Tagged: mobility

Arlington Bike Counts, Activity MapperArlington has a collection of automated trail counters which can distinguish between pedestrians and bikes, and can also record the direction of travel. This data is transmitted live to a database, managed with Eco Counter software. I recently got access to the data and was able to experiment with how to visualize it. I decided to add it as a dataset to my new Activity Mapper, which lets you animate and interact with a chronological and geographical data set.

For each coordinate, Activity Mapper lets you compare two numbers. For Metro it was entrances and exits at each station. For Capital Bikeshare it was bikeins and bikeouts at each station, and grew to include datasets comparing casual riders to registered riders, and trips that fell within or beyond the 30-min time limit. For my first look at Arlington’s trail counters, I chose to look at the past year of bike traffic (excluding pedestrian traffic), broken down into 365 days. The two data sets compare “trips in” to “trips out,” refering to the direction of the bike. Unfortunately, this version does not yet indicate the direction in the display. » Continue Reading…

Strava ExplorerThe Strava Explorer has been beefed up to not only reveal more information from Strava’s database, but to include street-view images from Google.

The first step is to get Strava’s top segments for geographic area. You can either use geolocation, or include a coordinate in the URL, like ?home=40.78,-73.97. Or you can just pan and zoom the map, or enter place names in the input field, then hit the “find segments” button.

Each segment is shown in red, with the name written by the starting point. Clicking on the name will bring up further information. A menu lets you choose between the leaderboard or “stats and streetviews,” or both. The leaderboard will show the names of the people with the top ten times (shown in the right-hand column). As you hover over a person’s name, their photo will be displayed, if available. Hovering over the table’s heading will pop up the “stats and streetviews” information, if it isn’t already on-screen. The “stats and streetviews” area displays more information about the selected segment. Additionally it will display streetview images from Google Maps. Use the menu to control how many images to display: either just the starting point, or also the ending point, or also the one-third and two-thirds mid-points, or all points. Points are determined by how to draw the route; they are not distributed to mark distance. Thus, a straight route will have fewer points than a curved route. As you hover over an image a marker will appear on the map showing the place and heading of the street view. Another option lets you animate all the images together, creating a flip-book impression of the route. » Continue Reading…

Mashing Up the Strava API

Strava ExplorerStrava, the software used by athletes to track their activities, has a new API available for accessing their data. I looked through it to see if I could do anything quick and interesting with it.

Earlier this year they angered many developers by unplugging their previous API, leaving many third-party apps stranded. My own interest is in seeing how cyclists use the city: which routes are preferred. Unfortunately, the data available in Strava’s v3 API is extremely limited. Most of the API is designed to reveal a selected user’s data, assuming they have specifically granted the program access. But there doesn’t seem to be a way to look at aggregate data.

In fact there seems to be only a single API function which accepts a geographic bounding box as an input. That API, the “segment explorer” returns up to 10 “popular” segments.

I wanted to see how easy it would be to use the API in a little test program. The result, my Strava Explorer, doesn’t really do anything interesting other than prove I can connect to the Strava API. » Continue Reading…

cabitypesNew Capital Bikeshare data sets have been added to the Activity Mapper visualization tool. I started with showing bikein & bikeout totals for each station for a single day in September; the new data sets all look at the entire 3rd quarter of 2013, from July through September. Instead of summarizing subtotals in 5-minute chunks, the new data sets have a subtotal for every day. The 3rd quarter includes the 13 CaBi stations that debuted in Montgomery County at the end of September, but since their traffic is dwarfed by the rest of the system, there is not much to see.

New controls let you customize the display. Use the + and – keys to zoom in or out (clicking will zoom and center). Ctrl+ and Ctrl- increase and decrease the size of the circle. ← and → can control the time sequence by moving backwards or forwards in time, one segment at a time.

