This is a guest post from Werner Pretorius
In a city planning context, it’s impossible to be everywhere all the time. Which is why we use data – to help us see the unseen, to fill the gaps in our knowledge.
The Census Commuter datasets are one of those that help you see the bigger picture and detail aspects of commuting patterns.
In New Zealand, it’s surveyed every five years and aims to capture the entire population during the Census collection. These datasets are often used by central and local government, academics, transit companies, transport and city planners for a wide spectrum of use cases.
With the latest 2018 release, Stats NZ included the travel to education and work data by mode. Which is much more exciting to look at.
The main reason I prepared this post was to share with you the latest 2018 Commuter data in a new and hopefully more engaging way. Also wanted more city planning professionals to be able to understand commuter data very fast.
This interactive data visualisation below, using NZ 2018 Census data and flowmap.blue will make it easy to ask questions ranging from micro-detail to macro-regional movement in and between cities.
You can see how people travel to work and education (combined) for the entire New Zealand using different travel modes. I created a short video to show the key features here:
Each geographical area unit (SA2) is represented as a centroid point. The flows between centroids show the desired lines of how people travel to work and education.
Clipped a few screenshots of Auckland’s commuter data, which highlight the desired travel demand by mode:
Total Trips (all modes): travel to work and education, combined all modes
At Home: did not travel to work or education
Train: travel to work and education using the train
Public Bus: travel to work and education using the public bus
School Bus: travel to education using the school bus
General Traffic: travel to work and education using car, van or truck
Bicycle: travel to work and education using the bike
Walk or Jog: travel to work and education by walking or jogging
To explore the interactive data visualisation yourself use this link.
I am interested to understand the potential different use cases and how it could help key decisions in shaping Auckland’s future. Leave a comment if anything comes to mind.
- This data only represents the journey to work and education and does not amount to all the demand on the network.
- This data only represents the main method of travelling to work and education
- Every dataset has strengths and weaknesses. Always apply ABC when working with large datasets.
- I found some anomalies (surprisingly long distance travelled) in the ‘walk or jog’ category, which seems to be a misclassified or misrepresented in the original data. Speculation only but suspect it is people living in another city, flying in but walking from their hotel to work.