Suitability analysis is the process and procedures used to establish the suitability of a system – that is, the ability of a system to meet the needs of a stakeholder or other user. Before GIS (a computerized method that helps to determine suitability analysis) was widely used in the mid to late 20th century, city planners communicated their suitability analysis ideas by laying transparencies in increasing darkness over maps of the present conditions. This technique's descendant is used in a GIS application called multicriteria decision analysis. In the 1960s, a mechanism called the ecological inventory process was developed to document existing surrounding land conditions to help inform the analysis for the land in question. These mechanisms were computerized upon the advent of computers due to inefficiencies in the methods, such as the inability to overlay a large number of transparencies. In order to feed a growing population that is pushing on the ability to extensively farm, suitability analysis is becoming more necessary to utilize the most productive land to its fullest potential, matching the needs of the plants more carefully to the existing assets in the environment. This technique is known as precision farming. Suitability analysis can also be used to track and label potential hazards, like earthquakes, contamination, or even crime. It can also be used to locate advantageous locations for commercial centers.
Suitability model A suitability model is a model that weights locations relative to each other based on given criteria. Suitability models might aid in finding a favorable location for a new facility, road, or habitat for a species of bird. Overlay analysis is a common method for creating a suitability model which involves using GIS techniques and software. Overlay techniques were originally advanced by Ian McHarg, who used a manual overlay cartographic process which he describes in his 1969 book Design with Nature. With the advancement of computer mapping software, suitability modeling has become much easier and faster to implement, and today it is used for many varying tasks.
Overview There are seven general steps required to create an acceptable suitability model:
Define the problem Break the problem into submodels Determine significant layers Reclassify or transform the data within a layer Weight the input layers Add or combine the layers Analyze
Define the problem Without a clear understanding of the problem that needs to be solved a suitability model cannot be successful. All other steps in the process will contribute to the objective of solving this problem. The components of this objective should also be defined, as well as a way of knowing when the problem has been solved. Considering the issue of deforestation, to lower deforestation rates a suitability model could be created to model areas most likely to be deforested in the immediate future; laws and regulating entities could then be focused on those areas most susceptible to deforestation. The overall goal of the deforestation suitability model would be to slow the rate of deforestation.
Break the problem into submodels The complexity of most suitability modeling problems can be overwhelming and confusing; for this reason, it is advisable to break the model into submodels. For deforestation there are many different drivers, therefore, a variety of submodels would be needed. Population, population density, movement of people, elevation, slope, land cover type, hydrology, location of protected areas, soil type, laws, roads and infrastructure, the list could go on, all of these things affect where deforestation happens and the intensity. Combining these factors could lead to a submodel for physical environment (elevation, slope, land cover, land use, soil type, and hydrology), for built environment (roads, infrastructure, and other relevant transportation networks), and for demographic characteristics (population, population density, population growth rate, and poverty rate).
Determine significant layers Each submodel should be defining an aspect of the overall model, and only submodel factors which contribute to solving the original problem should be included in a submodel. It is in this step that data must be gathered, and layers created; for example, it may be known that deforestation usually happens a certain distance from city/road/agricultural areas, therefore a Euclidean distance tool (within a GIS software package) could be used to create a distance raster around these areas.
Reclassification/transformation There are many different datasets going into the model, all with varying number systems; this means that attempting to combine these datasets would give meaningless results. Therefore, a common number scale should be chosen (usually 1 to 9 for a weighted overlay and 0 to 1 for a fuzzy overlay; with larger values signifying more favorable areas) and each dataset reclassified to the new scale (there should be a tool for this in most GIS applications).
Weight If there is strong evidence that some factors contribute more to the main goal these factors should be weighted based on their level of contribution. For instance, focusing specifically on deforestation in Africa, previous research shows that one of the main causes of deforestation is fuel wood extraction; therefore, variables associated with fuel wood extraction should be weighted more heavily than other variables. Weighting should not be done if a fuzzy overlay is used.
Add/combine To complete the model, all factors must be combined, usually through a weighted overlay or fuzzy overlay technique. For a weighted overlay all the factors would be added together and reclassified to form a new data layer where high values signify more favorable locations and low values less favorable locations. A fuzzy overlay analysis produces the same type of results but through more complex methods.
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