The program compares two sets of numbers for each station. Originally, I showed the number of bikeins and bikeouts per station. (CaBi also calls these actions rentals and returns, or un-dock and dock.) A new data set compares subscribers to casual riders. Subscribers buy memberships for a month or a year, while casual riders buy the 1- or 3-day memberships. Another new data set compares “on-time” rides to “late” rides. On-time means the trip took 30 minutes or less. The rides are counted against the station where the trip ended. » Continue Reading…

More Options for Activity Mapper

Activity DisplayHere’s another way to analyze trip history data from Capital Bikeshare. Back in March, I had used Processing to create QuickTime animations that I uploaded to YouTube (see Neighborhood CaBi Animations). But I wanted a tool that let the user control the flow of time, as well as how to customize the display. A few weeks ago, I created a tool to do this for Metro (see A Day of Metro, Entries and Exits). Now you can use the same tool to show CaBi data: go to the Activity Display home page to select which data set to use, or add ?system=cabi to the URL to go straight to the CaBi display.

I show data in 5-minute increments for Saturday, September 14, 2013. That day was the busiest day in the 3rd quarter, with 84,8755 trips made in a single day, using the system’s 243 stations. (Today the system has 300 stations.)

This is now the third tool in my collection of data visualization programs. The Stat Mapper shows collections of single points; the Trip Visualizer is meant for displaying point-to-point data; and now the Activity Mapper is for chronological data. Over time I hope to add new data sets as well as new features.

Animating a Day of CaBi Data

Want to see how people move in and out of Metro stations? I made a Metro Activity animation using data from April 10, 2013. The data shows the numbers of entries and exits for each station in 15-minutes increments, from 4:45am to 1:00am (that’s 81 records).

WMATA has already visualized this same data set, in Visualization of Metrorail Station Activity. The date was picked because it had the 4th-highest ridership, with 871,000 trips, compared to 750,000 on an average weekday.

My goal for this new visualization was to design a tool that’s fluid and interactive. I used the HTML canvas element to create the animation. I can scale the canvas to fit the window. On top of each station, I draw an image that’s scaled to the data for that station. I can change the color and shape to indicate other values. A form on top shows the user controls. » Continue Reading…

The Citi Bike Mapper application shows all of the new bikesharing stations in New York City. Like the official Citi Bike station map, my version plots markers across a map of the city. But to make it easier to gauge how full or empty a station is, I write the totals across each marker.

Whether you care more about avoiding empty stations or full stations depends on whether you’re planning to check in or out. So, to de-clutter the screen, you can select to show only the number of bikes or docks at each station. You can also show this as a percentage of total slots. Use the “wordy” check box to shorten the markers to just numbers. Or, another option lets you display just the name of each station.

The color or each bubble will always indicate how full or empty the station is, ranging from blue for empty to red for full. Shades of purple are used for proportions in-between. » Continue Reading…

Mapping Citi Bikes

At Mobility Lab‘s recent hack day, WMATA released a copy of the GTFS data they use for their trip planner. The trip planner includes schedule data for a total of 19 different transit agencies. GTFS, General Transit Feed Specification, is a collection of files that together form a complete description of all the routes, stops, and schedules. WMATA released an animation of the data last November (see Maps in Motion: Telling Stories from Transit Data). With the GTFS data now publicly available, I wanted to try making my own animations.

» Continue Reading…

Voronoi Diagram of CaBi StationsI was introduced to Voronoi diagrams at today’s Data Visualization Hack Day, at Mobility Lab. Justin Grimes showed a Voronoi diagram of Capital Bikeshare stations he had overlaid on a map of the Washington, DC region: capital_bikeshare_voronoi_diagram.

The map plots each CaBi station. The boundaries are drawn at the half-way point between the closest two stations. When three or more cell boundaries meet, you are equidistant to them all. Inside the cell of a station, that station is the one that’s closest to you.

Justin showed me an interactive example of Voronoi Tesselation on GitHub. The interactive demo adds a new “seed” wherever your mouse is pointing. That point creates a Voronoi cell. » Continue Reading…

A few days ago I animated a day of Capital Bikeshare trips using a new Java program I had written in Processing (see Animating Data with Processing). I wanted the program to be flexible enough to allow people to customize for their own uses, so I put it to the test myself by making slight modifications to its display.

For the first customization, I wanted to zoom into Dupont Circle. It turns out that at this scale, drawing a frame every 60 seconds means bikes disappear from view without giving an impression of movement. So, I had to slow down the speed. The video below samples the data every 5 seconds (12 times slower than before), so you can follow individual bikes. The video displays 30 frames per second. The data is from October 5, 2012, from 8am to 8pm.

» Continue Reading…