International Journal of Web Information Systems
Analyzing street crimes in Kobe city using PRISM
Takuhiro Kagawa, Sachio Saiki, Masahide Nakamura,

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Takuhiro Kagawa, Sachio Saiki, Masahide Nakamura, (2019) "Analyzing street crimes in Kobe cityusing PRISM", International Journal of Web Information Systems, Vol. 15 Issue: 2, pp.183-200,
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Analyzing street crimes in Kobecity using PRISM

Analyzingstreet crimes

Takuhiro Kagawa, Sachio Saiki and Masahide Nakamura

Downloaded by KOBE UNIVERSITY, Professor Masahide Nakamura At 02:34 17 June 2019 (PT)Kobe University Graduate School of System Informatics, Kobe, Japan

Abstract
Purpose – In a previous research, the authors proposed a security information service, called Personalized

183
Received 21 April 2018
Revised 21 July 2018
Accepted 7 August 2018

Real-time Information with Security Map (PRISM), which personalizes the incident information based on living
area of individual users. The purpose of this paper is to extend PRISM to conduct sophisticated analysis of street
crimes. The extended features enable to look back on past incident information and perform statistical analysis.
Design/methodology/approach – To analyze street crimes around living area in more detail, theauthors add three new features to PRISM: showing a past heat map, showing a heat map focused on speciﬁedtype of incidents and showing statistics of incidents for every type. Using these features, the authors visualize
the dynamic transition of street crimes in a speciﬁc area and the whole region within Kobe city. They also
compare different districts by statistics of street crimes.
Findings – Dynamical visualization clariﬁes when, where and what kind of incident occurs frequently.
Most incidents occurred along three train lines in Kobe city. Wild boars are only witnessed in a certain region.
Statistics shows that the characteristics of street crimes is completely different depending on living area.
Originality/value – Previously, many studies have been conducted to clarify factors relevant to street
crimes. However, these previous studies mainly focus on interesting regions as a whole, but do not consider
individual’s living area. In this paper, the authors analyze street crimes according to users’ living area using
personalized security information service PRISM.

Keywords Visualization, Smart city, Web service, Security information service, Street crimes
Paper type Research paper

1. Introduction
Many local governments in Japan recently started providing security information service forresidents. The service distributes information of street crimes and incidents that occurred in
the region to residents using the Internet. The residents can make use of the information to
avoid crimes. A typical security information service provides a list of recent incidents and asecurity map within a Web site. Or, it delivers the incident information by e-mail. For
example, Hyogo Prefectural Police in Japan provides “Hyogo Bouhan Net” (Hyogo
Prefectural Police, 2017). The service publishes incident information recognized by Hyogo
prefectural Police on the Web. By registering an e-mail address, a user can also receive theinformation by e-mail. Similarly, Tokyo Metropolitan Police Department provides the e-maildelivery service, called “Mail Keishicho”. The Department also publishes “Tokyo CrimeMap” (Tokyo Metropolitan Police Department, 2017). It is a security map showing where
and when every suspicious person appeared.
In these existing security information services, every incident information is uniformly
delivered to all users. Various types of incidents occur every day at various locations in theThis research was partially supported by the Japan Ministry of Education, Science, Sports, andCulture [Grant-in-Aid for Scientiﬁc Research (B) (16H02908, 15H02701), Grant-in-Aid for ScientiﬁcResearch (A) (17H00731), Challenging Exploratory Research (15K12020)] and Tateishi Science andTechnology Foundation (C) (No.2177004).

International Journal of Web
Information Systems
Vol. 15 No. 2, 2019
pp. 183-200© Emerald Publishing Limited
1744-0084
DOI 10.1108/IJWIS-04-2018-0032

