Fransisco Benita and Padmapriya Srinivasan
UROP Students: Chavi Mangla, Tessa Dougan, Reina Peh Shu Ting, Stephanie Ann Min Long, Fenet Demssie
Singapore University of Technology and Design
In the realm of urban planning and community development, understanding the walkability of a neighborhood is crucial. In this post, we'll explore a simple Python script designed to compute walkability features using a variety of powerful libraries.
The goal of our project was to develop a set of simple scripts capable of computing walkability features for a given location. We leveraged OpenStreetMap data to extract information about the street network.
After setting up our Python environment with the necessary libraries, the next crucial step is to define the geographical boundaries of the city we want to analyze. This information is pivotal for extracting the pedestrian network within the specified area.
Now that we have defined the geographical boundaries of our city using the boundary files, the next step is to extract the pedestrian network within this defined area. This is where the real exploration of walkability begins!
One of the fundamental aspects of walkability is the physical length of pedestrian pathways within a city. In this section, we introduce a function that calculates and quantifies the total length of the pedestrian network in kilometers.
In this section, we introduce a function that calculates both the total area of the defined boundary polygon and the network density.
In this section, we introduce a function that calculates both the total area of the defined boundary polygon and the network density.
In this section, we introduce code snippets that bring population data, building footprints, and elevation contours into the narrative.
The first step involves constructing an isochrone graph centered around a point of interest (POI) with latitude and longitude coordinates over our pedestrian network. The isochrone is created based on a specified walking distance (500 meters in this example) from the POI.
As we explore the intersection of walkability metrics within the isochrone and other geographical layers, our journey brings us to visualizing and calculating the density of the overlapping space.