Social networking app has been growing very rapidly in the past decade. One of the important features of social media is the ability of system that can attach coordinate where users are located (check-in). The aim of this study is to identify the characteristic of human mobility patterns in Bandung city. We proposed a technique uses pixel matching approach. In this paper, we describe the visualization of the city is determined by the activity of people on Twitter social media. Our work includes firstly, characterize the pattern of user’s interest to different types of places. Secondly, to characterize the pattern of user visits to different neighborhoods with way choose the user’s activity pattern on the weekdays and weekends. We then categorize the existing place based on the period of time that people visiting. Meanwhile, to define the existing areas, we used official map the city planning department as parameters to determine the user’s movement. Our research will answer the question whether the Twitter App data is a viable resource to measure the human movement? The result indicates that it can be used as the one of the sources of information data to understand urban human mobility

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Using Social Networking Data to Understand Urban Human Mobility
Yuyun1, 2
, Bart Julien Dewancker3
1
Lecturer, School of Management Informatics and Computer (STMIK Handayani), Makassar, Indonesia, yuyunwabula@gmail.com
2
Doctoral student, Graduate School of Environmental Engineering, The University of Kitakyushu, Japan, yuyunwabula@gmail.com
3
Professor, Department of Architecture, The University of Kitakyushu, Japan, bart@kitakyu-u.ac.jp
Abstract
Social networking app has been growing very rapidly in the past decade. One of the important features of
social media is the ability of system that can attach coordinate where users are located (check-in). The aim of
this study is to identify the characteristic of human mobility patterns in Bandung city. We proposed a
technique uses pixel matching approach. In this paper, we describe the visualization of the city is
determined by the activity of people on Twitter social media. Our work includes firstly, characterize the
pattern of user’s interest to different types of places. Secondly, to characterize the pattern of user visits to
different neighborhoods with way choose the user’s activity pattern on the weekdays and weekends. We then
categorize the existing place based on the period of time that people visiting. Meanwhile, to define the
existing areas, we used official map the city planning department as parameters to determine the user’s
movement. Our research will answer the question whether the Twitter App data is a viable resource to
measure the human movement? The result indicates that it can be used as the one of the sources of
information data to understand urban human mobility.
Keywords: social networking; human mobility; check-in data
1. Introduction
Social media are software tools to create the social
network that allows people to share and exchange
information [1]. Social media word became popular
when the Twitter and Facebook began to be known by
internet users. With the increasing of social
networking application, users are able to express
happiness, pleasure and opinion about what they see of
places visited. One important feature on social media
is the ability to display the location in real time, which
is called geolocation. It does allow people to share the
virtual activity through the mobile device
(smartphones) that can show a location maps
when and where the devices are located. In addition
to location and timing, this data informs us activities
towards specific types of location (e.g. Shop, Parks,
Hotels or other places) that we are visiting. From this
information, we argue that there is an exciting
opportunity for creating new ways to understand
human mobility behaviors. With the result that, we can
infer user’s interaction from their activity location in
different part a city. Currently, a variety of researches
were conducted for implementing the location-based
data, such as recommendation potential places for
marketing or recommendation places to assign tourist
service and determining touristic location based on a
user’s visiting [2, 3, 4, 5]. This data has potential to
provided new opportunities for others science
including urban planning, marketing, industrial and so
on. For that, a great opportunity exists for the
researcher to analyze this large of data that allow one
to understand the social and behavior characteristics of
the people on virtual location.
Currently, analysis of human mobility has become
a new paradigm in which the activity of people can be
monitored directly through social media application.
On the previous approach, there are many ways to
measure the movement of urban citizens. Researcher
have found that human mobility plays vital roles in
human urban development and human migration,
planning urban infrastructures, developing transport
and commuting alternatives [6, 7, 8]. Most of the
research has focused on big cities, the fact that human
mobility increasing are complex in densely populated
areas [6]. The current trend of research on human
mobility focuses on understanding the movement
trajectories of individuals. On the previous approach,
there are many ways to measure the movement of
Contact author: Yuyun, Doctoral student, The University of
Kitakyushu, 1-1 Hibikino, Wakamatsu-ku Kitakyushu-shi,
Fukuoka-ken 808-0135, Japan.
Tel: +81-9086680584.-
e-mail: yuyunwabula@gmail.com
Published in the Journal Book of Asian Institute of Low Carbon Design
(JAILCD). ISSN 2189-1400. In the International conference on Low Carbon
City Design, 2016 Feb. 15th
-19th
, Kitakyushu, Japan.
http://iss.ndl.go.jp/books/R100000002-I025940365-00?locale=en&ar=4e1f

