Regional Development in the Age of Big Data
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DOI: 10.21120/LE/12/1/1 Landscape & Environment 12 (1) 2018. 1-9 REGIONAL DEVELOPMENT IN THE AGE OF BIG DATA JÓZSEF ATTILA JANKÓ1*, GYÖRGY SZABÓ2 1,2 Budapest University of Technology and Economics, Hungary *Email: [email protected] Received 20 November 2016, accepted in revised form 14 April 2017 Abstract Our paper presents a forward looking analytical approach to the territorial development in a region of the Transylvanian Plain situated in the vicinity of Cluj-Napoca, Romania. We outlined the development of this region with the means of landscape architecture supported by a comparable assessment. In the age of Big Data we settled at creative usage of traditional analysis. We extracted yet undetected information from a limited amount of available as yet loosely related data. The key feature of the employed model is the ontological traceability of cause and effect. Although technology is available to collect enormous data, expert knowledge gained by education and professional practice cannot be overlooked. We demonstrate that this method of location based analysis is capable of delivering value added to established principles of spatial planning in the age of trustworthy, large volume, heterogeneous data. Keywords: Spatial Planning, Location Based Analysis, Heterogeneous Data, Thematic Overlay 1. Introduction to the characteristics of the phenomena examined. If change in assessment results The profession of landscape architecture becomes desired, the necessary change in gained not only substantial technological the determining phenomenon can be pointed tools over the 20th century but a pressure from the IT and GIS sectors as well. In the of available data, while at the same time age of Big Data, not all locations abound beingout. This capable method of can including assume thelarge deficiency volume data of heterogeneous type from different sources. This model stands in contrast to andwith technicalcurrent andderivatives thematically of the diversifiedadvanced technologiesdata. We also (deep identified learning thate.g.) scientificare con- analysis or probabilistic approaches where strained compared to the employment of thean artificial connections intelligence between like the neural input datanetwork and functional models. Therefore, we developed descriptive results are chaotic or even hidden. a comparable assessment method to Our approach is best described by the term transform obtained and collected data into ‘inverse fuzzy logic’, where phenomena of the a form of working knowledge (Jankó & region in the form of pseudo fuzzy sets are Szabó 2013). In this paper, we only highlight transformed into crisp values on a continuous some of the techniques where spatial data and heterogeneous statistical data were et al. 2015) of the assessment outcomes employed. The aim of the compa-rable enablesscale. Ultimately, design and the planningdefuzzification proposals (Skalna to settlement diagnosis is to identify cause be based on a controlled interpretation and effect relationships in such a way that of the landscape utilising the strengths assessment results can be traced back 2 Landscape & Environment 12 (1) 2018. 1-9 and addressing weakness of the site while was divided into four phenomena: considering opportunities and threats. This the natursphere, the sociosphere, the method differs from landscape character opussphere and the urbanosphere (Kiss assessment (Tudor 2014) in the term of 1996). This four-fold thematic approach character because we also take into account contains the state of art in our understanding remotely imperceptible factors. Examples of the environment, the society, the economy of this distinction will be put forward in the and the built heritage respectively (Jankó & later discussion. Szabó 2013). The data was aggregated within A better understating of spatial connections each local authority, scaled and normalized, and interactions within the area of interest providing a comparable relative rank (1-9) (the subject of design and planning) can of the local authorities in each sphere. The be attained from using thematic overlays numeric class represents weak, medium or represented on maps drawing on statistical strong quality of the components of each data. It is not the same case in our work sphere. Strong characteristics are considered environment where only raw and weakly to be relatively well developed. The weak connected data is present to incorporate characteristics provided the areas that into the design principles. Data availability could be addressed by planning proposals. is limited, and documentation suggests that The aggregated indices or ranks are the data reliability might be in question (Advameg result of base indices which are the result of 2015). While physical features of a landscape detailed indices directly measuring certain are straightforward to visualise and analyse on thematic layers, maps yet contain only goal of the regional development process is limited and purposefully selected elements tophenomenon reduce inequalities identified recognisable in the region. from Thethe from the reality. Our approach complements ranks. Examples of tracing back cause and this practice with such thematic data analysis effects will be provided in later chapter. We that connects information from diverse also determined opportunities and threats sources in order to construct an elaborated that challenge the achievement of the design general overview of the current state of the targets. Relevant geographical information 2 design area. Between 2010 and 2014 we was aggregated in 1 km hexagon cells engaged in an extensive data collection effort to identify locations with high risks or to overcome these challenges. The team remarkable potential for the development. evaluated historical maps, General Urban A professional pursuit like landscape Plans and current remote sensing sources architecture is performed within a discipline such as CORINE Land Cover of the European bounded by interdisciplinary values Environment Agency (EEA) and SRTM Digital concerning the development of the built Elevation Data of CGIAR Consortium for and cultivated environment regarding the Spatial Information (CGIAR-CSI). The team gathered an abundance of socio-economic requires a unique hierarchy of prioritization. information in a variety of forms including Thebeneficiary phenomena inhabitants. within thisHowever, landscape each were site statistical data, site visits and interviews evaluated with the real needs of the local with local elders who possess a lifetimes of society in mind while not passing over yet unpublished knowledge. We established ecological and economic considerations. a framework for spatial assessment capable The comparable assessment intended to of utilizing both historic and current data answer the following types of questions. regardless of its collection techniques. This How much does the current land use support method provides systematized information the society? What kind of change may lead to of the area of interest. more dependable self-sustaining wellbeing? The extracted information provided a What sort of intervention could cause the desired change? Where the interventions significant base for our planning process Landscape & Environment 12 (1) 2018. 1-9 3 Sunshine availability, visual accessibility means will not be discussed. could be realized? Institutional and financial Recursive solar irradiation analysis (GRASS function r.sun with sequential moments) 2. Methods and Workflow was performed to segment and classify the site to gain evidence for agriculture related The assessment and spatial planning proposals. Those areas with high irradiation proposal concerned ten local authorities on visually less exposed declivities and located in proximity to settlements provided sites for possible greenhouse culture. (Aiton/Ajton, Aşchileu/Esküllő, Borşa/ Irradiation is the process by which the land is Kolozsborsa, Căianu/Magyarkályán, exposed to radiation. We use the term ‘visual Cămăraşu/Pusztakamarás, Jucu/Zsuk, accessibility’ to describe the phenomenon in Mociu/Mocs, Pălatca/Magyarpalatka, Suatu/ encompassing 49 villages spread over a which the land is exposed to visual contact Magyarszovát,2 Vultureni/Borsaújfalu) 600 km region. The administrative centres during daily activities. listed in alphabetic order are in Romanian (ANCPI 2014) and Hungarian (Szabó & Szabó The visual accessibility analysis determined those areas of potential land of three major phases: 1) Investigation of the2003) natural respectively. and societal The workflowfacilities, landscapeconsisted (GRASS function r.los with default settings) use conflicts. Line-of-sight raster analysis shaping factors and applicable development was executed in nested loops over each strategies; 2) Landscape assessment based digital elevation model (DEM) pixel. Area of viewing positions (number of pixels) having the enhancement of the quality of life based visual contact with target locations in each on thisthese assessment. research findings; 3) Proposals for loop were collected and aggregated over the 1 km2 cell size tessellated hexagonal grid Fig. 1: Recreational complexity classes with area of high visual accessibility and irradiation 4 Landscape & Environment 12 (1) 2018. 1-9 of our assessment did not require such a and comparison. Spatial units visible from largerutilised area for thewere rest considered of spatial visually classification more susceptible on our visual accessibility scale. Complexityrefinement. assessment The algorithmic