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region. However, user’s living area varies from one person to another. Therefore, even if an
incident is critical for a user, it may not be so serious for another user who is living at distantplace. Thus, how the incident is severe depends on where the user is living. However, thisfact is not taken into account in the existing security information services. All information ofincident is distributed uniformly to all users. Hence, when much information is delivered in aday, a user may miss important information.
In our previous research, we proposed and implemented a new security information service,called Personalized Real-time Information with Security Map (PRISM), which personalizes theincident information based on living area of individual users (Kagawa et al., 2017a). For every
incident information provided by the existing security information services, PRISM computes
severity of the incident according to the living area of a user. More speciﬁcally, based on the
distance between the living area and the incident, the time elapsed from the occurrence, and thetype of the incident, PRISM adds a weight to the incident, so that closer and newer incidents
become more serious for the user. It then displays the weighted incidents on a heat map. As theweight of severity varies depending on user’s living area, the resultant heat map becomes apersonalized and real-time security map.
The current version of PRISM visualizes the latest incident information only. It does nothave features to look back on past incident information, or perform statistical analysis.
Although PRISM is useful to capture the current incident status, it cannot be used for deeper
analysis or visualization considering past incidents.
To visualize and analyze street crimes in more details, we add three new features to PRISM
in this paper. The ﬁrst feature is showing a past heat map. A user can display the heat map atany date in the past. By using this feature, the user can see how the incidents around the area
have been changing as the time went by. The second feature is narrowing down the type ofincident displayed. This allows the user to display only interesting type of incidents on the heat
map. The third feature is displaying statistics of the number of occurrences for each type ofincident. The user can see the number of incidents around the living area as statistical data.
Using three new features, around Kobe city in Japan, we visualize the transition of street
crimes in a certain living area and the whole region. We also investigate ecology of wildboars, and compare statistics of crimes for different living areas. Using extended
functionality of PRISM, the user can know when, where, and what kind of incident occurs
around the registered living area.
The original version of this paper has been published as an international conference
paper (Kagawa et al., 2017b). Changes made to this paper are most signiﬁcantly in the
addition of new data corrected in 2017. Based on the new data, we added a new section
comparing street crimes between 2016 and 2017.
2. Previous research: security information service Personalized Real-time
Information with Security Map
In our previous research, we have proposed PRISM (Personalized Real-time Information
with Security Map), which personalizes the incident information based on living area ofindividual users (Kagawa et al., 2017a).
2.1 Overview of Personalized Real-time Information with Security Map
To provide personalized incident information, PRISM exploits two key ideas. The ﬁrst idea
is to put a weight of severity on every incident. The severity is computed based on the
distance to the living area, the elapsed time, and the type of the incident. The second idea is
to visualize the weighted incidents on a heat map.

Downloaded by KOBE UNIVERSITY, Professor Masahide Nakamura At 02:34 17 June 2019 (PT)A user of PRISM ﬁrst registers his/her living areas. The living areas represent places where
the user often visits in the daily life, such as a house, a station, a working place, a school ofuser’s children, or a shopping center. Every user can register multiple places as living areas.
Then, for each incident information delivered, PRISM calculates the distance between the
point of incident occurrence and the living area of the user. PRISM also calculates the elapsed
time from date and time of occurrence to the current time. Based on the distance and theelapsed time, PRISM adjusts a weight of the default severity pre-determined for each type ofincident so that closer and newer incidents are more severe. The resultant weight is the severityof the incident personalized for the user. Finally, PRISM generates a heat map based on the
personalized severity, which achieves a personalized and real-time security map.

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2.2 Architecture
Figure 1 shows the system architecture of PRISM. In the ﬁgure, the dotted rectangle
represents the system boundary of PRISM. A crawler, at the bottom left of the ﬁgure,periodically obtains incident information from an existing security information service.
Then, the crawler analyzes the retrieved text, extracts attributes, and inserts the attributes
into an incident DB. A user at the top right of the ﬁgure registers his/her living areas data. In
the registration, the user can refer to a facility DB, where open data about facilities provided
by the local government is imported. Based on the incident DB and the living areas data,
PRISM computes the severity of every incident. Finally, PRISM generates a heat map basedon the weighted incident information, and presents the map to the user.
In the current version of PRISM, we use “Hyogo Bouhan Net” as a security information
service and Kobe city facility open data (Kobe city, 2017). Using PRISM, a user can browse
personalized and real-time information within Hyogo prefecture.
2.3 Severity
Severity is a degree of how the incident is serious for a user. Based on keywords contained in
the incident information, PRISM ﬁrst deﬁnes the default severity of incidents by thefollowing four categories:(1) Alert (severity 3)
 The most serious incident that can threaten life of citizens. Incidents involving
the following words are classiﬁed in this category: murder, robbery, shooting,assault, gun, knife, etc.
(2) Warning (severity 2)
 Incidents that may cause physical damage to citizens. Incidents involving thefollowing keywords are classiﬁed in this category: snatching, pickpocket, theft,
stalking, groping, etc.

