Section 5 - Site selection - Locational analysis and location/allocation - Other forms of operations research in spatial analysis - Spatial decision support systems - Linking spatial analysis with GIS to support spatial decision-making:

• Shortest path, traveling salesman, traffic assignment.

• What is location/allocation, and where can it be applied?

• Modeling the process of retail site selection. Criteria.

• Electoral districting and sales territories.

• What is an SDSS? What are its component parts? How does it compare to a GIS or a DSS? Why would you want one? Building SDSS.

• Examples of SDSS use - site selection, districting.

A spatial database can be used to support the solution of a variety of network problems, including optimal location, routing and vehicle scheduling

• these include:

• P-median problem
  Coverage problems
  Minimax location problems
  Plant location problem
• many of these are implemented in current GIS, e.g. network extensions in ArcGIS, TransCAD and GIS*PLUS from Caliper Corp.
• • additional code interfaced with GIS has been developed

• solution of many of these problems raises a number of issues of data modeling
• • some of these have been raised earlier in the example of modeling a street network for shortest path analysis
  •  

• oil extraction from the field generates large quantities of waste fluid

• there are 14 active producers in the field, each operating a single extraction facility

• the only effective method of disposal is by pumping to a formation below the oil producing layer

• options include:
• • a single, central disposal facility
  • requiring each producer to install a facility
  • some intermediate configuration of shared facilities

• maximum capital cost

• zero transport cost

• minimum capital cost

• maximum transport cost

• find locations for one or more central facilities and allocate producers to them in order to minimize the total of capital and transport costs
•  

Pipe cost:
 

Slide: Petrolia area

Slide: Transport cost functions
 

GIS implementation:

Network of streets and rights of way - potential routes for trucks/pipes

Links with attributes of length

Nodes with attributes of volume produced - producer sites plus other potential well locations

GIS database with nodes and links and associated attributes:

• data input functions (editing)

• data display - graphics, plots

• storage of geographic data

• provides data to the analysis module
•  

• obtains nodes and links from the GIS

• performs analysis, reports results directly to the user

• includes several heuristic methods for solving the optimization problem

• allows the user access to the display/analysis functions of the GIS
•  

Location-allocation analysis module:

Spatial search

Boolean search

Forest stands - area object type, non-overlapping

Two or more coverages can be overlayed to obtain new object types with concatenated attributes. This allows Boolean search and related operations to be conducted on multiple object types, i.e. with more information available.

Example:

A buffer zone allows Boolean searches to include criteria based on distance

Example:

In many cases criteria are not commensurate and cannot be summed.

Example

Single Utility Functions (SUFs) for each criterion

Multiple Utility Functions (MUFs) to combine criteria.
 
 

Both SUFs and MUFs can be determined by experimental designs involving groups of decision-makers

Decision theoretic methods can be incorporated into GIS technology. The GIS is used to evaluate the criteria for each alternative, then to weigh them using SUFs and MUFs to arrive at a decision.
 
 
 

A model for spatial analysis with a GIS
 

Example of multi-stage GIS analysis

Generation of a Recreation Opportunity Spectrum (ROS) map for a National Forest 1:24,000 quad (7.5 minute)
 
 

Problem: generate zones and associated ROS classes for Forest Service land based on distance from transportation features, with urban exclusions.
 

Data needed:

D1: Roads and railways (1:24,000) - line objects

D2: Forest Service ownership map (1:24,000) - area objects

D3: City and town boundaries map (1:24,000) - area objects

Reclassify attributes (B2)

Dissolve and merge (B3)

Generate corridors (B24)

Topological overlay (B50)

Measure size of areas (B35)

Centroid calculation and sequential numbering (B8)

Plot (A12)

Create list and report (B1)

SPM                forest land, non-urban and within 0.5 miles of road/rail

SPN                 forest land, non-urban, outside 0.5 mile and inside 1.0 mile corridors

P                      forest land, non-urban, outside both 0.5 mile and 1.0 mile corridors (B2)

Initial data sets: D1, D2, D3

1. B2 on D2 -> E1

2. B3 on E1 -> E2

3. B24 on D1 -> E3

4. B24 on D1 -> E4

5. B50 on E2, E3, E4 -> E5

6. B50 on E5, D3 -> E6

7. B2 on E6 -> E7

8. B3 on E7 -> E8

9. B35 on E8 -> E9

10. B2 on E9 -> E10

11. B8 on E10 -> E11

12. A12 on E11, D1, D3

13. B1 on E11
 
 

Many GIS applications require complex decision rules in reclassification operations.

