Analyze successful stores in existing markets

You'll use ArcGIS Business Analyst Desktop to analyze your nine current laundry and dry-cleaning facilities that exist within 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 ArcGIS Pro or an ArcGIS account, you can sign up for an ArcGIS free trial.

  2. Double-click Expansion Study.ppkx.

    Map pane displaying Facilities features and customer features in Grand Rapids, Michigan

    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.
  4. Scroll to the bottom of the Environments pane. Under Business Analyst, for Data Source, click Browse.

    data source selection on the Environments dialog box

  5. In the left pane, under Portal, select North America. Expand United States and select 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.

    Selection of United States dataset for

  6. Click OK, and then click OK again.

    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.

    Tools button in the Geoprocessing group on the Analysis tab

    The Geoprocessing pane appears.

  2. On the Toolboxes tab, expand Business Analyst Tools and Trade Areas, and then 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. For the Generate Customer Derived Trade Areas tool, edit the following parameters:
    • For Stores, choose Facilities.
    • For Store ID Field, choose Store ID.
    • Set Customers as Customers.
    • For Associated Store ID, choose Store ID.
    • For Output Feature Class, type TradeArea_Count.
    • For Radii (%), type 70.

    The radii value indicates the percent 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.

    Configured parameters in the Customer Derived Trade Areas tool

  4. Click Run.

    Result map of customer-derived trade areas that capture 70 percent of customers for each store

    Note:

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

    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.

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

    Configured parameters for the Customer Derived Trade Areas tool

  6. Click Run.

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

    Result map of customer-derived trade areas that capture 70 percent of sales

    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. Right-click the Sales column header and select Sort Descending.

    Facilities attribute table sorted by descending sales

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

    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. For Classification Variable, click Add.

    Color Coded Layer tool with Classification Variable parameter

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

  4. Search for Renter. In the results, expand 2019 Key Demographic Indicators (Esri) and select 2019 Renter Occupied HUs. Select % and deselect #.

    data browser with 2019 Renter Occupied Housing Units percentage selected

    Note:

    Business Analyst data is updated periodically. Please use the latest available data.

  5. Click OK and run the tool.

    Map displaying census tract data on 2019 renter-occupied housing units

    The 2019 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.

  6. In the Contents pane, turn off the TradeArea_Sales, TradeArea_Count, and Customers layers.

    The 2019 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.

  7. In the Contents pane, select 2019 Renter Occupied HUs Layer.
  8. On the ribbon, click the Appearance tab, and in the Effects group, adjust the transparency to 50%.

    Layer transparency option on the Appearance tab set to 50

    As the layer's transparency changes, 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.

    Map results of the Color Coded Layer tool displaying 2019 renter-occupied housing units at 50 percent transparency

  9. In the Contents pane, right-click Facilities, choose Selection, and choose Make this the only selectable layer.
  10. On the ribbon, on the Map tab, in the Selection group, click Select.
  11. In the Map pane, draw a selection box around the two stores.
  12. In the Contents pane, select the Facilities layer and press Ctrl+T to open the attribute table.

    Most successful stores are selected in the attribute table and the selection is reflected in the map.

    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.

  13. Close the attribute table.

    You'll run the Color Coded Layer tool again to add population density data.

  14. In the Geoprocessing pane, next to Classification Variable, click the Add button.
  15. Search for Population density. Then expand 2019 Key Demographic Indicators (Esri) and select 2019 Population Density.
  16. Click OK and click Run.

    Map results of the Color Coded Layer tool displaying 2019 population density by block group

    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.

  17. On the ribbon, on the Map tab, in the Selection group, click Clear.
  18. In the Contents pane, turn off the 2019 Population Density and 2019 Renter Occupied HUs layers.
  19. Close the attribute table.
  20. Press Ctrl+S to 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 select Zoom to Layer.

    The Candidate_Markets layer has been created based on potential market areas in select counties. 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.
    • For Input Features, select Candidate_Markets.
    • For Layer Name, type New Market Suitability Analysis.

    Configured parameters for the Make Suitability Analysis Layer tool

  3. Click Run.

    When the tool completes, the New Market Suitability Analysis layer is added to the Contents pane. Your layer color may be different, but the results are the same.

    Potential candidate market counties in the Great Lakes region

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. Please use the latest available data.