Figure 1.
Architecture of
PRISM

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186

(3)

Caution (severity 1)
Incidents to be paid attention, not directly linked to life or physical damage.
Incidents involving the following keywords are classiﬁed in this category:
scam, animal, wild boar, etc.
(4) Notice (severity 0)
 Supplementary information other than street crime. Information involving thefollowing keywords are classiﬁed in this category: arrested, resolved, notice, etc.
 To distinguish the supplementary information from the incident information,
we set its severity to 0.
Next, PRISM weights the above default severity based on following two viewpoints:(1) Distance: The shorter the distance between the place of incident and user’s living area is,the more serious the incident is for the user, as a new incident may happen
again nearby. Thus, higher weight is given to the incident. On the other hand,
the longer the distance is, the smaller the severity is.
(2) Time:
 The shorter the elapsed time from the incident occurrence, the more
serious it is for the user as a new incident may happen again soon. Thus,
higher weight is given to the incident. On the other hand, the longer thetime is, the smaller the severity is.
Now, for an incident x and a user u, let d be the distance from living area of u to the place
where x occurred. Also, let t be the elapsed time from the time when x occurred. Then, wedeﬁne the severity of x for u, denoted by severity(x, u), as follows:
(


0

 severityðxÞ ¼ 0
severityðx; uÞ ¼
1=3  WDðdÞ þ WT ðtÞ þ severityðxÞ  1=3 ðotherwiseÞ
where WD(d) and WT(t) are weight functions with respect to distance d and time t,
respectively. severity(x) represents the default severity of x (ranging over 0, 1, 2 or 3)
determined by the keywords. As severity(x) takes the maximum value 3, PRISM ﬁrst
multiplies it by 1/3 to normalize the maximum value to 1. After that, severity(x, u) is
calculated by taking the average value of WD(d), WT(t), and severity(x).
Currently, we use a function that maintains the weight 1.0 until d reaches 2 km anddecrease the weight linearly from 1.0 to 0.0 up to 4 km as WD(d). The threshold 4km wasderived from the maximum recommended distance of commuting to schools in Japan.
Similarly, WT(t) maintains the weight 1.0 until t reaches 7 days and decrease the weightlinearly up to 14 days. As Hyogo Prefectural Police lists every incident information on the
Web up to two weeks from the occurrence, we set the threshold of WT(t) to 14 days. Note,however, that the above forms of WD(d) and WT(t) are experimentally determined as
default setting of PRISM, but are not based on crime philology or speciﬁc features of theregion. PRISM has a room for a user to freely customize these functions, in case that thedefault setting does not ﬁt user’s sense of value for the severity.
2.4 Heat map
PRISM displays the incident data with personalized severity on heat map. The value ofseverity(x, u), which is the severity of incident x for user u, is between 0.0 and 1.0. PRISM

Downloaded by KOBE UNIVERSITY, Professor Masahide Nakamura At 02:34 17 June 2019 (PT)creates a heat map such that the severity value is scaled into the seven colors, [purple,
indigo, blue, green, yellow, orange, red]. For each incident x, PRISM puts a data point on
coordinates (lat, lng) of x using a color associated with severity(x, u). This generates a heat
map adapted to individual living area and the current time.
Figures 2 and 3 show two heat maps generated for two users A and B, where the incident
information within Kobe City at a certain date is visualized. The pins in the maps indicate
locations of living areas registered by the users. The colored points indicate the places whereincidents occurred. In this example, user A registered Kobe Sannomiya Station and Hankyu
Rokko Station as living areas. On the other hand, user B registered Hankyu Rokko Station,
Shukugawa Station, and Hanshin Fukae Station. We can see that completely different heat maps
are generated depending on the living area, even though the map area and the time are the same.

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187

2.5 Limitation
PRISM is good at visualizing real-time incident information. However, we consider that thecurrent version PRISM has these three limitations.
(1) L1: PRISM cannot look back on the past incidents. Thus, we cannot analyze where
and what kind of incidents occurred in the past.

Figure 2.
User A’s heat map

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188

Figure 3.
User B’s heat map

(2)

(3)

L2: Although PRISM has set the severity based on the type of incident, all types ofincidents are displayed on the map. In other words, we cannot focus on speciﬁctype of incident.
L3: PRISM is a tool for visualizing the place where the incidents occurred. It
does not have the feature of counting the number of incidents or performing
statistical analysis.

