Neighborhood Patterns (Deprecating)

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This data will be no longer available as of March 1st, 2026. Please refer to the new Neighborhood Patterns Plus data from Advan Research.

Overview

Advan Research’s Neighborhood Patterns dataset contains footfall data aggregated by census block
group (CBG) in the U.S. Learn which day of the week a CBG is busiest, what time of the day a CBG is busiest, where devices that stop during breakfast, lunch, and dinner travel from, and how weekday and weekend demographics compare. This data is ideal for site-selection use cases and other use cases where you need to understand how busy an area is, when it is busy and the demographics of the visitors.

Key Concepts

Visit Attribution

Neighborhood patterns determines if a visit or stop has occurred within a specific area by using the Census Block Group (CBG) delineation/polygon. Fields such as day_counts, stop_counts, device_counts, and stops_by_day are calculated based on this. Please note that for a "stop" to be counted, it requires a minimum duration of one minute within the CBG, rather than just a brief visit.

The presence or absence of POIs within a CBG does not impact these basic fields. POIs only affect metrics related to POI/brand cross-visitation, such as top_same_day brand.

Advan Research computes the visits/visitors and other metrics inside a POI using the POI’s geometry. They do not apply any dwell time or any concept of “stops”; they rely on the polygon for accuracy. We have tested our data on 1,500 publicly traded tickers versus (a) top line revenue as reported from the companies and (b) credit card transaction counts on physical locations, and we have determined consistently that in the vast majority of cases filtering for dwell time reduces the signal and makes the correlation/forecasting worse.

Differential privacy is applied to visit fields. A random number between 0 and 5 in the US is added or subtracted to the number of stops and census block group visitors. In addition, if there is only one device in the home and daytime areas it is not reported at all; if there are between 2 and 4 devices, they are reported as 4; and, starting January 2023 in the US, only the 65th percentile of areas are included.

Determining Home Location

Advan Research computes a device’s home/work (night/day) location by looking at the previous calendar month and computing the time a device spent in each building in the country; then taking the most frequented building. They define daytime (work location) as 8am-6pm M-F. We define nighttime (home location) as 6pm-8am.

Panel

Along with the the Neighborhood Patterns, Advan Research also provides a Panel Overview Data to help you better understand the context of the data appearing in Neighborhood Patterns.

Home Location Distributions by State/Census Block Group ↗️

US and Canada POI

On Dewey, US and Canada are separated into distinct datasets. Depending on the geographic region of interest, select the corresponding dataset.