Suitability Analysis allows you to identify the most suitable sites from a set of candidates, as defined by applying a set of individually weighted criteria.
If you aren't able to access certain functionalities of this feature, your custom roles, as defined by your organization and set up by your administrator, may be limited. Check with your administrator to set up roles specific to your work.
- From the Create Maps from Data, click Suitability Analysis.
- Click Get Started in the Suitability Analysis dialog box.
You can check the Skip this in the future check box to bypass this introduction page and go directly to Suitability Analysis.
Define your sites
There are two ways to select the candidate site to use in the suitability analysis:
- Add sites from project
- Start with items on the map
You may uncheck a selected site to deselect it, and click Prefilter to filter the selected sites based on the values of attributes of the sites – this is useful when you want to run a suitability analysis only on those sites that already meet specific conditions e.g., stores that have a certain minimum number of parking spaces.
For instance, Sales Volume and City are chosen attributes.
For the numerical Sales Volume attribute, a range of 2,000,000 to 20,000,000 is set as the filter. For the City attribute, all values containing sa are returned by the search, and then selected by clicking Select All.
For a text field, press the space key to see all available values. The user entered search string will be highlighted in available options as shown below.
Select criteria for your analysis
Use one or more options to select criteria for your analysis:
- Add variables from data browser
Use the Data Browser to find and add variables.
- Add attributes from sites (e.g. square footage)
Information on how to add attributes for your sites can be found here.
- Add point layer (e.g. competitor layer)
- Use saved criteria
Use previously saved criteria (a list of variables) from the Saved Criteria List dialog. Click, then click Use criteria.
The selected criteria will be weighted equally by default, but you can use the sliders to adjust any of the weights. The weights for all your criteria must always add up to 100%. Therefore, if you increase or decrease the weight for any criteria, the weights for each of the remaining criteria will automatically be decreased or increased proportionately. To prevent the weight of any of the criteria from changing in this way, click the lock icon next to it.
You may also click More options to modify how a variable is to be used in the analysis. Positive is selected by default, and means that the higher value for the variable, the higher its effect on the score will be. Selecting Inverse specifies the opposite: the lower the value for the variable, the higher its effect on the score will be. If the option Ideal is selected, a value for the Ideal must be selected on the graph that appears with it. The closer the value of the variable to the specified Ideal value on the graph, the higher its effect on the score will be.
- When a point layer is used as the criteria, the variable will be the number of points from the layer that fall within a site. By default, negative influence is used for point layer as competitor locations.
- This default influence for point layers can be changed under Preferences.
- When a point layer is selected as a criteria, and at least one of the selected sites has a large area, The suitability analysis may take a few moments to process.
View Results Table
Use the table to view a list of your sites, ordered by the final score. You can also filter, change the color ramp, and export.
- Click View Results Table.
- Hover the pointer over an item in the table and the corresponding site is highlighted on the map.
- Click on any column header to sort the table by the data in that column.
Filter and Colors
You can filter the view of the analysis for the sites you have chosen. This allows you to highlight sites that meet a certain score visually on the map. For example, when viewing the population by age in a block group, you can create a filter by selecting greater than and entering a value of 5,000. The map will only show color-shaded block groups where the population by age is more than 5,000 people.
To filter your analysis you will perform the following steps:
- Click the Filter icon.
- Move the sliders to filter the map.
- Click Ok.
The Colors drop-down menu allows you to select from a palette of colors for coding the results of your analysis on the map and in the results table.
- To change the colors, click the Colors drop-down menu and the color choices appear.
- Click the color you want to view in your map.
You can export the results of your analysis to an Excel file, or to a new suitability layer.
- Click Export in the Suitability Analysis panel.
The Export dialog will appear.
- Choose your export format: Excel file, or a new suitability layer.
- Click Export.
