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Show Guideguide Web Searching for GIS Services and Web Mapping Have Been Either Published Or an Introduction Adopted by OGC
showshow guideguide Web searching for GIS services and Web Mapping have been either published or An Introduction adopted by OGC. Who are OGC’s members? to OpenGIS There are three main levels of OGC membership: Principal, Technical and By Adam Gawne-Cain and Chris Holcroft Associate. The higher membership levels confer more voting rights. There are now OpenGIS is the activity pursued by the Open GIS over 300 members featuring 20 Principal members and 25 Technical members from Consortium (OGC). OpenGIS seeks to achieve around the globe. transparent access to disparate geo-data and geo- The membership of OGC is impressively processing resources in a networked environment by broad. Most major GIS companies are members, along with database companies, providing a rich suite of open interface specifications. operating system companies, national mapping agencies, large government These interface specifications will enable GIS developers departments, universities and research to create inter-operable components that provide this institutes. The membership is international, but mainly focused in North transparency. America and Europe. The difference between OpenGIS and A second advantage of OGC’s interface- How does OGC make Interface previous attempts to standardise GIS is based approach is that users can be sure Specifications? that OGC agrees interfaces, and not that they are looking at genuine, live data, Every two months, the OGC Technical neutral file formats. The problem with and not at a static file generated some time Committee (TC) and Management neutral file formats (e.g. SDTS or NTF) in the past. Committee (MC) meet for a week. was that specifications grew so complex The meetings are held in different that each GIS could only support a sub-set A third, practical advantage of interfaces locations around the world, hosted by one of the format. -
IR2 Trees for Spatial Database Keyword Search NSF Award
IR2 Trees for Spatial Database Keyword Search NSF Award: CNS-0220562 Mil: Infrastructure for Research and Training in Database Management for Web-based Geospatial Data Visualization with Applications to Aviation PI: Naphtali Rishe Florida International University School of Computing and Information Sciences Co-PI: Boonserm Wongsaroj Florida Memorial University Department of Computer Sciences and Mathematics ABSTRACT Our Mil-provided infrastructure and research support has enabled us to perform basic research in keyword search. Many applications require finding objects closest to a specified location that contains a set of keywords. For example, online yellow pages allow users to - specify an address and a set of keywords. In return, the user obtains a list of businesses whose description contains these keywords, ordered by their distance from the specified address. The problems of nearest neighbor search on spatial data and keyword search on text data have been extensively studied separately. However, to the best of our knowledge there is no efficient method to answer spatial keyword queries, that is, queries that specify both a location and a set of keywords. In this work, we present an efficient method to answer top-k spatial keyword queries. To do so, we introduce an indexing structure called IR2-Tree (Information Retrieval R-Tree) which combines an R-Tree with superimposed text signatures. We present algorithms that construct and maintain an IR2-Tree, and use it to answer top-k spatial keyword queries. Our algorithms are experimentally and analytically compared to current methods and are shown to have superior performance and excellent scalability. 1. INTRODUCTION An increasing number of applications require the efficient execution of nearest neighbor queries constrained by the properties of the spatial objects. -
Spatial Databases and Spatial Indexing Techniques
Spatial Databases and Spatial Indexing Techniques Timos Sellis Computer Science Division Department of Electrical and Computer Engineering National Technical University of Athens Zografou 15773, GREECE Tel: +30-1-772-1601 FAX: +30-1-772-1659 e-mail: [email protected] Spatial Database Systems Timos Sellis Spatial Databases and Spatial Indexing Techniques Timos Sellis National Technical University of Athens e-mail: [email protected] Aalborg, June 1998 Outline • Data Models • Algebra • Query Languages • Data Structures • Query Processing and Optimization • System Architecture • Open Research Issues Spatial Database Systems 1 1 Spatial Database Systems Timos Sellis Introduction : Spatial Database Management Systems (SDBMS) QUESTION “What is a Spatial Database Management System ?” ANSWER • SDBMS is a DBMS • It offers spatial data types in its data model and query language • support of spatial relationships / properties / operations • It supports spatial data types in its implementation • efficient indexing and retrieval • support of spatial selection / join Spatial Database Systems 2 Applications of SDBMS Traditional GIS applications • Socio-Economic applications • Urban planning • Route optimization, market analysis • Environmental applications • Fire or Pollution Monitoring • Administrative applications • Public networks administration • Vehicle navigation Spatial Database Systems 3 2 Spatial Database Systems Timos Sellis Applications of SDBMS (cont'd) Novel applications • Image and Multimedia databases • shape configuration -
