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 June 23, 2023, using ArcGIS Pro 3.1. 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
• Publisher or Administrator role in an ArcGIS organization or ArcGIS Enterprise
• ArcGIS Enterprise GeoEnrichment (Enterprise only)

Analyze successful stores in existing markets

You'll use ArcGIS Business Analyst Desktop to analyze your nine current laundry and dry-cleaning facilities in a single market area to understand what geographic attributes indicate a successful store. Then, you'll apply that information as criteria to search for a new market, narrow it to a submarket, and finally pinpoint a candidate site.

Set the Business Analyst data source

First, you'll download and open the ArcGIS Pro project package.

1. Download Expansion Study.ppkx to a known location on your computer.

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

   Note:

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

2. Double-click Expansion Study.ppkx.

   The project opens in ArcGIS Pro.

   

   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 Business Analyst 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 and under Business Analyst, for Data Source, confirm it is set to United States (Standard).

   

   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 now set to access variables in the United States.

Generate customer-derived trade areas

There are two stores in the Grand Rapids, Michigan, market that outperform the other seven. You'll use the Business Analyst toolbox to find the characteristics unique to the two highest-performing 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 created 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, expand Business Analyst Tools and Trade Areas, and double-click Generate Customer Derived Trade Areas.

   

   The tool opens in the Geoprocessing pane. You will 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.

3. In the Generate Customer Derived Trade Areas pane, set the following parameters:
   • For Stores, choose Facilities.
   • For Store ID Field, choose Store ID.
   • Set Customers as 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.

4. Click Run.

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

   

   Note:

   The color of your layer may differ, but the results are the same.

   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 edit the tool to aggregate customer sales based on a weight instead of a count.

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

   

6. 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.

7. In the Contents pane, right-click Facilities and select Attribute Table.

   

8. In the attribute table, right-click the Sales column header and select 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.

9. Close the Attribute table.
10. On the Quick Access Toolbar, click the Save button to save your project.

    

    Tip:

    You can also press Ctrl+S to save your project.

Add demographic variables as color-coded layers

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

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

   

2. In the Business Analyst Tools toolbox, expand the Analysis toolset and double-click Color Coded Layer.

   

   The Color Coded Layer tool pane appears.

   The first parameter is Classification Variable, which allows you to select demographic information for data enrichment. Previously, you set your Business Analyst data source to the United States, so you will have access to United States demographics.

3. In the Color Coded Layer tool pane, for Classification Variable, click Add.

   

   The Data Browser appears to show the data organized by category. You can explore data by category, or you can search for specific variables.

4. In the search bar, type Renter and press Enter.
5. In the results, expand 2021 Key Demographic Indicators (Esri) and choose 2021 Renter Occupied HUs. Click % and deselect #.

   

   Note:

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

6. Click OK.
7. In the Color Coded Layer tool pane, click Run.

   The 2021 Renter Occupied HUs layer is added to the map.

   

   In the Contents pane, you can see that this choropleth layer contains multiple geographies, such as states, counties, and so on. This layer is scale-dependent and will display the most appropriate geography based on the scale at which you are viewing it.

8. In the Contents pane, uncheck the TradeArea_Sales, TradeArea_Count, and Customers layers to turn them off.
9. Drag the Facilities layer above the 2021 Renter Occupied HUs Layer.

   

   The Facilities layer is now visible, but the 2021 Renter Occupied HUs Layer obscures the basemap and blends in with the Facilities features. You'll adjust the transparency to make it easier to see the underlying streets and the Facilities layer.

10. In the Contents pane, select 2021 Renter Occupied HUs Layer to select it.
11. On the ribbon, click the Group Layer tab, and in the Effects group, for Transparency, type 50 and press Enter.

    

    The layer's transparency changes, and you can more easily locate your stores. Notice that two stores are in areas with a relatively high percentage of renter-occupied housing units, which is indicated by red shading. You'll select the two stores and further explore how they perform in areas with high renter housing.

    

12. In the Contents pane, right-click Facilities, point to Selection, and choose Make this the only selectable layer.
13. On the ribbon, on the Map tab, in the Selection group, click Select.

    

14. On the map, draw a selection box around the two stores.

    

    The two stores highlight, indicating they are selected.

15. In the Contents pane, ensure the Facilities layer is selected and press Ctrl+T to open the 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.

16. Close the attribute table.

    Next, you'll run the Color Coded Layer tool again to add population density data.

17. In the Color Coded Layer tool pane, next to Classification Variable, click the Add button.

    

18. In the Data Browser window, search for Population density. Under 2021 Key Demographic Indicators (Esri), select 2021 Population Density.
19. Click OK and in the Color Coded Layer tool pane, click Run.

    The 2021 Population Density layer is added to the map.

20. In the Contents pane, drag the Facilities layer above the 2021 Population Density layer.

    The Facilities layer is now visible.

