Expand a small business

As a small business owner of laundry and dry-cleaning facilities, you are interested in expanding into new markets. Expansion can be risky, so you'll first analyze your most successful stores to establish what factors contribute to a successful laundry and dry-cleaning store. Then, you'll use those factors in a suitability analysis to determine a new market area for expansion.

This tutorial was last tested on December 3, 2024, using ArcGIS Pro 3.4. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.

Requirements

• ArcGIS Pro (see options for software access)
• ArcGIS Business Analyst Pro
• ArcGIS Enterprise GeoEnrichment (Enterprise only)

Analyze successful stores in existing markets

You'll use ArcGIS Business Analyst Pro to analyze your nine current laundry and dry-cleaning facilities to understand what geographic attributes indicate a successful store.

Set the data source

First, you'll download and open an ArcGIS Pro project package with data on your facilities and customers. Then, you'll set the data source for the Business Analyst data you'll use.

1. Download Expansion Study.ppkx.

   An ArcGIS Pro package includes the project file (.aprk), the toolbox (.tbx), and the geodatabase (.gdb).

2. Double-click Expansion Study.ppkx to open it in ArcGIS Pro. If prompted, sign in to your ArcGIS organizational account.

   Note:

   If you don't have access to ArcGIS Pro or an ArcGIS organizational account, see options for software access.

   The project contains feature layers for your nine existing laundry facilities in Grand Rapids, Michigan, and a feature layer of customers that has attributes related to the stores they visit. There are also feature layers for gyms and movie theaters, which attract customers who are using your laundry drop-off service. Additionally, there is a feature layer of locations of competitor laundromats.

   

   To discover what attributes are present for your most successful stores, you'll add variables using Business Analyst data. To access the correct data, you'll set the Business Analyst data source to use United States data.

3. On the ribbon, click the Analysis tab. In the Geoprocessing group, click Environments.

   

   The Environments window appears.

4. Scroll to the bottom of the Environments window. Under Business Analyst, confirm that Data Source is set to US (esri2024).

   Note:

   If your data source is different, click the Browse button. In the Business Analyst Data Source window, click North America. Under United States, select Esri 2024 and click OK.

   

   Note:

   ArcGIS Enterprise users need to ensure GeoEnrichment services are configured to view the data sources. To configure the services, you can review the documentation Configure utility services.

5. Click OK.

   The Business Analyst data source is set to access variables in the United States.

Generate trade areas

There are two stores in the Grand Rapids market that outperform the other seven. You'll use the Business Analyst toolbox to find the characteristics unique to these stores that facilitate higher sales.

The first step in your analysis is to create customer-derived trade areas around the stores. These areas capture a specific percentage of customers closest to each store. Alternatively, trade areas can capture other attributes, such as sales. The first set of trade areas you create will capture the closest 70 percent of each store's customers.

1. On the ribbon, click the Analysis tab. In the Geoprocessing group, click Tools.

   

   The Geoprocessing pane appears.

2. In the Geoprocessing pane, click the Toolboxes tab.

   

3. Expand Business Analyst Tools and Trade Areas. Double-click Generate Customer Derived Trade Areas.

   

   The tool opens. You'll set parameters to determine how many customers are captured in each store. The Store ID field is used to associate customers with their primary store.

4. Set the following parameters:
   • For Stores, choose Facilities.
   • For Store ID Field, choose Store ID.
   • For Customers, choose Customers.
   • For Associated Store ID Field, choose Store ID.
   • For Output Feature Class, type TradeArea_Count.
   • For Radii (%), type 70.

   

   The radii value indicates the percentage of each store's customers that will be captured to define store trade area polygons. In this scenario, the closest 70 percent of customers for each store will be encompassed in each trade area.

5. Click Run.

   A new layer showing the customer trade areas is added to the map. There is no overlap between trade areas, indicating distinction of customers by store, which is advantageous.

   

   Next, you'll create trade areas based on 70 percent of sales to customers for each store. To create trade areas based on sales, you'll change the tool parameters to aggregate customer sales based on a weight instead of a count.

6. In the Generate Customer Derived Trade Areas pane, change the following parameters:
   • For Output Feature Class, type TradeArea_Sales.
   • For Customer Aggregation Type, choose Weight.
   • For Customer Weight Field, choose Sales.

