Ever since Richard Florida cited this data set in Who's Your City, I knew this was both a novel way to look at economic data, and would have problems that need to be sorted out. Florida just wrote a feature about this data set for city lab, and it really highlights a few of the easy improvements that can be done to improve this data. With a little training, this can very accurately predict economic activity.
The first paragraph highlights one of the first problems one would run into:
The potential problem this can cause is explained further into the piece:
My first impression of this statement is what would lead someone to think wages are correlated with light levels within a country? I could definitely think of reasons why wages would be correlated with light levels when comparing countries, and would definitely be useful in comparing wages between urban and rural areas. When comparing urban areas within a country however, I'm not surprised smaller cities are underrepresented. I definitely believe that, within the same country, larger cities would have higher wages than smaller cities. I simply think that this is not proportional to the amount of light you emit. To put it another way, adding one more household to a small city will have a greater impact on the light output than adding one more household to a large city, since larger cities are more efficient.
This may seem unrelated to the energy economy in Texas, but that picture allows me to highlight how to fix this problem. The new energy economy in the United States is perhaps one of the biggest examples of economic activity taking a unique spatial form in a very short time. Over a period of years, the prospect of fossil fuel independence became very real for North America.
The pinprick pattern of energy development would be very easy to pick apart from more general urban economic activity. Combined with existing data of well production (full disclosure: I work for a company that could provide this data!) A computer could look at these satellite images and automatically pick up energy regions.
This is a very clear example, but this could also be used to try to improve this model for wages. Once again, there's a fairly clear pattern: smaller cities are getting overestimated and larger cities are getting underestimated. One could train a model to pick out contiguous metro areas, measure their size, and properly control for the efficiency effect I hypothesized above.
In this way, satellite data can be fine tuned to the economic concepts we want to. Premise has done amazing work in analyzing pictures to track price and quality of goods data across the developing world. With a similar sort of evolving model, other macro concepts can be predicted as well.
One more thing satellite data could be used for is determining whether growth is "healthy." In Cities and the Wealth of Nations, Jane Jacobs explains 5 ways city regions can grow. When they act in concert, they can transform formerly inert land into economically productive city economies. When they act in an unbalanced way, they lead to economic imbalances that may seem healthy in the short term, but in the long run are damaging and can only lead to ruin.
In the context of satellite data, this sort of balanced city growth should have a very noticeable pattern. There's a reason why slime molds can accurately predict train systems. If done in a healthy way, balanced growth should follow an organic pattern.
Compared to organic looking cities, the growth in the US energy sector seems very haphazard at first glance. Using Jacob's categorization, these regions look like supply regions, which instead of replacing their imports become addicted to the outside city economic activity that led to its founding. Some of the effects of this are positive: Texas and North Dakota have some of the healthiest job markets in the country, but it won't lead to long lasting economic development, and once this activity dries up, the regions will return to inert and we'll have a lot of upset unemployed oil workers.
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The first paragraph highlights one of the first problems one would run into:
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.Looking at a picture of Texas they linked to in another article, the difference between these sorts of economic activity becomes apparent:
The potential problem this can cause is explained further into the piece:
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.
My first impression of this statement is what would lead someone to think wages are correlated with light levels within a country? I could definitely think of reasons why wages would be correlated with light levels when comparing countries, and would definitely be useful in comparing wages between urban and rural areas. When comparing urban areas within a country however, I'm not surprised smaller cities are underrepresented. I definitely believe that, within the same country, larger cities would have higher wages than smaller cities. I simply think that this is not proportional to the amount of light you emit. To put it another way, adding one more household to a small city will have a greater impact on the light output than adding one more household to a large city, since larger cities are more efficient.
This may seem unrelated to the energy economy in Texas, but that picture allows me to highlight how to fix this problem. The new energy economy in the United States is perhaps one of the biggest examples of economic activity taking a unique spatial form in a very short time. Over a period of years, the prospect of fossil fuel independence became very real for North America.
The pinprick pattern of energy development would be very easy to pick apart from more general urban economic activity. Combined with existing data of well production (full disclosure: I work for a company that could provide this data!) A computer could look at these satellite images and automatically pick up energy regions.
This is a very clear example, but this could also be used to try to improve this model for wages. Once again, there's a fairly clear pattern: smaller cities are getting overestimated and larger cities are getting underestimated. One could train a model to pick out contiguous metro areas, measure their size, and properly control for the efficiency effect I hypothesized above.
In this way, satellite data can be fine tuned to the economic concepts we want to. Premise has done amazing work in analyzing pictures to track price and quality of goods data across the developing world. With a similar sort of evolving model, other macro concepts can be predicted as well.
One more thing satellite data could be used for is determining whether growth is "healthy." In Cities and the Wealth of Nations, Jane Jacobs explains 5 ways city regions can grow. When they act in concert, they can transform formerly inert land into economically productive city economies. When they act in an unbalanced way, they lead to economic imbalances that may seem healthy in the short term, but in the long run are damaging and can only lead to ruin.
In the context of satellite data, this sort of balanced city growth should have a very noticeable pattern. There's a reason why slime molds can accurately predict train systems. If done in a healthy way, balanced growth should follow an organic pattern.
Compared to organic looking cities, the growth in the US energy sector seems very haphazard at first glance. Using Jacob's categorization, these regions look like supply regions, which instead of replacing their imports become addicted to the outside city economic activity that led to its founding. Some of the effects of this are positive: Texas and North Dakota have some of the healthiest job markets in the country, but it won't lead to long lasting economic development, and once this activity dries up, the regions will return to inert and we'll have a lot of upset unemployed oil workers.