    

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

21. On the ribbon, on the Map tab, in the Selection group, click Clear.

    

    The two stores are no longer selected.

22. In the Contents pane, turn off the 2021 Population Density and 2021 Renter Occupied HUs layers.
23. Save the project.

    These variables, along with other relevant data, will be applied as suitability analysis criteria to determine a market for expansion.

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 need to 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 narrow the best market area.

2. In the Geoprocessing pane, click Back. Search for and select the Make Suitability Analysis Layer tool.

   

3. In the Make Suitability Analysis Layer tool pane, for Input Features, select Candidate_Markets. For Layer Name, type New Market Suitability Analysis.

   

4. Click Run.

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

   

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. This is done by selecting the suitability analysis layer, New Market Suitability Analysis, and running the Add Variable Based Suitability Criteria tool.

You'll add the following variables:

Note:

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

Business Analyst variable	Description
2021 Renter Occupied HUs (Housing Units): Percentage	Percentage of housing units occupied by renters in 2021
2021 Daytime Population Density	Density of population per square mile only present during business hours
2019 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)
2019 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)
2021 Coin-Op Apparel Laundry & Dry Cleaning: Index	Tendency of people to spend money on this service, as compared to the average spender
2021 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. On the Suitability tab, in the Criteria group, click Add Criteria.

   

   The Add Variable Based Suitability Criteria tool pane appears.

4. In the Make Suitability Analysis Layer pane, for Variables, click Add.

   

   The Data Browser window appears.

5. In the Data Browser window, search for and select the following variables:
   • 2021 Renter Occupied HUs—Click % and deselect #.
   • 2021 Population Density

   Note:

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

   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, located in the upper right, below the search bar.

   

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

7. Click Selected Variables to close the window.
8. Search for apparel, expand 2021 Apparel (Consumer Spending) and select the following variables:
   • 2021 Coin-Op Apparel Laundry & Dry Cleaning—Select Index and deselect #.
   • 2021 Apparel Laundry/Dry Cleaning—Select Index and deselect #.

   

   The 2021 Coin-Op Apparel Laundry & Dry Cleaning variable will be used as a proxy variable for customers who walk to stores. The related 2021 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 ACS Workers and select the following variables under 2015-2019 Population by Journey to Work (ACS):
   • 2019 Workers 16+: Public Transportation (ACS 5-Yr)—Select % and deselect #.
   • 2019 Workers 16+: Walked (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.

    

11. Click Run.

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

    

    However, 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. In the Geoprocessing pane, click the Back button. Under Suitability Analysis, double-click Add Point Layer Based Suitability Criteria.

   

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 will be scored based on the count of competitors in it. Next, you'll add criteria for the count of medium-sized cities (also known as midsized cities), 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 MidSized_Cities.

   

5. Click Run.

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

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

   

   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 here. 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, expand Additional Options. Under Influence, choose 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.

   

9. Close the Suitability Analysis pane.

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

10. In the Contents pane, right-click New Market Suitability Analysis and select Attribute Table.
11. In the attribute table, right-click Final Score and choose Sort Descending.

    Note:

    You may have to scroll right to find the attribute.

    

12. Select the first row and click Zoom To.

    

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

    

13. 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 will employ the same suitability analysis methods to determine a specific candidate site.

Generate block groups for submarkets

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. If necessary, from the New Market Suitability Analysis layer, ensure Dane County is selected.
2. In the Geoprocessing pane, click the back button, search for generate geographies, and select Generate Geographies From Overlay.

   

3. 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.
   • For ID Field select IDField.
   • For Output Feature Class, type Sub_Markets.
   • For Relationship, choose Have their center in.

   

4. Click Run.

   

   After the tool runs, the Sub_Markets layer is added to the Contents pane and appears on the map. You'll employ the same suitability analysis criteria that was previously used to select a market location with the addition of including a walkability index variable.

   The Walkability Index is a dataset created by the United States Environmental Protection Agency (EPA) that characterizes all block groups based on how suitable the area is for walking as a means of transportation. Areas with high walkability correlate with higher usage of coin-operated laundry facilities.

   Note:

   More information can be found on the EPA's Walkability Index.

5. In the Contents pane, right-click Sub_Markets layer, point to Joins and Relates, and choose Add Join.

   

   The Add Join window appears.

6. In the Add Join window, enter the following parameters:
   • Confirm Input Table is set to Sub_Markets.
   • For Input Join Field, select ID.
   • For Join Table, select WalkabilityIndex.
   • For Join Table Field, select GEOID10.

   

7. Click OK.

   After the tool runs, the walkability index attribute, WalkIndex, is added to the Sub_Markets layer.

8. In the Contents pane, select the Sub_Markets layer and press Ctrl+T to open the attribute table and view the WalkIndex attribute.