If you choose to export as a new suitability layer, the results will be saved under the Other Layers section of your project. Click and click Open analysis to reopen the analysis and modify it. If the sites for your suitability analysis were created from a point or polygon layer, they will be saved to the project as a new site layer.
If any of the sites in this site layer are deleted, the saved suitability layer not will not open, and will return an error message.
Use Suitability Analysis to rank and score sites based on multiple weighted criteria. Suitability can be ranked based on data variables from Esri’s living Atlas of Demographic and Socioeconomic data and your site attributes. Once you select your criteria, you can assign weights to them, get weighted scores for each potential site, and review their final score ranks from most suitable to least suitable site. Suitability Analysis can be run on a set of point location sites, polygon areas, standard geographies, or any combination thereof.
The following guide will lead you through an example of how to execute this workflow.
From the Create Maps from Data tab click Suitability Analysis.
You will see the Introductory screen that gives you a very brief overview of the Suitability Analysis workflow. If you do not wish to see this screen every time, select Skip this in the future, before you click Get Started.
For the purposes of this example, you are a real estate agent who has taken on a new client interested in buying a home. Your client is a young professional with an advanced degree, and has an elderly parent living with him who relies on being in close proximity to a senior center for socializing and leisure activities. Your client wishes to buy a home to reside in, that is within walking distance to one of his preferred senior centers, so that his parent has convenient access. You need to present him with a neighborhood analysis for these senior center locations, to help evaluate their suitability based on his criteria:
- Greater number of individuals with advanced degrees
- Lower median home value, which would indicate greater affordability of purchasing a home
- A median age of the population close to his own age (32)
To begin the Suitability Analysis workflow you will select the sites that you want to use in the analysis. For this walkthrough, five sites were already loaded on the map, each representing an area 0.75-mile radius around a senior center.
- Click Start with items on the map. Alternatively, we could click Add sites from project, to add sites not already on the map.
- Click Next.
- Expand the Add Criteria drop-down and select Add variables from data browser.
- Select the following three variables and click Apply.
- 2016 Education: Grad/Professional Degree
- 2016 Median Home Value
- 2016 Median Age
- You want a site to score higher if it has greater number of individuals holding Grad/Professional degrees, so you will keep the Influence setting Positive for that variable.
- For Median Home Value, a lower value is more desirable as it is indicative of greater home affordability. Therefore, you will change the Influence setting to Inverse.
- For the Median Age, the closer the value is to 32 the more ideal the site. Therefore, you set the Influence to Ideal. Use the slider to specify the ideal value of 32.
- Assign a Weight of 60% to 2016 Median Home Value. You want to do this as home affordability is a higher priority for your client over his other preferred criteria and it will be a better use of his time to look for a home in the vicinity of those senior centers where home prices are lower.
- To show sites that meet a certain minimum score. In this case, say, only those sites with a score > 0.5. Click the filter icon
- Drag the slider for the lower limit from 0 to 0.5 (or type the value 0.5 in the text box).
You may also adjust the thresholds to exclude sites from the Results table that do not meet must-have criteria for a variable. E.g. you may set a maximum value of $500,000 for Median home value so sites that have a median home value above $500,000 are excluded. Because you are not scoring too many sites in this example, you will not adjust the threshold values and keep the default setting to include their full range.
The color ramp is applied to color-code the sites on the map by their final score. You may click on the View Results Table link to open the Results Table. With the above settings, Candidate 2 receives the highest final score in the Results Table.
Further details regarding how the below scores are calculated can be found in this section.
Weighted score calculations
The following are details regarding how the above scores are calculated, using some of the values in the annotated table above to illustrate. Each weighted score is calculated as a percent difference of the value for a given site compared to the target value selected by the user. Here, GP stands for the Grad/Professional degree variable, HV stands for the Median Home Value variable and MA stands for the Median Age variable.
As the above table illustrates, Candidate 3 site has the highest suitability score of 0.86 (Cell 1A). This score is calculated by adding the weighted scores for each of the three variables (GP, HV, MA) used in the analysis.
