15230406.2013.777139

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Spatial, temporal, and socioeconomic patterns in the
use of Twitter and Flickr
Linna Li
a
, Michael F. Goodchild
a
& Bo Xu
b
a
Department of Geography, Center for Spatial Studies, University of California, Santa
Barbara, CA, USA
b
Department of Geography and Environmental Studies, California State University, San
Bernardino, CA, USA
Version of record first published: 19 Apr 2013.
To cite this article: Linna Li , Michael F. Goodchild & Bo Xu (2013): Spatial, temporal, and socioeconomic patterns in the use
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Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr
Linna Lia
*, Michael F. Goodchilda
and Bo Xub
a
Department of Geography, Center for Spatial Studies, University of California, Santa Barbara, CA, USA; b
Department of Geography
and Environmental Studies, California State University, San Bernardino, CA, USA
(Received 11 September 2012; accepted 27 January 2013)
Online social networking and information sharing services have generated large volumes of spatio-temporal footprints,
which are potentially a valuable source of knowledge about the physical environment and social phenomena. However, it is
critical to take into consideration the uneven distribution of the data generated in social media in order to understand the
nature of such data and to use them appropriately. The distribution of footprints and the characteristics of contributors
indicate the quantity, quality, and type of the data. Using georeferenced tweets and photos collected from Twitter and Flickr,
this research presents the spatial and temporal patterns of such crowd-sourced geographic data in the contiguous United
States and explores the socioeconomic characteristics of geographic data creators by investigating the relationships between
tweet and photo densities and the characteristics of local people using California as a case study. Correlations between
dependent and independent variables in partial least squares regression suggest that well-educated people in the occupations
of management, business, science, and arts are more likely to be involved in the generation of georeferenced tweets and
photos. Further research is required to explain why some people tend to produce and spread information over the Internet
using social media from the perspectives of psychology and sociology. This study would be informative to sociologists who
study the behaviors of social media users, geographers who are interested in the spatial and temporal distribution of social
media users, marketing agencies who intend to understand the inﬂuence of social media, and other scientists who use social
media data in their research.
Keywords: spatio-temporal footprints; socioeconomic; Flickr; Twitter; georeference
Introduction
There has been a rapid expansion in the use of social
media and data sharing services recently. Data generated
by these sources have been widely used to study social
networks (Huberman, Romero, and Wu 2008; Lerman and
Ghosh 2010) and behavioral trends (Sakaki, Okazaki, and
Matsuo 2010; Bollen, Mao, and Zeng 2011). However, it
is critical to understand the uneven distribution of such
data sources in order to evaluate their validity, accuracy,
representativeness, and uncertainty when they are used to
imply the social and behavioral characteristics of the
users. This article, using Twitter and Flickr as two exam-
ples, explores spatiotemporal patterns of geographic data
generated in social media, within the bounding box of the
contiguous United States and further infers the character-
istics of the users by examining the relationships between
geographic data densities and the socioeconomic charac-
teristics of local residents at the county level using
California as a case study.
Twitter is a popular social media site that is widely
used for daily chatter, conversations, sharing information,
and reporting news (Java et al. 2007). Flickr is an online
photo management service that allows uploading and shar-
ing photos within and outside of groups. Tweeting and
photo-taking behaviors, speciﬁcally the distribution of
tweets and photos, may suggest the diverse characteristics
of different users. To understand spatiotemporal patterns
of tweets and photos, we study two cases: georeferenced
tweet messages in Twitter and georeferenced photos in
Flickr, which are used as proxies for the spatio-temporal
footprints of their creators. In the past few years, the two
data sources have been widely used to investigate research
questions in different disciplines. For example, the loca-
tion and spatial boundary of places may be delineated
based on the aggregation of geotagged photos in Flickr
(Hollenstein and Purves 2010; Li and Goodchild 2012).
Representative photos at different locations and tourist
paths can also be extracted by analyzing spatial, temporal,
and visual information associated with Flickr photos
(Crandall et al. 2009). Both Antoniou, Morley, and
Haklay (2010) and Purves, Edwardes, and Wood (2011)
carried out comparative studies of Flickr and other image
collections and have investigated issues in such collec-
tions, including bias. Ames and Naaman (2007) have
studied motivations in tagging. The similarity of spatial
and temporal information associated with photos provided
by different contributors may even indicate the probability
of a social tie between them (Crandall et al. 2010). Twitter,
the other rich data source, has been used to study people’s
response to emergencies (Goodchild and Glennon 2010;
*Corresponding author. Email: linna@geog.ucsb.edu
Cartography and Geographic Information Science, 2013
Vol. 40, No. 2, 61–77, http://dx.doi.org/10.1080/15230406.2013.777139
© 2013 Cartography and Geographic Information Society
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Sakaki, Okazaki, and Matsuo 2010), the automatic detec-
tion of local events (Lee and Sumiya 2010), and predict
election results based on the sentiments expressed in
tweets (Tumasjan et al. 2010). Furthermore, check-ins
collected from location-sharing services were used to
study human mobility patterns (Cheng et al. 2011).
Although data collected from social media, such as
Twitter, have been increasingly used to study geographic
landscapes and human behaviors (Li and Goodchild,
2013), it is difficult to estimate the representativeness of
such data. Despite the various studies, thus far there is no
research on the socio-demographic characteristics of users,
which is of great value since georeferenced data from
Twitter and Flickr, are implicative of the characteristics
of places, as well as local residents.
However, research has been done on socio-demo-
graphic characteristics of Internet users using surveys in
many countries. For example, Soule, Shell, and Kleen
(2003) found that gender is not a significant variable in
explaining heavy Internet usage, but education is, based
on the data from the Tenth Graphic, Visualizations, and
Usability Center (GVU) Survey conducted on the Web. A
study in the Philippines showed that younger, more afflu-
ent, and well-educated people in places with better infra-
structure are more capable of using Information and
Communications Technology (ICT, Alampay 2006).
Different Internet usage patterns of people from different
socio-economic groups were identified in central
Queensland (Taylor et al. 2003). As demonstrated in
these studies, the characteristics of Internet users are cru-
cial for understanding a range of relevant phenomena,
such as Internet addiction, social opportunities through
the access to ICT, and behavioral patterns in using such
technologies. Since conducting surveys is time-consuming
and labor-intensive, all the studies primarily collect data
through questionnaires, so they can only rely on a small
number of participants. In our study, we use geographic
location as a link to associate social media usage and
characteristics of local residents based on the data auto-
matically collected using social media APIs and the aux-
iliary census data.
This study provides an exploratory analysis of a subset
of Twitter and Flickr users, those who provide locational
information for tweets and photos, in terms of their demo-
graphic and socioeconomic properties at the county level
in California. Georeferenced tweets and photos indicate
the presence of their creators at that location. There are
three major reasons why people are present at a particular
location: location of residence, location of work, or loca-
tion of tourist attractions. In this article, we select geor-
eferenced tweets and photos contributed by local residents
to explore the demographic and socioeconomic character-
istics of these users. A user is considered a local resident
in a county only when the time interval between two
tweets or photos produced in that specific county by the
user is longer than 10 days.
The remainder of the article is structured as follows.
The section “Twitter and Flickr data collection and pre-
processing” describes the collection and pre-processing of
georeferenced data from Twitter and Flickr. The section
“The spatial distribution of georeferenced tweets and
photos” presents the spatial distributions of georeferenced
tweets and photos over the contiguous United States,
followed by a discussion of the temporal patterns of geor-
eferenced tweets and photos in the section “Temporal
patterns of tweets and photos.” We propose two descrip-
tive models in the section “Descriptive models of tweet
and photo densities in California” to illustrate the relation-
ships between the tweet and photo densities and the char-
acteristics of people in different counties of California.
The article concludes with a discussion of implications
and future research directions.
Twitter and Flickr data collection and pre-processing
Tweets and photo metadata were collected using Twitter
and Flickr’s public APIs and stored in a MySQL database.
We collected data from 21 January to 7 March 2011; these
dates were chosen to avoid major events that might cause
unusual patterns. In total, there are 19,758,954 records for
Twitter and 4,263,227 records for Flickr within the bound-
ing box of the contiguous United States. Location asso-
ciated with each tweet is in a variety of forms with
different precision levels. It may be automatically captured
by built-in Global Positioning System (GPS) receivers in
mobile devices like smart phones, calculated according to
the relative position of the user’s equipment in a cellular
network, or manually selected by a user from a set of
place names provided by Twitter. In the first case, location
is in the form of latitude and longitude, while in other
cases location is usually recorded as a neighborhood, a
city, or even a country. Other than coordinates, Twitter
takes the estimated location of a user’s device or an
Internet Protocol (IP) address of a computer and reverse
geocodes it to a few possible places provided to the user
for selection. The positional accuracy varies from one
method to another. For location recorded by GPS, it is
usually at the magnitude of several meters. For location
determined by triangulation in a cellular network, accu-
racy ranges from 30 to 3000 m, depending on the spatial
distribution of cells (Zandbergen 2009). For IP address,
the positional accuracy of georeference depends on the
method used to convert IP addresses to geographic coor-
dinates, usually at the level of ZIP code, city, state, or even
country. For example, Maxmind’s free GeoLite City data-
base claims that the spatial accuracy of georeference is
“over 99.5% on a country level and 78% on a city level
for the U.S. within a 40 kilometer radius.” Finally, the
62 L. Li et al.