Business Analyst variablesDescription

2019 Renter Occupied HUs (Housing Units): Percentage

Percent of housing units occupied by renters in 2019

2019 Daytime Pop Density

Density of population only present during business hours

ACS Workers 16+: Walked: Percentage

Percent of population at age 16 or over that walks to work—determined by the United States Census Bureau's American Community Survey (ACS)

ACS Workers 16+: Public Transportation: Percentage

Percent of population at age 16 or over that takes public transportation to work—determined by the United States Census Bureau's American Community Survey (ACS)

Apparel Laundry & Dry Cleaning (Not Coin-Op): Index

Tendency of people to spend money on this service, as compared to the average spender

Apparel Laundry & Dry Cleaning (Coin Op): 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, you can view the Business Analyst Variable and Report List.

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

    Contextual Suitability tab under

  3. On the Suitability tab, in the Criteria group, click Add Criteria.

    Suitability analysis tools accessed on the Suitability tab

  4. In the Geoprocessing pane, for Variables, click Add.
  5. In the Data Browser, search for and select the following variables:
    • 2019 Renter Occupied HUs—Select % and deselect #.
    • 2019 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, located in the upper right, below the search bar.

    Selected Variables button in the Data Browser displays two selected variables

    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 again to close the window.
  8. Search for apparel and select the following variables under 2019 Apparel (Consumer Spending):
    • Coin-op Apparel Laundry & Dry Cleaning—Select Index only.
    • Apparel Laundry & Dry Cleaning (Not Coin-Op)—Select Index only.

    Coin-op Apparel Laundry & Dry Cleaning and Apparel Laundry & Dry Cleaning (Not Coin-op) variables selected as Index

    The Coin-Op Apparel Laundry & Dry Cleaning variable will be used as a proxy variable for customers who walk to stores. The related Not Coin-op 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 2013-2017 Population by Journey to Work (ACS):
    Note:

    Business Analyst data is updated periodically. Please use the latest available data.

    • ACS Workers 16+: Public Transportation—Select % and deselect #.
    • ACS Workers 16+: Walked—Select % and deselect #.

    ACS Workers 16+: Public Transportation and ACS Workers 16+: Walked variables selected as %

    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. Click OK.

    Configured parameters for the Add Variable Based Suitability Criteria tool

  11. Click Run.

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

    Map results and Contents pane updated to show the recalculated candidate markets

    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, search for and open Add Point Layer Based Suitability Criteria. Then 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.

    Configured parameters with competitors for the Add Point Layer Based Suitability Criteria tool

  2. Click Run.

    Each candidate market will be scored based on the count of competitors that exist within 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.

  3. For Point Features, choose MidSized_Cities.

    Configured parameters with midsized cities for the Add Point Layer Based Suitability Criteria tool

  4. Click Run.

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

  5. In the Contents pane, ensure New Market Suitability Analysis is selected.
  6. 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.

  7. In the Suitability Analysis pane, for Competitors Count, expand Additional Options.
  8. Under Influence, select Inverse.

    Competitors Count parameter set to Inverse influence

    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 in the map.

    Result map shows the final ranking for candidate markets.

  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 then 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.

    In the attribute pane, sort features by descending Final Score.

  12. Select the first row and click Zoom To.

    Use Zoom To to zoom to the selected feature.

    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 that demographic statistics are reported for. 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, search for and select Generate Geographies From Overlay. Then set the following parameters:
    • For Geography Level, choose 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.

    Configured parameters for the Generate Geographies from Overlay tool

  3. Click Run.

    Block groups are added for the candidate market county.

    After the tool runs, the Sub_Markets layer is added to the Contents pane and appears in 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. More information can be found on the EPA's Walkability Index.

  4. In the Contents pane, right-click Sub_Markets, choose Joins and Relates, and choose Add Join.

    Choose Add Join for the Sub_Markets suitability layer.

  5. In the Add Join tool, edit the following parameters:
    • Ensure Layer Name or Table View is set to Sub_Markets.
    • For Input Join Field, select ID.
    • For Join Table, select WalkabilityIndex.
    • For Output Join Field, select GEOID10.

    Configure parameters for the Add Join tool.

  6. Click Run.

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

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

    The WalkIndex attribute is joined to the Sub_Markets layer.

    The WalkIndex scores are classified as follows:

    Walkability scoreDescription

    1–5.75

    Least walkable

    5.76–10.50

    Below average walkable

    10.51–15.25

    Above average walkable

    15.26–20

    Most walkable

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

  8. 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, search for and select Make Suitability Analysis Layer. Set the tool parameters:
    • For Input Features, choose Sub_Markets.
    • For Layer Name, type Sub Market Suitability Analysis.