Compressed Spatial Hierarchical Bitmap (Cshb) Indexes for Efficiently Processing Spatial Range Query Workloads
Compressed Spatial Hierarchical Bitmap (cSHB) Indexes for Efficiently Processing Spatial Range Query Workloads ∗ Parth Nagarkar K. Selçuk Candan Aneesha Bhat Arizona State University Arizona State University Arizona State University Tempe, AZ 85287-8809, USA Tempe, AZ 85287-8809, USA Tempe, AZ 85287-8809, USA [email protected] [email protected] [email protected] ABSTRACT terms of their coordinates in 2D space. Queries in this 2D In most spatial data management applications, objects are space are then processed using multidimensional/spatial in- represented in terms of their coordinates in a 2-dimensional dex structures that help quick access to the data [28]. space and search queries in this space are processed using spatial index structures. On the other hand, bitmap-based 1.1 Spatial Data Structures indexing, especially thanks to the compression opportuni- The key principle behind most indexing mechanisms is to ties bitmaps provide, has been shown to be highly effective ensure that data objects closer to each other in the data for query processing workloads including selection and ag- space are also closer to each other on the storage medium. gregation operations. In this paper, we show that bitmap- In the case of 1D data, this task is relatively easy as the based indexing can also be highly effective for managing total order implicit in the 1D space helps sorting the ob- spatial data sets. More specifically, we propose a novel com- jects so that they can be stored in a way that satisfies the pressed spatial hierarchical bitmap (cSHB) index structure above principle. When the space in which the objects are to support spatial range queries. -
Building a Spatial Database in Postgresql
Building a Spatial Database in PostgreSQL David Blasby Refractions Research [email protected] http://postgis.refractions.net Introduction • PostGIS is a spatial extension for PostgreSQL • PostGIS aims to be an “OpenGIS Simple Features for SQL” compliant spatial database • I am the principal developer Topics of Discussion • Spatial data and spatial databases • Adding spatial extensions to PostgreSQL • OpenGIS and standards Why PostGIS? • There aren’t any good open source spatial databases available • commercial ones are very expensive • Aren’t any open source spatial functions • extremely difficult to properly code • building block for any spatial project • Enable information to be organized, visualized, and analyzed like never before What is a Spatial Database? Database that: • Stores spatial objects • Manipulates spatial objects just like other objects in the database What is Spatial data? • Data which describes either location or shape e.g.House or Fire Hydrant location Roads, Rivers, Pipelines, Power lines Forests, Parks, Municipalities, Lakes What is Spatial data? • In the abstract, reductionist view of the computer, these entities are represented as Points, Lines, and Polygons. Roads are represented as Lines Mail Boxes are represented as Points Topic Three Land Use Classifications are represented as Polygons Topic Three Combination of all the previous data Spatial Relationships • Not just interested in location, also interested in “Relationships” between objects that are very hard to model outside the spatial domain. • The most -
Cityjson: a Compact and Easy-To-Use Encoding of the Citygml Data Model
CityJSON: A compact and easy-to-use encoding of the CityGML data model Hugo Ledoux Ken Arroyo Ohori Kavisha Kumar Balazs´ Dukai Anna Labetski Stelios Vitalis This is the author’s version of the work. It is posted here only for personal use, not for redistribution and not for commercial use. The definitive version will be published in the journal Open Geospatial Data, Software and Standards soon. H. Ledoux, K. Arroyo Ohori, K. Kumar, B. Dukai, A. Labetski, and S. Vitalis (2018). CityJ- SON: A compact and easy-to-use encoding of the CityGML data model. Open Geospatial Data, Software and Standards, In Press. DOI: http://dx.doi.org/10.1186/s40965-019-0064-0 Full details of the project: https://cityjson.org The international standard CityGML is both a data model and an exchange for- mat to store digital 3D models of cities. While the data model is used by several cities, companies, and governments, in this paper we argue that its XML-based ex- change format has several drawbacks. These drawbacks mean that it is difficult for developers to implement parsers for CityGML, and that practitioners have, as a con- sequence, to convert their data to other formats if they want to exchange them with others. We present CityJSON, a new JSON-based exchange format for the CityGML data model (version 2.0.0). CityJSON was designed with programmers in mind, so that software and APIs supporting it can be quickly built. It was also designed to be compact (a compression factor of around six with real-world datasets), and to be friendly for web and mobile development. -
Understanding and Working with the OGC Geopackage Keith Ryden Lance Shipman Introduction
