“The View from Above: Applications of Satellite Data in Economics”, 2016-09-01 (; similar):
The past decade or so has seen a dramatic change in the way that economists can learn by watching our planet from above. A revolution has taken place in remote sensing and allied fields such as computer science, engineering, and geography.
Petabytes of satellite imagery have become publicly accessible at increasing resolution, many algorithms for extracting meaningful social science information from these images are now routine, and modern cloud-based processing power allows these algorithms to be run at global scale.
This paper seeks to introduce economists to the science of remotely sensed data, and to give a flavor of how this new source of data has been used by economists so far and what might be done in the future.
We group the main advantages of such remote sensing data to economists into 3 categories:
access to information difficult to obtain by other means:
The first advantage is simply that remote sensing technologies can collect panel data at low marginal cost, repeatedly, and at large scale on proxies for a wide range of hard-to-measure characteristics. We discuss below economic analysis that already uses remotely sensed data on night lights, precipitation, wind speed, flooding, topography, forest cover, crop choice, agricultural productivity, urban development, building type, roads, pollution, beach quality, and fish abundance. Many more characteristics of potential interest to economists have already been measured remotely and used in other fields. Most of these variables would be prohibitively expensive to measure accurately without remote sensing, and there are settings in which the official government counterparts of some remotely sensed statistics (such as pollution or forestry) may be subject to manipulation…
unusually high spatial resolution:
The second advantage of remote sensing data sources is that they are typically available at a substantially higher degree of spatial resolution than are traditional data. Much of the publicly available satellite imagery used by economists provides readings for each of the hundreds of billions of 30-meter-by-30-meter grid cells of land surface on Earth. Many economic decisions (particularly land use decisions such as zoning, building types, or crop choice) are made at this same level of spatial resolution. But since 1999, private companies have offered submeter imagery and, following a 2014 US government ruling, American companies are able to sell imagery at resolutions below 0.5 meters to nongovernment customers for the first time. This is important because even when a coarser unit of analysis is appropriate, 900 1-meter pixels provide far more information available for signal extraction than a single 30-meter pixel covering the same area. In addition, some innovative identification strategies used by economists exploit stark policy changes that occur at geographic boundaries; these high-spatial-resolution research designs rely intimately on high-spatial-resolution outcome data (for example, Turner, Haughwout, and van der 2014)…
wide geographic coverage.
The third key advantage of remotely sensed data lies in their wide geographic coverage. Only rarely do social scientists enjoy the opportunities, afforded by satellites, to study data that have been collected in a consistent manner—without regard for local events like political strife or natural disasters—across borders and with uniform spatial sampling on every inhabited continent. Equally important, many research satellites (or integrated series of satellites) offer substantial temporal coverage, capturing data from the same location at weekly or even daily frequency for several decades and counting.
An example of this third feature—global scope—can be seen in work on the economic impacts of climate change in agriculture by Costinot, Donaldson, and 2016. These authors draw on an agronomic model that is partly based on remotely sensed data. The agronomic model, when evaluated under both contemporary and expected (2070–292099) climates, predicts a change in agricultural productivity for any crop in any location on Earth. For example, the relative impact for 2 of the world’s most important crops, rice and wheat, is shown in Figure 5: Costinot, Donaldson, and Smith feed these pixel-by-pixel changes into a general equilibrium model of world agricultural trade and then use the model to estimate that climate change can be expected to reduce global agricultural output by about 1⁄6th (and that international trade is unlikely to mitigate this damage, despite the inherently transnational nature of the shock seen in Figure 5). Given the rate at which algorithms for crop classification and yield measurement have improved in recent years, future applications of satellite data are likely to be particularly rich in the agricultural arena.