One of the demographic attributes available in Places is "work from home" (WFH) status. This enables you to see detailed characteristics of the individuals who work from home on a given day in a given season. With this attribute, you can answer questions like:
What percentage of residents in a specific area work from home on a typical weekday?
How do these individuals travel during the week? Where do their trips start and end, and for what purpose are they traveling?
What are their individual and household characteristics? Income level, race and ethnicity, home and work locations?
In this tutorial, we'll walk through how you can view WFH data in Places. Note: If you prefer to query data directly, you can request direct database access to Replica data via BigQuery here.
Click here to view the example Study we walk through below. You can click "Make a copy" to make changes to this Study.
Step 1: Set up your Study
Access the "Studies" page in Replica's platform
Click "New Study" in the upper right-hand corner of the page
Name your study, select your megaregion, season, and day of the week (choose between seeing patterns for a typical weekday or weekend day) for which you'd like to see WFH data.
Step 2: Select your Study Area
In Places, you can set different geographic areas as your study area. For example, you can select a census-based geography (e.g., a county, a city, a census tract), custom geography (e.g., a neighborhood), or a specific roadway. In this example, we will look at WFH patterns for the residents of Chicago, IL. We'll set up this study area by following the steps below:
Leverage the Map Layer Panel or click the "+" icon in the filter bar to select your study area. As a default, your Study will enable you to select a geographic area off of the map to set as your Trip Origin points. As noted above, in this example, we want to understand WFH patterns by the residents of a given city, so we'll use the "Layer Data" dropdown in the left-hand layer panel and set our layer data as "By Home Location."
Next, select the "Geo Breakdown" that is applicable for your Study. As a default, your Study will show data at the census tract level. In this example, we will update the Geo Breakdown to "Cities", so that we can set our home location as Austin.
Next, select your geography from the interactive map. Tip: Save time by using the search functionality on the left-hand side of the map. You can find this via the magnifying glass icon. Once you type in your geography, the map will zoom into that location. Once you've selected your geography off of the map, click the "Apply as Filter" button on the right-hand side of the map. Now that we've selected Chicago, IL as a home location, our Study will dynamically update to show data for the residents of Chicago on a typical Thursday in Fall of 2021 season.
Step 3: Expand the Summary panel to see WFH data
At this point, we see data for all residents of Chicago. We'll expand the Summary panel to learn more about them, including their WFH status.
Click the "Show Summaries" button on the upper right-hand side of your Study.
Click on the "People" tab and scroll down to the "Work from Home" module:
In this module, we see the following breakdown for all Chicago residents:
~50% are unemployed, under 16, or not in the labor force
~26% worked in-person on a typical Thursday in this season
~15% are employed but did not work on a typical Thursday in this season
~10% worked from home on a typical Thursday in this season
Tip: You can scroll up to the "Employment Status" module to see how many residents in Chicago are employed vs. unemployed. You can also apply an "Employment Status" filter to just see data for employed residents. For instance, to see the percentage of employed residents (as opposed to the percentage of all residents) who worked from home on a typical Thursday in this season, you need to first filter to just the "Employed" population in the "Employment Status" module, and then reference the "Work From Home" module.
Learn more about our WFH module and our WFH methodology here.
Step 4: Apply filter to look at trip and demographic patterns of individuals working from home
We want to better understand who is working from home in Chicago. To do this, we select "Worked from home" from the module and click the "Filter to 1 selected" button. Now, our Study will dynamically update to show the trips and the people who are working from home on a typical weekday in this given season in Chicago.
Now that we've applied this filter, we can learn more about their demographic characteristics, including:
Understanding where within the city they live: Update the geo in the Geo Breakdown dropdown from "Cities" to "Tract" or "Block groups" to see more detailed information on where individuals who work from home live.
Understanding their household income: Use the Summary panel and scroll up to the "Household Income" module to learn more.
Understanding their race and ethnicity: Use the Summary panel and scroll up to the "Race and ethnicity" module to learn more.
We can also learn more about their travel behavior, including:
Understanding their travel purposes: Use the "Trips" tab in the Summary panel and scroll to the "Trip Purpose" module to learn more.
Step 5 (Optional): Download your dataset
Click the "Dataset" tab near the top left corner of your Study to download detailed records of the trips and travelers that meet your filter criteria. In this example, we've filtered down to ~1M trips and ~220K trip takers. The trip table will contain a detailed record of each one of these trips (via a unique activity_id), including origin and destination points, trip purpose, and more. The population table will contain an individual record of each trip taker, including detailed individual and household characteristics, like income, race and ethnicity, home and work location, and more.
Additional tools to see workforce information
Click here to view our Workforce Profile. This report contains summary metrics for the employed populations both working in and living in a specific geography.
Learn more about our WFH module and our WFH methodology here.