Urban Mobility Prediction Using Twitter
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2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing,
Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress
Urban Mobility Prediction Using Twitter
Saeed Khan Ash Rahimi Neil Bergmann
School of ITEE School of ITEE School of ITEE
University of Queensland University of Queensland University of Queensland
Brisbane, Australia Brisbane, Australia Brisbane, Australia
s.khan@uq.edu.au a.rahimi@uq.edu.au n.bergmann@itee.uq.edu.au
Abstract—The characteristics and dynamics of human mobility and predictability [5]. Various entropy and predictability mea-
have vital implications in areas such as disaster management, sures are used to determine whether future movements of users
transportation planning and infrastructure management. While have a strong correlation with past locations or they depend
aggregate mobility modeling is useful for getting a broader
overview of the system, the prediction of future movements only on the current location. Such analysis helps provide
of people in urban areas is also of significance. This work an upper bound for the prediction accuracy a state-of-the-
investigates the individual-level mobility of Twitter users in three art prediction algorithm [6] can achieve. Then, the prediction
Australian cities using the concepts of entropy and predictability. problem is discussed to ascertain how predictable the users
Twitter users are distinguished on the basis of their movement are in general, and whether all cities are equally predictable
patterns and two distinct groups are identified. The randomness
and regularity in their movements are calculated via multiple in terms of their respective user movements.
metrics, and prediction for the most active users in these cities
is also performed. The top 10% of Brisbane users have 76.6% II. R ELATED W ORK
prediction accuracy, much higher than the other cities, suggesting A number of studies have attempted to calculate the
heterogeneity among various cities. theoretical limits of predictability. In [5], Song et al.
Index Terms—mobility, twitter, entropy, prediction, Markov investigated the limits of predictability of user trajectories
chain by calculating their entropies using cell phone records and
observed 93% theoretical predictability in their mobility
I. I NTRODUCTION behaviour. Based on the results, they argue there is a huge
Urban mobility modeling aims to understand the mobility potential to explore the regularities of human mobility by
dynamics of people with applications in numerous domains using cell phones. In [6], Lu et al. calculated the movement
such as disaster management [1], transportation network uncertainties of a large number of people among cell phone
planning [2] and infrastructure management [3]. With this subscribers in Ivory Coast by using the frequencies as well as
goal, we looked at the system-wide mobility of Twitter users the temporal correlations of their trajectories. Their analysis
in our previous work [4], whereas in this paper we explore revealed a high theoretical predictability of up to 88%. In
the individual-based mobility patterns of users in Australia. another study, using a cell phone data of 500 users, Qin et al
A desirable goal of any mobility study is the ability to [7] demonstrated that movement patterns and entropy relate to
predict future movements of people. Such prediction can the degree of activities and locations with 78% predictability.
be done at individual or aggregate level for area under
study. The ability to predict the next location of people In [8], entropy and predictability measures are used to
can have benefits such as improving various services being observe the behaviour and mobility of a large set of players
used at a location. Previous work [4] has shown that most in the virtual world of an online game. In the game, players
Twitter users are active only for a limited period of time, do not make any physical movements, but rather navigate a
and only few users are active on a regular basis. These virtual avatar. It is observed that the movements in virtual
relatively active users can be monitored to learn long term human lives follow the same high levels of predictability as
predictable patterns, and conversely, the patterns learned can the real-world mobility.
help to predict the next location of visit for the users. Thus, it
is imperative to distinguish such users from the irregular ones. Different data sources capture different usage patterns
which may cause uncertainty in subsequent prediction. In
Consequently, this paper discusses how to identify and [9], the authors investigate the effect of using various types
separate regular from irregular users based on their activity of data sources on the predictability of check-in patterns of
patterns, and investigates the prediction of their movements. users. They observe that using multiple data sources does
The user groups are chosen on the basis of their move- not necessarily raise the predictability limits, and may even
ment/tweeting patterns on Twitter from three Australian cities: decrease it in some cases. The volume of data, however,
Sydney, Melbourne and Brisbane. The regularity and random- can affect the predictability in case of a single data source
ness of each group are examined using the concepts of entropy being used. In addition, the differences in user behaviour
978-1-7281-6609-4/20/$31.00 ©2020 IEEE 435
DOI 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00082
patterns across various social networks can also affect how Random Entropy: This takes into account the number of
their check-in patterns are acquired. They, however, do not unique locations N visited by each user i and is given as:
measure the accuracy of next-place-of-visit prediction using
distance metrics such as Euclidean distance. Moreover, the SiRand = log2 (N )
types of venues at which users may check-in are not taken where N > 0 since all users visit at least one location and
into account. also because log2 (0) is undefined.