3. Extending Personalized Real-time Information with Security Map for
deeper analysis
3.1 Extended features
To maximize the safety and security, it is important for every citizen to grasp when, where,
and what kind of incident occurs frequently around them. In this section, we propose amethod to analyze street crimes in their own living area by extending PRISM.
To cope with the limitations described in Section 2.5, we add the following three featuresto PRISM in this paper. Using these additional features, a user can analyze temporal andstatistical characteristics of incidents around his/her own living area.

(1)

(2)

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F1 (Visualizing past heat map): This feature shows the heat map on any given date
and time speciﬁed by the user. It allows the user to see the heat map at any point oftime in the past. This feature solves L1.
F2 (Narrowing down by type of incident): This feature ﬁlters only speciﬁed type ofincident on the heat map. It allows the user to focus on special types of incidents
that he/she wants to analyze. This feature solves L2.
F3 (Showing incident frequency): This feature shows statistics of the number ofincidents of each type as a stacked bar chart. It allows the user to analyze which
type of incidents occurred and how often they did. This feature solves L3.

We will describe the details of each feature in the following subsections.
3.2 F1: visualizing past heat map
PRISM proposed in Section 2 only displays the current incident information, and cannot
look back the past information. In this feature, therefore, we extend PRISM so that the user
can analyze street crimes with respect to the time axis. Speciﬁcally, we allow the user to
input any date and time. Regarding the given date and time as the current date and time,
PRISM re-computes the severity, and re-draws the heat map as described in Section 2.4. As aresult, the past heat map can be generated.
Figure 4 shows a screenshot of PRISM with the extended features. At the bottom of themap, there is a textbox for specifying the date and time The user inputs the past date andtime he/she want to analyze in the text box, and presses Go button. Then, PRISM displays
the heat map on the speciﬁed date and time.
On the left side of the text box, there are also buttons to look back one hour ago, or one
day ago. Similarly, on the right side, there are buttons to forward the time to one hour, or one
day. By using these interfaces, the user can display the heat map at any past time andanalyze the distribution of street crimes.
Using feature F1 with external software taking screen shots, it is also possible tovisualize the transition of incidents as an animated movie, where time-series snapshotsof the heat map are linked together. Speciﬁcally, a user displays a heat map of any datein the past, and saves a screenshot with any screen capture software. Then, the user
displays another heat map after one day using F1 and saves another screenshot. By
repeating this d times, the screenshots of d days are obtained. Finally, by joining the
saved screenshots, the user can generate the transition of the heat map during d days
into an animated movie. Like this, the user can dynamically analyze where and whenincidents occurred frequently.
3.3 F2: narrowing down by type of incident
In PRISM proposed in Section 2, all types of incident were displayed on the heat map.
Therefore, in this feature, we extend PRISM so that street crimes can be ﬁltered by the typesof incident. Speciﬁcally, when the user designates a keyword, PRISM searches only theincidents including the keyword with API and generates heat map.
We will explain F2 using Figure 5. When the user clicks on the “Narrow down” tab at thebottom of the screen, a dropdown list for selecting keywords appears. The user selects thetype of incident he/she want to analyze from the list and presses the search button. PRISM
then only displays incidents which have the selected keyword on the heat map.

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Figure 4.
Feature F1: showing
heat map of past
incidents

3.4 F3: showing incident frequency
This feature displays the statistics of the number of incidents of each type, which we call theincident frequency. The incident frequency is represented for each registered living area, in
a form of a stacked bar chart.
More speciﬁcally, for each location point p of the registered living areas, PRISM retrieves
incidents that occurred within a R-kilometer radius of p and within the past D days. PRISM
then counts the frequency of incidents for each incident type (see Section 3.3), and displays
the statistics as a stacked bar chart. In the default setting, R is set to 2.0 km and D is set to
365 days. Thus, it is possible to take statistics of the incident frequency in the past one year
within a two-kilometer radius of each point.
We explain F3 using Figure 6. When the user clicks the “Statistics” tab at the bottom of
the screen, and presses the “Show statistics” button, PRISM shows stacked bar charts for allAnalyzing
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191

Figure 5.
Feature F2: ﬁltering
incidents by typelocations registered as living area. In each chart, a user can see which type of incidents occur
and their respective occurrence rate. Thus, the user can visually analyze features of street
crimes at each location point with a stacked bar chart.
4. Visualizing and analyzing street crimes using Personalized Real-time
Information with Security Map
Using the extended features of PRISM, we conduct deeper analysis of street crimes within
Kobe city in this section.
4.1 Transition of street crimes in living area
We analyze how the street crimes in the living areas change throughout the year. In thisanalysis, we adopt the Hankyu Imazu station (Nishinomiya city) and the Hankyu Rokko

