OPEN SUPPLY CHAIN DATA IN GREECE AND THE WORLD

School of Economics

MSc in Logistics & Supply Chain Management

Supervisor: Tsadiras Athanasios

Author: Silvestri Miro

Thessaloniki, 2020 Abstract

This research is about the Open Data and Visualization techniques. More specifically, an introduction and discussion of the Open Data in Greece and the World will be done, by presenting the eight fundamental principles of the open data, the progress and maturity of the European countries, starting from 2016 all through 2019, the G20's anti-corruption principles and the G8's Open data principles. Also, in order to connect the Open Data with the supply chain, some constraints will be created, so as to collect all Greek Open Data Portals and filter them, in order to collect all the datasets related to the supply chain. Furthermore, the impacts and benefits of the Open data will be discussed, as well as the benefits of the Visualization techniques. Following, with the help of the Tableau and Power BI software, some of the data collected related to the supply chain, will be analyzed and visualized and a comparison of these two software will be done. Finally, in the last chapter, the conclusions of this research will be presented, in addition to some proposals so as to ameliorate the collection and record, process and publications of the Open Data.

Keywords: open data, Greece, world, supply chain, portal, gov, Tableau, Power BI, visualization

1 Acknowledgements

With this thesis, my course of study has come to an end. It was a path I had in mind since the beginning of my university commitment, when I chose the faculty of Economics and International Commerce with the idea of continuing with a Master in Logistics. My wish came true when I had the honor of being admitted to the prestigious Aristotle University of Thessaloniki, a city in which I spent a period of serious and demanding studies and which gave me a broad and updated vision of the subject to which I hope to dedicate my future working activity. I found a group of students, open to discuss and collaborate, with whom I hope to maintain a friendly relationship.

Above all, I was able to appreciate the teaching staff, made up of highly qualified Professors. I especially would like to thank Professor Tsadiras Athanasios for his collaboration and availability in following my work.

Finally, allow me to express my thanks to my parents, who for many years of studies have always supported and encouraged me, morally and economically, without sparing me praises and criticisms, also constructive in my educations as a young man.

2 Contents Abstract ...... 1 Acknowledgements ...... 2 List of Figures ...... 6 List of Tables ...... 9 Abbreviations ...... 10 Defined Terms ...... 10 1 Chapter One: Introduction ...... 11 1.1 Overview ...... 11 1.2 International historic overview of Open Data ...... 12 1.3 Definitions ...... 15 1.3.1 Definition of Data ...... 15 1.3.2 Definition of Information ...... 15 1.3.3 Definition of Data processing ...... 16 1.3.4 Definition of Open Public Data ...... 16 1.3.5 Definition of Public Data ...... 16 1.3.6 Definition of Government data ...... 17 1.3.7 Definition of Open ...... 17 1.3.8 Definition of Open Data ...... 18 1.3.9 Definition of Supply Chain ...... 18 1.3.10 Definition of Datasets ...... 19 1.3.11 Data Catalogue Vocabulary – Application Profile ...... 19 1.4 Types of Open Data ...... 20 1.5 Value of the Open Data ...... 20 1.6 Aim and Objectives ...... 22 1.7 Research scope ...... 22 1.8 Methodology ...... 23 1.9 Outline of the thesis ...... 23 2 Chapter Two: Methodology and Review ...... 25 2.1 Overview ...... 25 2.2 Methodology ...... 25 2.3 Select keywords and define search strings ...... 26 2.4 Inclusion and exclusion criteria ...... 30 2.4.1 Inclusion criteria for the websites ...... 31 2.4.2 Exclusion criteria for the websites ...... 31 2.4.3 Inclusion criteria for the datasets ...... 31

3 2.4.4 Exclusion criteria for the datasets ...... 32 2.5 Select data sources ...... 32 2.6 Aim and Objectives ...... 33 2.7 Results of the research ...... 33 3 Chapter Three: Thematic Analysis and Discussion ...... 35 3.1 Overview ...... 35 3.2 The Open Data Principles ...... 36 3.2.1 The eight fundamental principles of Open Data ...... 36 3.2.2 Secondary principles of the Open Data ...... 38 3.3 The Open Data in Greece ...... 40 3.3.1 The Central Open Data Portal in Greece ...... 40 3.3.2 Transparency project in Greece ...... 45 3.3.3 Greek Open Data Portals, Governmental and non ...... 47 3.3.4 The Evolution of Open Data in Greece ...... 50 3.4 Evolution of the European Unions’s Open Data Portals ...... 56 3.4.1 The EU Open Data Portal ...... 57 3.4.2 The European Data Portal ...... 57 3.4.3 The resource with Persistent URIs ...... 58 3.4.4 The European Web Archive ...... 58 3.4.5 The Support Center for Data Sharing ...... 58 3.5 Overview and Maturity of the Open Data in EU28+ ...... 59 3.5.1 Open Data Maturity in EU+28 in 2016 ...... 60 3.5.2 Open Data Maturity in EU+28 in 2017 ...... 62 3.5.3 Open Data Maturity in EU+28 in 2018 ...... 66 3.5.4 Open Data Maturity in EU+28 in 2019 ...... 69 3.6 The Open Data in the rest of the World ...... 72 3.6.1 International Tools for the Open Data ...... 72 3.6.2 The Open Data in the G8 ...... 74 3.6.3 G20 Anti-Corruption Open Data Principles ...... 78 3.6.4 International Open Data Charter ...... 83 3.7 Impacts and Benefits of the Open Data ...... 84 3.8 Licenses for the Publication of the Data ...... 91 3.9 Formats of the Data ...... 93 4 Chapter Four: Visualization and Data Analysis ...... 95 4.1 Overview ...... 95 4.2 Data Visualization ...... 95 4.3 Visualization Techniques ...... 96

4 4.3.1 Bar Chart ...... 96 4.3.2 Scatter Chart ...... 97 4.3.3 Pie Chart ...... 98 4.3.4 Block Chart ...... 98 4.3.5 Radar Chart ...... 99 4.3.6 Line Chart ...... 99 4.3.7 Mekko Chart ...... 100 4.4 Introduction to the Tableau and Power BI software ...... 100 4.4.1 The Tableau Software ...... 101 4.4.2 The Power BI Software ...... 106 5 Chapter Five: Case Studies ...... 110 5.1 Overview ...... 110 5.2 Case study one: Greek Open Data Portals, a statistical analysis and visualization with Tableau ...... 111 5.3 Case study two: Greek Open Data Portals, a statistical analysis and visualization with Power BI ...... 121 5.4 Case study three: Geographical visualization of road freight transport of goods, per type of package, in the thirteen Regions of Greece ...... 134 5.5 Cases study four: Unloaded and Loaded goods in Greek ports, by ports and type of cargo ...... 156 5.6 Comparison of Power BI and Tableau...... 203 6 Conclusions and Future Research ...... 206 References ...... 208 Appendix / Appendices ...... 214

5 List of Figures

Figure 1.1: Chapter’s One Structure 11 Figure 1.2: Supply Chain Process 18 Figure 1.3-Outline of the thesis 24 Figure 2.1: Chapter's two overview 25 Figure 3.1: Chapter's three overview 35 Figure 3.2: Greek Central Open Data Portal 40 Figure 3.3: Open Data Impact in Greece 2017 44 Figure 3.4: Questionnaire for the maturity of the Open Data 50 Figure 3.5: Open Data Progress in Greece from 2016 to 2019 55 Figure 3.6: Open Data Maturity Groups in Europe 2016 62 Figure 3.7: Open Data Maturity Groups in Europe 2017 65 Figure 3.8: Open Data Maturity Groups in Europe 2018 68 Figure 3.9: Open Data Maturity Groups in Europe 2019 71 Figure 3.10: Open Data Taxonomy of Impact 85 Figure 4.1: Chapter's four overview 95 Figure 4.2: Bar Chart 97 Figure 4.3: Scatter Chart 97 Figure 4.4: Pie Chart 98 Figure 4.5: Block Chart 98 Figure 4.6: Radar Chart 99 Figure 4.7: Line Chart 99 Figure 4.8: Mekko Chart 100 Figure 4.9: Tableau Connect Data 103 Figure 4.10: Tableau Selection of Data 104 Figure 4.11: Tableau Data Preparation 105 Figure 4.12: Tableau Visualization 106 Figure 4.13: Power BI Connect Data 108 Figure 4.14: Power BI Selection of Data 108 Figure 4.15: Power BI Data Preparation 109 Figure 5.1: Chapter’s five overview 110 Figure 5.2: Example of Table in Excel 111 Figure 5.3: Central Greek Open Data Portal Datasets 112 Figure 5.4: Greek Statistical Authority Datasets 113 Figure 5.5: Open Data Portal - Municipality of Thessaloniki Datasets 113 Figure 5.6: Percentage of Datasets 114 Figure 5.7: Organization Name and Datasets 115 Figure 5.8: Percentage of Datasets per Organization 116 Figure 5.9: Data.gov Percentage of Type 117 Figure 5.10: Data.gov Number of Type 117 Figure 5.11: Greek Statistical Authority Percentage of Type 117 Figure 5.12: Greek Statistical Authority Number of Type 117 Figure 5.13: Open Data Portal Thessaloniki Percentage of Type 118

6 Figure 5.14: Open Data Portal Thessaloniki Number of Type 118 Figure 5.15: Total Percentage of Type 118 Figure 5.16: Total Number of Type 118 Figure 5.17: Supply Chain Related Percentage 119 Figure 5.18: Supply Chain Related Data in Data.gov 121 Figure 5.19: Number of Datasets in Data.gov 123 Figure 5.20: Number of Datasets in Greek Statistical Authority 124 Figure 5.21: Number of Datasets in Thessaloniki Portal 124 Figure 5.22: Percentage of Datasets 125 Figure 5.23: Percentage of Datasets by each Organization 127 Figure 5.24: Percentage of Data relevant to Supply Chain in Data.gov 129 Figure 5.25: Percentage of Type in Data.gov 130 Figure 5.26: Percentage of Type in Greek Statistical Authority 130 Figure 5.27: Percentage of Type in Thessaloniki 131 Figure 5.28: Percentage of Type in all three Portals 132 Figure 5.29: Power BI Percentage of Description 133 Figure 5.30: Power BI Graph Percentage of Description 133 Figure 5.31: Tons Liquid Bulk 135 Figure 5.32: Tonometer Liquid Bulk 136 Figure 5.33: Tons Solid Bulk 137 Figure 5.34: Tonometer Solid Bulk 138 Figure 5.35: Tons Container 140 Figure 5.36: Tonometer Container 141 Figure 5.37: Tons Pallets 142 Figure 5.38: Tonometer Pallets 143 Figure 5.39: Tons of Goods Packaged in Artana 144 Figure 5.40: Tonometers of Goods Packaged in Artana 145 Figure 5.41: Tons of Mobile and Self-Propelled Units 146 Figure 5.42: Tonometer of Mobile and Self-Propelled Units 147 Figure 5.43: Tons of Other Mobile Unites 149 Figure 5.44: Tonometer of Other Mobile Unites 150 Figure 5.45: Tons of Other Types of Cargo 151 Figure 5.46: Tonometer of Other Types of Cargo 152 Figure 5.47: Number of Total Load and Unload, of all ports, per type of package for all 2019 158 Figure 5.48: Percentage of Total Load and Unload, of all ports, per type of package for all 2019 158 Figure 5.49: Total Load and Total Unload, of all ports, per type of pack for all 2019 159 Figure 5.50: Total Load and Unload, of all ports, per package and per quarter 160 Figure 5.51: Total Load and Total Unload, of all ports, per type of package and per quarter 161 Figure 5.52: Total Load and Unload for the 2nd quarter, of all ports, per type of package and per quarter, as a percentage 162 Figure 5.53: Total Load and Unload for the 1st quarter, of all ports, per type of package and per quarter, as a percentage 162

7 Figure 5.54: Total Load and Unload for the 4th quarter, of all ports, per type of package and per quarter, as a percentage 162 Figure 5.55: Total Load and Unload for the 3rd quarter, of all ports, per type of package and per quarter, as a percentage 162 Figure 5.56: Total Load and Total Unload, of all ports, per type of package for the 2nd quarter, as a percentage 163 Figure 5.57: Total Load and Total Unload, of all ports, per type of package for the 1st quarter, as a percentage 163 Figure 5.58: Total Load and Total Unload, of all ports, per type of package for the 4th quarter, as a percentage 164 Figure 5.59: Total Load and Total Unload, of all ports, per type of package for the 3rd quarter, as a percentage 164 Figure 5.60: Loaded Container and Unloaded Container per port and per quarter 166 Figure 5.61: Loaded Liquid Bulk and Unloaded Liquid Bulk per port for the 1st quarter 167 Figure 5.62: Loaded Liquid Bulk and Unloaded Liquid Bulk per port for the 2nd quarter 168 Figure 5.63: Loaded Liquid Bulk and Unloaded Liquid Bulk per port for the 3rd quarter 169 Figure 5.64: Loaded Liquid Bulk and Unloaded Liquid Bulk per port for the 4th quarter 170 Figure 5.65: Loaded Other General Cargo and Unloaded Other General Cargo per port for the 1st quarter 171 Figure 5.66: Loaded Other General Cargo and Unloaded Other General Cargo per port for the 2nd quarter 172 Figure 5.67: Loaded Other General Cargo and Unloaded Other General Cargo per port for the 3rd quarter 172 Figure 5.68: Loaded Other General Cargo and Unloaded Other General Cargo per port for the 4th quarter 173 Figure 5.69: Loaded Roll-on Roll-off and Unloaded Roll-on Roll-off per port for the 1st quarter 174 Figure 5.70: Loaded Roll-on Roll-off and Unloaded Roll-on Roll-off per port for the 2nd quarter 174 Figure 5.71: Loaded Roll-on Roll-off and Unloaded Roll-on Roll-off per port for the 3rd quarter 175 Figure 5.72: Loaded Roll-on Roll-off and Unloaded Roll-on Roll-off per port for the 4th quarter 176 Figure 5.73: Loaded Solid Bulk and Unloaded Solid Bulk per port for the 1st quarter 177 Figure 5.74: Loaded Solid Bulk and Unloaded Solid Bulk per port for the 2nd quarter 177 Figure 5.75: Loaded Solid Bulk and Unloaded Solid Bulk per port for the 3rd quarter 178 Figure 5.76: Loaded Solid Bulk and Unloaded Solid Bulk per port for the 4th quarter 179 Figure 5.77: Load Container per port for the 1st quarter as percentage 180 Figure 5.78: Load Container per port for the 2nd quarter as percentage 181 Figure 5.79: Load Container per port for the 3rd quarter as percentage 182 Figure 5.80: Load Container per port for the 4th quarter as percentage 182 Figure 5.81: Load Other General Cargo per port for the 1st as percentage 183 Figure 5.82: Load Other General Cargo per port for the 2nd as percentage 184 Figure 5.83: Load Other General Cargo per port for the 3rd as percentage 184 Figure 5.84: Load Other General Cargo per port for the 4th as percentage 185

8 Figure 5.85: Load Liquid Bulk per port for the 1st quarter as percentage 186 Figure 5.86: Load Liquid Bulk per port for the 2nd quarter as percentage 186 Figure 5.87: Load Liquid Bulk per port for the 3rd quarter as percentage 187 Figure 5.88: Load Liquid Bulk per port for the 4th quarter as percentage 187 Figure 5.89: Load Roll-on Roll-off per port for the 1st quarter as percentage 188 Figure 5.90: Load Roll-on Roll-off per port for the 2nd quarter as percentage 188 Figure 5.91: Load Roll-on Roll-off per port for the 4th quarter as percentage 189 Figure 5.92: Load Roll-on Roll-off per port for the 3rd quarter as percentage 189 Figure 5.93: Load Solid Bulk per port for the 1st quarter as percentage 190 Figure 5.94: Load Solid Bulk per port for the 2nd quarter as percentage 190 Figure 5.95: Load Solid Bulk per port for the 3rd quarter as percentage 191 Figure 5.96: Load Solid Bulk per port for the 4th quarter as percentage 192 Figure 5.97: Unload Container per port for the 1st quarter as percentage 193 Figure 5.98: Unload Container per port for the 2nd quarter as percentage 193 Figure 5.99: Unload Container per port for the 4th quarter as percentage 194 Figure 5.100: Unload Container per port for the 3rd quarter as percentage 194 Figure 5.101: Unload Liquid Bulk per port for the 1st quarter as percentage 194 Figure 5.102: Unload Liquid Bulk per port for the 2nd quarter as percentage 195 Figure 5.103: Unload Liquid Bulk per port for the 4th quarter as percentage 195 Figure 5.104: Unload Liquid Bulk per port for the 3rd quarter as percentage 195 Figure 5.105: Unload Other General Cargo per port for the 1st quarter as percentage 196 Figure 5.106: Unload Other General Cargo per port for the 2nd quarter as percentage 197 Figure 5.107: Unload Other General Cargo per port for the 3rd quarter as percentage 198 Figure 5.108: Unload Other General Cargo per port for the 4th quarter as percentage 198 Figure 5.109: Unload Roll-on Roll-off per port for the 1st quarter as percentage 199 Figure 5.110: Unload Roll-on Roll-off per port for the 2nd quarter as percentage 199 Figure 5.111: Unload Roll-on Roll-off per port for the 4th quarter as percentage 200 Figure 5.112: Unload Roll-on Roll-off per port for the 3rd quarter as percentage 200 Figure 5.113: Unload Solid Bulk per port for the 1st quarter as percentage 201 Figure 5.114: Unload Solid Bulk per port for the 2nd quarter as percentage 201 Figure 5.115: Unload Solid Bulk per port for the 3rdquarter as percentage 202 Figure 5.116: Unload Solid Bulk per port for the 4th quarter as percentage 202

List of Tables

Table 1: Results of Constraint 34 Table 2: Open Data Groups 43 Table 3: Open Data websites of Older Ministries in Greece 47 Table 4: Open Data websites of Decentralized Administrations in Greece 47 Table 5: Open Data websites of Regions in Greece 48 Table 6: Open Data websites of Ministries in Greece 49 Table 7: Evolution and Maturity of Open Data in Greece 2016 -2017 51 Table 8: Evolution and Maturity of Open Data in Greece 2018 - 2019 53

9 Table 9: Maturity of the Open Data in Europe 2016 60 Table 10: Maturity of the Open Data in Europe 2017 63 Table 11: Maturity of the Open Data in Europe 2018 66 Table 12: Maturity of the Open Data in Europe 2019 69 Table 13: European countries central Open Data Portal 71 Table 14: G8 countries central Open Data Portal 74 Table 15: G8 valuable areas for the re-use of the Open Data 77 Table 16: G20 countries central Open Data Portal 82 Table 17: Types of Datasets 94 Table 18: Path for Data in Case Study Three 134 Table 19: Case Study Two Min/Max 155 Table 20: Path for Data in Case Study Four 157 Table 21: Municipalities of Greece 2020 214 Table 22: Other Greek Organizations that publish open data 225

Abbreviations

DCAT-AP: Data Catalogue Vocabulary – Application Profile

EU: European Union

A.D.: stands for anno domini and means “after Christ”

B.C.: stands for “before Christ”

Defined Terms

Central Greek Open Data Portal: Data.gov

10 1 Chapter One: Introduction

1.1 Overview

This chapter is an introductory chapter of this thesis. In Figure 1.1, as shown below, Chapter’s one structure is presented and the particulars about every section of this chapter will be discussed.

1.2 International History of the Open Data

1.3 Definitions

1.4 Types of Open Data

1.5 Value of the Open Data 1.1 Overview

1.6 Aim and Objectives

1.7 Research scope

1.8 Methodology

1.9 Outline of the thesis

Figure 1.1: Chapter’s One Structure

Source: the author (2020)

11 1.2 International historic overview of Open Data

It is difficult to precise historically when exactly the open data concept was born. A large number of sources and events, driven by some key factors such as governmental and civil society, started referring to this term as a technical definition and over the course of time it took valid legal fundaments. More specifically, starting from the ancient times, there was some events that created a sort of idea, like we know it today, of the Open Data. Starting from the Hammurabi code, from 1792 to 1750 B.C., Ancient Greece in the 6th century B.C., the Twelve Boards of Rome in 450 B.C. all through the American Colonies, during the period from 1710 to 1742 A.D. and ending in the 7th of December in 2007, in Sebastopol, California where for the first time the term Open Data was officially established as an international term with valid principles and also legislation made around it.

Starting from the first reference in the history of the idea of the term Open Data as we know it nowadays, around 3791 years ago, in Babylonia, where the King Hammurabi, from 1792 to 1750, created a collection of laws, that were inscribed on a column, accessible to the public view. On this column 282 articles were gathered, concerning various sectors of laws, such as economic and commercial relationships and crime laws (ETc - HSOD, n.d.). This Reference can be considered as an Open Government Data, since these 282 laws were made public and accessible to everyone, an unprecedented phenomenon because up to this specific time in history, only the King could know such things and not the common people.

Continuing with Ancient Greece, in Athens, laws were initially propagated and transmitted in the traditional oral way. At that time, only a small part of the whole number of laws and reforms, created by the archon Solon, started being written down and codified. Since the majority of the public, back then, was not so literate, the main reason for the codification of the laws was not for the public to be able to see the laws (Joshua Tauberer, 2014). That small amount of laws written down was for the preservation of some rights that the public had, and in a democratic society as Athens was back then, these laws were specifically the ones that kept wealth in times of peace. Moreover, in order for the people to be able to consult and have access to them, these laws were written down on boards, known also as áxones, and were raised on a

12 spinning pillar in the center of the city, so that the plebs have access to it. This codification of the laws seems to be part of a program of holding the society into a democratized structure, since at that times, Athens was going through an important incensement of population and also economic growth (Federico Condello, n.d.).

The most ancient legislative work of ancient Rome is the Twelve Boards, made in 450 - 451 B.C. and was made in order for the people to know better the laws of the empire. As in ancient Athens so in ancient Rome, since this particular time, laws were transmitted from one generation to another by the traditional oral way, but because it was difficult for the public to know and remember all the laws, the people asked that the laws had to be written down. In 451 B.C. a commission of ten patricians was established. This commission created firstly a group of laws, written on ten boards. The year after, another commission of ten people was created, but this time it was composed by seven patricians and three people. This new commission created, in the 450 B.C., two additional boards of laws. Finally, in 449 B.C., the two consuls, L. Valerio and M. Orazio, wrote these laws on twelve bronze boards that were exposed in the forum of the city of Rome were anyone could see them (ETDTR, n.d.). A thousand years later, around 530 A.C., the Imperator Justinian created the “Corpus iuris civilis” rearranging and substituting all the laws made until then. This was made because in a thousand years, many laws were created and there was a lot of confusion and contradictions between them. This “Corpus iuris civilis” was used as a reference by a great part of European countries until the 19th century (ETcG, n.d.).

In the times of the American Colonies, during the period from 1710 to 1742, the lack of information between the various Public Authorities and inexperience back then, led to a big economic confusion. This circumstance was the key factor that drove the government so as to take action and end this confusion. Moreover, the constituents between the sessions of the legislators, didn’t have copies of the laws and as a result they got confused, because of the frequent changes made to the laws and also did not know their rights. Furthermore, the legislators were also confused, because of all these frequent changes of the laws. So, in 1710, firstly in Pennsylvania, the assembly of legislators started policing twice a week copies of the laws, in order for the members to have updated valid copies of the current laws. After this first event in 1729 and in 1740, the House started publishing codification of its laws and finally, the state of Massachusetts, in 1742 and in contrast with the Government, started

13 making a systematic list of all laws and so in this period of the American Colonies, the codification of the laws started taking place (Joshua Tauberer, 2014).

Arriving nowadays, more specifically at the 7th of December in 2007, in Sebastopol, California, an event was hosted for the first time, involving political and public figures so as to create an official international term, the Open Data. Thirty entrepreneurs, scholars and internet activists were gathered in order to originate a set of eight principles for the open government data and to establish definitively the term Open Data (OGWG, 2007). These principles, which will be analyzed furthermore in chapter 3 of this paper, are:

• Complete • Primary • Timely • Accessible • Machine Processable • Non-Discriminatory • Non-proprietary • License Free

This event was hosted by Sunlight Foundation, Google and Yahoo and the thirty entrepreneurs, scholars and internet activists invited were Carl Malamud and Joel Hardi from the Public Resource Organization, Tim O'Reilly from the O'Reilly Media, Greg Elin and Micah Sifry from the Sunlight Foundation, Adrian Holovaty and Daniel X. O'Neil from the EveryBlock, Michal Migurski and Shawn Allen from the Stamen Design, Josh Tauberer from the GovTrack.US, Lawrence Lessig from Stanford, Dan Newman from the MapLight.Org, John Geraci from outside.in, Edwin Bender from the Institution for Money, Tom Steinberg from My Society, David Moore from Participatory Politics, Donny Shaw from Participatory Politics, JL Needham from Google, Ethan Zuckerman from Berkman, Greg Palmer from NewCo, Jamie Taylor from MetaWeb, Bradley Horowitz from Yahoo, Zack Exley from New Organizing Institute, Karl Fogel from Question Copyright, Michael Dale from Metavid, Joseph Lorenzo Hall from UC Berkeley, Marcia Hofmann from EFF, David Orban from Metasocial Web, Will Fitzpatrick from Omidyar Network and Aaron Swartz from Open Library. By using these

14 eight principles, these thirty advocates created, governments could become more transparent, effective and relevant for the citizens and the citizens surely changed their thoughts about open data and the use of them (OGWG, 2007).

After this first international meeting, another important event regarding the open data took place two years later in January 2009, where the President of the United States of America Barack Obama announced his support for the open government data and the creation and implementation of the Data.gov.uk. (Bastiaan van Loenen, 2018). Later on, another seven principles were created, in association with the first eight fundamental ones, so as to conclude the whole idea of how to manage, publish, process and the legal rights of the Open Data. These seven additional principles are: Online and Free, Permanent, Trusted, a Presumption of Openness, Documented, Safe to Open and Designed for the Public and will also be further analyzed with the eight fundamental ones, in Chapter 3 of this research (8ODP, 2007).

1.3 Definitions

1.3.1 Definition of Data

As data, we define a set of discrete, objective elements and events. Data, also can be numbers, words, symbols, that describe or represent quantities, objects, situations and functionalities (Papadopoulos, 2011).

1.3.2 Definition of Information

Information is the result of the process of data that has as object to add value to the data. As adding value to the data in this case, we mean the knowledge that is given to us, and which help us understand the reality, through the process of data. As a result, the collection, the process and the interpretation of data, can give us valuable

15 information and that information can help us understand the real situation of what we are studying (Papadopoulos, 2011).

1.3.3 Definition of Data processing

The data, alone, do not mean anything, are not important and do not have any connection with something specifically, but from data, especially from the organization and process of data, valuable information can be extracted, for many purposes. By adding value to the data, by formatting, organizing, with mathematical and statistical analysis, error correction and with clusters, we can produce the information. This process is called data processing (Papadopoulos, 2011).

1.3.4 Definition of Open Public Data

The term open public data refers to data sets and information that are made available by public entities and that are accessible to everyone, without any restrictions. Although the public sector publishes the data, they may not always be produced by the public. They can be also be produced by the private sector and the civil society and these data can be used for statistical and mathematical processes, so as to further create valuable information (Papadopoulos, 2011).

1.3.5 Definition of Public Data

Public data is a lever for economic and social development in a variety of ways, either as a stand-alone financial asset such as raw materials for creating value added services, for example in education, or as an adjunct to lower costs and increase productivity in public administration, by collecting, disseminating and continually publicly evaluating financial data from various public services. Finally, Public Data can be a significant element in the proper functioning of the market, by disclosing the

16 terms and progress of public agreements and contribute to good governance, by ensuring transparency in the exercise of public authority (EPSET, n.d.). Public data are not just data that are made only by public agencies but are data that that can be made also from private entities. Three characteristics of public data are (Papadopoulos, 2011):

• The owner of these data are public institutions • These data have been produced with public foundlings • Can be produced by the Public entities but also by Private organizations, in order to serve and deliver the Public

1.3.6 Definition of Government data

The term Public Data is a wider definition that includes Government Data, as Government Data means the subset of Public data and information, that are produced and collected from the Government of entities subject to the Government. Such information can be laws, public revenue sources and public costs (Papadopoulos, 2011).

1.3.7 Definition of Open

The definition of Open is that a specific content can be processed, be re- disposed and become public again and re-used, without any restrictions, with the only exception being that the publisher has to mention the creator of that content. Moreover, there must be two ways of ensuring that the open content can be reused. Firstly, with the use of laws, so that the access is free for everyone and re-usage to that content and secondly, the technologies used to provide the open content must facilitate both the use and the publication of it (Papadopoulos, 2011; OKFT, n.d.).

17 1.3.8 Definition of Open Data

In order for the public data to be considered open, for everyone who wants to elaborate these data, there must be the potential for further process and analysis, and the re-publication of them. The level of how much “open” the data are, is defined from the opportunity to process and public the data, so as to create further information (Papadopoulos, 2011).

1.3.9 Definition of Supply Chain

A supply chain is the cooperation of the manufacturers, suppliers, transporters, warehouses, retailers, customers and all parties that can be involved directly or even indirectly. The main scope of the supply chain is to fulfill the requests of customers at any time. In an organization, the term supply chain is referred to all functionalities involved in receiving and fulfilling the requests of customers. The organization’s functionalities that help to do so, are the development of new products, marketing, customer service, distribution, operations and finance (Sunil Chopra & Peter Meindl, 2013). In Figure 1.2 the arrows show the physical flow of a product in the supply chain.

Figure 1.2: Supply Chain Process

Source: (Sunil Chopra & Peter Meindl, 2013)

18 The flow of information, funds and products along all the supply chain and between its various stages is necessary, in order for the entities involved in it to make the optimal decisions, so as to satisfy the customer’s needs. Furthermore, it is important that information, funds and product flows, run along to both directions of the supply chain. A usual supply chain consists of the following components (Sunil Chopra & Peter Meindl, 2013):

• Raw material supplier • Manufacturer • Distributor • Retailer • Customer

1.3.10 Definition of Datasets

A dataset is a group of separate sets of information, treated by a computer as a single unit. It can have lots of formats, that are machine readable and usually it regroups information and data that have the same content (CDD, n.d.).

1.3.11 Data Catalogue Vocabulary – Application Profile

The DCAT-AP means Data Catalogue Vocabulary – Application Profile and it is also used by the European Union, so as to describe the metadata of the public sector’s datasets. The pros are (EDP, 2015):

• The data publishers, using this tool, can increase the discoverability of the datasets, so to simplify the users that need it. • The users that need the data can search for it through various platforms and websites, with no difficulties, such as language difference or separate models.

19 1.4 Types of Open Data

Nowadays, data and information are something that is of most value. Usually, the people create data, just by doing their everyday life, using and consuming services provided both by the Private and the Public sector. Both sectors save all these data and create datasets based on statistics and empirical methods. These datasets are created so to better understand and manage future similar situation, forecast demand and get prepared to satisfy it, upgrade the services provided, create more value and also better manage the resources. Furthermore, these data gathered and created by the Government can not only help improve the services but also decrease the costs of them, so to better manage the public sector and do better investments for the country and the people more properly, where these are needed most. Until now, there is no any official method in order to distinguish the datasets into various types. Generally, the types of data can be distinguished by whom it is produced, which means that there are two providers of datasets in the world, the Government and the Private sector.

1.5 Value of the Open Data

Achieving impacts with the open data is as essential as producing those data. The impacts of the data make the projects and initiatives of the Public and Private sector to be more viable and trustful. Open data is a natural resource of the digital age and it can be used by everyone at the same time. Open data is driving innovation in growth by revealing opportunities to the private sector such as to deliver new services, improve efficiency, identify savings and reduce the time needed so to find valuable data. Moreover, open data can help any company adapting to a fast-changing world, due to the fast-technological progress that there is nowadays and also let them take advantage of these new resources created by the open data. For example, open data is already being used so to improve crop fields and so to feed the growing population (EDPV, n.d.).

Furthermore, open data is helping Governments in many aspects, such as the efficiency of the public services, boosting entrustment in the citizens and increasing

20 engagement in the political process. Open data is also helping unlock economic value, by providing the raw material from innovation (and from the government’s aspect, the digital transformation of governments and it is increasing the core of public policy to release data openly). Also, open data is becoming a key source of evidence for policy makers, as they use open data from a variety of sources so to improve the policy process. As population grows, Governments need to balance the demand of data needed so to solve some local issues. Services of this type can only be achieved by delivering detailed data on local levels. Some local level services have already started being provided in the UK, Hungary and Norway. In the UK, a website was created in order to help citizens in the local level by providing them services like the one through which they can report neighborhood problems to the local council (UKODV, n.d.). The same service is provided in Norway (NOPV, n.d.). In Hungary, a similar webpage was created so to help citizens to request public interest data easily and fast. Furthermore, open data can help Governments better manage resources from all the public activities (HuODV, n.d.). As an example, in the UK, 200£ million were saved in the health services because of the open data. In addition to all this, Governments, in order to support the already existing businesses and to help new ones to emerge, have to publish data. Also, data infrastructure that connects the public with the data needed to them, has to be maintained and supported by the Government. Doing these, Governments take in exchange all the benefits from a growing economy. A good example in this case is Finland, where the big and small businesses grow 15% faster by having access to open data than those who do not. Other valuable impacts that the Open Data has is in the environmental dimension, where farmers using open data can be helped by them in improving yields, so to support the growing population, without destroying valuable natural habitats. What is more, humanitarian groups use aid statistics and open geographical data, so to deliver in disaster zones targeted supplies (EDPV, n.d.). Having mentioned all these, the first re-user of this data is the public sector that also makes public these data, which means the more data the public sector provides and publishes, the better it will be for it because of the advantages it has through the economic growth, transparency and becoming trustful to the citizen and worldwide.

21 1.6 Aim and Objectives

The digital transformation of services provided by both the Private and the Public sector, the growth of population, the fast-technological progress and also the environmental crisis, are all factors that demonstrate the need for the implementation of the Open Data projects worldwide. As it will be presented in this thesis, the main objective is to gather all Open Data websites in Greece, Governmental or not, the main Open Data portals from all the European Countries and also from those of the G8 countries. Then, from those portals found in Greece, a division will be made so to gather all those that have data about the supply chain and finally select some datasets and process them with two analytics software, that are Tableau and Power Bi, so to create a graphical visualization of those datasets. Furthermore, the eight principles and the impacts of the open data will be presents and also the evolution and maturity of the Open Data in Greece and in Europe.

1.7 Research scope

The Open Data project has to do both with the Private and the Public sector, since these two are the ones that produce and publish data. Nowadays, data is of major importance since it can help in many ways. The data provided freely by these Open Data portals is of great value, because it can improve the management of raw materials, reduce costs and deliver the services needed where and when they are needed. As in all areas, and thus in the supply chain, the importance of data is a major factor for the optimization of the process. The research scope of this thesis is to identify and catalogue all Greek Open Data web portals. After this first step, they will be filtered so to create a list of those that have in them datasets regarding the supply chain.

22 1.8 Methodology

In order to find all the Open Data portals in Greece, a large variety of key words and strings have to be used. Moreover, articles and papers, mainly from the European Union, but also from some Open Data sources have been used, so to explain the terminology of the definitions used in this thesis. Since this particular thesis has to do with the Open Data, the Systematic Literature Review is difficult to be created, since the thirty entrepreneurs that in 2007 created the eight fundamental principles of the open data and it’s definition, are scholars and internet activists and because the Open Data project is implemented by Governments and Private organizations that promote it, few papers and articles have been published for this subject. The main source from where the theoretical part of this thesis was extracted, is from Governmental web pages and mainly from the Europeans Union, as Europe contains many states, many of which have very high global scores in the maturity and implementation of the Open Data project. After finding all Greek Open Data portals, they will be filtered so to create a list of those that have data regarding the supply chain. Finally, two data analytics software will be used (Tableau and Power BI), so to create graphical visualizations of two datasets regarding the supply chain. Furthermore, the main portals of the EU, the G8 and from all countries of Europe will be presented, regarding the Open Data project.

1.9 Outline of the thesis

As shown in Figure 1.3, the outline of this thesis is:

23 ➢ Overview ➢ Value of the Open Data ➢ International historic ➢ Aim and Objectives Chapter 1: Introduction overview of Open Data ➢ Research scope ➢ Definitions ➢ Methodology ➢ Types of Open Data

➢ Overview ➢ Inclusion and exclusion ➢ Methodology criteria Chapter 2: Methodology and review ➢ Select keywords and ➢ Select data sources define search strings ➢ Aim and Objectives ➢ Results of the research

➢ Overview ➢ The Open Data in the ➢ The Open Data Principles rest of the World ➢ The Open Data in Greece ➢ Impacts and Benefits of Chapter 3: Thematic Analysis and Discussion ➢ Evolution of the European the Open Data Union’s Open Data Portals ➢ Licenses for the ➢ Overview and Maturity of Publication of the Data the Open Data in EU28+ ➢ Formats of the Data ➢

➢ Overview Chapter 4: Visualization and Data Analysis ➢ Data Visualization ➢ Visualization techniques ➢ Introduction to the Tableau and Power BI software

➢ Overview ➢ Greek Open Data Portals, a statistical analysis and

visualization with Tableau ➢ Greek Open Data Portals, a statistical analysis and

Chapter 5: Case Studies visualization with Power BI ➢ Geographical visualization or road freight transport of goods,

per type of package, in the thirteen Regions of Greece ➢ Unloaded and Loaded goods in Greek Ports, by ports and type of cargo ➢ Comparison of Power BI and Tableau

Chapter 6: Conclusions and Future Research ➢ Conclusions and suggestions for future research

Figure 1.3-Outline of the thesis Source: the author (2020)

24 2 Chapter Two: Methodology and Review

2.1 Overview

In this chapter the methodology will be described alongside with the processes needed for the implementation of this thesis. In Figure 2.1, as shown below, Chapter’s two structure is presented and the particulars about every section of this chapter will be discussed.

