Inspiration

A big problem in our current cities is the cost of living. The increasing rental costs put especially low-income families in hard situations. Should they uproot their life and move out of the city, or can they live without previous amenities? Building more apartments in the spot of older, smaller houses, can lead to an increase in the population density, without the need to grow the city ever outwards, decreasing the access to nature for the city population. The information what houses could be replaced, and the fear of the neighbors that the cityscape would change rapidly in a bad direction, are often reasons why new building projects are delayed or not even started.

What it does

We extract features from cities, for example, the position of houses, parks, and public transport points. We also take the average size of surrounding houses into consideration, to recommend houses which could be replaced by houses with more apartments inside, without drastically changing the shape of the city.

How we built it

We used open LIDAR datasets and OpenStreetmap data which we analyze in python to find houses and their neighbors, to then analyze their volume, and the potential volume of a replacement.

Challenges we ran into

We never worked with LIDAR data before, which meant that we had to spend a considerable amount of time reading ourselves into this topic.

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