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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
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 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
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
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