2.2 Methodology

2.3 Select keywords and define search

strings

2.4 Inclusion and exclusion criteria 2.1

Overview 2.5 Select data sources

2.6 Aim and Objectives

2.7 Results of the research

Figure 2.1: Chapter's two overview

Source: the author (2020)

2.2 Methodology

In order to firstly gather all Open Data portals in Greece, the Europe and the European Union and also the G8, some keywords and strings have been selected. After having gathered all these Open Data portals, a selection has been made using some constraints called inclusion and exclusion criteria, so to determine which portals

25 have datasets relevant to the supply chain. Although the number of portals gathered was initially large, only few had the pre-determined requirements. Moreover, since this is a new project which began in the 7th of December in 2007 in Sebastopol, California, but with a big history behind and a brilliant future ahead, the selection of articles, the bibliography about this topic were not vast and so the articles, journals and publications about this topic were selected, so to form a clear view and easily understand what Open Data is, to create a background of this topic, show the work that is done worldwide, so to improve and implement this project alongside with the evolution and maturity and also the benefits that the Open Data can give to the citizens, the private and the public sector. However, only articles and publications from “trusted” entities have been selected, such as the thirty co-founders of the eight principles, the European Union, the G8 and some internet activists and entrepreneurs that collaborate with the public sector, so to improve the Open Data project and are known as “experts” on this field.

2.3 Select keywords and define search strings

When typing in a search, it is necessary to identify first some keywords about the research topic. These keywords are deliberately selected by the author. In this way, the database starts searching from those particular words, so to retrieve relevant records. The definition of the keywords will help gather relative information to the topic and also synthesize the thesis. After the selection of the keywords, the strings and combinations of them are defined, by combining the keywords selected previously (Jill K. Jesson, 2011). Therefore, the strings are defined as follows:

➢ String 1: “Municipality” ➢ String 2: “Alexandria” OR “Veria” OR “Filotheis – Psychikou” OR “Edesa” OR “Agios Efstratios” OR “Markopoulo Mesogaias” OR “Voiou” OR “Zagoras – Mouresiou” OR “Agathonisi” OR “Agia Paraskevi” OR “Agia Varvara” OR “Agias” OR “Agii Anargiri” OR “Kamaterou” OR “Agios Dimitrios” OR “Agios Nikolaos” OR “Agios Vasilios” OR “Agistri” OR “Agrafa” OR “Agrinio” OR “Aharnon” OR “Aktio-Vonitsa” OR

26 “Alexandrupolis” OR “Aliartos-Thespieon” OR “Alimos” OR “Almiros” OR “Almopia” OR “Alonnisos,Amari” OR “Amaroussi” OR “Amfiklias-Elatias” OR “Amfilochia” OR “Amfipolis” OR “Amorgos” OR “Ampelokipi- Menemeni” OR “Amyntaio” OR “Anafi” OR “Anatoliki Mani” OR “Anatoliki Samos” OR “Andravida-Killini” OR “Andritsena-Krestena” OR “Andros” OR “Anogeia” OR “Antiparos” OR “Apokoronas” OR “Archanes-Asterousia” OR “Argitheas” OR “Argos Orestiko” OR “Argous-Mykinon” OR “Arhaia Olympia” OR “Aristoteli” OR “Arriana” OR “Arta” OR “Aspropyrgos” OR “Astipalea” OR “Athens” OR “Avdira” OR “Baris - Bulas – Buliagmenis” OR “Belu – Bohas” OR “Central Corfu” OR “Chalandri” OR “Chalkidona” OR “Chalkis” OR “Chania” OR “Chios” OR “Corfu” OR “Corinth” OR “Dafnis – Imittu” OR “Delphi” OR “Delta” OR “Deskati” OR “Didymoteicho” OR “Dion Olympos” OR “Dionysos” OR “Dirfion-Messapion” OR “Distomou Arahovas Antikyras” OR “Dodoni” OR “Domokos” OR “Dorida” OR “Doxatos” OR “Drama” OR “Dytiki Achaia” OR “Dytiki Lesbos” OR “Dytiki Mani” OR “Dytiki Samos” OR “Egaleo” OR “Egina” OR “Elafonisos” OR “Elassonas” OR “Elefsina” OR “Elliniku – Argirupolis” OR “Emmanouil Pappa” OR “Eordea” OR “Epidavros” OR “Eretria” OR “Ermionidas” OR “Erymanthos” OR “Eurota” OR “Farkadona” OR “Farsalon” OR “Festos” OR “Filiates” OR “Florina” OR “Folegandros” OR “Fournoi Korseon” OR “Fyli” OR “Galatsi” OR “Gavdos” OR “Georgios Karaiskakis” OR “Glyfada” OR “Gortyna” OR “Gortynia” OR “Grevena” OR “Haidario” OR “Heraklion” OR “Heraklion of Attica” OR “Hersonissos” OR “Iasmos” OR “Iera Poli Messolonghi” OR “Ierapetra” OR “Igoumenitsa” OR “Ikaria” OR “Ilida” OR “Iliou” OR “Ilioupoli” OR “Ioannina” OR “Ios” OR “Iraklia” OR “Iroikis Poleos Naoussas” OR “Istiea-Edipsos” OR “Ithaki” OR “Kaisariani” OR “Kalamaria” OR “Kalamata” OR “Kalavrita” OR “Kallithea” OR “Kalymnos” OR “Kamena Vourla” OR “Kantanou-Selinou” OR “Karditsa” OR “Karistou” OR “Karpathos” OR “Karpenisi” OR “Kasos” OR “Kassandra” OR “Kastoria” OR “Katerini” OR “Kato Nevrokopi” OR “Kavala” OR “Kea” OR “Kefallonia-Argostoli” OR “Kefallonia-Lixouri” OR “Kefallonia-Sami” OR “Kentrika Tzoumerka” OR “Keratsini – Drapetsona” OR “Kifissia” OR “Kileler” OR “Kilkis” OR “Kimolos” OR “Kissamos” OR “Komotini” OR “Konitsa” OR “Kordeliou – Evosmos” OR “Koridallu” OR “Kos” OR

27 “Kozani” OR “Kropia” OR “Kymis Aliveriou” OR “Kythira” OR “Kythnos” OR “Lagadas” OR “Lamia” OR “Larissa” OR “Lavreotiki” OR “Lefkada” OR “Leros” OR “Limni Plastiras” OR “Limnos” OR “Lipsi” OR “Livadia” OR Locri” OR “Loutraki-Perahora-Agii Theodori” OR “Lykovrisis Pefki” OR “Madudi - Limnis - Saint Anna” OR “Makrakomis” OR “Maleviziu” OR “Mandras-Eidyllias” OR “Marathon” OR “Maronia Sapon” OR “Megalopoli” OR “Meganisi” OR “Megara” OR “Megisti” OR “Messini” OR “Metamorfosi” OR “Meteora” OR “Metsovo” OR “Mikis” OR “Milopotamu” OR “Milos” OR “Minoa plain” OR “Monemvasia” OR “Moschatu – Tavru” OR “Muzaki” OR “Mykonos” OR “Mytilini” OR “Nafpaktia” OR “Nafpli” OR “Nea Filadelfia-Nea Halkidon” OR “Nea Zihni” OR “Neapolis-Sikeon” OR “Nemea” OR “Nestorio” OR “Nestos” OR “New Ionias” OR “New Propontidas” OR “New Smirne” OR “Nikaia - Agiou I. Renti” OR “Nikolaos Skoufas” OR “Nisyros” OR “North Corfu” OR “Notia Kynouria” OR “Notios Pilios” OR “Oihalia” OR “Oinousses” OR “Orchomenos” OR “Oreokastro” OR “Orestiada” OR “Oropedio Lasithiou” OR “Oropos” OR “Pageo” OR “Palamas” OR “Paleo Faliro’ OR “Pallini” OR “Papagos-Holargos” OR “Paranesti” OR “Parga” OR “Paros” OR “Patmos” OR “Patra” OR “Pavlos Melas” OR “Paxi” OR “Peania” OR “Pella” OR “Pentelis” OR “Peonia” OR “Peristeri” OR “Petroupoli” OR “Philadelphia – Chalkidonos” OR “Pidnas- Kolindrou” OR “Pilaia-Hortiatis” OR “Pilos-Nestoros” OR “Pineiou” OR “Piraeus” OR “Platanias” OR “Pogoni” OR “Polygyro” OR “Poros” OR “Prespes” OR “Preveza” OR “Prosotsani” OR “Psara” OR “Pyli” OR “Pyrgos” OR “Rafina – Pikermiou” OR “Rethymnis” OR “Rhodes” OR “Rigas Feraios” OR “Salamina” OR “Samothraki” OR “Saroniko” OR “Serifos” OR “Serron” OR “Servion” OR “Sfakia” OR “Sifnos” OR “Sikinos” OR “Sikonion” OR “Sintiki” OR “Sithonia” OR “Sitia” OR “Skiathos” OR “Skopelos” OR “Skydra” OR “Skyros” OR “Sofades” OR “Soufli” OR “Souli” OR “South Corfu” OR “Spartis” OR “Spata-Artemis” OR “Spetses” OR “Stylida” OR “Symi” OR “Syros – Ermoupolis” OR “Tanagra” OR “Tempon” OR “Thasos” OR “Thermaikos” OR “Thermi” OR “Thermos” OR “Thessalonikis” OR “Thira” OR “Thiva” OR “Tilos” OR “Tinos” OR “Topiros” OR “Trifilia” OR “Trikala” OR “Tripolis” OR “Trizinia-Methanon” OR “Tyrnavos” OR “Velventos” OR “Viannou” OR “Virona” OR “Visaltias”

28 OR “Volos” OR “Volvis” OR “Voria Tzumerka” OR “Vorias Kinourias” OR “Vrilissia” OR “Xanthi” OR “Xiromerou” OR “Xilokastro-Evrostini” OR “Ydra” OR “Zagori” OR “Zaharo” OR “Zakynthos” OR “Zirou” OR “Zitsa” OR “Zografu” ➢ String 3: “Region” ➢ String 4: “Eastern Macedonia and Thrace” OR “Attica” OR “North Aegean” OR “Western Greece” OR “Epirus” OR “Thessaly” OR “Ionian Islands” OR “Central Macedonia” OR “Crete” OR “South Aegean” OR “Peloponnese” OR “Central Greece” OR “Western Macedonia” ➢ String 5: “Ministry” ➢ String 6: “Finance” OR “Development and Investments” OR “Foreign Affairs” OR “Civil Protection” OR “Defense” OR “Education and Religion” OR “Labor and Social Affairs” OR “Health” OR “Environment and Energy” OR “Culture and Spot” OR “Justice” OR “Interior Affairs” OR “Digital Governance” OR “Infrastructures and Transport” OR “Shipping and Island Policy” OR “Rural Development and Foodstuff” OR “Tourism” OR “Migration and Asylum” OR “Administration and Management” OR “Internal Affairs of Macedonia and Thrakis” OR “Digital Culture, Telecommunications and Information” ➢ String 7: “Decentralized Administration” ➢ String 8: “Attica” OR “Epirus and West Region” OR “Thessaly and Central Greece” OR “Crete” OR “Macedonia and Thrace” OR “Peloponnese, Western Greece and the Ionian” OR “Aegean” ➢ String 9: “Open data” ➢ String 10: “Gov” OR “Governmental” OR “Government” ➢ String 11: “Canada” OR “France” OR “Germany” OR “Italy” OR “Japan” OR “Russia” OR “United Kingdom” OR “United States” ➢ String 12: “Australia” OR “Argentina” OR “Brazil” OR “Canada” OR “France” OR “Germany” OR “India” OR “Indonesia” OR “Italy” OR “Japan” OR “Mexico” OR “Russia” OR “Saudi Arabia” OR “South Africa” OR “ South Korea” OR “Turkey” OR “United Kingdom” OR “United States” OR “European Union” OR “China” ➢ String 13: “European” OR “Europe”

29 ➢ String 14: “Austria” OR “Belgium” OR “Bulgaria” OR “Croatia” “Cyprus” OR “Czech” OR “Denmark” OR “Estonia” OR “Finland” OR “France” OR “Germany” “Greece” OR “Hungary” OR “Ireland” OR “Italy” OR “Latvia” OR “Lithuania” OR “Luxembourg” OR “Malta” OR “Netherlands” OR “Poland” OR “Portugal” OR “Romania” OR “Slovakia” OR “Slovenia” OR “Spain” OR “Sweden” OR “United Kingdom”

After the definition of strings, the combinations of keywords are made, so to make the research and find the content needed for this thesis. The search strings combinations are created as follows:

❖ String 1+2: So as to find all Open Data Portals of the Municipalities in Greece ❖ String 3+4: So as to find all Open Data Portals of the Regions in Greece ❖ String 5+6: So as to find all Open Data Portals of the Ministries in Greece ❖ String 7+8: So as to find all Open Data Portals of the Decentralized Administrations in Greece ❖ String 9+11 ”OR” Sting 9+10+11: So to find all Open Data Portals of the G8 member countries ❖ String 9+12 “OR” String 9+10+12: So as to find all Open Data Portals of the G20 member countries ❖ String 13+14: So as to find all the Open Data Portals for all countries of Europe ❖ String 9+13: So as to find all the European Union’s Open Data Portals

2.4 Inclusion and exclusion criteria

The inclusion and exclusion criteria must be predetermined, in order to make the selection of data easier, so to be included in the theoretical part and in this case, the selection of web portals and databases (Jill K. Jesson, 2011).

30 2.4.1 Inclusion criteria for the websites

In order for a website to be an open data portal, it must have data of any type, free to everyone interested in this data. Also, the provider of the data can be both from the Public and the Private sector. By the word “free”, we mean that there will be no necessity of a subscription, registration or creation of an account at that website in order to view, download and process the data. However, if someone is interested in viewing, downloading and processing that data, there is the option of creating an account and the data has to be provided free of any charge.

2.4.2 Exclusion criteria for the websites

The exclusion criteria in order for a website not to be an open data portal are:

• The website does not contain data • There is data in the website but it is not free • At least one of the following procedures is not free: view, download, process of the data

2.4.3 Inclusion criteria for the datasets

In order for the datasets to be included in this research, they have to be relevant to the supply chain. Moreover, the datasets must contain some actions that are included in the supply chain definition. By supply chain, we mean the following actions: purchase and sale for ex. of a quantity of products or raw materials, supply and feedback, production for ex. of raw material or products, storage, services, transportation and processing. Furthermore, the datasets must have data relevant to the supply chain that can be used and processed.

31 2.4.4 Exclusion criteria for the datasets

The exclusion criteria in order for the datasets not to be included in this research are:

• The datasets are not relevant to the supply chain • Datasets are relevant to supply chain but do not have data that can be processed • The datasets that contain announcements, deliberations, press releases, consultations, declarations types, which usually do not have data that can be processed, are also excluded

Additionally, after having completed the research with the above constraints, the files published as decisions and studies usually did not have data that may be useful in this research, with very few exeptions.

2.5 Select data sources

Due to some specific characteristics of this research, as that the Open Data Project was initially created by privates and then implemented by Governments, the data sources used for this research are from trusted and important publishers and sources, connected to this topic. Some sources are private but strictly connected to the foundation of the Open Data, the development and implementation of this, as well as Governments, International Institutions and Supranational bodies. The web browser we used in this thesis, was Google Chrome and more specifically, we used Google as our main search engine, in order to find all the 699 websites (see section 2.7), after having used the search strings mentioned in section 2.3.

32 2.6 Aim and Objectives

The open data term was officially created and recognized as a term at the 7th of December in 2007, in Sebastopol, California, where for the first time an event was hosted, involving political and public figures so to create it and make it an international term. Since then, all countries started developing their own open data strategies and webpages, so to be more trustful and more transparent in their decisions. As in the rest of the Europe and thus in Greece, there are some central national web pages that gather all the public and also the private data. These data are freely available to the public so to be viewed, downloaded, processed and re- published. The three main objectives of this thesis are:

➢ Objective one: To find all Greek open data websites ➢ Objective two: To filter all the previously Greek open data websites found, so to gather only those with data related to the supply chain ➢ Objective three: To find all datasets, in these open data websites, relevant to the supply chain

2.7 Results of the research

After applying all these combinations of strings mentioned in Chapter 2.3, the total number of Open Data portals found, in Greece and the world is of 675. More specifically, from these 675 portals, eight are from the countries of the G8, as shown in Table 14, twenty are from the countries of the G20, as shown in Table 16, twenty- eight are from the countries of Europe as they were in 2020, before the decision of the United Kingdom to leave the European Union, as shown in Table 13 and four are of the European Union. The remaining 615 are Greek Open Data portals, as presented in Chapter 3.3.3, of which only three have data related to the supply chain, as presented in Table 1. Furthermore, from these 615 Greek Open Data portals, eighteen are Ministries, two are from older Ministries, three houndred and thity eight are the Municipalities, thirteen are regions, seven are decentralized administrations and 237 are other private and public open data portals. From these 615 portals, only three have

33 Datasets and Data related to the supply chain, using the restrictions mentioned above in this Chapter. In these three Open Data websites, there are 81 Datasets, and in these Datasets, 2,784 files of Data related to the supply chain were found that meet the constraints. Finally, from these 2,784 files, two fiels of data will be chosen so to make a visualization and graphical representation of them. Also, the Excel file, created by the author, which has all datasets and data, from the three Greek open data portals that meet the constraints, will be processed using the Tableau and Power BI software, so to make visualisatios and statistical representations, for a total of four Case Studies, as shown in Chapter 5.

Table 1: Results of Constraint

Source: the author (2020)

Portal Name URL No. of No. of Datasets Data Open Data Portal of the Municipality of https://opendata.thessaloniki.gr/el 2 62 Thessaloniki Greek Statistical https://www.statistics.gr/el/home 44 2,364 Authority Central Greek Open http://www.data.gov.gr/ 35 358 Data Portal

Total 81 2,784

34 3 Chapter Three: Thematic Analysis and Discussion

3.1 Overview

In this chapter, the analysis and discussion of the main theme will be presented alongside with the presentation of the Greek Open Data portals, the European Union’s and those from the member states of the EU and also the portals of the states of the G8. In Figure 3.1, as shown below, Chapter’s three structure is presented and the particulars about every section of this chapter will be discussed further on, in each respective subchapter.

3.2 The Open Data Principles

3.3 The Open Data in Greece

3.4 Evolution of the European Open Data Portals

3.5 Overview and Maturity of the Open Data in EU+28

3.1 Overview 3.6 The Open Data in the rest of the World

3.7 Impacts and Benefits of the Open Data

3.8 Licenses for the Publication of the Data

3.9 Formats of the Data

Figure 3.1: Chapter's three overview Source: the author (2020)

35 3.2 The Open Data Principles

3.2.1 The eight fundamental principles of Open Data

1. Complete

The data provided and published must be registered in a complete way, in order to give valuable and accurate information about a subject. Apart from the raw data sets, the mathematical explanation of how this data was accumulated and formulated in that form, must be given. In this way, it would be easy for the users to understand the data and to further elaborate and to re-publish it with high accuracy (SFODPG, 2014; GCODP, n.d.).

2. Primary

The data collection must be accurate and must be referred to the raw data samples. In the data sets, the way the data was collected must be included and also the prototypes from where it was collected, so for the users of the data to be sure that it was collected in the right way (GCODP, n.d.).

3. Timely

The data collected and published by the Government has to be quickly distributed, especially if that data is time sensitive, so for the users to be able to acquire real time information through it (GCODP, n.d.).

4. Accessible

The data provided must be in easily readable forms for all the users and also for all the machines, in order to be accessible by anyone and all public (OKFOD, n.d.).

5. Machine processable

As the machines can read and process all formats of data content, most users do not have the knowledge to do so and in helping themselves in solving this kind of problems. Formats such as the PDF format, is a common one but it is also difficult to process data through it. Therefore, data must be in some specific electronic forms,

36 such as Excel format, which is one of the most common ones, so to facilitate machine users to elaborate and re-publish the data. Also, with this data, there has to be an explanation table connected to the format of the data, as an explanation on how to use it (SFODPG, 2014; GCODP, n.d.).

6. Non discriminatory

The accesses to the data on these Open Data websites must be, as the name says, open to everyone that wants to process and view the data, at any time and place a user is interested to do so. In some cases, the users can also register first to the website and view the data, so for the owners of the website to know who and how many different unique IP have requested to see it. This procedure has also to do with the collection of data and statistical reasons for many purposes, such as to meliorate the website’s interface and provide a better user experience and personalization of advertisings. Generally, an anonymous access to the data must be allowed to all the public interested in it for non-discriminatory reasons (SFODPG, 2014; GCODP, n.d.; OKFOD, n.d.).

7. Non proprietary

Data, on Open Data websites, has to be in more than one format type. The main reason for this is that a wider number of population and machines can have access to that data. Moreover, another reason for the data to be published in more than one electronic format is to prevent unpleasant restrictions to some users interested in that. Unlikely, the main format type of data nowadays, is the Excel format. Without any second thoughts, the most widely common format used all over the world is the Excel format, also for facilitating the largest number of users, but for this specific fundamental principle of Open Data this is a contradiction that may in the future procure some complications to a small amount of users (SFODPG, 2014).

8. License free

As there can be some Open Data websites that need to be registered first in order to view their content, that does not mean that the content is locked. There are also other options such as trial versions and guest modes so to view the content of the websites. The License Free principle has to do with the freedom of the users to view the data at any time and place, without any restrictions, except for some cases that the

37 re-publish of the data must be accompanied with the author’s name, and without the need of payment subscription to do so. Also, data on Open Government Data websites is not subject to any particular trademark or patent and that’s why it has to be free of charge (USALFD, 2013; SFODPG, 2014).

3.2.2 Secondary principles of the Open Data

Besides these eight fundamental principles made for the open data websites, there are another seven (secondary) made later on, which are worthy to be mentioned:

1. Online and Free

The data, in order to be useful and easily accessible for the public, has to be available to the internet and with no charges requested for it and also the information must be easily findable on the internet (SFODPG, 2014; OKFOD, n.d.).

2. Permanent

Information and data should be available for as long as possible, in an easily readable and stable format and preferably always in the same location, so to simplify things for the public (SFODPG, 2014), (GCODP, n.d.).

3. Trusted

In order for the digital content of these open data websites to be trustful, it has to be digitally signed and must include the publication date, integrity and authenticity, because digital signatures can be useful to the audience and the public interested in that specific paper/article, to validate the source of the data and also through the date, to find if the data was modified since the first date of publication (8ODP, 2007).

4. A presumption of openness

As the Sunlight Foundation stated on the Open Data Policy Guidelines, “Setting the default to open means that the government and parties acting on its behalf will make public information available proactively and that they’ll put that information within reach of the public, with low to no barriers for its reuse and consumption. Setting the default to open is about living up to the potential of our information, about

38 looking at comprehensive information management, and making determinations that fall in the public interest.” This presumption relies on the Freedom of Information Act. That includes data catalogues, procedures, management records and governmental data (8ODP, 2007).

5. Documented

For the data to be trustful, useful and also have high value, it has to be as much meticulously documented as possible. The documentation of the data includes the author’s name, the date, the methods used to collect the data, the process operation, if there are any, that was used to process the data and also some documentation about the format of the data (8ODP, 2007).

6. Safe to open

In order to keep safe whoever needs to download data from open data websites, datasets have to be published in formats that does not support executable content. The risks of having executable content within a dataset are that it may have some kind of malware such as viruses or worms. So, to prevent all this, Governments publishing data in their open data sites must always guarantee security to the public (8ODP, 2007).

7. Designed with public input

The open Government data can help create new public goods and also increase the transparency, reliability, accountability, honesty and efficiency of a Government because these are posted online so for the public to freely access these files. As such, these data must be designed with public input, which means that Governments must highlight and also state the achievements and missions needed, in order to realize some goals. In this way, more and more public will be interested in these data and also stakeholders from the public sector and the value of these data will increase (8ODP, 2007).

39 3.3 The Open Data in Greece

3.3.1 The Central Open Data Portal in Greece

In Greece, after the provision taken by the European Parliament and Council and also the Greek government, the government opened an open data portal with URL: http://www.data.gov.gr/ in 2013.

Figure 3.2: Greek Central Open Data Portal

Source: (GODPD.G, n.d.)

More specifically, the Data.gov.gr is an open data policy implementation tool that follows the implementation of the relevant legislation and has integrated the instructions taken in the 2013/37 / EU. The framework that leads this action is (GODPTP, 2013):

40 • Law 4305/2014 (Government Gazette 237 / A) “Open disposal and re-use of public sector’s documents, information and data, amendment of Law 3448/2006 (Α 57), an adaptation of the national legislation to the provisions of the Directive 2013/37 / EU of the European Parliament and of the Council, further enhancing transparency, Regulations of the ECHR Introductory Contest and other provisions • Law 3448/2006 (Government Gazette 57 / A) "On the re-use of public sector information and the regulation of matters of competence of the Ministry of Interior, Public Administration and Decentralization". (GODPTP, 2013) • Explanatory Memorandum of the Law 4305/2014 • Circular No. DD / F.40 / 407 / 8.1.2015 on the "Application of the provisions of Chapter A of the Law 4305/2014 (Government Gazette 237 / A) on the" open disposal and re-use of documents, public sector information and data, amending the provisions of Chapter One of Law 3448/2006, adapting national legislation to the provisions of Directive 2013/37 of the European Parliament and of the Council and further enhancing transparency in the public sector, (APA: OORMX-MBL) • SDN No. F. / 19710 / 16.6.2015 circular (APA: 7XX465FTHE-B2I) on "Open disposal and further use of public sector documents, information and data in accordance with Chapter A of Law 4305/2014" • No. Ref.: DHD / F.40 / 2369 / 24.1.2017 circular (appoint: 6Γ07465ΧΧΧ-473) on "Accelerating actions by the obliged entities for the availability and further use of open data in application of Law 4305/2014 (Government Gazette, 237a) • No. Entrance: DDD / 17544 / 11.5.2018 circular (APA: 676N465XHC-KEP) on the subject "Updating decision of article 10 of Law 4305/2014" • No. Circular: DDD / 3274 / 22.1.2019 circular on "Open data availability in accordance with Law 4305/2014"

This Open Governmental Database was created in order to gather as much information as possible from all the municipalities, regions, prefectures and ministers, but also the geographical data, traffic accidents, road construction, electromagnetic fields, oceanographic data, the energy and whatever data is possible to gather, in order for the Greek people to be informed in whatever they are interested and happens in their country. So, in order to be easier for them to gather all this data, many regional

41 and local portals were also created. Nowadays almost 338 operators are in collaboration with the Greek government so to create, publish and elaborate data and upload them all in this central website. All these portals and data are a hundred per cent free to everyone (not only to the Greek community) to be browsed, downloaded, processed and re-published. The website open.gov.gr is the central catalogue of all public data and with it, access is granted to all databases of the Greek public sector. The purpose of data.gov.gr is to increase the access to high-value, machine readable datasets by providing integrated cataloging, indexing, storage, retrievability and availability of public sector data and information, as well as online services to citizens and third-party information systems. This policy is provided by some special licenses called Creative Commons (CC) licenses that enable the free distribution of public sector’s data. In order to achieve this free distribution and publication of data there is a need of National coordination with National guidelines on publications, with all regional initiatives coordinated to a National level, with as much regional portals integrated into this main open portal (the data.gov.gr) as possible and with a lot of regional initiatives. As the statistics shows, these data are really appreciated and used by the users with 2,500 unique IP visitors on average per month of which only the 6% of them are foreign (EDPCFG7, 2017). Since now, the five most processed datasets on this website are the Management of Information and Communication that had been processed 23 times, the Decisions of the Department of Urban Planning & Applications (Directorate of Environmental and Spatial Planning of Thessaly) that had being processed 19 times, the Archive of Primary Education of Lesvos Prefecture that had being processed 14 times, the Administration Address by 14 times and the file of demarcated settlements in the Prefecture of Ioannina by 13 times (MEGDG, n.d.). In this site, there are 10,183 datasets available created by 338 operators, since the 17th of January 2020. These websites of these 338 operators are included, along with the rest of the organizations the publish open data in Greece, in the tables of Chapter 3.3.3.

Furthermore, in this website there are also 180 Ideas and Applications. These applications are created so to process specific datasets and the ideas are about what can be done in the future with those applications. Moreover, there are 17 groups so to create and manage various collections of datasets, as shown in Table 2. Also, these

42 collections can be for a specific project group of a topic, so to help users search and find the datasets easier (CGODP - G, n.d.).

Table 2: Open Data Groups

Source: the author (2020)

Name of Group Number of datasets International Issues 2 Government and Public Sector 398 Statistical Data 88 Energy 20 Economic and Financial Issues 55 Geospatial and Environmental 95 Education, Culture and Sport 30 Population and Society 16 Justice, Legal System and Public Security 25 Elections 9 Work 35 Transports 103 Agriculture, fisheries, forestry and foo 24 Science and Technology 4 Tourism 18 Regions and Cities 9 Health 52 Total 983

Moreover, the five most viewed datasets are Finance and contracts, geospatial, statistics, education and Governmental accountability and democracy, with the most downloaded dataset to be the Registry of Accountants by Economic Chamber of Greece (EDPCFG7, 2017). In 2015 a program was launched so to monitor the impacts of these data and in 2017, the open data made 100% impact on the Political Sector of Greece, had 75% impact on the Social Sector and 0% impact on the Economical Sector of the country as shown in Figure 3.3 (EDPCFG7, 2017).

43

Figure 3.3: Open Data Impact in Greece 2017

Source: (EDPCFG7, 2017)

Starting from 2013 since today, there have been measurements for the impact, policy, portal and quality of the open data in all countries of the EU. According to the European Data Portal, from the “country factsheet Greece 2019”, Greece has an overall maturity score equal to the 66% which is exactly on the average of the rest of all the other 28 European countries participating in the open data campaign. More specifically, there are four dimensions in which the maturity of open data in the various European countries that are Policy, Impact, Portal and Quality, is measured. According to the “country factsheet of Greece in 2019” Greece scores are (EDPCFG9, 2019):

• In the Policy dimension, the indicators of Policy Framework equal to 84%, the Governance, which is the Coordination at National Level, is at 77% and the Implementation, that is the Licencing norms, is at 81%. • In the Impact dimension, the indicators of Strategic Awareness equal to 75%, the Political equal to 69%, the Social of 42%, the Environmental equal to 60% and the Economic is of 23%. • In the Portal dimension, the indicators of Portal features equal to 69%, the Portal Usage is of 100%, the Data Provision is of 55% and the Portal sustainability is of 43% and with an Overall score of 79% • In the Quality dimension, the indicators of Currency and Completeness is of 63%, the Monitoring and Measures is of 72%, the DCAT-AP Compliance is of 68% and the Deployment Quality is of 38%, with an Overall score of 61%

44 The main problems preventing the further progress and publication of data to the open portals in Greece, has to do with how well-organized a country is and the lack of national and regional events, in order to further promote the Open Data policies. Due to the lack of informatization and automation, according to the “country factsheet of Greece 2016” only the 25% of data was uploaded automatically. Another problem connected to the barriers of the development of open data in Greece is the lack of competent personnel for the diffusion of data in some of the Public Sector’s bodies. Moreover, the impact of Open Data has decreased because of the few economic studies that have been conducted through the last four years. The next steps that will be done in Greece, in order to promote the Open Data, are to identify and activate the major important Public Sector Entities, upgrade the quality of the datasets, the transition to an updated and upgraded data portal and the promotion for the reuse of the open data by both Public and Private sectors (EDPCFG6, 2016).

3.3.2 Transparency project in Greece

Starting from the 1st of October 2010, all Greek institutions of the government are obligated to upload every act and decision on the Internet so to be judged as valid, more specifically on the website of the project Diavgeia, with URL: https://diavgeia.gov.gr/. To each of the acts and decision, a unique digital signature is assigned, called (IUN) Internet Uploading Number. In this way, each decision uploaded to the Transparency portal in certificated and valid. This process follows the initiative of the Ministry of Administrative Reform and e- Governance (Law 4210/2013) that declares that all acts and decision if not published online lose their validity. By uploading all the acts and decisions on this website, the Inspectors- Controllers Body for Public Administration (I.C.B.P.A.) can easily observe and investigate issues concerning legality and if a good administration of the public entities is done. This project introduces a high level of transparency between all levels of the Greek public sector and the citizens. In order to promote and implement this project, each Ministry has its own Project Task Force and thirteen Ministries take part in a group of supporting the project, called Joint Task Force, that focuses on the coordination and education of their associates as well as the collection of feedbacks,

45 the solvation of common problems and the share of best practices along its participants of the Task Force. An educational and training program, concerning all technical, business and legal issues, takes place in each Region. Also, assistance and information are provided on the official website of the project Diavgeia, solving all technical and operational issues, by providing supporting and training material. Moreover, the impact of this project is found on how the officials use their executive power. The main objectives of this Transparency project have to do with (GrD, n.d.):

• For all government actions their transparency to be safeguarded • The exposition of corruption is made easier and so it is gradually eliminated • Observation of fair administration and legality • The participation of the citizens in the Information Society is reinforced • The existing decision’s and administrative act’s publication system is modernized and enhanced • Regardless of the knowledge level of a citizen about the internal processes of the administration, all administrative acts and decisions are uploaded in formats that are easily accessible, readable and understandable to everyone

The public authorities, starting with the Ministries in October 2010, a month later, in November of 2010 and last in March 2011, adopted this program making the rate of uploads per work day of 16,000 decisions per day. This project is internationally recognized at a technological and organizational level, as a designer of a prototype for the future e-Government interventions. Furthermore, during a quality conference cycle of European Public Administration Network (EUPAN) called “Doing the right things right - Towards a more result-oriented public sector in Europe”, the Transparency project Diavgeia was presented at the 6th European Quality Conference as a Best Practice. In 2014 some improvements have been made to this Transparency project concerning the tools and policies of this program. These improvements were made to strengthen the Program through the Law 4210/2013 (O.G. A’ 254) building the new enhanced transparency portal and using the program as a tool for monitoring and controling the public sector’s activities (GrD, n.d.).

46 3.3.3 Greek Open Data Portals, Governmental and non

The Open Data term includes the Open Governmental Data. In Greece, there are Open Data websites for the Government, the 18 Ministries, the 13 Regions, the 338 Municipalities, the 7 Decentralized Administrations and some other entities like the Greek Statistics Authority or the Bank of Greece and some Universities, that collaborate with the government so to create and provide valuable data to the public and also some private Open Data websites that collect various types of data. The majority of these operators upload their datasets on the central webpage of the Greek Government as well, that is http://www.data.gov.gr/. Following, in Table 6 all Greek Ministries’ portals are presented, in Table 5 all Greek portals for the Regions, in Table 21 all the portals of the Municipalities, in Table 4 all Greek Decentralized Administration portals, in Table 3 some older Ministries that also have data in their portals and in Table 22 all the other entities that publish open data in Greece are presented.

