Embedded Files
Data:
- The purpose of this visualization is to display changes in public transit ridership, service characteristics, and demographics for the 55 most populous U.S. Metropolitan Statistical Areas (MSAs) and their transit agencies.
- Transit data are from 2006 to 2017, collected from the National Transit Database (NTD). The highest-ridership transit agencies per MSA are displayed individually, and the rest are aggregated into “Other.” Agency data are assigned by headquarter location for MSA-level tabulations. Bus ridership includes “unlinked passenger trips” (UPTs) on local, express, commuter, and trolley buses. Rail ridership includes UPTs on light, heavy, commuter, hybrid, and streetcar rail, as well as on monorail and cable car. Transit ridership and other indicators are reported for all modes. All transit indicators are presented as percent change.
- Demographic data are from the U.S. Census Bureau's 5-year American Community Survey estimates from 2010 to 2016. Jobs data are from the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics estimates from 2010 to 2015. All demographic indicators are presented as absolute, percentage point change, except for total population, median household income, and density (presented as percent change).
- Transit service shapefiles and frequency designations are from 2018. Interline exported the data from the TransitLand API. Transit service shapefiles for Memphis, Tennessee and Richmond, Virginia are unavailable. Note: low-frequency service is only visible by zooming in from the regional map view.
- View the data dictionary for term definitions. Download the ridership data by agency or export static images with the icons in the top right. Visit the project’s GitHub repository to learn more about the data collection, analysis, and production. Visit this repository to access scripts and documentation on exporting transit stop and route shapefiles. Email ridership@transitcenter.org with any questions, comments, or suggestions for improving Transit Insights.
Features
- You can select between two datasets, there is an option to download as well as export the visualization present on the screen.
- Sparklines: On selecting this option you see a lot of filter (as shown in the left pane in the image), selecting which displays data on the map and the bar graph. You can also get top 10 agencies by ridership and top 10 MSAs by population for each filter.
Using the select your own from the compare menu, you can choose multiple transits and compare them, the map, the line and bar graph changes on selecting it.
The graph beside a filter show the change in that filter from 2006 to 2017.
- Parallel Coordinate Plot: On selecting this option you see a lot of paths, selecting one shows the route on the map. It also has a lot of filters (as shown in the drop down menu in the image), selecting one shows the distribution on map and on the bar graph. The graph shows the filter again the number of census tracts.
The other way to select an individual transit ridership is to use the jump to option present on the top left side of the image.
There is also an option to see tracts within a selected distance to a high frequency transit stop.
Other Information (Website Questions):
- Any person who uses or wishes to use a particular transit. A government employee or a normal user to understand the general trends or trends for a particular or multiple transits.
- They can ask questions like which of the transit is most populous, which is in profit, compare fares or profit or usage of multiple transits.
- They can select the individual transits using jump to or compare multiple transits (using search your own option from compare menu) and then use their desired filter from the left pane.
Things I liked:
- The small graph present in the right side of the filter when you select the sparklines. This gives you some idea before choosing that filter.
- The color selection, its very subtle and pleasing.
- The color in the bar graph is same as the color in the map which makes it easier to see the category as well as the actual number.
- There are a lot of interesting filters to do a better visualization.
Things that can be improved:
- They could also show some predictions on how things are gonna change in future.
- They should have given the option to change the basemap for this visualization.
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