   

7. Click Run.

   A new layer showing 70 percent of the sum of sales for each store is added to the map.

   

   The sales trade areas are smaller than the customer trade areas for seven of the nine stores, but for two stores, both trade areas are roughly the same size. This indicates a more even distribution of sales among customers, meaning that more revenue is captured from customers. You'll investigate the Facilities layer's attribute table to understand any differences between these stores and the rest.

8. In the Contents pane, right-click Facilities and choose Attribute Table.

   

9. In the attribute table, right-click the Sales column header and choose Sort Descending.

   

   The stores with the highest sales are Creston and Westside GR. These stores have two temporary parking spots and both are dedicated to the store, meaning they are not shared with other businesses and there is less competition with customers visiting other businesses. Without vacant parking spots, a customer may drive to a different laundry and dry-cleaning store or have to wait until parking is available later. For customers using the premium drop-off service, convenience is attractive, and therefore dedicated parking spots are a high priority.

10. Close the attribute table.
11. On the Quick Access Toolbar, click the Save Project button to save your project.

    

    Tip:

    You can also press Ctrl+S to save your project. If you receive a message saying this project was created using a previous version of ArcGIS Pro, click Yes.

Create a color coded map

Areas with relatively high percentages of renter-occupied housing and relatively high population density positively affect business success. The Color Coded Layer workflow allows you to add demographic variables from Business Analyst as a choropleth layer, which can then be used to evaluate market opportunities. First, you'll add a layer for renter-occupied units.

1. On the Analysis tab, in the Workflows group, click Business Analysis.

   

2. Under General Workflows, click Color Coded Layer.

   

   The Data Browser window appears. It shows the available data organized by category. The data is determined by the project's data source, which you set to the most recent United States data. You'll search for the specific variables you want.

3. In the search bar, type Renter and press Enter.
4. Click 2024 Renter Occupied HUs to select it. Click % to select it and confirm # is not selected.

   

   Note:

   Business Analyst data is updated periodically. Use the latest available data.

5. Click OK.

   The color coded layer is created and added to the map.

   This layer contains multiple geographies, such as states and counties. The layer is scale-dependent and will display the most appropriate geography based on the scale at which you are viewing it. You'll change the area of interest to cover only Michigan, which will give you access to additional levels of detail.

6. In the Symbology pane, for Area of interest, type Michigan and choose Michigan.

   

   The map extent changes to show all of Michigan. Additional levels of detail are added under the Color Coded Layer group layer in the Contents pane.

7. In the Contents pane, right-click Facilities and choose Zoom To Layer.

   The map zooms back to your study area. At this scale, the color coded layer displays using block groups as the geography. However, it's difficult to read the map because there are many overlapping layers. You'll turn some of them off and rearrange others to increase their visibility. You'll also rename the color coded layer to be more descriptive.

8. In the Contents pane, click Color Coded Layer to select it. Click it again to make its name editable and rename it to Renter Occupied HUs.

   

9. Uncheck the TradeArea_Sales, TradeArea_Count, and Customers layers to turn them off.
10. Drag the Facilities layer above the Renter Occupied HUs layer.

    

    The Facilities layer is now visible, but the Renter Occupied HUs layer obscures the basemap. You'll increase the transparency to make it easier to see the underlying streets.

11. In the Contents pane, select Renter Occupied HUs to select it.
12. On the ribbon, click the Color Coded Group Layer tab. In the Effects group, for Transparency, type 50 and press Enter.

    

    The layer's transparency changes.

    

Examine specific stores

Two stores are in areas with a relatively high percentage of renter-occupied housing units, indicated by red shading. You'll select the two stores and investigate further.

1. In the Contents pane, right-click Facilities, point to Selection, and choose Make this the only selectable layer.
2. On the ribbon, click the Map tab. In the Selection group, click the Select button.

   

3. On the map, draw a box around the two stores in red block groups.

   

   The two stores are highlighted, indicating they are selected.

4. In the Contents pane, confirm that the Facilities layer is selected and press Ctrl+T to open its attribute table.

   

   In the attribute table, the two selected stores are highlighted in blue. These are the same stores you previously identified as having the highest sales. Their success in this location indicates that a relatively high percentage of renter-occupied housing can be beneficial for your new location.

5. Close the attribute table.

   You'll run the Color Coded Layer tool again to add population density data, another variable that might affect business success, and see if the two successful stores are in areas with high population density.