Table 3: Open Data websites of Older Ministries in Greece

Source: the author (2020)

Older Ministries URL MINISTRY OF ADMINISTRATIVE http://minadmin.ypes.gr/ RECONSTRUCTION MINISTRY OF INTERIOR http://www.mathra.gr/ (MACEDONIA - THRACE)

Table 4: Open Data websites of Decentralized Administrations in Greece

Source: the author (2020)

Name of Decentralized URL Administration Decentralized Administration of http://www.apdattikis.gov.gr/ Attica Decentralized Administration of http://www.apdhp-dm.gov.gr/ Epirus and Western Macedonia

47 Decentralized Administration of http://www.apdthest.gov.gr/Intro/Default.aspx Thessaly and Central Greece Decentralized Administration of https://www.apdkritis.gov.gr/ Crete Decentralized Administration of http://www.damt.gov.gr/ Macedonia and Thrace Decentralized Administration of Peloponnese, Western Greece http://www.apd-depin.gov.gr/ and the Ionian Decentralized Administration of http://www.apdaigaiou.gov.gr/ the Aegean

Table 5: Open Data websites of Regions in Greece

Source: the author (2020)

Name of Region URL Region of Eastern Macedonia and http://www.pamth.gov.gr/index.php/el/ Thrace Region of Attica http://www.patt.gov.gr/site/ Region of North Aegean https://www.pvaigaiou.gov.gr/ Region of Western Greece https://www.pde.gov.gr/gr/ Region of Epirus http://www.php.gov.gr/ Region of Thessaly https://www.thessaly.gov.gr/ Region of Ionian Islands https://www.pde.gov.gr/gr/ Region of Central Macedonia http://www.pkm.gov.gr/ Region of Crete https://www.crete.gov.gr/ Region of South Aegean http://www.pnai.gov.gr/ Region of Peloponnese https://www.ppel.gov.gr/ Region of Central Greece https://pste.gov.gr/ Region of Western Macedonia https://www.pdm.gov.gr/

48 Table 6: Open Data websites of Ministries in Greece

Source: the author (2020)

Name of Ministries URL Ministry of Finance https://www.minfin.gr/ Ministry of Development and http://www.mindev.gov.gr/ Investments Ministry of Foreign Affairs https://www.mfa.gr/ Ministry of Civil Protection http://www.mopocp.gov.gr/main.php Ministry of Defense http://www.mod.mil.gr/ Ministry of Education and https://www.minedu.gov.gr/ Religion Ministry of Labor and Social https://www.ypakp.gr/ Affairs Ministry of Health https://www.moh.gov.gr/ Ministry of Environment and http://www.ypeka.gr/ Energy Ministry of Culture and Spot https://www.culture.gr/el/SitePages/default.aspx Ministry of Justice https://www.ministryofjustice.gr/ Ministry of Interior https://www.ypes.gr/ Ministry of Digital Governance https://mindigital.gr/ Ministry of Infrastructures and http://www.yme.gr/ Transport Ministry of Shipping and Island http://www.yen.gr/ Policy Ministry of Rural Development http://www.minagric.gr/index.php/el/ and Foodstuff Ministry of Tourism http://www.mintour.gov.gr/ Ministry of Migration and Asylum http://asylo.gov.gr/

49 3.3.4 The Evolution of Open Data in Greece

Starting from 2016, the EU has created a method of measurement of the maturity and progress of the Open Data in Europe. More specifically, a questionnaire has been made for this specific evaluation, as shown in Figure 3.4. In this questionnaire, each question and answer are associated with points, so to make the final evaluation of each country’s Open Data for all Dimensions and Indicators. Also, from this evaluation questionnaire, the maximum of points that can be acquired in each dimension is showed. This maximum of point for each dimension is the sum of the maximum of points of all questions, as shown in Table 7 and Table 8 (EODMQ, 2019).

Figure 3.4: Questionnaire for the maturity of the Open Data

Source: (EODMQ, 2019)

According to the European Data Portal, the evolution and maturity of the Open Data in Greece, starting from the Country’s factsheet of 2016 until the one of 2019 is shown in Table 7 and Table 8 (EDPCM, n.d.).

50 Table 7: Evolution and Maturity of Open Data in Greece 2016 -2017

Source: (EDPCM, n.d.)

Greece Dimension Indicator Greece 2016 Mix Max 2017 Open Data Readiness - Presence Policy 295 330 350 400 Policy and Use National 120 130 130 140 Coordination

Licensing Norms 70 70 75 80 Use of Data 140 260 165 300 Portal Maturity Usability 20 60 25 90

Re-usability of data 60 140 90 140 Spreads of data 35 50 50 50 across domains Open Data Readiness - Political 120 120 120 120 Impact

Social 45 60 45 60 Economic 70 120 30 120 Open data

Readiness- Policy 625 790 915 1220 and Use Open Data 235 300 195 300 Readiness Impact

Portal Maturity 115 250 165 280 Total Score 975 1340 1080 1500

As shown in Table 7, in the years 2016 and 2017, there are three dimensions so to evaluate through them the progress and maturity of the Open Data in a country. Each dimension has some indicators so to better understand them and make the

51 evaluation more specific. The first dimension is the Open Data Readiness - Policy and Use with four indicators, such as the Presence Policy, the National Coordination, the Licencing Norms and the Use of Data. The second dimension is the Portal Maturity that has three indicators. These indicators are the Usability, the Re-usability of data and the Spreads of data across domains. Finally, the third dimension is the Open Data Readiness Impact with three indicators that are the Political, the Social and the Economic. More specifically, in 2016 in Greece, in the Open Data Readiness - Policy and Use dimension, the overall score is 625 points, in the Portal Maturity dimension the overall score is 115 points and the Open Data Readiness Impact has an overall score of 235 and the total score is 975 points. Continuing with 2017, the Open Data Readiness - Policy and Use dimension has an overall score of 915 points, the Portal Maturity dimension has an overall score of 165 points, the Open Data Readiness - Impact has an overall score of 195 and the total score is 1,080 points. Having mentioned all these, in confrontation to the maximum that can be achieved in each dimension, the percentage of maturity in the Open Data achieved by Greece in 2016 was (EDPCFG6, 2016):

• In the Open data Readiness- Policy and Use was 79.11% • In the Open Data Readiness Impact was 78.33% • In the Portal Maturity was 46% • With a total of Open Data maturity 72.76%

Furthermore, in 2017, the percentage of the progress in maturity of the Open Data in Greece, in confrontation to the maximum that can be achieved in each dimension was (EDPCFG7, 2017):

• In the Open data Readiness- Policy and Use was 75% • In the Open Data Readiness Impact was 65% • In the Portal Maturity was 58.98% • With a total of Open Data maturity 72%

Continuing with the two next years, 2018 and 2019, the progress of the Open Data project is as shown in Table 8.

52 Table 8: Evolution and Maturity of Open Data in Greece 2018 - 2019

Source: (EDPCM, n.d.)

Dimension Indicator Greece Max Greece 2019 Max 2018 Policy Policy 150 180 185 220 framework National 275 350 165 215 Coordination Licensing 130 150 170 210 Norms Portals Portal features 185 250 165 240 Portal Usage 110 120 160 160 Data provision 130 160 55 100 Portal 85 120 65 150 Sustainability Impact Strategic 170 200 105 140 awareness Political Impact 100 130 90 130 Social impact 70 110 50 120 Environmental 40 80 90 150 impact Economic 80 130 25 110 impact Quality Automation 45 100 115 160 Data & 120 210 95 150 metadata currency DCAT-AP 150 210 115 170 compliance Deployment - 65 170 Quality Policy 555 680 520 645

53 framework Portal 510 650 450 650 Impact 460 650 360 650 Data quality 315 520 390 650 Open data 73.6% 2500 66.0% 2595 maturity

Through the years, as the Open Data evolved and grew, changes had been made for its evaluation so to better determine the points that need focus so to meliorate the service provided by them. In 2018, as shown in Table 8, instead of three dimensions that had being used in the previous two years, changes have been made in the method of evaluation of the Open Data and another dimension has been created. Each dimension is followed by some indicators as in the previous years and the first one is the Policy dimension with three indicators, such as the Policy Framework, the National Coordination and the Licencing Norms. The second dimension is the Portals with four indicators that are: The Portal features, the Portal Usage, the Data provision and the Portal Sustainability. The third dimension is the Impact, with five indicators, that are: The Strategic awareness, the Political Impact, the Social impact, the Environmental impact and the Economic impact. Finally, the fourth dimension is the Quality, with four indicators such as the Automation, the Data and metadata currency, the DCAT-AP compliance and the Deployment Quality. More specifically, in 2018 in Greece, the overall score in the Policy dimension is 555 points, in confrontation to the maximum that is 680. The overall score of the Portal dimension is 510 points, in confrontation to the maximum that can be accomplished, that is 650 points. Continuing with the Impact dimension that the data has when published, Greece has a score of 460 points, over the 650 points that is the maximum and finally, in the Data Quality dimension, Greece has a total score of 315 points, with the maximum being 520.

Having mentioned all these, in confrontation to the maximum that can be achieved in each dimension, the percentage of maturity in the Open Data achieved by Greece in 2018 was (EDPCFG8, 2018):

54 • In the Policy Dimension was 80.88% • In the Portals dimension was 74.46% • In the Impact dimension was 70.46% • In the Quality dimension was 60.57% • With a total of Open Data maturity 73.6%

Moreover, in 2019, the percentage of the progress in maturity of the Open Data in Greece, in confrontation to the maximum that can be achieved in each dimension was (EDPCFG9, 2019):

• In the Policy Dimension was 80.62% • In the Portals dimension was 69.23% • In the Impact dimension was 55.38% • In the Quality dimension was 60% • With a total of Open Data maturity 66%

Following, as shown in Figure 3.5, a graphical representation of the maturity of the Open Data in Greece, starting from 2016 and ending in 2019, can better help understand the progress of this project.

3000

2500

2000

Achieved 1500 Max Dif. from Max 1000

500

0 2016 2017 2018 2019

Figure 3.5: Open Data Progress in Greece from 2016 to 2019

Source: the author (2020)

55 In Europe, four groups have been created so to determine the point that a country is in its progress and maturity with the implementation of the Open Data project. These four groups are the Beginners, the Followers, the Fast Trackers and the Trendsetters. Starting from 2016, Greece had an average maturity of 72.76%, a difference of 365 points from the max points, which made Greece be in the Fast Trackers group. At this stage, Greece had two barriers that did not help improve the implementation of the Open Data project. There was not enough awareness and promotion of the Open Data policies with no national or regional event so to promote this project and the financial barrier. In the financial barrier, there are two points to be taken into account by Greece, the poor investments of the Public sector in raising awareness from the benefits that the re-use of data has as result and the lack of competent personnel to promote and diffuse the data. Continuing with 2017, Greece has an average on the maturity of the Open Data of 72%, a difference of points from the max points of 420, making it to be in the Fast Trackers group. At this point, the barriers that Greece has were of financial nature and little awareness with no regional or national events so to promote the Open Data in the country. In 2018, Greece had an overall score of 73.6%, a difference of points from the max points of 660, making it become in the 8th place as Open Data maturity in Europe and also being in the Fast Trackers group. In 2019, Greece fell to the Follower's group, with an overall average of 66% on the maturity of the Open Data, with a difference of points from the max points of 875. In those two last years, the barriers preventing the progress of the open data were the same as those of the previous years, meaning of financial nature and awareness on the benefits of the Open Data and as it seems, since those two problems were not solved through the years, the evolution and maturity of the Open Data project in Greece lost some points and from the category Fast Trackers, Greece felt to the category of Followers (InVEDP, n.d.).

3.4 Evolution of the European Unions’s Open Data Portals

The European Union has created through the years five specific websites in order to collect and manage both the European Countries Open Data and the European institutions and buddies. The central webpage of all those five ones is the

56 https://data.europa.eu/ and through it the public can access all the rest. These five websites are the EU Open Data Portal, the European Data Portal, the Persistent URIs, the EU web archive and the Support Center for Data Sharing (EUODPH, n.d.).

3.4.1 The EU Open Data Portal

With the conference of the European Commission 2011/833/EU of the 12th December of 2011, on the reuse of the European documents, the decision was taken so to set up a unique data portal as a single access point for the upload of documents. These documents on this specific website, refer only to the ones regarding the European institutions and buddies only and can be re-used for both commercial and non-commercial applications. Starting from 2012, the European Data Portal was opened, with URL: https://data.europa.eu/euodp/ so, to collect all these data and documents and as a standardized portal, easily accessible to the public. The nature of the data included in this site is of statistics, legal acts, data regarding crime, election results, geopolitical and financial, health, geographical, transportation, scientific research and environmental. The aim of this portal is to increase the transparency of the European institutions and to boost the economic growth and development (EUODP, n.d.).

3.4.2 The European Data Portal

For the development and creation of this portal, the European commission collaborated with the help of a consortium, the members of which are Capgemini Consulting, INTRASOFT International, Fraunhofer FOKUS, The Lisbon Council, Time.Lex and the University of Southampton, in order to create this European Portal. The portal was lunched on 16 November 2015 and serves as a central key web-portal for the collection of data published by the administration of the European Countries. The URL of this portal is https://www.europeandataportal.eu/ and can serve the countries involved in two main ways. The first one is to centralize and provide a large number of datasets from all countries across Europe and the second one is to help

57 each individual country, by having freely access to it and to see other countries’ datasets and publications. The last one can help each individual country by impelling them to public or make that kind of implementations seen by others and that they could have not though about (EuDPUK, n.d.; OSOR, 2015; EDPT, n.d.).

3.4.3 The resource with Persistent URIs

The abbreviation of URI stands for Uniform Resource Identifier and is an identifier for all short of things, like locations, buildings and people. The European Commission made a Persistent URI with URL: https://data.europa.eu/URI.html as a trusted and dedicated service, which is independent from the originator of the information. More specifically a URI is “a compact sequence of characters that identifies and abstract or physical resource” (T. Berners-Lee, 2005). In other words, this European URI is a central webpage from which the public can access other pages and databases like the Food Safety Data Catalogue, the European Interoperability Framework and the European Legislation Identifier (EUURI, 2014).

3.4.4 The European Web Archive

Because web technologies progress in a quick way, the European Union, starting from 2013, made a web archive so to save information and data about older versions of webpages, events and older domains of sites for all the EU agencies, institution and buddies (EUWA, n.d.).

3.4.5 The Support Center for Data Sharing

Finally, the last web service hosted by the European Union is the Support Center for Data Sharing, with URL: https://eudatasharing.eu/. The SCDS is an initiative of the EU and focuses on documenting, reporting and researching of the

58 European Union’s Framework, the distribution technologies and access technologies and the data sharing practices that are only on-topic to organizations and that only imply legal changes, novel models and technological changes (EUSCDS, n.d.).

3.5 Overview and Maturity of the Open Data in EU28+

According to the European Data Portal, the evolution and maturity of the Open Data in Europe, starting from 2016 until 2019 is showed in Table 9, Table 10, Table 11 and Table 12. What is more, four levels have been created considering the level of maturity of the open data. These groups are the Beginners, the Followers, the Fast Tracker and the Trend Setters (EDPCM, n.d.). The definition of each level is listed below:

• Beginners: are called those who in Open Data policy and portal features are in an early stage. However, because of the limitations that have to do with the portal functionalities, accessibility and availability, a small amount of data sets exist for the public to be re used • Followers: are called those who have developed a basic Open Data policy successfully and have upgraded that portal with more advanced features. However, limitations still exist for the public in terms of data released and in the re use and use of data • Fast Trackers: are those that have significantly developed either a portal or a policy for the open data. However, a small number of drawbacks still exist in this level in the harvest of benefits in either their portal or policy • Trend Setters – Leaders: are those that have implemented a national coordination and open data policy across all its domains, with extensive features in their portals

59 3.5.1 Open Data Maturity in EU+28 in 2016

Following, in Table 9, the maturity of the Open Data in the European countries in 2016 is showed.

Table 9: Maturity of the Open Data in Europe 2016

Source: (EDPCM, n.d.)

Open data Open Data Portal Year: 2016 Readiness- Readiness Total Score Maturity Policy and Use Impact Country/Max 790 300 250 1340 Score Austria 660 175 210 1045 Belgium 400 50 190 640 Bulgaria 565 245 205 1015 Croatia 585 30 190 805 Cyprus 470 130 170 770 Czech Republic 545 50 140 735 Denmark 265 110 175 550 Estonia 425 80 230 735 Finland 615 195 205 1015 France 675 245 230 1150 Germany 410 70 210 690 Greece 625 235 115 975 Hungary 350 140 80 570 Ireland 660 210 200 1070 Italy 435 90 170 695 Latvia 200 0 0 200 Lithuania 215 185 130 530 Luxembourg 490 30 250 770 Malta 225 0 0 225 Netherlands 715 130 210 1055

60 Norway 500 155 180 835 Poland 445 170 135 750 Portugal 275 100 170 545 Romania 580 80 180 840 Slovakia 555 270 160 985 Slovenia 510 115 185 810 Spain 725 280 220 1225 Sweden 410 80 100 590 Switzerland 425 70 160 655 United Kingdom 640 220 180 1040 Iceland No data No data No data No data Liechtenstein 0 0 0 0

In 2016, the country with the biggest maturity on the Open Data was Spain, that in the dimension of Open data Readiness- Policy and Use had the biggest total score, with 725 out of 790 points, in the dimension of Open Data Readiness Impact also had the biggest score, with 280 out of 300 points, in the Portal Maturity was in the fifth place, with 220 out of 250 points, in contrast to Luxemburg that in this dimension, was the first that year, with 250 out of 250 points, and with Spain’s total score of 1,225 points. Furthermore, Greece acquired the tenth place in Europe that year with a maturity of 975 points out of 1,340 that was the maximum in 2016, making it be in the group of Fast Trackers. Moreover, Iceland did not take part in the project this year and so no data was published and Liechtenstein, because of not having a legal basis definition from a political perspective, the project was not defined and also due to little awareness on the open data about its existence, it was not able to publish any data (EUOD, 2016; Lcf, 2016; EDP- NonI, 2017). Following a representation of the maturity group of the countries in Europe is showed in Figure 3.6.

61

Figure 3.6: Open Data Maturity Groups in Europe 2016

Source: (EDPODM6, 2016)

In 2016, eight countries had achieved high enough scores so to be considered to be in the group of Trend Setters, which are the United Kingdom, Ireland, Bulgaria, Estonia, France, Finland, Austria and the Netherlands. In addition, eight countries are in the Fast Tracker level, twelve countries are in the Followers level and only three are in the Beginners level. The countries that achieved the Trend Setters level constantly develop their portals and have strong policies (EDPODM6, 2016).

3.5.2 Open Data Maturity in EU+28 in 2017

Following, in Table 10, the maturity of the Open Data in the European countries in 2017 is showed (EDPODM7, 2017).

62 Table 10: Maturity of the Open Data in Europe 2017

Source: (EDPODM7, 2017)

Open data Open Data Readiness- Readiness Portal Year: 2017 Policy and Use Impact Maturity Total Score Country/Max Score 1220 300 280 1500 Austria 910 135 240 1150 Belgium 790 105 230 1020 Bulgaria 920 130 215 1135 Croatia 895 180 250 1145 Cyprus 950 160 180 1130 Czech Republic 815 90 200 1015 Denmark 710 135 160 870 Estonia 655 30 220 875 Finland 1100 245 255 1355 France 1120 300 245 1365 Germany 790 160 260 1050 Greece 915 195 165 1080 Hungary 610 50 110 720 Iceland 270 30 135 405 Ireland 1185 300 260 1445 Italy 985 230 230 1215 Latvia 820 160 205 1025 Liechtenstein 5 0 0 5 Lithuania 685 200 160 845 Luxembourg 1000 135 275 1270 Malta 485 30 75 560 Netherlands 1140 270 240 1380 Norway 980 165 230 1210 Poland 735 110 195 930 Portugal 495 50 215 710

63 Romania 920 100 260 1180 Slovakia 930 170 230 1160 Slovenia 1025 200 210 1235 Spain 1150 300 260 1410 Sweden 755 145 220 975 Switzerland 600 30 170 770 United Kingdom 965 240 235 1200

Continuing with 2017, the country with the highest score in Europe was Ireland that in the dimension of Open data Readiness- Policy and Use was in the first place, with a score of 1185 out of 1220 points, in the Open Data Readiness Impact was also in the first place, with a score of 300 points out of 300, in the Portal Maturity dimension was in the third place, with a score of 260 out of 280 points and with a total maturity of 1445 out of 1500 points. The country with the highest score in the Portal Maturity dimension for that year was Luxemburg with 275 points out of 280. Furthermore, Greece felt from the tenth place in 2016 to the seventieth place in 2017, with an overall score of 1080 out of 1500 points. In addition, Liechtenstein still is in the last place because it continues to have political and legal barriers on the implementation of the open data project, being the only country this year to be in the Beginners level (EDPODM7, 2017; EDPL, 2017). Following a representation of the maturity group of the countries in Europe for the year 2017 is showed in Figure 3.7.

64

Figure 3.7: Open Data Maturity Groups in Europe 2017

Source: (ODMEU, 2017)

As shown in Figure 3.7, a near doubling of countries of the Trend Setters group was this year, in comparison to the previous one, with fifteen countries out of thirty- two. Half of the EU28+ countries were in the Trend Setter’s group and a twenty five percent in the Fast Trackers that specific year. Comparing this year’s results with the previous ones we see that in 2016, in the Beginner level, there were three countries in comparison to the one country in 2017. In the Followers level, from twelve countries in 2016, only eight remained in this level in 2017. In the Fast Trackers level, the same number of countries remained in 2016 and 2017 and finally in the Trendsetters level, where in 2016 were just eight, in 2017 fifteen countries had reached this level. Worthy to mention at this stage is Latvia that made a frog leap and from the Beginners level in 2016 went to the Followers level in 2017 (ODMEU, 2017).

65 3.5.3 Open Data Maturity in EU+28 in 2018

Following, in Table 11, the maturity of the Open Data in the European countries in 2018 is showed.

Table 11: Maturity of the Open Data in Europe 2018

Source: (EDPODM8, 2018)

Policy Data Open data Year :2018 framework Portal Impact quality maturity Country/Max Score 680 650 650 520 2500 Austria 595 480 340 205 64,8 Belgium 585 420 235 390 65,2 Bulgaria 610 400 305 330 65,8 Croatia 610 370 200 365 61,8 Cyprus 610 545 455 380 79,6 Czech Republic 550 280 285 425 61,6 Denmark 430 185 155 145 36,6 Estonia 550 310 135 105 44 Finland 525 545 190 285 61,8 France 645 520 515 395 83 Germany 525 350 250 465 63,6 Greece 555 510 460 315 73,6 Iceland 200 165 25 25 16,6 Ireland 620 525 625 425 87,8 Italy 655 455 475 415 80 Latvia 530 400 355 370 66,2 Liechtenstein 10 0 25 0 1,4 Lithuania 480 280 130 255 45,8 Luxembourg 620 495 485 300 76 Malta 420 0 50 0 18,8 Netherlands 595 455 340 435 73

66 Norway 475 360 30 300 46,6 Poland 550 465 400 230 65,8 Portugal 450 360 125 330 50,6 Romania 490 520 250 290 62 Slovakia 620 465 385 370 73,6 Slovenia 650 505 345 365 74,6 Spain 635 505 630 405 87 Sweden 420 370 115 385 51,6 Switzerland 455 415 165 365 56 United Kingdom 560 410 445 350 70,8

Continuing with 2018, the country with the highest overall score was Ireland, with 2125 points. At this point, the different dimensions for the measurement of the maturity of the open data had changed and from three became four, due to the evolution of the portals and the need of more efficient calculation of the scores. More specifically, instead of the three dimensions that were used in the previous years, that are Open data Readiness- Policy and Use, Open Data Readiness Impact and Portal Maturity, starting from 2018, the four dimensions that will be used are Policy framework, Portal, Impact and Data quality. Also, the indicators of these dimension were rearranged and the now ones are:

• For the Policy dimension, the indicators are the Policy framework, National Coordination and Licencing Norms will define the maturity level of each country • For the Portals dimension, the indicators are the Portal features, Portal Usage, Data provision and Portal Sustainability that will define the maturity level of each country • For the Impact dimension, the indicators are the Strategic awareness, Political Impact, Social impact, Environmental impact and Economic impact that will define the maturity level of each country

67 • For the Quality dimension, the indicators are the Automation, Data and metadata currency, DCAT-AP compliance and Deployment Quality that will define the maturity level of each country

As the dimension was rearranged, the indicators changed as well and from ten became fifteen. The previous two years, the country that scored first, was also usually first in each separate dimension. However, in 2018 the countries that scored first were Italy, with 655 points out of 680 in the Policy framework dimension, Cyprus, with 545 points out of 650 for the Portal dimension, Spain, with 630 out of 650 points in the Impact dimension and Germany, with 465 points out of 520 for the Data quality dimension (EDPODM8, 2018). Following a representation of the maturity group of the countries in Europe is showed in Figure 3.8.

Figure 3.8: Open Data Maturity Groups in Europe 2018

Source: (EDPODMC8, 2018)

In 2018, only five countries obtained high scores so to achieve the Trend Setters level due to their advanced level of maturity in all four dimensions. These first five countries also achieved high overall scores due to the variety, volume and quality of the data published, the updates made in their respective portals, the various projects so to boost impact of the data in the Political, Social, Economic and Environmental dimension and the re use of data published. Comparing this year’s results with the one of the previous year, in the Beginners level, from one country in 2017 to three

68 countries in 2019, in the Followers level, from eight became seven, in the Fast Trackers level, from eight countries became sixteen and in the Trend Setters, from fifteen countries that were at this level in 2017, only five remained in 2018. Moreover, Hungary was invited, but did not participate in the Open Data program in 2018 (EDPODMC8, 2018; EDP- NonH, 2018).

3.5.4 Open Data Maturity in EU+28 in 2019

Following, in Table 12, the maturity of the Open Data in the European countries in 2019 is showed.

Table 12: Maturity of the Open Data in Europe 2019

Source: (EDPODM9, 2019)

Policy Data Open data Year: 2019 Portal Impact framework quality maturity Country/Max 645 650 650 650 2595 Score Austria 435 535 330 410 66 Belgium 440 435 330 475 65 Bulgaria 475 385 320 310 57 Croatia 575 445 335 435 69 Cyprus 575 560 530 530 80 Czech 490 290 325 545 64 Republic Denmark 610 420 510 480 78 Estonia 540 435 350 425 67 Finland 465 540 445 515 76 France 630 580 600 505 89 Germany 480 450 435 410 68 Greece 520 450 360 390 66 Hungary 215 220 165 240 32

69 Iceland 20 130 10 50 8 Ireland 590 575 600 595 91 Italy 555 455 510 470 77 Latvia 530 510 425 480 75 Liechtenstein 120 0 30 0 6 Lithuania 490 305 315 265 53 Luxembourg 390 530 320 395 63 Malta 340 370 95 275 42 Netherlands 565 530 415 515 78 Norway 475 390 375 440 65 Poland 585 510 470 450 78 Portugal 245 335 85 420 42 Romania 355 535 195 405 57 Slovakia 260 290 55 285 33 Slovenia 535 540 425 445 75 Spain 580 580 650 520 90 Sweden 365 315 370 380 55 Switzerland 360 340 160 315 45 United 450 385 455 275 60 Kingdom

Finally, in 2019, the country with the highest overall score was also Ireland with 2360 points. Also, the countries with the highest score in each dimension were France, with 630 points out of 645 for the Policy framework dimension, also France, with 580 points out of 650 for the Portal dimensions, Spain, for the Impact dimension, with 650 out of 650 points and Ireland with 595 out of 650 points for the Data maturity dimension (EDPODM9, 2019). Following a representation of the maturity group of the European countries is showed in Figure 3.9.

70

Figure 3.9: Open Data Maturity Groups in Europe 2019

Source: (EDPODMC9, 2019)

This year’s clustering of the different European countries in the four levels shows large distance between the groups and less distance of the countries within each group. Comparing this year’s results with the previous one, in the Beginners level, the three countries became seven, in the Followers level, the seven became fourteen, in the Fast Trackers level, the sixteen countries became eight and in the Trend Setters level, from the five countries of 2018, only five remained (EDPODMC9, 2019). Following, as showed in Table 13, the official portals of each European country are listed:

Table 13: European countries central Open Data Portal

Source: the author (2020)

Name of country URL Austria https://www.data.gv.at/ Belgium https://data.gov.be/en Bulgaria https://data.egov.bg/ Croatia http://data.gov.hr/ Cyprus https://www.data.gov.cy/ Czech https://data.gov.cz/english/ Denmark https://www.opendata.dk/

71 Estonia https://opendata.riik.ee/en/ Finland https://www.avoindata.fi/en France https://www.data.gouv.fr/en/ Germany https://www.govdata.de/ Greece http://www.data.gov.gr/ Hungary http://opendata.hu/ Ireland https://data.gov.ie/ Italy https://www.dati.gov.it/ Latvia https://data.gov.lv/eng Lithuania https://www.stat.gov.lt/ Luxembourg https://data.public.lu/en/ Malta https://open.data.gov.mt/ Netherlands https://www.cbs.nl/ Poland https://dane.gov.pl/ Portugal https://dados.gov.pt/en/ Romania http://data.gov.ro/ Slovakia https://data.gov.sk/ Slovenia http://nio.gov.si/nio/ Spain https://datos.gob.es/en Sweden https://www.scb.se/en_ United Kingdom https://data.gov.uk/

3.6 The Open Data in the rest of the World

3.6.1 International Tools for the Open Data

There are three websites that compare and measure the progress of the open data for a large number of countries. Two of them are private, the Global Open Data Index and the Open Data Barometer and one of those is the European Open Data Portal. These websites practically are international tools that measure the progress of the

72 open data for a big amount of countries. Starting with the European Data Portal, it gathers in one place all the data published by the public sector of all European countries. It also provides information about the benefits of the open data and the re- use of it and forecasts of data. This portal’s strategic objective is to increase the value and also to improve the accessibility to the data. Apart from providing data, this European Portal also has a library with training and e-learning materials about the open data. Furthermore, it unifies the method for the valuation of the maturity and progress of the open data in the various countries of Europe, making it easier for the countries participating to understand the key points to implement, so to improve the open data project (EDPT, n.d.). Continuing with the Global Open Data Index, that it also provides open data published by governments, it measures the openness of the data published by governments. It also works as an annual global benchmark for those data. This website was created by the Open Knowledge Foundation, which is a nonprofit organization and that supports the open data project. Also, the Global Open Data index is supported by the Global Affairs of Canada (GAC), the World Bank, by the United Kingdom’s Department for International Development (DFID) and by the Canada’s International Development Research Centre (IDRC). In addition, this website can help governments understand where data gaps are and also how to make the data published more impactful and usable (GODI, 2016; OKFT, n.d.). Finally, the Open Data Barometer analyzes global trends, provides comparative data to governments and also aims to demonstrate the impacts of the various open data initiatives around the world. The first editions of this world-wide open data toll were covering over a hundred countries around the world, measuring the progress and maturity of their open data projects. However, nowadays the Open Data Barometer focuses on the government members of the G20 that had signed the G20 Anti- Corruption Open Data Principles and those who had adopted the Open Data Charter. In total, the Open Data Barometer ranks thirty Governments, which having made such commitments, should be leaders on these fields. The Governments are ranked according to three factors: the impacts that the published data has on the civil society, the businesses and politics, the readiness for the initiatives on the open data and the implementation of programs about the open data. These thirty countries are tested by the Barometer, which measures their progress on the open data and compares it to the global results of the previous years (ODBT, n.d.).

73 3.6.2 The Open Data in the G8

The Group of Eight as it is known nowadays, is a group, the members of which are the richest industrial countries worldwide. It was firstly created in 1975 as a group of six countries and its members were Britain, France, Germany Italy, Japan and USA. A year after the establishment of this group in 1976, Canada also joined the group and it became the Group of Seven. Two years later in 1998, Russia became also a member of this group and so it became the Group of Eight, but in 2014, because of its activities in Ukraine, Russia was suspended from the group and now it became again a Group of Seven. The G8 is not a union between states, nor a confederation or alliance. Its members’ politicians have ordinary meetings, usually to talk about policies and economic issues (Mingst). Furthermore, as shown in Table 14, the official portal of each country member of the G8 is listed.

Table 14: G8 countries central Open Data Portal

Source: the author (2020)

Country URL Canada https://open.canada.ca/en France https://www.data.gouv.fr/en/ Germany https://www.govdata.de/ Italy https://www.dati.gov.it/ Japan https://www.data.go.jp/ Russia https://data.gov.ru/?language=en United Kingdom https://data.gov.uk/ United States https://www.data.gov/

Each one of the countries members of this group has its open data portal but since this group can change and it is not a union of states, there is no central portal for the open data of the group. However, since the open data project is globally known and all countries have interests in this, in 2013 the G8 published a charter. In this charter, the G8 members agreed that the open data are valuable resources with enormous potential to encourage the creation of more interconnected and strong societies, that allow prosperity and innovation and also meet the needs of the citizens.

74 Furthermore, the G8 agreed to follow five principles on the open data that are Open Data by Default, Quality and Quantity, Useable by All, Releasing Data for Improved Governance and Releasing Data for Innovation and that these principles will be the foundation for the publication, re-use and access to the data provided by the government's members of the G8. More specifically, the G8 members agreed to (G8ODCnTA, n.d.):

1. Open Data by Default: • The G8 members recognized the significant value that the re-use and free access of the open data will have for the economy and society • Agreed that the G8 Governments will be orientated towards the open data by default • Recognized that the Government data term is used with a sider sense, meaning that it is applied to the wider public sector’s data such as local, international government bodies, national and federal • Recognized that for the personal identifiable and intellectual property, there are some international and national legislations that will be observed • The G8’s expectation established is that openly and by default all government data will be published. However, they recognized that some data will not be released due to legitimate reasons 2. Quality and Quantity: • The G8 recognized that the big amounts of data and information that the private and public sector holds, will be of interest to the citizens • Recognized the importance of consultation with the open data users and also with other countries about improvements that can be done and which data may need priority among other so to be published and also that it will take some time until the final preparation of high-quality data to be published • The G8 members agreed that the data released will be of high quality and also will be published accurately, in clear language and a comprehensive format so all will understand it, that it will remain in its original format, published in time, meaning that it will be released as soon as possible so for the data not to lose value and that the data published will be

75 accompanied with analytical descriptions and information, so that the users of this data get to know how to process them 3. Usable by All: • Agreed that the release of data will be done in ways that everyone can re- use and obtain it • Agreed that the open data, in order to find its most widespread use, has to be available for free • Agreed that no administrative or bureaucratic barriers should exist for the access of the people to the open data, such as requirements for registration • Agreed that the release of data will be done, as much as possible in open and multiple formats, so that they will be accessible and available to everyone and for every purpose and also to publish as much data as possible for free 4. Releasing Data for Improved Governance: • The members of the G8 recognized that in order to encourage the policymakers to meet the citizens’ needs and in order to make the democracy stronger, the release of open data is needed • Recognized that a lot of initiatives and multilateral organizations are interested in the open data. As a result, the G8 members will share with each other and also with the other countries their experience and expertise on the open data • Declared that the G8 members will be transparent by documenting online all their publication processes, data collection and standards 5. Releasing Data for Innovation: • The G8 members recognized that the more organizations and people use these data, the greater the benefits generated for the economy and society will be and also that diversity will stimulate creativity. The above mentioned are related to both non-commercial and commercial uses of the data • Agreed to encounter civil society organizations that promote the open data and developers of applications for these data, so to unlock the value of the data and also to provide these data in formats that are machine-readable, so for the next generations of data innovators to be empowered

76 The G8 also recognized some areas to be of high value both in terms of encouraging the re-use of data and for the improvement of democracy. These areas are listed in Table 15.

Table 15: G8 valuable areas for the re-use of the Open Data

Source: (G8ODCnTA, n.d.)

