4. 4 Smarter Planet SIG SPATIAL Spatial Computing: Recent Trends

5. Group Members Faculty Professor Shashi Shekhar Current Ph.D. Studnets Pradeep Mohan Mike Evans Dev Oliver Xun Zhou Abdussalam Bannur KwangSoo Yang Viswanath Gunturi Zhe Jiang Jeff Wolff Changqing Zhou Others/Visitors Lydia Manikonda Ivan Brugere Group Members Faculty Professor Shashi Shekhar Current Ph.D. Studnets Pradeep Mohan Mike Evans Dev Oliver Xun Zhou Abdussalam Bannur KwangSoo Yang Viswanath Gunturi Zhe Jiang Jeff Wolff Changqing Zhou Others/Visitors Lydia Manikonda Ivan Brugere

6. Ongoing Projects Overview Applications Transportation, virtual environments, Earth science, epidemiology and cartography. Spatial Data Mining Flow anomalies Teleconnection Cascade pattern discovery K-Main-Route (KMR) summarization Pattern of life Abrupt change detection Spatial Database Eco-Routing Evacuation planning Ongoing Projects Overview Applications Transportation, virtual environments, Earth science, epidemiology and cartography. Spatial Data Mining Flow anomalies Teleconnection Cascade pattern discovery K-Main-Route (KMR) summarization Pattern of life Abrupt change detection Spatial Database Eco-Routing Evacuation planning

7. Courses Topics Application Domains Conceptual Data Models Logical Data Models Physical Data Models Spatial Networks Spatial Data Mining Others Course Website http://www.spatial.cs.umn.edu/Courses/Fall11/8715 Topics Data Model Representation & access Architecture Others Courses Topics Application Domains Conceptual Data Models Logical Data Models Physical Data Models Spatial Networks Spatial Data Mining Others Course Website http://www.spatial.cs.umn.edu/Courses/Fall11/8715 Topics Data Model Representation & access Architecture Others CSCI 8715 – Spatial Databases and Applications CSCI 5980 – GIS: a computational perspective National Research Council

8. Flow Anomalies Problem Problem –Discover dominant time periods that exhibit anomalous behavior Why is it hard? Why is it hard? –A single dominant time period may have subsets that are not anomalous  No Dynamic Programming Contributions Contributions –A SWEET (Smart Window Enumeration and Evaluation of persistent- Thresholds) Approach 8 http://www.esri.com/news/arcuser/0405/ss_crimestats2of2.html Sensor 5 Sensor 1 Sensor 2 Sensor 4 Sensor 3 Ex. An Oil Spill (Source: http://www.sfgate.com/cgi-bin/news/oilspill/busan) (Source: Shingle Creek, MN Study Site) J. M. Kang, S. Shekhar, C. Wennen, P. Novak, Discovering Flow Anomalies: A SWEET Approach, In the Eighth IEEE International Conference on Data Mining (ICDM '08), pp. 851- 856, Pisa, Italy, December 15-19, 2008.

9. Problem Problem –Find remote connections Example Example –El Niño in Pacific Why is it hard? Why is it hard? –Large spatial dataset –Long time series 9 Dead Zone, Gulf of Mexico Global Influence of El Nino during the Northern Hemisphere Winter (D: Dry, W: Warm, R: Rainfall) Teleconnections

10. Cascading spatio-temporal patterns (CSTPs) Aggregate(T1, T2,T3) Time T1 Assault(A ) Drunk Driving (C)Bar Closing(B) Time T3>T2Time T2 > T1 a  Input: Crime reports with location and time.  Output: Cascading spatio-temporal patterns Courtsey: www.startribune.comwww.startribune.com Bar closing a generator for crime related CSTP! Bar locations in Lincoln, NE Why are CSTPs important ? Why is discovering CSTPs hard ?  Trade off between computational efficiency and statistical interpretation.  Pattern space exponential in number of event types. Why are CSTPs Novel/better ?  Current STDM literature ignores spatio-temporal semantics(e.g. partial order) BA C CSTP: P1 Contributions  Interest measure: Cascade participation index lower bound on conditional probability.  Computational Structure  Compute measure efficiently  Avoid unnecessary measure computations Results: {Bar Closing} {Vandalism}{Assault} CPI = 0.022; CPI-Downtown = 0.11 Pradeep Mohan, Shashi Shekhar, James A. Shine, James P. Rogers. Cascading spatio-temporal pattern discovery: A summary of results. In Proc. of 10th SIAM International Data Mining (SDM) 2010, Columbus, OH, USA Pradeep Mohan, Shashi Shekhar, James A. Shine, James P. Rogers. Cascading spatio-temporal pattern discovery. IEEE Transactions on Knowledge and data engineering(Accepted, In Press).