6. In the Contents pane, uncheck Renter Occupied HUs to turn it off.
7. On the ribbon, click the Analysis tab. In the Workflows group, click Business Analysis and choose Color Coded Layer.
8. In the Data Browser window, search for Population Density. Select 2024 Population Density.

   

9. Click OK.

   The color coded layer is added to the map.

10. In the Symbology pane, for Area of interest, type Michigan and press Enter.
11. In the Contents pane, rename Color Coded Layer to Population Density.
12. Right-click Facilities and choose Zoom To Layer. Drag the Facilities layer above the Population Density layer.

    Now, you can compare the successful facilities and the population density map.

    

    The two highest-performing stores are also located in areas with relatively high population density, indicated by orange shading. Areas with high population density, indicated by red shading, might be less desirable because they are usually in major urban markets that are saturated with competition and expensive to break into.

13. On the ribbon, click the Map tab. In the Selection group, click Clear.

    

    The two stores are no longer selected.

14. In the Navigate group, click the Explore button.

    Now, you can explore the map as usual, instead of selecting features.

15. In the Contents pane, turn off the Population Density layer.
16. Save the project.

You've used Business Analyst tools and data to analyze variables that affected your two most successful stores. Next, you'll apply this information to conduct a suitability analysis for markets in the surrounding region.

Determine suitable markets

Previously, you analyzed the characteristics of successful stores in your existing market. Next, you'll perform a suitability analysis to determine the best candidate market. A suitability analysis consists of adding criteria, such as population density; weighting that criteria according to how important it is to the success of your store; and calculating a total score based on those weights. The total scores are part of the final suitability analysis layer, which ranks locations to determine the best candidate site.

Create a suitability analysis layer

Before adding criteria, you'll define the area of analysis using the Make Suitability Analysis Layer tool. Currently, you own nine stores in Grand Rapids, Michigan, so you'll define the area of analysis to be in the broader Great Lakes region. This region encompasses portions of the states that border Lake Superior, Lake Michigan, Lake Huron, Lake Erie, and Lake Ontario.

1. In the Contents pane, right-click Candidate Markets and click Zoom To Layer.

   The Candidate Markets layer includes potential market areas in select counties throughout the Midwest region of the United States.

   

   You'll conduct a suitability analysis to find the best market area.

2. On the ribbon, click the Analysis tab. In the Workflows group, click the Business Analysis button.
3. Under General Workflows, choose Suitability Analysis.

   

   In the Geoprocessing pane, the Make Suitability Analysis Layer tool opens.

4. For Input Features, choose Candidate Markets. For Layer Name, type New Market Suitability Analysis.

   

5. Click Run.

   When the tool finishes, the New Market Suitability Analysis layer is added to the Contents pane.

6. In the Contents pane, turn off the Candidate Markets layer.

Add variable-based criteria

In addition to adding variables from Business Analyst as color coded layers, you can add variables as criteria in your suitability analysis. You'll add the following variables:

Business Analyst variable	Description
2024 Renter Occupied HUs (Housing Units): Percentage	Percentage of housing units occupied by renters in 2021
2024 Daytime Population Density	Density of population per square mile only present during business hours
2022 Workers 16+: Walked (ACS 5-Yr): Percentage	Percentage of population age 16 or over that walks to work—determined by the United States Census Bureau's American Community Survey (ACS)
2022 Workers 16+: Public Transportation (ACS 5-Yr): Percentage	Percentage of population age 16 or over that takes public transportation to work—determined by the United States Census Bureau's American Community Survey (ACS)
2024 Coin-Op Apparel Laundry & Dry Cleaning: Index	Tendency of people to spend money on this service, as compared to the average spender
2024 Apparel Laundry/Dry Cleaning: Index	Tendency of people to spend money on this service, as compared to the average spender

Note:

To read more about the variables available in Business Analyst, see the Business Analyst Variable and Report List.

1. In the Contents pane, select New Market Suitability Analysis.
2. On the ribbon, click the Suitability Analysis tab.

   

3. In the Criteria group, click the Add Criteria button.

   

   In the Geoprocessing pane, the Add Variable Based Suitability Criteria tool appears.

4. For Variables, click the add button.

   

   The Data Browser window appears.

