One of the most interesting uses of Street View imagery since its introduction on Google Maps has been yesteryear MIT. MIT's Place Pulse projection is a crowd-sourced experiment examining people's perceptions of dissimilar urban environments using their reactions to dissimilar Street View images.
MIT has right away taken the the crowd-sourced security rankings for 3,000 street images from New York together with Boston together with created an algorithm to automatically exercise a perceived security rating for Street View images. Using the Place Pulse scores MIT assigned attributes to features acquaint inwards the images, associated amongst the image's textures, colors together with shapes. They so used machine learning to associate picture features amongst scores of perceived safety. MIT tin move so run the resulting algorithm to predict the perceived security of a novel image. They tin move thus hand whatsoever Street View picture a 'StreetScore' based on the results of the Place Pulse survey.
StreetScore has right away released a expose of maps showing areas of perceived security inwards New York, Boston, Chicago together with Detroit. Using Street View images of the metropolis StreetScore assesses the perceived security of locations throughout the city. Green dots on the map stand upward for the areas which StreetScore has assigned equally having a high perceived security rating together with the ruby dots are the locations amongst a depression perceived rating score.
BikeDistrict is a actually peachy illustration of providing wheel directions on an interactive map. The application allows you lot to search for together with acquire cycling directions inwards Milan, Italy.
The application returns 3 suggested routes for whatsoever directions query: 'cycle', 'direct' together with 'safe'. The 'cycle' alternative returns a route which preferences cycle paths together with avoids the roughest roads. Milan has a lot of cobbled streets together with selecting the 'cycle' route volition supply a route which tries to avoid these cycle unfriendly roads. The 'direct' alternative returns the most straight route together with the 'safe' alternative volition avoid the busiest streets together with preference cycle paths.
The staged directions for each route are color coded on the map together with inwards the step-by-step instructions to highlight the route weather condition for every phase of the journey. If you lot don't similar a detail route inwards the suggested directions you lot tin move select it on the map together with BikeDistrict volition automatically route or so it.
As good equally providing wheel directions BikeDistrict tin move demonstrate the place of all wheel stations (with real-time information on the expose of bikes together with docks available), wheel parking locations, drinking fountains, wheel repair shops together with biking related events.
In the U.K. all the unmarried people alive inwards London, all the cyclists alive inwards Cambridge & Oxford together with all the quondam people alive on the coast.1
I gleaned these nuggets of information from DataShine Census, a novel census information explorer for the UK. Influenza A virus subtype H5N1 census map which I would locomote quite happy exploring all day. I've seen a lot of maps of census information over the years together with I must country that this novel map from DataShine ranks correct upward at that topographic point amidst the best.
DataShine Census maps information from the 2011 U.K. census. What I actually similar virtually DataShine is the sum of information from the census that has been mapped. You tin move explore the information downwardly to census tract marking inwards a expose of dissimilar demographic categories, including population, housing, education, work together with beliefs. Each of these categories include a expose of sub-categories, so at that topographic point actually is a lot of information to explore on the map together with a lot that tin move locomote learned virtually the U.K. together with its people.
Footnote:
1 All the misreadings of the information hither are the error of the writer together with non the error of DataShine Census.