    Configure parameters for the Make Suitability Analysis Layer tool.

  2. Click Run.

    The Sub_Markets 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 that exist within a layer's attribute table. In this case, the WalkIndex attribute, which you previously Joined, will be used.

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

    In the Suitability tab, select the Add Fields from Input Layer tool.

    The Add Field Based Criteria Geoprocessing tool opens.

  3. From the Fields drop down, select WalkIndex then click Add.

    For the Field parameter, add 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 resymbolizes 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, select Sub Market Suitability Analysis.
  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 opens.

  3. For Variables, click Add.
  4. In the Data Browser, double-click Housing.

    In the Data Browser, select the Housing category.

  5. Click Owner & Renter.
  6. Expand 2019 Key Demographic Indicators (Esri) and select 2019 Renter Occupied HUs. Select % and deselect #.

    In the Owner & Renter category, select 2019 Renter Occupied HUs and percent.

    Note:

    Business Analyst data is updated periodically. Please use the latest available data.

  7. In the left pane, under United States (Standard), click Categories.
  8. Search for Population Density. Then, under 2019 Key Demographic Indicators (Esri), select 2019 Population Density.

    Next, you will select the Consumer Expenditure Variable Coin-Op Apparel Laundry and Dry Cleaning Index variables.

  9. Search for Apparel Laundry & Dry Cleaning.
  10. Expand 2019 Apparel (Consumer Spending) and select Coin-op Apparel Laundry & Dry Cleaning and Apparel Laundry & Dry Cleaning (Not Coin-Op). For both layers, 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 ACS Workers.
  12. Expand 2013-2017 Population by Journey to Work (ACS) and select ACS Workers 16+: Public Transportation and ACS Workers 16+: Walked. For both layers, select % and deselect #.
  13. Click OK.

    All six variables are added to the tool.

    All six variables are added to the Add Variable Based Suitability Criteria 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 resymbolizes. The results indicate a cluster of highly suitable block areas near the center of the county, shaded in red. You'll adjust transparency of the Sub Market Suitability Analysis layer to better understand the characteristics of these neighborhoods.

    Result map displaying submarket suitability analysis results

  15. In the Contents pane, select the Sub Market Suitability Analysis layer.
  16. On the ribbon, click the Appearance tab. In the Effects, group, adjust the layer transparency to 70.

    The layer's transparency is adjusted to 70 percent.

  17. In the Contents pane, ensure all layers except Sub Market Suitability Analysis and World Topographic Map are turned off.

    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.

    Map zoomed in to the block groups with the highest suitability score

    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. Nex, 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, search for and select Generate Trade Area Rings. Then set the following parameters:
    • For Input Features, choose Candidate_Sites.
    • For Output Feature Class, type Candidate_Sites_Rings.
    • For Distances, type .5.
    • For Distance Units, ensure Miles is chosen.
    • For ID Field, choose ID.

    Configure parameters for the Generate Trade Area Rings tool.

  3. Click Run.

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

    Map results display 0.5-mile rings around 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, search for and select Make Suitability Analysis Layer. For the tool, set the following parameters:
    • For Input Features, choose Candidate_Sites_Rings.
    • For Layer Name, type Suitability Analysis Candidate Sites.

    Configure parameters for the Make Suitability Analysis Layer tool.

  2. Click Run.

    The tool runs and the suitability analysis layer is created. 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 attributes:

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

  1. In the Contents pane, select Suitability Analysis Candidate Sites.
  2. On the Suitability tab, in the Criteria group, click the Add Criteria drop-down menu and select Add Fields from Input Layer. Then edit the following parameters:
    • For Input Suitability Analysis Layer, ensure Suitability Analysis Candidate Sites is selected.
    • For Fields, click the Add Many button and select Tmp Parking Spots and Pct Tmp Parking Assigned. Then click Add.

    Configure parameters for the Add Field Based Suitability Criteria tool.

  3. Click Run.

    The tool runs. The two selected attributes are formatted 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, select Suitability Analysis Candidate Sites.
  2. On the Suitability tab, in the Criteria group, click the Add Criteria drop-down menu and select Add Point Layer. Then 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, choose Straight Line.
    • For Measure Units, choose Miles.

    Configure parameters for the Add Point Layer Based Suitability Criteria tool.