5. In the Data Browser window, search for and select the following variables:
   • 2024 Renter Occupied HUs—Select % and deselect #.
   • 2024 Population Density

   The data browser keeps track of your selections in the Selected Variables window. You can open this window at any time to view or edit your selections.

6. Click the Selected Variables button.

   

   The window displays the two variables you previously selected. The Selected Variables button also displays how many variables are selected in total.

7. Click the Selected Variables button to close the window.
8. Search for Apparel and select the following variables:
   • 2024 Coin-Op Apparel Laundry/Dry Cleaning—Select Index and deselect #.
   • 2024 Apparel Laundry/Dry Cleaning—Select Index and deselect #.

   

   The Coin-Op Apparel Laundry/Dry Cleaning variable will be used as a proxy variable for customers who walk to stores. The related Apparel Laundry/Dry Cleaning variable will serve as a proxy for customers who drive to stores.

   Index variables measure the probability of involvement in an activity for a specific area as compared to the national average, which is represented with an index value of 100. Indices above 100 mean that people in the area are more likely to engage in the activity. An index of 200 would indicate that residents are twice as likely to engage in the activity compared to the national average.

9. Search for Workers. Select the following variables:
   • 2022 Workers 16+: Walked (ACS 5-Yr)—Select % and deselect #.
   • 2022 Workers 16+: Public Transportation (ACS 5-Yr)—Select % and deselect #.

   

   There is a positive correlation between walking customers who use coin-operated machines and areas with relatively high percentages of commuters who walk or take public transportation.

10. Confirm you have six selected variables and click OK.

    The selected variables appear in the Add Variable Based Suitability Criteria pane.

    

    Before you run the tool, you'll save the list of variables so you can access them again quickly in subsequent analysis.

11. Under the list, click Save List. Name the list Laundry Market Criteria and click OK.
12. Click Run.

    After the tool finishes, the variables are added to the New Market Suitability Analysis layer as attributes and the layer changes its symbology to display their values.

    

    These are not the final scores for the market areas. You'll add more information on competitors and city size.

Add point layer-based criteria

Next, you'll add criteria on medium-sized cities and competitors. Medium-sized cities, defined as having a population between 100,000 and 350,000, and areas with few competitors are considered an advantage and are correlated to successful business. Conversely, larger cities tend to be saturated with competition and are very expensive to move into.

1. On the ribbon, on the Suitability Analysis tab, in the Criteria group, click the Add Criteria drop-down menu and choose Add Point Layer.

   

2. In the Add Point Layer Based Suitability Criteria pane, set the following parameters:
   • For Input Suitability Analysis Layer, choose New Market Suitability Analysis.
   • For Site Layer ID Field, choose IDField.
   • For Point Features, choose Competitors.

   

3. Click Run.

   Each candidate market is scored based on the count of competitors in it. Next, you'll add criteria for the count of medium-sized cities as centroid points. A centroid is the geometric center of a feature, which in this case will be the center point of the city.

4. For Point Features, choose Mid-sized Cities.

   

5. Click Run.

   All criteria for the candidate market suitability analysis are now added.

6. In the Contents pane, confirm that New Market Suitability Analysis is selected.
7. On the Suitability tab, in the Criteria group, click Suitability Analysis Pane.

   

   The Suitability Analysis pane appears.

   Each variable-based criterion you added from Business Analyst and the two point-based criteria you previously added are available in this pane. Initially, all criteria have equally distributed weighting, but you can adjust this number to indicate greater or lesser importance of the criteria. For now, you'll adjust the influence of competitor variables.

8. In the Suitability Analysis pane, for Competitors Count, change Influence to Inverse.

   

   By default, Influence is set to Positive, which results in higher values receiving a higher score. Since less competition in a candidate market is more attractive, setting Influence as Inverse will return higher scores for lower values. The new suitability score is automatically calculated and reflected on the map.

   

   The final suitability score for each candidate market is returned in three places: the map, the attribute table, and the Contents pane. You'll select the highest-scoring candidate from the attribute table and narrow your analysis area.

9. In the Contents pane, right-click Candidate Sites and choose Attribute Table.
10. In the attribute table, right-click Final Score and choose Sort Descending.

    Note:

    You may have to scroll right to find the attribute.

11. Select the first row and click Zoom To.

    

    The market area with the highest suitability score is Dane County, Wisconsin.

