A New Frontier: Combining GIS & Economics (Assigning Who Benefits)

by | Jun 4, 2015 | Uncategorized

Part 2: Assigning who benefits

This is the second part of a series of four blog posts on the potential uses of GIS in cost-benefit analysis.

When new infrastructure is designed and built decision makers often need to know is who the project impacts, in what way they are impacted, and the extent of the impacts. For example, if you build a new high-speed rail line through a residential neighborhood people will get from point A to B faster, but this is at the expense of additional noise in people’s back yards. Or, if you demolish part of a highway, it will take some people longer to get around, but you free up space for green development and new recreational space. Cost-benefit analysis is about balancing these trade-offs and deciding if the benefits to the ‘winners’ outweigh the costs to the ‘losers’. However, traditional cost-benefit analysis ignores exactly who those winners and losers are. This is another area where GIS can help.

It’s not always easy to assign who wins and losses. Especially if you have no way of tracking the geography of a projects costs and benefits. Often cost and benefit calculations are done and assumed to affect everyone in the entire city equally. But people who have infrastructure projects being built in the neighborhood know that this assumption is not always true.

For example, take the calculation of air pollution. Economists often assume a change in pollution from one source affects everyone equally, but the methodology behind the mapping air pollution shows us how wrong this is. When you see a map of air pollution over an area, like the one below, it is often shown as a continuous flow of pollution. Some parts of the map show pollution is very bad in some areas and much better in other areas. Interestingly, this map is created from only a handful of air quality monitoring stations. How is that done? A statistical method called interpolation. This is where you infer what is actually happening between each data point, which is spatially referenced, using regression analysis. The graphic and map below show this process visually. The first map shows the air pollution stations across Western Europe; the second map shows a continuous flow of air pollution across the region – based solely on those points.

Interpolate Points

air pollution stations across Western Europecontinuous flow of air pollution across the region

What does all this mean for a cost-benefit analysis? Well, if we know that a project will reduce air pollution in one area, we can alter the above map. Take the EU high-speed rail project displayed below as an example. After the completion of all the lines which are either under construction, or are in the planning stage, vehicle traffic across the region will decline as more people opt to use the new high-speed rail project. This reduction in car usage implies less air pollution – the above map will now have to change. This change is clearly not the same across the region, or even within a given country, or city. It’s dependent on where car travel declines, which is itself dependent on where the rail lines themselves are built and the cities they connect together.


For the most part, in a cost-benefit analysis, the calculation of how much pollution is removed due to reducing vehicle travel is standardized; it’s based mainly on the amount of vehicles on the road and the average speed they’re travelling. However, if we know the specific road/area where vehicle travel is reduced we can add another piece of information to the calculation of the air pollution map. More specifically, we could now create two maps: one before the reduction in vehicle travel and one after. A hypothetical example of this is displayed below. The difference between these two maps would give us the specific location where air pollution is reduced. Now we can use this information to learn more about who benefits due to the reduction in air pollution. By using information on the number of people in that area, the distribution of ages, gender, ethnicity, or any number of spatially reference socioeconomic variables, we could create a list of which countries, cities, postal codes, etc., benefit the most, and which benefit none, or are negatively impacted. In other words, being able to assign a location to the calculation of a benefit allows us to begin to study who the ‘winners’ and ‘losers’ of a project are. And the benefit of this? Better project communication.

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