March 7th, 2014 | 2 comments
What would the Washington, DC region look like if you never went further than 500 meters from a Metro station? Well, there’s an app for that! I was inspired by a car-free friend who pointed out the difficulty of finding a Metro-accessible dentist when moving to DC. So, let’s put the Internet to work to make that simpler.
I used the Places Library of the Google Maps API to discover dentist locations for a geographic region. To connect to Metro stations, I submitted a separate search for each Metro station. Of course, “dentist” is just one option for a type of place. The API has 96 Supported Place Types, from airport to zoo.
Try the Metro Places app to discover businesses near your favorite Metro station. To make your own search, select the type of place from the drop-down list, which station you want and how many stops you’re willing to travel (I assume no one wants to transfer), and how far you’re willing to walk from the end-station. You can display the results as a collection of icons or a heat map, or both. The icons returned are part of the Places API, such as a giant tooth for dentists, and a martini glass for bars. continued » » »
March 2nd, 2014 | no comments
Should the bikeshare industry adopt an open data standard? As bikesharing spreads to more cities, having a common method for accessing and analyzing data will become more important. We know that transit systems work best when agencies concentrate on their core mission. Transit agencies are not in the information technology business; all they should do is release their data to let third parties build apps that let passengers use the systems.
To use open data, programmers need to know: Where is the data? What are the files called? Which fields are available? What are the fields called?
Bikesharing systems should adopt the standard of having a “data” page which can be found by appending “data” immediately after the main URL. This is what many U.S. government web sites are doing (like justice.gov/data, dot.gov/data, state.gov/data, etc.) It would be awesome to have consistent URLs like capitalbikeshare.com/data and velib.paris.fr/data. continued » » »
February 27th, 2014 | no comments
You may visit Aspen, Colorado to go skiing, but how do you get around when visiting in the summer? We-Cycle has introduced bikesharing to this mountain town, giving residents and tourists a new option for getting around. They recently shared some trip history data with me, letting me create this short animation of how cyclists travel between the 13 stations.
February 21st, 2014 | no comments
Why do so many APIs offer geographic searches based on a single point and a radius, but not based on a bounding box using two points? Does your computer or mobile device have a round screen? If so, a radius search is perfect for you. But if your screen is rectangular, your search should be too. API designers need to wake up and realize that nobody has a round screen, and thus radius-based geo searches are vastly inferior to rectangular searches!
Here’s an example from Times Square. For a search at 42nd and Broadway, if you set the radius to be half the width of the screen, your search area would look like this green circle. On a square screen, those four corners outside the circle add up to 21% of the display (given a square display of width w, (w² – π×w²/4)/w² = 21%). For rectangular screens, it becomes higher. For example, putting a circle in a rectangle that’s twice as long as it is high means you are missing 61% of the display. This means you might not be including results that the user would expect to see. continued » » »
February 19th, 2014 | no comments
The San Francisco region has joined the bikesharing movement, with the introduction of Bay Area Bikeshare in August 2013. I wanted to see if I could adapt any of my CaBi tools for the “BABS” system, but their open data is too limited to be of much use. They have a System Metrics page which offers only ridership and membership data, which is not very interesting. To analyze the system we need trip history data, like Capital Bikeshare shares every quarter.
Luckily, I discovered Eric Fischer, who has been tracking station statuses since late August. Every minute, he records the number of available bikes and docks at each station. While not as valuable as trip history data, this data does let us discover when stations are either full or empty.
The data he records is a copy of the current station data, available at bayareabikeshare.com/stations/json. I had to reduce the size of the file by writing a Java program to remove redundancy and unnecessary fields. Still, storing data for a single day takes a megabyte of space for even the condensed JSON file. continued » » »
February 17th, 2014 | no comments
It used to be Valentine’s meant heart-shaped boxes filled with chocolates, but nowadays it means people running through the streets in red and pink underwear. Cupid’s Undie Run is a brilliant fundraising event that supports the Children’s Tumor Foundation while also making Capitol Hill a much more interesting place.
Here are the best shots from the 2014 Cupid’s Undie Run in Washington, DC:
February 15th, 2014 | no comments
If you’re a total transit nerd, this will be exciting. To prepare for a bus-themed event for the Transportation Techies meetup group, we’re making public APC data sets. That’s automated passenger counter; electronic devices that measure people boarding and alighting. We’re sharing it in hopes that local programmers will use it to create visualizations of how people use the bus.
2013-09 Raw Stop Data.xlsx is from Arlington Transit. It has 12 columns and 20,460 rows (1.2MB). The data is for weekdays in September 2013. I’ve created a CSV version, 2013-09 Raw Stop Data.csv. Here’s what 3 sample rows looks like: continued » » »
February 14th, 2014 | no comments
What is the minimum information you need when planning a trip on the Metro system? If all you want to see is which stations are connected, the Force Diagram of WMATA Metro Stations is the Metro map for you.
The code is relatively compact, and customizing it was a good way for me to learn D3. That’s the same tool I used to create the Voronoi Diagram of CaBi Stations and the interactive bar chart I used for Looking Back at 2013 CaBi Data.
January 27th, 2014 | no comments
The Trip Visualizer has been updated with new data for Capital Bikeshare. Instead of just posting the new quarter, I made all of 2013 a single data set. That’s over two-and-a-half million trips (2,585,010 bikeouts) using 309 stations, summarized into a big origin-destination table.
Montgomery County joined the network in late September, introducing bikesharing to four regions in Maryland: Rockville, Bethesda/Chevy Chase, Silver Spring, and Takoma Park. 73% of bikeouts from Maryland went to other stations in Maryland, with 26% headed to DC. Just under 0.5% (21 trips out of 4,675) went from Maryland to Virginia. The fastest Maryland-to-Virginia ride took 33 minutes, from Friendship Heights Metro to Rosslyn Metro, a trip that takes 27 minutes on Metro. The longest MD/VA trip was 1 hour and 48 minutes, when someone biked from Crystal City Metro to Battery Lane in Silver Spring.
The Trip Visualizer lets you select a single station to see the most-significant trips to/from that station. You can use some hidden features to select clusters of stations, to examine networks. Hitting “M” will select all stations in Maryland. You can see how isolated Rockville is, with its closest station to Bethesda still over five miles away. continued » » »
January 26th, 2014 | 1 comment
When I saw Dan Macy’s aerial photo of Washington, DC, I knew I had to turn it into the background for a map. Dan’s plane was flying into National Airport from New York City, and made an unusual entry over the Anacostia River, giving him a spectacular view of East Capitol Street, the Anacostia and Potomac Rivers, and the National Mall.
I had to figure out a not-too-complex way of mapping the latitude and longitude coordinates to the oblique photo. Unlike a map, the meridians (longitudes) aren’t vertical and the parallels (latitudes) aren’t horizontal. The bird’s-eye perspective also meant the meridians and parallels would be skewed. continued » » »
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