# Applications of the Index Model: Index Models Commonly Used for Suitability Analysis and Vulnerability Analysis-4 Examples (Especially for GATE-Geospatial 2022)

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For filter 2, Rohde et al. (2006) run a sensitivity analysis with four different weighting schemes to evaluate the relative influence of criteria weights on the ecological restoration suitability index. Development of an index model is therefore more involved than a binary model.

### Example 1

One type of suitability analysis is site selection. In their study of the site selection of emergency evacuation shelters in South Florida, Kar and Hodgson (2008) calculate the suitability score for each location by

Where is factor rating for factor j, n is the number of factor, and is the weight assigned to factor j such that the sum of equals 100.

### Example 2

The U. S. Environmental Protection Agency developed the DRASTIC model for evaluating groundwater pollution potential. The acronym DRASTIC stands for the seven factors used in weighted linear combination: Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, and hydraulic Conductivity. The use of DRASTIC involves rating each parameter, multiplying the rating by a weight, and summing the total score by:

Where is factor i and is the weight applied to . A critical review of the DRASTIC model in terms of the selection of factors and the interpretation of numeric scores and weights is available in Merchant.

### Example 3

Habitat Suitability Index (HSI) models typically evaluate habitat quality by using weighted linear combination and factors considered to be important to the wildlife species. An HIS model for pine marten developed by Kliskey et al. is as follows:

Where are the ratings for biogeoclimatic zone, site class, dominant species, canopy closure, and seral stage, respectively. The model is scaled so that the HIS values range from 0 for unsuitable habitat to 1 optimal habitat.

### Example 4

Wildfire hazard and risk models are usually index models based on factors such as vegetation species, slope, aspect, and proximity to roads. Lein and Stump (2009) use the weighted linear combination method to construct the following wildfire risk model in southeastern Ohio:

When fuel is based on vegetation species and canopy cover, solar is annual solar radiation calculated from a DEM and a slope raster, and TWI is topographic wetness index also calculated from a DEM and a slope raster. (TWI is defines as ln () , where is local upslope contributing area and is local slope) .

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