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

  4. For the Add Point Layer Based Suitability Criteria tool, edit the following parameters:
    • For Point Features, choose Theaters.
    • For Criteria Type, choose Count.

    Configure parameters for the Add Point Layer Based Suitability Criteria tool.

  5. Click Run.

    Processing completes, and a criterion based on the count of theaters that fall within each Suitability Analysis Candidate Sites polygon has been created. Next, you will edit the tool again to add gyms as point-based criteria.

  6. For the Add Point Layer Based Suitability Criteria tool, for Point Features, choose Gyms.
  7. Click Run.

    Processing completes, and a criterion based on the count of gyms that fall within each Suitability Analysis Candidate Sites polygon has been created.

    In the lesson 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.

Add variable-based criteria

The last type of criteria to be added to suitability analysis candidate sites is variable based.

  1. In the Contents pane, 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 opens.

  3. For Variables, click Add.

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

    Note:

    Business Analyst data is updated periodically. Please use the latest available data.

  4. In the Data Browser, double-click Housing and double-click Owner & Renter.
  5. Under 2019 Key Demographic Indicators (Esri), for 2019 Renter Occupied HUs and select % and deselect #.
  6. Under United States (Standard), click Categories.
  7. Search for Population Density.
  8. Under 2019 Key Demographic Indicators (Esri), select 2019 Population Density.
  9. Search for Apparel Laundry.
  10. Under 2019 Apparel (Consumer Spending), select Coin-Op Apparel Laundry & Dry Cleaning and Apparel Laundry & Dry Cleaning(Not Coin-Op). Next to the variables, select Index and deselect #.
  11. Click OK.

    Variables are added to the tool from the Data Browser.

  12. Click Run.

    The tool processes and criteria are added.

Adjust suitability criteria weights

Before calculating the final score, all suitability criteria will be reviewed in the Suitability Analysis pane.

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

    On the Suitability tab, click Suitability Criteria.

    The Suitability 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 Tmp Parking Spots, do the following:
    • For Weight, type 17.
    • Next to Weight, click Lock.

    Adjust the weight of the Tmp Parking Spots criterion.

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

  3. For Pct Parking Assigned, set Weight to 17, and then 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.

    Map results of the candidate site suitability analysis

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 .5 miles and 3 miles. The .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 ring areas

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. On the ribbon, on the Map tab, in the Selection group, click Select.
  2. Click the University Lake candidate site point feature, surrounded by the dark red ring in the center of the map, to select it.

    Highest-scored candidate site, University Lake, is selected.

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

  3. In the Geoprocessing pane, search for and select Generate Trade Area Rings. Edit the following parameters:
    • For Input Features, choose Candidate_Sites.
    • For Output Feature Class, type Selected_Site_Rings.
    • For Distances, type .5, press Enter, and then type 3 in the next text box.
    • For ID Field, choose ID.

    Configure parameters for the Generate Trade Area Rings tool.

  4. Click Run.

    The .5-mile and 3-mile trade ring areas are created and added to the map.

Create summary reports for selected site trade areas

You'll use the trade ring areas as an input to create summary reports.

  1. In the Geoprocessing pane, search for and select Summary Reports. Edit the following parameters:
    • For Boundary Layer, choose Selected_Site_Rings.
    • For Create Reports, click Add Many and select Community Profile, Graphic Profile, Housing Profile, and Market Profile. Then click Add.

    For the Summary Reports tool, select reports to be generated.

    The four selected report templates are loaded to the tool. You'll specify an output location.

    Configured parameters for the Summary Reports tool

  2. For Output Folder, 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.

  3. Expand Report Header Options and configure 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.

    Summary Reports tool report header options

  4. 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.

    Tool completion notification options to view details

  5. In the Details pane, next to 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 View Details window displays information on the generated reports.

    The report opens.

    Housing profile summary report for University Lakes

    The reports can be viewed on-screen, printed, or shared as files. Before you finish the lesson, 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.

  6. Close the report and return to ArcGIS Pro.

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 Infographics button is located in the Inquiry group on the Map tab.

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

    An infographic of key facts for University Lake

    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.

    An infographic of nearby restaurants for University Lake

    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 Transportation to Work template for the same area.

  4. For Template, choose Transportation to Work.

    Infographic of Transportation to Work variables for University Lake

    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 lesson, 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 lessons in the Learn ArcGIS Lesson Gallery.