Data Category Example datasets Companies Company/business register Crime and Justice Crime statistics, safety Earth observation Meteorological/weather, agriculture, forestry, fishing and hunting Education List of schools; performance of schools, digital skills Energy and Environment Pollution levels, energy consumption Finance and contracts Transaction spend, contracts let, call for tender, future tenders, local budget, national budget (planned and spent) Geospatial Topography, postcodes, national maps, local maps Global Development Aid, food security, extractives, land Government Accountability Government contact points, election results, and Democracy legislation and statutes, salaries (pay scales), hospitality/gifts Health Prescription data, performance data Science and Research Genome data, research and educational activity, experiment results Statistics National Statistics, Census, infrastructure, wealth, skills Social mobility and welfare Housing, health insurance and unemployment benefits Transport and Infrastructure Public transport timetables, access points broadband penetration

77 3.6.3 G20 Anti-Corruption Open Data Principles

In 2014, the G20’s Anti-corruption Working Group (ACWG) set the open data as one of the issues regarding the transparency of the public sector and developed six principles based on good practices, international standards and by taking into consideration three pillars. The first one is that by taking into consideration the increase in data quality, sources and amount due to the exponential technological progress, the promotion of the open data is necessary, so to be used as a tool against corruption. The second one is that by collaboration and availability of open data, the transparency of governments can be of great help in fighting corruption and the third one is that open data can help not only detect and prevent corruption, but also investigate and reduce it. By taking into account all these, the G20’s principles for the open data to be used as an anti-corruption tool are (G20P, 2015):

1. Open Data by Default: • The G20 recognized that the free access to data is significant for the economy and the society and also promotes the transparency of governments and therefore it should be open by default • They also acknowledge that the exchange of open data related to anti-corruption is needed in order to be used as a global tool so to fight corruption • The term government data applies to the data created by the public sector and also by some organizations related to it that can provide significant data • The G20 recognized that open data can be unlocked only when the citizens will be sure that their rights of privacy will not be compromised and that citizens can influence the use and collection of their personal data. • Therefore, the G20 members, in order to implement this principle, they will promote the development of best practices, adoption of policies and information technologies, so to ensure that the open by default principle will be applied to all government data except data that, for legitimate reasons, cannot be released. Also, they will work

78 in preventing corruption with the use of guidelines, tools, awareness programs and training, so to increase the amount of data published by the private sector, the government and the civil society with communication strategies and to establish an anti-corruption culture and promote the open data benefits. In addition, the G20 will take into account the internationall standards and laws, so to ensure that trademarks, intellectual work, personal data and sensitive data will be removed before publication, in order to ensure privacy 2. Timely and Comprehensive: • In order for the G20 to imply this principle, they recognized that the open data must be accurate and comprehensive so to be used as an anti-corruption tool, that the consultation with the private sector, civil society and other governments, data experts and users and also citizens is an important factor so to determine which data are more important and relevant to corruption so to be released first and also that technical and human resource and time is required for the identification and publication of data and that the countries of the G20 will work toward these goals • Therefore, the G20 members, in order to implement this principle they will work towards the identification of anti-corruption tools, standards and datasets. They will ensure that datasets will be available for a reasonable amount of time and that historical copies will be kept. Thet will ensure that data users will be able to provide feedbacks and also that the quality of data will be improved with constant revisions. They will provide, as much as possible data to its original form and with descriptions so to help data users analyze and visualize the data and publish high quality and accurate open data as soon as possible, in comprehensive and fully described formats. 3. Accessible and Usable • In order for the G20 to imply this principle, they recognized that open data, when released, must be easily accessible and discoverable and also that administration barriers and bureaucracy

79 may inflict in the accessibility of the data by the people, especially to the transparency and anti-corruption related data. • Therefore, the G20 members, in order to implement this principle, they will promote initiatives so to promote the open data awareness, release free data, without need of registrations and under open licenses. They will publish data on central portals so to increase the accessibility to the data and also ensure that the data will be available to everyone by releasing it in open, multiple and standardized formats 4. Comparable and Interoperable • In order for the G20 to imply this principle, they recognized that in order for the data to support effective reuse, traceability and interoperability, it should be published in standard and structured formats and also, in oder to be more useful and effective, it should be easily comparable over time, crosswise geographic locations and between and within sectors • Therefore, the G20 members, in order to implement this principle, they will ensure that data users will have enough information and that data will be accompanied with clear descriptions so the analytical limitations, strengths, sources and weaknesses of the data to be understood and that data will be available in machine and human readable formats and metadata will be also included. Moreover, the G20 will implement open standards that are related to the structure, format and interoperability of the data and that in order to support the increase of international standards, the creation of global and common data standards and also to ensure that every new standards will be interoperable with the existing ones, they will engage with international and domestic anti-corruption standards bodies, in order to achieve that 5. For improved Governance and Citizens Engagement • In order for the G20 to imply this principle, they recognized that by consulting and engaging with citizens, governments can detect high demand datasets, that the use and publication of open data can

80 improve the public services’ provision, the management of public institutions and decision making processes, so to better meet the needs of populations and also that for the better implementation and development of policies and programs, so that between governments and citizens there is a better informed engagement, to prone civic participation and also meet and satisfy the need of populations, the open data is essential • Therefore, the G20 members, in order to implement this principle, they will improve the procedures and mechanisms for the government and citizens, relevant to the application and use of the open data, provide guidelines, success stories and tools that will be designed for the efficient and effective use of the open data by the government officials, implement review, oversight and research processes so to regularly report to the public about the impacts and progress of the open data, that will be used as an anti-corruption tool and also, in order for the organizations that work on the anti- corruption, accountability and transparency domains to have the data needed, the promotion of digital participation platforms will be implemented 6. For Including Development and Innovation • In order for the G20 to imply this principle, they recognized the importance of the open data in stimulating innovation, creativity and accountability and that the more private organizations, civil society, citizens and governments use the open data, the greater the economic and social benefits will be. Also recognized the open data value on identifying challenges and achieving, monitoring and delivering worldwide development by promoting inclusive and transparent institutions as well. Furthermore, the G20 members recognized that governments’ role in promoting development and good governance is not achieved only by the publication of data but also by ensuring the private sector’s organizations, civil society, citizens and public employees that the tools and data needed, will be available for the effective use of data and also that data can be

81 accessed by everyone and to be considered as a resource that empowers citizens, when this data is used in combination with data from the civil society, private organizations and governments • Therefore, the G20 members, in order to implement this principle, they will support and create initiatives and programs that cultivate the co-creation and development of data mashups, visualizations, APIs and applications for the open data by sharing experience and expertise between international organizations and governments. Also in order to maximize the impacts and support the release of data, the exploration and creation of partnerships with organizations and institutions related to the anti-corruption sector is essential and in addition, the promotion of the adoption of the open data related items, where its application can be useful, such as other activities and principles will be supported by the G20’s ACWG. Lastly, the G20 members will prevent corruption, strengthen integrity and transparency and also create a richer open data ecosystem by encouraging multilateral institutions, private sector organizations, civil society and citizens to open (G20P, 2015).

Moreover, as shown in Table 16, the Governmental Open Data websites of each of the G20 countries, as they are classified to be in the G20 for 2019, are presented:

Table 16: G20 countries central Open Data Portal

Source: the author (2020)

Country URL Australia https://data.gov.au/data/ Argentina https://datos.gob.ar/ Brazil http://www.dados.gov.br/ Canada https://open.canada.ca/en France https://www.data.gouv.fr/en/ Germany https://www.govdata.de/ India https://data.gov.in/ Indonesia https://data.go.id/ Italy https://www.dati.gov.it/

82 Japan https://www.data.go.jp/ Mexico https://www.gob.mx/ Russia https://data.gov.ru/?language=en Saudi Arabia https://data.gov.sa/en South Africa https://southafrica.opendataforafrica.org/ South Korea http://data.seoul.go.kr/ Turkey http://acikveri.sahinbey.bel.tr/dataset United Kingdom https://data.gov.uk/ United States https://www.data.gov/ European Union https://data.europa.eu/ China http://zwgk.dl.gov.cn/

3.6.4 International Open Data Charter

The open data is a very significant globally and has the requisites for generating significant economic and social benefits for everyone. Governments, through the adoption and creation of principles for the open data can work towards the establishment of a more prosperous society. In the July of 2013, the G8 members signed and created the signed the G8 Open Data Charter, with five principles and till now, a lot of nations and open government advocates were positive and implemented those principles, however the general sense that these principles could be meliorated so to support a larger global adoption and implementation of those principles. Some years after, in May of 2015, in Ottawa, the Omidyar Network, the Open Data for Development (OD4D), the International Development Research Center, the Government of Mexico and the Open Government Partnership (OGP) Open Data Working Group, that is co-chaired by the Web Foundations and the Government of Canada, had a meeting of “open data champions” from all over the world in order to discuss for the development of an International Open Data Charter. This meeting’s members were multilateral institutions, representatives of nations and civil society organizations from all over the world. In this meeting, the participants gathered from all around the world, discussed about the importance of openness of data so to enable a “data revolution”. An action plan, so to create an international collaboration on the

83 open data subject was discussed between the thousand participants so to achieve and create a more sustainable development. In the July and August of 2015, more than three hundred and fifty comments were submitted from all around the world that improved significantly the Principles of this Charter. Moreover, additional officials, experts and stewards of the open data committed to collaborate with the partners of the open data charted so to improve the charted and during the Nations General Assembly, to propose for a global adoption of the Charted, which if adopted, will have as a result for the citizens, the civil society organizations, multilaterals, private sector and governments to promote sustainable development, make better decisions and create new solutions due to the fact of free access to the big amount of data published. In this Charter, the principles created for the implementation and development of the open data are six and are the open by default, accessible and usable, timely and comprehensive, comparable and interoperable, for inclusive development and innovation and for improved governance and citizen engagement. Furthermore, this charter was based on previous efforts and findings so to create a unique global charter for the open data (IODC, 2015).

3.7 Impacts and Benefits of the Open Data

Nowadays, data is of most value for the Public and Private sectors but also for the people. The open data project, allows private organizations and the public sector to improve the life of others, by providing all the best services and products. Also, for the Public sector the open data can meliorate the flow of information between countries, having as a result many benefits. Also, the Government’s transparency is increased due to the open data, giving people the opportunity to be informed about all the decisions a country takes and its management. This gives accountability to the Government and the citizens are more trustful about the management of the country. Furthermore, people nowadays expect to be able to have access electronically to the services and information they need (G8ODCnTA, n.d.). An example of the taxonomy of the impacs of the open data is shown in Figure 3.10.

84

Figure 3.10: Open Data Taxonomy of Impact

Source: (ODTI, 2016)

However, the evidence of the Open Data impacts is not such concrete to be identified, because of the difficulty of tracking the usability and re-usability of these data and the benefits they provide, the main two fields of impact of the open data, as many studies show, are the Economical and the Accountability. But in order to achieve impact with the open data, some challenges and obstacles have to be surpassed. These challenges are the Readiness, the Responsiveness, the Risk and the Resource allocation of the open data. By Readiness, we mean the ability to provide the data as quickly as possible, by having the technical and technological capacity to do this and also trained personnel. In addition, by Responsiveness we mean the effective definition of the needs of the data users and re users. The most successful way for the data to create value and impact is the recognition about the data providers of what data is needed most. Moreover, the Risks of providing data openly are the

85 security violation and privacy terms. These risks are inherited to the Open Data project and so great transparency is needed, not only because if an initiative of publishing data, does not take steps so to respect the privacy terms will harm its own prospects but it will harm, in a broader way, the reputation of the open data projects in general. Finally, a good resource allocation is needed when providing data. Because of the small investments that are needed so to lunch an open data project, many times the administrators of the portals focus more in providing the data than in security. Also, the financiation of such projects is critical due to its open and free nature. Furthermore, in order to enhance the impacts of the open data, some key conditions have to be enabled (ODTI, 2016). These conditions are:

• Partnership: the most successful open data projects are based on the collaboration between the various entities and sectors so for the data provided to be complete and accurate for its publication and for the users of this data, to be useful and helpful. Also, the partnership of the data providers with some civil society groups is useful so to educate and mobilize citizens on the open data project and the collaboration with the media, in order to inform citizens. The data collaboratives and intermediaries allow the better match of data demand and supply • Public Infrastructure: since the public sector is the main data provider, it has to train and educate its personnel and also to provide them the technical and technological tolls for the publication and reuse of data. Also, the public has to create an interoperable infrastructure so to be able to provide citizens a large variety of data that in addition, have to be in a big variety of formats. Moreover, the public has to train the reporters and the citizens so to make them create value out of the open data provided to them • Performance metrics and policies: another key condition so to determine the success of the open data is the existence of well-defined performance metrics and policies. Political leaders and policy makers have to create a legal environment that is forward-looking and flexible that encourages the technical innovation and the release of the open data, together with with mechanisms that enhance the accountability and project assessment. Furthermore, politicians and policy makers have to make sure that the law is respected when

86 processing and publishing data openly. In these ways, the necessary condition of success on the open data project is created • Problem definition: the identification of an existing need, the problem and target definition and the efficient provision of solutions that address a certain need are fundamental for a project that publishes data openly, in order to create more impact and value through the usage of the data

Considering the fast-changing world in terms of population growth and technological evolution, the open data creates value through the impacts they have on the Social, Economic, Environmental and Political fields, for the Public sector, the Private sector and also the citizens (ODTI, 2016).

Private

Also, it provides information and knowledge to the Private sector in order to better manage their resources and enhance their services. For example, in Finland, the medium and small businesses that have access to open data grow fifteen per cent faster than those who don’t (EDPV, n.d.). Furthermore, the open data can be a great support for the startup enterprises, by letting them begin their research at a higher level because of all these data provided by the open data projects around the world (ODBInB, n.d.). Also, in France, the Interdepartmental Digital Directorate (DINUM) lunched a mission called Dataconnexions that through its five first editions of this program, was able to identify more than two hundred projects and startups that are able to contribute to the economy of the country. These startups had to do mainly with initiatives of open data entrepreneurship with a potential to encounter various social issues (ODBInB, n.d.).

Public

Open data helps Governments and Public bodies to front corruption and become more accountable. Also, it provides information and knowledge to the Government in

87 order to better manage the resources and enhance the public services. In addition, the open data brings great transparency to the decisions and provisions a Government takes, having less impact on the citizens’ everyday life. Furthermore, the open data can help the policymakers better analyze the problems and needs of societies and for the Public sector, to solve many administrative problems (ODTI, 2016). Good examples at this point are in France, that practices for more efficient energy generation were driven by the usage of energy data, in the UK, where in the health services, the open data helped identify an amount of 200£ million of potential savings and in 2013, it was estimated that because of the publication of the outcome of cardiac surgeries, the number of deaths in health surgeries was reduced annually up to 1,000. Also, in Switzerland, in the canton of Bern, by using open data, it was managed to reduce the Public expenditure by 400€ million annually (EDPV, n.d.). Also, in Sierra Leone, the open data were used so to inform the people that were working on the ground to fight Ebola, on what actions to take and in Singapore, the government and the citizens used a Dengue Fever Cluster Map so to try to limit as much possible the spread of the Dengue Fever outbreak during 2013. The dengue cluster indicates a location where two or more cases of fever were spotted within fourteen days in a distance of a hundred and fifty meters from one another. The Dengue Fever Cluster Map can be found at this URL: https://www.nea.gov.sg/dengue-zika/dengue/dengue-clusters#onemap.

Also in Uruguay, the Health Minister launched an application that allows citizens compare the various health providers. In this way, the Health Ministry had increased effectiveness and efficiency because this platform made the citizens take better data-driven decisions and also helped to reveal issues in the quality of the data provided by the health suppliers. This application’s URL is https://data.org.uy/portfolio/a-tu-servicio/ (ODBInB, n.d.).

Moreover, due to the growing population, Governments have to respond not only to the main problems of the state but also to a local level. In order to do this, in Hungary, Norway and the UK, portals have been lunched so to provide citizens the services needed. The URL of the portals are for the UK: https://www.mysociety.org/, for Hungary: https://kimittud.atlatszo.hu/ and for Norway: https://www.fiksgatami.no/, that provide citizens with services like to report

88 neighborhood problems to the local council, the request of information from public bodies and what the Members of the Parliament are voting and saying (EDPV, n.d.).

Citizens

The opportunity given to the citizens by having free access to a vast variety and large amount of data, gives them the choice to take better decisions about health care and education or even for their country elections. Also, open data can create new forms of social mobilization. This social mobilization can be both in turns of new ways of information access and communication. In Addition, the citizens can (driven by the data) assess, participate and influence the activities of the Government (ODTI, 2016).

Economy

New economic opportunities can be created with the open data for everyone. The Government that publishes the data can help the Private sector and the citizens take advantage of those data. The public sector, having this information, can better manage their resources, create new services needed for the citizens and upgrade older ones by better analyzing the market for what is needed. Also, this big amount of free data can stimulate the economy by foresting innovation and open up new sectors having as a result to create more jobs for the citizens. The Government also, that is the first re-user of those data, can then provide better and more services and reduce taxes by taking advantage of this economic growth, both in terms of tax income and corruption eradication.

Environmental

Open data is already being used by farmers so to improve crop fields and so to feed the growing population, without destroying valuable habitats. Also, large amount

89 of data is being collected by private businesses so to inform the farmers about the diseases and the treatments to do to plants, without using products that can pollute the environment (EDPV, n.d.). Humanitarian groups in disinterred zones, in order to deliver target supplies, are using open geographical data in order to do this, like in 2010 during the Haiti earthquake and for the typhoon in the Philippines in 2014.

Culture

People, with important cultural issues, are connected together through the open data helping them create a more inform debate, around those issues and find better solutions (EDPV, n.d.).

Other

In the USA, the White House has released data, with the intention of increasing the Governments accountability about police officers and in particular about the issue of racial profiling that some police officers in the USA made about the African Americans. This new data that was publicly released made a real impact due to the fact that it included information about officers involved in shooting and use of force and police stops (ODBInB, n.d.).

In Japan, in 2011 at the Tohuko region, the Ministry of Economy joined efforts with the Electric Power Companies and the Trade and Industry (METI) so to develop a platform in order to inform citizens about the electricity consumption and electricity demand and supply. This was made because of an earthquake and tsunami that happened in this region, so to inform and allow citizens help the government, by knowing when the demand of electricity was at spike and avoiding power failures and minimize the environmental impact (ODBInB, n.d.).

90 3.8 Licenses for the Publication of the Data

When someone works on an article, book or project, has the rights to determine how that work will be used. For this reason, for the datasets published in the open data portals, licenses are needed. Practically, licenses give the permission to the owners of the open data portals to publish and use the datasets created by others. Publishers in Europe can have two types of rights over the work they have created. The copyrights over the content someone has created and the database right where someone has put an effort to collect, present, verify or obtain a large amount of data. This last right is unique to the EU and in some countries, for the collections of data, no protections may exist. The main differences in those two rights are explained in by following examples (ODL, 2013):

• If for the creation of a database, substantial effort was made so to gather the information to put in the database, then database rights will be given to the creator of the database • If for the creation of a database, the information gathered was filtered by restrictions made by the database creator, then copyrights will be given to the creator of the database • If for the creation of a database, substantial effort was made so to gather information and the information gathered was filtered by restrictions made by the database creator or the content of the database was entirely made by the creator of the database, then both rights will be given to this work

Furthermore, when a publication of data is made and the publisher does not own all the content or part of the content, then a license has to be given to the publisher about the not owned content. For the Brands, the protection used for the Brand’s logo and name of company is the trade mark. In addition, a Brand has copyrights on its logo. In order to use data that a company has created or their logo, some attribution requirements are required. As the copyrights and database rights are for the private work that someone has created, so is the open license for the content of the open data portals. Only two restrictions can exist for the open data, according to the open data definition. The first one is to give attribute to the author or source from where the data or content was published and the second one is the share-alike restriction, where the

91 re-publication of the data taken by another source has to be published under the same license. Despite their existence, an open license may have one or none of these restrictions, meaning that there are three levels of open licenses. The Public Domain license, where there is no restrictions on the use and republication of the data, the Attribution license, where an attribution must be given to the publisher of that data and the Attribution and Share-Alike license, where except from the attribution, the data also must be shared or re-published under the same license the author of that data used. Attribution must be provided when the publisher of the data specifies the wording that the attribution must have and how and where it must be presented. However, even if not necessary, it is good to give attribution to data, so to recognize both the generosity of the author that made the data available to be used by others and for the effort the author made so to create or gather that data. If an attribution is needed but neither the wording nor way for the attribution is specified, then it is recommended to get in touch with the author for further specifications. Moreover, the creation of new ways for the presentation of content do not count adaptation or derivation from the initial data and so, if published, the share-alike license is required. However, regarding the combination of two or more datasets so to create new ones, no share-alike license is required. If data has been created using open data, then the share-alike license is not required, but by publishing the new data as open data, value can be added to that work. For the Open Data, the license that exists is the open license for creative content that has to do with the content, like slides, text or photographs and has three levels of licenses (ODL, 2013):

1. The public domain license (CC0), where the content is open and free to be downloaded, processed and republished with no restrictions 2. The Attribution license (CC-by), where the content is open and free for any purpose but an attribution to the author is required 3. The Attribution and Share-alike (CC-by-sa), where as the previous one the content is open and free for any purpose but an attribution is required and also, if re-published, it has to be with the same license the author did

Another type of open license is the Open License for Databases and it also has three levels of licenses (ODL, 2013):

92 1. The public domain license named Open Data Commons Public Domain Dedication and Licenses (PDDL), where the database and the data in it is open and free to be downloaded, processed and republished with no restrictions 2. The Attribution license named Open Data Commons Attribution License (ODC-by), where the database and the data in it is open and free for any purpose but an attribution to the author is required 3. The Attribution and Share-alike named Open Data Commons Open Database Licensing (ODbL), where the database and the data in it is open and free for any purpose but an attribution is required and also, if re-published, it has to be with the same license the author did

Moreover, there are also some other licenses that enable the re-use of the data. These licenses are most commonly used by the public sector and are the Open Government license and the Ordinance Survey Open Data License. These two licenses are mainly used by the UK but also by other countries. They are both Attribution licenses that cover databases and copyrights. Their only difference is that the OS Open Data License is both attribution and share-alike license, meaning that the data with this license must be attributed to the Ordinance Service (ODL, 2013). The Open Government license, which is just an attribution license, gives the opportunity to the public to transmit, adapt and distribute the information, publish, copy and also use these datasets for non-commercial or commercial use for free. Therefore, there are some data exempted from this license, such as personal data than the datasets may include, logos of organizations, identity documents, intellectual property rights like design rights, patents and trademarks, military insignia and third-party information not licensed by the information provider (OGLUK, 2014).

3.9 Formats of the Data

In order for the data to be accessible to the public, they must be in some standard format types so for the machines to be able to read them. Also, in order to help people be able to read these data, the need of publishing the data in the most common formats, such as XLS for Excel and PDF is crucial so to make the Open Data

93 project viable and easily usable by everyone. According to the central Greek Open Government Data portal, from the 10,183 datasets, the 8,982 have a data format declared, with the most common format types to be the XLS, XLSX and PDF, with 4,645 datasets having these three format types, being half of the total amount. The format types are useful for the user to know how to use the dataset and the compatibilities with the various programs but they can also be used as a search key, so to find only datasets of a certain format type. As shown in Table 17, the format types of the various datasets that can be found in the central Greek Open Government Data portal (GODPD.G, n.d.).

Table 17: Types of Datasets

Source: (GODPD.G, n.d.)

Format Amount Format Amount Format Amount XLS 2660 xls 254 KML 85 XLSX 1191 url 207 RAR 80 CSV, XML, PDF 794 zip , shp 127 JSON 71 HTML 647 ZIP 117 DOCX 67 JSON 529 DOCX 114 JPEG 52 CSV 523 application/msword 113 html, pdf 50 .xlsx 296 SHP 110 .pdf 49 XML 266 URL 94 .doc 49 RSS 39 xml, rar 19 RAR, CSV 10 TXT 36 .ods 19 WORD 9 ODS 36 .html 18 .XLSX 9 arcgis 35 rar, xls 16 pdf, zip 8 application/x- TIFF 32 zip, doc 15 msdos-program 8 msword, msexcel, url, xml 24 pdf 12 XHTML 8 http 20 PNG 12 URL, PDF 8 zip, csv 19 dwg 11 WMS 7 RAR, CSV 7

94 4 Chapter Four: Visualization and Data Analysis

4.1 Overview

In this chapter, all the datasets, regarding the supply chain from the Greek open data webpages presented in the previous chapter, will be presented. Moreover, three of those datasets will be chosen so to be processed with two software, in order to create a graphical visualization for the better understanding of these datasets. The two software that will be used are Tableau and PowerBi. In Figure 4.1, as shown below, Chapter’s four structure is presented and the particulars about every section of this chapter will be discussed further on, in each respective subchapter.

4.2 Data Visualization

4.1 Overview 4.3 Visualzation Techniques

4.4 Introduction to the Tableau and Power BI software

Figure 4.1: Chapter's four overview

Source: the author (2020)

4.2 Data Visualization

The term Data Visualization refers to a set of techniques that have as a result the visual representation of data, using graphical media. With the help of visualization, the graphs can display data properties, relevance relationships, price comparisons, geographic dispersal of events, upward and downward trends and division of sets into subsets. Graphics are a tool for locating and recognizing structures and properties in a set of data. The human brain understands information better and faster when it is captured in an image than when it is described as an analytical text. In addition, the

95 graphic display of the information is more elegant and enjoyable to view than reading a text. Nowadays, the widespread application of information technology, the achievements in the development of hardware and software and the mass production of data, offers unprecedented opportunities for data visualization. Modern graphics are interactive and allow the user to choose between different levels for the detailation or generalization of the data. With the simultaneous appearance of many interconnected graphs, the comparison of data subsets and their different characteristics is achieved. The graphical display of data is not always easy and there is not a standard method so to ensure the quality of the results. However, there are some principles that the designer must follow so to effectively design graphics which are (Kirkos, 2015):

• To present the data • Do not change the meaning of the data • To present a large amount of data in a limited place • To give different perspectives of the data • To cluster large amount of data

4.3 Visualization Techniques

Nowadays, there are a lot of visualization techniques and types of graphs to be used and each one of these correspond better to the needs of what the designer has to present and the information of the data. Following, some of the main visualization techniques are presented.

4.3.1 Bar Chart

96 The Bar Chart is one of the most commonly used graphs and can offer a good and easy visual analysis. Its only drawback is that if there is a large amount of bars, the labeling of them becomes difficult and problematic (QVDV,

n.d.). A Bar Chart example is Figure 4.2: Bar Chart presented in Figure 4.2. Source: (QVDV, n.d.)

4.3.2 Scatter Chart

Figure 4.3: Scatter Chart

Source: (QVDV, n.d.)

The Scatter Chart represents the relationship between two variables. It is used so to show how scattered the data points are in the graphical area, as well as the connection between these two variables. In order to create this graph, at least two variables are needed, one for the x- axis and the other for the y-axis (QVDV, n.d.). A Scatter Chart example is presented in Figure 4.3.

97 4.3.3 Pie Chart

The Pie Chart is also one of the very popular charts and it is used for the comparison of the various parts of a set. These parts can also display the percentage values of the

Figure 4.4: Pie Chart total. However, in the case

Source: (QVDV, n.d.) of a large number of pieces in this graph, it may become hard to distinguish the various parts in it. An alternative and solution to this problem can be the use of the Bar Chart (QVDV, n.d.). A Pie Chart example is presented in Figure 4.4.

4.3.4 Block Chart

The Block Chart shows the hierarchical data structure and can help in the understanding of complex and big data structures. This chart represents the relationship of the variables with the whole

Figure 4.5: Block Chart amount of data in it, displaying the

Source: (QVDV, n.d.) data in it as rectangles that correspond to a percentage value of the whole and these rectangles also differ in size and color. In this way, the provided

98 information is enriched and easily readable (QVDV, n.d.). A Block Chart example is presented in Figure 4.5.

4.3.5 Radar Chart

Figure 4.6: Radar Chart

Source: (QVDV, n.d.)

The Radar Chart is used so to compare events over time. It shows whether there are changes in the behavior of the data, like consistency in the flow of the events or spikes, in order for the viewer of the graph to easily follow the behavior of them. Also, this type of chart connects the data points with lines and displays the movements of them in cyclic dimension that can include seasons and time (QVDV, n.d.). A Radar Chart example is presented in Figure 4.6.

4.3.6 Line Chart

Figure 4.7: Line Chart

Source: (QVDV, n.d.)

99 The Line Chart is used so to compare the relationship and behavior between two variables. The various data points presented in this graph are connected with lines that highlight the changes of quantities or trends over time (QVDV, n.d.). A Line Chart example is presented in Figure 4.7.

4.3.7 Mekko Chart

Figure 4.8: Mekko Chart

Source: (QVDV, n.d.)

The Mekko Chart is used so to display the relation between multiple data. It presents the data as an actual amount or as the percentage of the whole. Additionally, this chart has also another type of measurements, which is the width. In this way, a multidimensional graph is created, which makes it easier to identify the breakdowns of data in this graph (QVDV, n.d.). A Mekko Chart example is presented in Figure 4.8.

4.4 Introduction to the Tableau and Power BI software

The Tableau and Power BI software are between the best three Business Intelligence software that make it possible to count the times an item appears, make statistical analysis and optical visualization in a user-friendly and easy to use

100 environment. In this section, an introduction to these two software is made so to understand better the potentials and usefulness of these two software.

4.4.1 The Tableau Software

One of the most important Businest Intelligence software in the world, which will be used in this thesis, is Tableau. This software was developed by the Tableau Software Inc. in 2003, which was created by Christian Chabot, Pat Hanrahan and Chris Stolte. Nowadays, the Tableau Software Inc. is one of the three top business intelligence companies in the world. Initially, the Tableau software was created for the commercial exploitation of the researches conducted in the Department of Informatics in Stanford University, during the years from 1999 to 2002. During the years from 2010 to 2013, the Tableau Software Inc. had a growth of over eighty percent per year. Nowadays, the company has its headquartered in Seattle, Washington, United States of America and specializes in the production of software visualization with applications in Business Intelligence. Some of the well-known customers of the Tableau Software are Intel, Coca Cola, Yahoo and Amazone (Tsaousidis, 2019). The different versions that Tableau has are (TWP, n.d.):

• Tableau Server • Tableau Public Server • Tableau Online • Tableau Desktop • Tableau Public Desktop • Tableau Public • Tableau Reader • Tableau Prep Builder • Tableau Mobile

101 Furthermore, the Tableau software is one of the most advanced Business Intelligence and graphical visualization software because of its characteristics:

▪ Tableau supports a large number of possible connections to data sources, more specifically sixty-seven, which can be structured or not, both via live internet connections through the internet, to data clouds and also, to data loaded to the system’s memory ▪ Security in collaboration and exchange of visualizations with its use Tableau Server and Tableau Online ▪ Tableau is a very light program with few hardware requirements so it can be used easily both in desktop and laptops computers ▪ Easy to use, without the need of advanced programming knowledge, with drag and drop techniques and allowing the integration of data and creation of visual graphs clicks with a few clicks ▪ Very simple installation process, which allows the immediate use of the software for data analysis and creation visualizations ▪ Allows the management of all data sources from one central point, such as update data and defition of them

Following, an introductory representation of the Tableau software will be presented, with the help of figures:

1. After the program is opened, the initial page shows up. From this page, the user can select to which files or server to connect, so to begin the processing, and after, the visualization of the data. The various files are divided by their type, as well as the various servers that the program can connect to, are listed. For this research, the file type that will be processed is the .xls, which corresponds to the Excel files as shown in Figure 4.9.

102

Figure 4.9: Tableau Connect Data

Source: the author (2020)

2. After the selection of the type of file, a new tab opens so to browse and find the file the user wants to open, in order to process and then visualize it, as shown in Figure 4.10.

103

Figure 4.10: Tableau Selection of Data

Source: the author (2020)

3. After the selection of the file the user wants to open, a new page shows up. In this page, the user can select which sheets to open, from the document that was opened. As mentioned previously, one of the benefits and characteristics of the Tableau software is the ease of use with the drag and drop method. At this point, the user has to select and drag and drop the sheet in the upper left part, where “Drag sheets here” is written, as shown in Figure 4.11. Furthermore, at this stage, the user can also modify the selected sheet in the lower left part, as shown in Figure 4.11. The modification of the sheet consists of the following choices:

• Rename a row • Copy values of a row • Hide a row • Group two or more rows together • Split a row in two or more

104 For the next step, after the modification of the sheets, the user has to click on the “Sheet 1” so to go the next page, which is the page where the visualization and graphical representation begins.

Figure 4.11: Tableau Data Preparation

Source: the author (2020)

4. At this point, with the drag and drop method, the user can simply create a graphical visualization of the data. Moreover, the various graphics have some requirements in order to be used, which are showed as in the example in Figure 4.12. By selecting a “Dimension”, a “Measure” and then a graphic desired, the user can simply create the visualization.

105

Figure 4.12: Tableau Visualization

Source: the author (2020)

4.4.2 The Power BI Software

Another very important Business Intelligence software is the Power BI, developed by Microsoft and it was launched in the 24th of July 2015 (Microsoft, 2015). The Power BI software provides an interactive dashboard, data warehouse competence such as data discovery, which is a process of finding patterns in the data and data preparation. This software is one of the three top Busines Intelligence software in the world and some of the well-known customers of the Power BI software are Adobe, Pepsi, WorldSmart, Heathrow and AutoGlass.The different versions that Tableau has are (PowerBi.pg, n.d.):

• Power BI Desktop • Power BI Mobile • Power BI Pro • Power BI Premium • Power BI Report Builder

106 • Power BI Server

Furthermore, the Power BI software is one of the most advanced Business Intelligence and graphical visualization software because of its characteristics:

• Easy to use, with drag and drop techniques • Supports a large number of possible connections to data sources, via internet, to data clouds and also, to data loaded to the system’s memory • Security in collaboration and exchange of visualizations and tables of data with the use Power BI Server • Power BI is a light program with few hardware requirements so it can be used easily both in desktop and laptops computers • Easy to use, with drag and drop techniques • Simple installation process, which allows the immediate use of the software for data analysis and creation of visualizations • Allows the management of data sources from one central point, such as modification and transformation of tables, for a better use of the data

Following, an introductory representation of the Power BI software will be presented, with the help of figures:

1. After the program is opened, the initial page shows up. From this page, the user can select to which files or server to connect, so to begin the processing, and then, the visualization of the data. For this research, the file type that will be processed is the .xls, which corresponds to the Excel files as shown in Figure 4.13.

107

Figure 4.13: Power BI Connect Data

Source: the author (2020)

2. After the selection of the file, a new tab opens so to browse and find the file the user wants to open, so to process and then visualize it, as shown in Figure 4.14.

Figure 4.14: Power BI Selection of Data

Source: the author (2020)

108 3. After the selection of the file the user wants to open, a new page shows up. In this page, the user can select which sheets to open, from the document that was opened. Furthermore, at this stage the user can also modify the selected sheet, by selecting “Transform Data”, as shown in Figure 4.15. The modification of the sheet consists of the following choices:

• Rename a row • Copy values of a row • Hide a row • Group two or more rows together • Split a row in two or more

For the next step, after the modification of the sheets, the user has to select “Load”, so to go the next page, which is the page where the visualization and graphical representation begins.

Figure 4.15: Power BI Data Preparation

Source: the author (2020)

4. At this point, the user is redirected to the initial page, with the data modified and loaded. In this page, the user can further modify the data, create measures and create the visualization.

109 5 Chapter Five: Case Studies

5.1 Overview

In this Chapter, some datasets from the Greek Open Data Portals collected, will be analyzed and visualized with the Tableau and Powe BI software, in order to demonstrate that visualizations are very useful for the easier understanding of the data.

Moreover, a comparison between these two software will be done, by analyzing the same data in both of them. As shown in Figure 5.1, Chapter’s five structure is presented and the particulars about every section of this chapter will be discussed further on, in each respective subchapter.

5.2 Case study one: Greek Open Data Portals, a statistical analysis and visualization with Tableau

5.3 Case study two: Greek Open Data Portals, a statistical analysis and visualization with Power BI

5.4 Case study three: Geographical visualization of road

5.1 Overview freight transport of goods, per type of package, in the thirteen Regions of Greece

5.5 Case study four: Unloaded and Loaded goods in Greek ports, by ports and type of cargo

5.6 Comparison of Power BI and Tableau

Figure 5.1: Chapter’s five overview

Source: the author (2020)

110 5.2 Case study one: Greek Open Data Portals, a statistical analysis and visualization with Tableau

The first dataset that will be processed with Tableau is the excel file that was created so as to create a catalogue of all datasets and data in Greece, relevant to the supply chain. In this excel file, the path to each one of the datasets found is presented and also some keywords have been attached to each data, so as to determine the type, such as .xls, .pdf, .ods and a description, so as to determine which stage of the supply chain is referred to the data, such as transportation, loading, unloading, supply, production, imports, exports, orders, consumption, sales, processing, storage and transshipment. An example of this excel file is presented in Figure 5.2.

Figure 5.2: Example of Table in Excel

Source: the author (2020)

In order to create a graphical visualization of an excel file, this file has to be modified at first, in order to make the visualization easier in the Tableau software. The modification consists in organizing the various columns, in order for the type of values it contains, to be more clearly determined. By type, we mean numbers or text format. The next step, is to formulate some questions in terms of what we want to analyze and visualize.

Question One

1. How many datasets relevant to the supply chain are in each one of the Portals in which at least one dataset relevant to the supply chain was found?

Since all datasets have different names between them, this makes it impossible to count them automatically. This is the reason why the data has to be modified, as mentioned before in this chapter, so to be processed easier. In order to count the

111 datasets, the user can count how many times the respective “Site Name” appears, since each dataset is connected to only one “Site Name”, as showed in Figure 5.2. In order to answer this question in the Tableau software, the following steps have to be done:

• Under the “Dimension”, select the “Site Name” • Under the “Measures”, select the “Number of Records” • Select a type of visualization. In this case, the Stacked Bars graphic was selected

As presented in Figure 5.3, Figure 5.4 and Figure 5.5 the number of datasets relevant to the supply chain, published in each one of the three portals are:

• In the Central Greek Open Data Portal, there are thirty-five datasets • In the Greek Statistical Authority, there are forty-four datasets • In the Portal of the Municipality of Thessaloniki, there are two datasets

Figure 5.3: Central Greek Open Data Portal Datasets

Source: the author (2020)

112

Figure 5.4: Greek Statistical Authority Datasets

Source: the author (2020)

Figure 5.5: Open Data Portal - Municipality of Thessaloniki Datasets

Source: the author (2020)

Following, the percentage of datasets, relevant to the supply chain, published by each one of the previous three portals is presented. This percentage shows the relationship between the individual number and the total number of datasets, related to the supply chain published by each portal. As shown in Figure 5.6, from the total number of datasets, the Greek Statistical Authority has the 54.32%, the Central Greek Open Data Portal has the 43.21% and the Open Data Portal of the Municipality of Thessaloniki has the 2.47% of the datasets relevant to the supply chain.

113

Figure 5.6: Percentage of Datasets

Source: the author (2020)

Question two

2. How many datasets in each one of the Organizations has published datasets relevant to the supply chain in the central Greek Open Data Portal? Also, present the number of datasets of each Organization as a percentage of the total, for the central Greek Open Data Portal.

