Everything was going well; all the necessary markup was in place as recommended on the WebAIM site. Field sets, labels and buttons ordered correctly. I turned on screen reader options and everything was read back to me beautifully on my laptop as well as on iOS and Android devices. A nice accessible webapp, job done! Well, not exactly…
Just over a year ago I started a volunteer civic project with Code for Philly called UnlockPhilly at a Hackathon called Apps for Philly Transit. UnlockPhilly’s aim: to raise awareness of good and bad accessibility by mapping accessible stations, elevator outages and accessible venues in Philadelphia. This blog post reviews progress made so far and describes recent efforts to prototype an idea for an accessible new app.
The Hackathon provided a really unique opportunity for developers to pair up with subject matter experts and end users in the field of accessibility for people with disabilities.
I personally felt very proud to see a project that I’d worked on mostly on my own gain new team members, more visibility and make progress towards it’s goal to raise visibility of accessibility challenges.
In a previous post I wrote about Unlock Philadelphia; a web app we started developing at a transit hackathon to map accessibility in Philadelphia. Well, thanks to the app, and a few tweets about an abandoned elevator at 8th and Market Station, accessibility and civic hacking made the news this week.
NBC10 reporter Vince Lattanzio noticed tweets with photos I made to SEPTA_SOCIAL reporting an elevator that had been out of service for 2 months. The outage had prevented hundreds of people from getting between trains going east-bound and the street. I got back to him with more information about why I cared about this and shared the Unlock Philly app. He phoned me later that day for an interview.
In my previous post I talked about an app I worked on at a Hackathon that visualizes the accessibility of Philadelphia stations and services around them. After seeing how much open transit data is out there and being inspired by other open data mapping apps, including those by Chris Whong (in particular the Baltimore City Charm Circulator visualization he worked on with a team at Reinvent Transit), I thought it would be cool to see bus routes, positions and patterns by Philly neighborhood and integrate this into the ‘Unlock Philadelphia’ website.
Google transit searches go point to point and assume you know your exact departure and destination locations.
SEPTA’s website lets you see bus routes and positions, but for a single route that you already know by number.
How about if you’re new to an area, don’t have a car and just want to know where you can get to on the transit system and see where all the buses are? Or just interested in all the places it’s possible to get to easily without getting the car out? In other words, add a bit of serendipity to your search.
Apps For Philly Transit is an annual Hackathon that “aims to bring together transportation organizations and citizens of Philadelphia to rapidly conceive, design, and prototype uses of open data relating to transportation in Philly”.
I pitched an idea called Unlock Philadelphia: an application to help people with disabilities, parents with strollers and older people to find accessible stations and transit information. By combining transit data with with Yelp’s open data feed, I wanted to also find accessible shops, restaurants and services close to accessible stations.
The idea received votes and positive feedback so I formed a team and built it.
Open Data Nottingham has released traffic accident data covering incidents in Nottinghamshire for the last four years. For reported accidents, persons are listed (anonymously) if they suffer slight, severe or fatal injuries. Drivers are identified, along with age/gender, but only if they are injured. The quality and format of the data is good.
Nottingham City’s website already has a map representation of the data, but it doesn’t allow filtering and there is no colour coding or categorisation, making it difficult to spot patterns.
To provide a better visualisation, I reorganized the data using a custom Java app and categorised individual accidents by severity, time of accident and age group of persons injured in accidents. The most serious injury sustained by any person involved in an accident determines the overall severity of the accident; times of day have been split into bands, so have driver ages.