Weekly Patterns
Overview
Weekly Patterns data provides the same foot traffic data insights from Monthly Patterns on a
weekly basis, tracking data from Monday to the end of day on Sunday each week.
| Data Information | Value |
|---|---|
| Refresh Cadence | Monthly |
| Historical Coverage | 2018-Present |
| Geographic Coverage | United States |
| Observation Level | Visits per week, by point of interest |
Schema
| Name | Description |
|---|---|
| PLACEKEY | Unique and persistent ID tied to this POI. |
| PARENT_PLACEKEY | If place is encompassed by a larger place (e.g., mall, airport), this lists the placekey of the parent place; otherwise null. |
| SAFEGRAPH_BRAND_IDS | Unique and consistent ID that represents this specific brand. |
| LOCATION_NAME | The name of the place of interest. |
| BRANDS | If this POI is an instance of a larger brand that we have explicitly identified, this column will contain that brand name. |
| STORE_ID | The unique ID associated with the store as provided and maintained by the store/brand itself. |
| TOP_CATEGORY | The label associated with the first 4 digits of the POI’s NAICS category. |
| SUB_CATEGORY | The label associated with all 6 digits of the POI’s NAICS category. For POIs with a 4-digit NAICS category, this column is null. |
| NAICS_CODE | 4-digit or 6-digit NAICS code describing the business. |
| LATITUDE | Latitude coordinate of the place of interest. |
| LONGITUDE | Longitude coordinate of the place of interest. |
| STREET_ADDRESS | Street address of the place of interest. |
| CITY | The city of the point of interest. |
| REGION | The state, province, county, or equivalent of how "region" is understood in a given country for the place of interest. |
| POSTAL_CODE | The postal code of the place of interest. |
| OPEN_HOURS | A JSON string with days as keys and opening & closing times (in the POI's local time) as values. See open_hours for more details. |
| CATEGORY_TAGS | An array of descriptive tags indicating higher-resolution category information. |
| OPENED_ON | The outside year and month this POI opened in yyyy-mm format. If null, then we do not have enough metadata to determine an open date. |
| CLOSED_ON | The outside year and month this POI closed in yyyy-mm format. If null, then this POI is open. |
| TRACKING_CLOSED_SINCE | Indicates the year and month we started tracking "closed_on" for this POI. |
| WEBSITES | The web URL for the POI's publicly available website. |
| GEOMETRY_TYPE | The geometric shape associated with this POI. Possible values: POLYGON or POINT. |
| POLYGON_WKT | The shape of the place of interest, formatted as Well-Known Text (WKT). |
| POLYGON_CLASS | Classification of the polygon: OWNED_POLYGON or SHARED_POLYGON. |
| ENCLOSED | If true, the POI is completely enclosed indoors by its parent and is only accessible by entering the parent structure. |
| PHONE_NUMBER | The phone number of this POI. |
| IS_SYNTHETIC | If true, this is not a precise POI footprint polygon but an inferred polygon. |
| INCLUDES_PARKING_LOT | Whether or not the polygon includes the parking lot. |
| ISO_COUNTRY_CODE | The 2-letter ISO 3166-1 alpha-2 country code. |
| WKT_AREA_SQ_METERS | The calculated area of the polygon_wkt in square meters. |
| DATE_RANGE_START | Start date of the date range. |
| DATE_RANGE_END | End date of the date range. |
| RAW_VISIT_COUNTS | Number of visits in our panel to this POI during the date range. |
| RAW_VISITOR_COUNTS | Number of unique visitors from our panel to this POI during the date range. |
| VISITS_BY_DAY | The number of visits to the POI each day (local time) over the covered time period. |
| VISITS_BY_EACH_HOUR | The number of visits to the POI (local time) over the covered time period. |
| POI_CBG | The census block group the POI is located within. |
| VISITOR_HOME_CBGS | The number of visitors to the POI from each census block group based on visitor's home location. |
| VISITOR_HOME_AGGREGATION | Number of visitors from each census tract based on home location. |
| DISTANCE_FROM_HOME | Median distance from home traveled by visitors in meters. |
| MEDIAN_DWELL | Median minimum dwell time in minutes. |
| RELATED_SAME_DAY_BRAND | Other brands visited on the same day. Limited to top 20. |
| NORMALIZED_VISITS_BY_STATE_SCALING | Visits scaled using the mobile device sampling rate for the POI's state. |
| 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.
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.
*These are the same concepts as Advan Research's Monthly Patterns dataset
Panel
Along with the Weekly Patterns file, Advan Research also provides a Panel Overview Data to help you better understand the context of the data appearing in Weekly 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 about 2 months ago