Finally, we joined our distributions of commute distances with the publicly available distributions of modes of transport per ZIP code and of carbon emissions per mode of transport per passenger. Figure 2 shows our results in the form of heat maps, where color corresponds to the median carbon emission per commute across the people in each ZIP code. Colors are ordered so greener ZIP codes correspond to lower carbon emissions, with yellow, orange, red, and purple ZIP codes showing increasing emissions.

In the NY area, increasing distance from Manhattan correlates with an increasing carbon footprint; in contrast, LA is more uniform throughout, except for parts of Antelope Valley (northeast portion of the map) separated from downtown LA by a mountain range drivers must go around. The results for SF are between those for NY and LA.

These patterns match well with generally understood movement patterns in each city. Popular knowledge indicates that in NY, a great many people commute into Manhattan, while in LA, there is no single concentration of jobs. SF has at least two major job centers, one focused in the city of San Francisco proper, another in Silicon Valley approximately 40 miles to the south. Thus, unlike NY, SF has more than one strong jobs focus, but unlike LA, it has some clear areas of jobs focus.

Beyond identifying patterns of carbon emissions, we also compared raw carbon values. For instance, though difficult to see in Figure 2, Manhattan ZIP codes have the smallest carbon footprints of all ZIP codes studied, presumably due to the nearness to work of many people’s homes, as well as to an extensive public transportation infrastructure.

of the center of Morristown, NJ, a suburban city with approximately 20,000 residents. These 35 towers house approximately 300 antennas pointed in various directions and supporting various radio technologies and frequencies. Our goal was to capture cellular traffic in and around the town. Choosing the five-mile radius allowed us to cover both Morristown proper and its neighboring areas. We obtained anonymized CDRs for 60 consecutive days, March 1 to April 29, 2011, thus collecting more than 17 million voice CDRs and 39 million text CDRs for more than 472,000 unique phones.

We identified Morristown’s laborshed from the CDRs as follows: We classified as Morristown workers those cellphone users with significant activity inside Morristown during business hours ( 9 a.m. to 5 p.m., Monday to Friday). We then used billing ZIP codes to identify their places of residence. This method produced counts of Morristown workers by residential ZIP code.

We validated our results by compar-
ing them with data from the 2000 U.S.
Census, confirming that the number
of workers we attributed to each ZIP
code was strongly correlated with the
number of workers in the same ZIP
code as published in the “Journey to
Work” tables of the 2000 U.S. Census
Transportation Planning Package. 24
Our analysis and validation method-
ology are described in more detail in
Becker et al. 2

figure 3. Laborshed of Morristown, NJ; the red dot denotes the city center.

Contour lines divide regions of different concentrations of workers’ homes, with workers identified as those who use their cellphones in Morristown during weekday business hours. Most workers are from nearby areas, but some are from as far as 40 miles away in Manhattan.

Laborshed and Paradeshed

City and transportation planners are interested in knowing the home locations of people who work in and visit their city. The information is useful in, say, forecasting road-traffic volume during morning and evening rush hours. The set of residential areas that contribute workers to a city is known as the city’s laborshed.

To study an example laborshed, we captured all transactions carried by the 35 cell towers located within five miles