1. Sustainability: Spatial Computing Challenges Shashi Shekhar McKnight Distinguished University Professor University of Minnesota www.cs.umn.edu/~shekhar NSF Workshop on Information and Communication Technologies for Sustainability (WICS) (http://www.cs.ucdavis.edu/~liu/WICS/WICS.htm) June 27 rd, 2011.

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

3. Spatial Data Mining : Representative Projects Nest locationsDistance to open water Vegetation durability Water depth Location prediction: nesting sitesSpatial outliers: sensor (#9) on I-35Co-location PatternsTele connections

4. Why Sustainability? Next Decade Global Challenges [World Fed. Of United Nations Asso.]

5. What is Sustainable Development? Capacity to endure Long-term well-being Meet present needs without compromising ability of future generations to meet their needs –Environmental –Economic –Social Scale –Planet-scale –Economic sectors: Food Energy –Country, Municipality –Neighborhood, Individuals

6. What is Spatial Computing? 6 Smarter Planet SIG SPATIAL

7. Intersecting Spatial-Computing & Sustainability Spatial computing for sustainability –Spatial location bring rich context using other GIS layers –Sustainability-Sciences –Sustainable Development Economy Society Environment Sustainability of Spatial Computing –Geo-Data Collection, geo-registration, digitization is expensive & labor-intensive! –Challenge: Persistent Surveillance –Trends: Volunteered Geographic Information, e.g. OpenStreetMap Sustainability of Spatial Constructs –Urban plans, Regional economies, transportation planning, … –Are urban plans for US cities sustainable? –Is regional economy of rural areas sustainable with increased urbanization?

8. Spatial Computing for Sustainability Sciences Fourth Paradigm: Data-Intensive Sustainability Science Sustainability data is spatial –Land, Atmosphere, Ocean GIS gives Measurement framework –Shape of Earth: flat?, sphere, ellipsoid, … –Localization: GPS, surveying, … Spatial Database Management Systems –Data Types: Raster, Vector, Network –Operations: Topological, Metric, Euclidean Spatial Statistics provides richer models –Point-process, Spatial Auto-correlation, … –Heterogeneity, Krigging, … Cartography, Geo-visual Analytics –Symbols, map-generalization, … Key Challenge: –How might we better observe, analyze, and visualize a changing world?

9. Improving Climate Science via Spatial Computing Unknown unknowns Where are large d ifference (sensor data, Global Climate Models (GCMs)) ? –Southwest coast of Australia, Africa, Latin America –Northern North America, Andes, … Can GCMs be improved using Physics of local phenomena ? –Ex. Ocean upwelling 9 Upwelling areas map

10. Spatial Questions in Sustainability Sciences Environment –How are we changing the physical environment of Earth’s surface? –How can we best preserve biological diversity and protect endangered ecosystems? –How are climate and other environmental changes affecting the vulnerabilities of coupled human–environment systems? Economic –How and where will 10 billion people live? –How will we sustainably feed everyone in the coming decade and beyond? –How does where we live affect our health? Social –How is the movement of people, goods, and ideas changing the world? –How is economic globalization affecting inequality? –How are geopolitical shifts influencing peace and stability? Methods –How might we better observe, analyze, and visualize a changing world? –What are the societal implications of citizen mapping and mapping citizens?

11. Intersecting Spatial-Computing & Sustainability Spatial location bring rich context using other GIS layers Sustainability-Sciences Sustainable Development –Economy –Society –Environment

12. Economy & Spatial Computing

13. Real-time and Historic Travel-time Datasets 13

14. 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.

15. Eco-Routing: Spatial Computing Questions What are expected fuel saving from use of GPS devices with static roadmaps? What is the value-added by historical traffic and congestion information? How much additional value is added by real-time traffic information? What are the impacts of following on fuel savings and green house emissions? –traffic management systems (e.g. traffic light timing policies), –vehicles (e.g. weight, engine size, energy-source), –driver behavior (e.g. gentle acceleration/braking) –environment (e.g. weather) What is computational structure of the Eco-Routing problem? Does this problem satisfy the assumptions (e.g. stationary ranking of alternative routes) behind common shortest-path computation algorithms?

16. Intersecting Spatial-Computing & Sustainability Spatial location bring rich context using other GIS layers Sustainability-Sciences Sustainable Development –Economy –Society –Environment

17. Social Equity & Spatial Computing

18. Public Health Questions Sample Local Questions from Epidemiology [TerraSeer] –What’s overall pattern of colorectal cancer? –Is there clustering of high colorectal cancer incidence anywhere in the study area? –Where is colorectal cancer risk significantly elevated? –Where are zones of rapid change in colorectal cancer incidence? Geographic distribution of male colorectal cancer in Long Island, New York (Courtesy: TerraSeer)

19. Spatial Hotspot Detection in Public Health 1854 Cholera in London –Before germ theory –John Snow mapped disease –Hotspot near Broad St. water pump –except a brewery Sustainable City –1854: London was first large city –Without city-wide sanitation –2011: car-based suburbs in US –Obesity epidemic –Urban planning

20. Hotspots vs. Traditional Clustering Traditional Clustering: Find groups of tuples These may not have Spatial Statistical Significance –Complete spatial randomness, cluster, and decluster Inputs: Complete Spatial Random (CSR), Cluster, Decluster Traditional Clustering Spatial Statistical View

21. HotSpots What is it? Unusally high spatial concentration of a phenomena Accident hotspots Used in epidemiology, crime analysis Solved Spatial statistics based ellipsoids Almost solved Transportation network based hotspots Failed Classical clustering methods, e.g. K-means Missing Spatio-temporal Next Emerging hot-spots

22. Intersecting Spatial-Computing & Sustainability Spatial location bring rich context using other GIS layers Sustainability-Sciences Sustainable Development –Economy –Society –Environment

23. Environmental Sustainability Source: Planetary Boundaries: Exploring the Safe Operating Space for Humanity, (Rockström, et al), Ecology and Society, 14(2), 2009.