In [10], the authors explore human mobility predictability Shannon Entropy: The probability of visiting every unique
by analysing a set of tweets generated by Swedish users. location j by each user i, summed across all locations visited
They conduct the analysis in terms of temporal history of at least once:
mobility range showing how users disperse in space, and also
N
investigate entropy and its corresponding predictability. The SiShan = − pi,j log2 (pi,j )
results uncover a set of users routinely visiting some locations j=1
most of the time, and occasionally exploring new places as
well. By measuring the entropy of each user’s individual where pi,j is the number of visits to location j by user i
trajectory, a 70% theoretical predictability is achieved. They, divided by total visits for all locations visited by user i.
however, do not perform the actual prediction for users.
Conditional Entropy: Checks for correlation between a
previously visited location xt−1 and a subsequent one xt in
In [11], the Shannon and Real predictability of a particular
time series:
set of users are analysed and correlated with corresponding
entropy. Using Twitter data, users with at least 100 tweets are SiCond = − pi (xt−1 , xt ) log2 pi (xt |xt−1 )
chosen and analysis is performed based on a certain number xt ∈Xi xt−1 ∈Xi
of distinct locations visited by these users. The median
where Xi is the set of all locations visited by user
value for both entropies follows a linearly increasing trend
i, pi (xt−1 , xt ) denotes the probability of visiting the
as the number of locations increases, however, the Shannon
ordered pair of visited locations xt−1 and xt by user i,
entropy increases at a faster rate. Similarly, the decrease in
and pi (xt |xt−1 ) = pi (xt−1 , xt ) /pi (xt−1 ) represents the
predictability for higher number of locations is slower for
probability of visiting the location xt at time-ordered t given
Real predictability compared to Shannon’s. Overall, this work
a previously visited location xt−1 by user i. Only pairs that
suggests the importance of spatio-temporal correlations in
appear in a user history are used to ensure pi (xt |xt−1 ) > 0.
visitation patterns in predicting future tweet locations. The
actual prediction for users, however, is not analysed here.
Real Entropy: This considers the complete spatio-temporal
information of a visit, takes into account the frequency,
For prediction of user movements, a number of techniques
visitation sequence and time spent.
have been used by researchers in the literature. In [12], an
n −1
m=2 lm
Auto-Regressive Integrated Moving Average (ARIMA) model
is proposed to predict the spatio-temporal variations of taxi SiReal =
n log2 (n)
passenger numbers in China. The method uses GPS traces
from 4000 taxis and achieves a prediction performance with where lm is the length of the shortest sequence of locations
5.8% error. In [13], the authors use Mixed Markov Model starting at position m which does not show in the part of
(MMM) to predict pedestrian movement in Osaka, Japan sequences up to position m − 1.
and claim to achieve a prediction accuracy of 74.4%. Other B. Predictability
methods such as Gradient Boosting regression tree method
In the mobility scenario, predictability is a measure of the
(GBM) [14] and Support Vector Machine (SVM) [15] have
future whereabouts of a user and is inversely proportional to
also been used with varying degrees of accuracy.
entropy. The predictability Πi of user i is subject to Fano’s
inequality [17] and can be related to the user entropy Si as:
III. T HEORETICAL BACKGROUND
This section presents the theoretical background about the Si = H(Πi ) + (1 − Πi ) log2 (Ni − 1) (1)
concepts and methods used in this paper for data analysis. where H(Πi ) is the binary entropy function which is defined
as the entropy of a Bernoulli process with the probability of
A. Entropy success Πi that can take only two values: 0 and 1.
Entropy is a measure of the degree of randomness in a H(Πi ) = −Πi log2 Πi − (1 − Πi ) log2 (1 − Πi ) (2)
process. Since movement itself is a process and users usually
move between different locations, we are trying to find any where represents a placeholder for a particular kind of
patterns or predictable movements between the suburbs. There entropy, and Ni is the total possible locations visited by user
are four types of entropy as mentioned in [5], [16] and [8]. i based on their history. This means that given the entropy
436
S , we can find the predictability Π by solving Equation 1
numerically. For each type of entropy, we have corresponding
type of predictability such as Random predictability, Shannon
predictability, Conditional predictability, and the Real
predictability.
