Available with Spatial Analyst license.
A set of conceptual steps can be used to help you build a model. To understand the sequence of steps, you will work through a sample problem. As a town planner, you have been assigned the task of finding suitable locations for a new school. You can combine tools from ArcGIS Spatial Analyst extension to help you identify the potential sites.
Step 1: Stating the problem
To solve a spatial problem, you should first state the problem you are trying to solve and the goal you are trying to achieve. Start with a concept of the intended output of the study to visualize the type of map you want to produce.
Your problem is to find the best location for siting a new school. The result you seek is a map showing potential sites (ranked best to worst) that could be suitable for building a new school. This is called a ranked suitability map because it shows a relative range of values indicating how suitable each location is on the map, taking into account the criteria you put into the model.
To help you model your spatial problem, draw a diagram of the steps involved. Start with the statement of the problem. As you work through the problem, you will expand the diagram to show objectives, process models, and input datasets needed to reach your goal.
Step 2: Breaking down the problem
Once the problem is stated, break it down into smaller pieces until you know what steps are required to solve it. These steps are objectives that you will solve.
When defining objectives, consider how you will measure them. How will you measure what is the best area for the new school? In this fabricated school siting example, it is preferable to locate near recreational facilities, as many of the families who have relocated to the town have young children interested in pursuing recreational activities. It is also important to be away from existing schools to distribute their locations over the town. The school must also be built on suitable land that is relatively flat. There are obviously more objectives that could be included in this example, such as finding an area of land that is large enough for the school and its grounds or locating in an area with the highest density of children of an appropriate age, but this model is simplified for the purpose of the example.
To meet these objectives, you want to know the following:
- Where are locations with relatively flat land?
- Is the land use in these locations of a suitable type?
- Are these locations close enough to recreation sites?
- Are they far enough away from existing schools?
Where are locations with relatively flat land?
To find areas of relatively flat land, you need to create a map displaying the slope of the land. The process model here involves calculating the slope of the land.
- Input dataset needed: Elevation
Is the land use in these locations of a suitable type?
You need to decide what makes a suitable land-use type on which to build. This is a subjective process, according to your problem. Here, agricultural land is considered the least costly to build on and, therefore, the most preferable. Barren land is next, then scrub brush, forest, and lastly, existing built-up areas. There is no process model involved here, just an identification of the input land-use dataset and which land uses are most preferable to build on.
- Input dataset needed: Land use
Are these locations close enough to recreation sites?
You know that it is preferable to locate the school close to recreational facilities, so you need to create a map displaying distances to recreation sites to potentially locate the school in areas that are close to them. The process model here involves calculating distances from recreation sites.
- Input dataset needed: Location of recreational facilities
Are they far enough away from existing schools?
You want to site the school away from existing schools to avoid encroaching on their catchment areas. So you need to create a map displaying the distance to schools. Here, the process model involves calculating the distance from existing schools.
- Input dataset needed: Location of existing schools
Step 3: Exploring input datasets
Once you have broken down your problem into a series of objectives and process models and decided what datasets you will need, you should explore your input datasets to understand their content. This involves understanding which attributes within and between datasets are important for solving the problem and looking for trends in the data.
By exploring your data, you can often gain insights about the areas in which you want to locate the school, the weighting for input attributes, and alterations to your modeling process. You can see the locations of existing schools and recreation sites, and you can tell from the elevation dataset where the higher elevations are. The land-use dataset tells you what types of land use are in the area and where they are located in relation to the other datasets.
Step 4: Performing analysis
You have decided on your objectives, the elements and their interactions, the process models, and what input datasets you will need. You are now in the position to perform analysis.
Many tasks that can be solved by ArcGIS are discussed in the ESRI Guide to GIS Analysis book, available through ESRI Press .
When finding the best location for the new school, there are two ways to go about performing analysis. You can create a suitability map to find out the suitability of every location on the map, or you can query your created datasets to obtain a Boolean result of true or false.