accuracy of a place name depends on the spatial extent of
the place. Information about the tweets in the database
contains tweet ID, tweet text, time, location, and user ID.
In Flickr, photos were either georeferenced by built-in
GPS in cameras or manually georeferenced by a user who
identified photo location on a map. The location could
either be the place where a photo was taken or be the
location of an object in the photo. Automatic recording by
a GPS receiver is always the former case, while manually
georeferenced photos could be either way. One typical
error in location of photos occurs when a user uploads a
group of photos that involve several places to the same
location. Photo metadata contain information about photo
ID, photo title, description, tags, upload time, time when a
photo was taken, location, and owner ID.
For both tweets and photos, the locations are resolved
to five decimal places of latitude and longitude (approxi-
mately 1 m), but we should expect that the accuracy of
location is dependent on the accuracy of GPS in mobile
devices (which could be several meters) or the map scale
when a user specifies a photo location. Because the objec-
tive of this article is to study the spatial and temporal
patterns of tweets and photos, only data that have point
locational information with relatively high precision are
used, and those that are not georeferenced are excluded. It
is estimated that the percentage of georeferenced tweets is
less than 1% and geotagged photos around 3.33%.1
However, the total numbers of tweets and photos are
very large, so we can still obtain great volumes of geor-
eferenced data. In addition, we must be aware that these
data were contributed by users who are willing to share
their locations and not by everyone who uses the two
services. Therefore, the data are a subset of the entire
datasets of Twitter and Flickr given spatial and temporal
constraints, and the users are a subset of the entire user
groups. Like other data created by volunteers, there is bias
in terms of contributions made by different users, because
most contributions come from a very small percentage of
the total number of contributors. For instance, “In most
online communities, 90% of users are lurkers who never
contribute, 9% of users contribute a little, and 1% of users
account for almost all the action” (Nielsen 2006). Haklay
(2010) showed that most of the data for England were
contributed by only a few users and the difference of road
data coverage between wealthy areas and poor areas is
about 8% in OpenStreetMap (OSM). Contribution bias is
also present in our datasets. The 300 heaviest contributors
of local Twitter and Flickr users who share geographic
footprints are represented in Figure 1a and 1b, showing
the long tail effect: a large number of tweets and photos
are created by the first few hundred contributors.
When examining the relationships between georefer-
enced data densities and socioeconomic characteristics of
residents in California, we verify that the data were
produced by local users. First, we chose county as the
data aggregation level, because a person is more likely to
live in one census tract and work in another. Therefore, it
is difficult to tell whether a location is a user’s home or
work place at a finer spatial scale. By contrast, people are
more likely to live and work in the same county.
According to the 2000 Census Bureau county-to-county
commuting data for California, the percentage of resi-
dents who commute within the same county is as high as
83%. Second, we calculated the time a user stays in a
county by comparing the time interval between two
tweets and photos that are produced by the same user.
Only when a time interval is greater than 10 days, a user
is regarded as local, and data created by this user are
retained for further analysis.
Correlations between tweet and photo densities and
contributors’ properties were calculated at the county
level. Ideally, socioeconomic characteristics of users
would be determined at the individual level, but that
type of data is not available for obvious reasons, so loca-
tions were used to link the data densities and the residents.
This type of correlation based on group data rather than
individual data is called ecological correlation (Robinson,
1950). Ecological correlations between tweet and photo
densities and the socioeconomic characteristics of people
suggest that certain people with specific characteristics are
more involved in the generation of georeferenced tweets
and photos. However, it would be fallacious to infer
individual behaviors from data aggregated to geographic
areas (Openshaw 1984; Piantadosi, Byar, and Green 1988;
King 1997). For example, correlation between the number
of tweets from a place and the number of Native
Americans present in that place does not imply that
Native Americans are more likely to tweet. This study is
a first step toward an understanding of the relationships
between georeferenced tweets and photos and population;
the results suggest that it would be valuable to further
investigate these relationships.
The spatial distribution of georeferenced tweets and
photos
We plotted the locations of georeferenced tweets on a
map. As demonstrated in Figure 2, tweet locations roughly
describe the administrative boundary of the United States
and major roads at a very good resolution, which is similar
to the representation of Flickr photos in other research
(Crandall et al. 2009). Figure 3 shows georeferenced
tweets in part of Los Angeles. At this scale, the blocks
and local roads are delineated by tweet locations. For
instance, tweet locations are well aligned with the location
and shape of freeways, such as Interstate 405, as well as
some local roads. High density along major roads might
indicate people tweeting from vehicles, and perhaps from
locations adjacent to major roads such as hotels and gas
stations as well.
Cartography and Geographic Information Science 63

Flickr photos have similar spatial patterns to tweet
locations. However, the number of photos is substantially
smaller than that of tweets during the same time period. It
takes more effort to take and upload photos than it does to
generate tweets. Despite a smaller number of photos than
tweets, some places are associated with more photos.
Intensive tweets are usually generated at places with
high population density, such as big metropolitan areas;
3500
3000
2500
2000
1500
1000
20,000
18,000
16,000
14,000
12,000
10,000
8000
6000
4000
2000
0
500
0
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300
0 20 40 60 80 100 120
(b)
140 160 180 200 220 240 260 280 300
Numberofgeoreferencedtweets
Numberofgeoreferencedphotos
Ranked user - Top 300 users generating most tweets
Ranked user - Top 300 users generating most photos
(a)
Figure 1. (a) The number of georeferenced tweets generated by the top 300 contributors (highest: left; lowest: right). (b) The number of
georeferenced photos generated by the top 300 contributors (highest: left; lowest: right).
64 L. Li et al.

Figure 2. Georeferenced tweets within the bounding box of the contiguous United States.
Figure 3. A close-up of georeferenced tweets in part of Los Angeles.

however, many photos are also taken at places with low
population density, such as Yosemite National Park.
To estimate the number of tweet and photo occur-
rences per unit area, we performed a kernel density ana-
lysis of the national data using tweet and photo locations.
Kernel density is a way of estimating the intensity of
points by creating a smooth surface using a bivariate
probability density function (Bailey and Gatrell 1995).
The kernel estimator is defined as
f ðxÞ ¼
1
nh
Xn
i¼1
K
x À xi
h

(1)
where n is the total number of points, h is the bandwidth
that determines the amount of smoothing, K is the kernel
function, x is the location of estimation, and xi is known
point location. The kernel function K could have differ-
ent forms, such as a Gaussian distribution, negative
exponential, or a simple binary function (it is constant
within the bandwidth and zero otherwise). The quadratic
function we used in the analysis is given below
(Silverman 1986):
KðcÞ ¼
3
π ð1 À cT
cÞ2
ifcT
c 1
0 otherwise

(2)
There are two parameters in kernel density estimation:
kernel bandwidth and cell size. The kernel was 100 km
and the cell size was 1 km given the size of the region.
The kernel bandwidth of 100 km is a compromise between
a map that is too smooth to interpret and one that is too
noisy to interpret. The cell size of 1 km was used to show
fine detail. As shown in Figures 4 and 5, both tweets and
photos tend to cluster in major cities with high population
density. For example, Seattle, Portland, San Francisco, and
Los Angeles on the west coast and Boston, New York
City, Baltimore, and Washington DC on the east coast are
clusters of both tweets and photos. We can almost identify
all major cities with significant economic, political, and
social influence in the United States from these two maps.
Although there are consistent patterns of tweets and
photos occurring at cities with high population density,
there are some differences, too. We calculated the normal-
ized density difference as follows:
Dd ¼
Dp
max ðDpÞ
À
Dt
max ðDtÞ
(3)
where Dd measures the relative difference between tweet
density and photo density, Dp and Dt are photo density and
tweet density at a location, respectively, and max (Dp) and
max (Dt) are the maximum photo and tweet density within
the study area. To account for the total amount of differ-
ence between the two sources, we normalized the density
value by the maximum density in each source, so the
range of density for both sources is between 0 and 1.
This allows us to compare density at each location as
opposed to other locations. As shown in Figure 6, some
locations stand out in the map of density difference as
places with high photo density, such as Lake Tahoe and
Yosemite National Park in California, Charleston in South
Carolina, and Orlando in Florida – which are popular
tourist attractions. The normalized photo density for
these places is substantially higher than the normalized
tweet density. On the other hand, Atlanta in Georgia,
Figure 4. Tweet density within the bounding box of the contiguous United States.
66 L. Li et al.

Cincinnati and Columbus in Ohio, and Detroit in
Michigan have significantly higher normalized tweet den-
sity. Furthermore, there are many tweets in the city of
Denver but a considerable number of photos in the
Rockies west of Denver.
At a finer scale, we generated a tweet density surface in
Los Angeles using a kernel of 10 km and a cell size of
100 m. As shown in Figure 7a, downtown Los Angeles and
Beverly Hills have the highest tweet density and it gradu-
ally decreases in the surrounding areas. The photo density
surface in Los Angeles is demonstrated in Figure 7b, with
three major clusters in downtown Los Angeles, Pasadena,
and Santa Monica. In these two figures, density estimation
does not stop at the coast and the values are not zero in the
ocean; however, a spatial constraint clearly could be applied
in the density calculation.
Figure 5. Flickr photo density within the bounding box of the contiguous United States.
Figure 6. Normalized density difference between Flickr photos and tweets.

Figure 7. (a) Tweet density in Los Angeles County. (b) Flickr photo density in Los Angeles County.
68 L. Li et al.

Temporal patterns of tweets and photos
The density of tweets varies from place to place and also
through time. The hourly number of georeferenced tweets
in Los Angeles within a week is shown in Figure 8. The
highest rates of tweeting occurred between 8:00 in the
morning and at midnight. There are generally two tweet
peaks: one around 13:00–14:00 in the afternoon and the
other around 20:00–21:00 in the evening. The lowest rate
of tweeting is around 4:00–5:00 in the morning when most
people are sleeping. This trend is relatively consistent in
each day of the week and represents the activity pattern of
georeferenced tweets. A comparison of temporal patterns
of tweets and photos is shown in Figure 9a and 9b. In
contrast to the temporal pattern of tweets, Flickr users are
substantially more active during weekends and the rate of
photo-taking is highest during the afternoon hours.
However, temporal uncertainty should be considered
when interpreting the results. The time when a photo
was taken is provided by a camera, but not all photogra-
phers consistently keep the right time setting.
Descriptive models of tweet and photo densities in
California
In this section, we infer the characteristics of georefer-
enced tweet and photo users by studying the relationships
between tweet and photo densities and the socioeconomic
characteristics of people in different counties of California.
The hypothesis is that areas with high tweet or photo
density tend to have people with some speciﬁc character-
istics which may be age, race, educational attainment, the
type of occupation, and household income. The tweet
dataset contains 602,371 tweets in California that were
georeferenced by GPS, created by 44,097 users. Because
the study uses socioeconomic data of local residents only,
the raw data were preprocessed to exclude data that were
likely to be generated by tourists. As mentioned above, a
user is regarded as a local resident if he or she stays in a
county for a relatively long period of time (i.e., 10 days),
which is veriﬁed by the time interval between two tweets
or photos generated by the same user. As a result, there are
432,475 georeferenced tweets generated by 18,315 local
users, which represent about 71.80% of all georeferenced
tweets.
Data on distributions of age, race, educational attain-
ment, occupation, and household income were obtained
from the American Community Survey (ACS) 2006–
2010. These data made up the set of explanatory variables.
To create spatially intensive variables, all variables were
normalized by the total number of people in each county.
For instance, tweet density was calculated by the number
of tweets over the total population in a county. Hence, the
tweet density in the model is different from the tweet
density represented as a kernel density surface in the
section “The spatial distribution of georeferenced tweets
and photos”: It is the number of tweets per person in a
Figure 8. The average number of tweets per hour in Los Angeles County.

Figure 9. (a) Time chart for georeferenced tweets. (b) Time chart for georeferenced photos.
70 L. Li et al.

county, rather than the number of tweets per land area
unit. The explanatory variables consist of the percentage
of people who fall into each of the categories (e.g., there
are 23 age groups, ranging from “under 5 years” to “85
years and over,” so there are 23 variables for the percen-
tage of people in all age groups and they add up to 1).
Since there are many categories in each of these types
of data, the number of explanatory variables is large
compared to the number of observations, and some expla-
natory variables are correlated with each other, thus multi-
ple linear regression is not appropriate because it requires
the absence of multicollinearity. Partial least squares
regression (PLSR), on the other hand, is a method parti-
cularly useful for describing the correlation between a
dependent variable and a set of strongly collinear inde-
pendent variables. It aims to reduce the set of variables to
a smaller number of uncorrelated components that char-
acterize most of the covariance between the dependent
variable and independent variables. PLSR was introduced
by Wold (1966) in the social sciences, and was later
widely adopted in chemometrics (Wold, Sjöström, and
Eriksson 2001). PLSR is related to principal component
regression (PCR): Both extract components from original
independent variables for regression modeling; however,
they differ in several ways. The major difference is that
principal components in PCR are solely determined by the
variance of independent variables, while those in PLSR
are determined by the covariance between dependent and
independent variables (Garthwaite 1994). Therefore, the
methods for constructing components in PCR and PLSR
are different, and the latter has the capability to capture
most of the information in independent variables that
explains the dependent variable by avoiding the problem
in PCR of discarding important principal components with
a low variance (Jolliffe 1982).
Fifty-eight explanatory variables in the model can be
grouped into five categories: age, race, educational attain-
ment, income, and occupation. Performance of PLSR on
the data resulted in five components that explain most of
the variance in tweet density (70.81%) and in the original
58 independent variables (82.89%). Table 1 lists the per-
centages of variance in the dependent and independent
variables explained by each component, and Table 2
gives a sample loading matrix for the five components
obtained from the original variables (see Appendix 1 for
the entire loading matrix for PLS components in the tweet
density model). The loading measures the importance of
each variable in accounting for the variance of a compo-
nent. A high loading value means that a specific variable
accounts for much variance in a component. Table 3 gives
a brief description of the meaning of the five components
based on the loading values. The first component accounts
for 37.94% of the variation in tweet density and 28.59% of
the variation in the independent variables. It is positively
highly loaded on the occupation variable of management,
business, science, and arts, the education variables of
bachelor’s degree and graduate or professional degree,
and the household income variables of $200.000 or more
Table 1. The percentage of variances explained by components in the Twitter model.
Component
1 2 3 4 5
Explained variance in independent variables 0.2859 0.0987 0.0352 0.3199 0.0893
Explained variance in dependent variable 0.3794 0.1200 0.1381 0.0159 0.0548
Note: Independent variables are percentages of people falling into different subcategories of age, race, educational attainment, occupation, and household
income, respectively, obtained from ACS (2006–2010), and the dependent variable is tweet density.
Table 2. Sample loading matrix for PLS components in the Twitter model.
Component
Explanatory variables 1 2 3 4 5
Bachelor’s degree 0.401932 −0.0772 0.042405 0.024567 0.009941
Graduate or professional degree 0.29582 −0.03287 0.007762 0.025411 0.000464
$150,000 to $199,999 0.174187 0.013374 −0.04549 −0.02102 −0.0099
$200,000 or more 0.245107 0.042515 −0.02049 −0.00515 −0.02389
Management, business, science, and arts occupations: 0.49972 −0.17736 0.009559 0.054147 −0.02251
Service occupations: −0.14328 −0.05453 0.092773 0.047814 −0.03232
Sales and office occupations: 0.021245 0.022759 0.069738 −0.03638 −0.02886
Natural resources, construction, and maintenance occupations: −0.25131 0.09103 −0.11676 −0.03511 0.029064
Production, transportation, and material moving occupations: −0.12638 0.118096 −0.05531 −0.03048 0.054624

and $150,000 to $199,999. We may broadly call it a well-
educated people component. The second component
explains 12% of the variation in tweet density and
9.87% of the variance in the independent variables. It
has high positive loadings on low level of education
(i.e., less than 9th grade and 9th to 12th, no diploma)
and occupations in transportation and material moving.
This is a component for less-educated people. The third
component represents other race people and accounts for
13.81% of the dependent variable but only 3.52% of the
independent variables. The last two components both have
low explanatory powers for tweet density and are not
considered important in the model. Interestingly, there is
no obvious difference between male and female in the
behavior of generating georeferenced tweets, so sex was
not included in the final model. In simple correlations,
tweet density is also highly correlated with the percentage
of people between the ages of 25 and 44 years, but age is
correlated with income in this dataset, so variables of age
do not show up as highly loaded predictors on the
components.
The scores on each component may be mapped, as
demonstrated in Figure 10 for the first component. There
are five shades of color classified by natural breaks from
the darkest for the highest positive scores (the maximum:
0.36) to the lightest for the negative scores (the minimum:
–0.16). The San Francisco Bay area is described by high
positive scores, shown as the darkest area in the map. The
first component characterizes the percentage of people
with high education and salary, and associates this combi-
nation of characteristics with a high rate of tweeting. Take
San Francisco and Santa Clara Counties as an example.
These are places where many people work in high-tech
jobs with an advanced degree and where tweet density is
high. In contrast, northern and central California has a
dominance of negative scores, suggesting that the percen-
tage of well-educated people and tweet density are low in
these areas.
Component scores
–0.159129–0.122556
–0.122555–0.074462
–0.074461–0.013799
–0.013798–0.184898
0.184899–0.355314
0 100 200 400 km
Figure 10. First component scores for tweet density: linear combinations of the independent variables.
Table 3. Description of the PLS components in the Twitter
model.
Component Description
1 Well-educated people
2 Less-educated people
3 Other race people
4 White people
5 Asian people
72 L. Li et al.

The same procedure was applied to photo density. A
total of 752,176 georeferenced photos created by 19,594
users in California were collected from Flickr. Similarly,
only photos contributed by local residents were retained
for further analysis, resulting in 440,026 georeferenced
photos created by 7216 local users. Five components
constructed by PLSR capture 47.34% of the variation in
photo density and 81.49% of the variance in the original
independent variables (see Table 4). The entire loading
matrix for PLS components in the photo density model is
provided in Appendix 2. The explanatory power of this
model is not as high as the tweet density model for several
reasons. Although the total number of photos is about the
same as that of tweets, the number of unique photo con-
tributors (7216) is smaller than that of tweet creators
(18,315); therefore, photos were contributed by a much
smaller number of users compared to the tweet dataset. In
addition, the uncertainty of time when a photo is taken
may be present in Flickr photos, leading to judgmental
errors when time interval was used to infer whether a user
is a local resident or a tourist. The first component
explains 10.97% of the variance in the dependent variable
and 33.16% in the independent variables. The second
component captures only 7.33% of variation in the depen-
dent variable and 21.35% in the independent variables.
This contrast demonstrates the use of the covariance
between dependent and independent variables to construct
components in PLSR, rather than the use of only variance
of independent variables in PCA. The explanations of the
five components are listed in Table 5. The first component
is highly loaded on occupations of management, business,
science, and arts, bachelor’s degree, and graduate or pro-
fessional degree, and generally describes the percentage of
well-educated white people. The second component is
positively highly loaded on Asian people with bachelor’s
degree in the occupation of management, business,
science, and arts and is interpreted as well-educated
Asian people. The third component accounts for 9.52%
of variance in the dependent variable and 17.29% of
variance in the independent variables. It has positive high
loadings on white people, high school graduate, General
Educational Development (GED), or alternative, and
service occupations, which represents moderately edu-
cated white people. The last two components explain
10.87% and 8.67% of the dependent variable, but their
explanation powers are very low for independent vari-
ables, so they are not regarded as significant in the
model. Similar to the model of tweet density, gender
does not seem to make a difference in the interpretation
of photo density.
A straightforward interpretation of the models would
be the relationship between tweet and photo densities and
the demographic and socioeconomic characteristics of
people in these places. As the raw data were preprocessed
to retain only tweets and photos generated by local resi-
dents, socioeconomic properties of people who contribute
to these data may be inferred from this relationship, such
as race, education, occupation, and income. A distinction
between time intervals of tweets and photos indicate that
71.80% of georeferenced tweets are generated by local
people, while only 58.80% of georeferenced photos are
uploaded by local residents. Therefore, the tweet density
model may be more accurate in terms of inference about
properties of Twitter users from their spatial footprints.
Although at a coarse scale, these two models provide a big
picture of the properties of local people who contribute
georeferenced tweets and photos, and offer an exploratory
analysis on the representativeness of a subset of Twitter
and Flickr users.
Conclusion
The growing popularity of social networking and social
media services has attracted researchers from various dis-
ciplines, and this new form of geographic data has been
used in a variety of applications. However, many ques-
tions must still be answered in order to use these data
more appropriately. For example, who uses these services?
Why do people use them? How can we take advantage of
Table 4. percentage of variances explained by components in the Flickr model.
Component
1 2 3 4 5
Explained variance in independent variables 0.3316 0.2135 0.1729 0.0640 0.0359
Explained variance in dependent variable 0.1097 0.0733 0.0952 0.1087 0.0867
Note: Independent variables are percentages of people falling into different subcategories of age, race, educational attainment, occupation, and household
income, respectively, obtained from ACS (2006–2010), and the dependent variable is photo density.
Table 5. Description of the PLS components in the Flickr
model.
Component Description
1 Well-educated white people
2 Well-educated Asian people
3 Moderately educated white people
4 Less-educated people
5 Other race people