   

   The WalkIndex scores are classified as follows:

   Walkability score	Description
   1.00 - –5.75	Least walkable
   5.76 – 10.50	Below average walkable
   10.51 – 15.25	Above average walkable
   15.26 – 20.00	Most walkable

   You'll use the WalkIndex scores later in the submarket suitability analysis.

9. Close the attribute table.

Create a suitability analysis layer

To identify the submarket with the most potential for expansion, you'll create a suitability analysis layer from the Sub_Markets layer.

1. In the Geoprocessing pane, click the back button and search for and double-click the Make Suitability Analysis Layer tool.
2. In the Make Suitability Analysis Layer tool pane, for Input Features, choose Sub_Markets. For Layer Name, type Sub Market Suitability Analysis.

   

3. Click Run.

   The Sub Market Suitability Analysis layer is added to the Contents pane.

Add field-based criteria

The first criterion that you will add is the Walkability Index, which will be added as field-based criteria. Field-based criteria are created from fields in a layer's attribute table. In this case, you will use the WalkIndex attribute.

1. In the Contents pane, if necessary, select Sub Market Suitability Analysis.
2. On the Suitability Analysis tab, in the Criteria group, click the Add Criteria drop-down menu, and select Add Fields from Input Layer.

   

   The Add Field Based Suitability Criteria tool pane appears.

3. In the Add Field Based Suitability Criteria pane, for Fields, choose WalkIndex.

   

4. Click Run.

   After the tool completes, the WalkIndex field is added as an attribute to the Sub Market Suitability Analysis layer, and the map symbology updates based on the criteria.

   

Add variable-based criteria

You'll now 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, if necessary, select the Sub Market Suitability Analysis layer.
2. On the Suitability 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.

4. In the Data Browser window, double-click Housing.

   

5. Double-click Owner & Renter.
6. If necessary, expand 2021 Key Demographic Indicators (Esri) and check the box for 2021 Renter Occupied HUs. Select % and deselect #.

   

   Note:

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

7. Under United States (Standard), click Categories.

   

8. Search for Population Density. Under 2021 Key Demographic Indicators (Esri), check the box for 2021 Population Density.

   Next, you will select the laundry and dry cleaning-related variables.

9. Search for Apparel.
10. Expand 2021 Apparel (Consumer Spending) and check the boxes for 2021 Coin-op Apparel Laundry & Dry Cleaning and 2021 Apparel Laundry & Dry Cleaning. For each variable, select Index and deselect #.

    The last two variables to be added as criteria are the percentage of workers who commute by public transportation and by walking.

11. Search for Workers.
12. If necessary, expand 2015-2019 Population by Journey to Work (ACS) and check the boxes for 2019 Workers 16+ (ACS 5-Yr): Public Transportation and 2019 Workers 16+ (ACS 5-Yr): Walked. For each variable, select % and deselect #.
13. Confirm you have six selected variables and click OK.

    All six variables are added to the tool.

    

14. Click Run.

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

    

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

15. In the Contents pane, turn off all layers except the Sub Market Suitability Analysis and World Topographic Map layers.

    Note:

    You can turn off all the layers in the Contents pane by pressing Ctrl and uncheck one layer to uncheck all the layer.

16. Select the Sub Market Suitability Analysis layer and on the ribbon, click the Appearance tab. In the Effects group, adjust the layer transparency to 70 and press Enter.

    The basemap is now more visible.

    This area contains the city of 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 further analyze to pinpoint the ideal location.

You've further 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 will 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, and search for and select Generate Trade Area Rings.
3. 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, ensure Miles is chosen.
   • For ID Field, choose ID.

   

4. Click Run.

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

   

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

Rank candidate sites' trade areas

You will initiate a final suitability analysis to determine the best available site from the set of candidates.

1. In the Geoprocessing pane, click the back arrow, and search for and select Make Suitability Analysis Layer.
2. In the Make Suitability Analysis Layer tool pane, for Input Features, choose Candidate_Sites_Rings. For Layer Name, type Suitability Analysis Candidate Sites.

   

3. 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. Next, you'll add criteria sourced from all three types as follows.

Add field-based criteria

Field-based criteria will be created from candidate site attributes pertaining to 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.

You'll create field-based suitability criteria from 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, if necessary, click the Suitability Analysis Candidate Sites layer.
2. On the Suitability Analysis tab, in the Criteria group, click the Add Criteria drop-down menu and select 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, ensure Suitability Analysis Candidate Sites is selected.
   • For Fields, click the Add Many button, check the boxes for Pct Parking Assigned and Tmp Parking Spots, and click Add.

   

4. Click Run.

   The Suitability Analysis Candidate Site layer symbology updates based on the two selected attributes as criteria to be scored.

   

Add point layer-based criteria

You will next add point layer-based criteria to score each site based on proximity to theaters, gyms, and competitors.