It should be noted that the term “process” refers to timeline
of each user, and hence, all of the above measures are
calculated for every user in the system. Moreover, location
means the suburb where a tweet is posted, and sequence means
a time-ordered set of suburbs.
C. Markov Model
As mentioned earlier, our goal is to predict the next location Fig. 1: The number of tweets in the top 20 suburbs, and the
visited by users based upon the observation of their previous second top 20 suburbs active on Twitter in the data set.
location(s) over a period of time. The potential applications of
such analysis can include the development of location-based
services anticipating the next movement of a user or to the percentage of users making a certain minimum number
predict the spread of a disease from one place to another. of tweets throughout the data collection period. A very
This can also help in emergency situation, or understanding large share of users (79.8%) post a small number of tweets
short/long term migration patterns. For this purpose we will (between 1 and 24), while the users making a large number
use the Markov predictor. This type of predictor represents of tweets comprise of relatively much smaller percentage
the mobility behaviour of a user as a Markov model and (4.9%). This suggests that, broadly, users can be partitioned
predicts the next state based on the previous state(s). In into two groups: casual and frequent, and hence, for the
our case, the state refers to a suburb, hence, the transition analysis carried out in this paper, we separate the users who
between two states corresponds to the probability of moving have posted at least 25 tweets in the data set, and call them
from one suburb to another. The mobility behaviour is treated ‘frequent’ users, and term all other users as ‘casual’. We
as a discrete stochastic process. perform the entropy and predictability analysis for both these
groups.
IV. DATA S ET & E XPERIMENTAL S ETUP
The data set has been collected using Twitter Streaming API TABLE I: The percentage of users posting a minimum number
[18], geographically covering Australia and spanning a period of tweets in the data set. For example, 79.8% of users posted
from January 2015 till July 2016. It consists of 9462345 between 1 and 24 tweets.
tweets from 245796 unique users. For Sydney we have 947964
geo-tagged tweets and 40281 unique users, for Melbourne we No. of Tweets % of Users
Between 1 and 24 79.8
have 854821 geo-tagged tweets and 35556 unique users and Between 25 and 49 9.6
for Brisbane we have 276394 geo-tagged tweets and 14555 Between 50 and 99 5.7
unique users in the data set. For experiments, these three 100 and above 4.9
cities are chosen and only their top-20 suburbs are utilized
for the analysis. These suburbs are chosen w.r.t highest
We also do the prediction analysis for top users of each city
twitter activity since they account for the largest amount of
and discuss this in detail in §VI.
data compared to the remaining suburbs as shown in Figure 1.
V. P RELIMINARY E NTROPY AND P REDICTABILITY
As shown in Figure 1 the top-20 suburbs of each city
capture up to four times the twitter activity than the next 20 We start with analysing the entropy and predictability of
suburbs and as we go further down, the twitter activity drops all users among top-20 suburbs of Sydney, Melbourne and
significantly. Moreover, the top-20 suburbs cover key spatial Brisbane. This is done to get an idea about how random or
areas of a city where most of the movements usually take predictable the user movements are in general. Figure 2 (a-f)
place. The movements that users make between suburbs are shows the entropy and predictability measures for the three
referred to as fluxes. cities. Since the maximum value of entropy is about 4 for
random entropy, we divide entropy range 0-4 into 8 bins with
It is also important to give consideration to the number width 0.5, and assign each user to one of these bins. For
of tweets posted by each user in the data set. The total predictability measure, we assigned users to bins with width
number of unique users posting only geo-tagged tweets in 0.05, similarly. The number of users in each bin is shown in
our data set is 105641 and Table I provides an overview of Figure 2.
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(a) (b)
(c) (d)
(e) (f)
Fig. 2: The number of users in each entropy and predictability bin in the three cities.