In order to answer this question with the Tableau software, the user has to do the following:

• Under the “Dimension”, select the “Organization Name” • Under the “Measures”, select the “Number of Records” • Select a type of visualization. In this case, the Treemap graphic was selected

The number of datasets each Organization has published is showed in Figure 5.7 and the percentage of them is showed in Figure 5.8. As shown in Figure 5.8:

• In the first place, we see the Municipality of Thessaloniki and the Municipality of Ilios with the 20%

114 • In the second place we see the Decentralized Administration of Thessaly and Stereas Hellas, the Ministry of Environment and Energy and the Municipality of New Smirne with the 5.71% • In the third place we see the Region of Thessaly, Region of Sterea Hellas, Region of Northren Aegean, Region of Epirus, Municipality of Xiromerou, Municipality of Rafina – Pikermiou, Municipality of Egaleo, Municipality of Almopia, Municipality of Agias Paraskevi, Hellenic Center of Mental Health and Research, Hellenic Agricultural Organization – Dimitra, Greek Statistical Authority, General Hospital of Didimotichos, Decentralizes Administration of Epirus – West Macedoni and the Court of Appeal of Thessaloniki with the 2.85%

Figure 5.7: Organization Name and Datasets

Source: the author (2020)

115

Figure 5.8: Percentage of Datasets per Organization

Source: the author (2020)

Question three 3. From the total number of data recorded in these three portals, relevant to the supply chain, define as a percentage: A. The “Type” B. The keywords under the “Description”, that are the stage or stages of the supply chain in which the data refers to

A. In order to answer this question with the Tableau software, the following steps must be applied:

• Under the “Dimension”, select the “Type” • Under the “Measures”, select the “Number of Records” • Select a type of visualization. In this case the Pie Chart graphic was selected

As presented in Figure 5.10 and Figure 5.9 for the Central Greek Open Data portal, in Figure 5.12 and Figure 5.11 for the Greek Statistical Authority and in Figure

116 5.14 and Figure 5.13 for the Portal of the Municipality of Thessaloniki, the number and percentages of type of the data are:

• In the Central Greek Open Data Portal, there are 149 .ods and 209 .xls files • In the portal of Greek Statistical Authority are 2,030 .xls and 334 .pdf • In the Portal of the Municipality of Thessaloniki, there are 62 .xls

Figure 5.9: Data.gov Percentage of Type Figure 5.10: Data.gov Number of Type Source: the author (2020) Source: the author (2020)

Figure 5.12: Greek Statistical Authority Figure 5.11: Greek Statistical Authority Number of Type Percentage of Type

Source: the author (2020) Source: the author (2020)

117

Figure 5.13: Open Data Portal Thessaloniki Figure 5 .14: Open Data Portal Thessaloniki Percentage of Type Number of Type Source: the author (2020) Source: the author (2020)

Figure 5.15: Total Percentage of Type Figure 5 .16: Total Number of Type Source: the author (2020) Source: the author (2020)

118 As shown in Figure 5.16 and Figure 5.15, the vast majority of the files are of .xls type, with the 82.62% to be of xls type, the 12.03% to be of pdf type and the remaining 5.35% to be of .ods type.

B. In order to answer this question, the following steps must be done: • Under the “Dimension”, select the “Descriptions” • Under the “Measures”, select the “Number of Records” • Select a type of visualization. In this case the Packed Bubbles graphic was selected

As shown in Figure 5.17, the total number of each “Description” is measured as well as the percentage of each one. This visualization clearly shows that from the total number of data related to the supply chain, the 48.05% are related to the production, the 15.75% are related to the transportation, the 8.83% are related to the sales, the 7.42% are of import, the 5.49% are of supply, the 3.19% are of export, the 2.67% are of transshipment, the 2.45% are of storage, the 2.15% are of orders received, the 1.41% are of unload, the 1.41% are of load, the 0.52% are of processing, the 0.43% are about stock variation and the 0.25% are related to the consumption.

Figure 5.17: Supply Chain Related Percentage

Source: the author (2020)

119 Question four

4. How many data relevant to the supply chain have been published in each one of the Portals in which at least one dataset relevant to the supply chain was found? Also, the total number of data in all three portals, to be presented as a percentage, for each one of the Portals. • In the central Greek Open Data Portal: data.gov.gr • In the webpage of Greek Statistical Authority • In the web portal of the Municipality of Thessalonica

Furthermore, from the total number of data recorded, find the percentages of data each Portal has published. Also, the only website that declares the total number of data is the central Greek Open Data Portal. According to our record, as we also mentioned in Chapter 3, on 17-1-2020 the total number of data uploaded to this Portal was of 10,183. How many points (as a percentage) of the data recorded relevant to the supply chain, correspond to the total number of data in this Portal?

Because each data has a different name, this makes it impossible to count them automatically with the Tableau software. However, each data is related to only one “Type” of file. In order to count the number of data in each Portal and the total number of them, the user can count the number of “Type”, which is the same as the number of data. This procedure was already done in Question 3, and the answer is:

• In the Central Greek Open Data Portal, 358 different data were recorded • In the Greek Statistical Authority, 2,364 different data were recorded • In the Portal of the Municipality of Thessaloniki, 62 different data were recorded • With a total number of 2,784 data recorded in all these three Open Data Portals

In order to visualize with Tableau the percentage of points that the data recorded relevant to the supply chain in the Central Greek Open Data Portal, correspond to the total number of data uploaded since 17-1-2020, which is 10,183, an excel sheet must be created and the following steps must be followed:

120 • Select a column and in the first row write the “Name” of the column. This step is needed because Tableau reads the first row of each column as the “Name” of the column • Under the previously selected column, fill 358 cells with “Yes” • Under the previously selected column, fill the remaining 9,825 cells with “No”

In this way, the Tableau software can easily calculate and create a graphical visualization. In Figure 5.18, the percentage of the data relevant to the supply chain uploaded in the Central Greek Open Data Portal since 17-1-2020 is showed.

As shown in Figure 5.18, only the 3.52% of the total data uploaded in the Central Greek Open Data Portal are relevant to the supply chain

Figure 5.18: Supply Chain Related Data in Data.gov

Source: the author (2020)

5.3 Case study two: Greek Open Data Portals, a statistical analysis and visualization with Power BI

Question one

1. How many datasets relevant to the supply chain are in each one of the Portals, in which at least one dataset relevant to the supply chain was found? 1.1. In the central Greek Open Data Portal: data.gov.gr

121 1.2. In the webpage of Greek Statistical Authority 1.3. In the web portal of the Municipality of Thessalonica

Since all datasets have different names between them, this makes it impossible to count them automatically with the Power BI software. In order to count the datasets, the user can count how many times the respective “Site Name” appears, since each dataset is connected to only one “Site Name”, as showed in Figure 5.2. In order to answer this question in the Power BI software, a “Measure” has to be created for each one of the values we need to count. In order to create these “Measure” and after a visualization of the data, the following steps have to be done:

A. In the “Modeling” section, create a “New Measure” B. Select a type of visualization. In this case, the Stacked Column Chart graphic was selected C. From the list of “Fields”, select the “Measures” and the Data needed to be displayed in the visualization

The three Measures that were created so as to count the number of datasets in each one of the portals, are the following:

• Datasets in Data.gov.gr = CALCULATE(COUNTROWS('Data gov'), 'Data gov'[Site Name] = "data.gov.gr") • Datasets in ELSTAT = CALCULATE(COUNTROWS(ELSTAT), ELSTAT[Site Name] = "Greek Statistical Authority") • Datasets in Thessaloniki Portal = CALCULATE(COUNTROWS(Salonika), Salonika[Site Name] = "Open Data Portal - Municipality of Thessaloniki")

Following, an explanation of this measure and its parameters is presented:

• “Count A”: Is the name of the measure • “CALCULATE”: Evaluates an expression in a context modified by filters • “COUNTROWS(TourTable)”: Counts the number or rows in a table • “YourTable[Column Name]”: The name of the table and name of the row we have to process • “A”: the value we have to count

122 As presented in Figure 5.19, Figure 5.20 and Figure 5.21 the number of datasets relevant to the supply chain, published in each one of the three portals are:

• In the Central Greek Open Data Portal, there are thirty-five datasets • In the Greek Statistical Authorities, there are forty-four datasets • In the Portal of the Municipality of Thessaloniki, there are two datasets

Figure 5.19: Number of Datasets in Data.gov

Source: the author (2020)

123

Figure 5.20: Number of Datasets in Greek Statistical Authority

Source: the author (2020)

Figure 5.21: Number of Datasets in Thessaloniki Portal

Source: the author (2020)

124 Furthermore, all the data collected for each Portal, have to be copied in one unique sheet in the Excel software, so as to be processed in the Power BI software afterwards. In order to count the number of datasets in each one of the portals and to create a graphical visualization that presents the number of datasets in each portal, as a percentage of the total number of datasets in all three portals, the following measurements have to be created.

• Count ELSTAT = CALCULATE(COUNTROWS('All'), 'All'[Site Name] = "Greek Statistical Authority") • Count Thessaloniki = CALCULATE(COUNTROWS('All'), 'All'[Site Name] = "Open Data Portal - Municipality of Thessaloniki") • Count Data.gov.gr = CALCULATE(COUNTROWS('All'), 'All'[Site Name] = "Data.gov.gr")

Following, the percentage of datasets relevant to the supply chain, published by each one of the previous three portals is presented. This percentages show the relationship between the individual number and the total number of datasets, related to the supply chain published by each portal. As shown in Figure 5.22, from the total number of datasets, the Greek Statistical Authority has the 54.32%, the Central Greek Open Data Portal has the 43.21% and the Open Data Portal of the Municipality of Thessaloniki has the 2.47% of the datasets relevant to the supply chain.

Figure 5.22: Percentage of Datasets

Source: the author (2020)

125 Question two

2. How many different Organizations and how many datasets in each one of them have published datasets relevant to the supply chain in the central Greek Open Data Portal? Also, present the number of datasets of each Organization as a percentage of the total.

In order to count the times an Organization has published data in the Central Greek Open Data Portal, the following steps have to be done:

• In the “Modeling” section, select “New Measure” • Measure = COUNTA ('Data gov'[Organization Name]) • From the list of “Fields”, select the “Measure” that was previously created and the “Organization’s Names” • Select a graphical visualization. In this case, the Treemap graphic was selected

The COUNTA expression counts the number of values in a column. Moreover, the percentage of datasets published by each organization is showed in Figure 5.23. As shown in Figure 5.23: • In the first place, we see the Municipality of Thessaloniki and the Municipality of Ilios with the 20% • In the second place, we see the Decentralized Administration of Thessaly and Stereas Hellas, the Ministry of Environment and Energy and the Municipality of New Smirne with the 5.71% • In the third place, we see the Region of Thessaly, Region of Sterea Hellas, Region of Northren Aegean, Region of Epirus, Municipality of Xiromerou, Municipality of Rafina – Pikermiou, Municipality of Egaleo, Municipality of Almopia, Municipality of Agias Paraskevi, Hellenic Center of Mental Health and Research, Hellenic Agricultural Organization – Dimitra, Greek Statistical Authority, General Hospital of Didimotichos, Decentralizes Administration of Epirus – West Macedoni and the Court of Appeal of Thessaloniki with the 2.85%

126

Figure 5.23: Percentage of Datasets by each Organization

Source: the author (2020)

Question three

3. How many data relevant to the supply chain have been published in each one of the Portals in which at least one dataset relevant to the supply chain was found? Also, the total number of data in all three portals, to be presented as a percentage, for each one of the Portals. 3.1. In the central Greek Open Data Portal: data.gov.gr 3.2. In the webpage of Greek Statistical Authority 3.3. In the web portal of the Municipality of Thessalonica

Furthermore, from the total number of data recorded, find the percentages of data each Portal has published. Also, the only website that declares the total number of data is the central Greek Open Data Portal. According to our record, as we also mentioned in Chapter 3.3.1, on 17-1-2020 the total number of data uploaded to this Portal was of 10,183. How many point percent the data recorded, relevant to the supply chain, occupies from the total number of data in this Portal?

Because each data has a different name, this makes it impossible to count them automatically with the Power BI software. However, each data is related to only one

127 “Type” of file. In order to count the number of data in each Portal and the total number of them, the user can count the number of “Type”, which is the same as the number of data. This procedure was already done in Question 3, and the answer is:

• In the Central Greek Open Data Portal, 358 different data were recorded • In the Greek Statistical Authority, 2,364 different data were recorded • In the Portal of the Municipality of Thessaloniki, 62 different data were recorded • With a total number of 2,784 data recorded in all these three Open Data Portals

Furthermore, in order to count, make a percentage and visualize the results in the Power BI software, we can use the same sheet created previously, for doing this in the Tableau software. The previously mentioned sheet was created as follows:

• In the Excel software, create a new sheet • Select a column and in the first row write the “Name” of the column. This step is needed because Power BI reads the first row of each column as the “Name” of the column • Under the previously selected column, fill 358 cells with “Yes” • Under the previously selected column, fill the remaining 9,825 cells with “No”

After the creation of this sheet in the Excel software, the following steps have to be made:

• Create a measure for each value we have to count • For creating a measure, go to “Modeling” and the “New measure” • Count Yes = CALCULATE(COUNTROWS(Sheet1), Sheet1[Column1] = "Yes") • Count No = CALCULATE(COUNTROWS(Sheet1), Sheet1[Column1] = "No") • Select a graphical visualization. In this case the Pie Chart graphic was selected • From the list of “Fields”, select the measures “Count Yes” and “Count No”

As shown in Figure 5.24, only the 3.52% of the total data uploaded in the Central Greek Open Data Portal are relevant to the supply chain.

128

Figure 5.24: Percentage of Data relevant to Supply Chain in Data.gov

Source: the author (2020)

Question four

4. From the total number of data recorded in these three portals, relevant to the supply chain, define as a percentage: A. The “Type” B. The keywords under the “Description”, that are the stage or stages of the supply chain in which the data refers to A. In order to calculate the type of each data, the following steps have to be made: • Create a measure for each value we have to count • Go to “Modeling” and select “New measure”

Measure for the calculation of “Type” in the Central Greek Open Data Portal: ▪ PDF = CALCULATE(COUNTROWS('Data gov'), 'Data gov'[Type] = "pdf") ▪ XLS = CALCULATE(COUNTROWS('Data gov'), 'Data gov'[Type] = "xls") ▪ ODS = CALCULATE(COUNTROWS('Data gov'), 'Data gov'[Type] = "ods")

As presented in Figure 5.25 for the Central Greek Open Data portal, in Figure 5.26 for the Greek Statistical Authority and in Figure 5.27 for the Portal of the Municipality of Thessaloniki, the number and percentages of type of the data are:

• In the Central Greek Open Data Portal, there are 149 .ods and 209 .xls files • In the portal of Greek Statistical Authority are 2,030 .xls and 334 .pdf

129 • In the Portal of the Municipality of Thessaloniki, there are 62 .xls

Figure 5.25: Percentage of Type in Data.gov

Source: the author (2020)

Measure for the calculation of “Type” in the Greek Statistical Authority:

▪ ODS ELSAT = CALCULATE (COUNTROWS ('ELSTAT'), 'ELSTAT'[Type]= "ods") ▪ PDF ELSAT = CALCULATE (COUNTROWS ('ELSTAT'), 'ELSTAT'[Type]= "pdf") ▪ XLS ELSAT = CALCULATE (COUNTROWS ('ELSTAT'), 'ELSTAT'[Type]= "xls")

Figure 5.26: Percentage of Type in Greek Statistical Authority

Source: the author (2020)

130 Measure for the calculation of “Type” in the Municipality of Thessaloniki: ▪ XLS Saloniko = CALCULATE (COUNTROWS ('Salonika'), 'Salonika'[Type]= "xls") ▪ PDF Saloniko = CALCULATE( COUNTROWS ('Salonika'), 'Salonika'[Type]= "pdf") ▪ ODS Saloniko = CALCULATE (COUNTROWS ('Salonika'), 'Salonika'[Type]= "ods")

Figure 5.27: Percentage of Type in Thessaloniki

Source: the author (2020)

Measure for the calculation of “Type” for all three Portals:

▪ ODS ALL = CALCULATE(COUNTROWS('All'), 'All'[Type]= "ods") ▪ PDF ALL = CALCULATE(COUNTROWS('All'), 'All'[Type]= "pdf") ▪ XLS ALL = CALCULATE(COUNTROWS('All'), 'All'[Type]= "xls")

131

Figure 5.28: Percentage of Type in all three Portals

Source: the author (2020)

As shown in Figure 5.28, the vast majority of the files are of .xls type, with the 82.62% to be of xls type, the 12.03% to be of pdf type and the remaining 5.35% to be of .ods type.

B. In order to answer this question, the following measure has to be created so to count how many times a value exists in the row. • Measure = COUNT (Descrip [Descriprion])

The measure COUNT, counts the numbers in a column. Then, in order to answer this question, the following steps must be done: • Under the “Fields”, select the “Description” • Under the “Fields”, select the “Measure” • Select a type of visualization. In this case the Packed Bubbles graphic was selected

As shown in Figure 5.29 and Figure 5.30, the total number of each “Description” is measured as well as the percentage of each one. This visualization clearly shows that from the total number of data related to the supply chain, the 48.05% are related to the production, the 15.75% are related to the transportation, the 8.83% are related to the sales, the 7.42% are of import, the

132 5.49% are of supply, the 3.19% are of export, the 2.67% are of transshipment, the 2.45% are of storage, the 2.15% are of orders received, the 1.41% are of unload, the 1.41% are of load, the 0.52% are of processing, the 0.43% are about stock variation and the 0.25% are related to the consumption.

Figure 5.29: Power BI Percentage of Description

Source: the author (2020)

Figure 5.30: Power BI Graph Percentage of Description

Source: the author (2020)

133 5.4 Case study three: Geographical visualization of road freight transport of goods, per type of package, in the thirteen Regions of Greece

This case study was selected in order to present the Geographical visualization with the Tableau software. In this geographical visualization, the tons and tonometer of products, divided by type of cargo, transported by road freight transportation in the thirteen Regions of Greece during the year 2015 will be presented. Furthermore, the path in order to find this data is shown in Table 18.

Table 18: Path for Data in Case Study Three

Source: the author (2020)

Site Name Greek Statistical Authority URL of Site https://www.statistics.gr/el/home Tab 1 Statistics Tab 2 Industry, Trade, Services, Transportation URL of Tab https://www.statistics.gr/el/statistics/ind 2 Tab 3 Transportation Tab 4 Road transport Dataset Road Freight Transport Name URL of https://www.statistics.gr/el/statistics/-/publication/SME15/- Dataset TABLE 5. Transportation of products using both private and publicly Data Name owned trucks in tones and tonne-kilometers, by type of cargo (freight packaging) (Aggregate 2015) Type .xls Description Transportation

134 Firstly, in order for the Tableau software to recognize that the data in the “Region” column have to be processed as geo-data in the data source table, click on the type of column, then “Geographic Role” and select “State/Provinces”.

In order to create this visualization for each product’s tons and tonometer transported in the various Regions, the following steps must be done:

1. For the tons of liquid bulk goods transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Tons of Liquid goods” • Select a type of visualization. In this case, the Symbol Maps graphic was selected

Figure 5.31: Tons Liquid Bulk

Source: the author (2020)

As shown in Figure 5.31, the Region with the most tons of liquid goods transported by road freight transportation, during the year 2015, was the Attica Region, with 7,901,321 tons. Following, are the Region of Central Macedonia, with 3,828,308 tons, the Region of Central Greece, with 3,410,758 tons, the Thessaly Region, with 1,597,540 tons, the Region of West Macedonia, with 830,868 tons, the

135 Region of West Greece, with 775,984 tons, the Southern Aegean Region, with 758,464 tons, the Region of Crete, with 623,285 tons, the Region of Eastern Macedonia and Thrace, with 585,820 tons, the Epirus Region, with 296,237 tons, the Region of Peloponnese, with 287,922 tons and last, the Region of North Aegean, with 220,151 tons. Also, the Ionian Island Region had no data collected for the liquid goods or had no transportation of liquid goods, during this period.

2. For the tonometer of liquid bulk goods transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Tonometer of Liquid goods” • Select a type of visualization. In this case, the Symbol Maps graphic was selected

Figure 5.32: Tonometer Liquid Bulk

Source: the author (2020)

As shown in Figure 5.32, the Region with the most tonometer of liquid goods, transported with road freight transportation, during the year of 2015, was the Attica

136 Region, with 749,114,517 tonometers. Following, are the Region of Central Macedonia, with 322,027,474 tonometers, the Region of Central Greece, with 264,720,688 tonometers, the Region of West Greece, with 109,964,739 tonometers, the Regions of Eastern Macedonia, with 69,195,118 tonometers, the Region of Thessaly, with 37,213,084 tonometers, The Region of Southern Aegean, with 20,343,076 tonometers, the Regions of Crete, with 16108839 tonometers, the Region of Peloponnese, with 12,568,904 t tonometers, the Region of Epirus, with 5,321,212 tonometers, and last, the Region of North Aegean, with 3,268,982 tonometers. Also, for the Ionian Island Region, no data was collected or this Region had no transportation of liquid goods, during this period.

3. For the tons of the solid bulk, transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Solid Bulk Tons” • Select a type of visualization. In this case, the Symbol Maps graphic was selected

Figure 5.33: Tons Solid Bulk

Source: the author (2020)

137 As shown in Figure 5.33, the Region with the most tons of solid bulk, transported with road freight transportation, during the year of 2015, was the Region of Western Macedonia, with 132,527,983 tons. Following, are the Region of Central Macedonia, with 46,220,755 tons. The Region of Western Greece, with 37,257,753 tons, the Region of Peloponnese, with 17,900,630 tons, the Region of Thessaly, with 16,594,846 tons, the Region of Central Greece, with 16,594,846 tons, the Region of Attica, with 11,615,998 tons, the Region of Epirus, with 8,033,420 tons, the Region of Southern Aegean, with 7,865,654 tons, the Region of Crete, with 7,508,405 tons, the Region of Eastern Macedonia, with 7,281,597 tons, the Region of North Aegean, with 3,660,184 tons and last, the Ionian Island Region, with 507,489 tons.

4. For the tonometer of solid bulk, transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Solid Bulk Tonometer” • Select a type of visualization. In this case, the Symbol Maps graphic was selected

Figure 5.34: Tonometer Solid Bulk

Source: the author (2020)

138 As shown in Figure 5.34, the region with the most tonometer of solid bulk, transported with road freight transportation, during the year of 2015, was the Region of Central Macedonia, with 1,717,875,239 tonometers. Following, are the Region of Attica, with 1,144,663,747 tonometers, the Region of Western Macedonia, with 1,028,977,991 tonometers, the Region of Peloponnese, with 870,287,893 tonometers, the Region of Western Greece, with 701,960,177 tonometers, the Region of Eastern Macedonia, with 545,697,893 tonometers, the Region of Central Greece, with 540,831,466 tonometers, the Region of Thessaly, with 536,726,093 tonometers, the Region of Epirus, with 384,998,249 tonometer, the Region of Crete, with 219,247,602 tonometers, the Region of Southern Aegean, with 184,262,361 tonometers, the Region of North Aegean, with 18,217,646 tonometers and last, the Region of Ionian Islands, with 17,861,857 tonometers.

5. For the tons of containers, transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualisation: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Container Tons” • Select a type of visualization. In this case, the Symbol Maps graphic was selected

139

Figure 5.35: Tons Container

Source: the author (2020)

As shown in Figure 5.35, the region with the most tons of containers, transported with road freight transportation, during the year of 2015, was the Region of Attica, with 37,854,255 tons. Following, are the Region of Western Greece, with 3,208,303 tons, the Region of Eastern Macedonia, with 567,247 tons, the Region of Peloponnese, with 411,916 tons, the Region of Central Macedonia, with 159,691 tons, the Region of Crete, with 156165 tons, the Region of Thessaly, with 79,895 tons and the Region of Central Greece, with 71,943 tons. Also, for the Regions of Western Macedonia, Epirus, Ionian Islands, North Aegean and South Aegean, no data were collected or these Regions had zero transportation of tons, during this period.

6. For the tonometer of containers, transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Container Tonometer” • Select a type of visualization. In this case, the Symbol Maps graphic was selected

140

Figure 5.36: Tonometer Container

Source: the author (2020)

As shown in Figure 5.36, the region with the most tonometer of container, transported with road freight transportation, during the year of 2015, was the Region of Attica, with 261,676,601 tonometers. Following, are the Region of Easter Macedonia, with 102,541,508 tonometers, the Region of Peloponnese, with 23,189,956 tonometers, the Region of Thessaly, with 15,979,090 tonometers, the Region of Western Greece, with 11,285,194 tonometers, the Region of Central Greece, with 6834614 tonometers and the Region of Crete, with 1,234,960 tonometers. Also, for the Regions of Western Macedonia, Epirus, Ionian Islands, North Aegean and South Aegean, no data were collected or these Regions had zero tonometers, during this period.

7. For the tons of pallets, transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Pallet Tons” • Select a type of visualization. In this case, the Symbol Maps graphic was selected

141

Figure 5.37: Tons Pallets

Source: the author (2020)

As shown in Figure 5.37, the region with the most tons of pallets, transported with road freight transportation, during the year of 2015, was the Region of Attica, with 7,255,528 tons. Following, are the Region of Central Macedonia, with 5,610,706 tons, the Region of Peloponnese, with 3,051,353 tons, the Region of Epirus, with 2,202,841 tons, the Region of Crete, with 2,111,897 tons, the Region of Thessaly, with 1,785,858 tons, the Region of Western Greece, with 1,478,842 tons, the Region of Central Greece, with 1,255,993 tons, the Region of Western Macedonia, with 867,358 tons, the Region of Southern Aegean, with 679,569 tons, the Region of Eastern Macedonia, with 652,610 tons, the Region of North Aegean, with 175,596 tons and last the Ionian Islands Region, with 40,976 tons.

8. For the tonometer of pallets, transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Pallet Tonometer”

142 • Select a type of visualization. In this case, the Symbol Maps graphic was selected

Figure 5.38: Tonometer Pallets

Source: the author (2020)

As shown in Figure 5.38, the region with the most tonometer of pallet, transported with road freight transportation, during the year of 2015, was the Region of Central Macedonia, with 1,716,193,785 tonometers. Following, are the Region of Attica, with 1,328,724,546 tonometers, the Region of Epirus, with 647,002,798 tonometers, the Region of Thessaly, with 565,129,819 tonometers, the Region of Central Greece, with 394,091,504 tonometers, the Region of Peloponnese, with 391,636,287 tonometers, the Region of Western Greece, with 305,809,728 tonometers, the Region of Eastern Macedonia, with 183,260,234 tonometers, the Region of Crete, with 158,059,062 tonometers, the Region of Western Macedonia, with 89,230,643 tonometers, the Region of Southern Aegean, with 18,764,634 tonometers, the Region of Ionian Islands, with 4,122,875 tonometers and the Region of North Aegean, with 4,062,259 tonometers.

143 9. For the tons of goods packaged in artana, transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Artana Tons” • Select a type of visualization. In this case, the Symbol Maps graphic was selected

Figure 5.39: Tons of Goods Packaged in Artana

Source: the author (2020)

As shown in Figure 5.39, the region with the most tons of goods packaged in artana, transported with road freight transportation, during the year of 2015, was the Region of Central Macedonia, with 219,682 tons. Following, are the Region of Thessaly, with 169,982 tons, the Region of Epirus, with 164,758 tons, the Region of Crete, with 137,150 tons, the Region of Attica, with 103,625 tons, the Region of Central Greece, with 100,200 tons, the Region of Peloponnese, with 53,350 tons, the Region of Western Greece, with 45,118 tons, the Region of Western Macedonia, with 41,226 tons and the Region of Southern Aegean, with 4,413 tons. Also, the Region of

144 Ionian Islands and North Aegean had no data collected or zero transportation of tons, during this period.

10. For the tonometers of goods packaged in artana, transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Artana Tonometer” • Select a type of visualization. In this case the Symbol Maps graphic was selected

Figure 5.40: Tonometers of Goods Packaged in Artana

Source: the author (2020)

As shown in Figure 5.40, the region with the most tonometer of goods packaged in artana, transported with road freight transportation, during the year of 2015, was the Region of Central Macedonia, with 32,069,066 tonometers. Following are the Region of Attica, with 31,719,264 tonometers, the Region of Central Greece, with 20,911,133 tonometers, the Region of Peloponnese, with 11,415,217 tonometers, the Region of Western Greece, with 6,446,840 tonometers, the Region of Epirus, with 4350213 tonometers, the Region of Western Macedonia, with 3,682,365 tonometers,

145 the Region of Thessaly, with 2,520,431 tonometers, the Region of Crete, with 1,003,894 tonometers and the Region of Southern Aegean, with 70,604 tonometers. Also, for the Regions of Eastern Macedonia, Ionian Islands and North Aegean, no data was collected or there was zero tonometers, for this period.

11. For the tons of mobile and self-propelled units, transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Mobile Tons” • Select a type of visualization. In this case, the Symbol Maps graphic was selected

Figure 5.41: Tons of Mobile and Self-Propelled Units

Source: the author (2020)

146 As shown in Figure 5.41, the region with the most tons of mobile, self-propelled units, transported with road freight transportation, during the year of 2015, was the Region of Western Greece, with 1,626,754 tons. Following, are the Regin of Attica, with 1,116,086 tons, the Region of Central Macedonia, with 620,581 tons, the Region of Western Macedonia, with 269,373 tons, the Region of Thessaly, with 218,298 tons, the Region of Crete, with 207,364 tons, the Region of Peloponnese, with 168,391 tons, the Region of Epirus, with 136,471 tons, the Region of Eastern Macedonia, with 133,324 tons, the Region of Central Greece, with 62,457 tons and the Region of Southern Aegean, with 20,534 tons. Also, for the Regions of Ionian Islands and North Aegean, no data was collected or zero tons were transported, during this period.

12. For the tonometer of mobile and self-propelled units transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Mobile Tonometer” • Select a type of visualization. In this case the Symbol Maps graphic was selected

Figure 5.42: Tonometer of Mobile and Self-Propelled Units

Source: the author (2020)

147 As shown in Figure 5.42, the region with the most tonometer of mobile and self- propelled units, transported with road freight transportation, during the year of 2015, was the Region of Attica, with 78,273,273 tonometers. Following, are the Region of Central Macedonia, with 69,258,928 tonometer, the Region of Western Greece, with 22,107,348 tonometers, the Region of Thessaly, with 20,358,200 tonometers, the Region of Western Macedonia, with 13,392,553 tonometers, the Region of Epirus, with 10,455,449 tonometers, the Region of Crete, with 10215635 tonometers, the Region of Peloponnese, with 9,647,754 tonometers, the Region of Eastern Macedonia, with 2,107,541 tonometers, the Region of Central Greece, with 1968440 tonometers and the Region of Southern Aegean, with 802,302 tonometers. Also, for the Regions of Ionian Islands and North Aegean, no data were collected or there were zero tonometers, during this period.

13. For the tons of other mobile unites, transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Other Mobile Tons” • Select a type of visualization. In this case, the Symbol Maps graphic was selected

148

Figure 5.43: Tons of Other Mobile Unites

Source: the author (2020)

As shown in Figure 5.43, the region with the most tons of other mobile unites, transported with road freight transportation, during the year of 2015, was the Region of Central Greece, with 167,834 tons. Following, are the Region of Western Greece, with 109,264 tons, the Region of Thessaly, with 93,817 tons, the Region of Peloponnese, with 78,257 tons and the Region of Central Macedonia, with 37,747 tons. Also, for the Regions of Eastern Macedonia, Western Macedonia, Epirus, Ionian Islands, Attica, North Aegean, Southern Aegean and Crete, no data was collected or zero tons were transported, during this period.

14. For the tonometer of other mobile unites, transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • Under the “Dimension”, select the “Regions” • Under the “Measures”, select the “Other Mobile Tonometer”

149 • Select a type of visualization. In this case, the Symbol Maps graphic was selected

Figure 5.44: Tonometer of Other Mobile Unites

Source: the author (2020)

As shown in Figure 5.44, the Region with the most tonometers of other mobile unites, transported with road freight transportation, during the year of 2015, was the Region of Thessaly, with 10,788,917 tonometers. Following, are the Region of Central Macedonia, with 6,553,223 tonometers, the Region of Central Greece, with 177,185 tonometers, the Region of Western Greece, with 130,428 tonometers and the Region of Peloponnese, with 113,201 tonometers. Also, for the Regions of Eastern Macedonia, Western Macedonia, Epirus, Ionian Islands, Attica, North Aegean, Southern Aegean and Crete, no data was collected or there was zero tonometers, during this period.

The last two graphical visualizations of this data will be done with the Power BI software. In this way, a better comparison of the Power BI and Tableau software can be done in the last subchapter of Chapter five.

150 15. For the tons of other types of cargo, transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • After the selection of the data, so to be loaded in the software • From the various types of “Visualizations”, select the Map graphic • In the “Fields” of the graphic selected, drag and drop the “Regions” under the “Location”, so for the software to know that the Regions are not just names, but have to be shown as points on the map. • Then, just select the values that have to be showed in the visualization. In this case, the “Other Types of Cargo - Tons”

Figure 5.45: Tons of Other Types of Cargo

Source: the author (2020)

As shown in Figure 5.45, the region with the most tons of other types of cargo, transported with road freight transportation, during the year of 2015, was the Region of Central Greece, with 1,452,699 tons. Following, are the Region of Central Macedonia, with 1,300,568 tons, the Region of Western Greece, with 1,230,592 tons, the Region of Attica, with 1,212,580 tons, the Region of Peloponnese, with 980,987 tons, the Region of Thessaly, with 701,918 tons, the Region of Epirus, with 450,540 tons, the Region of Crete, with 386,920 tons, the Region of Eastern Macedonia, with

151 276,776 tons, the Region of Western Macedonia, with 208,820 tons, the Region of Southern Aegean, with 83,872 tons and the Region of Ionian Islands, with 37,479 tons. Also, for the Region of North Aegean, no data were collected or zero tons were transported, during this period.

16. For the tonometer of other types of cargo, transported by road freight transportation in each Region, the following steps must be done, so as to create a graphical visualization: • After the selection of the data, so to be loaded in the software • From the various types of “Visualizations”, select the Map graphic • In the “Fields” of the graphic selected, drag and drop the “Regions” under the “Location”, so for the software to know that the Regions are not just names, but have to be shown as points on the map. • Then, just select the values that have to be showed in the visualization. In this case, the “Other Types of Cargo - Tonometer”

Figure 5.46: Tonometer of Other Types of Cargo

Source: the author (2020)

152 As shown in Figure 5.46, the region with the most tonometer of other types of cargo, transported with road freight transportation, during the year of 2015, was the Region of Central Macedonia, with 354,637,615 tonometers. Following are the Region of Attica, with 315,924,816 tonometers, the Region of Central Greece, with 327,073,966 tonometers, the Region of Western Greece, with 129305418 tonometer, the Region of Thessaly, with 107504594 tonometer, the Region of Epirus, with 107,055,441 tonometers, the Region of Peloponnese, with 100,428,404 tonometers, the Region of Eastern Macedonia, with 63029198 tonometer, the Region of Western Macedonia, with 17,315,657 tonometers, the Region of Crete, with 17,315,657 tonometers, the Region of Ionian Islands, with 10,992,674 tonometer and the Region of Southern Aegean, with 8,570,569 tonometers. Also, for the Region of North Aegean, no data were collected, or there was zero tonometer, during this period.

After the analysis and visualization of the tons and tonometer of products, divided by type of cargo, transported by road freight transportation in each of the thirteen Regions of Greece during 2015, it is easier to understand and recognize in which regions the highest and lowest mobility of tons and tonnage per type of packaging were found, as shown in Table 19.

As a result, the minimum tons of liquid type of products transported, was in the Northern Aegean Region, with 220,151.2 tons and the maximum tons transported was in the Attica Region, with 7,901,321 tons. Regarding the tonometers of liquid type of products, the minimum was in the Northern Aegean Region, with 3,268,981.6 tonometers and the maximum was in the Attica Region, with 749,114,517 tonometers. For the tons of solid bulk products transported, the minimum was in the Ionian Islands Region, with 507,488.7 tons and the maximum was in the Western Macedonia Region, with 132,527,982.8 tons. For the tonometers of solid bulk products, the minimum was in the Ionian Islands Region, with 17,861,857.2 tonometers and the maximum was in the Central Macedonia Region, with 1,717,875,239 tonometers. For the tons container transported in the various Regions, the Region with the minimum was Central Greece, with 71,943.3 tons and the maximum tons transported was in the Attica Region, with 37,854,254.9 tons. For the tonometers of containers, the minimum was in the Region of Crete, with 1,234,960 tonometers and the maximum was in the Attica Region, with 261,676,601 tonometers. For the tons of pallets, the region with the minimum was the Ionian Islands, with 40,975.9 tons and the

153 maximum was in the Attica Region, with 7,255,527.5 tons. For the tonometers of pallets, the minimum was in the Northern Aegean Region, with 4,062,259.1 tonometers and the maximum was in the Region of Central Macedonia, with 1,716,193,785 tonometers. For the tons of goods packaged in artanas, the minimum was in the Southern Aegean Region, with 4,412.8 tons and the maximum was in the Region of Central Macedonia, with 219,681.7 tons. For the tonometer of goods packaged in artanas, the minimum was the Southern Aegean Region, with 70,604.4 of tonometer and the maximum was in the Region of Central Macedonia, with 32,069,066.2 of tonometers. For the tons of mobile, self-propelled units, the minimum was in the Southern Aegean Region, with 20,534.1 tons and the maximum was in the Region of West Greece, with 1,626,753.8 of tons. For the tonometer of mobile, self- propelled units, the minimum was in the Southern Aegean Region, with 802,301.8 tonometers and the maximum was in the Region of Attica, with 78,273,273 tonometers. For the tons of other mobile units, the minimum was in the Central Macedonia Region, with 37m747.4 tons and the maximum was in the Region of Central Greece, with 167,833.6 tons. For the tonometer of other mobile units, the minimum was in the Peloponnese Region, with 113,201.4 tonometers and the maximum was in the Region of Thessaly, with 10,788,916.7 of tonometer. For the tons of other types of cargo, the minimum was in the Ionian Island Region, with 37,478.5 of tons and the maximum was in the Region of Central Greece, with 1,452,698.9 of tons. For the tonometer of other types of cargo, the minimum was in Southern Aegean Region, with 8,570,568.5 tonometers and the maximum was in the Region of Central Macedonia, with 354,637,614.6 of tonometer.

