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