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192

Figure 6.
Feature F3: statistics
of incidents for eachpoint of living area

station (Kobe city) as personal living areas. These stations are used by the author on a daily
basis. We create a video of the transition of street crimes around these stations in 2016,using F1 with the aid of “Mado Photo [1]” and “UWSC [2]”The following steps show the concrete procedure for generating the video: Display a heat map on 2016-01-01 with PRISM.
 Capture and save a screen shot using Mado Photo.
 Forward the time of the heat map by one day using feature F1 of PRISM.
 Repeat capturing and saving the screen shot and forwarding the time by 366 times(for 366 days of the leap year). Use UWSC to automate the process.
 Use Mado Photo again to generate an animation GIF ﬁle by combining the saved
366 screen shot.

Downloaded by KOBE UNIVERSITY, Professor Masahide Nakamura At 02:34 17 June 2019 (PT)Convert the GIF ﬁle into an MP4 ﬁle. The format conversion is to just increase theportability of the ﬁle, which does not affect any observable result.

We used Mado Photo and UWSC in this paper. However, a user can use any software thatcan save screen shots and automate the window operations.
Figure 7 shows a scene of the animation. From this, the following facts were observed:
 More incidents occurred around Imazu station than around Rokko station.
 Street crimes occur throughout the year.
 Incidents occurs various places around both stations.

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The generated movie ﬁle can be watched at: https://youtu.be/rwaLJJG_QFA.
To validate the facts observed in the animation, we examined the number of incidents
around Imazu station and Rokko station. Figure 8 shows the number of incidents occurred
within 2km around each station for each month in 2016. In Figure 8, it can be seen that the
number of crimes around Imazu station is larger than that of Rokko station in 8 months outof 12. Similarly, as for the total number throughout the year, Imazu has more crimes thanFigure 7.
Showing incidents
around Imazu andRokko stations(February 1, 2016)

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Rokko. Although the number varies over months, the street crimes keep occurring
throughout the year. Thus, the observed facts can be validated by statistics.
The signiﬁcant merit of generating animations is to allow a user to quickly browse thetransition of street crimes within a vast area throughout a year. Although it may lack the
precision compared to other numerical analysis, we consider that the visual observation isquite useful to grasp whole situation at a time.
In this experiment, we used a day as time lapse, which may skip the exact time of anincident occurrence. However, as the time weight function WT(t) sustains the severity
weight at least seven successive days, every incident must appear in the video. Thus, a user
cannot overlook any single incident.
Depending on external software such as Mado Photo and UWSC may decline user
experiences. In the future, we will consider a server-side feature of generating videos tomake PRISM more self-contained.
4.2 Transition of street crimes in the whole region
We analyze the transition of the street crimes in the whole region of Kobe, by applying thesame method described in the previous subsection.
Speciﬁcally, we place points of living areas throughout Kobe in a mesh shape. Here, we
register nine points covering each ward of Kobe city, centering on Shin-Kobe station. Then,we created a movie ﬁle in the same way we described in the Section 4.1. Analyzing theanimation, we investigate in which part within Kobe city incidents often occur. Figure 9shows a snapshot of the movie. From the animation, the following facts were observed:
 Incidents occurred along three train lines (Hankyu, JR, and Hanshin) running
through Kobe city.
 As it goes to the north, the number of incidents decreases.
 There are no incidents observed in mountains.
The generated movie ﬁle can be watched at: https://youtu.be/2gzVi2aJCYA.
4.3 Ecology of wild boars
As there are many mountains in Kobe city, wild boars frequently appear. Wild boars
sometimes cause harm to people. Therefore, when a wild boar is observed, theFigure 8.
The number ofincidents around
Imazu and Rokko
stations each monthin 2016

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195

Figure 9.
Showing incidents inall Kobe area (June 7,
2016)

location and time where the boar is found are reported to the police. Then, the witnessreport is delivered as an incident by Hyogo Bouhan Net. Like this, especially in Kobe
City, residents need to pay attention to wild boars. Thus, for Kobe citizens, knowing
where and when wild boars appear is as important as knowing street crimes. Here, we
analyze the ecology of the wild boars that appeared in Kobe city in 2016, using thefeature F2.
First, as similar to Figure 9 and Section 4.2, we registered nine points centered on ShinKobe station. Then, we speciﬁed “wild boar” as a keyword with the feature F2. As a result,only witness information of wild boars is shown in the heat map. Finally, we generated amovie ﬁle in the same way as Sections 4.1 and 4.2. Figure 10 shows a snapshot of the movie.
From the animation, the following facts were observed:
 Most wild boars are observed within a mountain belt from Shin-Kobe station andHankyu Okamoto station.
 Wild boars are frequently observed from June to September.
 Wild boars are not observed during the winter season.
The generated movie ﬁle can be watched at https://youtu.be/cJSpXZlxITg.

