Representation of geographic concepts

1. Representation of geographic concepts Geog 495: GIS database design November 7, 2005

2. Outlines • [D] Review of the article: Egenhofer et al 1999 Discussion will be focused on • Relational DB and GIS • Cognitive approach to GIS • GIS and DBMS • GISystems and GIServices • [L] Representation of spatial entity Lecture is organized into three parts • Spatial data model • Spatial data structure • Design consideration

3. DB implementation of geographic concepts is divided into three subfields in GIScience • Spatial data model/spatial query language • Spatial data type/operators • Spatial access method Q. What are they about? Q. Indicate a level of abstraction for each Q. What are different focuses on these?

4. Mathematics and GIS • It is said that database implementation of geographic concepts heavily borrows from mathematics. So which subfields of mathematics are influential in the followings? • Representation of geographic entities • e.g. Spatial primitives of vector data (point, line, polygon) • Representation of spatial relationships • e.g. Connectivity in road network

5. Database and GIS • Since most of GIS is implemented using RDB, let’s focus on RDB. • Examples: building Country database, how can you store geometric features of Country in table? Is multi-valued attribute allowed? Then break it down to 4NF. And then… What about query such as displaying boundary of France? What about query such as returning adjacent countries of France? • Now generalizing your point, go to the next slide

6. Inadequacy of RDB to GIS • What is the inadequacy of employing relational database model for representing geographic concepts? • What is the legacy of storing spatial information in relational database when creating GIS database? • How can we accommodate this problem? Think from different perspectives. If you are DB developer, If you are network administrator who makes decision on GIS/DBMS purchase, if you work in ISO committee, if you are a researcher...

7. Cognitive approach to GIS • Do you agree with authors’ view on cognitive approach? • Where do you think cognitive approach will be useful for making a better GIS (that’s the goal of GIScience)? Remember the current GIS is a just snapshot. You could be a person who can create a better GIS. • Query based on natural language (for visually impaired?) • Design of better user interface design • Development of spatial data model (beyond vector/raster?)

8. GIS and DB systems • Four different approaches to integrating GIS with DB systems • Extension of GIS with DB functionalities • e.g. Arc/Info, Arcview support ODBC • Coupling of a GIS with commercial DBMS • e.g. spatial data is stored in relation DB so that SQL can be called upon the spatial data • Extension of DBMS with spatial functionalities • e.g. Illustra’s DataBlades, Oracle’s Cartridges • Open toolbox approaches where GIS provides specialized services • e.g. you work in your computer where you can download data or processing tools from server

9. GISystems and GIServices • Would a gradual shift toward GIServices replace GISystems? • What would be the likely impact of GIServices? What about the impact on the integration of GIS with DB systems?

10. Representation of spatial entity

11. Data hierarchy • Data model: how the real-world is viewed • e.g. Relational DB, Object-Oriented DB Model • e.g. Object view, Field view • Data structure: how data is stored in the computer • e.g. Vector, Raster, TIN • Data (file) format: how data storage is specified in s/w-specific way, you need particular viewer to display the data • e.g. VPF, shapefile, MrSID Level of abstraction

12. Spatial data model:how spatial things are viewed • Object view • See that the world is composed of discrete entity • Interested in precise location of geographic features • Space is measured, attribute is controlled • Human beings prefer discrete perception of things rather than fuzzy perception • e.g. parcel, lake, city • Field view • See that the world is composed of continuous field • Interested in smooth variation in attribute over space • Space is controlled, attribute is measured • Measuring attribute for all locations are infeasible due to its continuity • e.g. elevation, temperature Q. So which view is implicitly supported by DB model such as OO or relational?

