Spatial Analysis cont Optimization Network Analysis Routing Optimization

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Spatial Analysis cont. Optimization Network Analysis, Routing

Spatial Analysis cont. Optimization Network Analysis, Routing

Optimization • Spatial analysis can be used to solve many problems of design •

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

Lab 5 • Edges, junctions, and weights

Lab 5

Lab 5

Location-allocation Problems • Design locations for services, and allocate demand to them, to achieve

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

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

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

Traveling Salesman

Traveling Salesman – Georgia Tech http: //www. tsp. gatech. edu/maps/

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

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

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/chuck

Example: Santa Ynez Mtns. , CA More details at http: //www. ncgia. ucsb. edu/~ashton/demos/chuck 95/stochastic. html Chuck Ehlschlaeger, Ashton Shortridge

Least-cost path problem. Range of solutions across a friction surface represented as a raster.

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

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 Response Santa Barbara, Utah, San Diego

Optimization & Routing for Emergency/Disaster Response Santa Barbara, Utah, San Diego

Optimization & Routing for Emergency/Disaster Response • Kim et al. 2006 – PARs, Protective

Optimization & Routing for Emergency/Disaster Response • Kim et al. 2006 – PARs, Protective Action Recs d= interpolated, shortest-distance of wildfire to community d 1 = shortest distance before PAR d 2 = shortest distance after PAR t = time PAR was issued t 1 = time last known fire perimeter at d 1 t 2 = time last known fire perimeter at d 2

Fire Origin to Communities: Estimate Avg. Speed of Fire Between Known Perimeters Kim et

Fire Origin to Communities: Estimate Avg. Speed of Fire Between Known Perimeters Kim et al. 2006

Animations

Animations

Gateway to the Literature • Cova, T. and Johnson, J. P. , 2002. Microsimulation

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.

(Extra slide) Cova et al. 2005

(Extra slide) Cova et al. 2005