Moreover, from Table 19, we can easily distinguish similarities, commonalities and patterns of the data, such as: The Region with the minimum tons of a type of package transported, has a probability of four in eight, which is a fifty percent probability, to be also the Region with the minimum tonometer of that type of package. Also, the Region with the maximum tons of a type of package transported, has a probability of three in eight, which is a thirty-seven-point five percent, to be also the Region with the maximum tonometer of that type of package.

154 Table 19: Case Study Two Min/Max

Source: the author (2020)

Type of Package max Region min Region Liquid (no unit) Northern Tons 7,901,321 Attica 220,151.2 Aegean Liquid Northern Tonometer 749,114,516.9 Attica 3,268,981.6 Aegean Solid bulk (no Western unit) Tons 132,527,982.8 Macedonia 507,488.7 Ionian Islands Central Solid Tonometer 1,717,875,239 Macedonia 17,861,857 Ionian Islands Container Tons 37,854,254.9 Attica 71,943.3 Central Greece Container Tonometer 261,676,601 Attica 1,234,960 Crete Pallet Tons 7,255,527.5 Attica 40,975.9 Ionian Islands Central Northern Pallet Tonometer 1,716,193,785 Macedonia 4,062,259.1 Aegean Goods packaged Central Southern in Artanas Tons 219,681.7 Macedonia 4,412.8 Aegean Goods packaged in Artanas Central Southern Tonometer 32,069,066.2 Macedonia 70,604.4 Aegean Mobile, self- propelled units Western Southern Tons 1,626,753.8 Greece 20,534.1 Aegean Mobile, self- propelled units Southern Tonometer 78,273,273 Attica 802,301.8 Aegean Other Mobile Central Central Units Tons 167,833.6 Greece 37,747.4 Macedonia Other Mobile Tonometer 10,788,916.7 Thessaly 113,201.4 Peloponnese Other types of 1,452,698.9 Central 37,478.5 Ionian Island

155 cargo Tons Greece Other types of Central Southern cargo Tonometer 354,637,614.6 Macedonia 8,570,568.5 Aegean

5.5 Cases study four: Unloaded and Loaded goods in Greek ports, by ports and type of cargo

This case study was selected because of the complete data it has, as well as the type of data. In this case study, we will be present the loaded and unloaded goods in 171 ports of Greece, divided by type of package in the four quarters of 2019. Furthermore, to these 171 ports, a category named as “Other” is added, that the data does not precise which ports are in this category, making the row of ports having 172 values to analyze. More specifically, we will analyze and visualize the:

• Total Load and Unload, of all ports, per type of package for all 2019 • Total Load and Total Unload of all ports, per type of pack for all 2019 • Total Load and Unload, of all ports, per package and per quarter • Total Load and Total Unload, of all ports, per type of package and per quarter • Total Load and Unload of each quarter, of all ports, per type of package and per quarter, as a percentage • Total Load and Total Unload, of all ports, per type of package and per quarter, as a percentage • Loaded Container and Unloaded Container per port per quarter • Loaded Liquid Bulk and Unloaded Liquid Bulk per port per quarter • Loaded Other General Cargo and Unloaded Other General Cargo per port and per quarter • Loaded Roll-on Roll-off and Unloaded Roll-on Roll-off per port and per quarter • Loaded Solid Bulk and Unloaded Solid Bulk per port and per quarter • Load Container per port and per quarter as percentage • Load Other General Cargo per port and per quarter as percentage

156 • Load Liquid Bulk per port and per quarter as percentage • Load Roll-on Roll-off per port and per quarter as percentage • Load Solid Bulk per port and per quarter as percentage • Unload Container per port and per quarter as percentage • Unload Liquid Bulk per port and per quarter as percentage • Unload Other General Cargo per port and per quarter as percentage • Unload Roll-on Roll-off per port and per quarter as percentage • Unload Solid Bulk per port and per quarter as percentage

Furthermore, the gross weight of goods is in tons. Also, the path in order to find this data is shown in Table 20.

Table 20: Path for Data in Case Study Four

Source: the author (2020)

Site Name Greek Statistical Authority URL of Site https://www.statistics.gr/el/home TAB 1 Statistics TAB 2 Industry, Trade, Services, Transport URL TAB 2 https://www.statistics.gr/el/statistics/ind TAB 3 Transports TAB 4 Shipping Dataset Name Passenger and Freight Movement in Shipping (number of passengers / vehicles, goods) Dataset URL https://www.statistics.gr/el/statistics/- /publication/SMA06/- Year 2019, divided into quarters Data Name Unloaded and loaded goods in Greek ports, by port and cargo category

157 Total Load and Unload, of all ports, per type of package for all 2019

Figure 5.47: Number of Total Load and Unload, of Figure 5.48: Percentage of Total Load and Unload, of all ports, per type of package for all 2019 all ports, per type of package for all 2019 Source: the author (2020) Source: the author (2020)

In order to create these graphs, the user has to:

• From the “Dimensions”, select the the five type of package • From the “Measures”, select the total load and unload, for all five type of package • Select a visualisation. In this case the Pie Chart graphic was selected • In order show in the Pie Chart the values as numbers, as shown in Figure 5.47, form the toolbar, go to “Analysis”, and select “Percentage of: None” • In order to show in the Pie Chart the values as percentage, as shown in Figure 5.48, form the toolbar, go to “Analysis”, and select “Percentage of: Table”

As shown in this graphical visualisation, Figure 5.48, of the total ammount of transportation for each type of package, in 2019, the type of package with the most traffic was the Liquid Bulk, with the 35.12%. The secont type of package was the Container, with the 28.33% and then, in the third, fourth and fifth place were the Solid Bulk, with 19.17%, the Roll-on Roll-off, with the 15.10% and the Other general cargo, with the 2.28%. Also, the average of the year is of 19,381,667.4 tons.

158 Total Load and Total Unload, of all ports, per type of pack for all 2019

Figure 5.49: Total Load and Total Unload, of all ports, per type of pack for all 2019

Source: the author (2020)

In order to create this graph, the user has to:

• From Measures, select the Load for each type and Unload for each type of package • From Dimensions, select the “2019 Quarters” • From side bar, go to “Analitics” and select “Total”, so to see the total value in the graph • From side bar, go to “Analitics” and select “Average”, so to see the average value in the graph • Select a visualisation. In this case, the Side by Side graphic was selected

As shown in Figure 5.49, the Containers are the most loaded type of package and the Liquid Bulk is the most unloaded type of package. Also, the least loaded and unloaded type of packages are the Other general cargo. Furthermore, the Average for the total traffic of loads and unloads in 2019 was 9,690,834 tons.

159 Total Load and Unload, of all ports, per package and per quarter

Figure 5.50: Total Load and Unload, of all ports, per package and per quarter

Source: the author (2020)

In order to create this graph, the user has to:

• From the “Measure”, select the values of Load of all five types and Unload of all five types of package • From “Dinensions”, select the “2019 Quarters” • Select a visualisation. In this case, the Side by Side graphic was selected

As shown in Figure 5.50, for the first quarter of 2019, the type of package with the most traffic was Roll-on Roll-off. For the second quarter, also Roll-on Roll-off, was the type of package with the most traffic. For the third quarter, the Liquid Bulk was the type of package with the most traffic and for the fourth quarter of 2019, the Container was the type of package with the most traffic. The overall type of package with the most traffic, during 2019 was the Liquid Bulk in the third quarter. In addition, the average of the first quarter is 2,833,874.8 tons, of the second quarter is 3,579,705.6 tons, of the third quarter is 6,536,344.8 tons, of the fourth quarter is 6,431,742.2 tons and of all four quarters is 4,845,416.85 tons.

160 Total Load and Total Unload, of all ports, per type of package and per quarter

Figure 5.51: Total Load and Total Unload, of all ports, per type of package and per quarter

Source: the author (2020)

In order to create this graph, the user has to:

• From “Measure”, select the Load and Unload of all types of package • From “Dinensions”, select the “2019 Quarters” • From side bar, go to “Analitics” and select “Average” so to see the average in the graph • Select a visualisation. In this case, the Side by Side graphic was selected

As shown in Figure 5.51, from this visualisation, it is easy to see that in the first quarter of 2019, the Load and Unload values of all five types of packages are equal. Also, the type of package with the most traffic, during the first quarter, was the Roll- on Roll-off, with 2,366,416 tons of load and also 2,366,416 tons of unload. For the second semester, all Load and Unload values of all five types of packages are equal. Also, the type of package with the most traffic was the Roll-on Roll-off, with 3,235,375 tons of load and the same ammount of unload. For the third quarter, there is no more balance between the loaded and unloaded type of package, with the most load package being the Container, with 5,828,408 tons and for the unloaded type, the Liquid Bulk, with 9,487,687 tons. Finally, for the fourth quarter of 2019, also, the load and unload tons of the various types are uniqual, with the most loaded type of package being the Container, with 6,286,741 tons and the most unloaded type to be

161 the Liquid Bulk, with 8,401,297 tons. In addition, the average of the first quarter is 1,416,937 tons, of the second’s quarter is 1,789,853 tons, of the third’s quarter is 3,268,172 tons, of the fourth quarter is 3,215,871 tons and the total average of all four quarters is 2,422,708 tons.

Total Load and Unload of each quarter, of all ports, per type of package and per quarter, as a percentage

st Figure 5.53 : Total Load and Unload for the 1 nd quarter, of all ports, per type of package and per Figure 5.52: Total Load and Unload for the 2 quarter, quarter, as a percentage of all ports, per type of package and per quarter, as a percentage Source: the author (2020) Source: the author (2020)

Figure 5.55: Total Load and Unload for the 3rd Figure 5.54: Total Load and Unload for the 4th quarter, of all ports, per type of package and per quarter, of all ports, per type of package and per quarter, as a percentage quarter, as a percentage

Source: theIn author order (2020)to create these graphs, for all four quarters,Source: the the userauthor has (2020 to:)

• From the “Dimensions”, select the “2019 Quarters” • From the “Measure”, select the “Load and Unload” for all five types of

packages • Select a visualisation. In this case, the Pie Chart graphic was selected

162 • Right click on the “2019 Quarters”, select one quarter to be shown at a time

As shown in Figure 5.53, in the first quarter of 2019, there was a 33.40% of total traffic of Roll-on Roll-off, a 29.90% of total traffic of Solid Bulk, a 26.35% of total traffic of Liquid Bulk, an 8.86% of total traffic of Containers and a 1.49% of total traffic of other general cargo. For the second quarter of 2019, as shown in Figure 5.52, there was a 36.15% of total traffic of Roll-on Roll-off, a 30.20% of total traffic of Solid Bul, a 25.31% of total traffic of Liquid Bulk, a 7.42% of total traffic of Container and a 0.92% of total traffic of other general cargo. For the third quarter of 2019, as shown in Figure 5.55, there was a 41.55% of total traffic of Liquid Bulk, a 37% of total traffic of Container, a 13.68% of total traffic of Solid Bulk, a 5.19% of total traffic of Roll-on Roll-off and a 2.59% of total traffic of other general cargo. Last, for the fourth quarter of 2019, as shown in Figure 5.54, there was a 39.75% of total traffic of Container, a 37.91% of total traffic of Liquid Bulk, a 13.90% of total traffic of Solid Bulk, a 5.38% of total traffic of Roll-on Roll-off and a 3.06% of total traffic of other general cargo.

Total Load and Total Unload, of all ports, per type of package and per quarter, as a percentage

Figure 5.57: Total Load and Total Unload, of all ports, Figure 5.56: Total Load and Total Unload, of all nd per type of package for the 1st quarter, as a ports, per type of package for the 2 quarter, as a percentage percentage

Source: the author (2020) Source: the author (2020)

163

Figure 5.59: Total Load and Total Unload, of all rd ports, per type of package for the 3 quarter, as a Figure 5.58: Total Load and Total Unload, of all percentage ports, per type of package for the 4th quarter, as a percentage Source: the author (2020) Source: the author (2020)

In order to create these graphs, for all four quarters, the user has to:

• From the “Measure”, select the Load and the Unload for all types of package • From the “Dimension”, select the “2019 Quarter” • Select a visualisation. In this case, the Pie Chart graphic was selected • Right click on the “2019 Quarters”, select one quarter to be shown at a time

As shown in Figure 5.57, in the first quarter of 2019, the total unloads of solid bulk, was of 14.95%, of roll-on roll-off, was of 16.70%, of other general cargo, was of 0.75, of liquid bulk, was of 13.18% and of container, was of 4.43% and the total loads of solid bulk was of 14.95%, of roll-on roll-off, was of 16.70%, of other general cargo was of 0.75%, of liquid bulk was of 13.18% and of containers, was of 4.43%. For the second quarter of 2019, as shown in Figure 5.56, the total unloads of solid bulk was of 15.10%, of roll-on roll-off, was of 18.08%, of other general cargo, was of 0.46%, of liquid bulk, was of 12.66% and of container, was of 3.71% and the total loads of solid bulk, was of 15.10%, of roll-on roll-off, was of 18.08%, of other general cargo, was of 0.46%, of liquid bulk, was of 12.66% and of container, was of 3.71%. For the third quarter of 2019, as shown in Figure 5.59, the total unloads of solid bulk, was of 5.63%, of roll-on roll-off, was of 2.67%, of other general cargo, was of 1.74%, of liquid bulk, was of 29.03% and of container, was of 19.16% and the total loads of solid bulk, was of 8.05%, of roll-on roll-off, was of 2.51%, of other general cargo, was of 0.85%, of liquid bulk, was of 12.52% and of container, was of 17.83%. Last, for the fourth quarter of 2019, the total unloads of solid bulk, as shown in Figure 5.58, was of 6.30%, of roll-on roll-off, was of 2.84%, of other general cargo, was of 1.75%,

164 of liquid bulk, was of 26.12% and of container, was of 20.20% and the total loads of solid bulk, was of 7.60%, of roll-on roll-off, was of 2.54%, of other general cargo, was of 1.32%, of liquid bulk, was of 11.78% and of container, was of 19.55%.

Loaded Container and Unloaded Container per port and per quarter

Because some of the 172 ports may not have any transaction of some type of package, the following calculation has to be done in the Tableau software, so as to exclude these ports, with no load and no unload of some type of package, from the graphical visualization. In order to do this, we have to create a calculation. Calculations, in Tableau, are created by selecting “Create Calculated Field” from the Analysis. The calculations made, so as to show only the ports with a positive value of Loaded or Unloaded type of package, are as follows:

• if [Load-Containers] < 1 and [Unload-Containers] < 1 then [Load-Containers] = null and [Unload-Containers] = null else [Load-Containers] = [Load- Containers] and [Unload-Containers] = [Unload-Containers] END • if [Load-Liquid bulk] < 1 and [Unload-Liquid bulk] < 1 then [Load-Liquid bulk] = null and [Unload-Liquid bulk] = null else [Load-Liquid bulk] = [Load-Liquid bulk] and [Unload-Liquid bulk] = [Unload-Liquid bulk] END • if [Load-Solid bulk] < 1 and [Unload-Solid bulk] < 1 then [Load-Solid bulk] = null and [Unload-Solid bulk] = null else [Load-Solid bulk] = [Load-Solid bulk] and [Unload-Solid bulk] = [Unload-Solid bulk] END • if [Load-Roll-on roll-off] < 1 and [Unload-Roll-on roll-off] < 1 then [Load- Roll-on roll-off] = null and [Unload-Roll-on roll-off] = null else [Load-Roll- on roll-off] = [Load-Roll-on roll-off] and [Unload-Roll-on roll-off] = [Unload-Roll-on roll-off] END • if [Load-Other general cargo] < 1 and [Unload-Other general cargo] < 1 then [Load-Other general cargo] = null and [Unload-Other general cargo] = null else [Load-Other general cargo] = [Load-Other general cargo] and [Unload- Other general cargo] = [Unload-Other general cargo] END

In order to create the visualizations shown , for all four quarters, the user has to:

• From the “Dimensions”, select the “2019 Quarters”

165 • From the “Measure”, select the “Load and Unload” for one types of packages at a time • Select a visualisation. In this case the Side by Side Bars graphic was selected • Right click on the “2019 Quarters”, select one quarter to be shown at a time • Drag and drop, into the “Filters” box, the corresponding restriction of the type of package and select “True”

Figure 5.60: Loaded Container and Unloaded Container per port and per quarter

Source: the author (2020)

As shown in Figure 5.60, the loaded tons of containers and the unloaded tons of containers are presented, divided by each quarter of the year and also by the ports. In the first quarter, the port of Heraklion had 1,100 tons of load and 37,767 tons of unload, Piraeus had 259,047 tons of load and 368,544 tons of unload, the port of Thessaloniki had 340,522 tons of load and 192,436 tons of unloaded and Volos had 26,922 tons of load and 28,844 tons of unload.

In the second quarter, the port of Heraklion had 1,249 tons of load and 36,872 tons of unload, Piraeus had 325,418 tons of load and 334,071 tons of unload, Thessaloniki had 310,689 tons of load and 251,068 tons of unload and Volos had 26,337 tons of load and 41,628 tons of unload.

166 In the third quarter, the port of Elefsina had zero tons of load and 26 tons of unload, Lavrio had 3,879 tons of load and 5,230 tons of unload, Patra had 6,630 tons of load and 8,827 tons of unload, Piraeus had 5,608,341 tons of load and 6,080,349 tons of unload and Thessaloniki had 209,558 tons of load and 167,907 tons of unload.

In the fourth quarter of 2019, the port of Elefsina had 71 tons of load and 192 tons of unload, Lavrio had 5,474 tons of load and 4,877 tons of unload, Patra had 2,933 tons of load and 7,372 tons of unload, Piraeus had 6,030,290 tons of load and 6,338,537 tons of unload and Thessaloniki had 247,973 tons of load and 145,232 tons of unload.

Loaded Liquid Bulk and Unloaded Liquid Bulk per port and per quarter

Figure 5.61: Loaded Liquid Bulk and Unloaded Liquid Bulk per port for the 1st quarter

Source: the author (2020)

As shown in Figure 5.61, the five ports with the most loaded tons of liquid bulk, in the first quarter of 2019, are the ports of Agii Theodoroi, with 499,997 tons of loaded and zero tons of unloaded, Elefsina, with 497,168 tons of loaded and 55,535 tons of unloaded, Perama, with 389,315 tons of loaded and 201,121 tons of unloaded, Thessaloniki, with 229,525 tons of loaded and 219,181 tons of unloaded and Lavrio, with 135,900 tons of loaded and zero tons of unloaded.

Also, as shown in Figure 5.61, the five ports with the most unloaded tons of liquid bulk, in the first quarter of 2019, are the ports of Aegina, with 384,928 tons of unloaded and zero tons of loaded, Thessaloniki, with 279,181 tons of unloaded and

167 229,525 tons of loaded, Perama, with 201,121 tons of unloaded and 389,315 tons of loaded, Piraeus, with 187,553 tons of unloaded and zero tons of loaded and Aspropyrgos, with 187,185 tons of unloaded and 33,900 tons of loaded.

Figure 5.62: Loaded Liquid Bulk and Unloaded Liquid Bulk per port for the 2nd quarter

Source: the author (2020)

As shown in Figure 5.62, the five ports with the most loaded tons of liquid bulk, in the second quarter of 2019, are the ports of Elefsina, with 606,613 tons of loaded and 53,843 tons of unloaded, Agii Theodoroi, with 553,777 tons of loaded and zero tons of unloaded, Perama, with 497,808 tons of loaded and 128,284 tons of unloaded, Thessaloniki, with 239,537 tons of loaded and 193,475 tons of unloaded and Lavrio, with 169,917 tons of loaded and zero tons of unloaded.

Also, as shown in Figure 5.62, the five ports with the most unloaded tons of liquid bulk, in the first quarter of 2019, are the ports of Aegina, with 529,547 tons of unloaded and zero tons of loaded, Piraeus, with 218,327 tons of unloaded and 497,808 tons of loaded, Aspropirgos, with 197,998 tons of unload and 36,159 tons of loaded, Thessaloniki, with 193,475 tons of unloaded and 239,537 tons of loaded, Linoperamata Herakliou, with 183,598 tons of unloaded and 584 tons of loaded and Spetses, with 159,969 tons of unloaded and zero tons of loaded.

168

Figure 5.63: Loaded Liquid Bulk and Unloaded Liquid Bulk per port for the 3rd quarter

Source: the author (2020)

As shown in Figure 5.63, the only ports with loaded tons of liquid bulk, in the third quarter of 2019, are the ports of Agii Theodoroi, with 2,400,591 tons of loaded and 3,569,107 tons of unloaded, Elefsina, with 1,527,935 tons of loaded and 961,284 tons of unloaded, Thessaloniki, with 132,390 tons of loaded and 1,306,954 tons of unloaded and Kavala, with 30,404 tons of loaded and 35,345 tons of unloaded.

Also, as shown in Figure 5.63 , the only ports with unloaded tons of liquid bulk, in the third quarter of 2019, are the ports of Agii Theodoroi, with 3,569,107 tons of unloaded and 2,400,591 tons of loaded, Megara, with 3,313,482 tons of unloaded and zero tons of loaded, Thessaloniki, with 1,306,954 tons of unloaded and 132,390 tons of loaded, Elefsina, with 961,284 tons of unloaded and 1,527,935 tons of loaded, Lavrio, with 2,016,526 tons of unloaded and zero tons of loaded, Piraeus, with 144,460 tons of unloaded and zero tons of loaded, Ierapetra Lasithiou, with 81,013 tons of unloaded and zero tons of loaded, Kavala, with 35,345 tons of unloaded and 30,404 tons of loaded, Patra, with 14,995 tons of unloaded and zero tons of loaded, Antikyra, with 10,646 tons of unloaded and zero tons of loaded, Volos, with 10,150 tons of unloaded and zero tons of loaded, Chalkida, with 6,673 tons of unloaded and zero tons of loaded and last, the ports categorized as “Other”, with 854 tons of unloaded and zero tons of loaded.

169

Figure 5.64: Loaded Liquid Bulk and Unloaded Liquid Bulk per port for the 4th quarter

Source: the author (2020)

As shown in Figure 5.64, the only ports with loaded tons of liquid bulk, in the fourth quarter of 2019, are the ports of Agii Theodoroi, with 2,394,451 tons of loaded and 3,533,015 tons of unloaded, Elefsina, with 1,227,532 tons of loaded and 866,614 tons of unloaded, Thessaloniki, with 103,927 tons of loaded and 938,389 tons of unloaded and Kavala, with 59,126 tons of loaded and 53,895 tons of unloaded.

Also, as shown in Figure 5.64 , the five ports with the most unloaded tons of liquid bulk, in the third quarter of 2019, are the ports of Agii Theodoroi, with 3,533,015 tons of unloaded and 2,394,451 tons of loaded, Megara, with 2,658,314 tons of unloaded and aero tons of loaded, Thessaloniki, with 938,389 tons of unloaded and 103,927 tons of loaded, Elefsina, with 866,614 tons of unloaded and 1,227,532 tons of loaded and Piraeus, with 114,961 tons of unloaded and zero tons of loaded.

170 Loaded Other General Cargo and Unloaded Other General Cargo per port per quarter

Figure 5.65: Loaded Other General Cargo and Unloaded Other General Cargo per port for the 1st quarter

Source: the author (2020)

As shown in Figure 5.65, the five ports with the most loaded tons of liquid bulk, in the first quarter of 2019, are the ports of Volos, with 34,848 tons of loaded and zero tons of unloaded, Kavala, with 16,174 tons of loaded and zero tons of unloaded, Amaliapolis Magnisias, with 10,314 tons of loaded and zero tons of unloaded, Argostoli, with 7,200 tons of loaded and 1,248 tons of unloaded and Domvraina Magnisias, with 5,970 tons of loaded and zero tons of unloaded.

Also, as shown in Figure 5.65, the five ports with the most unloaded tons of liquid bulk, in the first quarter of 2019, are the ports of Skiathos, with 13,200 tons of unloaded and zero tons of loaded, Elefsina, with 7,234 tons of unloaded and 3,601 tons of loaded, Istmia, with 6,610 tons of unloaded and 1,673 tons of loaded, Korinthos, with 6,145 tons of unloaded and zero tons of loaded and Rodos, with 6,106 tons of unloaded and zero tons of loaded.

171

Figure 5.66: Loaded Other General Cargo and Unloaded Other General Cargo per port for the 2nd quarter

Source: the author (2020)

As shown in Figure 5.66, the five ports with the most loaded tons of liquid bulk, in the second quarter of 2019, are the ports of Volos, with 33,987 tons of loaded and 2,500 tons of unloaded, Argostoli, with 10,800 tons of loaded and zero tons of unloaded, Amaliapolis Magnisias, with 7,545 tons of loaded and zero tons of unloaded, Antikyra, with 6,640 tons of loaded and zero tons of unloaded and the ports categorized as “Others”, with 5,443 tons of loaded and 5,722 tons of unloaded.

Also, as shown in Figure 5.66, the five ports with the most unloaded tons of liquid bulk, in the second quarter of 2019, are the ports of Skiathos, 13,216 tons of unloaded and zero tons of loaded, Elefsina, with 12,720 tons of unloaded and 1,240 tons of loaded, Rodos, with 7,410 tons of unloaded and 25 tons of loaded, Thessaloniki, with 6,134 tons of unloaded and 300 tons of loaded and the ports categorized as “Other”, with 5,722 tons of unloaded and 5,443 tons of loaded.

Figure 5.67: Loaded Other General Cargo and Unloaded Other General Cargo per port for the 3rd quarter

Source: the author (2020)

172 As shown in Figure 5.67, the five ports with the most loaded tons of liquid bulk, in the third quarter of 2019, are the ports of Amaliapolis Magnisias, with 57,854 tons of loaded and 7,802 tons of unloaded, Volos, with 52,537 tons of loaded and 20,285 tons of unloaded, Elefsina, with 29,957 tons of loaded and 140,852 tons of unloaded, Kavala, with 26,196 tons of loaded and 2,020 tons of unloaded and Thessaloniki, with 25,262 tons of loaded and 125,182 tons of unloaded.

Also, as shown in Figure 5.67, the five ports with the most unloaded tons of liquid bulk, in the third quarter of 2019, are the ports of Elefsina, with 140,852 tons of unloaded and 29,957 tons of loaded, Dombraina Boiotias, with 132,367 tons of unloaded and 17,761 tons of loaded, Thessaloniki, with 125,128 tons of unloaded and 25,262 tons of loaded, Chalkida, with 69,599 tons of unloaded and zero tons of loaded and Volos, with 20,285 tons of unloaded and 52,537 tons of loaded.

Figure 5.68: Loaded Other General Cargo and Unloaded Other General Cargo per port for the 4th quarter

Source: the author (2020)

As shown in Figure 5.68, the five ports with the most loaded tons of liquid bulk, in the fourth quarter of 2019, are the ports of Nissyros, with 123,034 tons of loaded and zero tons of unloaded, Amaliapolis Magnisias, with 84,139 tons of loaded and 1,328 tons of unloaded, Volos, with 41,142 tons of loaded and 11,573 tons of unloaded, Kavala, with 34,830 tons of loaded and 21,037 tons of unloaded and Dombraina Boiotias, with 33,257 tons of loaded and 104,852 tons of unloaded.

Also, as shown in Figure 5.68, the five ports with the most unloaded tons of liquid bulk, in the fourth quarter of 2019, are the ports of Thessaloniki, with 159,448 tons of unloaded and 29,831 tons of loaded, Dombraina Boiotias, with 104,852 tons

173 of unloaded and 33,257 tons of loaded, Elefsina, with 99,843 tons of unloaded and 18,768 tons of loaded, Chalkida, with 84,353 tons of unloaded and zero tons of loaded and Kavala, with 21,037 tons of unloaded and 34,830 tons of loaded.

Loaded Roll-on Roll-off and Unloaded Roll-on Roll-off per port per quarter

Figure 5.69: Loaded Roll-on Roll-off and Unloaded Roll-on Roll-off per port for the 1st quarter

Source: the author (2020)

As shown in Figure 5.69, the five ports with the most loaded tons of roll-on roll- off, in the first quarter of 2019, are the ports of Piraeus, with 731,117 tons of loaded and 320,522 tons of unloaded, Perama, with 209,040 tons of loaded and 81,400 tons of unloaded, Heraklion, with 202,443 tons of loaded and 259,464 tons of unloaded and Souda Bay, with 118,949 tons of loaded and 208,157 tons of unloaded.

Also, as shown in Figure 5.69, the five ports with the most unloaded tons of roll-on roll-off, in the first quarter of 2019, are the ports of Piraeus, with 320,522 tons of unloaded and 731,117 tons of loaded, Heraklion, with 259,464 tons of unloaded and 202,443 tons of loaded, Paloukia Salaminas, with 209,040 tons of unloaded and 81,400 tons of loaded, Souda Bay, with 208,157 tons of unloaded and 118,949 tons of loaded and Corfu, with 92,664 tons of unloaded and 74,061 tons of loaded.

Figure 5.70: Loaded Roll-on Roll-off and Unloaded Roll-on Roll-off per port for the 2nd quarter

174 Source: the author (2020)

As shown in Figure 5.70, the five ports with the most loaded tons of roll-on roll- off, in the second quarter of 2019, are the ports of Piraeus, with 870,245 tons of loaded and 399,309 tons of unloaded, Heraklion, with 263,859 tons of loaded and 356,792 tons of unloaded, Perama, with 244,310 tons of loaded and 93,984 tons of unloaded, Thassos, with 162,190 tons of loaded and 143,416 tons of unloaded and Souda Bay, with 141,035 tons of loaded and 230,424 tons of unloaded.

Also, as shown in Figure 5.70, the five ports with the most unloaded tons of roll-on roll-off, in the second quarter of 2019, are the ports of Piraeus, with 399,309 tons of unloaded and 870,254 tons of loaded, Heraklion, with 356,792 tons of unloaded and 263,859 tons of loaded, Paloukia Salaminas, with 244,310 tons of unloaded and 93,984 tons of loaded, Souda Bay, with 230,424 tons of unloaded and 141,035 tons of loaded and Keramoti, with 157,370 tons of unloaded and 139,807 tons of loaded.

Figure 5.71: Loaded Roll-on Roll-off and Unloaded Roll-on Roll-off per port for the 3rd quarter

Source: the author (2020)

As shown in Figure 5.71, the ports with loaded roll-on roll-off goods, in the third quarter of 2019, were the ports of Patra, with 386,206 tons of loaded and 414,463 tons of unloaded, Igoumenitsa, 342,799 tons of loaded and 351,999 tons of unloaded, Piraeus, with 67,360 tons of loaded and 96,135 tons of unloaded, Lavrio, with 13,495 tons of loaded and 7,678 tons of unloaded, Thessaloniki, with 9,771 tons of loaded and 1,991 tons of unloaded, Corfu, with 1,262 tons of loaded and 1,585 tons of unloaded and the ports categorized as “Other”, with 372 tons of loaded and 326 tons of unloaded.

175 Also, as shown in Figure 5.71, the ports with unloaded roll-on roll-off goods, in the fourth quarter of 2019, were the ports of Patra, with 414,463 tons of unloaded and 386,206 tons of unloaded, Igoumenitsa, with 351,999 tons of unloaded and 342,799 tons of loaded, Piraeus, with 96,135 tons of unloaded and 67,360 tons of loaded, Lavrio, with 7,678 tons of unloaded and 13,495 tons of loaded, Thessaloniki, with 1,991 tons of unloaded and 9,771 tons of loaded, Corfu, with 1,585 tons of unloaded and 1,262 tons of loaded and the ports categorized as “Other”, with 326 tons of unloaded and 372 tons of loaded.

Figure 5.72: Loaded Roll-on Roll-off and Unloaded Roll-on Roll-off per port for the 4th quarter

Source: the author (2020)

As shown in Figure 5.72, the ports with loaded roll-on roll-off goods, in the fourth quarter of 2019, were the ports of Igoumenitsa, with 363,888 tons of loaded and 354,274 tons of unloaded, Patra, with 362,910 tons of loaded and 437,314 tons of unloaded, Piraeus, with 67,902 tons of loaded and 111,442 tons of unloaded, Lavrio, with 12,206 tons of loaded and 7,486 tons of unloaded and Thessaloniki, with 11,207 tons of loaded and 1,979 tons of unloaded.

Also, as shown in Figure 5.72, the ports with unloaded roll-on roll-off goods, in the fourth quarter of 2019, were the ports of Patra, with 437,314 tons of unloaded and 362,910 tons of loaded, Igoumenitsa, with 354,274 tons of unloaded and 363,888 tons of loaded, Piraeus, with 111,442 tons of unloaded and 67,902 tons of loaded, Lavrio, with 7,486 tons of unloaded and 12,206 tons of loaded and Thessaloniki, with 1,979 tons of unloaded and 11,207 tons of loaded.

176 Loaded Solid Bulk and Unloaded Solid Bulk per port per quarter

Figure 5.73: Loaded Solid Bulk and Unloaded Solid Bulk per port for the 1st quarter

Source: the author (2020)

As shown in Figure 5.73, the five ports with the most loaded tons of liquid bulk, in the first quarter of 2019, are the ports of Amaliapolis Magnisias, with 534,152 tons of loaded and 3,700 tons of unloaded, Larymna, with 248,400 tons of loaded and 267,450 tons of unloaded, Volos, with 197,412 tons of loaded and 509,145 tons of unloaded, Politika, with 187,912 tons of loaded and zero tons of unloaded and Itea, with 141,537 tons of loaded and zero tons of unloaded.

Also, as shown in Figure 5.73, the five ports with the most unloaded tons of liquid bulk, in the first quarter of 2019, are the ports of Volos, with 509,145 tons of unloaded and 197,412 tons of loaded, Larymna, with 267,450 tons of unloaded and 248,400 tons of loaded, the ports categorized as “Other”, with 250,700 tons of unloaded and 1,440 tons of loaded, Antikyra, 133,400 tons of unloaded and 5,750 tons of loaded and Drepano Riou, with 112,280 tons of unloaded and zero tons of loaded.

Figure 5.74: Loaded Solid Bulk and Unloaded Solid Bulk per port for the 2nd quarter

Source: the author (2020)

177 As shown in Figure 5.74, the five ports with the most loaded tons of liquid bulk, in the second quarter of 2019, are the ports of Amaliapolis Magnisias, with 625,005 tons of loaded and 5,170 tons of unloaded, Larymna, with 329,050 tons of loaded and 411,235 tons of unloaded, Volos, with 300,525 tons of loaded and 586,529 tons of unloaded, Politika, with 247,426 tons of loaded and zero tins of unloaded and Aliverio, with 195,982 tons of loaded and 53,750 tons of unloaded.

Also, as shown in Figure 5.74, the five ports with the most unloaded tons of liquid bulk, in the second quarter of 2019, are the ports of Volos, 586,529 tons of unloaded and 300,525 tons of loaded, Larymna, with 411,235 tons of unloaded and 329,050 tons of loaded, the ports categorized as “Other”, with 348,610 tons of unloaded and 10,140 tons of loaded, Antikyra, with 139,200 tons of unloaded and 15,338 tons of loaded and kos, with 117,310 tons of unloaded and 400 tons of loaded.

Figure 5.75: Loaded Solid Bulk and Unloaded Solid Bulk per port for the 3rd quarter

Source: the author (2020)

As shown in Figure 5.75, the five ports with the most loaded tons of liquid bulk, in the third quarter of 2019, are the ports of Elefsina, with 521,989 tons of loaded and 84,693 tons of unloaded, Milos (Adamas), with 350,034 tons of loaded and 17,900 tons of unloaded, Aliverio, with 286,143 tons of loaded and 75,980 tons of unloaded, Thessaloniki, with 214,029 tons of loaded and 558,430 tons of unloaded and Volos, with 198,740, tons of loaded and 87,665 tons of unloaded.

Also, as shown in Figure 5.75, the five ports with the most unloaded tons of liquid bulk, in the third quarter of 2019, are the ports of Thessaloniki, with 558,430 tons of unloaded and 214,029 tons of loaded, Antikyra, with 224,396 tons of unloaded and 167,538 tons of loaded, Amaliapolis Magnisias, with 127,467 tons of unloaded

178 and 23,028 tons of loaded, Istmia, with 122,478 tons of unloaded and 28,953 tons of loaded and Kavala, with 110,221 tons of unloaded and 114,795 tons of loaded.