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Figure 10.
Showing witness
information of wildboars (June 7, 2016)

Another promising approach to analyze the ecology of wild boars is to exploit a smart citysolution, where ambient sensors are deployed to detect wild boars. However, maintaining
such sensors within a vast area is quite expensive. Therefore, in reality, the local
government has to rely on the witness reports from human residents.

4.4 Comparing statistics of crimes
One of the biggest concerns for inhabitants is to know whether or not their living place andsurroundings are actually secure. Moreover, such security information becomes a criterion
for in-bound people to decide where to live. In this subsection, we analyze statistics ofincidents frequency for several locations within Kobe city using the feature F3. Then, we
compare the security of the locations from the viewpoint of the frequency of street crimes.
In this analysis, we compare the security around actual living houses of four users. User
A’s house is located in Nishinomiya city. User B’s house is located in a downtown area of
Nada ward, Kobe city. User C’s house is located near mountains in Nada ward, Kobe city.
Finally, User D’s house is located in Sanda city, which is a countryside of Hyogo prefecture.
We registered addresses of the four houses in PRISM, then displayed statistical information

Downloaded by KOBE UNIVERSITY, Professor Masahide Nakamura At 02:34 17 June 2019 (PT)using the feature F3. We took frequency of incidents that had occurred within a twokilometer radius in the year 2016.
Figure 11 shows four stacked bar charts generated by PRISM, each of which corresponds
to statistics of a user. For clear reading, we also summarize the number of occurrences foreach type of incident in Table I.
In the neighborhood of User A’s house, there are many exposures and groping.
Therefore, a woman need to be careful when she walks alone. Moreover, a few serious
incidents such as robbery, assault, and knife occurred. Thus, it is necessary to pay attention
to these incidents. On the other hand, in the neighborhood of User B’s house, there areseveral incidents of exposure and groping. Also, there is no incident that can threaten life ofresidents. However, more wild boars are observed compared to the downtown area. Thus, itis necessary to prepare for the wild boars. Fewer incidents are observed in the neighborhoodof User C’s house, compared to Users A and B’s. However, more wild boars are witnessed,
because the house is near a mountain. Thus, User C should be more careful of wild boars
than User B. As User D lives in the countryside, only six incidents are reported around his
house within the year of 2016. Therefore, it can be said that User D lives in a relatively safearea compared to the other users.

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4.5 Comparing street crimes between 2016 and 2017
In Sections 4.1-4.4, we analyzed street crimes which occurred in 2016. Based on the new data,
we compare street crimes between 2016 and 2017 in this subsection. Speciﬁcally, we analyze
street crimes in the same way as Sections 4.1-4.4 using the new data corrected in 2017 andcompare the result with 2016.
First, we analyze the street crimes in a certain living area. We adopt the Hankyu Imazu
station and the Hankyu Rokko station as personal living areas like Section 4.1. We display aseries of heat maps from January 1st, 2017 to December 31st, 2017. We capture each heat
map into an image ﬁle and create a movie ﬁle by joining 365 image ﬁles together. The
generated movie ﬁle can be watched at https://youtu.be/tn0_7WkcDmE.
Next, we place points of living areas throughout Kobe in a mesh shape and analyze street
crimes in the whole region and the ecology of wild boars in the same way as Sections 4.2 and4.3. The generated movie ﬁles can be watched at https://youtu.be/hmWDx6hUFpw andhttps://youtu.be/3PvuwdNY-MQ respectively.

