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 monthly.
• 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 Monthly data since 2019.
• Latency Data are published monthly. Each release adds the most recent available month of measured data.

Schema

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.

{
  "days": [
    {
      "day": "su",
      "percent": 0.15505222850949532,
      "visits": 835,
      "hours": [0.0015, 0.0027, 0.0041, ...]
    },
    {
      "day": "mo",
      "percent": 0.465464654646,
      "visits": 835,
      "hours": [0.0011, 0.0121, 0.0900, ...]
    },
    {
      "day": "fr",
      "percent": 0.564651463131111646,
      "visits": 835,
      "hours": [0.0255, 0.0002, 0.0054, ...]
    },
    ...
  ],
  "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 activities, visits, dropoffs, and passbys over time, beginning with January 2019. The history is reported in three arrays — month, year, and week — each labeled using ISO 8601: yyyy-mm for month, yyyy for year, and yyyy-Www for week. visit_observations reports the number of visits observed directly in the sample during the reported period.

• month reports the activity measured within each calendar month and is the basis for time-based analysis.
• year reports per-year figures. activities, visits, dropoffs, and passbys are the average of that year's monthly values (not an annual sum), while visit_observations is the total observed over the year. The current year is reported year-to-date, and week_count gives the number of weeks the year contributes.
• week reports, for each week, the value of its month. visits, activities, dropoffs, and passbys are therefore identical for every week within the same month, and the month's visit_observations is divided among its weeks.

In the example below (a real place), August 2025 has four weeks (2025-W32 through 2025-W35). Each week reports the August month figures (visits = 5769, activities = 6838, and so on), and the month's 202 visit_observations is divided among its four weeks (≈ 51 each). The year entry for 2025 reports the average of the monthly visits (5812) and the total observations for the year (2460).

{
  "year": [
    {
      "id": "2026",
      "imputed": false,
      "activities": 6061,
      "visit_observations": 918,
      "visits": 5168,
      "dropoffs": 893,
      "passbys": 1463,
      "week_count": 22
    },
    {
      "id": "2025",
      "imputed": false,
      "activities": 6850,
      "visit_observations": 2460,
      "visits": 5812,
      "dropoffs": 1038,
      "passbys": 1559,
      "week_count": 52
    },
    ...
  ],
  "month": [
    {
      "id": "2025-08",
      "imputed": false,
      "activities": 6838,
      "visit_observations": 202,
      "visits": 5769,
      "dropoffs": 1069,
      "passbys": 1476
    },
    {
      "id": "2025-07",
      "imputed": false,
      "activities": 6544,
      "visit_observations": 196,
      "visits": 5524,
      "dropoffs": 1020,
      "passbys": 1481
    },
    ...
  ],
  "week": [
    {
      "id": "2025-W35",
      "imputed": false,
      "activities": 6838,
      "visit_observations": 51,
      "visits": 5769,
      "dropoffs": 1069,
      "passbys": 1476,
      "week_start": "2025-08-25"
    },
    {
      "id": "2025-W34",
      "imputed": false,
      "activities": 6838,
      "visit_observations": 51,
      "visits": 5769,
      "dropoffs": 1069,
      "passbys": 1476,
      "week_start": "2025-08-18"
    },
    {
      "id": "2025-W33",
      "imputed": false,
      "activities": 6838,
      "visit_observations": 51,
      "visits": 5769,
      "dropoffs": 1069,
      "passbys": 1476,
      "week_start": "2025-08-11"
    },
    {
      "id": "2025-W32",
      "imputed": false,
      "activities": 6838,
      "visit_observations": 51,
      "visits": 5769,
      "dropoffs": 1069,
      "passbys": 1476,
      "week_start": "2025-08-04"
    },
    ...
  ]
}