    

12. Close the attribute table and the Suitability Analysis pane. Save the project.

You've successfully narrowed the search for new market expansion by performing a suitability analysis using related criteria. Next, you'll further this analysis by applying the same criteria to submarkets and then potential candidate sites.

Determine suitable submarkets

Previously, you performed a suitability analysis using the Business Analyst tools in ArcGIS Pro to select a suitable market area. Next, you'll explore the suitability of submarkets in Dane County. The submarkets are the size of block groups, which are subdivisions of census tracts and the smallest geographic unit for which demographic statistics are reported. After selecting a suitable submarket, you'll employ the same suitability analysis methods to determine a specific candidate site.

Generate block groups

To explore submarkets, you'll add block groups in Dane County using the Generate Geographies From Overlay tool. When running, if individual features are selected, such as a county, the tool will only return block group geographies that are within that county.

1. On the map, confirm that the feature representing Dane County is selected.

   Note:

   If Dane County is not selected, you can select it either using the attribute table or the Select tool.

2. In the Geoprocessing pane, click the Back button.

   

3. Search for generate geographies and choose Generate Geographies From Overlay.

   

4. In the Generate Geographies From Overlay tool pane, set the following parameters:
   • For Geography Level, choose Block Groups (US.BlockGroups).
   • For Input Features, choose New Market Suitability Analysis\Candidate Sites.
   • For ID Field, choose IDField.
   • For Output Feature Class, type Submarkets.
   • For Relationship, choose Have their center in.

   

   Because Use the selected records is turned on, the tool will only run on Dane County, the selected county.

5. Click Run.

   After the tool runs, the Submarkets layer is added to the Contents pane and appears on the map.

   

   Note:

   The default symbology for your Submarkets layer may differ from the example images.

Create a suitability analysis layer

To identify the submarket with the most potential for expansion, you'll create a suitability analysis layer from the Submarkets layer. You'll employ the same suitability analysis criteria that was previously used to select a market location.

1. On the ribbon, click the Analysis tab. In the Workflows group, click Business Analysis and choose Suitability Analysis.
2. In the Make Suitability Analysis Layer tool pane, for Input Features, choose Submarkets. For Layer Name, type Submarket Suitability Analysis.

   

3. Click Run.

   The Submarket Suitability Analysis layer is added to the Contents pane.

4. In the Contents pane, turn off the Submarkets and New Market Suitability Analysis layers.

Add variable-based criteria

You'll add the same variable-based criteria to the Sub Market Suitability Analysis layer that was used in the New Market Suitability Analysis layer.

1. In the Contents pane, select the Sub Market Suitability Analysis layer.
2. On the ribbon, click the Suitability Analysis tab. In the Criteria group, click the Add Criteria drop-down menu and choose Add Variables From Data Browser.

   The Add Variable Based Suitability Criteria pane appears.

3. Next to Variables, click the Add button.

   The Data Browser window appears. You plan to select the same six variables you used when you determined suitable markets. Because you saved those variables as a list, you can access them quickly, instead of needing to select them all individually.

4. In the Data Browser window, click the Variable Lists tab.

   

5. Double-click Laundry Market Criteria (6). Check the box next to Laundry Market Criteria to select all six variables in the list.

   

6. Click OK.

   The six variables are added to the Add Variable Based Suitability Criteria tool pane.

   

7. Click Run.

   The variables are added to the Submarket Suitability Analysis layer as criteria to be weighted. The layer automatically recalculates the suitability score and updates the layer symbology. The results indicate a cluster of highly suitable block areas near the center of the county, shaded in red.

   

   You'll adjust the transparency of the Sub Market Suitability Analysis layer to better understand the characteristics of these neighborhoods.

8. In the Contents pane, select the Submarket Suitability Analysis layer.
9. On the ribbon, click the Group Layer tab. In the Effects group, adjust the layer transparency to 70.0% and press Enter.

   The basemap is now more visible.

10. Zoom in and explore the city.

    This area contains Madison, Wisconsin, which is a medium-sized city with a population between 100,000 and 300,000. Additionally, the University of Wisconsin–Madison is located here, likely indicating a high volume of foot traffic.

    

    Satisfied that you have identified viable neighborhoods for expansion, you are now ready to review available commercial sites. There are three available sites that you will analyze to pinpoint the ideal location.