Understanding and Working with the OGC Geopackage Keith Ryden Lance Shipman Introduction - Introduction to Simple Features - What is the GeoPackage? - Esri Support - Looking ahead… Geographic Things 3 Why add spatial data to a database? • The premise: - People want to manage spatial data in association with their standard business data. - Spatial data is simply another “property” of a business object. • The approach: - Utilize the existing SQL data access model. - Define a simple geometry object. - Define well known representations for passing structured data between systems. - Define a simple metadata schema so applications can find the spatial data. - Integrate support for spatial data types with commercial RDBMS software. Simple Feature Model 10 area1 yellow Feature Table 11 area2 green 12 area3 Blue Feature 13 area4 red Geometry Feature Attribute • Feature Tables contain rows (features) sharing common properties (Feature Attributes). • Geometry is a Feature Attribute. Database Simple Feature access Query Connection model based on SQL Cursor Value Geometry Type 1 Type 2 Spatial Geometry (e.g. string) (e.g. number) Reference Data Access Point Line Area Simple Feature Geometry Geometry SpatialRefSys Point Curve Surface GeomCollection LineString Polygon MultiSurface MultiPoint MultiCurve Non-Instantiable Instantiable MultiPolygon MultiLineString Some of the Major Standards Involved • ISO 19125, Geographic Information - Simple feature access - Part 1: common architecture - Part 2: SQL Option • ISO 13249-3, Information technology — Database -
Introduction to Postgis
Introduction to PostGIS Tutorial ID: IGET_WEBGIS_002 This tutorial has been developed by BVIEER as part of the IGET web portal intended to provide easy access to geospatial education. This tutorial is released under the Creative Commons license. Your support will help our team to improve the content and to continue to offer high quality geospatial educational resources. For suggestions and feedback please visit www.iget.in. IGET_WEBGIS_002 Introduction to PostGIS Introduction to PostGIS Objective In this tutorial we will learn, how to register a new server, creating a spatial database in PostGIS, importing data to the spatial database, spatial indexing and some important PostGIS special functions. Software: OpenGeo Suite 3.0 Level: Beginner Time required: 3 Hour Software: OpenGeo Suite 3.0 Level: Beginner Prerequisites and Geospatial Skills Basic computer skills IGET_WEBGIS_001 should be completed Basic knowledge of SQL is excepted Readings 1. Introduction to the OpenGeo Suite, Chapter 2: PostGIS, pp. 21 – 38. http://presentations.opengeo.org/2012_FOSSGIS/suiteintro.pdf 2. Chapter 13. PostGIS Special Functions Index, http://suite.opengeo.org/docs/postgis/postgis/html/PostGIS_Special_Functions_Index.html Tutorial Data: The tutorial data of this exercise may be downloaded from this link: 2 IGET_WEBGIS_002 Introduction to PostGIS Introduction PostGIS is an open source spatial database extension that turns PostgreSQL database system into a spatial database. It adds support for geographic objects allowing location queries, analytical functions for raster and vector data, raster map algebra, spatial re-projection, network topology, Geodetic measurements and much more. In this tutorial we will learn how to register a server and creation of a spatial database, and importing the shapefiles into it, for this purpose we are using the shapefiles digitized during the tutorial IGET_GIS_005: Digitization of Toposheet using Quantum GIS. -
Tutorial for Course of Spatial Database
Tutorial for Course of Spatial Database Spring 2013 Rui Zhu School of Architecture and the Built Environment Royal Institute of Technology-KTH Stockholm, Sweden April 2013 Lab 1 Introduction to RDBMS and SQL pp. 02 Lab 2 Introduction to Relational Database and Modeling pp. 16 Lab 3 SQL and Data Indexing Techniques in Relational Database pp. 18 Lab 4 Spatial Data Modeling with SDBMS (1) Conceptual and Logical Modeling pp. 21 Lab 5 Spatial Data Modeling with SDBMS(2) Implementation in SDB pp. 25 Lab 6 Spatial Queries and Indices pp. 30 Help File Install pgAdmin and Shapefile Import/Export Plugin pp. 36 1 AG2425 Spatial Databases Period 4, 2013 Spring Rui Zhu and Gyözö Gidófalvi Last Updated: March 18, 2013 Lab 1: Introduction to RDBMS and SQL Due March 27th, 2013 1. Background This introductory lab is designed for students that are new to databases and have no or limited knowledge about databases and SQL. The instructions first introduce some basic relational databases concepts and SQL, then show a number of tools that allow users to interact with PostgreSQL/PostGIS databases (pgAdmin: administration and management tool administration; QuantumGIS). Lastly, the instructions point to a short and simple SQL tutorial using an online PostgreSQL/PostGIS database and ask the students to write a few simple queries against this database. 2. Tutorial 2.1. What is SQL? As it is stated in Wikipedia, SQL (Structured Query Language) is a special-purpose programming language designed for managing data held in a relational database management system (RDBMS). SQL is a simple and unified language, which offers many features to make it as a powerfully diverse that people can work with a secure way. -
Analyzing the Performance of Nosql Vs. SQL Databases for Spatial And
Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings Volume 17 Boston, USA Article 4 2017 Analyzing the performance of NoSQL vs. SQL databases for Spatial and Aggregate queries Sarthak Agarwal International Institute of Information Technology Hyderabad Gachibowli, Hyderabad, India KS Rajan International Institute of Information Technology Hyderabad Gachibowli, Hyderabad, India Follow this and additional works at: https://scholarworks.umass.edu/foss4g Part of the Data Storage Systems Commons Recommended Citation Agarwal, Sarthak and Rajan, KS (2017) "Analyzing the performance of NoSQL vs. SQL databases for Spatial and Aggregate queries," Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings: Vol. 17 , Article 4. DOI: https://doi.org/10.7275/R5736P26 Available at: https://scholarworks.umass.edu/foss4g/vol17/iss1/4 This Paper is brought to you for free and open access by ScholarWorks@UMass Amherst. It has been accepted for inclusion in Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings by an authorized editor of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. Analyzing the performance of NoSQL vs. SQL databases for Spatial and Aggregate queries Sarthak Agarwala,∗, KS Rajana aInternational Institute of Information Technology Hyderabad Gachibowli, Hyderabad, India Abstract: Relational databases have been around for a long time and spatial databases have exploited this feature for close to two decades. The recent past has seen the development of NoSQL non-relational databases, which are now being adopted for spatial object storage and handling, too. While SQL databases face scalability and agility challenges and fail to take the advantage of the cheap memory and processing power available these days, NoSQL databases can handle the rise in the data storage and frequency at which it is accessed and processed - which are essential features needed in geospatial scenarios, which do not deal with a fixed schema(geometry) and fixed data size. -
Oracle Spatial
Oracle Spatial An Oracle Technical White Paper May 2002 Oracle Spatial Abstract...............................................................................................................1 1.0 Introduction...............................................................................................1 2.0 Spatial Data and ORDBMS.....................................................................3 2.1 Object-Relational Database Management Systems (ORDBMS)...3 2.2 Challenges of Spatial Databases.........................................................3 2.2.1 Geometry .......................................................................................3 2.2.2 Distribution of Objects in Space................................................4 2.2.3 Temporal Changes........................................................................4 2.2.4 Data Volume .................................................................................4 2.3 Requirements of a Spatial Database System.....................................4 2.3.1 Classification of Space..................................................................5 2.3.2 Data Model ....................................................................................5 2.3.3 Query Language ............................................................................5 2.3.4 Spatial Query Processing .............................................................6 2.3.5 Spatial Indexing.............................................................................6 3.0 Oracle Spatial.............................................................................................7 -
Towards Integrating Heterogeneous Data: a Spatial DBMS Solution from a CRC-LCL Project in Australia †
International Journal of Geo-Information Article Towards Integrating Heterogeneous Data: A Spatial DBMS Solution from a CRC-LCL Project in Australia † Wei Li * , Sisi Zlatanova , Abdoulaye A. Diakite , Mitko Aleksandrov and Jinjin Yan Faculty of the Built Environment, The University of New South Wales, Kensington 2052, Australia; [email protected] (S.Z.); [email protected] (A.A.D.); [email protected] (M.A.); [email protected] (J.Y.) * Correspondence: [email protected] † This Paper Is an Extended Version of Our Paper Published in GI4SDG 2019. Received: 30 December 2019; Accepted: 19 January 2020; Published: 21 January 2020 Abstract: Over recent decades, more and more cities worldwide have created semantic 3D city models of their built environments based on standards across multiple domains. 3D city models, which are often employed for a large range of tasks, go far beyond pure visualization. Due to different spatial scale requirements for planning and managing various built environments, integration of Geographic Information Systems (GIS) and Building Information Modeling (BIM) has emerged in recent years. Focus is now shifting to Precinct Information Modeling (PIM) which is in a more general sense to built-environment modeling. As scales change so do options to perform information modeling for different applications. How to implement data interoperability across these digital representations, therefore, becomes an emerging challenge. Moreover, with the growth of multi-source heterogeneous data consisting of semantic and varying 2D/3D spatial representations, data management becomes feasible for facilitating the development and deployment of PIM applications. How to use heterogeneous data in an integrating manner to further express PIM is an open and comprehensive topic.