Spatial hexagon mapping of commute patterns in Belgium

original post february 2017 - last updated march 2019

Commute patterns refer to the regular movements of people between their homes and workplaces. Based on Belgian census data, we visualized various commute patterns as outflow, inflow or resident (see an earlier post for the description of the data). In this post we use spatial hexagon mapping to represent the proportions of the active population that is comprised of people moving in from nearby or more distant areas to access jobs or job opportunities (inflow).

We partition our study area (Belgium) into identifiable grid cells. Spatial gridding is the process of dividing a continuous geographical area into a regular grid of discrete cells. Grid cells can tessellate, i.e. cover an area by the repeated use of a single shape without gaps or overlapping. We use hexagons for a regular tessellation of a geographical area and apply a spatial overlay operation to map data points into the hexagonal structure. One way of modifying point data is to create an interpolated surface that calculates predicted values over areas where points do not exist and convert these values to a raster surface. This can be used to create a map where each hexagon represents a geographical area that is shaded or coloured based on the commute data (i.e. darker shades might indicate higer inflow counts).

The description of the workflow to produce a spatial hexagon grid analysis can be found on my github.