New Approaches to Human Mobility: Using Mobile Phones for
Demographic Research
John R.B. Palmer,
Woodrow Wilson School of Public and International Affairs and Office of Population Research,
Princeton University, 284 Wallace Hall, Princeton, NJ, 08544, USA jrpalmer@princeton.edu
Thomas J. Espenshade,
Office of Population Research and Department of Sociology, Princeton University, Princeton, NJ,
USA
Frederic Bartumeus,
Center for Advanced Studies of Blanes, CEAB-CSIC, Blanes, Spain
Chang Y. Chung,
Office of Population Research, Princeton University, Princeton, NJ, USA
Necati Ercan Ozgencil, and
Syncsort, Woodcliff Lake, NJ, USA
Kathleen Li
Google, New York, NY, USA
Abstract
This article explores new methods for gathering and analyzing spatially rich demographic data
using mobile phones. It describes a pilot study (the Human Mobility Project) in which volunteers
around the world were successfully recruited to share GPS and cellular tower information on their
trajectories and respond to dynamic, location-based surveys using an open-source Android
application. The pilot study illustrates the great potential of mobile phone methodology for
moving spatial measures beyond residential census units and investigating a range of important
social phenomena, including the heterogeneity of activity spaces, the dynamic nature of spatial
segregation, and the contextual dependence of subjective well-being.
Keywords
Spatial demography; Activity space; Segregation; Subjective well-being; Ecological momentary
assessment
Introduction
Questions of human location and movement are of central importance in demography, but
answers are limited by our frequent inability to observe and measure where people are and
where they are going. Census data tell us about general patterns of human settlement and
migration but very little about where people spend their time, or where and how they travel.
Interviews and surveys give us more detail, but these are constrained by scale and selfreporting
errors, and they often fail to provide a dynamic picture. We are thus confronted
with an important obstacle as we try to better understand a wide range of human activity,
NIH Public Access
Author Manuscript
Demography. Author manuscript; available in PMC 2014 June 01.
Published in final edited form as:
Demography. 2013 June ; 50(3): . doi:10.1007/s13524-012-0175-z.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
from immigration and urbanization to social networks, and from the environmental effects
of population growth and the health effects of environmental exposure to the spread of
diseases and the distribution of social or cultural capital.
Mobile phone techniques offer a way around this obstacle. With their increasing worldwide
popularity and sophistication, the research potential of mobile phones is quickly becoming
apparent, and the possibilities for using them to record location and movement are especially
promising. Any given mobile phone can typically be traced to the cellular tower of closest
proximity, and a user’s movement can be estimated by analyzing the phone’s transitions
from one tower to another. More accurate location data may be obtained if a phone is
capable of processing signal information from multiple cellular towers, from nearby devices,
or from satellites (Eagle et al. 2009b). Phones with these capabilities are now firmly
entrenched in the market and gaining widespread use around the world. That many of these
phones have a variety of additional sensing capabilities beyond location, store huge amounts
of data, and function as miniature computers adds to their usefulness (Raento et al. 2009).
In this article, we describe a pilot study—the Human Mobility Project (HMP)—that we
conducted to explore the use of mobile phones in demographic research and to test one
particular technique for which they may be especially well suited: a dynamic, location-based
survey. The pilot study uses global positioning system (GPS) and cellular tower data from
mobile phones not only to determine subjects’ residence locations (to which we are limited
when using census data) and observe how they respond to questions at a fixed time and
place (to which we are limited when relying on traditional survey methods) but also to
examine where they spend time when not at home, their trajectories, and how they respond
to questions at a variety of different times and places. The study provides a partial glimpse at
the answers to such questions as how traditional concepts of neighborhood might be
adjusted to better account for the varied spaces in which people carry out their day-to-day
activities, how patterns of spatial segregation observed in residential housing permeate other
aspects of life, and how subjective well-being depends on spatial context. This study
demonstrates the powerful ways in which demographers may use mobile phones to move
beyond residential census units and “put people into place” (Entwisle 2007), more fully
capturing the complex relationship between spatial behavior and local environment. In
addition, this study identifies some of the limitations and risks associated with mobile phone
research, including the need for appropriate safeguards to protect subjects’ privacy in light
of the particular problems presented by spatial data (Gutmann et al. 2008; VanWey et al.
2005).
We start with an overview of the basic problem of studying human movement, the mobile
phone technology that can be used for this purpose, and the research that has made use of
this technology to date. We then describe our pilot study and present our results. Finally, we
evaluate the study methodology and discuss ways in which this research complements and
extends the field of demography.
Mobile Phones as Demographic Instruments
The scientific value of observing human spatial variation has long been apparent (e.g.,
Anderson and Massey 2001; Du Bois 1899, Park and Burgess 1925), and spatial questions
are increasingly at the core of research in a variety of disciplines. Specific research questions
are diverse but may be grouped into three basic categories: (1) those that aim to describe and
explain patterns of location and movement (e.g., Golledge and Stimson 1997; Hägerstrand
1970), (2) those concerned with how people influence their spatial context (e.g., Cassels et
al. 2005), and (3) those concerned with how spatial context influences people (e.g., Inagami
et al. 2007). In calling on demographers to take the lead in researching how neighborhoods
Palmer et al. Page 2
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
influence health, Entwisle (2007) demonstrated the interconnectedness of the three
categories and the importance of treating them together. People are not merely passive
objects on which neighborhoods operate, she argued, but rather agents who make choices
about where to spend their time and who modify those places through their presence and
activities (see also Cummins et al. 2007). Entwisle’s point can be extended beyond health
research and highlights a need that mobile phone methods are well suited to address.
By making it possible to systematically follow people as they move, mobile phones help
overcome the limitations of censuses and other methods that focus on static places.1 They
allow us to reconstruct detailed movement trajectories and analyze the space through which
people move as they go about their daily activities (Golledge and Stimson 1997). Although
this can be done using more traditional methods, such as time-space diaries or interviews
(Basta et al. 2010; Goodchild and Janelle 1984; Janelle et al. 1998; Jones and Pebley 2012;
Kwan 2008, 1999; Lee and Kwan 2011; Matthews 2011; Wang et al. 2012; Wong and Shaw
2011), mobile phones avoid many of the accuracy and logistical problems inherent in selfreporting
(Golob and Meurs 1986; Kwan and Lee 2004; Murakami and Wagner 1999;
Stopher et al. 2007). Moreover, because people incorporate mobile phones into their daily
lives naturally, these devices are particularly well suited for measuring people’s reactions
and capturing information about their surroundings in real time, facilitating techniques such
as ecological momentary assessment or the construction of precise individual environmental
exposure profiles (Ahas 2011; Borrell 2011; Kwan 2009; Nusser et al. 2006; Raento et al.
2009; Shiffman et al. 2008).
Mobile phone location tracking has been the subject of a body of research that has expanded
rapidly over the past decade as an array of commercial and scientific applications have
become technologically and socially feasible (Ahas 2011; Ahas and Mark 2005; Asakura
and Hato 2004). Many mobile phones can estimate their own locations using signals from
cellular towers, satellites, or other beacons, but even those that lack this capability can be
localized externally based on the traces they leave on cellular networks and other receivers
(such as Bluetooth or Wi-Fi devices). Data collection techniques can be divided between
active positioning, in which subjects volunteer to be tracked and data are collected from
their phones or their phones’ traces, and passive positioning, in which trace data are
obtained directly from network operators without contacting the individuals being tracked
(and also, consequently, without much information about these individuals, apart from
location) (Ahas 2011). Alongside these collection techniques has come the development of
methods for organizing and evaluating spatiotemporal data, including techniques for
analyzing geospatial lifelines—the paths formed in space-time by successive location
estimates of each tracked individual (Laube et al. 2005, 2007).
