Geographical Information System (GIS) to Knowledge

1. Geographical Information System (GIS) to Knowledge

2. Outline • Problem Statement • Top Level Overview • Input Information Extraction and Representation • Georeferencing and Raster Information Extraction • Feature Driven Boundary Aggregation and Evaluation • Error Evaluation of New Boundary Aggregations and Decision Making • Summary

3. Acknowledgement • Project Team Members: Peter Bajcsy, Peter Groves, Sunayana Saha, Tyler Alumbaugh • Support: Michael Welge, Loretta Auvil, Dora Cai, Tom Redman, David Clutter, Duane Searsmith, Lisa Gatzke, Andrew Shirk, Ruth Aydt, Greg Pape, David Tcheng, Chris Navaro, Marquita Miller.

4. Problem Statement • Problem Statement: search for the best partition of any geographical area that is • (a) based on raster or point information, • (b) formed by aggregations of known boundaries, • (c) constrained or unconstrained by spatial locations of know boundaries and • (d) minimizing an error metric. • Raster or Point Information: • Grid-based information, e.g., from satellite or air-borne sensors • Geographical point information, e.g., from GPS or address data base • Boundaries (Vector Data): • Man-made, e.g., Counties, US Census Bureau Territories • Defined by environmental characteristics, e.g., Eco-regions, Historical iso-contours • Spatial Constraints and Error Metric: • Defined by applications

5. Top Level Overview • References: • ALG Technical Reports: TR-20030226-1.doc, TR-20030211-1.doc, TR-20021011-1.doc • Conferences: Peter Bajcsy and Tyler Jeffrey Alumbaugh, “Georeferencing Maps With Contours,” Proceedings of the 7th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2003), Orlando, Florida, July 27-30, 2003. • Peter Bajcsy, “Automatic Extraction Of Isocontours From Historical Maps,” Proceedings of the 7th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2003), Orlando, Florida, July 27-30, 2003.

6. Input Information Extraction and Representation

7. Input Information Extraction and Representation

8. Data Types and Representation: Examples • Raster Information: GeoImage Object • Boundary Information: Shape Object • Tabular Information: Table Object • Neighborhood Information: NBH Object

9. Raster Data: File Formats • USGS Digital Elevation Data (DEM) Files • Header file with georeferencing information • Floating point values, 30 m spatial resolution, IL coverage, published in 2002 • TIFF Files • Georeferencing information from: • One or more standardized files are distributed along with TIFF image data as .tfw and/or .txt files. • The metadata is encoded in the image file using private TIFF tags. • An extension of the TIFF format called GeoTIFF is used. • Forest labels, 1km spatial resolution, • Forest Cover Types: 29 labels, USA coverage, published in 2000 • Forest Fragmentation Index Map of North America, 8 labels, USA coverage, published in 1993 • Land use labels, 1km spatial resolution, world wide coverage, published in 2001

10. Vector Data: File Formats • Computational Tradeoffs Between Vector Information Retrieval and Data Storage • US Census Bureau TIGER Files • Elaboration of the chain file structure (CFS) • Used record files 1, 2, I, S, P • Environmental Systems Research Institute (ESRI) Shapefiles • Location list data structure (LLS) • shp, shx, dbf files • TIGER to ESRI Shapefiles

11. Point Data: File Formats • FBI Crime Reports • United States Crimes Database, years 94-98, USA states, reports per county, published in 2001 • United States Crimes Database, years 98-00, IL state, reports per county, published in 2002 • Entries • Theme_Keyword: crime, arrests, murder, forcible rape, rape, robbery, aggravated assault, assault, burglary, larceny, motor vehicle theft, theft, arson • Challenges • Multiple Files • Varying notation • Association with geographical boundary information

12. Data Size Data size driven operations : • Sub-setting • Sub-sampling • Cropping • Zooming

13. Formation of Vector Data • Iso-contour extraction from historical maps • Segmentation and clustering of raster data into homogeneous regions

15. Georeferencing Data Sets and Raster Information Extraction

16. Georeferencing Data Sets and Raster Information Extraction

18. Georeferencing Based on Data Types • Raster and Raster • Vector and Vector • Raster and Vector

19. Georeferencing Based on Coordinate Systems

20. Raster Information Extraction: Categorical Variable Frequency of Occurrence

21. Raster Information Extraction: Continuous Variable Elevation Statistics Per County Standard Deviation Sample Mean Skew Kurtosis

22. Feature Driven Boundary Aggregation and Evaluation

23. Feature Driven Boundary Aggregation and Evaluation

25. Spatially Unconstrained Boundary Aggregation • Hierarchical clustering of crime data with the exit criterion being the number of clusters and the clustered feature being “auto theft in 2000” leads to six aggregations. Tabular Display Geographical Display Boundaries Boundary Aggregations

26. Spatially Constrained Boundary Aggregation • Hierarchical segmentation and hierarchical clustering of oak hickory feature with the exit criterion of 18 numbers of county aggregations With Spatial Constraint Without Spatial Constraint Boundaries Boundary Aggregations

27. Boundary Aggregation With Hierarchical Output • Hierarchical segmentation of extracted forest statistics (oak hickory occurrence) with two output partitions. 43 aggregations 21 aggregations Boundaries Boundary Aggregations

28. Error Evaluations of New Territorial Partitions • Error evaluation of partitions obtained by clustering and segmentation of mean elevation feature per Illinois county with Variance error metric

29. Geographical Error Evaluations and Decision Making • Geographical error evaluation of partitions obtained by clustering and segmentation of mean elevation feature per Illinois county with Variance error metric Partition Index Eval#0 Eval#1 Eval#2 Eval#3

30. Decision Making • Which global partition minimizes a chosen error metric? • Which partition minimizes a chosen error metric at a selected fundamental area definition? • What is the geographical error distribution given a territorial partition?

31. Documentation

32. Summary • Applications of GIS tools • Remote Sensing • Agriculture • Hydrology • Water Quality Survey • Atmospheric Science • Military • Socio-Economics • Interested ? Useful ? Let us know.