Geospatial Data Analysis in Python

Getting started with performing geographical data analysis in Python using OSMnx and Kepler.gl

Photo by Aaron Burden on Unsplash

Introduction

Geospatial data is ubiquitous and used for many different applications across all businesses (e.g. calculating the risk of properties depending on their location, designing new architecture development, planning shipment of goods, and finding possible routes between different locations).

Geospatial data is typically stored in two possible formats: Raster and Vector:

• Rasters represent data as a matrix of pixels (therefore having a fixed resolution). In this representation, each pixel can be assigned a different value and multiple grids stacked together can be used in order to augment even more the same image. For example, the same image could be stored using 3 channels/bands (e.g. RGB — Red, Green, Blue) or with a single channel.
• Vectors can be used to abstract geometries of the real-world using elements such as points, lines, polygons, etc… and they can usually be stored in conjunction with some useful metadata about the objects they are representing (e.g. name, address, owner, etc…). Since they are stored as mathematical objects, it is possible to zoom in on vectors without compromising resolution.