We observe that entropy distributions do not seem to follow of each other depends upon how the binning is performed for
the standard order of S Real ≤ S Cond ≤ S Shan ≤ S Rand plotting the data points.
and the corresponding predictability distributions also exhibit
coarse trend. This suggests that twitter users have wide ranging A. Frequent vs Casual User Groups
behaviour reflecting some degree of randomness as well as We compute various entropies and predictabilities for fre-
some regularity. If users with similar mobility behaviour, either quent and casual users belonging to Sydney as shown in
with high randomness or high regularity are separated into Figure 3.
different groups, then it is possible that highly predictable We can see that the behaviour of frequent users is different
users may be identified more easily. Visually, the extent to from casual users whereas the casual users still show coarse
which entropy and predictability measures are mirror image distributions like the case of all users as shown in Figure
438
(a) (b)
(c) (d)
Fig. 3: Sydney’s frequent vs. casual users
2 (a, b). Table II summarises the percentages, averages and than 50% of overall movements between its suburbs.
upper bound threshold for each group of all three cities’ users.
The entropy measure can also be used to infer the
significance of Markov chain transition probabilities i.e.
TABLE II: User behaviour for each city
whether the next location is highly predictable based on
Users No. of Fluxes per User % of % of
current location. On the other hand, predictability measure
Users Fluxes can be perceived as a theoretical upper bound of prediction
Max Avg StDev that can possibly be achieved using a suitable prediction
Syd Frequent 198 46.5 27.9 3.06% 29.17% algorithm. In Figure 4, we show the plots for remaining two
Casual 24 3.57 4 96.94% 70.83%
Mel Frequent 1218 48.43 69.67 3.00% 29.67% cities.
Casual 24 3.56 3.98 97.00% 70.33%
Bri Frequent 6044 160 809.4 2.08% 53.72%
Casual 24 2.94 3.39 97.92% 46.28%
In Figure 4 (c, g), we see that entropies of casual users
do not follow the standard ordering rule and such users are
difficult to analyse. The large proportion of very low entropy
It is expected that frequent users will have different values corresponds to a large number of users having a small
prediction characteristics than casual users and we analyse number of fluxes, and prediction accuracy for these users is
the most predictable users in section VI. We also observe unlikely to be high [19]. In contrast, the entropy distributions
that average value for frequent users is much higher than of frequent users look better, exhibiting normal distribution as
that of casual users, and although the percentage of number shown in Figure 4 (a, e). This also suggests that in general,
of frequent users is much smaller than the casual users, we have reasonably differentiated and grouped the users
the frequent users still account for a proportionately good together from entropy point of view. It is important to note
percentage of overall movements. Brisbane shows a different that estimation of various types of entropies and their standard
trend whereby its average value is more than three times that order becomes exact only for infinitely long sequences where
of Sydney and Melbourne and its frequent users record more all locations and transition probabilities between them can
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(a) Melb. frequent entropy (b) Melb. frequent predictability
(c) Melb. casual entropy (d) Melb. casual predictability
(e) Bris. frequent entropy (f) Bris. frequent predictability
(g) Bris. casual entropy (h) Bris. casual predictability
Fig. 4: Frequent vs Casual users entropy and predictability
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accurately be calculated [19]. TABLE IV: Average predictability for cities
Table III shows the average values for all types of entropies City Avg. Predictability
Random Shannon Conditional Real
for all three cities and we utilise these figures to get further Sydney 0.17 0.59 0.75 0.73
insights. Melbourne 0.16 0.57 0.75 0.71
Brisbane 0.32 0.73 0.82 0.79
TABLE III: Average entropies for cities and their user groups
City/User Group RandEnt ShanEnt CondEnt RealEnt history of locations. This means prediction problem can be
Sydney/Frequent 2.7 1.95 1.4 1.49 presented where the actual predictability can be represented
Melbourne/Frequent 2.71 2.01 1.4 1.54 by the conditional predictability whereby considering just
Brisbane/Frequent 1.9 1.16 0.92 1.06 the last location returns almost the same predictability as if
considering the entire previous history of movements.