Within the social sciences, mobile phones have been used to address such topics as social
networks (Eagle et al. 2009a; Pentland 2007), mobility and migration (Ahas 2011; Gonzalez
et al. 2008; Wesolowski and Eagle 2010), crowds and urban dynamics (Calabrese et al.
2007, 2010; Reades et al. 2007), transportation behavior (Ahas 2011; Asakura and Hato
2001, 2004; Reddy et al. 2010), health behavior (Anokwa et al. 2009; Madan et al. 2010),
and happiness (Killingsworth and Gilbert 2010; MacKerron 2012). All this mobile phone
research has come alongside the emergence within the social sciences of a “mobilities”
paradigm, which emphasizes the role of movement in creating social and material realities
1The difference is between selecting people and observing the space through which they move and selecting places and observing the
people who move through them. This is a basic choice that must be confronted in studying any collection of moving objects, and it is
often the case that the first approach (which mobile phones facilitate) is more illuminating but also harder to implement (Ōkubo and
Levin 2001).
Palmer et al. Page 3
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
(Adey 2010; Büscher and Urry 2009; Büscher at al. 2011; Sheller and Urry 2006; Urry
2007).
The results of this research have been impressive, but much remains to be explored in terms
of methods and their integration within theoretical frameworks (D’Andrea at al. 2011).
Scholars have complained that existing methods deal poorly with the “fleeting,” transitory
nature of movement (Büscher et al. 2011; Law and Urry 2004). D’Andrea et al. (2011:151)
noted that methodological development has lagged behind empirical and conceptual
advances and “the examination of basic research protocols has only emerged recently.” In
particular, they emphasized the need for methods that link micro- and macro-level
observation.
From the perspective of demography, mobile phone methods offer new sources of data to
answer traditional questions while also suggesting new ways of formulating questions and
thinking about populations to begin with. Mobile phones can be used to measure traditional
migration events in populations that are otherwise hard to reach. For example, Wesolowski
and Eagle (2010) used cellular tower data to infer changes of residence within a Nairobi
slum that lacked the administrative or census records that would have been used for this
purpose in other settings. By taking this approach, they were able to measure a high rate of
residential turnover, much of it attributable to movement within the slum—something that
would have been very difficult to detect and quantify using traditional methods.
Mobile phones also make it possible to understand population in a much more dynamic way,
untethered to residence, adding an empirical, demographic grounding to the emerging
mobilities paradigm (D’Andrea et al. 2011). Isaacman et al. (2010) used mobile phone call
records to analyze daily movement patterns in New York City and Los Angeles, finding
significant variation by day of the week, city, and even neighborhood. Calabrese et al.
(2007, 2010), Carioua et al. (2010), Ratti et al. (2007), Ratti et al. (2006), and Reades et al.
(2009) tracked the ebbs and flows of urban populations throughout the day as people go to
and from work, attend concerts, visit their weekend homes, and engage in all the other
activities that we tend to ignore when asking traditional demographic questions like, “What
is the city’s population?”
Viewing population from this perspective can help us to think about what we even mean
when we talk about a city and how we should draw urban boundaries. It can help us to better
understand what Bertolini and Dijst (2003) described as the increasing divergence between
modern cities’ social and physical dimensions. Similarly, it can help us find more
meaningful ways to define neighborhoods for research purposes given the inherent
limitations of commonly used census units, which exclude a great deal of spatial information
(Lee et al. 2008; Reardon et al. 2008) and fail to capture individuals’ actual experiences and
exposures (Basta et al. 2010; Chaix et al. 2009; Entwisle 2007; Kwan 2009; Matthews
2011).
The dynamic perspective can also help us to investigate a variety of questions that blend
demography with other disciplines. For example, the individual health consequences of
environmental exposure and the environmental and epidemiological impacts of population
growth depend on the activities in which the population’s members are engaged and the
spaces through which they move (Cassels et al. 2005; Cummins et al. 2007; de Castro et al.
2006; Inagami et al. 2007; Martens and Hall 2000). Tracking human movement and
environmental exposure can thus provide more insight than simply counting the number of
residents within an administratively defined boundary, and mobile phones offer a way to do
this at high resolutions (Wiehe et al. 2008). Similarly, many of the social consequences of
residential segregation depend on where people spend time when not at home (Ellis et al.
Palmer et al. Page 4
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
2004). Although place of residence may sometimes be a good proxy for this, there is much
to be gained by measuring movement outside the home more precisely. Studies based on
self-reported places of work and travel diaries show notable differences in urban space use,
depending on individuals’ sociodemographic characteristics (Ellis et al. 2004; Jones and
Pebley 2012; Zandvliet and Dijst 2006).
Objectives
In our Human Mobility Project (HMP) pilot study, we tested the feasibility of using mobile
phone positioning to obtain spatially rich demographic data and administer a dynamic,
location-based survey using participants from around the world who volunteered by
downloading an application and installing it on their own phones. The linking of phone
position with survey administration is at the forefront of methodological developments in
this area (Ahas 2011), providing a way to merge localized response information with largerscale
movement patterns (D’Andrea et al. 2011). Moreover, the active positioning of
volunteers with their own phones anywhere in the world is an approach that has been less
well explored than the collection of cellular tower data (Blumenstock et al. 2010; Calabrese
et al. 2007, 2010; Carioua et al. 2010; Isaacman et al. 2010; Ratti et al. 2006, 2007; Reades
et al. 2009; Wesolowski and Eagle 2010) or the tracking of volunteers who are given phones
and who often travel through fixed areas containing pre-positioned beacons (Asakura and
Hato 2004; Eagle and Pentland 2006, 2009; Eagle et al. 2009a; Raento et al. 2009; Wiehe et
al. 2008). These latter two approaches can be quite powerful because cellular towers yield
massive quantities of data, while phone distribution improves sample representativeness and
pre-positioned beacons boost accuracy; however, the former lacks demographic detail, and
the latter has limited scalability.
Because our approach makes it possible to ascertain respondents’ characteristics, it has the
potential to provide richer data than that obtained from cellular towers while also allowing
larger scales and broader ranges of geographic coverage than phone distribution studies. By
harnessing the efforts of large numbers of volunteers to participate remotely in the creation
of knowledge, our approach is in the spirit of what Goodchild (2007) described as the
volunteered geographic information movement. It is similar to approaches currently being
implemented by researchers at the London School of Economics (MacKerron 2012)2 and
Harvard (Killingsworth and Gilbert 2010).3 Whereas those studies focus on, respectively,
environmental and psychological determinants of happiness, we focused on methodology
and explored its application to demographic and sociological questions.
We centered the study on three substantive topics: activity space, spatial segregation, and
subjective well-being.
Activity space is the term behavioral geographers use to describe the “locations within which
an individual has direct contact as a result of his or her day-to-day activities” (Golledge and
Stimson 1997:279). It is the component of the individual’s overall environmental interaction
that involves movement and direct contact, as opposed to communication. Activity space is
potentially a much better measure of environmental exposure than measures based on census
units (Basta et al. 2010; Kwan 2009; Matthews 2011), but the latter continue to dominate
research in many fields. It has long been recognized that people spend significant time far
from home and that the very notion of “neighborhood” is immensely difficult to define
(Foley 1950; Matthews 2008). Gathering data on activity space and refining our
understanding of place have been flagged as important priorities, particularly for research on
2See www.mappiness.org.uk.
