Data Tab

Use the Data tab to select the output data format and the data apportionment method.

Advanced apportionment settings

Different methods are provided to more accurately retrieve data. This is important when a trade area cuts across a demographic layer. More traditional selection methods use an apportionment method based on area alone. This process can compromise accuracy when equally distributed data across geographies is unavailable. In most cases, population or housing is distributed in irregular fashion across geographic boundaries, such as ZIP Codes, counties, or provinces.

Business Analyst offers the following data apportionment methods. You have the ability to modify these settings to reach your desired data retrieval approach.

BDS Performance Indexes

Business Analyst allows you to create custom BDS performance indexes that are optimized for your custom data. Performance indexes allow you to aggregate data and create summary reports efficiently. These performance indexes can be created during Custom Data Setup or you can use the Build Index button, at any time, in Advanced Tolerance Settings. If the Build Index button is active, no indexes currently exist for your custom layer. Custom BDS performance indexes can only be used with the standard BDS geographic levels as a base (block groups, census tracts, ZIP Codes, and so forth). Each performance index is copied to the location where your custom BDS layer is saved. All ESRI Data levels have BDS performance indexes created by default, thus the Rebuild Index button will be inactive.

Distance threshold settings

The data apportionment threshold area settings in Business Analyst are shown in squared area units, such as square miles or kilometers. Extensive research was done to identify data retrieval performance drop-offs by the geographic size of an area, the calculation method, and the data hierarchy. It is because of this that default area thresholds are set for the hybrid, block apportionment, and cascading centroid methods. You can change these settings for your own custom methodology.

This graphic highlights the Data tab options and the default distance settings. Because most trade areas are irregularly shaped, the thresholds are based on the maximum distance of either the height or width.

Data tab

The distance threshold is calculated using the maximum value of a trade area's height and width. Unlike the example above, many trade areas are not perfect circles but are irregular shapes. The example below shows how a maximum value is calculated for an irregularly shaped trade area.

Threshold distances

Block Apportionment versus Cascading Centroid

This section explains the block apportionment and cascading centroid methods when small areas of geography are used. In this example, both methods are used in the exact same area to retrieve data using Spatial Overlay from ZIP Code boundaries.

Use the image below for reference to the following notes:

General Apportionment

The example below shows the block apportionment data apportionment method. ZIP Codes are used in this case. This highlights that block apportionment is more accurate, especially in smaller geographic areas.

HinweisHinweis:

ZIP Codes are used in this example, but you can use any demographic boundary layer. Block groups are effectively used for smaller geographic areas and are set as the block apportionment default layer.

Block Apportionment method

Here is an attribute table output using block apportionment. You can see that the block points included in the trade area are summed to equal the total population figure. So 13,547 + 10,798 + 8,585 + 14,042 + 15,881 + 1,854 + 12,115 + 1,066 = 77,888.

Block Apportionment output

The example below shows the cascading centroid data apportionment method. ZIP Codes are used in this case. This highlights why cascading centroid can be less accurate, especially in smaller geographic areas.

Cascading Centroid

Here is an attribute table output using cascading centroid. You can see that the ZIP Code centroids included in the trade area are summed to equal the total population figure. So 12,749 + 15,881 + 21,676 = 50,306.

Cascading Centroid output

9/16/2010