11. Problem Statement: The spatial network activity summarization (SNAS) problem: Given a spatial network and a collection of activities (e.g., crime reports, emergency requests), find a set of k paths to summarize the activities. A K-Main Routes Approach to Spatial Network Activity Summarization Importance: SNAS is important for crime analysis and disaster response. Challenge: Computational Complexity Choose(N,2) paths, given N nodes Exponential number of k subsets of paths Contribution The K-Main Routes (KMR) algorithm Discovers k paths to summarize activities. Generalizes K-means for network space but uses paths instead of ellipses to summarize activities. Improves performance by using a network voronoi technique to assign activities to summary paths and a divide and conquer method to recompute summary paths. Dev Oliver, Shashi Shekhar, James M. Kang, Renee Bousselaire, Abdussalam Bannur Related Work: Input K-Means OutputKMR Output KMR uses paths instead of ellipses in summarizing activities Results Proposed two new algorithms for improving the performance of KMR: Network Voronoi activity Assignment (NOVA) and Divide and conquer Summary PAth REcomputation (D-SPARE). Validation via case studies, experiments and analytical evaluation to verify correctness in context of real workloads. Successfully transferred software for direct evaluation by the National Geospatial-Intelligence Agency. Input K-Means OutputKMR Output

12. Abrupt Change Interval Detection Publication: Xun Zhou, Shashi Shekahr, Pradeep Mohan, Stefan Liess, Peter K. Snyder, Discovering Interesting Sub-paths in Spatiotemporal Datasets: A Summary of Results. In Proc. 19 th Intl’ Conf. Advances on Geographical Information Systems (ACM GIS 2011), Nov 2011, Chicago, IL, USA. Given: A path S in a Spatiotemporal Dataset A unit-interval change abruptness threshold  A unit-interval change abruptness threshold  a A sameness degree threshold  A sameness degree threshold  sd Find: Dominant ST sub-intervals of S with persistently abrupt change Objective: Reduce Computational CostConstraints: Correctness & Completeness Vegetation cover in Africa, August 1-15, 1981. Abrupt vegetation cover change in Africa, August 1-15, 1981. Results:  Temporal intervals of abrupt rainfall change in Sahel, Africa.  Longitudinal spatial abrupt change of vegetation cover in Africa.

13. Fuel Efficient Routing Venkata M. V. Gunturi, Ernesto Nunes, KwangSoo Yang, and Shashi Shekhar. 2011. A critical-time-point approach to all-start-time lagrangian shortest paths: a summary of results. In SSTD'11, pp 74--91 INPUT : Road network; a source and destination; a time interval OUTPUT : A path between source and destination for each start time OBJECTIVE : The path should be fuel efficient.

14. Evacuation Planning University of Minnesota 2006 Annual Report (http://www.research.umn.edu/communications/publications/documents/OVPRAnnualRpt06.pdf)

15. Evacuation Planning System in Cloud Environment Given Transportation network with capacity constraints Initial number of people to be evacuated and their initial locations Evacuation destinations Output Routes to be taken and scheduling of people on each route Objective Minimize total time needed for evacuation Minimize computational overhead Constraints Capacity constraints: evacuation plan meets capacity of the network Network data size is too large. (Data are stored into secondary storage) Utilize cloud environment for scalability Problem Statement Why Evacuation Planning? Hurricane Andrew Florida and Louisiana, 1992 ( National Weather Services) Hurricane Rita Gulf Coast, 2005 ( www.washingtonpost.com ) ( National Weather Services) ( FEMA.gov) System Architecture for Cloud Environment Lack of effective evacuation plans Traffic congestions on all highways 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 ) Hurricane Rita evacuees from Houston clog I-45. A Real Scenario (Monticello): Result Routes