3See www.trackyourhappiness.org.
Palmer et al. Page 5
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
health and environment (Matthews 2011). In our study, we explore subjects’ activity spaces,
analyzing the sizes of these spaces using the minimum convex polygon method, which takes
the area of the smallest convex polygon that may be drawn around an individual’s locations
(Buliung and Kanaroglou 2006; Jones and Pebley 2012).
Spatial segregation along lines of race, ethnicity, and class is an important topic of study
because it is usually associated with deep and pervasive inequality (Anderson and Massey
2001; Massey and Denton 1993). What social scientists generally measure when examining
this topic is residential segregation (e.g., Massey and Denton 1988), but segregation occurs
throughout the day as people engage in a variety of activities (Ellis et al. 2004; Wang et al.
2012; Wong and Shaw 2011). This may be partly driven by the interplay between home
locations and the locations of jobs, schools, and other socially significant places. Indeed, the
mismatch between suburban job locations and segregated central city neighborhoods
provides one explanation of how segregation concentrates unemployment and poverty (Kain
1968; Wilson 1987, 1996). Segregation outside the home may be driven also by a variety of
other factors ranging from transportation network design to policing tactics to individual
preferences. As a starting point for investigating the question using mobile phones, we
explored the amount of time subjects spent in census blocks with different racial
characteristics.
Finally, subjective well-being has been a topic of social inquiry since ancient times, and it
constitutes an important measure by which to judge the impact of policies and events on
people’s lives (Helliwell 2003; MacKerron 2012). Subjective well-being is generally
conceived to encompass both affective components (such as feelings of happiness or pain)
and cognitive components (such as evaluations of life satisfaction) (Diener et al. 1999).
People’s self-reports about both types of components may be influenced by a variety of
time-varying personal and environmental factors, and their reports about affective
components, in particular, may depend on whether they are communicated as the event is
being experienced or recalled afterward (Kahneman and Krueger 2006). Consequently, there
are important reasons to employ methods that can record people’s subjective feelings along
with contemporaneous contextual data. This is what we attempted to do in this pilot study,
employing a location-based survey drawn from the group of methods known as experience
sampling (Csikszentmihalyi and Hunter 2003). Although experience sampling methods
generally ask a broader array of affect questions as well as questions about activities and
surroundings, we kept our survey simple and relied for context on basic location information
—namely, distance from home—to test what may be gained by combining this type of
survey with a positioning device.
Methods
The pilot study had three main components: (1) an open source application that subjects
could install for free on any device with an Android operating system, (2) a structured query
language (SQL) database that communicated with the phone application to trigger locationbased
surveys and collect data, and (3) a project website where subjects could securely
register and activate the application, provide us with basic demographic information about
themselves (age, sex, and race4), and view some of the data being collected. All user
interfaces were in English.
4We use “race” throughout the article to refer to both racial and ethnic self-identification. Subjects could identify as “White,” “Black,
African, African American,” “Spanish/Hispanic/Latino,” “Asian,” “American Indian or Alaska Native,” “Pacific Islander,” or
“Other,” and we treat these as nonoverlapping categories for simplicity.
Palmer et al. Page 6
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
We chose to write the application for the Android platform because it is an open source
operating system used by a growing number and variety of mobile devices around the world,
and because its ability to run applications and processes in the background makes it well
suited to location monitoring (Arnold 2010; Chang et al. 2010). The Android operating
system is used on hundreds of phone models available on every continent, and by late 2011,
it was installed on more than one-half of all new smartphones sold worldwide (Yarow
2011).
We made the application available for download for three weeks in early 2010 via the
Android Market (now, Google Play). Those who downloaded it were directed to the HMP
webpage, where they could activate the application and consent to participate in the study.
The application ran in the background of subjects’ phones and recorded location estimates at
intervals of 2, 5, 10, 30, or 60 minutes.5 The estimates were based on GPS satellite signals
when available, and otherwise on cellular tower signals. The application transmitted
estimates to a Princeton server, and this server triggered surveys on phones when they
entered predetermined locations. This feature would make it possible to focus, for instance,
on subjective well-being near major landmarks. For the pilot study, our interest was in the
ordinary places people visit throughout the day, so we continuously updated the trigger
points to included subjects’ actual locations, and we added a stochastic component to the
trigger itself to avoid overwhelming subjects with too many surveys.
Although it would be possible to use multiple survey questions, each one triggered at a
different location or time, we used just one question for the pilot: “How happy are you?”
Subjects could respond by touching the phone’s screen to select between zero and five stars
(with half-star increments) or the response of “Ask me later.”
Results
During the three weeks when the application was available from the Android Market, 869
people downloaded it, and 270 registered and consented to be research subjects. Their selfreported
races and sexes are summarized in Table 1, and self-reported ages are shown in Fig.
1.
We collected location estimates and survey responses from subjects during a five-week
period. Subjects’ participation times varied, but 128 subjects participated for at least 24
hours, 62 participated for more than one week, and 13 participated for more than two weeks.
The mean and median participation times were, respectively, 6.3 days and 2.2 days. We
received a total of 68,198 location estimates, of which 29 % were based on GPS signals and
the rest on cellular tower signals. We retained and analyzed 67,294 location estimates while
discarding 904 (1.3 %) because of accuracy concerns.6 Within this analyzed subset, the
mean and median Android-reported accuracies were, respectively, 1,210 meters and 1,000
meters for all estimates combined, 261 meters and 48 meters for the GPS estimates, and
1,586 meters and 1,318 meters for the cellular tower estimates. Figure 2 maps the
approximate centroid of each subject’s set of location estimates.7 Most subjects were in the
United States, but the project also drew volunteers also from Australia, Canada, China,
5We gave subjects the ability to select the interval.
6We discarded estimates with Android-reported accuracy measurements of more than 5 km and those that based on our own
calculations, would have required subjects to have been traveling faster than 54 m per second.
7The centroid is calculated as the mean longitude and mean latitude for each subject. A small amount of random noise is added to
each centroid to aid in visualization and protect subjects’ privacy, using the random perturbation masking technique described in
Armstrong et al. (1999).
Palmer et al. Page 7
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
France, Germany, Israel, Japan, Norway, South Korea, Spain, Sweden, and the United
Kingdom.
Activity Space
The individual movement patterns revealed by each subject’s location estimates exhibit
varying degrees of regularity. As an example, Figs. 3, 4, 5, and 6 plot the movement of two
subjects with notably different behavioral patterns. We selected these subjects because their
differences are so clear and because their participation times are among the highest, each
lasting a little more than 30 days. Figures 3 and 4 show the subjects’ full sets of location
estimates (points), with sequential estimates connected by straight lines, and scale indicated
by axis tick marks (1 km intervals). To protect privacy, we transformed the coordinates such
that the location estimates maintain their relative distances, but it is impossible to identify
subjects’ actual locations on Earth.8
Most of the estimates in these plots lie on top of one another and cannot be distinguished.
Figures 5 and 6, therefore, plot the same subjects’ patterns with separate panels for weekday
(A) and weekend (B) locations and a small amount of random noise added to each estimate
to produce artificial spreading and thus aid in visualization. Location estimates in these plots
are marked with points shaded (in the online version, colored) by the hour of the day when
transmitted (in the subject’s time zone). As in the previous plots, sequential location
estimates are connected by straight lines and scale is indicated by axis tick marks (1 km
intervals).
Each of the displayed subjects’ movement patterns is characterized by a high degree of
clustering, and this is the case for many of our other subjects as well. In addition, each
subject has at least two clusters, suggesting that daily movements are not random but rather
concentrated in specific locations that have some functional saliency to the subject.
