“Geospatial analysis at scale using Tableau Prep and Eqolines”
Electric Vehicle (EV) ownership is still in its infancy, but with the ban on the sale of new diesel or petrol cars and vans only eight years away there is much to do to prepare. With the new battery plants being built, are regions ready for this transition? There are questions to be answered about the affordability of electric vehicles as well as exploring the charging infrastructure and its reachability. Additionally, ‘range anxiety’, driver concern that the battery will run out before the destination or a suitable charging point is reached, is a major concern both for existing and prospective EV drivers.
Electric Vehicles are relatively expensive. The average price of a new electric vehicle is almost out of reach of many people. Out of the models currently available, some are less than £30k with the cheapest coming in around £17k. However, if you spend less than £30k on an EV, don’t expect much range, with an average of just 132 miles at this price point. It therefore will come as no surprise that uptake of EVs in many areas is relatively low and there are large variations in ownership between areas of the UK. Additionally, the UK Government have targeted transport infrastructure projects as being key to their ‘Levelling Up’ programme, with the potential for EV-related projects to receive significant investment. The charging infrastructure is also an important factor in getting people to transition to electric vehicles. It is also a massively expanding area with significant investment being made both privately and publicly.
A UK Government report (link) summarises the scale of the task to build a comprehensive and competitive electric vehicle charging sector that works for all drivers.
The scale of the shift to EVs – requiring the development of an entirely new network – should not be underestimated. While it is difficult to know precisely how much charging will be needed, forecasts suggest that at least 280,000 to 480,000 public chargepoints will be needed by 2030 – more than 10 times the current number.
They go on to explain that EV drivers find charging to be “complex, confusing and frustrating” with poor reliability and lack of information as to the status of the charging point. Simplicity of payment was also cited as being a particular issue.
It is a rapidly developing market with thousands of new points being installed each month. For many people, charging at home will be the most cost-effective and convenient method. However, access to off-street parking or availability of on-street chargers, for example as part of the street light network, could be limited or unavailable. There are many different kW ratings for charging points – they can be ranked as slow (e.g. home chargers up to 7kW), fast (from 11kw to 42kW) and rapid (above 42kW). For away from home charging, convenience may dictate that charging time needs to be less than an hour. For example, to 80% charge a 58kwh VW ID.3, it will take six to fifteen hours using a slow charger and one to four hours using a fast charger. Seeing the breakdown of chargers by strength in England, it highlights the problem.
Only rapid chargers can achieve this, but only a small number of England’s charging points are of this type with most being of the slow type which takes over 4 hours to charge a VW id.3. Another important consideration is journey time to the charging point – if home-based charging is not available, then the charging point would need to be within a 10 minute drive of the home location or on-route. This would be particularly important in densely populated areas with limited off-street parking.
There is also the issue of accessibility of the charging points. This is demonstrated again using the North East as an example where there is currently only one charging point per 2,000 people across the area. This rises to over 6,000 people for each point in the least covered area.
Using eqolines it is possible to visualise reachability of charging points. Traditionally, reachability would be measured in crow-flies distances. However, with transportation this doesn’t make sense, unless you’re a crow! Eqolines enables journey times to be mapped and places of equal travel time to be plotted by a boundary. This boundary is not a circle, but a polygon called an isochrone (more information) bounded by an isoline which denotes all places within a certain travel time of an origin point. We looked at the reachable population within a 10-minute drive of a charging point separated by region, by district and by charging point speed.
Eqolines is now available on Tableau Public. The Seattle team have been awesome, it’s a short list of extensions that work on Public and we happy to be on that list. Examples coming out soon.
Sneak preview of an upcoming dashboard. We also extended the analysis to look at EV owners range anxiety, highlighting which charging points were reachable in the USA, UK and Germany based on vehicle model, residual charge, vehicle model, and speed of charger required. Most EV car’s owners or prospective owners, tend to have significant worries about running out of charge. More on this later in the article…
So how did we analyse EV charging points in England
When it comes to large-scale geospatial analysis, Tableau Prep doesn’t necessarily come to mind and most people would associate Alteryx, QGIS or Esri to achieve this capability. We are here to demonstrate otherwise, potentially even simpler ways, to visualise this data.
Eqolines works as an integrated solution with Tableau Desktop and Tableau Prep which is available on the newly launched Tableau Exchange. Tableau and Eqolines coupled together create new geospatial capabilities for users previously either reserved for GIS teams and utilising specialist tools.
