Suitability Analysis in Raster GIS Combining Multiple Maps

The Challenge Thus far –Single or Dual Factor Overlay Analyses i.e. Land Cover on Private Land –Biophysical Analyses with Algebraic Formulas i.e. RUSLE Landscape Planning –Dozens to hundreds of spatial factors –Factors have “apples and oranges” characteristics –Combinations must reflect social values, not just (bio)physical processes “Best” for industrial development – from whose perspective?

Suitability Analysis General purpose –To rank potential sites according to suitability for a proposed type of activity Requirements –A set of “factor” or criteria maps, organized to rate sites relative to one or more characteristics –A technique for appropriately combining factors

Types of Criteria Absolute –Frequently hard-edged –Often include property ownership/management zones –Often involve legal standards Relative –Typically “fuzzy” edged E.g. “proximity to X” where closer = better, but no absolute distance known in advance –Often involved in trade-offs where values ranges come from specific data within a place Criterion 1 = “Low rent” and criterion 2 = “close to school”

Suitable For Whom? Suitability models have a “point of view” –Audience can be human “Affordable housing” Best sites for High-end commercial –Audience can be environmental Best habitat for black bear Most suitable multispecies conservation areas Can be implicit or explicit –But better to be explicit where possible

Common Units How do you “combine” –a map representing “meters to nearest road” Units = meters –with another representing “land cost”? Units = dollars Short Answer: find or create common units –Easiest: likert scale “preference” units A range of values: 1 to 5, or 1 to 9 Polar opposites on both sides of range –i.e. “Best”/”Worst”, “Most Suitable”/”Completely Unsuitable”

Cautions with Likert Scales –Consistent Application With multiple factors, must make sure that scale consistently applied – example analysis: want to be near streams and far from roads, using 1..9 with 9 = best –Calculate distance to streams, distance to roads –Reclassify stream distance to preference units »Closest = 0 distance = 9 –Reclassify road distance to preference units »Closest = 0 distance = 1 –In other words, may need to “flip” values when reclassifying Doesn’t really avoid scaling issues, just defers –Sensitivity and range in price/distance may be different –Often what’s needed from initial analysis is “range of the possible”