11. Save the project.

You've narrowed your market analysis into the submarkets around the city of Madison, Wisconsin. Next, you'll apply the same suitability criteria to determine a suitable candidate site.

Finalize candidate sites

Previously, you determined a suitable submarket in Madison, Wisconsin. You'll apply the same suitability criteria to determine a specific candidate site to expand your business into.

Generate trade area rings

Before starting the final analysis, you'll create half-mile rings around each of the three sites.

1. In the Contents pane, turn on the Candidate Sites layer.
2. In the Geoprocessing pane, click the Back button.

   

3. Search for and open the Generate Trade Area Rings tool.

   Tip:

   You can either search for the tool using the search bar or find it in the Trade Areas toolbox.

4. In the Generate Trade Area Rings tool pane, set the following parameters:
   • For Input Features, choose Candidate Sites.
   • For Output Feature Class, type Candidate_Sites_Rings.
   • For Distances, type 0.5.
   • For Distance Units, confirm that Miles is chosen.
   • For ID Field, choose ID.

   

5. Click Run.

   After the tool runs, half-mile rings are added around each of the three candidate sites.

   

   Since suitability analysis requires polygonal inputs, you'll use these half-mile rings to compare and score the three sites. The workflow will use all three types of criteria, but first you'll create a suitability analysis layer.

6. On the ribbon, click the Analysis tab. In the Workflows group, click Business Analysis and choose Suitability Analysis.
7. In the Make Suitability Analysis Layer tool pane, for Input Features, choose Candidate_Sites_Rings. For Layer Name, type Suitability Analysis Candidate Sites.

   

8. Click Run.

   The tool runs and the Suitability Analysis Candidate Sites layer is added to the Contents pane. The layer has the same features and attributes as Candidate_Sites_Rings, but it can now access the suitability analysis tools.

9. In the Contents pane, turn off Candidate_Sites_Rings.

Add field-based criteria

You'll set field-based criteria using candidate site attributes for the availability and exclusivity of temporary parking spaces. Your analysis of existing stores showed that these attributes correlate to higher sales from premium drop-off service users.

In particular, you'll use the following attributes:

• Tmp Parking Spots—Number of temporary parking spots available
• Pct Parking Assigned—Percentage of temporary parking spots assigned exclusively to the shop

1. In the Contents pane, select the Suitability Analysis Candidate Sites layer.
2. On the ribbon, click the Suitability Analysis tab. In the Criteria group, click the Add Criteria drop-down menu and choose Add Fields From Input Layer.

   The Add Field Based Suitability Criteria pane appears.

3. In the Add Field Based Suitability Criteria pane, set the following parameters:
   • For Input Suitability Analysis Layer, confirm that Suitability Analysis Candidate Sites is selected.
   • For Fields, choose Tmp Parking Spots and Pct Parking Assigned.

   

4. Click Run.

   The Suitability Analysis Candidate Sites layer symbology updates based on the two selected attributes.

   

Add point layer-based criteria

Next, you'll add point layer-based criteria to score each site based on proximity to theaters, gyms, and competitors.

1. If necessary, in the Contents pane, select Suitability Analysis Candidate Sites.
2. On the Suitability Analysis tab, in the Criteria group, click the Add Criteria drop-down menu and choose Add Point Layer.
3. In the Add Point Layer Based Suitability Criteria pane, set the following parameters:
   • For Site Layer ID Field, choose ID.
   • For Point Features, choose Competitors.
   • For Criteria Type, choose Minimal Distance.
   • For Distance Type, confirm that Straight Line is selected.
   • For Measure Units, confirm that Miles is selected.

   

4. Click Run.

   The tool runs and creates the criterion to be scored based on the straight-line distance from each candidate site to the nearest competitor. Earlier, you learned that gyms and theaters have an attracting effect for premium drop-off service customers. These customers have a tendency to visit a gym or theater while their laundry is serviced.

   Next, you'll add theaters as point-based criteria by editing the tool parameters.

5. In the Add Point Layer Based Suitability Criteria tool pane, set the following parameters:
   • For Point Features, choose Theaters.
   • For Criteria Type, choose Count.

   

6. Click Run.

   A criterion is added to each location in the Suitability Analysis Candidate Sites layer based on the count of theaters within each ring area. You'll edit the tool again to add gyms as point-based criteria.