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urban citizens. Such information is usually gathered
through a survey method or using questionnaires that
attempt to capture how the citizens interact with their
environment [9, 10, 11]. But on the other hand, this
approach has some limitation such as the accuracy of
the respondent to answer the questions or usually
costly to implement and it has the weakness to smother
the large number of individuals and some problems of
reliability [12].
On the other hand, some studies using data sets
collected from mobile phone traces as the alternative
to measuring user’s mobility [13, 14], GPS devices,
bank records movement, and subway smartcard notes
[15, 16, 17]. Through those devices, individuals can
spend their majority of time with visited specific
locations. Recently, Mobile phone data have been one
of technology devices which often used to describe
human movement within cities. Because most of the
information derived from mobile phone data provides
information of the location where calls occurred.
However, movement patterns and spatial behaviors of
individuals within cities remains hard to understand,
lack a quantitative validation of the results and
difficult to obtain due to privacy concerns [18, 19].
In the literature, we found some research discussed
about human mobility using geosocial networking data.
Cho et al. [21] used geo-location data on Gowalla and
Brightkite to investigate the human movement. They
developed a model of human mobility that combines
periodic short-range movements with travel due to the
social network structure. In another study, Nouclas et
al. [6] used Foursquare data to study human mobility
and answer the question how to measure the human
movement in different cities. They paper described the
rank-based model to measure users movement.
In this paper, we propose to use the Geolocation
data obtained from social networking check-in to
characteristic urban human mobility. To do this, we
propose a mobility model with pixel matching
approach. This approach is trying to count the number
of visitors in each region by transforming the image
pixel value into GPS (latitude and longitude)
coordinate. Our work consist of two steps (1)
characterizes existing place, to categorize the pattern
of user’s interest to different types of places. In this
step, count the number of visitors on each place for
each category. This activity produces activity maps
showing the functionality of each part within a city. (2)
Time distribution, to divide the pattern of user visits to
the different environment with way choose the users
activity on the weekdays and weekends. Then, we
categorize the existing place based on the period of
time that people visiting.
2. Methodology and Data Collection
2.1 Method.
We characterize each of geo-location Tweets with
matching pixel between the image on the map which is
released by city planning department of Bandung local
government (see figure 4). This model utilizes the
colors feature on the map based on the number of
pixels in each color and then is transformed on the
GPS scale. Determining the location areas is calculated
based on the thickness of pixel colors. As an example,
education area with the pixel 74-71 is located at
coordinate -6.85903, 107.59383 to -6.96792,
107.68094 (see table 1). As for the procedure of
system is as follows:
Fig.1. Flowchart model for data analyze
For each region group Ct, △w range of pixel colors
on certain area and p GPS position on color pixel is
built as:
1. Look for the pixel values of each color △w 0…255
on the map. There are thirteen area categories is
marked by color
2. Used the deviation standard to calculate the
average value of all data points. This function is
used to measuring how far the data values lie from
the mean
3. Each position which is marked by latitude and
longitude coordinate p is transformed into the pixel
value. Matching both of them produce the point or
position in a region. To find the frequency of
visiting place, we rank cells based on the range
value of check-ins for each activity category

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Data Collection
One of the Twitter app features allows users to tag
their current location. When a new post is made, it will
record their geographical information by specifying an
area or location in order to find their longitude and
latitude coordinates at that moment. This research is
focused in Bandung city, Indonesia which has an area
of 167, 30 km2
and population density 14,736 km2
with a population 1, 4 million.
For data collection, we utilized Twitter Streaming
Application Program Interface (API). It is a window
that applications provide to developers for accessing
them in a programmatic manner. The REST APIs
provide programmatic access to read and write Twitter
data. As example; Author a new Tweet, read author
profile, follower data, time zone and location
information that indicate where the Tweet is posted
[20]. our final Twitter data set consist of 35 days (five
weeks) is started from August 27th to October 1st. It is
constructed from Indonesian language Tweet, which
was filtered to find those tweets that contain the
geolocation. In this study we analyze 375,410 data
records from Twitter. For further steps, authors only
process data that contain user’s location.
3. Result
To locate the exact location, we match user’s data
position (geo-location) on Twitter with colors feature
that exist on the map. Color depth is measured by pixel
and check-in is marked with location coordinate.
Because the depth of color is very different, so that the
pixel value is also different. To count the number of
visitors (check-in) on the particular area then, the value
of GPS is transformed to pixel scale. In the table below
we can see the value of class for each category.
Meanwhile, colors feature on the map can be seen on
figure 4.
Fig. 2. Frequency distribution of daily activity
To divide the urban land, we group each coordinate
location within the thirteen categories. Each category
represents a set of land function. Determination the
name of categories is based on the map of the planning
department. For analytical purposes, we group
check-in across the city by pixel matching. This
analysis can be used to count the number of users in
each category. From these result, we can determine the
location of each user at the time the Tweet is posted.
3.1 Visitation Frequency
To find the distribution of visiting places, we rank
each individual visited places based on the number of
times one visits the places over the study period. For
example rank 1 represents the most visited category;
rank 2 the second most visited category and so on.
Then we calculate the frequency of each of these
ranked places.
Table 1. Average of each activity category, analyzed with the matching pixel approach
Activity
Category
Type of Visited
Location
Mean Deviation Min Max
Business
Commercial 67.5088 3.6172 64 71
Trading (services) 199.8739 3.5162 196 203
Airport 193.1594 2.0166 191 195
Works
Government office 205.5534 1.3064 204 207
Health 173.2847 0.7354 173 174
Parks
Green open space 153.2095 4.0980 149 157
Protected area 108.2344 2.6711 106 111
Artificial tourism 144.4783 3.5001 141 148
Home
High Density housing 214.6584 6.7190 208 221
Medium Density housing 229.4701 7.1054 222 237
Low Density housing 244.3594 6.5030 238 251
Industrial
Industries and warehouses 162.3676 3.9132 158 166
Defense and security 132.6000 4.3577 128 137

