The Proximity Dataset comes in two standard forms:
- Scores for commonly used geographies
- Typically these are government defined boundaries such as census block group, tract, etc.
- This standard data format pairs well with demographic and other commonly used data.
- Scores around a set of locations
- For example, we might provide scores for every Chipotle location, based on social media in a one-mile radius around each Chipotle
- These type of scores are custom and are typically provided on a project basis
While the above are the most common use cases, we are capable of working with custom geographies as well. Other examples of geographies could be drive-times, custom trade areas, and more. Many of the users who work with our data work with our USA national dataset, which is a dataset that covers the full USA at the census block group level. We also have standard data readily available for the Canadian and Mexican markets.
Scores for Commonly Used Geographies¶
The following table previews what the USA national dataset file looks like.
|BLOCKGROUP_ID||EA01 - Bookish||EA02 - Engine Enthusiasts||EA03 - Green Thumb||...||Index - Politically Liberal Affinity||Index - Politically Conservative Affinity||VOLUME INDEX||VOLUME PERCENTILE||DENSITY INDEX||DENSITY PERCENTILE|
As you can see, scores are provided for segments, indexes, and social media volume. Variable definitions can be found in the Data Dictionary.
Scores Around a Set of Locations¶
All variables remain the same for scores aggregated around a set of locations with one small difference. The "BLOCKGROUP_ID" field is replaced by "LOCATION_ID" to reflect the locations in the analysis.