Motionworks Placecast™ Profiles provides historical and near-real time data on the for places and regions from the beginning of 2019 to present. Data includes activities by type, audience, and origin.

Data Dimensions

Frequency Published weekly.
Geographic coverage Summarizes activities for residents of the U.S. for Motionworks Places and Regions.
Zone systems Not applicable. No zone systems are used in reporting data in this schema.
Date availability Weekly data since 2019.
Latency Data are released each Monday on a 15-day latency. For example, by 27 March 2023, data through 12 March 2023 will be released.

Schema

Name	Description	Type	Example
place_id	Unique and persistent identifier of this place or region defined by a data collection polygon (also sometimes called point or place of interest).	Integer	275640
name	Name of the place.	String	Grand Tetons National Park
street_address	Street address of the place.	String	103 Headquarters Rd
city	City of the place.	String	Moose
state	State or province of the place.	String	WY
postal_code	Postal delivery code of the place. In the US, this is the ZIP code.	String	83012
location	Latitude and longitude of the place	JSON {String: String}	{"lat":40.632881, "lon":-73.948035}
market	A variety of location descriptors using standard geographies, e.g. county, state, core-based statistical area, and designated market area.	JSON {String: String}	{"co_geoid": "36047","co_name": "Kings",...}
place_type_id	Unique identifier of the place type.	Integer	19
place_type	Description of the place type.	String	Grocery Store
place_audit_status	Audit status of the place.	String	Reviewed
place_modified_date	The last date the place's metadata, polygon, or audit status were modified, ISO 8601 with `yyyy-mm-dd`.	Date	2022-01-13
published_date	The date these weekly metrics were published, labeled using ISO 8601 with `yyyy-mm-dd`. For all metrics (unless expressly stated otherwise), we estimate a recent week using a rolling average over the current week and each of the previous five weeks separately. Then, these six values are averaged. In some cases where it makes sense, a weighted six-week average rather than a simple six-week average is used. For example, the rolling average for `visits` is a simple average (weighting each of the six weeks equally), whereas the rolling average of `visits_avg_dwell` is a weighted average (weighting the dwell time from each of the six weeks according to their respective total visits).	Date	2022-03-07
activities	Total weekly activities at the place, where an activity refers to any significant time spent at a place. Activities are the sum of `dropoffs` and `visits`, which are differentiated by a dwell time threshold.	Integer	1765
visits	Total weekly visits at the place. A visit is an activity with a measured dwell time at the place greater than or equal to `activities_dwell_threshold`.	Integer	1013
dropoffs	Total weekly dropoffs at the place. A dropoff is an activity with a measured dwell time at the place less than ‘activities_dwell_threshold‘.	Integer	752
passbys	Total weekly pass-bys of the place. A pass-by has zero dwell time at the place and is NOT an activity. They do not count as a dropoff or visit (or stay).	Integer	172
stays	Total weekly stays at the place. In short, a stay is an overnight visit. Stays are a subset of ‘visits‘, meaning a ‘stay‘ counts as a ‘visit‘, but not all ‘visits‘ count as a ‘stay‘. A stay is a visit with a measured dwell time of at least 4 hours inclusive of 03:00 a.m. in local time.	Integer	261
visits_ci	90% confidence interval of the ‘visits‘ estimate. If we were to repeatedly make new estimates of ‘visits‘ using the same procedure with new data from different samples of people, the confidence interval would contain the resulting estimate 90% of the time.	JSON [Integer, len=2]	[913, 1113]
visits_observations	The number of visits observed directly in the sample in total over the six weeks used to compute rolling averages.	Integer	59
imputed	A boolean flag indicating whether the estimates for this week were imputed with a larger time window than the standard rolling period due to missing data. If true, `activities`, `visits`, `dropoffs`, `passbys`, `stays`, and all other included metrics are imputed.	Boolean	true
visits_avg_dwell	Average dwell time (in minutes) per person over the week, ignoring those panel members who live in the associated block group(s) that intersect the place or that work in the place.	Number (Float)	47.1
activities_dwell_threshold	The dwell time threshold (in minutes) for the place. Activities measuring a dwell time less than this threshold at this place are counted as `dropoffs`, and activities measuring a dwell time greater than or equal to this threshold at this place are counted as `visits`. Values for Places are maximum of 15 minutes.	Number (Float)	2.4
visits_frequency_per_person	Average visits per person over the week, ignoring those panel members who live in the associated block group(s) that intersect the place or that work in the place.	Number (Float)	1.15
visits_unique_persons	The weekly number of unique persons visiting the place. Equals ‘visits‘ divided by ‘visits_frequency_per_person‘.	Integer	1532
visits_unique_long_trips	The weekly number of unique long-distance trips visiting the place. A long-distance trip is counted when a panel member is more than 50 miles from their home neighborhood. Panel members are only eligible to contribute to the estimate of unique long-distance trips if the place does not overlap with the home county of the panel member.	Integer	71
passbys_travel_mode	Weekly pass-bys by vehicular and pedestrian status. The determination of vehicular pass-bys and pedestrian pass-bys are determined by observed speed. The sum of all will equal ‘passbys‘. Vehicular are first, and pedestrian are second in the array. If NULL, the polygon of this place is large enough to be considered a region and is not calculated.	JSON [Integer, len = 2]	[172, 0]
local_radius	Weekly activities, pass-bys, dropoffs, and visits by local and non-local status. A local activity or pass-by is counted when the home block group of the panel member is within a 50-mile radius of the place. The radius is calculated between the observed lat/lon of the panel member and the center point of the home block group. The sum of all will equal ‘activities‘, ‘passbys‘, ‘dropoffs‘, and ‘visits‘, respectively. Local is first, and non-local is second in the array.	JSON {String: [Integer, len = 2]}	{"passbys": [75, 97], "dropoffs": [506, 246], "visits": [161, 100], "stays": [150, 111]}
percent_visits	Percent of visits by various dimensions, including the day of the week and hour of the day, demographic segments, and home locations. The sum of all will generally equal 100, except in the case of rounding error. Indices against the population are included for demographic segments. For example, with age group 0-17 having 3.1% of visits, an index of 100 indicates that 3.1% of the population of the surrounding DMA also is aged 0-17. Indices can range from 0 to infinity, where 0 occurs when there are no visits from a given segment, and infinity occurs when the segment is not present in the surrounding DMA. See the Percent Visits section for a more detailed example.	JSON {String: {String: {String: Float}}}	{"day": {...}, "segment": {...}, "home": {...}}
history	A history of ‘visits‘, ‘dropoffs‘, ‘passbys‘, and ‘stays‘, year over year or week over week, as measured within each separate time frame (not a rolling average). Labeled using ISO 8601 with ‘yyyy‘ or ‘yyyy-Www‘ beginning with January 2019. The current year reports year-to-date. See the Historical Summary section for a more detailed example.	JSON {String: {Date: {String: Integer}}}	{"year": {...}, "week": {...}}

