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The contents of this and page and subordinate links are part of a working paper by Jeannette Candau and Noah Goldstein and will be published in the forthcoming URISA 2002 Annual Conference proceedings. References to this work may not be made without the expressed permission of the authors. For more information please contact Jeannette Candau.

Using Scenario Difference Maps

1 Define Scenarios
By altering the composition of SLEUTH input layers, scenarios of land use change may be generated. For Santa Barbara, CA five scenarios were defined and implemented in the data. The first scenario (MSQ) assumed that the status quo would be maintained, and future growth would be allowed to expand in a manner similar to what had occurred in the past. The second scenario (ER) maintained the same assumptions as the MSQ scenario, but included an expanded roads network. The third scenario (EEP) instituted extreme protection of environmentally sensitive lands. The fourth scenario (MEP) allowed a more moderate environmental protection approach. The fifth scenario (UB) used an urban boundary to define the maximum extent of urban growth.

2 How the scenario inputs were generated using Arc/Info GIS
To modify the data inputs for the different planning scenarios used for modeling Santa Barbara's urban growth, the Exclusion and Transportation layers we modified.

The Maintain Status Quo (MSQ) Scenario:
The exclusion layer in this scenario gave all parks, protected areas, and water bodies (like the Pacific Ocean) a value of 100. A value of 100 corresponds to a 100% exclusion from possible urban growth. The geographic extents of parks were obtained from the Santa Barbara County Assessors' map of 1997. If a parcel was fully protected from urban growth by a permanent change of ownership (to the state or county parks department, for example), then it was included in the exclusion layer.

The Expanded Roads (ER) Scenario:
Transportation layers for the Santa Barbara data set used weighted classification. The classes highway, state route, and primary road were grouped together into the class Primary Road. Data of the Primary Road class were given a value of 100. The secondary road classification was given a value of 50. Non-road data had a value of zero. For the ER scenario the transportation layer was modified as follows:

All roads with potential to be expanded to freeway-level status were given values the same as freeways (i.e.; 100)
Roads were added to areas of current (2002) development projects. This primarily included additional intersections with Highway 101 in Goleta and Carpinteria and a "Ring Road" in Western Goleta.

The ER scenario used the MSQ exclusion layer.

The Moderate Environmental Protection (MEP) Scenario:
This scenario is based upon the MSQ exclusion layer. Environmentally sensitive areas were then identified and added to the MSQ exclusion layer. These additional data were given a value of 50, indicating a 50% probability of exclusion from urban growth. The data added is as follows:

All agriculture was added: agricultural areas were identified and digitized using aerial photography from 1998. All polygons from the landuse class "agriculture" were included in the MEP exclusion layer
Creek setbacks were added: these data, created by Tim Robinson at UCSB's Bren School, corresponds to a regular setback from all creeks and the ocean coastline. The distance of setback is dependent on water district and ownership (federal or private).
Recharge areas were included: the recharge areas were also made by Tim Robinson and correspond to areas that have the appropriate soils, geology and location to be necessary for appropriate aquifer recharge.

The Extreme Environmental Protection (EEP) Scenario:
This scenario is based upon the MEP Scenario’s exclusion layer, but all Exclusion values have a value of 100. This is in contrast to the MEP Scenario, where Exclusion values are both 100 (from the MSQ exclusion scenario) and 50 (from the agriculture, creek setbacks and recharge areas).

The Urban Boundary (UB) Scenario:
This scenario is based upon the MSQ exclusion layer, and uses an additional "Urban Boundary" GIS layer to constrain growth. The Urban Boundary data was derived through a series of workshops held by the Economic Forecasting Project of Santa Barbara where local stakeholders defined a desired spatial limit to urban expansion. All growth in this scenario is constrained within the urban boundary.

3 Calibrate SLEUTH and run projections
SLEUTH was calibrated for the Santa Barbara area using historical data from 1967 – 2001 (REF). Using the calibrated parameters, the five scenarios of land use policy described above defined unique projections of urban growth to the year 2030. Each scenario run was executed with 100 Monte Carlo iterations.

Santa Barbara forecast images
The output of each forecast run included a file of averaged annual growth metrics, annual, classified probability maps of urban growth, and a “cumulate” image which is an unclassified probability map of urban growth from 2030, the final forecast year. The classified probability maps for the year 2030 can be seen in here. The cumulate images from the five scenarios are illustrated here.

4 Generate Difference Maps
The cumulate images are unclassified probability maps from the final forecast year. These images were brought into ESRI’s Arc8 GIs as grids. Using map algebra, one cumulate grid was subtracted from another ([first scenario] – [second scenario]) to generate difference grids. The difference grids could contain values from –100 to 100. Negative values were generated if the second scenario forecasted urban growth at a location with a greater frequency than the first scenario did. The closer to –100 the values were, the greater the difference between the two scenarios and the larger the underestimate by the first scenario. Similarly, positive numbers at a location were a result of the first scenario forecasting urban growth with a higher probability than the second scenario did. The closer to 100 the values were, the greater the difference between the two scenarios and the larger the overestimate by the first scenario. Zero indicates agreement between the two scenarios.

For visualization purposes, the values of the difference maps were reclassified and then displayed using a colormap file. Using the GRID command RECLASS and a reclass file the difference grid values were divided into ranges and assigned new values. The first scenario underestimates –100 to –1 were remapped into categories from 1 to 10 respectively. Zero values were reclassified to the value 11. First scenario overestimates 1 to 100 were remapped to new values of 12 to 21 respectively. A ramped colormap file assigned RGB color values to the new categories and was used to display the difference grids in GRID with the GRIDPAINT command. Category 1 displayed as a dark orange and signified first scenario underestimation. Category 11 is white, indicating agreement between the two scenarios. Category 21 showed as a dark blue and represented first scenario overestimation. The colormap file was also used to export the difference grids as TIF images using the Arc command GRIDIMAGE.

The difference maps may be viewed from the following links: