behavior analysis of people in maebashi city using...

Proceedings of International Conference on Technology and Social Science 2019 (ICTSS 2019)

Behavior Analysis of People in Maebashi City

Using Location Information Data

Yasuko Kawahata and Takuya Ueoka

Gunma University, Department of Social Informatics1

4-2 Aramaki-machi, Maebashi, Gunma, 371-8510，Japan

a

Keywords: GPS, Human flow, Social Science

Abstract. In this study, we aimed to derive a quantitative opinion on the problem from the trend of

local people's trend in Maebashi City, Gunma Prefecture. It has been suggested that it is possible to

capture trends in human behavior and distribution in Maebashi City by subdividing location

information data by time and location, ie, by segmenting location information data. It is expected to

extract spatio-temporal characteristics of human behavior in the vicinity of city facilities.

1. Research background

An aging population, attributable to population decline and changing demographics, is causing

concern in Gunma Prefecture. Other local governments across Japan are dealing with similar issues by

formulating and implementing regional policies based on a variety of debates and regional

characteristics. Maebashi City, the object of this study, has also seen the creation and implementation

of projects aimed at combating population decline, for example, the "Habatake Gunma Plan" and the

"Gunma Prefecture Comprehensive Strategy" [1]. These plans tackle issues, such as the shortage of

care workers, which pose serious problems in the sphere of social security. In its description of the

issues faced by the prefecture, the "Gunma Prefecture Comprehensive Strategy" explores the

possibility of using location information data to quantify people's behavior. This study examines how

computational social science can contribute to realizing that research goal. More specifically, our aim

for this study was to analyze trends in human mobility in Maebashi City, Gunma Prefecture, to make

a quantitative assessment of data associated with the issue. Previous research that also used location

information data from smartphones (only obtained with the consent of users) include a study on human

mobility during the Kumamoto earthquake of April 2016 [2] and an analysis of tourism-related

mobility in the city of Kaga, Ishikawa Prefecture [3].

2. Purpose of this study

The aim of this study is to use location information data from smartphones (Obtained only with the

consent of users) to analyze how people move through Maebashi City, namely when and where they

move about and how they distribute themselves throughout city areas associated with particular

activities (commerce, transportation, education, etc.). To accomplish this, this study analyzes the

movements of people in Maebashi City, Gunma Prefecture, using quantitative methods involving

location information data.


3. Research methods

(1) Location information data (latitude, longitude, time in seconds, 100 m-mesh code, prefecture

number, etc.) for people who passed through Maebashi City, Gunma Prefecture, between January 2017

and June 2017.

(2) Statistical analysis of the data in (1) using R. (Tests, etc., were conducted at significance level

α＝0.05.)

4. Results

4-1. Overall trends

Fig. 1. Percent of movement data points recorded in Maebashi City for each month (vertical axis: relative percentage of total movement data points recorded for each month; horizontal axis: ay of the month)

The graph in Fig 1 shows that more movements tended to take place on Saturdays, and that people

tended to move relatively more often during four particular days in the course of the study: the second

day of the New Year holiday on January 2 (1), the Saturday two weeks after the New Year holiday (2),

a Saturday (March 19) that fell in a three-day weekend (3), and a day (May 4) that fell in the Golden

Week (4). Previous research on Shibuya [2] has shown comparable trends in average velocity

distribution. Similarly, we can infer from these relative percentages of total movement data points that

people tend to be more active on Saturdays.


Fig.2. Left: Percent for points(Hourly movement) on Saturdays and Sundays. (The vertical axis shows the relative frequency in %; the horizontal axis shows the time.) Right: Percent for points(Hourly movement) in Maebashi City on weekdays. (The vertical axis shows the relative frequency in %; the horizontal axis shows the time.)

Fig.3. Percent for points(Hourly movement) from January to March (Saturdays and Sundays, weekdays)

Fig 3 shows that movements tend to increase gradually from 5 am to 5 pm on weekends, while on

weekdays, movements tend to peak around 7 am to 8 am, noon to 1 pm, and 5 pm to 6 pm.


