figure 2. Median carbon emissions per home-to-work commute of cellphone users living in
the La, sf, and Ny metropolitan areas.
Greener ZIP codes denote smaller carbon footprints, ranging through yellow, orange, red, and purple as
footprints grow. all these maps use the same geographic and carbon scales; emissions are scaled linearly.
(a)
(b)
(c)
By analyzing similar datasets from
different time periods, we made additional spatial and temporal comparisons between the daily ranges of
various populations. For example,
people throughout the LA region travel
farther on a typical day than people
throughout the NY area. In contrast,
the longest trips taken by residents
of Manhattan are much longer than
those taken by residents of central Los
Angeles. Furthermore, people in both
the LA and NY regions tend to travel
shorter distances in the winter months
than in the summer months, with the
effect being more pronounced in NY.
For a more complete description of
our daily range results, see Isaacman
et al. 13 and Isaacman et al. 14
Carbon footprints
Evaluating the environmental impact
of human travel is of urgent interest to
society at large. A person’s commute
between home and work can account
for a significant portion of his or her
overall carbon footprint. We can estimate the carbon emissions due to
these commutes by combining our
datasets of cellphone locations with a
U.S. Census dataset on mode of transport to work (such as automobile, bus,
and train) 24 and a table of carbon emissions by mode of transport. 4
We devised an algorithm that uses
CDRs to identify important places in
people’s lives, defined as places a person visits frequently or spends a lot of
time. We further identified the likely
home and work locations from among
these important places, then calculated
the home-to-work commute distance.
Our approach, described in more detail
and validated in Isaacman et al., 12 uses a
series of clustering and regression steps
to accomplish these tasks. We found
our commute-distance estimates were
within one mile of the ground-truth distances provided by volunteers.
We then applied this approach to
our large CDR datasets for the LA, SF,
and NY metropolitan areas described
earlier and computed the distribution
of commute distances across the population of each ZIP code in our regions
of interest. We found that our estimates were within one mile of the average commute distances for these same
regions as published by the U.S. Bureau
of Transportation Statistics. 23
