Available with Business Analyst license.
The Measure Cannibalization tool calculates the amount of overlap between two or more polygons. This overlap refers to the area where polygon boundaries intersect. The cannibalized area could indicate whether there are enough potential customers/population to support a new facility. It could also indicate where you have operational inefficiencies, or market redundancy.
When the Measure Cannibalization tool is run, it creates a polygon feature class. The output features show the area of overlap, or cannibalization, between the input features. The attributes table of the output feature class includes statistics that describe the overlap of the affected input polygons. If variables are added under Additional Metrics, you will see additional cannibalization statistics that will help you assess the cannibalization impact and guide trade area revisions. This information is exported into a report when the Create Report option is enabled.
How the statistics are calculated
The Measure Cannibalization tool calculates two types of statistics and returns them in the output attribute table: geographic statistics and cannibalization statistics.
Geographic statistics compare the geographic shape of the input features with each other and with the output area of overlap. Cannibalization statistics compare the variables chosen, the amount of that variable within the geographic boundary of the input features, and the amount of those variables within the area of overlap.
Example tool workflow
Consider an analysis in which you are assessing the impact of opening a new store (Store 2) whose trade area is going to overlap with an existing store's (Store 1) trade area. You want to know how much overlap there is between the trade areas and what proportion of the overlap falls into each store's trade area. Additionally, you want to analyze how much the cannibalization between the trade areas will affect the amount of 2021 Total Daytime Population shopping at each store. You will add this variable under Additional Metrics to generate cannibalization statistics.
The two stores and their trade areas are displayed below as light-yellow polygons and labeled with their trade area descriptions.
The output feature class from the Measure Cannibalization tool creates a polygon showing the area of overlap between the two trade areas. This polygon is displayed in green.
The geographic and cannibalization statistics will be returned in the attribute table of the output feature class.
Geographic statistics
The geographic statistics are calculated by comparing the Shape_Area field from the input features (Store 1 and Store 2 polygons) and the Shape_Area field from the output feature class (polygon area that shows the overlap in green).
The field definitions are as follows:
| Field | Description |
|---|---|
ID | The trade areas being compared—-for example, 1 vs. 2 shows the comparison of 1 as compared to 2. |
NAME | The trade areas being compared—for example, Store 1 vs. Store 2. |
ID1/ID2 | Trade areas individually. Populated using the input Trade Area ID field. |
NAME1/NAME2 | Trade areas individually. Populated using the input Trade Area Description field. |
Proportion of Areas: ID 1 to ID2 | The comparison of the total area of the trade areas. In this case, Store 1's trade area is 125.5 percent as large as Store 2's trade area. |
Proportion of Areas: Overlap | The proportion of the two trade areas that overlap. In this case, 5 percent of the trade areas overlap. |
| Proportion of Areas: ID1 within ID2 | The proportion of Store 1's trade area that falls within Store 2's trade area. In this case, 8.5 percent of Store 1's trade area lies within Store 2's trade area. |
Proportion of Areas: ID2 within ID1 | The proportion of Store 2's trade area that falls within Store 1's trade area. In this case, 10.7 percent of Store 2's trade area lies within Store 1's trade area. |
Cannibalization statistics
When variables are added under Additional Metrics, statistics for each variable are returned. The calculation of these statistics depends on the enriched variable values for each trade area and the area of overlap.
The tool will first perform a data apportionment within the trade areas and the overlap area using the input variable. These enriched values are used to calculate the below proportion statistics.
| Field | Description |
|---|---|
2023 Total Daytime Population | The amount of variable in the overlap region. In this case, the Total Daytime population in the area of overlap is 39,254. |
Proportion of 2023 Total Daytime Population: ID 1 to ID 2 | The comparison of the amount of variable in the trade areas. In this case, the Total Daytime Population in Store 1's trade area is 70.7 percent of the variable in Store 2's trade area. |
Proportion of 2023 Total Daytime: Overlap | The proportion of the variable value in the overlap area when compared to the value in the combined area (variable value in 1 and 2 excluding variable value in overlap area). In this case, 13.9 percent of the Total Daytime Population is in the overlap area when compared to Total Daytime Population in the combined area. |
Proportion of 2023 Total Daytime: ID 1 within ID 2 | The proportion of the variable value in Store 1's trade area that falls within Store 2's trade area (the overlap portion). In this example, 29.5 percent of the Total Daytime Population from trade area 1 lies within the overlap area. |
Proportion of 2023 Total Daytime: ID 2 within ID 1 | The proportion of the variable value in Store 2's trade area that falls within Store 1's trade area (the overlap portion). In this example, 20.8 percent of the Total Daytime Population from trade area 1 lies within the overlap area. |
Note:
The enrichment values of variables for each trade area are not returned in the attribute table. This information is returned in the optional Measure Cannibalization report.
Review the report
Enabling the Create Report option in the Measure Cannibalization tool will generate a report. The report provides an understanding of the overlap between trade areas and summarizes the key cannibalization statistics that help you assess the impact of the overlap on each trade area.