1. In the Contents pane, if necessary, select Suitability Analysis Candidate Sites.
2. On the Suitability Analysis tab, in the Criteria group, click the Add Criteria drop-down menu and select 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 Straight Line is selected.
   • For Measure Units, confirm 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.

   In the tutorial introduction, 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 will 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. Next, you will 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 to be added to the Suitability Analysis Candidate Sites layer is variable based.

1. In the Contents pane, if necessary, select Suitability Analysis Candidate Sites.
2. On the Suitability 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.

   The Data Browser window appears. You'll add the same criteria you used for the previous site suitability assessments.

   Note:

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

4. In the Data Browser window, double-click Housing and double-click Owner & Renter.
5. Under 2021 Key Demographic Indicators (Esri), for 2021 Renter Occupied HUs, select % and deselect #.
6. Under United States (Standard), click Categories.
7. Search for Population Density.
8. Under 2021 Key Demographic Indicators (Esri), select 2021 Population Density.
9. Search for Apparel Laundry.
10. Under 2021 Apparel (Consumer Spending), select 2021 Coin-Op Apparel Laundry & Dry Cleaning and 2021 Apparel Laundry & Dry Cleaning. Next to the variables, select Index and deselect #.
11. Confirm that you have four selected variables and click OK.

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

    

12. Click Run.

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

Adjust suitability criteria weights

Before calculating the final score, all suitability criteria will be reviewed in the Suitability Analysis pane, where you can assign weight values to criteria.

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

   

   The Suitability Analysis pane appears, and all criteria are displayed.

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

2. For the Tmp Parking Spots criteria, for Weight, type 17. Click the Lock button.

   

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

3. For Pct Parking Assigned, set Weight to 17, and click the Lock button.

   When weight for a criterion is changed, all other criteria have 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 preserved.

   Scores are recalculated, and 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.

4. Turn off all layers except Suitability Analysis Candidate Sites, Candidate_Sites, and World Topographic Map.
5. In the Contents pane, drag the Candidate_Sites layer above the Suitability Analysis Candidate Sites layer.

   

6. Save the project.

You've methodically 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 run a series of infographics and summary reports on 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 will 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 will select the highest-scoring candidate site so that the trade area creation is limited to this single location.

1. In the Contents pane, right-click Candidate_Sites, point to Selection, and select Make this the only selectable layer.
2. On the ribbon, on the Map tab, in the Selection group, click Select.
3. Click the University Lake candidate site point feature, surrounded by the dark red ring in the center of the map, to select it.

   

   With the site selected, you will now create the two ring trade areas to be reported on.

4. In the Geoprocessing pane, click the back button, and search for and select Generate Trade Area Rings.
5. In the Generate Trade Area Rings tool pane, 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. Click Add another and type 3 in the next text box.
   • For ID Field, choose ID.

   

6. Click Run.

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

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

   

Create summary reports for selected site trade areas

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

1. In the Geoprocessing pane, click the back button and search for and choose Summary Reports.
2. In the Summary Reports tool pane, set the following parameters:
   • For Boundary Layer, choose Selected_Site_Rings.
   • For Create Reports, click the Add Many button and check the boxes for Community Profile, Demographic Profile, Housing Profile, and Market Profile. Click Add.

   

   The four selected report templates are loaded to the tool.

   

   Next, you'll specify an output location.

3. For Output Folder, click the Browse button and browse to a desired location to save your reports.

   Note:

   You can choose your project folder.

   The output templates created during processing will be stored within the path specified in the Output Folder parameter. Before running the tool, you will 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.

4. 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.

   

5. Click Run.

   After the tool completes, a message appears that allows you to view details and open the report history. By clicking View Details, you can examine more information about the process. Specifically, 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.

6. In the details window, for Output Files, click the first directory that ends with Community Profile.PDF.

   

   Note:

   Depending on where you saved the reports, your path may look different. Alternatively, you can browse to the output folder and open the Community Profile PDF from there.

   The report appears as a PDF document.

   

   The reports can be viewed on-screen, printed, or shared as files. Before you finish the tutorial, you will 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. The available templates provide readily consumable insight about the selected area.

7. Close the report and return to ArcGIS Pro.
8. Close the details window and save the project.

Create infographics for selected site trade areas

You will create and view Key Facts, Nearby Restaurants, and Transportation to Work infographics for the expansion site trade areas.

1. On the ribbon, on the Map tab, in the Inquiry group, click Infographics.

   

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

2. Click the University Lake (middle) feature.

   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 Nearby Restaurants template to see which restaurants are closest to the location.

   Note:

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

3. For Template, choose Nearby Restaurants.

   

   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. Hovering over 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 and close 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

• Walkability Index data was accessed from the U.S. Environmental Protection Agency (EPA).