24. Bio-Conservation: Nest Location Prediction Nest Locations Vegetation Water DepthDistance to Open Water

25. 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

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

27. Location Prediction What is it? Models to predict location, time, path, … Nest sites, minerals, earthquakes, tornadoes, … Solved Interpolation, e.g. Krigging Heterogeneity, e.g. geo. weighted regression Almost solved Auto-correlation, e.g. spatial auto-regression Failed: Independence assumption Models, e.g. Decision trees, linear regression, … Measures, e.g. total square error, precision, recall Missing Spatio-temporal vector fields (e.g. flows, motion), physics Next Scalable algorithms for parameter estimation Distance based errors

28. Summary Spatial Computing is critical for sustainability –Sustainability-Sciences: Fourth Paradigm –Sustainable Development Economy, e.g. eco-routing Society, e.g. public health Environment, e.g. conservation New spatial computing challenges –Eco-routing –Emerging hotspot –Auto-correlation –Non-stationarity –…–…

29. IGERT: Non-equilibrium dynamics across space and time: a common approach for engineers, earth scientists, and ecologists PI: S. Shekhar University of Minnesota Fall 2005 – Summer 2012. Sponsor: NSF

30. Faculty and Students 28 Faculty Members –Civil Eng. (9), CS (2), Electrical Eng. (1), Ecology (8), Geology (2), Applied Economics (1), Forest Resources (1), Soil-Water-Climate (3), Bio-based Products (1) 05-Cohort: 6 students (3-Ecology, 2-CivE, 1-CS) –4 completion, 1 placed at USDOD-NGA 06-Cohort: 4 students (1-Ecology, 2-CivE, 1-Geology) –1 completion. 07-Cohort: 4 students (3-Ecology, 1-CivE) 08-Cohort: 5 students (1 Ecology, 2-CivE, 1-CS, 1-Geo) –1 summer 2011 trainee at NGA

31. How a collaboration started? Water quality monitoring Hydrolab (1,2,3,5) –Battery Voltage –Temperature –pH –Specific Conductance –Water Depth –Dissolved Oxygen Rain Gage (4) –Precipitation Sensor 5 Sensor 1 Sensor 2 Sensor 4 Sensor 3 31 Shingle Creek Study Site

32. What is Interdisciplinary Research? Is it multiple Disciplines working on a single project? Is it one discipline helping another? My Thoughts: –Ideally: Perform research that enhances all disciplines involved. Not just a subset! –Very Hard To Do!!! –A lot of asking questions back and forth 32

33. Communication Barriers Language & terminology differences Goal differences Mis-understanding of what each discipline really is –e.g., “I thought Civil Eng. was all about building bridges!” –e.g., “I thought Computer Sc. was all about programming!” Break down barriers –Keep talking to each other and have an open mind when discussing each others interest 33

34. Brainstorming: In the Beginning… Civil Engineering: How is Computer Science involved in this work? CS : –I don’t know! –Need to understand the domain questions and the dataset first 34 Computer Science: Do you plan on having more than 5 sensors? Like 1000 or 10,000 or more? CE : No Way! The cost of each sensor ranges from 10 to 100k

35. Brainstorming: A litte later… Civil Engineer: Can you remove errors from the dataset? Computer Science: –Yes, –But, not really CS research –Existing techniques already exist e.g., Triggers 35 Computer Science: Do you want to know how fast the river is flowing? Civil Eng.: Not really, We can already determine that by the discharge, water depth, and physical characteristics of the river

36. Brainstorming: Light at the end of the tunnel Civil Engineering Can you find when and where interesting contaminants may enter the river? CS Ans: –Yes! –Flow Anomaly –Violates Dynamic Programming Principle! 36 Computer Science: Are you interested in finding point sources in both space and time? CE Ans: Yes! Its too hard to find this manually e.g., hours to sift through the data 50k data points per measured variable

37. Brainstorming 37 Time NitrateTime Nitrate Time Nitrate Two Use Cases: At the water treatment plant, when should it turn off the water supply from the river? Where is the source of the contaminant? Apply Threshold S1S1 S2S2 S3S3 Direction of Water Flow Flow Anomaly between these two sensors. Water Treatment Plant

38. 38 Detailed Example events between sensors? Sensor 5 Sensor 1 Sensor 2 Sensor 4 (rain gage) Sensor 3 Other Applications: Atmospheric Monitoring (e.g. Plumes), Pipe Systems Flow Networks: Transportation Networks, Intrusion Detection Networks Ex. An Oil Spill (Source: http://www.sfgate.com/cgi- bin/news/oilspill/busan) (Source: Shingle Creek, MN Study Site)

39. Dissolved Oxygen Flow Anomaly Top Flow Anomaly Result (Error: +/- 5, Accuracy: 80%) Start: 6/4/2008 13:06 End: 6/5/2008 19:34 39 Flow Anomaly (Error: +/- 5, Accuracy: 80%), 6/4/2008 13:06 to 6/5/2008 19:34

40. Lessons Learned Interdisciplinary Research is HARD Hardest part is trying to understand the other domain Crucial that both sides understand each other before research can begin A lot of trial and error between both sides Once an “Ah-ha” moment occurs –The number of opportunities can be unlimited! 40