Organic Bike Share Networks

If you consider the true costs, bicycles are by far the most efficient form of transportation.

We explore how individual actors in a city can collaborate asynchronously to create a city-wide bike-sharing network. At the core of this system is a bike that unlocks with your phone or RFID.

The lock is designed as a kickstand, so that when users in the network shakes a bicycle in this network, the bicycle recognizes them, unlocks the bike for them, and allows them to ride, then charging for the time they spend before putting the kickstand down, thus re-engaging the lock.

Such a system allows micro-entrepreneurs to keep adding bikes in the network in a peer-to-peer fashion, benefiting both the users and the providers.

Mapping Transportation

Bike Sharing, Washington, D.C.

This map shows the most active routes Capital Bikeshare cyclists are using in the city of Washington, D.C.

This map simulates the specific route and location of every Capital Bikeshare rider in the city of Washington, D.C. throughout a single day, and aggregates the data to uncover urban commuting patterns. Individual riders are represented by green dots based on their imputed location and speed while different line width and color opacity represents the amount of riders using specific routes at any given time in the day.

Open bike share data can help inform cities on how cyclists use roads so they can better plan for a safe and effective infrastructure. Simulated bicycle routes help visualize the daily flux and flow of people on the move.

We made this map using Capital Bikeshare's Washington, D.C. open-data of over a million rides in 2013. The data includes the time-stamp and station number for every trip made. From this we identified top 200 station pairings then estimated the different routes using Google Directions API. We verified that the majority of trips from those stations were within the same time-band, suggesting that the majority of people use the bike share to take a direct route from their point of departure to their destination.

Bike Sharing Efficiency, Chicago

This map shows the usage and efficiency of Divvy bike-stations in the city of Chicago over the course of a single day.

Each bike station is both a point of departure and a destination. In a balanced system the amount of bikes departing equals the ones arriving. When stations are unbalanced, due, for example, to commuting patterns, sightseeing riders or public transit usage, the system restocks depleted stations by hauling bikes from 'sinks' to 'sources' using cargo bikes and vans.

Each circle marks the location of a bike station. The circles expand and contract to show the amount of bikes arriving and departing within a 60 minute time-window. Blue circles represent more departures than arrivals, while red indicates more arrivals than departures. In each station, a dial points to the direction where the majority of bike traffic from that station goes.

We made this map using Divvy’s Chicago open data set of over a million rides in 2013. The data includes time-stamp and station number for every trip made. From this we identified the top 250 station pairings then estimated the different routes using Google Directions API. To determine how unbalanced the city is, we take a weighted average of the proportion of departures not being replaced by arrivals (or vice versa).

Assuming bike stations start at 50% full, 4% of trips made by Divvy bikes would be in vans hauling bikes during the day, and 8% would be by in vans hauling bikes at the end of the day.

Assuming bike stations start at 75% full, 9% of trips made by Divvy bikes would be in vans hauling bikes during the day, and 5% would be by in vans hauling bikes at the end of the day.

Bicycle Crashes, Cambridge

This map visualizes the 746 bicycle crashes in the city of Cambridge, Massachusetts from 2010-2013.

In Cambridge alone the number of daily bike commuters has quadrupled since 2006. This map helps to show where crashes tend to happen – like Mass Ave and Cambridge Street and Hampshire Street – in the hope that those streets might be made safer for riders.

All crashes were placed on the map as a point at the place where they occurred. Not all bike crashes are reported to the police, and this map only shows those that are reported. In order to show those streets where crashes were common, we used an average-link agglomerative clustering algorithm to cluster the crashes. The large clusters, in which many crashes on a single street length, are shown by a red line. Green lines on the map indicate existing bike paths.

The data extraction and geolocation was written in Python, and the visualization was written in JavaScript and D3. We used the Google Maps API to geolocate every crash point, since the data was given to us as street addresses or intersections.

Best Mode of Transit, San Francisco

We walk to our neighbors’ apartments and ride the subway to work. Our walking city is not our subway city is not our driving city. Each mode of transit creates a different scale and pattern of use.

The map visualizes the fastest mode of transportation from each point in the city to every other point in the city. Diverse modes of transit affect the efficiency of how a city works, and the reach of many of its citizens. We hope that these maps help shed light on the way accessibility shapes one’s experience of the city, and the need to plan our streets for multiple uses.

This map is activated by selecting a specific departure point. Once the departure point is selected, the rest of the city will be colored based on the fastest form of transportation. Those points to which it’s fastest to get to by bicycle are colored yellow, by public transit: blue, by walking: green, and by driving: red.

To make this map we gridded up the city at the block group level, and then computed the time using each mode of transit from the centroid of the source block group to the centroid of the destination block group using the Google Maps API. For driving, we added a buffer time for parking and walking, and then we compared the four resulting ties and colored the block group based on the minimum.

A more complete calculation of transportation efficiency would not only take into consideration the time it takes, but the true cost of each mode of transit, including the cost of the vehicle, the cost of fuel, and the effect on air quality. In these calculations, walking and bicycling would cover even more area, and we will explore this in later maps.

Income by Subway, Washington, D.C.

This map visualizes the median household income by station along Washington, D.C.’s Metro system. It is inspired by The New Yorker’s Inequality and New York’s Subway Project.

The New Yorker map showed that transit networks are powerful orientation features. Because they are so familiar, they form a nice backbone over which we can communicate data in an intuitive way.

To build on the work of The New Yorker map, we are in the process of developing an open-source library by which any census data for any city can be visualized over a transit network. This is the first usage of this library.

Using median household income data from the census at the block group level, we mapped the changes in income across each metro line. In Washington, D.C., the stops along the green line show lower median income than other lines. The westernmost stations show higher income than those on the other end of the line.

Station, line, and route information was constructed from a GTFS (General Transit Feed Specification) feed. A station neighborhood around each station was defined as a circle with radius of 0.5 mile was constructed. All block groups that intersect a station neighborhood were identified and the proportion of the intersection area to the block group area was used to weight the population for each block group. A weighted average of median incomes was calculated for each station neighborhood across the intersecting block groups.