Moreover, the clustering of shades in Figs. 5 and 6 suggests that when subjects return to
locations on multiple occasions, it is often at the same time of day. Finally, we see different
patterns on weekends than on weekdays. Some areas are visited only on weekdays, and
some only on weekends. Even in areas visited on both weekdays and weekends, the timing
of the visits may differ, as can be seen by the predominance of different sets of shades in
each panel of Figs. 5 and 6.
There are also notable differences between the activity spaces of these two subjects. For
example, Subject 1 (Figs. 3 and 5) traveled through a much larger area than did Subject 2
(Figs. 4 and 6). Likewise, the main clusters of location estimates are spread further apart in
the case of Subject 1 than in that of Subject 2. The area of the smallest convex polygon
within which each movement pattern fits (without the addition of any random noise) is 827
km2 for Subject 1 but only 8 km2 for Subject 2.
These types of differences can be seen in the movement patterns of our other subjects as
well. Among the 96 who participated for more than 24 hours and provided more than 50
location estimates, the smallest convex polygons within which their movement patterns fit
range from 0.01 km2 to 56,000 km2, with a median of 150 km2 and an interquartile range of
60 to 394 km2.
8We did this by applying what Armstrong et al. (1999) referred to as a “scale transformation mask.” We multiplied each subject’s
matrix of location estimates by a randomly generated number. Although one might assume that it is sufficient to simply remove
coordinates from the axes, the transformation is necessary because depending on how the graphs are generated, their files may contain
underlying coordinate data that can be extracted. We used a multiplicative transformation, rather than an additive one, to make it
impossible to reconstruct the original coordinates using scale information. Although this transformation does not preserve actual
distances between points, it is possible to retain approximate map scale by measuring and transforming the actual distances between
each subject’s maximum and minimum latitude and longitude coordinates.
Palmer et al. Page 8
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
For subjects in the United States, we compared activity-space areas to the areas of the
census blocks and tracts within which each subject resided, using U.S. census unit
boundaries from 2000. We estimated the home census block for each subject as the one in
which he or she spent at least four consecutive hours bracketing 3:00 a.m.9 We estimated the
amount of time subjects spent in any given census block by summing the times elapsed
between successive location estimates lying within that block. This is a modified version of
the approach taken by Bartumeus et al. (2010) and Barraquand and Benhamou (2008), and it
relies on the assumption that when successive points lie within the same block, the subject
remained in that block during the intervening time. The approach ignores time spent
between successive points that fall on either side of a block boundary as well as time spent
in a census block prior to the subject’s first location estimate or subsequent to his or her last.
Using this approach, we can estimate home U.S. census blocks for 65 of the subjects for
whom we calculated activity-space areas.10 We also identify the census tracts in which these
blocks were located. The median home block area was 0.2 km2, with an interquartile range
of 0.03 to 0.7 km2, and the median home tract area was 4.9 km2, with an interquartile range
of 2.7 to 10.9 km2. In contrast, the median activity-space area for these subjects was 156.8
km2, with an interquartile range of 61.0 to 377.2 km2. If we take the ratio of each subject’s
residential tract area to activity-space area, the median is 0.037 and the interquartile range is
0.009 to 0.155.
In terms of the percentage of each subject’s total participation time spent within his or her
home tract, the median was 61 %, with an interquartile range of 23 % to 84 %. For home
census blocks, the median was 38 %, and the interquartile range 15 % to 65 %. If we
recalculate the percentages for home tracts excluding time spent in the home block itself, the
median was 4 %, and the interquartile range 0 % to 36 %.
Spatial Segregation
The racial compositions of residential census units are the ingredients of traditional spatial
segregation measures (Massey and Denton 1988), and we used data from the 2000 census to
calculate the proportion of white residents (PWR) in subjects’ home census block
populations. For white subjects whose home blocks were estimated (n = 58), the median
PWR was .89, with an interquartile range of .79 to .98. For black and Latino subjects
combined (n = 9), it was .74, with an interquartile range of .70 to .77.
To move beyond the traditional focus on residence, we calculated, for each subject, the
mean PWR in the census blocks in which that subject spent time, weighted by the amount of
time spent in each block. Median results were similar to those based on residence, but the
interquartile ranges move closer together, overlapping at .76 to .96 for whites versus .72 to .
82 for blacks and Latinos. The ranges overlap further if we exclude time spent in the home
block, although this requires a loss of data because some of these subjects spent insufficient
time outside their home blocks to make the calculation possible. Subtracting home-block
PWR from mean non-home-block PWR for each of these subjects yields a median
difference of −.02 for whites (n = 51), with an interquartile range of −.10 to .06, and .01 for
blacks and Latinos (n = 8), with an interquartile range of −.10 to .03. In other words, at least
one-half of the subjects had greater exposure to members of their own race in their home
blocks than outside them. However, the reverse was also true: when they left their home
blocks, at least one-quarter of the whites spent time in higher-PWR areas, and one-quarter of
9When there was more than one such block, we used the one that occurred most frequently in the data.
10In Figs. 5 and 6, location estimates within the home block are marked with squares.
Palmer et al. Page 9
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
blacks and Latinos spent time in lower-PWR areas. Thus, time spent outside the home block
can both attenuate and intensify segregation.
Another way to view segregation outside the home block is in terms of the proportion of
each subject’s time spent in census blocks of given racial compositions. Figure 7
summarizes this information for blocks grouped into quartiles by PWR, with time in the
home block excluded. To increase sample size for these calculations, we consider all
subjects who we observed for at least four hours in non-home census blocks (including
subjects for whom we could not estimate a home census block). The biggest differences can
be seen in the percentage of time spent in blocks with PWR greater than .75: for white
subjects (n = 81), the median is 83 % of the subject’s time, with an interquartile range of 38
% to 99 %; for blacks (n = 5), it is 43 %, with an interquartile range of 0 % to 95 %; and for
Latinos (n = 9), it is 7 %, with an interquartile range of 0 % to 25 %.
We do not attempt to analyze the extent to which these results are driven by spatial
autocorrelation (cf. Zenk et al. 2005), and we recognize that our samples are small and likely
biased. Nonetheless, it is apparent from these data how a dynamic, time-and-space approach
to human movement can help us to advance beyond traditional measures of spatial
segregation.
Subjective Well-being
The approach also allows us to examine the link between subjective well-being and
geographic context. During the study, the application sent a total of 485 surveys to subjects’
phones, with each survey triggered by the location of the phone and asking subjects to rate
their happiness as between 0 (least happy) and 5 (most happy) in increments of 0.5. We
received 232 responses with a pooled mean and median of, respectively, 3.8 and 4. Given
the high share of rejections, it is possible that these results are biased upward by a
correlation between subjects’ happiness and their willingness to answer the survey at a given
time. In particular, it may be that subjects were more likely to respond when they were in a
better mood. Leaving that concern aside, however, we explored the relationship between
subjects’ self-reported happiness and their distance from home.
Having already estimated census blocks of residence, we inferred that each subject’s home
was at the point where the bivariate normal kernel density estimate of the subject’s locations
within the home census block reached its maximum value (Venables and Ripley 2002).11
We then calculated how far each subject was from home when sending each survey
response. In total, we analyzed 168 survey responses from 36 subjects (Fig. 8; color figure
available online).12
We fit a series of multilevel linear models to the data, treating each survey response i from
subject j as a continuous random variable drawn from a normal distribution with mean μij
and standard deviation σ. The multilevel approach provided a way to account for subjects
having different characteristic levels of subjective well-being and to focus on contextdependent
changes relative to these levels. We modeled the mean of the survey response
variable as
(1)
11In Figs. 3–6, this point is identified by the large circle drawn around it.
12In addition to excluding responses from subjects whose home locations were not estimated (meaning all outside the United States),
we also excluded one extreme outlier in terms of distance from home.
Palmer et al. Page 10
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
where αj[i] is a subject-specific constant, Xi is a matrix of observed covariates,13 and β is a
vector of estimated coefficients. For these models, we rely on Bayesian parameter estimates
that reflect compromises between pooled and unpooled (subject-level) means, depending on
the number of responses given by each subject (Gelman and Hill 2007). We obtained the
estimates using the Gibbs sampler implemented through the OpenBUGS software package
(Lunn et al. 2009).
We found that male subjects tended to describe themselves as less happy the farther they
were from their homes, whereas there was no statistically discernible relationship between
female subjects and distance.14 This pattern held even when we controlled for subjects’
race. Each 10 km increase in distance from home is associated with a decrease in reported
male happiness of between 0.6 and 1.1 points, estimated with 95 % confidence (Fig. 9). This
finding is also robust to alternate model specifications, including pooled-subject linear
models and ordered logit models.
Figure 9 displays the relationship between distance from home and observed and predicted
happiness responses in the multilevel model for white, Latino, and black men and women.
The lines in panels A–C are drawn using coefficients estimated in each of 999 final
iterations of the Gibbs sampler, saved after the sequences reached approximate convergence,
in order to indicate uncertainty (see Gelman and Hill 2007). The lines in panel D of Fig. 9
are drawn using the mean estimates.
Discussion
Our study demonstrates that it is possible to obtain detailed information on the locations,
demographic characteristics, and context-dependent responses of people all over the world
simply by having them download and run an application on their mobile phones. It
illustrates the great potential in adopting an approach that lies somewhere between the largescale
collection of coarse mobile network operator data and the small-scale collection of
detailed data from devices that have been distributed to subjects or pre-positioned in
bounded research areas. By capturing more subject-specific information than the former
while offering greater scalability than the latter, this middle approach is well suited for
pursuing a wide range of demographic inquiries. The capacity of this approach to
incorporate dynamic, location-based surveys is particularly valuable for a range of research
questions that depend on subjects’ descriptions of their surroundings, activities, or mental
states as they are experiencing them.
The study also helps to clarify many of the methodological issues that must be confronted in
this type of research. Representativeness and generalizability are clearly concerns when
subjects self-select into a study that requires them to possess a smartphone and register on
the Internet because there are systematic differences in mobile phone and Internet use across
demographic groups (Blumenstock et al. 2010; Goodchild 2007). However, traditional
survey methods present their own problems of representativeness and generalizability.
Although the problems may be more serious when using mobile phone methods for certain
purposes, the difference is one of degree, and it is likely to shrink with time as the
technology becomes more widely accessible. Moreover, mobile phone methods alleviate
many of the problems presented by traditional methods, inducing participation by people
who would not be willing to respond to paper surveys or interviews and minimizing
13These included variables for sex, race (black, white, and Latino were the only responses given in the data analyzed), distance from
home, and the interaction between distance from home and sex. Additional models were tested using variables for time of day,
weekend, working hours (9:00 a.m.–5:00 p.m.), and census block racial characteristics, but these variables did not have clear
relationships to the survey responses or sufficiently improve model fit to warrant inclusion in the final analysis.
14These conclusions are based on our estimated coefficients on the variable interacting sex with distance from home.
Palmer et al. Page 11
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
reporting errors and logistical complications. Indeed, there may be much to be gained from
using mobile phone methods in combination with traditional ones such that their strengths
complement each other.
Another issue that must be confronted is privacy. One of the major complications of mobile
phone localization studies is that the spatially explicit data they produce can be difficult to
anonymize properly, particularly if the data are linked to information on social
characteristics and particularly if the goal is to make data available publicly (Gutmann et al.
2008; VanWey et al. 2005). In our pilot study, access to the raw data we collected is limited
to our research group, with one exception: we gave subjects online access to their own
location data (as well as the option for them to request that we delete their data). In terms of
processed data, we allowed subjects to view online static maps showing aggregate survey
responses from all subjects, and we have presented additional aggregate data in the present
article. The only individual-level data we have presented here has been transformed to make
it impossible to identify any subject or the actual locations they visited during the study.15
From a substantive perspective, the study provides strong evidence of the importance of
moving beyond static, census-unit measurements when investigating the relationship
between people and place (Entwisle 2007). We found a great deal of variation in the sizes of
subjects’ home census tracts, with the top quartile living in tracts more than four times larger
than those of the bottom quartile. This alone should make one cautious about relying too
heavily on tract-based measures, as Matthews (2011) argued. The problem is made even
clearer by our finding that the residential census tract accounts for less than 15.5 % of the
total area in which three-quarters of the subjects moved and less than 3.7 % for one-half of
them. This is consistent with findings by Matthews (2011) and Basta et al. (2010), as well as
earlier work by Foley (1950) and others (see Matthews 2008). If we care about the
environment to which these people were exposed, their spatial choices, or the places they
may have influenced through their presence or activities (Entwisle 2007), their residential
census tracts alone tell only a small part of the story.
Incorporating time into the analysis gives more weight to home tracts but leads to the same
conclusion. One-half of the subjects we measured spent less than 61 % of the study time
within their census tracts of residence, and one-quarter spent less than 23 % of the time in
these tracts. Moreover, while in their census tracts of residence, subjects spent most of this
time in their census blocks of residence—and much of it presumably in their homes (Basta et
al. 2010). These subjects’ census blocks of residence accounted for a small fraction of the
areas of their census tracts of residence—less than 9 % for three-fourths of them—and if we
exclude time spent in the census blocks of residence, one-half of the subjects spent less than
4 % of the time in their census tracts of residence, and one-quarter spent none of the time in
these tracts. In other words, the residential census tracts are simultaneously overinclusive
and underinclusive.
Mobile phone location tracking helps us overcome both problems by tightening measures of
local environment to each individual’s immediate surroundings while also letting these
measures follow the individual as he or she moves through space. This gives us a dynamic
“personal exposure” (Chaix et al. 2009) that includes much more information than
residence-based measures. The racial composition of the populations our subjects
encountered outside their home blocks differed notably from the racial composition of their
home blocks themselves. Incorporating the non-home blocks into our analysis expanded our
view of segregation and interracial exposure.
15See footnotes 7 and 8 and accompanying text. For an overview of masking techniques, see Armstrong et al. (1999) and Gutmann et
al. (2008).
Palmer et al. Page 12
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
This is only part of what a moving local environment allows us to accomplish. We used U.S.
census information on residential racial compositions as our environmental variable, but
with a larger and more concentrated sample, we could estimate the changing racial
composition (or other characteristics) of each location based on the sampled individuals
themselves. In this way, we would not only temporally refine our measure of exposure but
also illuminate the ways in which individuals choose the places they visit and modify the
characteristics of these places through their presence and activities (Entwisle 2007).
Finally, just as we can tighten measures of local context around individuals as they move, so
too can we tighten our measures of individuals’ dynamic responses to this context. Our
location-based survey made it possible to identify significant differences between male and
female subjects in terms of the relationship between subjective well-being and distance from
home. That male subjects tended to respond differently (reporting lower happiness) the
farther they were from home is additional evidence of the limits of residential measures. Had
we surveyed these people only in their homes, our picture would have been much less
complete.
