city features

We run a series of queries on overpass turbo to get the most important features per city


running a query on overpass

We create a buffer around the new points and we dissolve the layer into a single multipolygon


we have different layers showing the most important city features

We calculate the distance from the closest polygon and label them by customer segment


we have different layers showing the public spaces

We calculate correlation between features


correlation between features and distances


feature and spot overlapping

Population density

We enrich poi information with official statistical data like:

We don’t know a priori which parameter is relevant for learning and we might have surprisingly perfomances from features that do not seem to have connection with the metric.

blue de

distribution of official census data

To obtain the value of population density we interpolate over the neighboring tiles with official census data using a stiff multiquadratic function.

Distribution of expense power across the city


expense power

dens interp

determination of the density value coming from the neighboring tiles of the official statistics

We than obtain an approximation on smaller geometries

popDens population density interpolation

And obtain an estimation on percentage of foregners, flat use, land use…


foreign distribution


Degeneracy is a measure of sparsity or replication of states, in this case we use the term to define the recurrency of pois in a spatial region.

The operative definition is to calculate the distribution of other pois at a certain distance. To reduce the complexity of the metric we perform a parabolic interpolation and define the degeneracy as the intercept of the parabola fitting the radial density distribution.

spatial degeneracy

spatial degeneracy, only the intercept is taken into consideration


For each location we download the local network and calculate the isochrones


isochrone, selected nodes and convex hulls


In the literature there are different examples of spatial forecast prediction property values.


property prediction