Our team used a range of publicly available datasets that were processed using Tableau prep and the new eqolines python library. The library has been designed to have simple inputs instead of requiring users to write extensive code. We did this so can democratise the geospatial capability among the existing Tableau users.
Eqolines and Tableau Prep create outputs ready to be used in Tableau Desktop to enable creation of insights which the wider community can consume.
The starting point for creating the dashboard was preparing the data using Tableau Prep. Data was downloaded from the Government charging point database. This gave the lat/long of every charging point in England. Sometimes, data may not contain lat/long information, we built a service into eqolines that will allow Tableau Prep or the Desktop extension to geocode the data. The charging point database didn’t contain information about region or district. Using an intersect spatial join, it was possible to match the lat/long of the charging point both to district and region boundaries.
Eqolines library comes with ready to use templates (fill in the blanks concept) for isolines (URL), routing, spatial aggregation, and spatial filtering (more information). For the purpose of this analysis, we created 10 minute isochrones estimated during rush hour for each charging point i.e. people arriving at charging points within 10 minute of driving – almost 20k isochrones were generated.
Tableau Prep calls the appropriate function from the library, as detailed in the configuration fields. This creates a shape file (multi-polygons) for each of the charging locations. Some of these shape files overlap, which could cause double-counting or other issues. Therefore we added a spatial function in eqolines python library (Union) to remove overlapping areas.
Although eqolines comes with global grid based population data. In this specific scenario, we used census data by postcode. Using a similar technique to before, we were able to calculate the postcodes that were contained within each spatially merged isochrone and therefore sum the total population in those areas. This calculation was repeated at both the region and district level.
The initial output from Prep was in excess of 17k isochrones – some isochrones were removed during the cleaning process. This number of isochrones would be difficult to handle without further manipulation. Again, a reminder that at this scale people wouldn’t think of Tableau Prep and would automatically opt for specialist GIS tools. To further spatially aggregate, a further spatial merge was performed with isochrones allocated to district and region as well as splitting by slow, fast and rapid charging speed giving 6 sets of isochrones that could easily be visualised and manipulated.
Population data at both the region and district level was then passed from Prep to Desktop as well as the isochrones.
It was then relatively simple to build-up using Tableau map layers consisting of geographic data and isochrones. Calculations could be made as to the populations that were within the specified isochrones based on the area. Isochrones could be viewed down to the district level with different charging speeds, displayed together or filtered.
The eqolines product enabled geospatial analysis to be performed at scale with over 20k catchment areas being calculated for each charging point. The nature of the data that we were using meant that these catchment areas did not need to be calculated in real-time. However, the power of Tableau Prep enabled these areas to be calculated quickly and efficiently thereby enabling the final dashboard to be updated on a periodic, or even daily basis. Alternative datasets can be introduced to provide different insights, for example different geospatial border files can be used depending on the area of interest. Additionally, different demographic data can be introduced so that specific population characteristics can be explored.
There are numerous use-cases for both the data that we have compiled and the dashboards we have created. There are many stakeholders in electric vehicles and their infrastructure, not only drivers of electric vehicles. CPOs at infrastructure providers and procurement leads as well as policy and strategy managers within local government and urban planning are just a few who may be interested in the insights provided by eqolines. The power of the Tableau combined with eqolines provides flexibility and interoperability. This is just the first in a substantial pipeline of use cases for eqolines in partnership with Tableau. We will go into further how-to details about the range anxiety dashboard in our next post. In the meantime, if you have any questions about how we are creating these exciting insights or any suggestions for other uses of our product that we’ve demonstrated, please get in touch.
EV Owners Range Anxiety in USA – UK – Germany
As an extension to looking at EV reachability we were able to use similar and additional datasets to create a dashboard about range anxiety. Additional data was imported that gave information about charging times for vehicles as well as adding calculations that took into account the amount of charging time available and the current status of the vehicle’s charge. All vehicles have a battery capacity and charging points have a rating that can be used to calculate an estimate of the time that charging will take. Vehicles also have a predicted range, although that is affected by factors such as driving style, elevation and ambient temperature. It is also necessary to consider the residual charge in a car’s battery when the calculation is performed. In this way it is possible to derive the range available based on the various parameters of both the vehicle and the charging point.
This dashboard will highlight those charging points that can be reached from the origin charging point based on how long you choose to charge at the origin and how much charge is left in the battery. This could be especially useful if the origin charging point is either not operational or is being used. Drivers can then easily ascertain which other charging points may be available to them. More on EV Range Anxiety in a separate post, please subscribe on articles page and we will let you know when its ready.