For now, let’s weight all amenities equally, and visualize distance to the fifth nearest amenity.plot_nearest_amenity('all',5)From network distances to walk timeLet’s make the same outputs for a second city: Minneapolis-St Paul..(The people are nice there, and the gridded streets visualize well.)Here, we see that plotting a compound measure of accessibility — distance to the fifth nearest amenity — gives a clearer picture of which neighborhoods are most walkable, compared with plotting just the distance to the single nearest cultural amenity (cafe, restaurant or school).To make the results more interpretable, let’s build a grid of 250 square meter cells behind our road network, attach each cell to its closest node, and translate the distances into walk, drive or public transit time..Minneapolis is not a very walkable city, though some downtown neighborhoods fare well.Minneapolis: Minutes walk to fifth-nearest amenityExtensionsThe library used here was developed as part of the UrbanSIM project at UC Berkeley, supporting a range of urban planning and real-estate use cases..Good quality, locally produced GIS data can be used instead of OSM (and indeed is preferable).An important extension: this method is a great input for property price modelling or building-level predictive models..Hedonic price models assume that, for example, home buyers pay more for a home where they can easily walk to a Starbucks or drive to a hospital..Snapping tax lot boundaries (or OSM building footprints) to the network, the same way as we just attached grid squares to it, allows us to derive this data for any given property.Likewise, machine learning models to predict crime or fire risk require as many attributes as possible to characterize each building’s place in the urban fabric.Rapid network-constrained queries through this kind of framework can answer many questions — starting with the walkability metrics discussed above.. More details