Using Raster Data for Site Selection

10/29/2014

Problem:

In this training course, we were introduced to the concept of using GIS to not only locate where things are, but to locate where things should be, or in other words, to perform site selection. Site selection can be performed on vector or raster data, but whereas with vector data that uses a binary approach, with raster data, there are a few options that can be used, such as binary, weighted, and fuzzy logic analyses. With binary analysis, the selection is based on a yes or no principle. With weighted analysis, individual cells can be ranked and a score of relative importance assigned to each layer. This technique allows results to provide next-best options. Fuzzy logic assigns membership values to locations, ranging from 0 to 1, with 0 constituting no membership and 1 constituting full membership.

Analysis Procedure:

Site selection analysis begins first with a definition of the problem to be solved, such as where to place a vineyard. After identifying the question, it is important to set criteria by which the solution will be made, for example the aspect of the slope upon which the vineyard will be placed for ideal light conditions. Gathering data about the areas into which the vineyard will be placed allows for the creation of map layers to visualize the final solution. Data comes in many different forms and units, so they will need to be reclassified into the same scale. Not all layers carry equal importance, so weights will be assigned to the parameters in accordance with their influence. The layers can then be overlaid, resulting in a final map with ranked locations.

Figure 1. Map of suitable vineyard locations near San Diego, CA - from the assignment of the ESRI training module

Fuzzy logic accepts conditions that are partially false or partially positive and makes recommendations about the best fit within that framework, and tends to be used when there are no "discrete" conditions. As with weighted overlay, the first step is to define the problem, such as suitable Bald Eagle nesting habitat. After identifying the question, it is important to set criteria by which the solution will be made, for example proximity to water. Gathering data about the areas into which the nests will be located allows for the creation of map layers to visualize the final solution. Fuzzy membership values can then be assigned and will help determine how likely a location will be suitable. The layers can then be overlaid, resulting in a final map with suitable locations.

Figure 2. Map of Bald Eagle nest locations near Big BearLake, CA - from the assignment of the ESRI training module

Figure 3. Certificate of completion for the ESRI training course, Using Raster Data for Site Selection

Application and Reflection:

The weighted overlay method of data analysis allows one to link layers of different units by putting them into a common scale thereby providing a way to determine by rank the most optimal conditions. The fuzzy logic method identifies suitability by calculating, essentially, a probability that a site is suitable. Returning to the mushroom hunting project my daughters and I are working on (see Our Mushroom Hunting), it may be more appropriate to approach the question of which mushrooms will grow where by attempting the fuzzy logic overlay approach. The data I would like to have for these analyses are soil moisture content, light level, temperature, relative humidity, barometric pressure, and GPS coordinates collected at the time of the mushroom sightings. I would also like to know the trail segment location, slope, aspect, elevation, and the information produced through linear referencing. Each mushroom variety, it seems, will have different requirements, and will have ranges over which their membership can be assigned within each environmental parameter. I will likely perform statistical analyses on the data to identify which parameters are more likely to influence the mushroom sightings, then use these in the fuzzy logic overlay.