Conclusions
Our work opens the door to new possibilities for incorporating mobile phones into
demographic research. We explored the potential that these new technologies have for better
understanding activity space, segregation outside the home, the geographic context of
subjective well-being and perhaps many other aspects of human behavior. That we were
able to recruit 270 volunteers in 13 countries, examine their demographic characteristics and
movement patterns, link these to census block data, and then have subjects respond in real
time to surveys triggered by their locations, all in a low-cost and time-efficient manner,
suggests the power of these new methodologies in a more broad-based inquiry.
Acknowledgments
The authors thank Hazer Inaltekin, Spencer Lucian, and David Potere for their contributions to this project during
its initial phases, and Matthew Salganik for his invaluable guidance and contributions throughout. The work was
funded by a grant from the Center for Information Technology Policy at Princeton University. Institutional support
was provided by National Institutes of Health Training Grant T32HD07163 and Infrastructure Grant
R24HD047879.
References
Adey, P. Mobility. Routledge; London, UK: 2010.
Ahas, R. Mobile positioning. In: Büscher, M.; Urry, J.; Witchger, K., editors. Mobile methods.
Routledge; London, UK: 2011. p. 183-199.
Ahas R, Mark Ü. Location based services—New challenges for planning and public administration?
Futures. 2005; 37:547–561.
Anderson, E.; Massey, DS. Problem of the century: Racial stratification in the United States. Russell
Sage Foundation; New York: 2001.
Anokwa Y, Hartung C, Brunette W, Borriello G, Lerer A. Open source data collection in the
developing world. Computer. 2009; 42(10):97–99.
Armstrong MP, Rushton G, Zimmerman DL. Geographically masking health data to preserve
confidentiality. Statistics in Medicine. 1999; 18:497–525. [PubMed: 10209808]
Arnold SE. Google and its strategy of “meh.”. KM World. 2010; 19:6–24.
Asakura Y, Hato E. Behavioral monitoring of public transport users through a mobile communication
system. Journal of Advanced Transportation. 2001; 35:289–304.
Asakura Y, Hato E. Tracking survey for individual travel behaviour using mobile communication
instruments. Transportation Research Part C: Emerging Technologies. 2004; 12:273–291.
Palmer et al. Page 13
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Barraquand F, Benhamou S. Animal movements in heterogeneous landscapes: Identifying profitable
places and homogeneous movement bouts. Ecology. 2008; 89:3336–3348. [PubMed: 19137941]
Bartumeus F, Giuggioli L, Louzao M, Bretagnolle V, Oro D, Levin SA. Fishery discards impact on
seabird movement patterns at regional scales. Current Biology. 2010; 20:215–222. [PubMed:
20116250]
Basta LA, Richmond TS, Wiebe DJ. Neighborhoods, daily activities, and measuring health risks
experienced in urban environments. Social Science & Medicine. 2010; 71:1943–1950. [PubMed:
20980088]
Bertolini L, Dijst M. Mobility environments and network cities. Journal of Urban Design. 2003; 8:27–
43.
Blumenstock J, Gillick D, Eagle N. Who’s calling? Demographics of mobile phone use in Rwanda.
AAAI Symposium on Artificial Intelligence and Development. 2010; 18:116–117.
Borrell B. Every bite you take. Nature. 2011; 470:320–322. [PubMed: 21331018]
Buliung RN, Kanaroglou PS. Urban form and household activity-travel behavior. Growth and Change.
2006:172–199.
Büscher M, Urry J. Mobile methods and the empirical. European Journal of Social Theory. 2009;
12:99–116.
Büscher, M.; Urry, J.; Witchger, K. Introduction. In: Büscher, M.; Urry, J.; Witchger, K., editors.
Mobile methods. Routledge; London, UK: 2011. p. 1-19.
Calabrese F, Colonna M, Lovisolo PPD, Ratti C. Real-time urban monitoring using cell phones: A
case study in Rome. IEEE Transactions on Intelligent Transportation Systems. 2007; 12:141–151.
Calabrese, F.; Pereira, F.; Di Lorenzo, G.; Liu, L.; Ratti, C. The geography of taste: Analyzing cellphone
mobility and social events. In: Floréen, P.; Krüger, A.; Spasojevic, M., editors. Pervasive
computing. Springer; Berlin, Germany: 2010. p. 22-37.
Carioua C, Ziemlickib C, Smoredab Z. Paris by night. NetMob. 2010; 2010:62–66.
Cassels S, Curran SR, Kramer R. Do migrants degrade coastal environments? Migration, natural
resource extraction and poverty in North Sulawesi, Indonesia. Human Ecology. 2005; 33:329–363.
Chaix B, Merlo J, Evans D, Leal C, Havard S. Neighbourhoods in eco-epidemiologic research:
Delimiting personal exposure areas. A response to Riva, Gauvin, Apparicio and Brodeur. Social
Science & Medicine. 2009; 69:1306–1310. [PubMed: 19692161]
Chang, G.; Tan, C.; Li, G.; Zhu, C. Developing mobile applications on the Android platform. In: Jiang,
X.; Ma, M.; Chen, C., editors. Mobile multimedia processing. Springer; Berlin, Germany: 2010. p.
264-286.
Csikszentmihalyi M, Hunter J. Happiness in everyday life: The uses of experience sampling. Journal
of Happiness Studies. 2003; 4:185–199.
Cummins S, Curtis S, Diez-Roux A, Macintyre S. Understanding and representing “place” in health
research: A relational approach. Social Science & Medicine. 2007; 65:1825–1838. [PubMed:
17706331]
D’Andrea A, Ciolfi L, Gray B. Methodological challenges and innovations in mobilities research.
Mobilities. 2011; 6:149–160.
de Castro MC, Monte-Mór RL, Sawyer DO, Singer BH. Malaria risk on the Amazon frontier.
Proceedings of the National Academy of Sciences of the United States of America. 2006;
103:2452–2457. [PubMed: 16461902]
Diener E, Suh EM, Lucas RE, Smith HL. Subjective well-being: Three decades of progress.
Psychological Bulletin. 1999; 125:276–302.
Du Bois, WEB. The Philadelphia Negro: A social study. University of Pennsylvania; Philadelphia:
1899.
Eagle N, Pentland A. Reality mining: Sensing complex social systems. Personal and Ubiquitous
Computing. 2006; 10:255–268.
Eagle N, Pentland A. Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and
Sociobiology. 2009; 63:1057–1066.
Eagle N, Pentland A, Lazer D. Inferring friendship network structure by using mobile phone data.
Proceedings of the National Academy of Sciences. 2009a; 106:15274–15278.
Palmer et al. Page 14
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Eagle, N.; Quinn, J.; Clauset, A. Methodologies for Continuous Cellular Tower Data Analysis. In:
Tokuda, H.; Beigl, M.; Friday, A.; Brush, A.; Tobe, Y., editors. Pervasive computing. Springer;
Berlin, Germany: 2009b. p. 342-353.
Ellis M, Wright R, Parks V. Work together, live apart? Geographies of racial and ethnic segregation at
home and at work. Annals of the Association of American Geographers. 2004; 94:620–637.
Entwisle B. Putting people into place. Demography. 2007; 44:687–703. [PubMed: 18232206]
Foley DL. The use of local facilities in a metropolis. American Journal of Sociology. 1950; 56:238–
246.
Gelman, A.; Hill, J. Data analysis using regression and multilevel/hierarchical models. Cambridge
University Press; New York: 2007.
