Replica Trends is a nationwide dataset updated weekly covering mobility, consumer spending, and COVID-19 cases.
Each week, the Trends dataset is generated from a full activity-based model, run for the entire country for a typical weekday and typical weekend day. Mobility and spend data are available for each week from the beginning of 2019 to the most recent complete week. COVID-19 data, sourced from the Centers for Disease Control and Prevention’s Covid Data Tracker, is available from the beginning of 2020 to the most recent week.
The geographies available in Trends match the U.S. Census definitions for States, Combined Statistical Areas, Metropolitan Statistical Areas, Micropolitan Statistical Areas, Cities, Counties, and Census Tracts. Customers can also aggregate this data into their own custom geographies.
Trends estimates are based on a composite of data sources, including but not limited to mobile location and financial transaction data. For spending data, advanced modeling and statistical weighting methods are applied to generate a representative weekly total estimate of consumer spending activity.
Together, these provide vital indicators for tracking, understanding, and comparing patterns of mobility and economic recovery across geographic regions in a high level of detail.
The Trends mobility table contains the aggregate trips and associated attributes in selected geographies.
Field Name | Content Type | Sample Value | Description |
|---|---|---|---|
geo_id | Integer | 36005014100 | The GEOID of the corresponding row’s geography, as defined by the US Census Bureau. |
[state, county, tract, city, msa, custom_geo] | String | 141 (Bronx, NY) | The name corresponding to the row’s geography, as defined by the US Census Bureau or specified by the user. |
week_starting | Date | 2022-06-13 | The first day of the week (Monday) of the corresponding row’s data. |
start_hour_local_time | Integer | 7 | The hour of the day in 24h format, local to the corresponding row’s geography. |
total_trips_count_typical_[days_of_week] | Integer | 15602 | The total number of trips starting or ending (depending on the selection made in the download menu) in the corresponding row’s geography on an average [days_of_week] (the entire day, from 00:00 to 23:59 local time). |
[mode]_count_typical_[days_of_week] | Integer | 6679 | The number of trips taken by [mode] on an average [days_of_week].
There is a single primary mode assigned to each trip. For example, a trip that involved a short walk and long bus ride would be classified as a single “Transit” trip. A trip that involved two separate bus segments with a brief transfer in between would also be counted as a single “Transit” trip. The summation of all modes’ trips will equal the total trip volume for the selected geography. Each mode option is defined below: • Private Auto: Trips made by drivers in private auto vehicles. This is equivalent to the number of private auto vehicle movements. • Auto Passenger: Trips made by passengers in private auto vehicles. Sum Auto Passenger and Private Auto trips to get the total number of people who traveled in private autos. • Transit: Trips that primarily used public transit, such as buses, light rail, and subways. Because Mode Split is based on trip origin, it should be evaluated for an MSA (versus a city) to capture all commuters. • Walking: Trips made by people walking. • Biking: Trips made by people biking. Replica does not model scooter trips and does not separate out e-bike trips. • Other Mode: Trips not included in any of the above categories and long trips of ~300 miles or greater. While rideshare, delivery, and long-haul freight are included in total trip volumes, we do not track them individually and do not recommend using Trends data to analyze them at this time. |
[purpose]_count_typical_[days_of_week] | Integer | 4669 | The number of trips taken for [purpose] on an average [days_of_week].
Each trip is assigned to a single primary purpose depending on the trip destination. Each purpose is defined below: • Home: All trips to a person’s own home. • Work: All trips that ended at a person’s workplace, including both direct commutes (home to work) and other trips (e.g. return trips to the workplace from lunch). • School: All trips to schools such as community colleges and universities. • Eat: All trips to restaurants. • Social: All trips to visit someone else’s home. • Shop: All trips to shops and retail stores. • Recreation: All trips to recreational destinations such as parks and swimming pools. Replica does not include looping trips without a destination, such as walking the dog, or jogging. • Maintenance: All trips to hairdressers, auto shops, banks, and a variety of other locations. • Other Activity Type: All trips not included in any of the above categories. While rideshare, delivery, and long-haul freight are included in total trip volumes, we do not track them individually and do not recommend using Trends data to analyze them at this time. |
residential_vmt_total_typical _[days_of_week] | Integer | 16520 | The total number of vehicle miles traveled by residents of the corresponding row’s geography, regardless of where the vehicle trips occurred, on an average [days_of_week]. |
residential_vmt_per_capita_typical_[days_of_week] | Float | 2.87 | The average number of vehicle miles traveled per resident of the corresponding row’s geography, regardless of where the vehicle trips occurred, on an average [days_of_week] equal to residential_vmt_total/population. |
employed_total | Integer | 2383 | The total number of employed people in the corresponding row’s geography, from our modeled population based on the 2019 ACS. |
wfh_total_typical_[days_of_week] | Integer | 573 | The total number of people estimated to be working-from-home in the corresponding row’s geography. This estimate is an average across all weekdays (i.e., if 10% of residents worked from home 3 days of the week, and 35% of residents worked from home 2 days of the week, the weekly average would be 20%). |
wfh_proportion_typical_[days_of_week] | Float | 0.24 | The proportion of people working from home in the corresponding row’s geography equal to wfh_total / employed_total. |
intra_geo_trips_count_typical_[days_of_week] | Integer | 5107 | The total number of trips that were intra-geo trips, defined as a trip that both started and ended in the corresponding row’s geography, on an average [days_of_week]. |
intra_geo_trips_proportion_typical_[days_of_week] | Float | 0.33 | The proportion of all trips starting or ending (depending on the selected parameter) in the corresponding row’s geography that were intra-geo trips, on an average [days_of_week] equal to intra_geo_trips_count/total_trips_count. |
start_hour_trips_count_typical_[days_of_week] | Integer | 799 | The number of trips starting or ending (depending on the selected parameter) in the corresponding row’s geography that started in the hour specified by start_hour_local_time, on an average [days_of_week]. |
The Trends consumer spending table shows the estimated consumer spending that occurred in the selected geography in a given week. Consumer spending includes all transactions — including credit card, debit card, and cash transactions — that take place at a point of sale, such as at retail stores, supermarkets, restaurants, taxis, and bars. It also includes e-commerce transactions in these same categories.
For each geography, Replica produces two data tables. The first includes all spending that takes place at brick and mortar locations in a given census tract, regardless of where the purchaser lives. The second table includes all money spent by residents of each geography, regardless of where the transaction takes place. This latter table also includes breakdowns of online and offline spending.
The data does not include all household expenditures; for example, rent, car payments, and healthcare spending is not included. This most closely aligns Replica’s consumer spending metric to the Census Bureau’s Monthly Retail Trade Estimates. Transactions are categorized by the merchant’s NAICS code.
Field Name | Content Type | Sample Value | Description |
|---|---|---|---|
geo_id | Integer | 36005014100 | The GEOID of the corresponding row’s geography, as defined by the US Census Bureau. |
[state, county, tract, city, msa, custom_geo] | String | 141 (Bronx, NY) | The name corresponding to the row’s geography, as defined by the US Census Bureau or specified by the user. |
week_starting | Date | 2022-06-13 | The date(s) of the week(s) in your selected time period. |
population | Integer | 5756 | The total population in the geography selected. |
[category]_[online/offline]_total__[days_of_week] | Float | 42157.484375 | The total consumer spend in [category] in a given week across the following categories:
• Airline/hospitality/car rental • Entertainment/recreation • Gas stations/parking/taxis/tolls • Grocery stores • Restaurants/bars • Retail Note: spend by home location includes both online and in-person spend estimates. Spend by merchant estimates only include in-person spending. |
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