7. In the Add Point Layer Based Suitability Criteria tool pane, for Point Features, choose Gyms.
8. Click Run.

   A criterion based on the count of gyms within each Suitability Analysis Candidate Sites ring area has been created.

Add variable-based criteria

The last type of criteria you'll add to the Suitability Analysis Candidate Sites layer is variable based. You'll use the same list of variables you used for your previous suitability analyses.

1. If necessary, in the Contents pane, select Suitability Analysis Candidate Sites.
2. On the Suitability Analysis tab, in the Criteria group, click the Add Criteria drop-down menu and choose Add Variables From Data Browser.

   The Add Variable Based Suitability Criteria tool pane appears.

3. Next to Variables, click the Add button.
4. In the Data Browser window, click the Variable Lists tab and double-click Laundry Market Criteria (6).
5. Check the box next to Laundry Market Criteria to select all six variables in the list. Click OK.

   The selected variables are added to the Add Variable Based Suitability Criteria pane.

   

6. Click Run.

   The selected variables are added as criteria. The layer symbology updates to reflect the new criteria scores.

Adjust suitability criteria weights

Before calculating the final score, you'll review all suitability criteria in the Suitability Analysis pane. You'll assign weight values to criteria so more important criteria have a larger impact on the suitability score.

1. In the Contents pane, ensure the Suitability Analysis Candidate Sites layer is selected. On the Suitability Analysis tab, in the Criteria group, click Suitability Analysis Pane.

   The Suitability Analysis pane appears. It lists all criteria used to calculate the suitability score.

   The presence of temporary parking has been shown in the existing market to significantly increase premium drop-off service customers, so you'll increase the weight for these criteria.

2. In the Suitability Analysis pane, click Settings.

   

3. Click the Weighting tab.
4. For the Tmp Parking Spots criteria, change the weight to 17 percent. Click the lock button.

   

   Locking the value ensures that changes to other criteria will not affect this weight.

5. For Pct Parking Assigned, change the weight to 17. Click the lock button.

   When weight for a criterion is changed, all other criteria have their weights recalculated so that distribution is even. Since you locked the values for Tmp Parking Spots and Pct Parking Assigned, these criteria were excluded from the redistribution and the values of 17 were preserved.

   You've finished adding and weighting criteria. On the map, the highest-ranked candidate site trade area, based on applied criteria, is shaded in red. In this case, the University Lake candidate is the ideal site.

6. In the Contents pane, turn off the Submarket Suitability Analysis layer.

   

7. Save the project.

You've narrowed your search from nine candidate markets to desirable neighborhoods within the most suitable market, and then pinpointed the best site. Next, to further validate your results, you'll create a series of infographics and summary reports for the selected site.

Generate summary reports

Previously, you determined the final site for your business expansion. Next, you'll generate infographics and reports to learn more about the site. Before running summary reports and infographics, you'll run the Generate Trade Area Rings tool again to create two rings with distances of 0.5 miles and 3 miles. The 0.5-mile trade area represents the area of potential walking customers and the 3-mile trade area represents the area of potential driving customers.

Generate trade area rings

Before creating the trade areas, you'll select the highest-scoring candidate site so that the trade area creation is limited to this single location.

1. In the Contents pane, drag the Candidate Sites layer to the top of the Drawing Order list.

   

2. Right-click the Candidate Sites layer, point to Selection, and choose Make this the only selectable layer.
3. On the ribbon, on the Map tab, in the Selection group, click the Select button.
4. Click the University Lake candidate site point feature, surrounded by the dark red ring in the center of the map, to select it.

   

   Next, you'll create the two ring trade areas.

5. In the Geoprocessing pane, click the Back button. Search for and open the Generate Trade Area Rings tool.
6. Set the following parameters:
   • For Input Features, choose Candidate Sites.
   • For Output Feature Class, type Selected_Site_Rings.
   • For Distances, type 0.5 and press Enter. In the next text box, type 3.
   • For ID Field, choose ID.

   

7. Click Run.

   The 0.5-mile and 3-mile trade area rings are created and added to the map.

8. In the Contents pane, turn off the Suitability Analysis Candidate Sites layer.

   

Create summary reports

You'll use the trade area rings as an input to create summary reports.