Districting

• GIS technology useful in designing sales areas, analyzing trade areas of stores

• similar applications occur in politics
• • design of voting districts (apportionment, gerrymandering) has enormous impact on outcome of elections
  • major interest in reapportionment after 1990 census

• GIS applications in these areas are still at early stage

• scale:
• • street centerline, census reporting zones - i.e. 1:24,000 and smaller
  • data at block group/enumeration district scale (250 households) is needed for locating smaller commercial operations like gas stations and convenience stores
  • data at census tract scale (2,000 households) is good for the location of larger facilities like supermarkets and fast food outlets

• data sources:
• • much reliance on existing sources of digital data
  • • especially TIGER and DIME
    • similar data available in other countries
    • additional data added to standard datasets by vendors
    • e.g. updating TIGER files by digitizing new roads, correcting errors
    • e.g. adding ZIP code boundaries, locations of existing retailers

• functionality:
• • dissolve and merge operations, e.g. to build voting districts out of small building blocks
  • modeling, e.g. to predict consumer choices, future population growth
  • overlay operations, e.g. to estimate populations of user-defined districts, correlate ZIP codes with census zones
  • point in polygon operations, e.g. to identify census zone containing customer's residence
  • mapping, particularly choropleth and point maps of consumers
  • geocoding, address matching

• data quality:
• • more concern with accuracy of statistics, e.g. population counts, than accuracy of locations

• districting
• • designing districts for sales territories, voting
  • objective is to group areas so that they have a given set of characteristics
  • "geographical spreadsheets" allow interactive grouping and analysis of characteristics
  • • e.g. Geospreadsheet program from GDT

• site selection
• • evaluating potential locations summarizing demographic characteristics in the vicinity
  • • e.g. tabulating populations within 1 km rings
  • searching for locations that meet a threshold set of criteria
  • • e.g. a minimum number of people in the appropriate age group are within trading distance

• market penetration analysis
• • analyzing customer profiles by identifying characteristics of neighborhoods within which customers live

• targeting
• • identifying areas with appropriate demographic characteristics for marketing, political campaigns

• many data vendors and consulting companies active in the field, many large retailers

• no organization unique to the field

• American Demographics is influential magazine
•  

• GIS has applications in design of electoral districts, sales territories, school districts

• each area of application has its own objectives, goals

• this example looks at designing school districts

• the Catholic school system of London, Ontario, Canada provides elementary schools for Kindergarten through Grade 8 to a city of approx. 250,000
• • about 25% of school children attend the Catholic system
  • 27 elementary schools were open prior to the study

• population data is available for polling subdivisions from taxation records
• • approx. 700 polling subdivisions have average population of 350 each

• forecasts of school age populations are available for 5, 10, 15 years from the base year at the polling subdivision level

• children are bussed to school if their home location is more than 2 miles away, or if the walking route to school involves significant traffic hazard

• minimal changes to the existing system of school districts

• minimal distances between home and school, and minimal need for bussing

• long-term stability in school district boundaries

• preservation of the concepts of community and parish - if possible a school should serve an identifiable community, or be associated with a parish church

• maintenance of a viable minimal enrollment level in each school, defined as 75% of school capacity and > 200 enrollment

• digitized boundaries of the polling subdivision "building blocks"

• an attribute file of building blocks giving current and forecast enrollment data
• • for forecasting, we must include developable tracts of land outside the current city limits, plus potential "infill" sites within the limits
  • 748 polygons
  • development tracts are isolated areas outside the contiguous polling subdivisions
  • infill sites are shown as points

• the ability to merge building blocks and dissolve boundaries to create school districts
• • school districts are not required to be conterminous
  • if necessary a school can serve several unconnected subdistricts

• a table indicating whether walking or bussing is required for each building-block/school combination

Current districts

• "starbursts" show allocations of building blocks to 29 current schools (includes two special education centers)
• • note bussed areas in NW and SW - separate enclaves of recent high-density housing allocated to distant schools
  • this strategy allows an expanding city to deal with
  • • dropping school populations in the core leading to an excess of capacity
    • rising school populations in the periphery but lack of funds for new school construction
    • without constantly adjusting boundaries
    •  

Projections of enrollment based on current school districts

• rapid increase in developing areas, e.g. St Joseph's (#3), St Thomas More (#4) NW

• decrease in maturing areas of periphery, e.g. St Jude's (#8) - SW area

• rejuvenation in some inner-city schools due to infilling, e.g. St Martin's (#15) - lower center

• stagnation in other inner-city schools, e.g. St Mary's (#17), decline e.g. St John's (#14) - center

• general strategy - begin with current allocations, shift building blocks between districts in order to satisfy objectives

• requires interaction between graphic display and tabular output
• • quick response to "what if this block is reassigned to the school over here?"

• implementation allowed School Board members to make changes during meetings, observe results immediately
• • using map on digitizer tablet, tables on adjacent screen

• one of the alternative plans developed

• note:
• • assumes closure of 6 schools
  • rise in enrollment as percent of capacity
  • stability of projections through time
  • reduction in number of "non-viable" schools (<200 enrollment)
  • increase in percent not assigned to nearest school
  • increase in average distance traveled

Slide: Projected enrolments

Slide: Planned enrolments