For Sydney frequent users, the averages for random,
In literature, studies have been conducted about predictabil-
Shannon, conditional and real entropies are 2.7, 1.95, 1.4
ity analysis using mobile phones. Song et al. [5], Lu et al. [6]
and 1.49 respectively. It means that the next location for a
and Qin et al. [7] found 93%, 88% and 78% predictability for
user could randomly be out of possible 2rand ≈ 22.7 ≈ 7
users respectively. Here, the predictability for Brisbane users
locations where each location is unique one. If we count the
is close to the calculation of Qin et al. [7]. However, both Song
movement frequency, then this uncertainty will be shown
et al. [5] and Qin et al. [7] did not further pursue their work to
by Shannon entropy, hence, 2Shan ≈ 21.95 ≈ 4 locations.
do actual prediction to show whether their high predictability
So Shannon entropy suggests fewer highly probable next
results can be achieved in practice. On the other hand, Lu
locations than random entropy. If we take into account the
et al. [6] used a Markov Chain model to perform prediction
sequence of locations visited, then the conditional entropy
and achieved an accuracy at par with their predictability level
will be 2cond ≈ 21.4 ≈ 3 locations. Finally, considering the
using the first order Markov model. In the following section,
whole history of movements, the real entropy average of 1.49
we explore the prediction problem to get further insights into
also yields 21.49 ≈ 3 locations. For Melbourne, the number
the movement patterns of Twitter users.
of possible locations based on each type of entropy are
7(random), 4(Shannon), 3(conditional) and 3(real) whereas VI. P REDICTION OF N EXT L OCATION
for Brisbane these numbers are 4(random), 2(Shannon),
Prediction estimates the next location based on the transition
2(conditional) and 2(real).
probability matrix. Given a current location, the next location
with the highest probability from the transition matrix
It is observed that real entropy is close to conditional assembled from the training set is used as the estimated next
entropy, suggesting the fact that entropy is strongly determined location. We take the entire data set for users and investigate
by location history with most of the information in last visited the previous location for a record to determine the chance of
location. correctly predicting the next location. Specifically, we choose
the top most predictable users for each city as per their
Next we look at predictability as shown in Figure 4 theoretical real predictability values and do the prediction for
(b,d,f,h). Examining the frequent users, we find that average them to see what their prediction accuracy is like. We use the
real predictability, ΠReal , for Sydney is 0.73, for Melbourne first order Markov Chain predictor to conduct this analysis.
it is 0.71, whereas for Brisbane it is 0.79. The predictability For each user, the origin-destination (OD) transition matrix
values suggest there is a possibility that about 73% of and their probabilities are calculated based on one previous
Sydney, 71% of Melbourne and 79% of Brisbane users’ state. For all the days that a user is active in the whole data
whereabouts can possibly be predicted with the help of a set, we use the first 80% of the days as training period, and
good prediction algorithm, whereas the remaining 27%, 29% the last 20% as the test period. The predictor checks in which
and 21% users of these cities are difficult to predict. Hence, suburbs the users have been mostly active in the training set
the predictability measure provides a theoretical upper bound and calculates prediction for the corresponding suburbs in the
of the prediction algorithm performance [19]. In other words, test set. The accuracy measure is calculated as the number of
for actual prediction accuracy, this is the target that could correct predictions divided by the total number of predictions
possibly be achieved by a good algorithm [6]. Table IV shows (correct plus incorrect).
the average predictability values for these cities.
After doing our prediction analysis for the top predictable
It can be seen that real predictability is close to conditional users of each city, we report the findings in Table V.
predictability and this clearly suggests that most of the It shows the prediction accuracies for top 1%, 5% and 10%
information about the probable next location is held in the of users (in terms of mobility) for each city along with their
current location, implying a weak dependence on the previous corresponding theoretical predictability ranges. Among all
441
TABLE V: Prediction accuracy & Real predictability Future work can involve tracking the movements of frequent
users itself over a period of time with a broader goal to
City Top 1% Users Top 5% Users Top 10% Users detect change in their routine activity patterns and exploit that
Accu. Pred. Accu. Pred. Accu. Pred.
information for some useful purpose.
Brisbane 100% All 1 100% All 1 76.6% 0.94-1
Melbourne 63.3% 0.88-1 47.1% 0.81-1 49.2% 0.79-1 R EFERENCES
Sydney 100% All 1 53% 0.83-1 54.8% 0.80-1
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