1. Spatial Database & Spatial Data Mining Shashi Shekhar Dept. of Computer Sc. and Eng. University of Minnesota shekhar@cs.umn.edu, www.cs.umn.edu/~shekhar www.spatial.cs.umn.edu

2. Spatial Data Location-based Services –E.g.: MapPoint, MapQuest, Yahoo/Google Maps, … Courtesy: Microsoft Live Search (http://maps.live.com)

3. Spatial Data In-car Navigation Device Emerson In-Car Navigation System (Courtesy: Amazon.com)

4. Book http://www.spatial.cs.umn.edu

5. Outline Spatial Databases –Conceptual Modeling Pictograms enhanced Entity Relationship Model –Logical Data Model Direction predicates and queries –Physical Data Model Query Processing – Shortest Paths, Evacuation Routes, –Correlated time-series Storage – Connectivity Clustered Access Method Spatial Data Mining –Location Prediction – fast algorithms –Co-location patterns – definition, algorithms –Spatial outliers – algorithms –Hot-spots – new work on “mean streets”

6. Geo-Spatial Databases: Management and Mining Nest locationsDistance to open water Vegetation durability Water depth 1. Recent book from our group!3. Shortest Path Queries4. Storing roadmaps in disk blocks 2. Parallelize Range Queries 6. Spatial outlier detect bad sensor (#9) on Highway I-35 5. Location prediction to characterize nesting grounds.

7. Spatial Data Mining (SDM) The process of discovering –interesting, useful, non-trivial patterns patterns: non-specialist exception to patterns: specialist –from large spatial datasets Spatial pattern families –Spatial outlier, discontinuities –Location prediction models –Spatial clusters –Co-location patterns –…

8. Spatial Data Mining - Example Nest locations Distance to open water Vegetation durabilityWater depth

9. Spatial Autocorrelation (SA) First Law of Geography –“All things are related, but nearby things are more related than distant things. [Tobler, 1970]” Spatial autocorrelation –Nearby things are more similar than distant things –Traditional i.i.d. assumption is not valid –Measures: K-function, Moran’s I, Variogram, … Pixel property with independent identical distribution Vegetation Durability with SA

10. Implication of Auto-correlation Computational Challenge: Computing determinant of a very large matrix in the Maximum Likelihood Function:

11. Outline Spatial Databases –Conceptual Modeling Pictograms enhanced Entity Relationship Model –Logical Data Model Direction predicates and queries –Physical Data Model Query Processing – Shortest Paths, Evacuation Routes, –Correlated time-series Storage – Connectivity Clustered Access Method Spatial Data Mining –Location Prediction – fast algorithms –Co-location patterns – definition, algorithms –Spatial outliers – algorithms –Hot-spots – new work on “mean streets”

12. Spatio-temporal Query Processing Teleconnection –Find (land location, ocean location) pairs with correlated climate changes Ex. El Nino affects climate at many land locations Global Influence of El Nino during the Northern Hemisphere Winter (D: Dry, W: Warm, R: Rainfall) Average Monthly Temperature (Courtsey: NASA, Prof. V. Kumar)

13. Auto-correlation saves computation cost Challenge –high dimensional (e.g., 600) feature space –67k land locations and 100k ocean locations (degree by degree grid) –50-year monthly data Computational Efficiency –Spatial autocorrelation Reduce Computational Complexity –Spatial indexing to organize locations Top-down tree traversal is a strong filter Spatial join query: filter-and-refine –save 40% to 98% computational cost at θ = 0.3 to 0.9

14. Evacuation Route Planning - Motivation No coordination among local plans means Traffic congestions on all highways e.g. 60 mile congestion in Texas (2005) Great confusions and chaos "We packed up Morgan City residents to evacuate in the a.m. on the day that Andrew hit coastal Louisiana, but in early afternoon the majority came back home. The traffic was so bad that they couldn't get through Lafayette." Mayor Tim Mott, Morgan City, Louisiana ( http://i49south.com/hurricane.htm ) Florida, Lousiana (Andrew, 1992) ( www.washingtonpost.com) ( National Weather Services) ( FEMA.gov) I-45 out of Houston Houston (Rita, 2005)