Figure 11.
Showing stacked bar
charts of incident
statistics in 2016

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Table I.
Number of incidents
every type aroundeach house of users

Type of incident

User A

User B

User C

User D

Robbery
Assault
Knife
SnatchingStalk
Groping
Exposure
Suspicious person
Act of speaking
Spy photo
Obscenity
Phone
RacoonWild boar
MonkeyOthers
Total

2
2
3
6
9
16
28
10
7
1
2
10
0
0
0
11
107

0
0
0
2
11
5
14
6
12
2
2
5
0
11
0
7
77

0
0
0
0
3
3
6
0
7
0
0
0
3
31
1
4
58

0
0
0
0
0
0
0
3
2
0
0
0
0
0
0
1
6

From the animation, it turns out that the place where the street crime occurs does not change
signiﬁcantly from year to year. On the other hand, the ecology of wild boars differed. In
2016, wild boar was only witnessed in a certain area(between Shin-Kobe station andOkamoto station), but in 2017 wild boars were witnessed in the other places such as JR Kobe
station and Ashiya town.
Finally, we register the same living area as that used in Section 4.4 and compare securityof the locations. We took frequency of incidents that had occurred within a two-kilometerradius in the year 2017. Figure 12 shows the number of occurrences for each type of incident
in 2017. In user A’s living area, street crimes such as exposure decreased, however,suspicious phone calls signiﬁcantly increased. In user B and C’s living area with a lot of
witness information of wild boars, there was no major change in the type of street crime. In
the user D’s living area, there were few incidents in both 2016 and 2017.
We summarize the found facts comparing street crimes between 2016 and 2017.
 the place where the street crime occurs does not change signiﬁcantly from year toyear; in 2017, wild boars were also witnessed in other places; and the type of incidents occurring in the living area changes depending on the year.
5. Related work
Previously, many studies have been conducted to clarify factors relevant to street crimes.
Hipp and Kane focused on the relationship between population and crime (Hipp and Kane,
2017). They state that cities with more population will experience larger increases in crime.
They also state that cities with increasing population will experience larger decreases incrime, regardless of the size of population. Morenoff and Sampson mentioned about therelationship between decrease of population and violent crime. They also visualized changes
in the population (Morenoff and Sampson, 1997). Roncek examined how the characteristicsof the area affect where crimes occur using the actual data of San Diego and Cleveland(Roncek, 1981). Stults and Hasbrouck focused on the relationship between commuting andAnalyzing
street crimes

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199

Figure 12.
Showing stacked bar
charts of incident
statistics in 2017

crime rates (Stults and Hasbrouck, 2015). Kester visualized and analyzed crime patterns
using Formal Concept Analysis (Kester, 2013). Their ﬁndings are interesting, however, theseprevious studies mainly focus on interesting regions as a whole, but do not consider
individual’s living area. Using the extended features of PRISM, it is possible to analyze
street crimes according to an individual’s living area. Tokyo Crime Map (Tokyo
Metropolitan Police Department, 2017), operated by the Tokyo Metropolitan Police
Department, is a security map visualizing street crimes within all cities of Tokyo. The map
covers ﬁne-grained incident information over vast area. However, Tokyo Crime Map does
not consider personalization for individual users, as PRISM does so using the living area.
Kajita and Kajita (2017) proposed a method that predicts street crimes from open data.
Based on Green’s Function method, their algorithm ﬁnds a causality of incidents. Theirstudy is similar to ours in the sense that they extensively use open data of street crimes.
However, their purpose is prediction, which is different from the visualization of the current
or past street crimes discussed in this paper.
6. Conclusion
In this paper, we have extended a personalized security information service PRISM to
perform deeper analysis of street crimes and incidents within a city. Using the three newfeatures, a PRISM user can refer to a heat map of past incidents, focus on speciﬁed type ofincidents, and see the statistics of incident frequency for each type of incidents. These newfeatures allow the user to know more deeply the characteristics of the incidents around hihe/sher living area.
Using the extended version of PRISM, we have conducted deeper analysis of Kobe city.
Transitions of street crimes within a year 2016 were analyzed for speciﬁc living area of an
author and Kobe city. It was also interesting to see the ecology of wild boars, unveiled fromincident information. Finally, we compare the security of different locations quantitatively
with the incident frequency.
In our future work, we will verify the validity of the value of the severity and the weightfunctions described in Section 2.3. In addition, although only security information in Hyogo
prefecture can be browsed as it stands, we plan to implement security map in other areas,
and analyze characteristics of incidents.

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Notes
1. www.otsoftware.net/software/madophoto.html
2. www.vector.co.jp/soft/winnt/util/se115105.html

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Corresponding author
Takuhiro Kagawa can be contacted at: kagawa@ws.cs.kobe-u.ac.jp

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