Geographical information system (GIS) is widely used to perform visualization of data that is better suited to be presented in map-like display. The GIS has become much more ubiquitous in the news reporting landscape thanks to the recent use cases like the tracking of coronavirus infection across the globe. Can the same system be used to perform other kinds of data analytics? That is a rhetorical question. Of course, it can.
In our role as cost estimators, we must pay special attention to the accuracy and reliability of the inputs which are driven by assumptions and their associated uncertainties. In other words, when there is uncertainty in the input, we need to put care in justifying the reason for settling on that value. Because not only the decision to pick a value can be subjective, those underlining reasons may change over time. Traceability is so important that without it, some of the best predictions could be viewed as garbage-in/garbage-out. One of the most difficult parameters to predict, and arguably the most fluid in early stages of the development process is the production volume. We need to understand the demand to be able to forecast how many should be built.
Our quest to determine when we will have flying orbs dotting our airspace sent us into the wonderful world of GIS analytics. There are several important questions that need to be investigated in a logical and visual way, and that is where GIS can be quite useful for our UAM demand forecast. – Who would likely be the early adopters of the service? – Where do they live? and how do they travel? – What is the traffic like in the area? – Do we have land or rooftop areas big enough to install new vertiports? – What is the population size of these early adopters in the catchment areas? – How much are they willing to pay?
By answering these questions, we arrive at the total eVTOL fleet size for our case study of an airport shuttle service for Hartsfield-Jackson Atlanta International airport (ATL). We can see potential economic benefit for those high net-worth passengers who need to go to the airport during rush hours. We can get an idea of how many of these people are there, where they live, and how likely they are to use the service. The series of graphics below shows the steps and layers of information used to arrive at our estimate. This is only the first small step toward a framework for economic feasibility analysis.