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.
| Data Information | Value |
|---|---|
| Refresh Cadence | Monthly |
| Historical Coverage | 2019-Present |
| Geographic Coverage | United States |
Schema
| Name | Description |
|---|---|
| PLACEKEY | Unique and persistent ID tied to this POI. |
| PARENT_PLACEKEY | Parent placekey if the place is within a larger location. |
| SAFEGRAPH_BRAND_IDS | Unique ID representing this specific brand. |
| LOCATION_NAME | The name of the place of interest. |
| BRANDS | Brand name if identified explicitly. |
| STORE_ID | Unique store ID provided by the store/brand. |
| TOP_CATEGORY | First 4 digits of the NAICS category. |
| SUB_CATEGORY | Full 6-digit NAICS category label. |
| NAICS_CODE | 4-digit or 6-digit NAICS code. |
| LATITUDE | Latitude of POI. |
| LONGITUDE | Longitude of POI. |
| STREET_ADDRESS | Street address of the POI. |
| CITY | City where the POI is located. |
| REGION | State/province/county of the POI. |
| POSTAL_CODE | Postal code of the POI. |
| OPEN_HOURS | JSON string of operating hours. |
| CATEGORY_TAGS | Descriptive tags indicating category details. |
| OPENED_ON | Year and month the POI opened. |
| CLOSED_ON | Year and month the POI closed (if applicable). |
| TRACKING_CLOSED_SINCE | Start of "closed_on" tracking. |
| WEBSITES | Publicly available website URL. |
| GEOMETRY_TYPE | Geometric shape type of the POI. |
| POLYGON_WKT | Shape of the POI in WKT format. |
| POLYGON_CLASS | Polygon classification (owned/shared). |
| ENCLOSED | Whether the POI is enclosed. |
| PHONE_NUMBER | Phone number of the POI. |
| IS_SYNTHETIC | Indicates if the polygon is inferred. |
| INCLUDES_PARKING_LOT | Whether the polygon includes parking lot. |
| ISO_COUNTRY_CODE | 2-letter ISO country code. |
| WKT_AREA_SQ_METERS | Calculated area of the polygon in square meters. |
| DATE_RANGE_START | Start of the date range. |
| DATE_RANGE_END | End of the date range. |
| RAW_VISIT_COUNTS | Number of visits to the POI during the date range. |
| RAW_VISITOR_COUNTS | Number of unique visitors. |
| VISITS_BY_DAY | Number of visits by day (local time). |
| VISITOR_HOME_CBGS | Number of visitors by home census block group. |
| VISITOR_HOME_AGGREGATION | The number of visitors to the POI from each census tract based on the visitor's home location. |
| VISITOR_DAYTIME_CBGS | The number of visitors to the POI from each census block group based on primary daytime location between 9 am - 5 pm. |
| VISITOR_COUNTRY_OF_ORIGIN | The number of visitors to the POI from each country based on visitor's home country code. |
| DISTANCE_FROM_HOME | Median distance from home travelled by visitors (of visitors whose home we have identified) in meters. |
| MEDIAN_DWELL | Median minimum dwell time in minutes. |
| BUCKETED_DWELL_TIMES | The distribution of visit dwell times based on pre-specified buckets. Key is the range of dwell time in minutes and value is number of visits that were within that range. |
| RELATED_SAME_DAY_BRAND | Other brands that the visitors to this POI visited on the same day as the visit to this POI. Limited to top 20. |
| RELATED_SAME_MONTH_BRAND | Other brands that the visitors to this POI visited in the same month as the visit to this POI. Limited to top 20. |
| POPULARITY_BY_HOUR | The number of visits in each hour over the course of the date range, in local time. First element in the array corresponds to the hour of midnight to 1 am, second is 1am to 2am, etc. |
| POPULARITY_BY_DAY | The number of visits in total on each day of the week (in local time) over the course of the date range. |
| DEVICE_TYPE | The number of visitors to the POI that are using Android vs. iOS. |
| NORMALIZED_VISITS_BY_STATE_SCALING | raw_visit_counts scaled using the mobile device sampling rate for the state in which the POI is located. |
| NORMALIZED_VISITS_BY_REGION_NAICS_VISITS | raw_visit_counts divided by the sum(raw_visit_counts) to the naics_code in the same state or province during the same time period. This measures changes in the category-specific popularity of the POI over time. |
| NORMALIZED_VISITS_BY_REGION_NAICS_VISITORS | raw_visit_counts divided by the sum(raw_visitor_counts) to the naics_code in the same state or province during the same time period. This measures changes in the visits per devices that visited the same category in Advan's panel to the POI over time. |
| NORMALIZED_VISITS_BY_TOTAL_VISITS | raw_visit_counts divided by the total_visits in the same state or province during the same time period. This measures changes in the relative popularity of POI over time. |
| NORMALIZED_VISITS_BY_TOTAL_VISITORS | raw_visit_counts divided by the total_devices_seen in the same state or province during the same time period. This measures changes in the visits per device in Advan's panel to the POI over time. |
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.
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.
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. Advan uses a calendar month window (instead of 45 days) to be consistent with the month window of the patterns, and defines “nighttime” as 6pm-8am.
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.
Panel
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. They also provide 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.
Home Location Distributions by State/Census Block Group ↗️
Updated 21 days ago