Fig.4. Percent for points(Hourly movement) from April to June (Saturdays and Sundays, weekdays)

Hourly frequencies in the April to June period appear similar to those recorded in the January to

March period. Distributions do not change significantly from month to month, but overall distribution

and peaks on graphs appear to differ depending on whether data were recorded on weekdays or on

weekends.

4-2. Trends near Maebashi Station

Fig.5. Percent for points(Hourly movement) for location information data obtained within a 100m mesh block near Maebashi Station.


Data obtained near Maebashi Station appears to show trends connected to the morning and evening

rush in a more pronounced manner than data in Fig 4.

Table 1. Table summarizing cosine similarities of histogram data for weekdays and holidays

We applied Welch's t-test to the results shown in Table 1 to evaluate the difference between the

average cosine similarity of 3 (weekdays and holidays) and the cosine similarity of 2 (weekdays).

Before the t-test, we conducted an F-test of the variance ratio to determine whether 1 and 3 or 2 and 3

have unequal variances. This resulted in a p-value of 3.11e-08, disproving the null hypothesis of "no

difference in variance" and showing that 1 and 2 have unequal variance. For 2 and 3, the result was a

p-value of 1.504e-07, also showing unequal variance. We then applied Welch's t-test, assuming a null

hypothesis of "there is no difference in mean between two groups." For 1 and 2 and for 2 and 3, the

result was a p-value of 2.2e-16, disproving the null hypothesis and showing that there is a difference

in the cosine similarities of weekdays and holidays.

5. Discussion

A comparison of Fig 1 and Fig 2 shows that movement of people begins to increase rapidly at 6 am

or 7 am on weekdays, while it increases gradually between 4 am and 10 am on holidays. Despite overall

similarities between the histograms for weekdays and holidays, statistical tests show a difference in

the cosine similarities of data for weekdays and holidays. That means the average cosine similarities

suggest that the movements of people on holidays and weekdays follow the trends described above,

meaning that movements can be divided into "weekday" and "holiday" movements. Breaking down

location information data by day and day of the week enabled us to ascertain how people move at

different times on holidays and weekdays. When we broke down data by facilities, such as hospitals

or stations, we found that the movements of people around hospitals are concentrated around particular

times and that many people from outside the prefecture move in the vicinity of stations.

6. Conclusion

This study suggests that it is possible to capture trends in the behavior and distribution of people in

Maebashi City by breaking down location information data by time and place. In other words, we

expect it is possible to determine the spatio-temporal characteristics of people’s behavior in and around

facilities in Maebashi City by breaking down relevant location information data. This study only used

location information data for Maebashi City. However, it may be possible to identify these behavioral

characteristics for people in the entirety of Gunma Prefecture by using prefecture-wide data. We aim

to use these methods in future studies to help uncover and solve problems in Gunma Prefecture.

Average cosine

similarity

Cosine similarity

standard deviation

1. Weekdays and

weekdays

0.9992566 0.0005474582

2. Holidays and

holidays

0.9989234 0.0006159447

3. Weekdays and

holidays

0.9902683 0.003011386


Acknowlegement

We would like to also thank you for the support from the 2018 Scholarly Scholars Research Grant

Program(Leading Initiative for Excellent Young Researchers (LEADER)), which provided support for

this research in general.

References

[1] Gunma Prefecture plans (Gunma Plan Portal), http://www.pref.gunma.jp/07/b0110184.html

[2] Y. Kawahata, M. Takayuki, and I. Akira I. “Measurement of human activity using velocity GPS

data obtained from mobile phones. “ arXiv preprint arXiv:1706.04301, 2017.

[3] Y. Ubukata, S. Yoshihide, and H. Teerayut. “Availability as tourism statistical data of large scale

and long term human mobility tracks by GPS: a study of Ishikawa Pref.” Journal of Japan Society of

Civil Engineers, Ser. D3, Vol. 69, No. 5, 2013.

behavior analysis of people in maebashi city using...

Documents