Richard Florida is a co-founder and editor at large of CityLab and a senior editor at The Atlantic. He is a university professor in the University of Toronto’s School of Cities and Rotman School of Management, and a distinguished fellow at New York University’s Schack Institute of Real Estate.
Getting satellite luminosity data right could help us better understand what works and what doesn't in urban development.
Looking at aerial images of nighttime lights can tell us a surprising amount about human activity on the ground. Satellite images of the world at night have been used to see how North Korea's political isolation has left its residents in the dark, and how Texas's booming oil industry has spread across the landscape. As Yale University economist William Nordhaus has noted, roughly 3,000 studies have used nighttime lights as a proxy for various economic activities just since 2000.
There is no doubt that satellite images have many uses, but can they also help measure the economic activity of cities and regions?
Two recent studies shed light on this question. One, by Nordhaus and Quinnipiac sociologist Xi Chen and published in the Journal of Economic Geography, takes a close statistical look at how well nighttime lights can help us gauge economic activity across the developed and developing world. The second, by my colleagues at the Martin Prosperity Institute, uses the case of Sweden—where very detailed data are available for very small geographic units like blocks and neighborhoods—to track how well the satellite data line up with actual economic statistics. The two together tell us great a deal about how well and in what cases satellite data can help us better understand the spread and clustering of economic activity in cities and regions across the world.
Nordhaus and Chen’s analysis finds that satellite data is of greatest utility in assessing the economies of cities and regions in the developing world, where traditional data sources are often far less reliable and far less frequently collected. The last usable census data from Somalia, for example, was collected in 1975, while the Democratic Republic of the Congo’s most recent is from 1981. Without good data, how can we truly know where and how cities are growing, or get a handle on why urbanization spurs economic growth in places like China but results in slum formation in other parts of the globe?
For cities and regions of the developing world, Nordhaus and Chen write that their “estimates suggest that there may be substantial information in the lights data.” The authors add that this light data is especially promising in estimating density of economic activity and output per person for these countries with very low data quality.
These findings echo those of a 2012 study in the American Economic Review by the urban economist J. Vernon Henderson and his colleagues, who found that the greatest economic utility of satellite data is to “tell us about economic growth in circumstances where we have no measures of income growth. Most compelling is that night lights data are available at a far greater degree of geographic fineness than is attainable in any standard income and product accounts.”
In contrast, Nordhaus and Chen find that satellite data are of far less value for the advanced nations, where existing economic data is of reasonably high quality. As they put it, “There is no reason to use luminosity data as a supplement to standard data in any context where standard data are available.”
But studying satellite data in more advanced countries has another purpose. We can use the much more detailed economic data available there to further test and refine the ways that we use satellite data.
My MPI colleagues took a detailed look at the connections between satellite images and actual economic activity in Sweden, where extremely geographically detailed economic data is collected. Their research compares satellite data from the U.S. Department of Commerce's NOAA National Geophysical Data Center to geo-coded economic data from Statistics Sweden, which offers fine-grained statistics on individuals and establishments at incredible detail—down to grids of just 250 meters.
Their research found that the satellite data correlated more strongly with population density than with economic measures, finding close statistical associations between luminosity levels and population levels, population density, the number of establishments, and the number of employees. But the satellite data were considerably less accurate in estimating for a key measure of the level of economic activity—wages. Based on a geographically weighted regression analysis, they found that nighttime light levels overestimated wages for the largest cities like Stockholm, Gothenburg, and Malmo, where together more than 40 percent of Sweden’s population resides. In contrast, satellite images generally underestimated wages for smaller towns and rural areas.
The maps above, from the report, show where light was most and least predictive of wages. The map on the left shows how radiant light predicted the density of take-home wages per person, while the map on the right looks at the locations of the firms that paid out those wages. In both cases, the oranges and yellows in densely populated areas like Stockholm and Gothenburg showed that light emissions could fairly accurately predict, though sometimes overestimate, the wages in these areas. All that green out in the country’s smaller towns and rural regions indicates that light emissions may underestimate the true productivity of rural regions. In other words, satellite images are better suited for measuring the extent and shape of urbanization than for measuring the level of economic activity within these urban centers.
Much of the problem stems from the fact that these data are being collected from satellite imaging that was meant for other purposes, and most data-collecting satellites lack the instrumentation required to more accurately detect emissions of artificial light. These satellite sensors are only able to pick up the presence of exterior lights that mark the presence of streets, parking lots, rooftops, traffic stops, and vehicle traffic. As a result, these data provide reasonable measurements of light coming off of buildings and infrastructure but have real trouble quantifying what exactly is going on inside these buildings. As the MPI study notes, while satellites can find
"lampposts at a factory’s parking lot, the satellites cannot detect light use inside a cavernous production plant, whether mostly empty or running at full capacity. Nor can the satellite’s sensors pick up the light emitted from offices in buildings, or distinguish between buildings with offices staffed by high-valued software developers and one crowded with textile workers."
The luminosity data coming from the existing satellites also run into trouble distinguishing areas of extremely low development from areas with no development at all, often interpreting these low levels of light as just noise, unable to be differentiated from zero. In places of higher development, light saturation can have the opposite effect, leading to overestimates of economic data.
But improvements in satellite technology may well allow us to more accurately use light images to detect and estimate urban economic activity. A dedicated instrument—the Visible Infrared Imaging Radiometer Suite—has recently been put into orbit on board the Suomi satellite. It promises to greatly improve the ability of urban researchers to use satellite images to provide more accurate estimates of economic activity for the cities and regions of the world.
Having more reliable, systematic, and comparable data would greatly improve our ability to plan for the ongoing wave of urbanization occurring especially in the developing world, where little if any data is available. It would enable us to more wisely spend the tens of trillions dollars that we will dedicate to building new cities and retrofitting old ones as billions more people head to urban centers over the next century. Getting the satellite luminosity data right could help us better understand what works and what doesn’t in terms of urban development, why some places grow while others stagnate, and what we can do to accelerate and advance the connection between urbanization, economic growth and rising living standards.