Collaborations between city savvy professionals happened back in the 60s in America, in an analog fashion. It has massively changed the way we understand cities, but it has since dissapeared due to the enourmous ammount of effort needed in order to maintain it, and to the complexity of cities. We find the possibilities of combinig technology with professional knowledge about the city(architecture, sociology, urbanism, etc.) fascinating - cities are for sure in our future, so is technology. We believe that it is a must to deliver both a better understanding of how they work and grow and of how they influence the quality of our life.

A REMAX study shows that 85% of Romanians have as first criteria when choosing a house, the price! At the same time, american studies show that living next to a window with a view to a park helps one to have a better focus, and also halves the time we need to recover from surgery for example.

Thus, we believe that where we live has quite a lot to do with the quality of our life.

What it does

It illustrates in a friendly fashion which are the qualities of our homes and city, by using open data.

How we built it

We selected an area in Timișoara, for which we generated some quality revealing indicators, by using sets of data that we prepared before joining HackTM.

What we used: Open Street Map API, mapbox, overpass API, Python, shapely, Django

Challenges we ran into

The open data (in Romania) subject in itself was the biggest challenge. Cleaning it and making it usable.

We worked specifficaly with data related to sound in the city, access to light, air quality, building permits, buildings valuable from an architectural point of view, city functions, walkability, building volumes, etc.

Accomplishments that we're proud of

Transforming quantitaive information into qualitative information. And, of course, the collaboration between different professionals.

We managed to develop formulas that transform some of these layers into qualities - for example we established the level of privacy by evaluating the distance between buildings, we analised a primary version of the access to light by analising the position of the buildings compared with the cardinal points, we transformed data about noise and air quality into grades by referencing them to national legislation, etc.

What we learned

Living in a context where open data is still very close to being a myth, we need to tackle this problem in a very creative fashion. Also, a further collaboration with a wider community of professionals is needed in order to develop more precisely the method that transforms the data into qualitative incentives.

What's next for undelocuim

Loads of partnerships, an MVP, and then, getting it done, and developing it continously.

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