MRCagney Works

Job Accessibility

Recently the Data Science Team conducted a study of multimodal job accessibility in Hamilton, New Zealand, the purpose of which was to test our tools on a timely topic of interest to our clients. Let me walk you through our approach and findings.

We chose Hamilton, New Zealand as our study area, because it is populous enough to be instructive and small enough to compute everything quickly on our laptops. By Hamilton, we mean the area within the Hamilton City Territorial Authority as shown below.

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We followed the sensible approach of Conveyal and measured job accessibility with travel time isochrones. In doing so, we aimed to answer, at least approximately, the following questions for a given mode of travel, e.g. public transport, and a given time bound, e.g. 30 minutes.

  1. Given a point in Hamilton, how many jobs can you reach from that point by that mode and within that time bound?
  2. What percentage of Hamiltonians can reach what percentage of jobs by that mode and within that time bound?

The first question addresses the geographic distribution of job accessibility in Hamilton and the second the statistical distribution.

Question 1

There are infinitely many geographic points in Hamilton, which is too many to compute isochrones for, so we whittled our source points down to a manageable finite set. Using all street address points in Hamilton was one option, but there are 64,859 of those, which is still too many for our laptops. So instead we partitioned Hamilton into a somewhat fine regular grid, chose one sample point from each grid cell, and used that point’s accessibility score to represent the accessibility score of the entire cell, a reasonable approximation.

We chose a hexagon grid with inradius 250m (the radius of the inscribed circle of each hexagon), because we like honeycomb and reckoned that those cells would be small enough to yield meaningful results but few enough (513 of them) to yield quick computations. For the source points we chose the most central address in each cell.

Here is what that grid looks like, clipping it to Hamilton’s boundary and removing cells lacking residential addresses. To see the source points, toggle them via the map’s layer control in the top right.

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Gather job locations

Now where are the jobs? The finest grained open data we knew of to answer that question is the statistical area 1 data set for 2018 Census. That data set divides Hamilton into statistical area 1 cells (SA1s) and groups job locations by them. Here is a choropleth map of the number of jobs by SA1.

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