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

Lab 5

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

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

Response

Santa 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 communityd1 = shortest distance before PARd2 = shortest distance after PARt = time PAR was issuedt1 = time last known fire perimeter at d1t2 = time last known fire perimeter at d2

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

Kim et al. 2006

Animations

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