Neighborhood Patterns Plus

Overview

Advan’s Neighborhood Patterns Plus dataset contains footfall data aggregated by census block group (CBG) in the U.S. and dissemination area (DA) in Canada. Learn which day of the week a CBG or DA is busiest, what time of the day a CBG or DA 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.

Neighborhood Patterns Plus are available starting from January 1st, 2019.

Differences between Patterns and Patterns Plus

Methodology Differences

• Neighborhood patterns+ normalizes the observed data to estimate the actual counts across the actual adult population, as opposed to Neighborhood patterns, which is measuring only devices in our panel.

Key Concepts

Differential Privacy

Differential privacy is applied to fields before adjusting the results to the adult population. A random number between 0 and 5 in the US, or 0 and 3 in Canada, 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.

Visit Attribution

Neighborhood Patterns Plus 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.

For POI dependent fields (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. Advan Research has tested their 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 they have determined consistently that in the vast majority of cases filtering for dwell time reduces the signal and makes the correlation/forecasting worse.

Determining Home Location:

Advan Research computes a device’s home/work (night/day) location by computing the time a device spent in each building in the country; then taking the most frequented building.