Figure 5.76: Loaded Solid Bulk and Unloaded Solid Bulk per port for the 4th quarter

Source: the author (2020)

As shown in Figure 5.76, the five ports with the most loaded tons of solid bulk, in the fourth quarter of 2019, are the ports of Elefsina, with 470,994 tons of loaded and 47,683 tons of unloaded, Milos, with 353,640 tons of loaded and 2,500 tons of unloaded, Volos, with 268,547 tons of loaded and 125,571 tons of unloaded, Aliverio, with 219,574 tons of loaded and 75,068 tons of unloaded and Kavala, with 174,248 tons of loaded and 84,374 tons of unloaded.

Also, as shown in Figure 5.76, the five ports with the most unloaded tons of solid bulk, in the fourth quarter of 2019, are the ports of Thessaloniki, with 728,466 tons of unloaded and 97,619 tons of loaded, Antikyra, with 173,030 tons of unloaded and 144,769 tons of loaded, Amaliapolis Magnisias, with 171,595 tons of unloaded and 21,994 tons of loaded, Volvos, with 125,571 tons of unloaded and 265,547 tons of loaded and Istmia, with 121,662 tons of unloaded and 41,667 tons of loaded.

Load Container per port and per quarter as percentage

Because some of the 172 ports may not have any transaction of some type of package, the following calculations have to be done in the Tableau software, so as to exclude these ports, for the visualization of the loads per ports. In order to do this, we have to create a calculation. Calculations, in Tableau, are created by selecting “Create Calculated Field” from the Analysis. The calculations made, so as to show only the ports with a positive value of Loaded or Unloaded type of package, are as follows and must be one for each type of package:

179 • if [Load-Containers] < 1 then [Load-Containers] = null else [Load-Containers] = [Load-Containers] END • if [Load-Liquid bulk] < 1 then [Load-Liquid bulk] = null else [Load-Liquid bulk] = [Load-Liquid bulk] END • if [Load-Other general cargo] < 1 then [Load-Other general cargo] = null else [Load-Other general cargo] = [Load-Other general cargo] END • if [Load-Roll-on roll-off] < 1 then [Load-Roll-on roll-off] = null else [Load- Roll-on roll-off] = [Load-Roll-on roll-off] END • if [Load-Solid bulk] < 1 then [Load-Solid bulk] = null else [Load-Solid bulk] = [Load-Solid bulk] END

In order to create the visualizations shown , for all four quarters, the user has to:

• From the “Dimensions”, select the “2019 Quarters” • From the “Measure”, select the “Load” for the visualization of loads and “Unload” for the visualization of the unload, for one types of packages at a time • Select a visualization. In this case the Pie Chart graphic was selected • Right click on the “2019 Quarters”, select one quarter to be shown at a time • Drag and drop, into the “Filters” box, the corresponding restriction of the type of package and select “True”

Figure 5.77: Load Container per port for the 1st quarter as percentage

Source: the author (2020)

180 As shown in Figure 5.77, the majority of containers loaded in the first quarter of 2019 was in the port of Thessaloniki, with the 54.26%, the second largest port, as loads of containers, was Piraeus, with 41.28%, the third was Volos, with 4.29% and last the port of Heraklion, with the 0.18%.

Figure 5.78: Load Container per port for the 2nd quarter as percentage

Source: the author (2020)

As shown in Figure 5.78, the majority of containers loaded in the second quarter of 2019 was in the port of Thessaloniki, with the 46.81%, the second one was the port of Piraeus, with the 49.03%, the third one was the port of Volos, with the 3.97% and last the port of Heraklion, with the 0.19%.

181

Figure 5.79: Load Container per port for the 3rd quarter as percentage

Source: the author (2020)

As shown in Figure 5.79, the majority of containers loaded in the third quarter of 2019 was the port of Piraeus, with the 96.22%, the second largest port as loads, was Thessaloniki, with the 3.60%, the third was Patra, with the 0.11%, and last was Lavrio, with the 0.07%.

Figure 5.80: Load Container per port for the 4th quarter as percentage

Source: the author (2020)

182 As shown in Figure 5.80, the majority of containers loaded in the fourth quarter of 2019, was the port of Piraeus, with the 95.921%, the second was Thessaloniki, with the 3.944%, the third one was Patra, with the 0.047% and last one, was the port of Elefsina, with the 0.001%.

Load Other General Cargo per port and per quarter as percentage

Figure 5.81: Load Other General Cargo per port for the 1st as percentage

Source: the author (2020)

As shown in Figure 5.81, the majority of other general cargo loaded in the first quarter of 2019, was the port of Agii Theodoroi, with the 26.78%. Following, are the port of Elefsina, with the 26.63%, the port of Perama, with the 20.85%, the port of Thessaloniki, with the 12.29%, the port of Lavrio, with the 7.28%, the port of Astros Arkadias, with the 3.77%, the port of Aspropirgos, with the 1.82%, the port of Souda Bay, with the 0.20%, the port of Kaloi Limenes Rethymno, with the 0.17%, the port of Kalymos, with the 0.16% and last the port of Alexandroupolis, with the 0.05%.

183

Figure 5.82: Load Other General Cargo per port for the 2nd as percentage

Source: the author (2020)

As shown in Figure 5.82, the majority of other general cargo loaded in the second quarter of 2019, was the port of Elefsina, with the 26.78%. Following, are the port of Agii Theodori, with the 24.45%, the port of Perama, with the 21.98%, the port of Thessaloniki, with the 10.57%, the port of Lavrio, with the 7.50%, the port of Astros Arkadias, with the 6.80%, the port of Aspropirgos, with the 1.60%, the port of Kalymos, with the 0.12%, the port of Alexandoupolis, with the 0.10%, the port of Kavala, with the 0.06%, the port of Linoperamata Herakliou, with the 0.03% and last the port of Mytiline, with the 0.02%

Figure 5.83: Load Other General Cargo per port for the 3rd as percentage

Source: the author (2020)

184 As shown in Figure 5.83, the majority of other general cargo loaded in the third quarter of 2019, was the port of Agii Theodoroi, with the 58.68%, the second one was Elefsina, with the 37.35%, the third one was Thessaloniki, with the 3.24% and las the port of Kavala, with the 0.74%.

Figure 5.84: Load Other General Cargo per port for the 4th as percentage

Source: the author (2020)

As shown in Figure 5.84, the majority of other general cargo loaded in the fourth quarter of 2019, was the port of Agii Theodori, with the 63.19%, the second one was the port of Elefsina, with the 32.40%, the third, was the port of Thessaloniki, with the 2.74%, the fourth, was the port of Kavala, with the 1.56% and last, the port of Stylida, with the 0.11%.

185 Load Liquid Bulk per port and per quarter as percentage

Figure 5.85: Load Liquid Bulk per port for the 1st quarter as percentage

Source: the author (2020)

As shown in Figure 5.85, the five ports with the largest percentage of liquid bulk loaded in the first quarter of 2019, was the port of Volos, with the 33%, Kavala, with the 15.32%, Amaliapolis Magnissias, with the 9.77%, Igoumenitsa, with the 6.96% and Argostoli, with the 6.82%.

Figure 5.86: Load Liquid Bulk per port for the 2nd quarter as percentage

Source: the author (2020)

As shown in Figure 5.86, the five ports with the largest percentage of liquid bulk loaded in the second quarter of 2019, was the port of Volos, with the 41.20%,

186 Argostoli, with the 13.09%, Amaliapolis Magnissias, with the 9.15%, Antikyra, with the 8.05% and last the ports in the category “Other”, with the 6.60%.

Figure 5.87: Load Liquid Bulk per port for the 3rd quarter as percentage

Source: the author (2020)

As shown in Figure 5.87, the five ports with the largest percentage of liquid bulk loaded in the first quarter of 2019, was the port of Amaliapolis Magnissias, with the 20.88%, Volos, with the 18.96%, Elefsina, with the 10.81%, Kavala, with the 9.45% and last the port of Thessaloniki, with the 9.12%.

Figure 5.88: Load Liquid Bulk per port for the 4th quarter as percentage

Source: the author (2020)

187 As shown in Figure 5.88, the five ports with the largest percentage of liquid bulk loaded in the first quarter of 2019, was the port of Nissyos, with the 29.08%, Amaliapolis Magnissias, with the 19.88%, Volos, with the 9.72%, Kavala, with the

8.23% and last the port of Dombraina Boiotias, with the 7.86%.

Load Roll-on Roll-off per port and per quarter as percentage

Figure 5.89: Load Roll-on Roll-off per port for the 1st quarter as percentage

Source: the author (2020)

As shown in Figure 5.89, the majority of containers loaded in the fourth quarter of 2019, was the port of Piraeus, with the 30.90%, Perama, with the 8.83%, Heraklion, with the 8.55%, Souda Bay, with the 5.03% and last the port of Igoumenitsa, with the 4.44%.

Figure 5.90: Load Roll-on Roll-off per port for the 2nd quarter as percentage

Source: the author (2020)

188 As shown in Figure 5.90, the majority of containers loaded in the fourth quarter of 2019, was the port of Piraeus, with the 26.90%, Heraklion, with the 8.16%, Perama, with the 7.55%, Thassos, with the 5.01% and last the port of Souda Bay, with the 4.36%.

Figure 5.92: Load Roll-on Roll-off per port for the Figure 5.91: Load Roll-on Roll-off per port for the th 3rd quarter as percentage 4 quarter as percentage

Source: the author (2020) Source: the author (2020)

As shown in Figure 5.92, the majority of containers loaded in the fourth quarter of 2019, was the port of Patra, with the 47.03%. Following, are the port of Igoumenitsa, with the 41.74%, the port of Piraeus, with the 8.20%, the port of Lavrio, with the 1.64%, the port of Thessaloniki, with the 1.19%, the port of Corfu, with the 0.15% and last the ports categorized as “Other”, with the 0.05%.

Moreover, as shown in Figure 5.91, the majority of containers loaded in the fourth quarter of 2019, was the port of Igoumenitsa, with the 44.48%. Following, are the port of Patra, with the 44.36%, Piraeus, with the 8.30%, Lavrio, with the 1.49% and last, the port of Thessaloniki, with the 1.37%.

189 Load Solid Bulk per port and per quarter as percentage

Figure 5.93: Load Solid Bulk per port for the 1st quarter as percentage

Source: the author (2020)

As shown in Figure 5.93, the five ports, with the majority of solid bulk goods loaded in the first quarter of 2019, were the ports of Amaliapolis Magnisias, with the 25.22%, Larymna, with the 11.73%, Volos, with the 9.32%, Politika, with the 8.87% and Itea, with the 6.68%.

Figure 5.94: Load Solid Bulk per port for the 2nd quarter as percentage

Source: the author (2020)

190 As shown in Figure 5.94, the five ports, with the majority of solid bulk goods loaded in the second quarter of 2019, were the ports of Amaliapolsi Magnisias, with the 23.13%, Larymna, with the 12.18%, Volos, with the 11.12%, Politica, with the 9.16% and Aliverio, with the 7.25%.

Figure 5.95: Load Solid Bulk per port for the 3rd quarter as percentage

Source: the author (2020)

As shown in Figure 5.95, the five ports, with the majority of solid bulk goods loaded in the third quarter of 2019, were the ports of Elefsina, with the 19.85%, Milos (Adamas), with the 13.31%, Aliverio, with the 10.88%, Thessaloniki, with the 8.14% and Volos, with the 7.56%

191

Figure 5.96: Load Solid Bulk per port for the 4th quarter as percentage

Source: the author (2020)

As shown in Figure 5.96, the five ports, with the majority of solid bulk goods loaded in the fourth quarter of 2019, were the ports of Elefsina, with the 19.26%, Milos (Adamas), with the 14.46%, Volos, with the 10.98%, Aliverio, with the 8.98% and the port of Kavala, with the 7.13%.

Unload Container per port and per quarter as percentage

Because some of the 172 ports may not have any transaction of some type of package, the following calculations have to be done in the Tableau software, so as to exclude these ports, for the visualization of unloads per ports. In order to do this, we have to create a calculation. Calculations, in Tableau, are created by selecting “Create Calculated Field” from the Analysis. The calculations made, so to show only the ports with a positive value of unloaded type of package, are as follows and must be one for each type of package:

• if [Unload-Containers] < 1 then [Unload-Containers] = null else [Unload- Containers] = [Unload-Containers] END • if [Unload-Liquid bulk] < 1 then [Unload-Liquid bulk] = null else [Unload- Liquid bulk] = [Unload-Liquid bulk] END

192 • if [Unload-Other general cargo] < 1 then [Unload-Other general cargo] = null else [Unload-Other general cargo] = [Unload-Other general cargo] END • if [Unload-Roll-on roll-off] < 1 then [Unload-Roll-on roll-off] = null else [Unload-Roll-on roll-off] = [Unload-Roll-on roll-off] END • if [Unload-Solid bulk] < 1 then [Unload-Solid bulk] = null else [Unload-Solid bulk] = [Unload-Solid bulk] END

Figure 5.97 : Unload Container per port for the 1st Figure 5.98: Unload Container per port for the 2nd quarter as percentage quarter as percentage

Source: the author (2020) Source: the author (2020)

As shown in Figure 5.97, the majority of containers unloaded in the first quarter of 2019, was in the port of Piraeus, with the 58.72%. Following, are the ports of Thessaloniki, with the 30.66%, Heraklion, with the 6.02% and Volos, with the 4.60%.

Moreover, as shown in Figure 5.98, the majority of containers unloaded in the second quarter of 2019, was in the port of Piraeus, with the 50.34%. Following, are the ports of Thessaloniki, with the 37.83%, Volos, with the 6.28% and Heraklion, with the 5.56%.

193

th Figure 5.100: Unload Container per port for the Figure 5.99: Unload Container per port for the 4 3rd quarter as percentage quarter as percentage

Source: the author (2020) Source: the author (2020)

As shown in Figure 5.100, the majority of containers loaded in the third quarter of 2019, was the port of Piraeus, with the 97.09%. Following are the ports of Thessaloniki, with the 2.68%, Patra, with the 0.14%, Lavrio, with the 0.08% and Elefsina with less than 0.01%.

Also, as shown in Figure 5.99, the majority of containers loaded in the fourth quarter of 2019, was the port of Piraeus, with the 97.553%. Following are the ports of Thessaloniki, with the 2.236%, Patra, with the 0.113%. Lavrio, with the 0.075% and last Elefsina, with 0.003%

Unload Liquid Bulk per port and per quarter as percentage

Figure 5.101: Unload Liquid Bulk per port for the 1st quarter as percentage

Source: the author (2020)

194 As shown in Figure 5.101, the five ports with the majority of liquid bulk goods unloaded in the first quarter of 2019, were the ports of Aegina, with the 20.62%, Thessaloniki, with the 11.74%, Perama, with the 10.77%, Piraeus, with the 10.05% and Aspropyrgos, with the 10.03%.

Figure 5.102: Unload Liquid Bulk per port for the 2nd quarter as percentage

Source: the author (2020)

As shown in Figure 5.102, the five ports with the majority of liquid bulk goods unloaded in the second quarter of 2019 were the ports of Aegina, with the 23.38%, Piraeus, with the 9.64%, Aspropyrgos, with the 8.74%, Thessaloniki, with the 8.54% and Linoperamata Herakliou, with the 8.10%.

Figure 5.103: Unload Liquid Bulk per port for Figure 5.104: Unload Liquid Bulk per port for the the 4th quarter as percentage 3rd quarter as percentage Source: the author (2020) Source: the author (2020)

As shown in Figure 5.104, the five ports with the majority of liquid bulk goods unloaded in the third quarter of 2019 were the ports of Agii Theodoroi, with the 37.62%, Megara, with the 33.01%, Thessaloniki, with the 13.78%, Elefsina, with the 10.13% and Lavrio, with the 2.18%.

195 Also, for the fourth quarter of 2019, as shown in Figure 5.103, the ports with the most tons of liquid bulk goods unloaded, were the ports of Agii Theodoroi, with the 42.05%, Megara, with the 31.64%, Thessaloniki, with the 11.17%, Elefsina, with the 10.32% and Piraeus, with the 1.37%.

Unload Other General Cargo per port and per quarter as percentage

Figure 5.105: Unload Other General Cargo per port for the 1st quarter as percentage

Source: the author (2020)

196 As shown in Figure 5.105, the five ports, with the majority of other general cargo unloaded in the first quarter of 2019, were the ports of Skiathos, with the 12.50%, Elefsina, with the 6.85%, Istmia, with the 6.26%, Korinthos, with the 5.82% and Rodos, with the 5.78%.

Figure 5.106: Unload Other General Cargo per port for the 2nd quarter as percentage

Source: the author (2020)

As shown in Figure 5.106, the five ports, with the majority of other general cargo unloaded in the second quarter of 2019, were the ports of Skiathos, with the 16.02%, Elefsina, with the 15.42%, Rodos, with the 8.98%, Thessaloniki, with the 7.43% and the ports categorized as “Other”, with the 6.94%.

197

Figure 5.107: Unload Other General Cargo per port for the 3rd quarter as percentage

Source: the author (2020)

As shown in Figure 5.107, the five ports, with the majority of other general cargo unloaded in the third quarter of 2019, were the ports of Elefsina, with the 24.74%, Dombraina Boiotias, with the 23.25%, Thessaloniki, with the 21.99%, Chalkida, with the 12.23% and Volos, with the 3.56%.

Figure 5.108: Unload Other General Cargo per port for the 4th quarter as percentage

Source: the author (2020)

198 As shown in Figure 5.108, the five ports, with the majority of other general cargo unloaded in the fourth quarter of 2019, were the ports of Thessaloniki, with the 28.37%, Dimbraina Boiotias, with the 18.66%, Elefsina, with the 17.77%, Chalkida, with the 15.01% and Kavala, with the 3.74%.

Unload Roll-on Roll-off per port and per quarter as percentage

Figure 5.109: Unload Roll-on Roll-off per port for the 1st quarter as percentage

Source: the author (2020)

As shown in Figure 5.109, the five ports, with the majority of roll-on roll-off goods unloaded in the first quarter of 2019, were the ports of Piraeus, with the 13.54%, Heraklion, with the 10.96%, Paloukia Salaminas, with the 8.83%, Souda Bay, with the 8.30% and Corfu, with the 3.92%.

Figure 5.110: Unload Roll-on Roll-off per port for the 2nd quarter as percentage

Source: the author (2020)

199 As shown in Figure 5.110, the five ports, with the majority of roll-on roll-off goods unloaded in the second quarter of 2019, were the ports of Piraeus, with the 12.34%, Heraklion, with the 11.03%, Paloukia Salaminas, with the 7.55%, Souda Bay, with the 7.12% and Keramoti, with the 4.86%.

Figure 5.111: Unload Roll-on Roll-off per Figure 5.112: Unload Roll-on Roll-off per port for port for the 4th quarter as percentage the 3rd quarter as percentage Source: the author (2020) Source: the author (2020)

As shown in Figure 5.112, the ports with roll-on roll-off goods unloaded in the third quarter of 2019, were Patra, with the 47.41%, Igoumenitsa, with the 40.27%, Piraeus, with the 11%, Lavrio, with the 0.88%, Thessaloniki, with the 0.23%, Corfu, with the 0.18% and the ports in the category “Other”, with the 0.04%

Also, as shown in Figure 5.111, the ports with roll-n roll-off goods unloaded in the fourth quarter of 2019, were Patra, with the 47.93%, Igoumenitsa, with the 38.82%, Piraeus, with the 12.21%, Lavrio, with the 0.82% and Thessaloniki, with the 0.22%.

200 Unload Solid Bulk per port and per quarter as percentage

Figure 5.113: Unload Solid Bulk per port for the 1st quarter as percentage

Source: the author (2020)

As shown in Figure 5.113, the five ports, with the majority of solid bulk goods unloaded in the first quarter of 2019, were Volos, with the 20.04%, Larymna, with the 12.63%, the ports categorized as “Other”, with the 11.84%, Antikyra, with the 6.30% and Drepano Riou, with the 5.30%.

Figure 5.114: Unload Solid Bulk per port for the 2nd quarter as percentage

Source: the author (2020)

As shown in Figure 5.114, the five ports, with the majority of solid bulk goods unloaded in the second quarter of 2019, were the ports of Volos, with the 21.70%,

201 Larymna, with the 15.22%, the ports categorized as “Other”, with the 12.90%, Antikyra, with the 5.15% and Kos, with the 4.34%.

Figure 5.115: Unload Solid Bulk per port for the 3rdquarter as percentage

Source: the author (2020)

As shown in Figure 5.115, the five ports, with the majority of solid bilk goods unloaded in the third quarter of 2019, were the ports of Thessaloniki, with the 30.35%, Antikyra, with the 12.20%, Amaliapolis Magnissias, with the 6.93%, Istmia, with the 6.66% and Kavala, with the 5.99%.

Figure 5.116: Unload Solid Bulk per port for the 4th quarter as percentage

Source: the author (2020)

As shown in Figure 5.116, the five ports, with the majority of solid bulk goods unloaded in the fourth quarter of 2019, were the ports of Thessaloniki, with the

202 35.98%, Antikyra, with the 8.55%, Amaliapolis Magnissias, with the 8.48%, Volos, with the 6.20% and Istmia, with the 6.01%.

Finally, after these visualizations of thοse data, the result shows that the visualization of data is very useful in order to understand large amount of data easier, with graphical representations that can combine multiple values, such as the names of ports, the four quarters of the year, the five types of packages and the loads and unloads. Also, with the variety of graphs, specific data can be shown better, such as the comparison of loads and unloads, with two parallel Side by Side graph, or the percentages of a type of package unloaded, with a Pie Chart. Moreover, the visualization of data makes the analysis of data as a whole very easy, starting from the total loads and unloads for the whole 2019, all the way down to the details, such as the number or percentage of a type of package loaded or unloaded in a specific port in a specific quarter of the year. Also, the maximum and minimum values and the average values are clearly showed in the graphical visualization, which would be difficult to be found by reading this data as it is, in the Excel.

5.6 Comparison of Power BI and Tableau

In this section, a confrontation of the Tableau and Power BI software will be made, mainly according to what was learned and practiced in this thesis.

On the one hand, Tableau has the drag and drop method that makes the use of this software easy and practical, except from most of the calculations, that are created manually, also, with an interface that is easy to learn and use. Moreover, the interface enables to create and customize the dashboards, according to the requirements of the user, in an easy way. Tableau provides two types of viewing the data. The data source, where the data is loaded into the software and where it can be modified before and during the process of visualization and the sheets, where the visualizations are created. It also has an inviting box space area, where the user can experiment with the data, in the creation of the visualization, in creative ways. The box-space area has different parts, such as the tool bar, side bar, data source sheet tabs, so to create multiple visualizations and analyze different data simultaneously, and the bar with the

203 visualizations, were it provides the user with the requirements needed in order to create graphical visualization. Tableau also, provides access to numerous data sources and servers, such as excel, pdf, dropbox, amazon redshift and google analytics. The creation of measures and calculations is easy because of the natural language capabilities this software has, which helps the inexpert user get the job done. However, for those who know one of the four programming languages that this software supports, such as C, C++, Java and Python, can create even more powerful visualizations and data analytics. The normal data visualizations are created by the drag and drop method. Finally, another feature of this software, which makes it easy to use and practical, is that some measurements are already ready, such as number of records, average, totals and trends.

On the other hand, Power BI is also easy to learn and use, with the drag and drop method. However, all the measures are created manually; the interface is easy to learn. Power BI, provides three types of viewing the data. The report, where reports and visuals are created, the data, where the user can see the tables, measures and other data used in the data model associated with the report, and also transform the data for the best use of it in the report model and the model, where the user sees and manages the relationships among data in the data model. Power BI supports a large amount of data sources, however, limited because it can connect mainly with Microsoft servers and databases. Also, Power BI is based on the Microsoft office; which means, for the more expert users of excel, the creations of parameters and measures is easier. Moreover, supports DAX, which are data analysis expressions and it can also connect to Power Query formula language, also known as m language, and R programming language, which is used for statistics, using Microsoft revolution analytics. The creation of data visualizations is easy and detailed, with the drag and drop method and features that can make the vacuolation more appealing. Furthermore, Power BI supports a wide range of detailed and attractive visualizations so to create reports and dashboards and also, the user can ask questions about the data and get some hints and insights, by using the Power BI service. Power BI can also find trends and forecasts; however, the equations must be provided by the user.

Finally, as a conclusion, from the comparison of the two top Business Intelligence software in the world, we can say that these two software are equal. The only difference can be that on the one hand, Tableau has more automated features and

204 can be easier to use for an inexpert user and very quick in the creation of simple visualizations and on the other hand, in Power BI, the user must know how to create DAX and measure, spending more time in the creation of visualizations. This difference is not of major importance if compared to the high-quality result, however, can affect the preference of the user, in the selection between those two software, depending on knowledge of the user.

205 6 Conclusions and Future Research

In this research, we learned about the open data and visualizations of data. Firstly, we created some conditions in order to collect data from open data sources, relative to the supply chain, so in the end, to visualize some of those data with the help of Power BI and Tableau software.

The open data theory, principles and usage is very vast and year by year, private organizations, independent states and supranational entities, constantly develop new strategies about how to collect, filter the more important data, organize and publish these large amounts of data with open licenses, so as for them to be accessible by everyone. As we have seen, the open data can help private organizations, countries and people, by giving them the tools and information they need so to judge, analyze and take actions. Moreover, the open data can also help and provide solutions to cultural issues, humanitarian groups, agriculture, economic growth and the environment. Furthermore, the optical visualization and graphical representation of data is very useful in order to understand, much easier, large amounts of data, by analyzing and visualizing them, so to find gaps and solutions.

Because most of the value of data comes from the chain effects of publication and re-publication and process and re-process, some actions and rules can be created for the better administration as to the collection, grouping, partition, formatting and then publication of those data. More specifically, the development of the above- mentioned actions, can blossom with regulations such as continuous update of a country's central open data portal, so all the public entities, bodies and services are able, but also binded, to publish their data per monthly basis, so that the public interested in those data, are be able to acquire updated information. Categorize the data according to the usefulness of those, as to the information they contain, so as to prioritize the publication of the important information before others. Categorize the data, depending on the content, so as to publish them by some specific types of files. Depending on the category and type of data, standardized procedures must be applied for the documentation and after that, publication of the data collected by the various entities. A standardized procedure also must be created for the above categorizations

206 of data as well as the continuous instruction and training of the entities, organizations and staff on the procedures of collection, categorization, process and publication of the data. Last but not least, the open data are valuable only if public and private organizations and entities, countries, citizens and also members of the scientific community can access, download, process and publish those data. For these reasons, a fundamental action is to advertise and inform the public about the open data, the importance, the opportunities and the benefits of having free access to this vast amount of information.

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213 Appendix / Appendices

Table 21: Municipalities of Greece 2020

Source: the author (2020)

Name of Municipality URL Municipality of Avdira http://www.avdera.gr/ Municipality of Agathonisi https://agathonisi.gr/ Municipality of Agia http://www.dimosagias.gr/ Municipality of Agia Varvara https://agiavarvara.gr/ Municipality of Agia Paraskevi https://www.agiaparaskevi.gr/portal/ Municipality of Agios Vasilios http://www.agios-vasilios.gr/ Municipality of Agios Dimitrios https://dad.gr/ Municipality of Agios Efstratios http://www.agios-efstratios.gov.gr/ Municipality of Agios Nikolaos http://www.dimosagn.gr/ Municipality of Agii Anargiri Kamaterou https://www.agan.gov.gr/ Municipality of Agistri http://www.agistri-island.gr/ Municipality of Agrafa https://agrafa.gr/ Municipality of Agrinio http://www.cityofagrinio.gr/agrinionews/ypiresies.php Municipality of Athens https://www.cityofathens.gr/ Municipality of Egaleo https://www.aigaleo.gr/ Municipality of Egina https://www.aegina.gr/web/ Municipality of Aktio-Vonitsa http://www.aktiovonitsa.gov.gr/ Municipality of Alexandria https://www.alexandria.gr/ Municipality of Alexandrupoli https://www.alexpolis.gr/ Municipality of Aliartos-Thespieon http://www.aliartos.gov.gr/ Municipality of Alimos http://www.alimos.gov.gr/ Municipality of Almiros https://almyros-city.gr/ Municipality of Almopia http://www.dimosalmopias.gov.gr/ Municipality of Alonnisos https://alonissos.gr/

214 Municipality of Amari https://www.amari.gr/ Municipality of Amaroussi http://www.maroussi.gr/ Municipality of Amorgos http://dimos.amorgos.gr/ Municipality of Ampelokipi- Menemeni http://www.ampelokipi-menemeni.gr/ Municipality of Amyntaio http://www.amyntaio.gr/ Municipality of Amfiklia-Elatia http://www.dimos-amfiklias-elatias.gr/ Municipality of Amfilochia https://www.dimosamfilochias.gr/ Municipality of Anatoliki Mani https://www.anatolikimani.gov.gr/ Municipality of Anafi http://www.anafi.gr/ Municipaliti of Andravida-Killini http://www.andravida-killini.gr/ Municipaliti of Andritsena-Krestena https://www.andritsainas-krestenon.gov.gr/ Municipality of Andros https://www.andros.gr/gr/ Municipality of Antiparos http://www.antiparos.gr/el/ Municipality of Amfipoli http://dimos-amfipolis.gr/ Municipality of Anogeia http://www.anogeia.gr/ Municipality of Apokorona https://www.apokoronas.gov.gr/ Municipality of Argithea https://www.dimosargitheas.gr/el/ Municipality of Argous-Mykinon http://www.newargos.gr/ Municipality of Argos Orestiko http://www.argosorestiko.gr/ Municipality of Aristoteli http://www.dimosaristoteli.gr/ Municipality of Arriana https://www.arriana.gr/index.php/el/ Municipality of Arta http://www.arta.gr/ Municipality of Arhaia Olympia https://www.arxaiaolympia.gov.gr/ Municipality of Archanes-Asterousia http://www.dimos-archanon-asterousion.gr/ Municipality of Aspropyrgos https://www.dimosaspropyrgou.gr/ Municipality of Astipalea http://www.visitastypalea.com/ Municipality of Aharnon http://www.acharnes.gr/ Municipality of Bari - Bula – Buliagmeni http://www.vvv.gov.gr/ Municipality of Belu – Boha https://velovocha.gr/ Municipaliti of Viannou http://www.viannos.gov.gr/

215 Municipality of Visaltia http://www.dimosvisaltias.gr/ Municipality of Voio https://dimosvoiou.gr/ Municipality of Volvi https://www.dimosvolvis.gr/ Municipality of Voria Kinouria http://www.boriakinouria.gov.gr/ Municipality of Voria Tzumerka http://www.voreiatzoumerka.gr/ Municipality of Vrilissia https://www.vrilissia.gr/ Municipality of Virona https://www.dimosbyrona.gr/ Municipality of Galatsi https://www.galatsi.gov.gr/ Municipality of Gavdos http://www.gavdos-dimos.com/ Municipality of Georgios Karaiskakis https://gkaraiskakis.gr/ Municipality of Glyfada https://www.glyfada.gr/ Municipality of Gortyna http://www.gortyna.gov.gr/ Municipality of Gortynia http://www.gortynia.gov.gr/ Municipality of Grevena http://www.dimosgrevenon.gr/ Municipality of Delta https://www.dimosdelta.gr/ Municipality of Deskati https://dimos-deskatis.gr/ Municipality of Didymoteicho https://www.didymoteicho.gr/en/ Municipality of Dionysos https://www.dionysos.gr/ Municipality of Dion Olympos https://www.dion-olympos.gr/ Municipality of Dirfion-Messapion https://ddm.gov.gr/ Municipality of Distomo – Arahova - Antikyra http://www.daa.gov.gr/ Municipality of Domokos http://www.domokos.gr/ Municipality of Dytiki Achaia https://dimosdymaion.gr/ Municipality of Dytiki Mani http://www.dimosdytikismanis.gr/ Municipality of Dodoni http://www.dodoni.gr/ Municipality of Dorida http://www.dorida.gr/ Municipality of Elassona https://www.dimoselassonas.gr/ Municipality of Elafonisos http://elafonisos.gov.gr/ Municipality of Elefsina https://www.elefsina.gr/ Municipality of Emmanouil Pappa http://edemocracy-empapas.gr/

216 Municipality of Epidavros http://www.epidavros.gr/ Municipality of Eretria http://eretria.gr/ Municipality of Ermionida http://www.dimosermionidas.gr/ Municipality of Erymanthos https://erymanthou.gov.gr/ Municipality of Eurota https://www.eurota.gr/ Municipality of Zagora - Mouresiou http://www.dimos-zagoras-mouresiou.gr/ Municipality of Zagori http://www.zagori.gov.gr/ Municipality of Zakynthos http://www.zakynthos.gov.gr/ Municipality of Zaharo https://www.zacharo.gr/ Municipality of Zirou http://dimoszirou.gr/ Municipality of Zitsa https://www.zitsa.gov.gr/ Municipality of Igoumenitsa https://igoumenitsa.gr/el/ Municipality of Ilida http://www.dimosilidas.gr/ Municipality of Ilioupoli http://www.ilioupoli.gr/ Municipality of Iraklia http://www.dimosiraklias.gr/ Municipality of Iroiki Poli Naoussas https://www.naoussa.gr/index.htm Municipality of Thasos http://www.thassos.gr/ Municipality of Thermaikos http://www.thermaikos.gr/ Municipality of Thermi http://www.thermi.gov.gr/ Municipality of Thermos https://www.dimos-thermou.gr/website/ Municipality of Thira https://www.thira.gr/ Municipality of Iasmos https://iasmos.gr/ Municipality of Iera Poli Messolonghi http://messolonghi.gov.gr/ Municipality of Ios http://ios.gr/ Municipality of Ithaki http://ithaki.gr/ Municipality of Ikaria http://ikaria.gov.gr/ Municipality of Istiea-Edipsos http://www.dimosistiaiasaidipsou.net/ Municipality of Kaisariani https://kaisariani.gr/ Municipality of Kalavrita http://www.kalavrita.gr/ Municipality of Kalamata https://www.kalamata.gr/el/ Municipality of Kalymnos https://www.kalymnos.gov.gr/el/

217 Municipality of Kantanou-Selinou http://www.kantanouselinou.gr/ Municipaliti of Karpathos http://karpathos.gr/ Municipality of Karistou http://www.dimoskarystou.gr/ Municipality of Kasos http://kasos.gr/ Municipality of Kassandra https://kassandra.gr/ Municipality of Katerini https://katerini.gr/ Municipality of Kato Nevrokopi http://www.nevrokopi.gr/ Municipality of Kea https://kea.gr/ Municipality of Kentrika Tzoumerka http://www.dhmosktzoumerkwn.gr/ Municipality of North Corfu https://diavgeia.gov.gr/f/DIMOS_VORIAS_KERKIRAS Municipality of Central Corfu https://www.corfu.gr/web/guest/home Municipality of South Corfu https://diavgeia.gov.gr/f/DIMOS_NOTIAS_KERKYRAS Municipality of Kefallonia-Argostoli http://www.kefallonia.gov.gr/pages/gr.php?lang=GR Municipality of Kefallonia-Lixouri https://www.diavgeia.gov.gr/f/DIMOS_LIXOURIOU Municipality of Kefallonia-Sami https://www.diavgeia.gov.gr/f/DIMOS_SAMIS Municipality of Kifissia http://www.kifissia.gr/ Municipality of Kileler https://www.kileler.gov.gr/ Municipality of Kimolos https://www.kimolos.gr/ Municipality of Kissamos http://www.kissamos.gr/ Municipality of Kozani https://cityofkozani.gov.gr/ Municipality of Komotini https://www.komotini.gr/ Municipality of Konitsa http://www.konitsa.gr/ Municipality of Kythira https://kythira.gr/ Municipality of Kythnos https://www.kythnos.gr/ Municipality of Kymi Aliveriou http://www.kimis-aliveriou.gr/ Municipality of Kos http://www.kos.gov.gr/default.aspx Municipality of Lagadas http://www.lagadas.gr/ Municipality of Lavreotiki https://www.lavreotiki.gr/ Municipality of Livadia https://dimoslevadeon.gr/ Municipality of Lipsi http://www.lipsi.gov.gr/ Municipality of Leros https://www.leros.gr/ Municipality of Mytilini http://www.mytilene.gr/