16. Spatial Computing in Government

17. Economy & Spatial Computing

18. Group Alumni Academia: Mete Celik (Erciyes Univ.) Jin Soung Yoo (IU-Purdue Univ. Indy) Hui Xiong (Rutgers Univ.) Yan Huang (Univ. of North Texas) Wei Li Wu (U. of Texas, Dallas) Chang-Tien Lu (Virginia Polytechnic Univ) Sanjay Chawla (Univ. of Sydney) Du-Ren Liu (National Chiao Tung Univ.) Andrew Yang (Univ. of Houston). Government Agency: James Kang (USDOD) Ranga Raju Vatsavai (USDOE-ORNL) Industry: Betsy George (Oracle Spatial) Qingsong Lu (Microsoft Research) Sangho Kim (ESRI) Baris Kazar (Oracle Spatial) Pusheng Zhang (Microsoft Virtual Earth) Xuan Liu (IBM TJ Watson) Siva Ravada (Oracle) Mark Coyle (Appirio) Babak Hamidzadeh (Boeing Research) Group Alumni Academia: Mete Celik (Erciyes Univ.) Jin Soung Yoo (IU-Purdue Univ. Indy) Hui Xiong (Rutgers Univ.) Yan Huang (Univ. of North Texas) Wei Li Wu (U. of Texas, Dallas) Chang-Tien Lu (Virginia Polytechnic Univ) Sanjay Chawla (Univ. of Sydney) Du-Ren Liu (National Chiao Tung Univ.) Andrew Yang (Univ. of Houston). Government Agency: James Kang (USDOD) Ranga Raju Vatsavai (USDOE-ORNL) Industry: Betsy George (Oracle Spatial) Qingsong Lu (Microsoft Research) Sangho Kim (ESRI) Baris Kazar (Oracle Spatial) Pusheng Zhang (Microsoft Virtual Earth) Xuan Liu (IBM TJ Watson) Siva Ravada (Oracle) Mark Coyle (Appirio) Babak Hamidzadeh (Boeing Research)

19. 19 Spatial/Spatio-temporal Data Mining: Representative Project Nest locationsDistance to open water Vegetation durability Water depth Location prediction: nesting sitesSpatial outliers: sensor (#9) on I-35Co-location PatternsTele connections (Ack: In collaboration w/V. Kumar, M. Steinbach, P. Zhang)

20. 20 Spatial Databases: Representative Projects only in old plan Only in new plan In both plans Evacutation Route Planning Parallelize Range Queries Storing graphs in disk blocksShortest Paths

21. Co-location Patterns Yan Huang, Shashi Shekhar, and Hui Xiong, Discovering Co-location Patterns from Spatial Datasets: A General Approach, IEEE Transactions on Knowledge and Data Engineering (TKDE), 16(12), pp. 1472-1485, December 2004. (Earlier version appeared in SSTD ’01) Given: A collection of different types of spatial event Find: Co-located subsets of event types Objective: Minimize computation time

22. Spatial Outlier Detection S. Shekhar, C.T. Lu, and P. Zhang. A unified approach to detecting spatial outliers. GeoInformatica, 7(2), 2003 (Earlier version appeared in SIGKDD ’01). Given: A spatial graph G={V,E} A neighbor relationship (K neighbors) An attribute function f : V -> R An aggregation function : faggr :R k - > R Confidence level threshold  Find: O = {vi | vi  V, vi is a spatial outlier} Objective: Correctness: The attribute values of v i is extreme, compared with its neighbors Computational efficiency Computational efficiency Constraints: Constraints: Attribute value is normally distributed Computation cost dominated by I/O op.

23. Nest locations Distance to open water Vegetation durability Water depth Location Prediction: Spatial Auto-regression S. Shekhar, P. Schrater, R. Vatsavai, W. Wu, and S. Chawla, Spatial Contextual Classification and Prediction Models for Mining Geospatial Data, In IEEE Transactions on Multimedia (special issue on Multimedia Dataabses) p174-188, 2002. Given : Spatial Framework S={s1,…,sn} Explanatory functions: fxi : S->R A dependent class: fy : S->[0,1] A family ζ of function mappings: R x…x R -> [0,1] Find : Classification model: f ^ y Є ζ Objective : Maximize classification accuracy Constraints : Constraints : Spatial Autocorrelation exists

24. Eco-Routing U.P.S. Embraces High-Tech Delivery Methods (July 12, 2007) By “The research at U.P.S. is paying off. ……..— saving roughly three million gallons of fuel in good part by mapping routes that minimize left turns.” Minimize fuel consumption and GPG emission –rather than proxies, e.g. distance, travel-time –avoid congestion, idling at red-lights, turns and elevation changes, etc. Do you idle at green light during traffic congestion?

25. 25 Evacuation Planning: A Real Scenario, New Plan Routes 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.