this new source of information that may be potentially
used for any possible topic but with uncertainty and
bias? Understanding the spatial and temporal distribution
of georeferenced data would provide insight into these
questions. This article visualizes the spatial and temporal
patterns of georeferenced tweets and Flickr photos col-
lected within the contiguous United States. The tweets
collected within only a few weeks delineate the adminis-
trative boundaries of the United States and the major roads
at a very good resolution, especially in areas with high
population density. Flickr photos have similar spatial pat-
terns, although the total number of photos taken during the
same period of time is substantially smaller than that of
tweets. However, some places have considerably higher
normalized photo density than tweet density – a character-
istic of tourist attractions, such as Yosemite National Park.
The temporal patterns of tweets are relatively consistent
each day of the week, with two major peaks around
13:00–14:00 and 20:00–21:00 hours, but there are sub-
stantially more photos taken over weekends.
Two descriptive models using PLSR were con-
structed to explain the variation of tweet and photo
densities from place to place in California, using demo-
graphic and socioeconomic variables of people in each
county. According to the first model, tweet density is
highly dependent on the percentage of well-educated
people with an advanced degree and a good salary who
work in the areas of management, business, science, and
arts. The second model suggests that high photo density
is correlated with a high percentage of white and Asian
people with an advanced degree in the areas of manage-
ment, business, science, and arts. This study would be
informative to sociologists who study the behaviors of
social media users, geographers who are interested in the
spatial and temporal distribution of social media users,
marketing agencies who intend to understand the influ-
ence of social media, as well as other scientists who use
social media data in their research.
This research provides an exploratory analysis of the
characteristics of the contributors of georeferenced data,
so we may be aware of the representativeness of such
specific groups of people in the total population when
using the data. Two major sources of bias may be
reduced in the future: the bias caused by people’s move-
ment and the bias due to ecological correlation. Finally,
further research from the perspectives of psychology and
sociology is required to explain why people with some
specific social and demographic properties are more
involved in creating georeferenced tweets and photos.
Note
1. http://code.flickr.com/blog/2009/02/04/100000000-geo-
tagged-photos-plus/
Acknowledgments
The research was supported by the US National Science
Foundation, award 0849910, and by the U.S. Army Research
Office, award W911NF-09-1-0302.
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Appendix 1. Loading matrix for PLS components in the tweet density model
Component
Under 5 years −0.01474 0.077405 −0.04545 −0.01982 −0.01143
5–9 years −0.02025 0.057787 −0.04379 −0.01927 −0.01
10–14 years −0.03172 0.058438 −0.02302 −0.02861 −0.01334
15–17 years −0.02153 0.022426 −0.02407 −0.01397 −0.00691
18 and 19 years 0.00196 0.016601 −0.01363 −0.00271 −0.00794
20 years 0.000168 0.006627 −0.00405 0.002784 −0.00437
21 years 0.005339 0.013494 −0.00895 −0.00116 −0.00942
22–24 years 0.008545 0.042466 0.018536 0.001079 −0.01888
25–29 years 0.022469 0.091848 0.036858 0.005309 0.002426
30–34 years 0.026894 0.080128 0.02624 0.005096 −0.00324
35–39 years 0.0304 0.042774 0.018096 0.00483 0.00986
40–44 years 0.039194 0.004091 −0.02617 −0.00161 −0.00537
45–49 years 0.017316 −0.03679 −0.00857 0.002174 0.001503
50–54 years 0.00373 −0.0826 −0.00635 0.011897 0.008826
55–59 years −0.01004 −0.10516 0.011584 0.019274 0.027798
60 and 61 years −0.00336 −0.03628 0.013421 0.002809 0.002329
62–64 years −0.01243 −0.05618 0.011172 0.010629 0.009505
65 and 66 years −0.00818 −0.03081 0.005977 0.003103 0.008006
67–69 years −0.01376 −0.04628 0.009472 0.004551 0.003888
70–74 years −0.01464 −0.04668 0.014862 0.005115 0.006863
75–79 years −0.00534 −0.03874 0.011525 0.009119 0.010758
80–84 years −0.00167 −0.01497 0.012574 −0.0014 −0.00086
85 years and over 0.001647 −0.01959 0.013749 0.00079 7.76E-06
White alone −0.04727 0.041288 −0.10895 0.855233 −0.33653
Black or African American alone 0.019714 0.001025 0.043088 −0.15585 0.061714
American Indian and Alaska Native alone 0.039311 −0.03672 −0.049 0.040688 0.018066
Asian alone 0.02731 0.032401 −0.03394 −0.3589 0.364497
Native Hawaiian and Other Paciﬁc Islander alone 0.003158 −1.33E-05 0.000767 −0.0057 0.00913
Some other race alone −0.04648 −0.03246 0.129032 −0.3555 −0.13824
Two or more races: 0.004259 −0.00552 0.019004 −0.01998 0.021366
Less than 9th grade −0.08597 0.331632 −0.10665 −0.02253 −0.00902
9th–12th grade, no diploma −0.16101 0.136008 0.007883 0.003644 −0.01523
High school graduate, GED, or alternative −0.302 −0.11136 0.021482 0.009998 0.058959
Some college, no degree −0.13348 −0.18998 0.02426 −0.03015 −0.04115
Associate’s degree −0.01529 −0.05624 0.002854 −0.01094 −0.00396
Bachelor’s degree 0.401932 −0.0772 0.042405 0.024567 0.009941
Graduate or professional degree 0.29582 −0.03287 0.007762 0.025411 0.000464
Less than $10,000 −0.05965 0.007578 0.041237 0.033263 0.014456
$10,000–$14,999 −0.11884 −0.0168 0.083771 0.034953 0.007335
$15,000–$19,999 −0.0885 −0.00257 0.045553 0.019674 −0.00656
$20,000–$24,999 −0.08984 0.020739 0.051756 0.005561 −0.03398
$25,000–$29,999 −0.06982 −0.00308 −0.00224 0.006647 0.015886
$30,000–$34,999 −0.05892 −0.01624 0.004458 −0.00272 −0.01196
$35,000–$39,999 −0.06398 −0.00129 0.025394 −0.00427 0.006125
$40,000–$44,999 −0.03699 −0.02119 0.001722 −0.00392 0.000198
$45,000–$49,999 −0.03612 −0.01511 −0.01185 0.011192 0.017964
$50,000–$59,999 −0.04508 0.019009 −0.0025 −0.01275 −0.00141
$60,000–$74,999 −0.01536 −0.03518 −0.03475 −0.0045 0.019347
$75,000–$99,999 0.047511 −0.00607 −0.04302 −0.01023 0.023652
$100,000–$124,999 0.112712 0.001461 −0.05712 −0.02623 −0.01345
$125,000–$149,999 0.10356 0.012866 −0.03643 −0.02051 −0.00383
$150,000–$199,999 0.174187 0.013374 −0.04549 −0.02102 −0.0099
$200,000 or more 0.245107 0.042515 −0.02049 −0.00515 −0.02389
Management, business, science, and arts occupations: 0.49972 −0.17736 0.009559 0.054147 −0.02251
Service occupations: −0.14328 −0.05453 0.092773 0.047814 −0.03232
Sales and ofﬁce occupations: 0.021245 0.022759 0.069738 −0.03638 −0.02886
Natural resources, construction, and maintenance occupations: −0.25131 0.09103 −0.11676 −0.03511 0.029064
Production, transportation, and material moving occupations: −0.12638 0.118096 −0.05531 −0.03048 0.054624
76 L. Li et al.