Golledge, RG.; Stimson, RJ. Spatial behavior: A geographic perspective. The Guilford Press; New
York: 1997.
Golob TT, Meurs H. Biases in response over time in a seven-day travel diary. Transportation. 1986;
13:163–181.
Gonzalez MC, Hidalgo CA, Barabasi A-L. Understanding individual human mobility patterns. Nature.
2008; 453:779–782. [PubMed: 18528393]
Goodchild MF. Citizens as sensors: The world of volunteered geography. GeoJournal. 2007; 69:211–
221.
Goodchild MF, Janelle DG. The city around the clock: Space-time patterns of urban ecological
structure. Environment and Planning A. 1984; 16:807–820.
Gutmann MP, Witkowski K, Colyer C, O’Rourke JM, McNally J. Providing spatial data for secondary
analysis: Issues and current practices relating to confidentiality. Population Research and Policy
Review. 2008; 27:639–665. [PubMed: 19122860]
Hägerstrand T. What about people in regional science? Papers in Regional Science. 1970; 24:6–21.
Helliwell JF. How’s life? Combining individual and national variables to explain subjective wellbeing.
Economic Modelling. 2003; 20:331–360.
Inagami S, Cohen DA, Finch BK. Non-residential neighborhood exposures suppress neighborhood
effects on self-rated health. Social Science & Medicine. 2007; 65:1779–1791. [PubMed:
17614175]
Isaacman, S.; Becker, R.; Cáceres, R.; Kobourov, S.; Rowland, J.; Varshavsky, A. A tale of two cities.
Proceedings of the Eleventh Workshop on Mobile Computing Systems and Applications;
Annapolis, MD: ACM; 2010. p. 19-24.
Janelle DG, Klinkenberg B, Goodchild MF. The temporal ordering of urban space and daily activity
patterns for population role groups. Geographical Systems. 1998; 5:117–137.
Jones, M.; Pebley, A. Redefining neighborhoods using common destinations: Social characteristics of
activity spaces and home census tracts compared. Presented at the annual meeting of the
Population Association of America; San Francisco. May. 2012 Retrieved from http://
paa2012.princeton.edu/papers/120246
Kahneman D, Krueger AB. Developments in the measurement of subjective well-being. Journal of
Economic Perspectives. 2006; 20:3–24.
Kain JF. Housing segregation, Negro employment, and metropolitan decentralization. The Quarterly
Journal of Economics. 1968; 82:175–197.
Killingsworth MA, Gilbert DT. A wandering mind is an unhappy mind. Science. 2010; 330:932.
[PubMed: 21071660]
Kwan M-P. Gender, the home-work link, and space-time patterns of nonemployment activities.
Economic Geography. 1999; 75:370–394.
Kwan M-P. From oral histories to visual narratives: Re-presenting the post-September 11 experiences
of the Muslim women in the USA. Social and Cultural Geography. 2008; 9:653–669.
Kwan M-P. From place-based to people-based exposure measures. Social Science & Medicine. 2009;
69:1311–1313. [PubMed: 19665828]
Kwan, M-P.; Lee, J. Geovisualization of human activity patterns using 3D GIS: a time-geographic
approach. In: Goodchild, MF.; Janelle, DG., editors. Spatially integrated social science: Examples
in best practice. Oxford University Press; New York: 2004. p. 48-66.
Palmer et al. Page 15
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Laube P, Dennis T, Forer P, Walker M. Movement beyond the snapshot: Dynamic analysis of
geospatial lifelines. Computers, Environment and Urban Systems. 2007; 31:481–501.
Laube P, Imfeld S, Weibel R. Discovering relative motion patterns in groups of moving point objects.
International Journal of Geographical Information Science. 2005; 19:639–668.
Law J, Urry J. Enacting the social. Economy and Society. 2004; 33:390–410.
Lee BA, Reardon SF, Firebaugh G, Farrell CR, Matthews SA, O’Sullivan D. Beyond the census tract:
Patterns and determinants of racial segregation at multiple geographic scales. American
Sociological Review. 2008; 73:766–791.
Lee JY, Kwan M-P. Visualisation of socio-spatial isolation based on human activity patterns and social
networks in space-time. Tijdschrift voor economische en sociale geografie [Journal of Economic
and Social Geography]. 2011; 102:468–485.
Lunn D, Spiegelhalter D, Thomas A, Best N. The BUGS project: Evolution, critique, and future
directions. Statistics in Medicine. 2009; 28:3049–3067. [PubMed: 19630097]
MacKerron G. Happiness economics from 35,000 feet. Journal of Economic Surveys. 2012; 26:705–
735. doi: 10.1111/j.1467-6419.2010.00672.x.
Madan, A.; Cebrian, M.; Lazer, D.; Pentland, A. Social sensing for epidemiological behavior change.
Proceedings of the 12th ACM International Conference on Ubiquitous Computing; New York:
ACM; 2010. p. 291-300.
Martens P, Hall L. Malaria on the move: Human population movement and malaria transmission.
Emerging Infectious Diseases. 2000; 6:103–109. [PubMed: 10756143]
Massey DS, Denton NA. The Dimensions of Residential Segregation. Social Forces. 1988; 67:281–
315.
Massey, DS.; Denton, NA. American apartheid: Segregation and the making of the underclass.
Harvard University Press; 1993.
Matthews SA. The salience of neighborhood: Some lessons from sociology. American Journal of
Preventive Medicine. 2008; 34(3):257–259. [PubMed: 18312814]
Matthews, SA. Spatial polygamy and the heterogeneity of place: Studying people and place via
egocentric methods. In: Burton, LM.; Kemp, SP.; Leung, M.; Matthews, SA.; Takeuchi, DT.,
editors. Communities, neighborhoods, and health: Expanding the boundaries of place. Springer;
New York: 2011. p. 35-55.
Murakami E, Wagner DP. Can using global positioning system (GPS) improve trip reporting?
Transportation Research Part C: Emerging Technologies. 1999; 7:149–165.
Nusser, SM.; Intille, SS.; Maitra, R. Emerging technologies and next-generation intensive longitudinal
data collection. In: Walls, TA.; Schafer, JL., editors. Models for intensive longitudinal data.
Oxford University Press; New York: 2006. p. 254-277.
Ōkubo, A.; Levin, SA. Diffusion and ecological problems: Modern perspectives. Springer; New York:
2001.
Park, RE.; Burgess, EW. The city. University of Chicago Press; Chicago, IL: 1925.
Pentland A. Automatic mapping and modeling of human networks. Physica A: Statistical Mechanics
and its Applications. 2007; 378:59–67.
Raento M, Oulasvirta A, Eagle N. Smartphones: An emerging tool for social scientists. Sociological
Methods and Research. 2009; 37:426–454.
Ratti, C.; Sevtsuk, A.; Huang, S.; Pailer, R. Mobile landscapes: Graz in real time. In: Gartner, G.;
Cartwright, W.; Peterson, MP., editors. Location based services and telecartography. Springer;
Berlin, Germany: 2007. p. 433-444.
Ratti C, Williams S, Frenchman D, Pulselli RM. Mobile Landscapes: using location data from cell
phones for urban analysis. Environment and Planning B: Planning and Design. 2006; 33:727–748.
Reades J, Calabrese F, Ratti C. Eigenplaces: Analysing cities using the space-time structure of the
mobile phone network. Environment and Planning B: Planning and Design. 2009; 36:824–836.
Reades J, Calabrese F, Sevtsuk A, Ratti C. Cellular census: Explorations in urban data collection.
IEEE Pervasive Computing. 2007; 6(3):30–38.
