Spatial Database & Spatial Data Mining

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. Nest locations Distance to open water Vegetation durability Water depth Geo-Spatial Databases: Management and Mining 1. Recent book from our group! 3. Shortest Path Queries 4. Storing roadmaps in disk blocks 2. Parallelize Range Queries 5. Location prediction to characterize nesting grounds. 6. Spatial outlier detect bad sensor (#9) on Highway I-35

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 durability Water 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. Houston (Rita, 2005) Florida, Lousiana (Andrew, 1992) ( National Weather Services) ( National Weather Services) ( www.washingtonpost.com) I-45 out of Houston ( FEMA.gov) 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 )

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

16. Monticello Emergency Planning Zone Emergency Planning Zone (EPZ) is a 10-mile radius around the plant divided into sub areas. 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 Total 41,950 Estimate EPZ evacuation time: Summer/Winter (good weather):  3 hours, 30 minutesWinter (adverse weather): 5 hours, 40 minutes Data source: Minnesota DPS & DHS Web site: http://www.dps.state.mn.us http://www.dhs.state.mn.us

17. A Real World Testcase Experiment Result Total evacuation time: - Existing Plan: 268 min. - New Plan: 162 min. Monticello Power Plant Source cities Destination 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

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

22. Resource Description Framework (RDF) Multimodal Transportation System (between BU Central and Blandford St) Commonwealth Ave. and Subway (Green Line), Boston [source: http://maps.google.com/] N2 N3 N4 N5 N1 Road Intersections Subway Stations R3 R1 R2 Transition Edge Graph Representation

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

24. Nest locations Distance to open water Vegetation durability Water depth Geo-Spatial Databases: Management and Mining 1. Recent book from our group! 3. Shortest Path Queries 4. Storing roadmaps in disk blocks 2. Parallelize Range Queries 5. Location prediction to characterize nesting grounds. 6. Spatial outlier detect bad sensor (#9) on Highway I-35