Household Demographics and Privacy

All of our demographics, even if person-level, like age or gender, are estimated by considering the demographic profiles of the household unit. If we observe visits from a neighborhood characterized by households that tend to be single parents as well as multi-generational, all of this neighborhood-typical household’s ages contribute to the characterization of the estimated visit. Specifically, let’s say a neighborhood's typical household tends to have equal distributions of people in age segments 0-17, 35-44, and 75+. If we observe visits to a bar from this neighborhood, the percentage of visits by age segment at the bar would be 33.3% for each of the three age segments.

JSON Schema

All metrics reported as percentages are rounded to the nearest tenth of a percent, where 25.1 is 25.1%.

Percent Visits

Percent of visits by a variety of dimensions, including by day of the week and hour of the day, demographic segments, and home locations. Some of these dimensions are mutually exclusive groups, and some of them overlap. These differences determine whether all possible categories within the groups are reported and what the sum of the percentages should equal. The bulleted list highlights these differences, and the JSON sample below demonstrates the format of the object.

The daily_dwell_bins measure the total time spent in a day by a panel member, regardless of the number of times coming and going. For example, when an individual spends 3.5 hours at work, leaves for lunch, and returns for another 4.5 hours, their daily dwell will be 8 hours.

{
  "day": [
    {
      "id": "mo",
      "description": "Monday",
      "value": 0.213,
      "hourly": [0.0017, 0.0017, 0.0015, ...]
    },
    {
      "id": "tu",
      "description": "Tuesday",
      "value": 0.223,
      "hourly": [0.0186, 0.009, 0.0027, ...]
    },
    {
      "id": "we",
      "description": "Wednesday",
      "value": 0.223,
      "hourly": [0.0015, 0.0027, 0.0041, ...]
    },
    ...
  ],
  "daily_dwell_bins": [
    {
      "id": "persons_000_030min",
      "description": "Less than 30 minutes",
      "value": 0.5456
    },
    {
      "id": "persons_030_060min",
      "description": "30 minutes to 59 minutes",
      "value": 0.2342
    },
    {
      "id": "persons_060_120min",
      "description": "1 hour to 1 hour 59 minutes",
      "value": 0.1689
    },
    {
      "id": "persons_120_240min",
      "description": "2 hours to 3 hours 59 minutes",
      "value": 0.0272
    },
    {
      "id": "persons_240_plus",
      "description": "4 hours or more",
      "value": 0.0241
    }
  ],
  "segment": {
    "basic_demographics": [
      {
        "category": "age_plus",
        "segments": [
          {
            "id": "age_00plus",
            "description": "All Persons",
            "value": 1.000,
            "index": 1.0
          },
          {
            "id": "age_18plus",
            "description": "Population, Age 18+",
            "value": 0.7862,
            "index": 0.9796
          },
          ...
        ]
      },
      {
        "category": "age",
        "segments": [
          {
            "id": "age_00t17",
            "description": "Population, Age 0 - 17",
            "value": 0.031,
            "index": 0.3
          },
          {
            "id": "age_18t24",
            "description": "Population, Age 18 - 24",
            "value": 0.052,
            "index": 1022.3
          },
          ...
        ]
      },
      ...
    ],
    ...
  },
  "home": {
    "state_province": [
      {
        "id": "50",
        "alias": "VT",
        "description": "Vermont",
        "value": 0.252
      },
      {
        "id": "33",
        "alias": "NH",
        "description": "New Hampshire",
        "value": 0.137
      },
      ...
    ],
    ...
  }
}

