People are finding more and more interesting things to do with open data and smart data in cities. A new project at MIT Media Labs called You Are Here, led by Sep Kamvar, of the Social Computing Group has given itself a task of creating 100 different maps of 100 different US cities.

They hope that by making aspects of city life visible there will encourage public participation to campaign for more of the things that they like and need in their city.

Below are just three examples showing public transit efficiency, greenspace extent, and cycle accident locations.

This map visualizes the efficiency of the public transit infrastructure for different neighborhoods in New York CityThis map, right (larger and interactive on the website), visualizes the efficiency of the public transit infrastructure for different neighborhoods in New York City.

For each point in the city, the team queried the times it takes to reach every other point by riding public transit and by driving a car, then divided the former with latter. This ratio represents the efficiency of a transit system. They normalized each score by dividing it by the maximum ratio in a city and multiplying by 10, giving a relative transit efficiency score for each neighborhood. Darker areas (closer to 0) are transit deserts where cars are a necessity, while lighter areas (closer to 10) are places where public transit represents a more viable alternative.

To make this map, the team gridded up the city at the block-group level, and then computed the time using each mode of transport from the centroid of the source block group to the centroid of the destination block group using the Google Maps API. For driving, they added a buffer time for parking and walking, and then divided both resulting times and colored the block-group based on the minimum.

A more complete calculation of transit efficiency would not only take into consideration the time it takes, but the true cost of each mode of transport, including the cost of the vehicle, the cost of fuel, and the effect on air quality, and the team will explore this in future maps.

Data Sources: Google Maps Directions Services API; 2010 US CENSUS Block Groups - NYC Open Data Portal

This map visualizes the approximate amount of greenery on the streets in Washington DC.

This map visualizes the 2043 reported bicycle crashes in Los Angeles in 2012.

The relative amount of greenery at each point is represented by a marker's size and opacity. It was based on Google Street View images. These maps are approximate, in part because of seasonal changes and weather conditions, yet they give us a sense of which streets are full of life. The histogram in the lower left-hand corner gives a distribution of street greenery. The left-hand side of the histogram represents the number of streets with low levels of street greenery, and the right-hand side of the histogram represents the number of streets with a high levels of street greenery.

Data Sources: Google Maps StreetView Image API; Washington DC GIS Data program

This map visualizes the 2043 reported bicycle crashes in Los Angeles in 2012.

This map visualizes the 2043 reported bicycle crashes in Los Angeles in 2012.

This map helps to show where crashes tend to happen — like Olympic Avenue, Venice Avenue and Sunset Avneue — in the hope that those streets might be made safer for riders. On their website you can explore the map on three levels: city, street and location. Each reported bike crash is recorded as a dot on the map. Streets on which bike crashes are common are outlined in red. You may hover over an crash to see the street or intersection on which it occurred. You can even make street-wise comparison of crashes. Places where many crashes are on a single street length, are shown by a red line. Green lines indicate existing bike paths.

Data Sources: Los Angeles Police Department Reports (Raw Data); Google Maps Geolocation API; Google Maps StreetView Image API.