- First, we will examine how the number of households with graduate degrees (GP) contributed to this score.
- Site Candidate 3 has 1013 households with Grad/Professional Degree (Cell 1B).
- The maximum value for GP across all the sites is 1272 households for the site Candidate 2 (Cell 2B).
- Similarly, the smallest value for GP across all the sites is 821 households for site Candidate 1 (Cell 3B).
In this example, the greater number of people with graduate degrees is desired. This is a positive relationship, so these values are plugged into this formula to calculate the score for GP for Candidate 3:
We can calculate what the score is for GP for Candidate 3 using the values outlined above:
This means, that Candidate 3 has a score of 0.43 (Cell 1C). Once the score is calculated, the weight is then applied to the value to determine how much GP will contribute to the total suitability score for the site. In our example, a weight of 20% was applied to GP. Therefore, the weighted score for GP is calculated as 0.09 (Cell 1D):
0.20*0.43 = 0.09
The whole weighted score calculation for Candidate 3 can be expressed as:
abs is the absolute value function.
- Next, let us see how the Median Home Value of the site (HV) contributed to the score:
- The homes within the area of site Candidate 3 have a Median Home Value of $281,545 (Cell 1E).
- The maximum value for HV across all the sites is $569,638 for the site Candidate 5 (Cell 5E).
- Similarly, the smallest value for HV across all the sites is $281,545 for site Candidate 3 (Cell 1E).
Here, a lower Median Home Value is desired, as an indicator of better affordability. This is an inverse or negative relationship, so these values are plugged into this formula to calculate the score for GP for Candidate 3:
We can calculate what the score is for HV for Candidate 3 using the values outlined above:
This means, that Candidate 3 has a score of 1 (Cell 1F). Once the score is calculated, the weight is then applied to the value to determine how much HV will contribute to the total suitability score for the site. In our example, a weight of 60% was applied to HV. Therefore, the weighted score for GP is calculated as 0.60 (Cell 1G):
0.60*1 = 0.60
The whole weighted score calculation for Candidate 3 can be expressed as:
- Lastly, let us examine how the Median Age of the people living in the area (MA) contributed to the score:
- The population living in the area of site Candidate 3 has a Median Age of 29.9 (Cell 1H).
- The maximum value for MA across all the sites is 51.5 for the site Candidate 5 (Cell 5H).
- The minimum value for MA across all sites is 29.9 for the site Candidate 3 (Cell 1H).
Here, an ideal value of 32 was selected, as a median age closer to 32 is more desirable. These values are plugged into this formula to calculate the score for MA for Candidate 3:
This means, that Candidate 3 has a score of 0.89 (Cell 1I). Once the score is calculated, the weight is then applied to the value to determine how much MA will contribute to the total suitability score for the site. In our example, a weight of 20% was applied to MA. Therefore, the weighted score for MA is calculated as 0.18 (Cell 1G):
0.20*0.89 = 0.18
In this example we have not adjusted the threshold for any of the variables. If, for example, the threshold had been set such that the GP value for Candidate 3 did not fall within the specified range, then the weight for that variable would default to 0 and effectively GP wouldn’t be used in the final score calculation - filtering would be applied and that particular suitability score would not be used in the final results table.
Final score is calculated as:
The final score you see in the table is slightly lower, at 0.86, but that's only because it adds the unrounded values instead of the rounded values we used here:
This walkthrough gave you a basic understanding of how suitability analysis works. You were able to create a ranked list of the top 3 most suitable senior centers in the area, for your client's weighted criteria from the point of view of house hunting.
You may add additional sites and variables, and further adjust the settings to perform an even more sophisticated suitability analyses. You could further enhance your analysis by including relevant variables that are attributes of the sites being scored. For example, every senior center may have a rating, which is an indicator of quality. You may want to factor that into your suitability analysis, with the Add point layer (e.g. competitor layer) option under Add Criteria.