Small Mutually-Exclusive Dimensions

The following dimensions have mutually exclusive groups and include a small enough set of groups that all of the groups are reported. In these cases, the sum of all of the values will generally equal 100, except in the case of rounding error.

day Special Note: Some places may return NULL values for certain hours. Measurement during these hours are being suppressed at these places for privacy purposes.
daily_dwell_bins
segment:basic_demographics:age See the Household Demographics and Privacy section for details on how this is estimated.
segment:basic_demographics:hh_income
segment:basic_demographics:race See the Household Demographics and Privacy section for details on how this is estimated.
segment:basic_demographics:gender See the Household Demographics and Privacy section for details on how this is estimated.
segment:basic_demographics:ethnicity See the Household Demographics and Privacy section for details on how this is estimated.
segment:basic_demographics:prizm
home:state_province Labeled by 2-digit FIPS code (including leading zeros).
home:dma Labeled by DMA identifier.

Large, Mutually-Exclusive Dimensions

The following dimensions also have mutually exclusive groups, but there are too many groups to report percentages for all the groups. In these cases, the groups representing the top 75th percentile of all visits are reported (with a minimum of 10 groups). Also, any segment with an index value greater than 1.05 is included. Accordingly, the sum of all the values will NOT equal 100, but the sum will be at least 75.

home:metro_area Labeled by core-based statistical area (CBSA) identifier in the United States.
home:county Labeled by 5-digit county FIPS code (including leading zeros).
home:postal_code Labeled by 5-digit postal code.
home:census_neighborhood Labeled by block group's 12-digit FIPS code (including leading zeros) in the United States and by dissemination area's 8-digit identifier in Canada.

Overlapping Dimensions

The following dimensions have overlapping groups.

segment:basic_demographics:age_plus
segment:consumer:alcohol
segment:consumer:automotive
segment:consumer:commuting_and_transportation
segment:consumer:digital_video_displays
segment:consumer:environment
segment:consumer:financial
segment:consumer:food_and_beverages
segment:consumer:health
segment:consumer:items_in_the_home
segment:consumer:mri_apparel_and_jewelry
segment:consumer:mri_home_improvements
segment:consumer:mri_psychographics
segment:consumer:restaurants
segment:consumer:retail_shopping
segment:consumer:sports_and_leisure
segment:consumer:telecommunications
segment:consumer:travel
segment:consumer:voting

Historical Summary

A history of visits, dropoffs, passbys, and stays, year over year or week over week, as measured within each separate time frame (not a rolling average). Labeled using ISO 8601 with yyyy or yyyy-Www beginning with January 2019. The current year reports year-to-date. Also, visits_observations reports the number of visits observed directly in the sample during each specific reported period (year or week).

{
  "year": [
    {
      "id": "2019",
      "imputed": false,
      "activities": 5086,
      "visits": 4152,
      "visits_ci": [4142, 4162],
      "visits_observations": 102,
      "dropoffs": 934,
      "stays": 2273,
      "passbys": 10562,
      "week_count": 52
    },
    {
      "id": "2020",
      "imputed": false,
      "activities": 3247,
      "visits": 2135,
      "visits_ci": [2130, 2140],
      "visits_observations": 192,
      "dropoffs": 1112,
      "stays": 178,
      "passbys": 40874,
      "week_count": 52
    },
    ...
    {
      "id": "2022",
      "imputed": false,
      "activities": 4580,
      "visits": 3546,
      "visits_ci": [3540, 3552],
      "visits_observations": 142,
      "dropoffs": 1034,
      "stays": 134,
      "passbys": 40436,
      "week_count": 11
    }
  ],
  "week": [
    {
      "id": "2019-W01",
      "imputed": true,
      "activities": 64,
      "visits": 52,
      "visits_ci": [32, 72],
      "visits_observations": 1,
      "dropoffs": 12,
      "stays": 45,
      "passbys": 34,
      "week_start": "2019-01-07"
    },
    ...
  ]
}