Creating a suitability map
Creating a suitability map enables you to obtain a suitability value for every location on the map.
Once you have created the necessary layers (in this example, layers are Slope, Distance to recreation sites, Distance to schools, and Landuse) for your analysis, how are these created layers combined to create a single ranked map of potential areas to site the school? You need a way to compare the values of classes between layers. One way to do this is to assign numeric values to classes within each map layer, or to reclassify.
Each map layer is ranked by how suitable it is as a location for a new school. For example, you might assign a value to each class in each layer on a scale of 1 to 10, with 10 being the best.
This is often referred to as a suitability scale. NoData can be used to mask off areas that should not be considered. Having all measures on the same numeric scale gives them equal importance in determining the most suitable locations. The model is initially constructed in this way. Then, while testing alternative scenarios, weight factors can be applied to layers to further explore the data and its relationships.
Creating suitability scales
As is the case with this example, many scales are synthetic. These are often a ranked measure of suitability, or preference, from best to worst. It is based on something you can measure, such as distance to schools, but in the end, it is a subjective measure of how suitable a certain distance is from a school for locating another school.
There are natural scales that are commonly associated with some objectives. Cost is a good example but needs to be defined in sufficient detail. In a study of building suitability, an objective of low real estate cost would be measured on a scale of dollars. Be sure to adequately define the scale. For something as well understood as dollars, there are other variables, such as whether it's U.S. dollars, Australian dollars, or an exchange rate between monies.
Many scales are not linear relationships, although they are often presented that way to save time and money or because all options were not considered. For example, if assigning a scale to travel distance, traveling 1, 5, or 10 kilometers would not be ranked as a suitability of 10, 5, and 1 if you were walking. Some people may think that walking 5 kilometers is only two times as bad as 1 kilometer, while others may think it's 10 times as bad.
When you construct a suitability scale, work with experts to find the best and worst of a scenario and as many intermediate points as possible. Experts should be knowledgeable about the objective being studied. For example, it is more meaningful to ask commuters to rank their opinions on drive time desirability than to ask a city official when he thinks traffic is worst.
See GIS and Multicriteria Decision Analysis by Jacek Malczewski for more information on dealing with conflicting objectives and evaluation criteria.
Ranking the areas close to recreation sites
To site the school close to recreational facilities, you need to know the distance to them. The Spatial Analyst Euclidean Distance tool will create such a map, calculating the straight-line (Euclidean) distance from any location to the nearest recreation site. The result is a raster dataset in which every cell represents the distance to the nearest recreation site. To rank this map, use the Reclassify tool. As it is preferable to locate close to recreation sites, give a value of 1 to distances far from recreation sites and a value of 10 to distances close to recreation sites, then rank the distances linearly in between as the following illustration shows.
Ranking the areas away from existing schools
To avoid the catchment areas of the other schools, you need to know the distance to them. The Euclidean Distance tool will create such a map, calculating the straight-line distance from any location to the nearest school. The result is a raster dataset in which every cell represents the distance to the nearest school. To rank this map, use the Reclassify tool. As it is preferable to locate away from existing schools, give a value of 1 to distances close to existing schools and a value of 10 to distances far from existing schools, then rank the distances linearly in between as the following illustration shows.
Ranking the areas on relatively flat land
To avoid steep slopes and find areas that are relatively flat to build on, you need to know the slope of the land. The Slope tool will create such a map, identifying for each cell the maximum rate of change in value from each cell to its neighbors. To rank this map, use the Reclassify tool. As it is preferable to locate on relatively flat slopes, give a value of 1 to locations with steep slopes and 10 to locations with the least steep slopes, then rank the values linearly in between as the following illustration shows.
Ranking the areas on suitable land-use types
To rank the map representing land-use types, use the Reclassify tool. As it is preferable to build on certain land-use types due to the costs involved, you need to decide how to rank the values.
