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Spatial Analysis cont. Optimization Network Analysis, Routing . Optimization. Spatial analysis can be used to solve many problems of design A spatial decision support system (SDSS) is an adaptation of GIS aimed at solving a particular design problem . Lab 5. Edges, junctions, and weights.
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Optimization • Spatial analysis can be used to solve many problems of design • A spatial decision support system (SDSS) is an adaptation of GIS aimed at solving a particular design problem
Lab 5 • Edges, junctions, and weights
Location-allocation Problems • Design locations for services, and allocate demand to them, to achieve specified goals • Goals might include: • minimizing total distance traveled • minimizing the largest distance traveled by any customer • maximizing profit • minimizing a combination of travel distance and facility operating cost
Optimizing Point Locations • One service location and the goal of minimizing total distance traveled • The operator of a chain of convenience stores or fire stations might want to solve for many locations at once • where are the best locations to add new services? • which existing services should be dropped?
Routing Problems • Search for optimum routes among several destinations • Draws on location-allocation • The traveling salesman problem • find the shortest (cheapest) tour from an origin, through a set of destinations that visits each destination only once
Traveling Salesman – Georgia Tech http://www.tsp.gatech.edu/maps/
Routing service technicians for Schindler Elevator. Every day this company’s service crews must visit a different set of locations in Los Angeles. GIS is used to partition the day’s workload among the crews and trucks (color coding) and to optimize the route to minimize time and cost.
Optimum Paths • Find the best path across a continuous surface • between defined origin and destination • to minimize total cost • cost may combine construction, environmental impact, land acquisition, and operating cost • used to locate highways, power lines, pipelines • requires a raster representation
Example: Santa Ynez Mtns., CA More details at http://www.ncgia.ucsb.edu/~ashton/demos/chuck95/stochastic.html Chuck Ehlschlaeger, Ashton Shortridge
Least-cost path problem. Range of solutions across a friction surface represented as a raster. The area is dominated by a mountain range, and cost is determined by elevation and slope.
Solution of the least-cost path problem. The white line represents the optimum solution, or path of least total cost. The best route uses a narrow pass through the range. The blue line results from solving the same problem using a 90-m DEM.
Optimization & Routing for Emergency/Disaster ResponseSanta Barbara, Utah, San Diego
Optimization & Routing for Emergency/Disaster Response • Kim et al. 2006 – PARs, Protective Action Recs d= interpolated, shortest-distance of wildfire to community d1 = shortest distance before PAR d2 = shortest distance after PAR t = time PAR was issued t1 = time last known fire perimeter at d1 t2 = time last known fire perimeter at d2
Fire Origin to Communities:Estimate Avg. Speed of Fire Between Known Perimeters Kim et al. 2006
Gateway to the Literature • Cova, T. and Johnson, J.P., 2002. Microsimulation of neighborhood evacuations in the urban-wildland interface. Environment and Planning A, 34: 2211-2229. • Cova, T. J., P. E. Dennison, et al. 2005. Setting wildfire evacuation trigger points using fire spread modeling and GIS. Transactions GIS, 9(4): 603-617. • Kim, T.H., Cova, T.J., and Brunelle, A., 2006. Exploratory map animation for post-event analysis of wildfire protective action recommendations. Natural Hazards Review, 7(1): 1-11. • Monteiro, C., Ramirez-Rosado, I., Zorzano-Santamaria, P. and Fernandez-Jimenez, L.A., 2005. GIS spatial analysis applied to electric line optimization. IEEE Transactions on Power Delivery, 20(2): 934-942.