Appendix 2. Loading matrix for PLS components in the photo density model.
Component
Under 5 years −0.03985 −0.06968 −0.04157 0.035035 0.009508
5–9 years −0.03863 −0.06033 −0.03379 0.018745 0.005705
10–14 years −0.04948 −0.04034 −0.00729 0.030802 −0.01749
15–17 years −0.02739 −0.0298 −0.0105 0.001409 0.003521
18 and 19 years −0.00368 −0.02068 −0.01886 −0.00211 −0.01066
20 years 0.001142 −0.00986 −0.00504 −0.00208 −0.00873
21 years 0.001123 −0.01489 −0.01485 0.000999 −0.00771
22–24 years 0.001153 −0.01669 −0.00644 0.024757 −0.02822
25–29 years −0.00304 −0.02076 −0.00858 0.078787 −0.03802
30–34 years 0.00358 −0.02021 −0.01408 0.070264 −0.02725
35–39 years 0.010636 0.000369 −0.01345 0.047709 −0.01351
40–44 years 0.022184 −0.00871 −0.04678 0.000559 0.014569
45–49 years 0.020005 0.019069 −0.00946 −0.02387 0.013253
50–54 years 0.027076 0.038446 0.014259 −0.05507 0.028873
55–59 years 0.024689 0.058246 0.040607 −0.06575 0.022882
60 and 61 years 0.008444 0.027431 0.020304 −0.01986 −0.00012
62–64 years 0.011037 0.032313 0.032863 −0.03226 0.014433
65 and 66 years 0.002372 0.017243 0.015113 −0.01997 0.009119
67–69 years 0.004825 0.025543 0.026478 −0.02984 0.011136
70–74 years 0.004335 0.029439 0.030894 −0.02749 0.012094
75–79 years 0.009549 0.023947 0.020369 −0.02343 0.006073
80–84 years 0.002783 0.018798 0.015552 −0.00183 −2.78E-05
85 years and over 0.007138 0.02111 0.01425 −0.00551 5.71E-04
White alone 0.64126 −0.54537 0.384937 −0.04463 −0.05466
Black or African American alone −0.11371 0.098182 −0.08834 −0.0114 −0.04436
American Indian and Alaska Native alone 0.051597 −0.02843 −0.05185 −0.03109 0.121559
Asian alone −0.33726 0.269738 −0.17645 0.099653 −0.19034
Native Hawaiian and Other Paciﬁc Islander alone −0.00434 7.60E-03 −0.00337 0.003228 −0.00463
Some other race alone −0.22297 0.165412 −0.06804 −0.02706 0.172535
Two or more races: −0.01458 0.032869 0.003109 0.011299 −0.0001
Less than 9th grade −0.14529 −0.25007 −0.05677 0.209129 −0.00659
9th–12th grade, no diploma −0.12839 −0.14642 0.06076 0.02307 −0.03606
High school graduate, GED, or alternative −0.17274 −0.03592 0.212701 −0.14922 0.089943
Some college, no degree −0.05122 0.058598 0.100471 −0.2111 −0.0332
Associate’s degree −0.00306 0.032506 0.01773 −0.05174 −0.03535
Bachelor’s degree 0.288224 0.213345 −0.18656 0.095178 0.006815
Graduate or professional degree 0.212482 0.127951 −0.14833 0.084679 0.014442
Less than $10,000 −0.02021 −0.02446 0.057107 −0.01381 0.001242
$10,000–$14,999 −0.04735 −0.01043 0.123375 −0.03851 −0.00763
$15,000–$19,999 −0.0397 −0.02443 0.07639 −0.03061 −0.00025
$20,000–$24,999 −0.04602 −0.02101 0.090227 0.001759 −0.01519
$25,000–$29,999 −0.04342 −0.0292 0.038342 −0.02533 0.014401
$30,000–$34,999 −0.0333 −0.0215 0.028601 −0.04265 0.013107
$35,000–$39,999 −0.0432 −0.00371 0.053541 −0.00873 0.003684
$40,000–$44,999 −0.02043 0.00364 0.030614 −0.01959 0.008193
$45,000–$49,999 −0.01826 −0.02243 0.008614 −0.03616 0.011423
$50,000–$59,999 −0.04024 −0.01621 0.026354 0.008339 −0.00548
$60,000–$74,999 −0.01251 −0.00814 −0.01824 −0.0494 0.013102
$75,000–$99,999 0.017281 0.015004 −0.0481 0.006423 −0.03166
$100,000–$124,999 0.056167 0.027812 −0.09461 0.03855 0.021724
$125,000–$149,999 0.048939 0.026345 −0.0844 0.03963 −0.00837
$150,000–$199,999 0.094066 0.044338 −0.13391 0.058537 0.004909
$200,000 or more 0.148192 0.064382 −0.15391 0.111557 −0.02321
Management, business, science, and arts occupations: 0.393605 0.217233 −0.29509 −0.0328 0.032189
Service occupations: −0.03144 0.015353 0.190332 −0.04594 −0.02918
Sales and ofﬁce occupations: −0.00541 0.056445 0.020075 0.03938 −0.06822
Natural resources, construction, and maintenance occupations: −0.21088 −0.16301 0.090635 0.009872 0.037683
Production, transportation, and material moving occupations: −0.14587 −0.12602 −0.00595 0.029492 0.027525

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