Ranking distance or slope values is relatively straightforward. You have to decide whether short or long distances are preferable and whether steep slopes or less steep slopes are preferable, then rank the rest of the values linearly or specify a maximum distance or slope to consider. Here you have to decide which land-use types are preferable. This is subjective depending on your study. The easiest way to decide what type of land is preferable for building on and what is not is to decide on the most preferable and then the least preferable. Then, out of the land-use types left, again decide on the most and least preferable. Do this until you have put the land-use types in order of preference. Land uses of water and wetlands have been excluded from the analysis, as you cannot build on water and there are restrictions against building on wetlands. The illustration below shows how the land-use types have been ranked.
Combining the suitability maps
The last step in the suitability model is to combine the reclassified outputs (the suitability maps) of Distance to recreation sites, Distance to schools, Slope, and Land-use types.
To account for the fact that some objectives have more importance in the suitability model, you can weight the datasets, giving those datasets that should have more importance in the model a higher percentage influence (weight) than the others. If all datasets are of equal importance, you can assign the same weight to each one.
In the example, you know from breaking down the problem that the most preferable objective to satisfy is to locate the school close to recreational facilities, and the next is to locate away from existing schools. The following percentage influences will be assigned to the suitability maps. The values in parentheses are the percentage divided by 100 to normalize the values. This normalized value will be assigned to each suitability map:
|Suitability factors||Percent influence||Percentages normalized|
Distance to recreation sites
Distance to schools
The Distance to recreation sites suitability map has an influence of 50 percent (0.5) on the final result, and Distance to schools has an influence of 25 percent (0.25). Slope and Land-use types both have a 12.5 percent (0.125) influence. Like assigning scales of suitability, assigning weights is a subjective process, depending on what objectives are most important to your study.
The final suitability map is produced by combining all the maps. Weights can be assigned at the same time as combining the suitability maps. The final suitability map for locating sites for the school is shown below. Most suitable locations are in the darkest green color. The least suitable locations are in orange shades.
You can use Map Algebra to weight and combine datasets. Alternatively, you can use the Weighted Overlay tool. If you use this tool in a model, you have the option of going back and easily altering weights (percent influence) and any scale values you might have set. Connecting geoprocessing tools inside a model means you only need to create the model once, then you can alter parameter values to experiment with different outcomes.
Querying your data
The alternative way to find suitable locations for the new school (rather than creating a suitability map) is to query your data. Once you have created all the datasets you need (Slope, Distance to recreation sites, and Distance to schools), you can query the data to find the suitable locations. Such a query would be to find all locations on agricultural land with slopes less than 20 degrees, where the distance to recreation sites is less than 1,000 meters and the distance to schools is greater than 4,000 meters.
The result is a Boolean true or false map of locations that meet or do not meet the criteria. The areas in green are suitable for the school site, and the areas not suitable are shown in brown.
Compare this result to the suitability map from the previous step. The difference between querying your data and creating a suitability map is that when you query your data to get a Boolean true or false map, there are no areas of medium suitability. A location either meets all the criteria, or it is deemed not suitable. If your analysis warrants more flexibility, you should create a suitability map, where every location (cell) has a suitability value. There may be a location that is deemed as totally suitable according to your suitability analysis, but when you investigate further, you find that there are restrictions on building in that location. Since you haven't restricted the locations to suitable or not suitable (as with the Boolean approach), you can find a location close by that isn't quite as perfect (it might be in a location where the land-use type isn't quite as suitable, for example), but it will still make a good location to build.
Step 5: Verifying the result
Once you have your result from any spatial analysis, you should verify that it is correct. If possible, this should be done by visiting the potential sites in the field. Often the result you achieve has not accounted for something important. For example, there may be a chicken ranch upwind of the site that is producing foul odors, or by examining the town hall records you may discover a restriction on building on the desired land of which you were not aware. If either is the case, you will need to add this information to the analysis.
Step 6: Implementing the result
The final step in the spatial model is to implement the result, which is to commence the planning and construction of the new school in the chosen location.