15. A Real Scenario Nuclear Power Plants in Minnesota Twin Cities

16. Monticello Emergency Planning Zone Monticello EPZ Subarea Population 2 4,675 5N 3,994 5E 9,645 5S 6,749 5W 2,236 10N 391 10E 1,785 10SE 1,390 10S 4,616 10SW 3,408 10W 2,354 10NW 707 Total41,950 Estimate EPZ evacuation time: Summer/Winter (good weather): 3 hours, 30 minutes Winter (adverse weather): 5 hours, 40 minutes Emergency Planning Zone (EPZ) is a 10-mile radius around the plant divided into sub areas. Data source: Minnesota DPS & DHS Web site: http://www.dps.state.mn.us http://www.dhs.state.mn.us

17. A Real World Testcase Source cities Destination Monticello Power Plant Routes used only by old plan Routes used only by result plan of capacity constrained routing Routes used by both plans Congestion is likely in old plan near evacuation destination due to capacity constraints. Our plan has richer routes near destination to reduce congestion and total evacuation time. Twin Cities Experiment Result Total evacuation time: - Existing Plan: 268 min. - New Plan: 162 min.

18. Outline Spatial Databases –Conceptual Modeling Pictograms enhanced Entity Relationship Model –Logical Data Model Direction predicates and queries –Physical Data Model Query Processing – Shortest Paths, Evacuation Routes, –Correlated time-series Storage – Connectivity Clustered Access Method Spatial Data Mining –Location Prediction – fast algorithms –Co-location patterns – definition, algorithms –Spatial outliers – algorithms –Hot-spots – new work on “mean streets”

19. Resource Description Framework (RDF) Physical model  Representation  Directed Acyclic Graph, TAGs  Storage method  Connectivity-Clustered Access Method (CCAM)  Frequent Operations  Breadth First Search  Path Computation

20. Semantics in Databases Ontology - Shared Conceptualization of knowledge in a specific domain. Resource Description Framework (RDF) - Representation of resource information in World Wide Web. Patterns

21. Ontology based Semantic Computing  Example Query SELECT * FROM travelmode WHERE ONT_RELATED (transport, ‘IS_A’, ‘ Road’, ‘Transport_Ontology’, 123) = 1; Result: All walk and drive modes. … Drive Walk Transport Road Commuter Rail Bus  Applications Homeland Security, Life Sciences, Web Services

22. Resource Description Framework (RDF) Multimodal Transportation System Commonwealth Ave. and Subway (Green Line), Boston [source: http://maps.google.com/] Subway Stations Road Intersections Transition Edge N1 N2N3N4N5 R1R2 R3 Graph Representation (between BU Central and Blandford St)

23. Resource Description Framework (RDF) : Street : TrafficLight : RailRoute : bus :busTerminals : busStops crosscuts used_by parallel has Start/end halts Light Rail System : Rail_line : Streets start/end has serves crosscuts parallel : Terminals used_by Road System : TrafficLight : Stations : Trains Transit Edges(*) Multimodal Transportation System : Streets SELECT S.street, S.busStop, R.Stations, R.RailRoute,R.Terminal FROM TABLE(SDO_RDF_MATCH( ‘(?x : halts ?b) SDO_RDF_Models(‘rail_line R’,’street S’)), ‘(?rr :serves ? z), WHERE S.b hasTransitTo R.z and S.Street = ‘Commonwealth’ ‘(?rr :start/end ?tr), Find all routes from the Commonwealth Avenue to the Logan Airport using bus and subway systems. *Note: A subset of possible transition edges is shown. and R.terminal = ‘Logan airport’;

24. Geo-Spatial Databases: Management and Mining Nest locationsDistance to open water Vegetation durability Water depth 1. Recent book from our group!3. Shortest Path Queries4. Storing roadmaps in disk blocks 2. Parallelize Range Queries 6. Spatial outlier detect bad sensor (#9) on Highway I-35 5. Location prediction to characterize nesting grounds.