#TransitVizThursday – Transit Data Visualisations

Earlier this year, Craig Taylor, our expert in data visualisation, launched a mini campaign showcasing some of his data animation projects at Ito. The visualisations below are all examples of creatively deriving insight from complex transit data and descriptions taken directly from his social posts.

#TransitVizThursday: Where does your bus fill or empty?

A #dataviz exploring the pattern of occupancy data in Newcastle with data via BODS (https://lnkd.in/dz7ym8dr). This is an older project but wanted to re-share it again as I think it’s really effective in highlighting the pattern of occupancy data in an area.

I utilise spheres to represent the net change of people on the bus between stops. Blue spheres show people boarding the bus whilst yellow show people alighting. The data only shows us totals so we can only estimate the net change i.e. zero change between stops could mean 2 people board and 2 people alight.

The visualisation also shows two periods of time during the day so morning and afternoon commuter periods can be compared.

Visualising occupancy data is quite complex and creating a slightly abstract depiction of this helps us to comprehend the patterns between space and time within a city centre more clearly

#TransitVizThursday: โšก London’s EV Buses โšก

I started looking into the possibility of identifying EV vehicles in BODs over the weekend and (with help from Robin Hawkes previous experiments in EV) found a nice audit from TFL which identified electric vehicle IDs. It didn’t take long to throw this ‘experimental’ viz together showing bus movement for about an hour on Monday morning.
โš— The size of the sphere is random for now just to create some variance but it could be linked to any number of metrics; on time performance, occupancy, speed etc.
๐Ÿ“Š It would be interesting to take this to a more analytical level and start looking at the proportions of ev to normal vehicles or saving on emissions etc

#TransitVizThursday Brighton Occupancy DataViz

๐ŸšŒ Summer holidays are slowing the cadence of my weekly transit #dataviz posts but this one from the locker is a nice example of how innovation in visual design can help reveal patterns.
๐Ÿš The visual uses clustering and rigid body dynamics to showcase the patterns of people entering and exiting a bus at a specific stop over 24 hours. When a delta occurs a number of spheres are deposited from that stop proportional to the volume of people entering/exiting with colour being utilised to highlight high/low volumes.
โš— The cumulative effect of spheres being deposited pushes previous ones out to create zones, these zones help identify where clusters of people utilise the network. The concept does get tricky around inner city regions where zones merge but the pattern of occupancy on the periphery of a city is useful for identifying commuter zones/school drops offs etc.
๐Ÿ’ก I like this concept as an overview visual, it highlight patterns quickly but in order to be more accurate the spheres would need to be smaller to create distinguishable zones in inner city regions. Or, I guess, 3d pillars could show this data – but it’s less fun!

#TransitVizThursday: Occupancy Heartbeats ๐ŸšŒ

โšก This week’s 3d data visualisation looks at slightly longer-term trends in occupancy and how the heartbeat and pulse of passenger change occurs over a week.

โ“ So what is it showing? Similar to last week we are looking at the net change of passenger counts between stops in a rolling 3-hour window. This window gives us enough data to analyse patterns throughout the day. Positive net change ( ๐Ÿ”ต) signifies an increase in passenger count between stops and so shows where people are boarding buses and negative net change ( ๐Ÿ”ด ) shows where people are alighting or leaving the bus.

โš— Each sphere on the map represents a person and has a physicality to it so when multiple people alight the spheres get pushed away creating an effect that looks like growing embers in a fire. The larger zones indicate a greater change in occupancy with the chart at the bottom indicating the total net change in the region for 3-hour bins.

๐Ÿ’ก As with last week’s experiment this one also has pitfalls, the physicality and weight of the spheres pushing other out to create zones creates a disconnect with the base map. Most of those spheres will be allocated to a few bus stops in the city centre so this visual can’t be used for accurate insight. What it can give is a decent overview of activity within the region, identifying hotspots or activity outside of the CBD. Plus I find it kind of mesmerising to watch alongside the ambient music!

๐Ÿ“Š All data is sourced via BODS (https://lnkd.in/evDazHfU) although this data is taken from an archive a few years ago when passenger counting was more prevalent in the data.

Stay tuned for next weeks #TransitVizThursday – I am always interested in new ideas for how we can analyze transit data; ideas for stories, insight or just experimentation. If you have any I would love to hear your ideas!

#TransitVizThursday: ๐Ÿ—ป Occupancy Mountains in Newcastle ๐Ÿš

โšก I’m trying to keep up the cadence of sharing a mobility-based dataviz every Thursday now – #TransitVizThursday! They won’t all be new but worth checking out in case you missed them the first time I posted them.

โš— This experimental animation takes last week’s concept of occupancy change and maps it onto a 3d topographic landscape. When the buses (blue/orange orbs) record a change in occupancy (net change) between stops they deposit balls proportional to this number. These balls form the basis of a landscape that cumulates and grows over the course of 24 hours. The higher the ‘mountains’ the more people have boarded or alighted the bus over that 24-hour period.

๐Ÿ’ก As with any ‘experimental’ dataviz there are pitfalls, the visual can’t be used for detailed insight as the landscape propagation tends to spread along the basemap, however it works as a quick analysis of broad trends across a city. Of course, a simple pillar extrusion would have been simpler and arguably more insightful, but it’s not as fun as seeing occupancy mountains form!

#TransitVizThursday | Philadelphia Buses: Average Lateness

โš— This weeks experimental transit #dataviz looks at the on-time performance of buses in Philadelphia. It’s a fairly complex visual as there are competing elements at play but the below should explain it clearly.

There are two metrics showing covering different time periods shown at any one point…

๐Ÿ”ด Spheres represent the current time and current movement of buses. The colour indicates whether they are early or late and the size indicates the intensity of that metric. Big red spheres are buses that are very late, big blue spheres are buses that are very early.

๐„œ The grid represents the average lateness for buses within that cell for the previous 3 hours. It allows us to show patterns easier as there are more buses to sample over a 3 hour period so you get a less sporadic distribution – it’s less jittery. Colour ranges show the average value of lateness, so seeing red along key corridors indicates high late value for past 3 hours. Blue is early and opposite to this. The size of grid cells are linked to the frequency of activity for a whole day, so large red circles are worse than small red circles.

โฉ‡โฉ‡:โฉ‡โฉ‡ The time widget shows the 3 hour interval move along. The current time is indicated by the triangle which shows where the busses are at the moment.

As I mentioned it’s quite complicated and needs a few views to understand it; two time driven metrics of which each have a colour and size proxy linked to the values of lateness… Basically we could strip the spheres and just look at the underlying grid to give a better indication of pattern, hence the motion blur on the spheres.
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