10 exemples concrets d'utilisation des données pour construire des bases de connaissances et des outils d'aide

Ces 10 exemples d'utilisation des données dans le domaine des transports de marchandises (déchets notamment) et des personnes, préfigurent les futurs outils d'aide qui seront orientés en fonction des utilisateurs : citoyen, ménage, collectivité, entreprise, état…

Traffic accidents in the U.S. between 2001 to 2009

ITO-Road fatalities USA

 

An impressive work that collects all traffic accidents on different roads of the United States by type of accident (pedestrian, driver, year, etc..) And all in one map that has accumulated a huge range of information for the period 2001 – 2009. The same team has prepared one for the UK. Guns of mass destruction? A silent tragedy? The map is shocking.

 

The long journey of trash

Trash track

I wrote some lines about this project from MIT some time ago. What is worth watching in the video is how it explains the concept of the project and the result of adding location aware tags to different types of trash and see how each of them travel a huge amount of miles until final disposal. Waste management and removal is an obscure and secret system (throw away and forget about them) and the project helps to visualize and understand there is a much more extensive life than we imagine for the trash we throw away.

 

A public hire bike system in real time

London Bike Share Map

This map visualizes all bikes of the public hire schemes in London. From the same site, in fact, you can access and check other cities (Zaragoza, Toronto, Lille, etc.). The project displays information on the distribution of all the checkin points, the level of use at any given time, temporal progression of use of each terminal and the availability or not of bicycles at each point.

 

The intense activity of a subway network

Examining MetroCard usage

What to do with the data from every user entries in the extensive network of subway in New York? This phenomenal work published by Wall Street Journal is a good example of how to use information from seemingly irrelevant individual data: types of tickets, stations, schedules, fares, etc. Put this in a map and add logic to the data to understand, among other things, the variation in use according to the tariff changes introduced in the price system.

 

Real-time use of bicycles

London Hire Bikes animation

Another one about bikes. The video shows the flow dynamically of the bicycles used moving through the 18 hours of the day. I also mentioned this and other projects about London some weeks ago.

 

A U.S. map block by block

Mapping America: Every City, Every Block

What can you do with the census data? With this map you can reach the level of detail of every building anywhere in the country and see the distribution of population by race, by income, by type of household, type of housing or education, and understand the dynamics of spatial distribution at national, regional, urban or neighborhood level.

 

Time distance to get around the city

Mapumental

MySociety developed years ago this project that perfectly illustrates the utility of georeferenced data. Mapumental tool displays the travel time to reach a certain point from anywhere in the city, thereby helping to understand the temporal distance mobility, a much more useful and practical information than just physical distance.

 

The changing city. Day and night

Day vs. Night population maps

A simple but powerful idea. The population of New York during the day and at night, reflecting the density of different areas.

 

Singapore real time

LIVE Singapore!

Another well-known MIT project from Seansable City Lab. Using different data sets and maps designed to explain the impact of rain on the level of use of the taxi in the city, predicted travel time based on changing traffic conditions , the heat island effect or continuous flow of arrivals and departures of peopl
e and goods in a city that serves as a hub of the global economy. The video explains it all.

Understanding air pollution

In the air

Make visible the invisible dirty air we breathe, nothing less. That's what Nerea Calvillo proposed in a dynamic model to visualize and map the footprint of air pollution in Madrid.

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