Palmer et al. Page 16
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Reardon SF, Matthews SA, O’Sullivan D, Lee SA, Firebaugh G, Farrell CR, Bischoff K. The
geographic scale of metropolitan racial segregation. Demography. 2008; 45:489–514. [PubMed:
18939658]
Reddy S, Mun M, Burke J, Estrin D, Hansen M, Srivastava M. Using mobile phones to determine
transportation modes. ACM Transactions on Sensor Networks. 2010; 6(2):1–27. article 13. doi:
10.1145/1689239.1689243.
Sheller M, Urry J. The new mobilities paradigm. Environment and Planning A. 2006; 38:207–226.
Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annual Review of Clinical
Psychology. 2008; 4:1–32.
Stopher P, FitzGerald C, Xu M. Assessing the accuracy of the Sydney Household Travel Survey with
GPS. Transportation. 2007; 34:723–741.
Urry, J. Mobilities. Polity; Cambridge, UK: 2007.
VanWey LK, Rindfuss RR, Gutmann MP, Entwisle B, Balk DL. Confidentiality and spatially explicit
data: Concerns and challenges. Proceedings of the National Academy of Sciences. 2005;
102:15337–15342.
Venables, WN.; Ripley, BD. Modern applied statistics with S. 4th ed. Springer; New York: 2002.
Wang D, Li F, Chai Y. Activity spaces and sociospatial segregation in Beijing. Urban Geography.
2012; 33:256–277.
Wesolowski, AP.; Eagle, N. Parameterizing the dynamics of slums. Proceedings of the AAAI
Artificial Intelligence for Development Symposium; 2010. p. 103-108.
Wiehe SE, Carroll AE, Liu GC, Haberkorn KL, Hoch SC, Wilson JS, Fortenberry JD. Using GPSenabled
cell phones to track the travel patterns of adolescents. International Journal of Health
Geographics. 2008; 7:22. doi:10.1186/1476-072X-7-22. [PubMed: 18495025]
Wilson, WJ. The truly disadvantaged: The inner city, the underclass, and public policy. University of
Chicago Press; Chicago, IL: 1987.
Wilson, WJ. When work disappears: The world of the urban poor. Alfred Knopf; New York: 1996.
Wong DWS, Shaw S-L. Measuring segregation: An activity space approach. Journal of Geographical
Systems. 2011; 13:127–145. [PubMed: 21643546]
Yarow, J. GARTNER: Android market share doubles, iOS drops in Q3. Business Insider. 2011.
Retrieved from http://articles.businessinsider.com/2011-11-15/tech/30400455_1_ios-iphone-
smartphone-market\
Zandvliet R, Dijst M. Short-term dynamics in the use of places: A space-time typology of visitor
populations in the Netherlands. Urban Studies. 2006; 43:1159–1176.
Zenk SN, Schulz AJ, Israel BA, James SA, Bao S, Wilson ML. Neighborhood racial composition,
neighborhood poverty, and the spatial accessibility of supermarkets in metropolitan Detroit.
American Journal of Public Health. 2005; 95:660–667. [PubMed: 15798127]
Palmer et al. Page 17
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Fig. 1.
Age and sex of subjects. Number of subjects is shown for each age group and sex
Palmer et al. Page 18
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Fig. 2.
Centroids of each subject’s location estimates on world map. A small amount of random
noise has been added to each location estimate to protect privacy and aid in visualization of
high-density areas
Palmer et al. Page 19
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Fig. 3.
Individual movement pattern of Subject 1. Straight lines connect sequential location
estimates (points) and the circle marks the estimated place of residence (circle’s center).
Many location estimates lie on top of one another and thus cannot be distinguished. Tick
marks are 1 km apart. This subject’s total participation time was 30 days
Palmer et al. Page 20
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Fig. 4.
Individual movement pattern of Subject 2. Straight lines connect sequential location
estimates (points) and the large circle marks estimated place of residence (circle’s center).
Many location estimates lie on top of one another and so cannot be distinguished. Tick
marks are 1 km apart. This subject’s total participation time was 31 days
Palmer et al. Page 21
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Fig. 5.
Location estimates of Subject 1 on weekdays (A) and weekends (B) with random noise
added to aid in visualization. Lines connecting sequential location estimates during both
weekdays and weekends are displayed in both plots in light grey. In panel A, weekday
location estimates are plotted with shaded markers (colored in the online version of the
article), and dark grey lines connect sequential estimates. In panel B, weekend location
estimates are plotted with shaded markers (colored in the online version), and dark grey
lines connect sequential estimates. In both cases, filled circles indicate locations outside
subject’s estimated census block of residence, and filled squares indicate those within this
block. The large circle marks the estimated place of residence (circle’s center). Axis tick
marks are 1 km apart. This subject’s total participation time was 30 days
Palmer et al. Page 22
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Fig. 6.
Location estimates of Subject 2 on weekdays (A) and weekends (B) with random noise
added to aid in visualization. Lines connecting sequential location estimates during both
weekdays and weekends are displayed in both plots in light grey. In panel A, weekday
location estimates are plotted with shaded markers (colored in the online version of the
article), and dark grey lines connect sequential estimates. In panel B weekend location
estimates are plotted with shaded markers (colored in the online version), and dark grey
lines connect sequential estimates. In both cases, filled circles indicate locations outside
subject’s estimated census block of residence, and filled squares indicate those within this
block. The large circle marks estimated place of residence (circle’s center). Axis tick marks
are 1 km apart. This subject’s total participation time was 31 days
Palmer et al. Page 23
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Fig. 7.
Box-whisker plots of the percentage of time that black, Latino, and white subjects spent in
census blocks in which the proportion of white residents was (0.75, 1] (A), (0.50, 0.75] (B),
(0.25, 0.50] (C), or [0, 0.25] (D), based on the 2000 census. Boxes show medians and
interquartile ranges (calculated as Tukey’s lower and upper hinges). Each whisker extends to
the maximum and minimum values lying at a distance of up to 1.5 times the length of the
interquartile range. All data points are displayed (with a small amount of noise added to
their x-coordinates to avoid overlap), rather than simply the outliers, for better visualization
of different sample sizes and distributions. Figures exclude subjects with less than 4 hours
observed outside of home, and they also exclude time spent in the census block of subject’s
residence. Not displayed here is time spent in census blocks with total census populations of
zero
Palmer et al. Page 24
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Fig. 8.
Survey responses analyzed in models with distance from home as a covariate and without
census block racial characteristics. Main plot shows responses grouped by research subject,
with shades (colors in the online version of the article) corresponding to the pooled
responses shown in inset A.
Palmer et al. Page 25
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Fig. 9.
Observed and predicted happiness by distance from home for white (A), Latino (B), and
black (C) men (crosses and darker lines) and women (filled circles and lighter lines). Lines
in panels A–C represent uncertainty and are drawn using coefficients estimated in each of
999 final iterations of the Gibbs sampler. Lines in panel D are drawn using mean estimates
for each gender and race/ethnicity (dotted = white, dashed = Latino, solid = black)
Palmer et al. Page 26
Demography. Author manuscript; available in PMC 2014 June 01.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
Palmer et al. Page 27
Table1
Raceandsexofsubjects
Race
FemaleMaleMissingTotal
N%N%N%N%
Asian10.37155.5600.00165.93
Black103.7072.5900.00176.30
Latino145.19197.0400.003312.22
White6524.0712747.0400.0019271.11
Missing/Other72.5941.4810.37124.44
Total9735.9317263.7010.37270100.00
Demography. Author manuscript; available in PMC 2014 June 01.