218 Municipality of Dytiki Lesbos http://www.mwlesvos.gr/ Municipality of Limnos https://limnos.gov.gr/ Municipality of Limni Plastiras https://www.plastiras-ota.gr/ Municipality of Locri http://www.dimos-lokron.gov.gr/ Municipality of Loutraki-Perahora- Agii Theodori http://new.loutraki-agioitheodoroi.gr/ Municipality of Makrakomis http://www.dimosmakrakomis.gr/ Municipality of Mandra-Eidyllia http://mandras-eidyllias.gr/ Municipality of Marathon http://site.marathon.gr/ Municipality of Markopoulo Mesogaia http://www.markopoulo.gr/ Municipality of Megalopoli https://www.megalopoli.gov.gr/ Municipality of Meganisi http://meganisi.gov.gr/ Municipality of Megisti http://megistinews.blogspot.com/ Municipality of Messini http://www.messini.gr/ Municipality of Meteora https://www.dimosmeteoron.com/ Municipality of Metsovo https://metsovo.gr/ Municipality of Milos https://milos.gr/ Municipality of Monemvasia https://monemvasia.gov.gr/ Municipality of Miki http://www.dimosmykis.gr/ Municipality of Mykonos https://mykonos.gr/en/ Municipality of Neapoli-Sikies http://www.dimosneapolis-sykeon.gr/web/guest/home Municipality of Nea Zihni http://www.dimos-neaszixnis.gr/ Municipality of Nea Filadelfia-Nea Halkidon http://www.dimosfx.gr/el Municipality of Nemea http://www.nemea.gr/ Municipality of Nestorio http://www.nestorio.gr/ Municipality of Nestos http://nestos.gr/ Municipality of Nikolaos Skoufas http://www.nskoufas.gr/index.php?lang=en Municipality of Nisyros https://nisyros.gr/ Municipality of Notia Kynouria http://www.notiakynouria.gov.gr/ Municipality of Notios Pilio http://www.dimosnotioupiliou.gov.gr/

219 Municipality of Xanthi https://www.cityofxanthi.gr/ Municipality of Xilokastro-Evrostini https://www.xylokastro-evrostini.gov.gr/ Municipality of Oinousses http://www.oinousses-municipality.gr/frontend/index.php Municipality of Orestiada https://www.orestiada.gr/ Municipality of Oropedio Lasithiou https://oropediolasithiou.weebly.com/ Municipality of Orchomenos https://orchomenos.gr/ Municipality of Peania https://paiania.gov.gr/ Municipality of Peonia http://paionia.gov.gr/ Municipality of Palamas https://www.palamas.gr/ Municipality of Pallini http://www.pallini.gr/ http://www.paxi.gr/web/index.php/home/0/1/0/dhmos_paxon.ht Municipality of Paxi ml Municipality of Papagos-Holargos https://www.dpapxol.gov.gr/ Municipality of Paranesti https://www.paranesti.gr/ Municipality of Parga https://dimospargas.gr/ Municipality of Paros http://dimos.paros.gr/ Municipality of Patmos http://www.patmos.gr/ Municipality of Pavlos Melas https://pavlosmelas.gr/ http://www.dimospineiou.gov.gr/portal/page/portal/municipality/ Municipality of Pineios Home Municipality of Platanias https://www.platanias.gr/en/ Municipality of Poros https://www.poros.gr/ Municipality of Prespes http://www.prespes.gr/prespa/ Municipality of Prosotsani https://www.prosotsani.gr/el/ Municipality of Pidna-Kolindros https://www.pydnaskolindrou.gr/ Municipality of Pilaia-Hortiatis https://www.pilea-hortiatis.gr/web/guest/home Municipality of Pyli https://dimospylis.gr/ Municipality of Pilos-Nestoros http://www.pylos-nestor.gr/portal/ Municipality of Pyrgos https://www.cityofpyrgos.gr/ Municipality of Pogoni http://www.pogoni.gr/ Municipality of Rigas Feraios https://www.rigas-feraios.gr/ Municipality of Salamina http://www.salamina.gr/

220 Municipality of Samothraki http://samothraki.gr/ Municipality of Anatoliki Samos http://www.islandofsamos.gr/ Municipality of Dytiki Samos https://diavgeia.gov.gr/f/dimos_dyk_samou Municipality of Saroniko http://www.saronikoscity.gr/wil.aspx?a_id=1 Municipality of Servion http://www.dservionvelventou.gr/ Municipality of Velventos https://velventos.gr/ Municipality of Serifos http://www.serifos.gr/ Municipality of Sitia http://www.sitia.gr/ Municipality of Sithonia https://www.dimossithonias.gr/ Municipality of Sikinos http://www.sikinos.gr/files/intro/intro2.html Municipality of Sintiki https://www.sintiki.gov.gr/ Municipality of Sifnos https://www.sifnos.gr/dimos Municipality of Skiathos http://www.skiathos.gr/en/ Municipality of Skopelos http://www.skopelos.gov.gr/ Municipality of Skyros http://helios.grserver.gr/index-1.html or www.skyros.gr Municipality of Souli https://dimossouliou.gov.gr/ Municipality of Soufli https://www.soufli.gr/index.php/el/ Municipality of Sofades https://sofades.gr/ Municipality of Spata-Artemis http://www.spata-artemis.gr/ Municipality of Spetses http://www.spetses.gr/ Municipality of Stylida http://www.stylida.gr/ Municipality of Symi http://symi.gr/ Municipality of Sfakia http://www.sfakia.gr/el/ Municipality of Tanagra http://www.tanagra.gr/ Municipality of Tilos https://www.tilos.gr/ Municipality of Topiros http://www.topeiros.gr/portal/ Municipality of Trizinia-Methanon http://www.dimostroizinias-methanon.gr/articlesnew/ Municipality of Tyrnavos http://www.tirnavos.gr/el/ Municipality of Ydra http://ydra.gov.gr/articles/ Municipality of Festos http://www.dimosfestou.gr/ Municipality of Farkadona https://farkadona.gr/ Municipality of Filiates http://www.filiates.gr/

221 Municipality of Florina https://www.cityoflorina.gr/ Municipality of Folegandros https://www.folegandros.gr/ Municipality of Fournoi Korseon https://fournoikorseon.gr/ Municipality of Fyli https://fyli.gr/ Municipality of Chalkidona http://dimos-chalkidonos.gr/ Municipality of Chalki http://www.dimoschalkis.gr/ Municipality of Hersonissos http://www.hersonissos.gr/ Municipality of Psara http://www.dimospsaron.gr/ Municipality of Oropos http://www.oropos.gov.gr/ Municipality of Veria http://www.veria.gr/new/ Municipality of Volos https://dimosvolos.gr/el Municipality of Dafnis - Imittu https://www.dafni-ymittos.gov.gr/ Municipality of Delphi http://www.dimosdelfon.gr/ Municipality of Doxatos https://diavgeia.gov.gr/f/dimosdoxatou Municipality of Drama https://dimos-dramas.gr/ Municipality of Edesa http://www.dimosedessas.gov.gr/ Municipality of Elliniko – Argirupoli https://www.elliniko-argyroupoli.gr/ Municipality of Eordea http://www.ptolemaida.gr/ Municipality of Zografu https://www.zografou.gov.gr/ Municipality of Heraklion https://www.heraklion.gr/ Municipality of Heraklion of Attica https://www.iraklio.gr/ Municipality of Thessaloniki https://thessaloniki.gr/ Municipality of Thiva https://thiva.gr/ Municipality of Ierapetra http://www.ierapetra.gov.gr/ Municipality of Ilion http://www.ilion.gr/web/guest/home Municipality of Ioannina https://www.ioannina.gr/ Municipality of Kavala https://kavala.gov.gr/ Municipality of Kalamaria https://kalamaria.gr/ Municipality of Kallithea http://www.kallithea.gr/ Municipality of Kamena Vourla http://www.dimos-kamenon-vourlon.gr/ Municipality of Karditsa https://dimoskarditsas.gov.gr/

222 Municipality of Karpenisi https://www.karpenissi.gr/ Municipality of Kastoria http://www.kastoria.gov.gr/ Municipality of Keratsini – Drapetsona https://keratsini-drapetsona.gr/index.php/el/ Municipality of Corfu https://www.corfu.gr/web/guest/home Municipality of Kilkis http://www.e-kilkis.gr/ Municipality of Kordelio – Evosmos http://www.kordelio-evosmos.gr/ Municipality of Corinth http://www.korinthos.gr/ Municipality of Koridallos http://www.korydallos.gr/ Municipality of Kropia https://www.koropi.gr/ Municipality of Lamia https://www.lamia.gr/ Municipality of Larissa https://www.larissa-dimos.gr/el/ Municipality of Lefkada http://www.lefkada.gov.gr/ Municipality of Lykovrisi Pefki https://www.likovrisipefki.gr/ Municipality of Maleviziu http://www.gazi.gov.gr/ Municipality of Madudi - Saint Anna http://www.malian.gov.gr/ Municipality of Maronia Sapon https://dimosmaroneiassapon.gr/ Municipality of Megara http://www.megara.gr/portal/index.php Municipality of Metamorfosi https://www.metamorfossi.gr/ Municipality of Minoa plain https://www.minoapediadas.gr/ Municipality of Moschato - Tavros http://dimosmoschatou-tavrou.gr/ Municipality of Muzaki http://www.mouzaki.gr/ Municipality of Milopotamos http://www.dimosmylopotamou.gr/ Municipality of Nafpaktia http://www.nafpaktos.gr/ Municipality of Nafplio https://www.nafplio.gr/ Municipality of New Ionia http://www.neaionia.gr/ Municipality of New Propontida http://www.nea-propontida.gr/ Municipality of New Smirne https://neasmyrni.gr/ Municipality of Nikaia - Agiou I. Renti https://www.nikaia-rentis.gov.gr/ Municipality of Xeromeros https://www.dimosxiromerou.gr/

223 Municipality of Oihalia https://oichalia.gov.gr/ Municipality of Pageo https://www.dimospaggaiou.gr/ Municipality of Paleo Faliro https://palaiofaliro.gr/ Municipality of Patra http://www.e-patras.gr/ Municipality of Piraeus https://piraeus.gov.gr/ Municipality of Pella https://www.giannitsa.gr/ Municipality of Penteli https://www.penteli.gov.gr/ Municipality of Peristeri http://www.peristeri.gr/ Municipality of Petroupoli https://petroupoli.gov.gr/ Municipality of Polygyros http://polygyros.gr/index.php/en/ Municipality of Preveza http://www.dimosprevezas.gr/ Municipality of Rafina – Pikermio http://www.rafina-pikermi.gr/ Municipality of Rethymni https://www.rethymno.gr/ Municipality of Rhodes https://www.rhodes.gr/ Municipality of Serres https://www.serres.gr/ Municipality of Sikonion https://kiato.gov.gr/ Municipality of Skydra https://www.skydra.gr/ Municipality of Sparti https://www.sparti.gov.gr/ Municipality of Syros – Ermoupoli http://www.syros-ermoupolis.gr/ Municipality of Tempon https://www.dimostempon.gr/ Municipality of Tinos http://www.dimostinou.eu/ Municipality of Trikaia https://trikalacity.gr/ Municipality of Tripoli http://www.tripolis.gr/ Municipality of Trifilia http://www.dimostrifylias.gr/ Municipality of Farsala http://www.farsala.gr/ Municipality of Philadelphia – http://dimosfx.gr/el Chalkidona Municipality of Filothei - Psychiko https://www.philothei-psychiko.gov.gr/ Municipality of Haidario https://www.haidari.gr/ Municipality of Chalandri https://www.chalandri.gr/ Municipality of Chalki https://dimoschalkideon.gr/ Municipality of Chania https://www.chania.gr/

224 https://www.chios.gov.gr/index.php?option=com_acymailing&ct rl=archive&task=view&mailid=27&key=UhkY6LFr&tmpl=com Municipality of Chios ponent Municipality of Oreokastro http://www.oraiokastro.gr/ Municipality of Oropos http://oropos.gov.gr/

Table 22: Other Greek Organizations that publish open data

Source: the author (2020)

NAME URL "AGIOS PAVLOS" GENERAL HOSPITAL OF http://www.agpavlos.gr/ THESSALONIKI "IPPOKRATIO” GENERAL HOSPITAL OF http://www.ippokratio.gr/ THESSALONIKI "KONSTANTOPOULIO-PATISION" GENERAL https://www.konstantopouleio.gr/ HOSPITAL OF NEA IONIA "PAPAGEORGIOU" GENERAL HOSPITAL OF https://www.papageorgiou-hospital.gr/ THESSALONIKI "SISMANOGLIO" GENERAL HOSPITAL OF http://www.komotini-hospital.gr/ KOMOTINI "VOSTANIO" GENERAL HOSPITAL OF https://www.vostanio.gr/ MITILINI “ACHILOPOULIO” GENERAL HOSPITAL OF http://www.ghv.gr/?page_id=11 VOLOS “AGIA ELENI” SPILIOPOULIO HOSPITAL http://www.spiliopoulio.gr/ “AGIOI ANARGYROI” GENERAL http://www.gonkhosp.gr/cms.asp?id=4 ONCOLOGICAL HOSPITAL OF KIFFISIA “AGIOS DIMITRIOS” GENERAL HOSPITAL https://www.oagiosdimitrios.gr/ OF THESSALONIKI “ANDREAS SYGGROS” HOSPITAL OF http://www.hda.gr/nosokomeio-dermatikwn- CUTANEOUS & VENEREAL DISEASES afrodisiakwn-noswn-athinwn-andreas-suggros/ “BOSDAKIO” GENERAL HOSPITAL OF http://www.mpodosakeio.gr/ PTOLEMAIDA

225 “ELENA VENIZELOU” REGIONAL GENERAL http://www.hospital-elena.gr/ HOSPITAL - MATERNITY HOSPITAL “EV ZIN” L.E.P.L. OF MUNICIPALITY OF https://diavgeia.gov.gr/f/eyzhn_npdd_koinonikh_pr EDESSA ostasia_allhleggyh_kai_athlitismos_dhmoy_edessas “GEORGIOS GENNIMATAS” GENERAL http://www.gna-gennimatas.gr/ HOSPITAL OF ATHENS “MAMATSIO” GENERAL HOSPITAL OF https://www.mamatsio.gr/ KOZANI “METAKSAS” CANCER HOSPITAL https://www.metaxa-hospital.gr/ “SISMANOGLIO-AMALIA FLEMING” http://www.sismanoglio.gr/sismanoglio.gr GENERAL HOSPITAL OF ATTICA “SKILITSIO” GENERAL HOSPITAL OF CHIOS http://www.xioshosp.gr/aggregator/sources/1 “THEAGENEION” CANCER HOSPITAL OF http://www.theagenio.gov.gr/ THESSALONIKI “TZANIO” GENERAL HOSPITAL OF PIRAEUS http://www.tzaneio.gr/ 3rd HEALTH REGION OF MACEDONIA http://www.3ype.gr/index.php?lang=en 4th HEALTH REGION OF MACEDONIA AND https://www.4ype.gr/ THRACE 7th HEALTH REGION OF CRETE https://www.hc-crete.gr/ ACADEMY OF ATHENS http://www.academyofathens.gr/ ACHARNES MORTGAGE OFFICE https://diavgeia.gov.gr/f/YPACHARNWN AEGEAN CORONER’S SERVICE https://diavgeia.gov.gr/f/iatrodikastikisyrou AINO NATIONAL FOREST MANAGEMENT http://www.foreasainou.gr/ BODY AMVRAKOS GULF - LEFKADA https://www.amvrakikos.eu/ MANAGEMENT BODY ANTI-MONEY LAUNDERING AUTHORITY http://www.hellenic-fiu.gr/index.php?lang=el ARISTOTLE UNIVERSITY OF THESSALONIKI https://www.auth.gr/ ARMY PENSION FUND https://mts.army.gr/ ASSOCIATION OF GREEK JUDGES AND https://ende.gr/ PROSECUTORS ASSOCIATION OF SOLID WASTE https://esdak.gr/ MANAGEMENT OF CRETE (ESDAK)

226 ATHENS CHAMBER OF COMMERCE AND https://www.acci.gr/acci/shared/index.jsp?context= INDUSTRY 101 ATHENS CHAMBER OF TRADESMEN https://www.eea.gr/ ATHENS CORONER’S SERVICE https://diavgeia.gov.gr/f/iatrodikastikiathinas ATHENS PUBLIC TRANSPORT http://www.oasa.gr/ ORGANIZATION (O.A.S.A. S.A.) ATHENS SCHOOL OF FINE ARTS http://www.asfa.gr/ ATTIKO METRO S.A. https://www.ametro.gr/ AUTHORITY FOR THE HEARING OF PRE- http://www.aepp-procurement.gr/ JUDICAL OBJECTIONS BANK OF GREECE https://opendata.bankofgreece.gr/el/home BENAKI PHYTOPATHOLOGICAL INSTITUTE https://www.bpi.gr/ CENTRAL MARKET AND FISHERY https://www.okaa.gr/ ORGANIZATIONS (CMFO SA) CENTRAL UNION OF GREEK https://www.kedke.gr/el/ MUNICIPALITIES (KEDE) CENTRE OF INTERNATIONAL AND http://www.cieel.gr/gr/index.jsp EUROPEAN ECONOMIC LAW CENTRE OF PLANNING AND ECONOMIC https://www.kepe.gr/index.php/el/ RESEARCH (KEPE) CENTRE OF SOCIAL WELFARE OF ATTICA http://www.kkppa.gr/ CENTRE OF SOCIAL WELFARE OF CENTRAL http://www.kkp-km.gr/ MACEDONIA CENTRE OF SOCIAL WELFARE OF EPIRUS https://kkppe.gr/ CENTRE OF SOCIAL WELFARE OF SOUTH https://aegeantherapy.wordpress.com/ AEGEAN CENTRE OF SOCIAL WELFARE OF http://www.kkppthessaly.gr/ THESSALY CENTRE OF SOCIAL WELFARE OF WESTERN https://kkppde.gr/ GREECE CENTRE OF SOCIAL WELFARE OF WESTERN http://kkppdm.gr/ MACEDONIA CHALANDRI MORTGAGE OFFICE https://diavgeia.gov.gr/f/ypothxal

227 http://www.epimetol.gr/aitnia/shared/index.jsp?cont CHAMBER OF AITOLOAKARNANIA ext=101 https://www.epichal.gr/chalkidiki/shared/index.jsp? CHAMBER OF CHALKIDIKI context=101&js=1 CHAMBER OF DODECANESE https://www.ebed.gr/ CHAMBER OF FINE ARTS OF GREECE http://www.eete.gr/ CHAMBER OF LASITHI http://www.epimlas.gr/ http://www.trikala- CHAMBER OF TRIKALA chamber.gr/trikala/shared/index.jsp?context=101 COLLECTIONS OF MUSEUMS / CULTURAL https://www.openarchives.gr/ INSTITUTIONS / HISTORICAL ARCHIVES CONSIGNMENT DEPOSITS AND LOANS https://www.tpd.gr/ FUND CORFU CORONER’S SERVICE https://diavgeia.gov.gr/f/iatrodikastiki_thrakis COUNTRY COURT OF THESSALONIKI https://eirthess.wordpress.com/ COURT OF AUDIT https://www.elsyn.gr/ CRETE CORONER’S SERVICE https://diavgeia.gov.gr/f/iatrodikastikhkrhths CRIMINAL COURT OF THESSALONIKI https://www.ptaismatodikeio-thes.gr/ DIRECTORATE OF PRIMARY EDUCATION https://dipesamou.blogspot.com/ OF SAMOS DIRECTORATE OF SECONDARY http://dide.sam.sch.gr/ EDUCATION OF SAMOS DODECANESE CORONER’S SERVICE https://diavgeia.gov.gr/f/iatrdod ECONOMIC CHAMBER OF GREECE https://www.oe-e.gr/en/home/ EGNATIA MOTORWAY OBSERVATORY http://observatory.egnatia.gr/index.htm EGNATIA MOTORWAY S.A. http://www.egnatia.eu/page/ E-GOVERNMENT CENTRE FOR SOCIAL http://www.idika.gr/ SECURITY (IDIKA) ENTERPISE GREECE-INVEST AND TRADE https://www.enterprisegreece.gov.gr/ ERGOSE S.A. https://www.ergose.gr/ GENERAL HOSPITAL OF AITOLOAKARNANIA-HOSPITAL UNIT OF https://hospital-agrinio.gr/ MESSOLONGHI

228 GENERAL HOSPITAL OF CORFU http://www.gnkerkyras.gr/ GENERAL HOSPITAL OF DIDIMOTICHO https://did-hosp.gr/ GENERAL HOSPITAL OF DRAMA http://www.dramahospital.gr/ GENERAL HOSPITAL OF GREVENA http://www.ghv.gr/ GENERAL HOSPITAL OF IMATHIA- http://www.gnnaousas.gr/ HOSPITAL UNIT OF NAOUSA GENERAL HOSPITAL OF IMATHIA- http://www.verhospi.gr/ HOSPITAL UNIT OF VERIA GENERAL HOSPITAL OF KASTORIA https://www.kastoriahospital.gr/ GENERAL HOSPITAL OF KATERINI http://www.gnkaterini.gr/ GENERAL HOSPITAL OF KAVALA https://kavalahospital.gr/ GENERAL HOSPITAL OF LASITHI- “DIALINAKEIO” GENERAL HOSPITAL- https://www.agnhosp.gr/ HEALTH CENTRE OF NEAPOLI- GENERAL HOSPITAL OF AGIOS NIKOLAOS GENERAL HOSPITAL OF PELLA – HOSPITAL http://www.gnedessas.eu/gne/ UNIT OF EDESSA GENERAL HOSPITAL OF PELLA – HOSPITAL http://www.gng.gr/ UNIT OF GIANNITSA GENERAL HOSPITAL OF SERRES http://www.hospser.gr/ GENERAL INSPECTOR OF PUBLIC https://www.gedd.gr/ ADMINISTRATION GENERAL SECRETARIAT OF LEGAL AND http://www.ggk.gov.gr/ PARLIAMENTARY ISSUES GENERAL SECRETARIAT OF SPORTS http://gga.gov.gr/ GENERAL STATE ARCHIVES http://www.gak.gr/index.php/el/ GEOTECHNICAL CHAMBER OF GREECE https://www.geotee.gr/ (GEOTEE) GREEK ATOMIC ENERGY COMISSION https://eeae.gr/ GREEK NATIONAL AUTHORITY OF http://eaiya.gov.gr/ ASSISTED REPRODUCTION GREEN FUND http://www.prasinotameio.gr/index.php/el/ GUIDE FOR SERVICE PROVISIONING IN http://www.eu-go.gr/sdportal/

229 GREECE HELLENIC AGENCY FOR LOCAL DEVELOPMENT AND LOCAL GOVERNMENT https://www.eetaa.gr/ (E.E.T.A.A.) S.A. HELLENIC AGRICULTURAL INSURANCE http://www.elga.gr/ ORGANIZATION (ELGA) HELLENIC AGRICULTURAL ORGANIZATION https://www.elgo.gr/ – DIMITRA HELLENIC AUTHORITY FOR COMMUNICATION SECURITY AND http://www.adae.gr/ PRIVACY HELLENIC BANK ASSOCIATION https://www.hba.gr/ HELLENIC CADASTRE https://www.ktimatologio.gr/ HELLENIC CENTER FOR MENTAL HEALTH http://www.ekepsye.gr/ AND RESEARCH HELLENIC CHAMBER OF HOTELS https://www.grhotels.gr/ HELLENIC CHAMBER OF SHIPPING https://nee.gr/ HELLENIC COMPETITION COMMISSION https://www.epant.gr/ HELLENIC CONSUMERS' OMBUDSMAN http://www.synigoroskatanaloti.gr/index.html HELLENIC DATA PROTECTION AUTHORITY https://www.dpa.gr/ HELLENIC FISCAL COUNCIL https://www.hfisc.gr/ HELLENIC FOOD AUTHORITY http://www.efet.gr/ HELLENIC GAMING COMMISSION (HGC) https://www.gamingcommission.gov.gr/ HELLENIC NATIONAL METEOROLOGICAL http://www.hnms.gr/emy/el/ SERVICE (EMY) HELLENIC NATIONAL OCEANOGRAPHIC http://mapserver.ath.hcmr.gr/pagin/v27/ DATA CENTRE HELLENIC OPEN UNIVERSITY https://www.eap.gr/el/ HELLENIC PARLIAMENT https://www.hellenicparliament.gr/ HELLENIC PARLIAMENT RESOLUTIONS http://diafaneia.hellenicparliament.gr HELLENIC PASTEUR INSTITUTE https://www.pasteur.gr/ HELLENIC PENSION MUTUAL FUND http://www.hpmf.gr/ MANAGEMENT COMPANY (HPMF)

230 HELLENIC QUALITY ASSURANCE AND https://adip.gr/el/ ACCREDITATION AGENCY (HQA) HELLENIC REPUBLIC (PRIME MINISTER) https://primeminister.gr/ HELLENIC SINGLE PUBLIC PROCUREMENT https://www.eaadhsy.gr/ AUTHORITY (H.S.P.P.A.) HELLENIC STATISTCAL AUTHORITY https://www.statistics.gr/ HELLENIC TELECOMMUNICATIONS https://www.eett.gr/opencms/opencms/EETT_EN/i AND POST COMMISSION (EETT) ndex.html HELLENIC TOURISM ORGANIZATION http://www.gnto.gov.gr/ HOME FOR AUTISTIC PERSONS "AGIOS http://www.psychargos.gov.gr/Default.aspx?id=125 NIKOLAOS" 45&nt=156 INDEPENDENT AUTHORITY FOR PUBLIC https://www.aade.gr/ REVENUE (IAPR) https://www.gcloud.ktpae.gr/wps/portal/gcloud/ktp/ !ut/p/z1/jZDBDoIwEES_xQNXdpVC0FsjRINyEY zYi0FTCwYoKRV- X_RGguLeJnkvsxlgkACr0jYXqc5llRZ9PjPnEiw9f 77eYIghIUgD14-iiFhIbDgNATc- INFORMATION SOCIETY S.A. vIGtR0Nvh4vYAfaPj1- O4rTPhsjIBx_gR0UwVdKvQKur5Qpgit- 54sp8qn6cTOu6WRloYNd1ppBSFNy8ydLAMSW TjYZkSEJdHhN82EW7p7MXi3egqg!!/dz/d5/L2dB ISEvZ0FBIS9nQSEh/ INSPECTORS-CONTROLLERS BODY FOR http://www.seedd.gr/ PUBLIC ADMINISTRATION –I.C.B.P.A. INSTITUTE OF AGRICULTURAL SCIENCES https://ige.gr/index.php/el/ (I.A.S) INSTITUTE OF EDUCATIONAL POLICY http://iep.edu.gr/el/ INSURANCE FUND OF EMPLOYEES OF http://www.tayteko.gr/ BANKS AND UTILITIES (TAYTEKO) KERATEA MORTGAGE OFFICE https://diavgeia.gov.gr/f/YKERATEAS KOTYCHIOS - STROFILIA AND KYPARISSIA https://diavgeia.gov.gr/f/YG.KO.ST.KY.KO GULF WETLAND MANAGEMENT BODY

231 LAMIA CORONER’S SERVICE http://www.iatrodikastikiypiresialamias.gr/ LARISA CORONER’S SERVICE https://diavgeia.gov.gr/f/iatrodyplar LINKED OPEN DATA ON THIS PORTAL ARE BEING PUBLISHED BY THE UNIVERSITY OF http://linkedopendata.gr/ ATHENS GROUP PARTICIPATING IN THE TELEIOS PROJECT MANAGEMENT BODY OF THE NATIONAL PARK OF SCHINIA-MARATHON, YMITTOS https://diavgeia.gov.gr/f/FODEPASMYNA AND SOUTHEAST ATTICA MANAGEMENT BODY OF THE PROTECTED https://diavgeia.gov.gr/f/FDTHERMAIKOY AREAS OF THERMAIKOS GULF MANAGEMENT COMMITTEE OF THE ENVIRONMENTAL EDUCATION CENTER OF http://kpe-kastor.kas.sch.gr/index1.htm KASTORIA MANAGEMENT ORGANIZATION UNIT OF https://www.mou.gr/el/Pages/Default.aspx DEVELOPMENT PROGRAMMES S.A. MANPOWER EMPLOYMENT http://www.oaed.gr/ ORGANIZATION (OAED) MEGARA MORTGAGE OFFICE https://diavgeia.gov.gr/f/YPMEGARON MINISTRY OF DIGITAL POLICY, TELECOMMUNICATIONS AND https://mindigital.gr/old/ INFORMATION. MORTAGE OFFICE – CADASTRAL OFFICE https://diavgeia.gov.gr/f/YPILIOUPOLIS OF ILIOUPOLI MORTAGE OFFICE – CADASTRAL OFFICE https://diavgeia.gov.gr/f/YPOTHKGPAPAGOU OF PAPAGOS https://diavgeia.gov.gr/f/dhmotikosorganismosthiva MUNICIAPL ORGANIZATION OF THEBES s MUNICIPAL INFORMATION, SHOW AND https://thessaloniki.gr/ COMMUNICATION ENTERPRISE MUNICIPAL KINDERGARTENS OF PALEO https://www.dpspfaliro.eu/ FALIRO MUNICIPAL PORT FUND OF CHIOS https://chiosport.gr/

232 MUNICIPAL PORT FUND OF NAXOS https://www.portofnaxos.com/index.php?lang=el MUNICIPAL PORT FUND OF SIROS https://www.portofsyros.gr/ MUNICIPAL WATER SUPPLY AND SEWERAGE COMPANY OF THE https://www.deya-thermis.gr/ MUNICIPALITY OF THERMI MUNICIPALITY OF GORTINA http://opendatagortynia.gr/ MUNICIPALITY OF THESSALONIKI (E- https://opengov.thessaloniki.gr/ GOVERNMENT GATEWAY) NAFPLIO AND KALAMATA CORONER’S https://yperdiavgeia.gr/search/signer_id:127067 SERVICE NATIONAL ACTUARIAL AUTHORITY http://www.eaa.gr/ NATIONAL AND KAPODISTRIAN https://www.uoa.gr/ UNIVERSITY OF ATHENS NATIONAL CENTRE FOR PUBLIC ADMINISTRATION AND LOCAL https://www.ekdd.gr/ GOVERNMENT (EKDDA) NATIONAL COUNCIL FOR RADIO AND https://www.esr.gr/ TELEVISION (NCRTV) NATIONAL COUNCIL OF PUBLIC HEALTH https://diavgeia.gov.gr/f/ea_esydy (ESYDY) NATIONAL DOCUMENTATION CENTRE http://www.ekt.gr/ http://www.eprocurement.gov.gr/webcenter/faces/o racle/webcenter/page/scopedMD/sd0cb90ef_26cf_4 NATIONAL ELECTRONIC PUBLIC 703_99d5_1561ceff660f/Page119.jspx?_afrLoop=1 PROCUREMENT SYSTEM (ESIDIS) 2292510665249909#%40%3F_afrLoop%3D12292 510665249909%26_adf.ctrl- state%3Di071zz8ya_97 NATIONAL EXAMS ORGANIZATION http://eoe.minedu.gov.gr/ NATIONAL FOUNDATION FOR THE DEAF https://idrimakofon.gr/ NATIONAL INFRASTRUCTURES FOR https://grnet.gr/ RESEARCH AND TECHNOLOGY NATIONAL OBSERVATORY OF https://paratiritirioemf.eeae.gr/index.php/el/ ELECTROMAGNETIC FIELDS

233 NATIONAL ORGANIZATION FOR https://www.eof.gr/web/guest;jsessionid=f107adce0 MEDICINES 913830c7b86a6910060 http://www.et.gr/index.php/nomoi-proedrika- NATIONAL PRINTING HOUSE OF GREECE diatagmata NATIONAL TRANSPARENCY AUTHORITY http://www.aead.gr/ NEA IONIA MORTGAGE OFFICE https://diavgeia.gov.gr/f/YPNEASIONIAS NESTOS DELTA - VISTONIDA - ISMARIDA https://fd-nestosvistonis.gr/ AND THASSOS MANAGEMENT BODY NORTHERN PINDOS NATIONAL PARK https://www.pindosnationalpark.gr/ MANAGEMENT BODY OFFICE OF SOCIAL PROTECTION, SOLIDARITY AND SPORTS AND https://ykpaaplagada.gr/ EDUCATION OF LAGADAS MUNICIPALITY OITIS NATIONAL FOREST, SPERCHIOS VALLEY AND MALIAKOS GULF https://oiti.gr/en/home/ MANAGEMENT BODY OPEN GEOSPATIAL DATA AND SERVICES https://geodata.gov.gr/ FOR GREECE ORGANIZATION OF CULTURE AND SPORTS OF THE MUNICIPALITY OF NEA https://diavgeia.gov.gr/f/politismos_athlitismos_np PROPONTIDA ORGANIZATION OF WELFARE BENEFITS https://opeka.gr/ AND SOCIAL SOLIDARITY PARNONA, MOUSTOS, MAINALOS AND http://www.fdparnonas.gr/ MONEMVASIA MANAGEMENT BODY PATRA CORONER’S SERVICE https://diavgeia.gov.gr/f/iatrodikastikipatras PATRIARCHAL UNIVERSITY http://www.aeahk.gr/joomla/index.php/en/ ECCLESIASTICAL ACADEMY OF CRETE PAYMENT AND CONTROL AGENCY FOR GUIDANCE AND GUARANTEE AID https://opekepe.gr/el/ (O.P.E.K.E.P.E.) PENTELI GENERAL HOSPITAL FOR https://www.paidon-pentelis.gr/ CHILDREN

234 PIRAEUS CHAMBER OF COMMERCE AND http://www.pcci.gr/evep/shared/index.jsp?context= INDUSTRY 101&js=1 PIRAEUS CHAMBER OF TRADESMEN https://eep.gov.gr/ PIRAEUS CORONER’S SERVICE https://diavgeia.gov.gr/f/iatrodikastikipeiraia PIRAEUS MORTGAGE OFFICE https://diavgeia.gov.gr/f/ypothikofilakiopirea PRIMARY AND SECONDARY EDUCATION ASSURANCE AND ACCREDITATION http://www.adippde.gr/ AGENCY PRIME MINISTER TO THE PRIME https://primeminister.gr/primeminister/proedria-tis- MINISTER’S OFFICE kivernisis PRIVATE SECTOR WELFARE FUND (L.E.P.L.) https://diavgeia.gov.gr/f/tapit PUBLIC PROSECUTOR’S OFFICE AT THE COURT OF FIRST INSTANCE https://ppothess.gr/ (THESSALONIKI) REGIONAL DEVELOPMENT FUND OF http://www.pta.stereahellas.gr/ CENTRAL GREECE REGIONAL DEVELOPMENT FUND OF CRETE https://www.pta.gr/ REGIONAL DEVELOPMENT FUND OF http://pta-emth.gr/ EASTERN MACEDONIA AND THRACE REGIONAL DEVELOPMENT FUND OF http://www.ptaepirus.gr/ EPIRUS REGIONAL DEVELOPMENT FUND OF http://www.ptaba.gr/ NORTH AEGEAN REGIONAL DEVELOPMENT FUND OF https://www.ptapnai.gr/ SOUTH AEGEAN REGIONAL DIRECTORATE OF PRIMARY & SECONDARY EDUCATION OF NORTH http://vaigaiou.pde.sch.gr/newsch/ AEGEAN REGIONAL DIRECTORATE OF PRIMARY https://srv-ipeir.pde.sch.gr/ AND SECONDARY EDUCATION OF EPIRUS http://www.rae.gr/site/portal.csp;jsessionid=c3fb77 REGULATORY AUTHORITY FOR ENERGY 6630d7badae37cd56a4d7382858c67a59f4b3e.e3aP (RAE) b3iLbxySe34Nch0Sa3iNch10n6jAmljGr5XDqQLv

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236 INSTITUTE OF ATHENS TECHNOLOGICAL EDUCATIONAL https://cm.ihu.gr/ INSTITUTE OF CENTRAL MACEDONIA TECHNOLOGICAL EDUCATIONAL http://www.teiep.gr/ INSTITUTE OF EPIRUS THE GOULANDRIS NATURAL HISTORY MUSEUM-GREEK BIOTOPE/WETLAND http://www.ekby.gr/ekby/el/EKBY_home_el.html CENTRE THE GREEK OMBUDSMAN https://www.synigoros.gr/ THESSALONIKI CENTRAL MARKET S.A. https://www.kath.gr/ THESSALONIKI CORONER’S SERVICE https://diavgeia.gov.gr/f/iatrodikastikithess THESSALONIKI COURT OF APPEAL http://www.efeteio-thess.gr/web/ THRACE CORONER’S SERVICE https://diavgeia.gov.gr/f/iatrodikastiki_thrakis TRAINING VOCATIONAL SCHOOL OF FINE http://www.tinosartschool.gr/ ARTS OF PANORMOS TINOS UNIFIED FUND FOR AUXILIARY https://www.eteaep.gov.gr/web/ INSURANCE AND LUMP SUM BENEFITS UNION OF HELLENIC CHAMBERS OF https://www.uhc.gr/ COMMERCE UNIVERSITY ECCLESIASTICAL ACADEMY https://www.aeavellas.gr/new/?page_id=113&lang= OF VELLA OF IOANNINA en UNIVERSITY FOREST ADMINISTRATION http://uniforest.auth.gr/ AND MANAGEMENT FUND UNIVERSITY OF CRETE https://www.uoc.gr/ UNIVERSITY OF WESTERN MACEDONIA https://uowm.gr/ URBAN RAIL TRANSPORT S.A. http://www.stasy.gr/index.php VOULIWATCH (HELLENIC PARLIAMENT) https://vouliwatch.gr/ WATER DISTRICTS (hydroscope.gr) http://www.hydroscope.gr/ WESTERN MACEDONIA CORONER’S https://diavgeia.gov.gr/f/iatrdytmak SERVICE YOUTH & LIFELONG LEARNING https://www.inedivim.gr/ FOUNDATION

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