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Figure 2 shows frequency distribution of daily
visiting activity. We observe the data distribution is
dominated by home users. Meanwhile, the user’s
activity in places such as work, business and park
categories is running normally. In addition, the home
category that has the highest Tweet, the majorities of
activity also come on Friday, Saturday, and Sunday. If
the user is ranked based on daily activity then, the
tendency of people are at the home category (52, 9%),
Business (19%), Park (16, 7%), work (7, 9%) and
industrial (3, 4%). Furthermore in figure 5, we show
the spread of data in each category. Each area
category displays the density of user activity when
they are check-in specific venue
3.2 Data Distribution
We observe that check-in activity in figure 5 is
comparison during weekdays and weekends. In
average, during weekdays and weekends, highest
tweeting activity is coming at around 20-21AM.
Meanwhile, the peak of the tweeting activity during
the weekends is reduced when compared to weekdays.
We also analyze the weekly rhythm of these visits.
During afternoon highest tweeting activity is reached
at around 18 AM which might be associated to the
time at which people typical get home from work.
And then for night activity is coming at 21 PM. This
activity might be associated to the time at which
people typical visiting the entertainment activities like
bars and night clubs. While weekly patterns suggest
that shopping and recreation trips are predominant in
the weekends. Fig. 3. Check-in density for different categories
Fig. 4. Map of city planning department of Bandung local government

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Fig. 6. Comparison of user frequency distribution
Based on Figure 5, we found that distribution
pattern of Tweeter activity on weekdays and weekends
is running normally although the number of user
activity has decreased. On table 2, we have shown the
average of visiting in different places. We observe that
there is a significant difference between each of them
Such as the number of the visitor at work and
industrial categories that have increased on weekdays.
We assumed that it is related to the activity of people
at the workplaces while at the weekends has decreased
amount. This is because, they visited places such as the
park, business or stay at home. It can be proved by the
increasing the number of them on weekends.
Table 2. Average of weekdays and weekends period
Area categories Weekdays Weekends
Business 18.6% 19.6%
Work 8.0% 4.8%
Park 15.9% 18.0%
Home 54.0% 55.3%
Industrial 3.4% 2.4%
4. Conclusion
Our paper presented the use of geo-location of
social networking as the one source of data to measure
urban human mobility. In this paper, we introduced the
matching pixel image method. This model utilizes
Fig. 5. Comparisons of visiting different places on weekdays and weekends

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pixel feature on each color and transformed it on GPS
coordinate. To determine the location areas is
calculated based on the thickness of each color. Our
work is characterizing the pattern of user’s interest to
different types of places and characterize the pattern of
user visits to different neighborhoods. We observed
that data distribution is dominated by home users. Next,
we also observed the distribution of data in weekdays
and weekends. We found significant differences in
each category, such as work and business categories is
dominant on weekdays while business, park, and home
categories on weekends. In addition, the home
category that has the highest Tweet, the majorities of
activity categories also come on Friday, Saturday, and
Sunday. If check-in is ranked based on daily activity
then, the tendency of people is the home category (52,
9%), business (19%), park (16, 7%), work (7, 9%) and
industrial (3, 4%). We believe that using the
geo-location of social networking approach is a new
way to view the shape of the city. Because this study
only shows one city and for future work, we will
compare it with other cities.
5. Acknowledgement
This research was supported by the University of
Kitakyushu, Directorate General Higher Education of
Indonesia, and STMIK Handayani Makassar.
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