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Data apportionment
ArcGIS Pro uses a Weighted Centroid geographic retrieval methodology to aggregate data for rings and other polygons. The Weighted Centroid retrieval approach uses census block data to better apportion block groups that are not exclusively contained in a polygon. You can apply the following apportionment methods to data fields:
- NONE—No apportionment is used.
- GEOM—Uses the geographic area of a polygon. No block point apportionment is used.
- POP_W—Uses weighted population from the decennial census year.
- HH_W—Uses weighted households from the decennial census year.
- HU_W—Uses weighted housing units from the decennial census year.
- POP_W_CY—Uses weighted population from the current year's dataset.
- HH_W_CY—Uses weighted households from the current year's dataset.
- HU_W_CY—Uses weighted housing units from the current year's dataset.
- BUS_W_CY—Uses weighted businesses from the current year's dataset.
- Daytime Workers Population—Uses weighted workforce population locations from the current year’s dataset.
- Daytime Residents Population—Uses weighted residential population locations from the current year’s dataset.
注：
The list of apportionment methods is specific to United States local data. Your list is dependent on the local data installed and is derived from the block centroid point layer.
Apportionment layers
An apportionment layer is a point feature layer containing a weight field that is used in Statistical Data Collections (SDCX) to estimate and aggregate data to other layers. When using a local dataset in Business Analyst, apportionment layers, by default, are census block centroids. Statistical Data Collections allow you to customize the apportionment layer to use any point layer. This connects your custom polygons to a custom apportionment layer to refine the results beyond default methods. No locally installed dataset is required.
Examples of apportionment layers
International location and nondemographic data area are examples of apportionment layers.
International location example
You can create an SDCX in Japan to analyze the historical household population—for example, the population in 1900—using data derived from research sources. You can start with Japan prefecture administrative division polygons. These are large boundaries where a remedial geometry apportionment does not return accurate results. To increase accuracy and granularity—and results specific to that time period—you can load a new point feature layer containing population settlement locations with weights for the year 1900. The weights can contain household counts in that year. By connecting the Japanese boundaries to the new apportionment layer, you can understand what the household populations were like in any boundary, such as a 5-kilometer area around Tokyo.
Nondemographic data area example
You can create an SDCX in the oil fields of Texas, where there might be minimal human population, but you still need to accurately estimate the underground resource levels. Instead of administrative boundaries, such as block groups, you can start with a custom 2x2-mile grid layer containing aggregated locations of underground fuel sources, such as natural gas or crude oil. To increase accuracy and granularity, you can load a new point feature layer containing oil and gas well locations with monthly tallied weights for each type of natural resource. By connecting the oil field grid layer to the new apportionment layer, you can understand what the current resource levels might be like in any boundary, such as a defined area of seismic activity.
Create an apportionment layer
To create an apportionment layer, do the following:
- Build an SDCX using any custom boundary layer.
- On the SDCX Edit dialog box, on the Source tab, set Apportionment Layer to any point feature layer. For best accuracy, the point feature should intersect the custom boundary. The point feature layer must contain a numeric field used for Apportionment Method weighting. The first numeric field found is used.
- Optionally change the Apportionment Method to any numeric field on the Variables tab.
Any changes made should be reflected in an updated SDCX performance index. You can build the index from the Source tab. Your custom variables can be selected in the Custom Data node for any tool that uses the data browser, such as the Enrich Layer workflow.