13. Spatial data structure:how spatial things are stored in the machine Way to represent spatial things • Vector: point, line, polygon, node, vertices • Raster: a set of grid cell Way to represent spatial relationships • TIN (Triangulated Irregular Network): a set of triangulated line between neighboring points • Matrix: stores attributes of relationships between spatial objects

14. Note on spatial data structure • Spatial data structure appears to correspond to spatial data model (i.e. nature of phenomenon: continuous/discrete) • However, data structure is largely determined by the manner of data input regardless of the nature of geographic phenomenon • Vectorization of paper map (i.e. digitizing) • Satellite image, Air photo • You should choose data structure suitable for the nature of phenomenon given users’ need

15. More on vector • Higher-level vector • Overlapping polygon (e.g. flycircle) • Multi-part polygon (e.g. Hawaiian island) • Hole in polygon (e.g. lake in island) • Spaghetti model vs. topological model • Cartographic purpose: spaghetti will do • What is topology? Why do we need topology? • e.g. Network model commonly uses link-node representation • Planar vs. non-planar • How to represent overpass in contrast to intersection?

16. How topology is stored Q. What kind of topology is measured? {containment, connectivity, contiguity}

17. How to build topology • Do some exercise in the lab • 1) Digitize features over backdrop (say airphoto) say at arcview • 2) Save the file and import this file into arc/info coverage (use SHAPEARC) • 3) Use CLEAN command in Arc/Info workstation • 4) Read attributes at feature attribute table, say AAT and PAT

18. Planar vs. non-planar

19. More on Raster • Same values are often repeated in the cell • Efficient storage method (or compression method) is needed as it uses space rather inefficiently • These are methods for storing • Run-length code: linear representation of cell values • Quadtree: hierarchical organization of raster structure (e.g. SPAN)

20. Run-length code, Quadtree Run-length code 3A1B2A2B2A2B1A3B Quadtree is also known for spatial access method

21. See how, run-length code can save space, and save time in searching the location of Well

22. Triangulated Irregular Network (TIN) • Point is not sampled in a regular manner • Sample points are connected by lines to form triangles • Compact representation of surface (compare this to point-based DEM)

23. Matrix • Used to represent attributes of spatial interaction (e.g. commuting flow, traffic flow, commodity flow, migration, and so on) • Conversion between matrix and relational table often necessary as matrix is not well supported in GIS software

24. Same phenomenon can be represented differently (data structure) Q. How topography is stored in GIS? • In Raster • In TIN • In Vector

25. Spatial file format • This is system-specific, so you need a specific software to view them • TIGER/Line, DXF, SHP, TIFF • SDTS (Spatial Data Transfer Standard) • MrSID • Need for metadata to exchange info • FGDC standard, XML • Need for interoperability • Converting file formats are tedious and sometime it loses information • OpenGIS

26. Special aspect of spatial data • Spatial data, unlike attribute data, are designed for display in addition to query • Therefore, some technical aspects of cartographic representation should be taken into account when you design database for GIS • e.g. some cartographic symbol can be exaggerated to enhance readability without regard to its positional accuracy

27. Which model to choose will be influenced by … • Users need/application • e.g. routing application requires link-node model, road maintenance application requires polygon representation of road as width of road can be important • Geographic scale • Line generalization (e.g. Douglas-Peuker algorithm) • Multiple representation • Annotation

28. Terms • Interoperability: the ability of two or more systems or components to exchange information and to use the information that has been exchanged • Open GIS Consortium: Non-profit organization that works for standardizing the implementation specification of geospatial information; designed to form the architecture for interoperability; promotes COM technology • Middleware: software that glues two separate applications; e.g. link a database system to application; balances traffic between the two

29. Terms • MBR (Minimum Bounding Rectangle): spatial access method uses filter-refine step. Where filter select candidates using minimum bounding rectangle. • R-trees: spatial access method that stores overlapping MBR into tree-structure • Quadtree: spatial access method that stores non-overlapping multi-scale grid into tree-structure, also known as the method for compressing raster data structure • Which version of SQL has the spatial extensions? 1) SQL1 2) SQL2 3) SQL3