1. Peter Bajcsy, Ph.D. Research Scientist Adjunct Assistant Professor, CS Department, UIUC Automated Learning Group National Center for Supercomputing Applications University of Illinois pbajcsy@ncsa.uiuc.edu April 2003 Geographical Information System (GIS) to Knowledge

2. alg | Automated Learning Group 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. alg | Automated Learning Group 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. alg | Automated Learning Group 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. alg | Automated Learning Group 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 7 th 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 7 th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2003), Orlando, Florida, July 27-30, 2003.

6. alg | Automated Learning Group Input Information Extraction and Representation

7. alg | Automated Learning Group Input Information Extraction and Representation

8. alg | Automated Learning Group Data Types and Representation: Examples Raster Information: GeoImage Object Boundary Information: Shape Object Tabular Information: Table Object Neighborhood Information: NBH Object

9. alg | Automated Learning Group 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. alg | Automated Learning Group 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. alg | Automated Learning Group 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. alg | Automated Learning Group Data Size Data size driven operations : Sub-setting Sub-sampling Cropping Zooming

13. alg | Automated Learning Group Formation of Vector Data Iso-contour extraction from historical maps Segmentation and clustering of raster data into homogeneous regions

14. alg | Automated Learning Group

15. Georeferencing Data Sets and Raster Information Extraction

16. alg | Automated Learning Group Georeferencing Data Sets and Raster Information Extraction

17. alg | Automated Learning Group

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

19. alg | Automated Learning Group Georeferencing Based on Coordinate Systems

20. alg | Automated Learning Group Raster Information Extraction: Categorical Variable Frequency of Occurrence

21. alg | Automated Learning Group Raster Information Extraction: Continuous Variable Sample MeanSkew Standard Deviation Kurtosis Elevation Statistics Per County

22. alg | Automated Learning Group Feature Driven Boundary Aggregation and Evaluation

23. alg | Automated Learning Group Feature Driven Boundary Aggregation and Evaluation

24. alg | Automated Learning Group

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. Boundaries Boundary Aggregations Geographical Display Tabular Display

26. alg | Automated Learning Group Spatially Constrained Boundary Aggregation Hierarchical segmentation and hierarchical clustering of oak hickory feature with the exit criterion of 18 numbers of county aggregations Boundaries Boundary Aggregations With Spatial ConstraintWithout Spatial Constraint

27. alg | Automated Learning Group Boundary Aggregation With Hierarchical Output Hierarchical segmentation of extracted forest statistics (oak hickory occurrence) with two output partitions. Boundaries Boundary Aggregations 43 aggregations21 aggregations

28. alg | Automated Learning Group 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. alg | Automated Learning Group 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#0Eval#1Eval#2Eval#3

30. alg | Automated Learning Group 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. alg | Automated Learning Group Documentation

32. alg | Automated Learning Group Summary Applications of GIS tools —Remote Sensing —Agriculture —Hydrology —Water Quality Survey —Atmospheric Science —Military —Socio-Economics Interested ? Useful ? Let us know.