Monthly Patterns

Overview

Aggregated raw counts of visits to POIs from a panel of mobile devices over a given month, detailing how often people visit, how long they stay, where they came from, where else they go, and more.

This dataset includes visitor and demographic aggregations for points of interest (POIs) in the US over the course of a month. This contains aggregated raw counts of visits to POIs from a panel of mobile devices, answering how often people visit, how long they stay, where they came from, where else they go, and more. Data is anonymized and aggregated to provide insights into the volume of visitors to certain locations and overall behavioral patterns. SafeGraph Places and Geometry datasets which can be added to Patterns via the Placekey unique identifier for further context.

Schema

Monthly Patterns Important Variables

🔒 Advan Research does not report data when less than 2 visitors are observed from that group. If there are
between 2 and 4 visitors this is reported as 4.

Advan Research Normalized Variables

Panel Overview Data

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

Home Location Distributions by State/Census Block Group

Dataset on Dewey ↗️

Number of Visits/Visitors by State

Dataset on Dewey ↗️

Normalization Stats

Advan Research also provides a file containing information on the panel for each day of the week to help with
normalization. This file will have rows for each region for each day of the week.

Dataset on Dewey ↗️

Additional Variable Information

city

• In the US, all centroids (latitudes/longitudes) are referenced against a geospatial file of
  city boundaries as defined by the US Census Bureau. In edge cases, the preferred city name in the address line reflects a pre-annexed city name, and Advan Research tries their best to preserve those city names where possible.

raw_visit_counts

• These are the aggregated raw counts that we see visit the POI from our panel of mobile devices.
• The duration of the visit must last at least 4 minutes to count as a visit to a given POI.

visits_by_day

• This is an array of visits on each day in the week, Monday through Sunday.
• Advan Research breaks up days based on local time.

visitor_home_cbgs

• These are the home census block groups (U.S.) of the visitors to the POI.
• For each census block group, we show the number of associated visitors (as opposed to the number of visits). If visits by home CBG is desired, we recommend taking the visitors from each CBG and multiplying by the average visits/visitor (i.e., raw_visit_counts / raw_visitor_counts) as an approximation.
• Advan Research does not have a home census block group for each visitor and not each visitor originates from the US. The number of US visitors listed in the visitor_country_of_origin column represents the total number of visitors which Advan Research has determined originate from the US versus Canada.

visitor_home_aggregation

• This is similar to visitor_home_cbgs except they represent the home census tracts (U.S.) or aggregate dissemination areas (Canada) of the visitors to the POI.
• Advan Research recommends using this column when you do not need to know visitor homes areas at such a fine level (CBGs represent 600-3000 people while DAs represent 400-700), but can aggregate to the next-level-up geographic unit (CTs represent 2,500 to 8,000 people while ADAs represent 5,000 to 15,000).

visitor_daytime_cbgs

• These are the daytime census block groups of the visitors to the POI.
• For each census block group, we show the number of associated visitors (as opposed to the number of visits).

visitor_country_of_origin

• These are the countries of origin of the visitors to the POI.

distance_from_home

• This is the median distance from home to the POI in meters for the visitors Advan Research has identified a home location.
• This is calculated by taking the haversine distance between the visitor's home geohash-7 and the location of the POI for each visit. Advan Research then takes the median of all of the home-POI distance pairs.
• If there are fewer than 5 visitors to a POI, the value will be null.
• Advan Research does not adjust for visits - each visitor is counted equally.

median_dwell

• This is the median of the minimum dwell times we have calculated for each of the visits to the POI.
• Advan Research determines the minimum dwell time by looking at the first and last ping we see from a device during a visit. This is a minimum dwell because it is possible the device was at the POI longer than the time of the last ping.
• It is possible to have a minimum dwell of 0 if we only saw 1 ping and determined the visit
  based on factors such as wi-fi.

bucketed_dwell_times

• This is a dictionary of different time spans and the number of visits that were of each
  duration.
• The time spans are in minutes.
• Data contains the following bins: { "\<5", "5-10", "11-20", "21-60", "61-120", "121-240", ">240"}

related_same_day_brand

• These are the brands that the visitors to this POI also visit, on the same day that they visit the POI. The number mapped to each brand is an indicator of how highly correlated a POI is to a certain brand. The value is a simple percent of POI visitors that visited the other brand on the same day.
• Only the first 20 brands are returned.

Key Concepts

Visit Attribution

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.

Backfills

Backfill is when we take our most recent locations (i.e., addresses + geofences) and run our visit attribution algorithm backward in time to generate a new history of “backfilled” Patterns. Backfills are typically generated every time new Advan POIs are added (typically monthly, with the exception of August and December) on Advan POIs only. This means historical Patterns will only be present for all Advan POIs, including over 20,000 Industrial POIs, or any other POIs that were released on or before December 2022.

FAQs

What is a place_key?

Placekey is an identifier for any physical place, so that the data pertaining to those places can be shared across organizations easily.

How is the data sourced?

How can I be confident the geographic coverage will meet my research needs?

What is the source of the POI?