1. In the Geoprocessing pane, click the Back button. Search for and open the Summary Reports tool.
2. In the Summary Reports tool pane, for Boundary Layer, choose Selected_Site_Rings.
3. For Create Reports, add Community Profile, Demographic and Income Profile, Housing Profile, and Market Profile.

   

   Next, you'll specify an output location.

4. For Output Folder, click the Browse button.

   

5. In the Output Folder window, under Project, click Folders and select Expansion_Study. Click OK.

   Next, you'll set values for the Report Header Options parameters. These parameters add information to report headers to indicate which input polygon corresponds to each section of displayed data.

6. Expand the Report Header Options section and set the following parameters:
   • For Store ID Field, choose ID.
   • For Store Name Field, choose Name.
   • For Store Latitude Field, choose STORE_LAT.
   • For Store Longitude Field, choose STORE_LON.
   • For Ring ID Field, choose RING.
   • For Area Description Field, choose AREA_DESC.

   

   This tool consumes credits. Before running, you'll estimate the credits to ensure you aren't consuming more credits than you have available.

7. At the top of the tool, click estimate credits.

   

   The tool will consume 40 credits. The amount of available credits in your account is shown.

   Note:

   If you do not have enough credits to run the tool, contact your ArcGIS organization administrator for help acquiring more credits.

8. Click Run.

   After the tool completes, a message appears that allows you to view more details about the tool process and open the report history. Through the tool details, you can directly click the output directory to open the reports.

   At the bottom of the Geoprocessing pane, click View Details.

   

   The details window appears.

9. In the details window, click the Parameters tab. For Output Files, click the first directory that ends with Community Profile.PDF.

   The report appears as a PDF document.

   

   The reports can be viewed on-screen, printed, or shared as files.

10. Close the report and return to ArcGIS Pro.
11. Close the details window and save the project.

Create infographics

Next, you'll create a series of infographics to better understand characteristics of the neighborhood surrounding the expansion site. Infographics are graphically enhanced on-screen reports, created by clicking a point, line, or polygon.

1. On the ribbon, click the Map tab. In the Inquiry group, click the Infographics button.

   

   The pointer changes and a small infographics icon is added to indicate that the tool is active.

2. Click the University Lake feature (the same one you selected before creating the rings around the site).

   An infographic window appears, displaying data aggregated from the feature's underlying administrative boundaries.

   Note:

   The data is aggregated through Data Apportionment, which you can read more about in the Data apportionment documentation.

   

   The Key Facts template displays a quick overview of important suitability variables. You can select from a variety of other templates to view information based on the suitability variables you added. You'll look at the Eating Places template to see information about restaurants close to the location.

   Note:

   Business Analyst data is updated periodically. Updates may result in slightly different infographic and report numbers.

3. For Template, choose Eating Places.

   

   Clicking the plus and minus signs in the templates allows you to zoom in and out. The availability of nearby restaurants can have an attracting quality on your premium drop-off service customers. It is useful to understand those who are nearby, as well as their distance from the shop. Pointing to individual restaurant points in the template map highlights their name and distance from the ring centroid in the left pane.

   Finally, you'll view the Commute Profile template for the same area.

4. For Template, choose Commute Profile.

   

   The template opens and is populated with data, providing information about commuting trends of area residents. This information graphically supplements suitability analysis results. The data in the summary reports and infographic templates further supports your selection of the expansion site.

5. Close the window.
6. Save your project.

In this tutorial, you analyzed your best-performing stores to identify unique characteristics and applied this and other known criteria to a search for the most suitable expansion market. You then created submarkets within the selected market and analyzed them to narrow the search to the most suitable neighborhoods. Your final suitability analysis was conducted on available commercial sites to identify the best location. Finally, you ran summary reports and infographics to validate the site's selection and create supporting information to share.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• The Facilities, Customers, and Candidate Sites layers are fictitious datasets created by the tutorial author.
• The Competitors, Gyms, and Theaters layers are derived from Data Axle data accessed via ArcGIS Business Analyst.
• The Mid-sized Cities layer was derived from the USA Major Cities layer in ArcGIS Living Atlas.
• The Candidate Markets layer was derived from Esri United States demographic and boundary data.
• Banner photo by Jeremy Sallee on Unsplash.
• Cover photo by Kyle Watson.
• World Topographic Map sources: Esri, TomTom, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community