For those of us in the fortunate position to be in paid work, we are lucky if our travel to work is a simple one. The last century has been the increase in flexible working practices in many businesses, the geographic centralisation of economy and rise of private transport in most Western countries. This has led to it being common for many to not live in the same city/area, let alone the same part of town as your place of work. Therefore, commuting time has become of greater interest to companies and employees, as well as influencing property prices, climate change and transport infrastructure. This is true whether you use public or private transport; I don’t currently drive, meaning my average commute on public transport is 1 hour, despite being in the same town as my office.
Beyond grumbling time though, geospatial network analyses with live and archived traffic and travel data have provided great insight. This is both for interests sake, but also in ‘real-world’ solutions and knowledge to not only influence our choices of commute home and where to live, but also for emergency services. Most of us will be aware of live traffic map information available on our GPS devices or Google Maps apps, but I thought I’d share some recent resources of mapped average commute times, which I think are particularly interesting both in insight and presentation.
For American viewers, this Auto Accessories Garage have utilised Mapbox GL JS API and data from the US Census Bureau to create this fantastic interactive map which compares the average commuting time per US ZIP Codes to the rest of the country. A choloropleth map highlights the areas with the longest commuting times, with a ZIP code and simple search option, as well as a neat pop-box with graphs and rankings comparative to the rest of the state, and general information about commuting in US cities.
There are other approaches to traffic map cartographic design – Peter Kerpedjiev at ‘Emtpy Pipes’ uses data from a Swiss public transport API to create an isochrone map of travel times from most major European cites. This utilises leaflet.js and d3.js to create ‘contouring’ of travel times by public transport. Whilst not as precise as other transport maps, it is a useful indicative reference tool, especially if I want to estimate my travel plans from where I live to London, Manchester, Amsterdam or further.
Possibly my favourite of these maps, is one from Mark Evans at ‘I Like Big Bytes’, which provides an animated categorised coloured dots on Google Maps, to show the pattern and timing of commuters travelling to a location. It is both informative of the commuting times, but also a visual pattern of the human shift of population in daily life. Frustratingly however, the map appears to not be working at the time of writing, so I’m hoping this will be fixed soon and or a cached version works shortly. For the time being, I hope this gif below will illustrate what I mean for the time being.
I realise this is more Maps of the Day, but I think it wouldn’t be fair to single one of these out, there are loads of informative and innovative ways to display traffic data, and indeed our daily lives; for me this is one of many exciting examples where GIS and big data comes into its own. This isn’t exhaustive either, I’d love to hear and see any similar maps from regions of the world not included in this post. I hope you enjoyed and hope it’s an influence on your cartographic or non-cartographic work. It’s great to see some of the great work being done by geo/web enthusiasts out there.
Happy Mapping,
David