.

1. Density of Route Frequency for Enforcement
Norazah Abd Aziz, Raja Mohamad Fairuz R. Mohamad Yusoff
MIMOS Berhad
Technology Park Malaysia
57000 Kuala Lumpur
+603-8995 5000
azahaa@mimos.my, fairuz.yusoff@mimos.my
ABSTRACT
Recently, big data has become one of the hot topics that lead the
organization, either public or private, to generate more efficient
management solutions. Not for business purposes only, the big data
has the potential to create significant value to assist law
enforcement or to improve the policy for the decision-making
process. The features of big data that have the ability to handle
massive data volume and variety at high velocity, can be used in
fields of policing to improve organization operation. The data are
valuable for Geographic Information System (GIS) based decision-
making. With intelligent GIS, information can be updated across
the organization immediately. Geospatial Modelling (GM) is to
help in the analysis solution in order to improve the decision-
making process in the daily operation of the police. The GM
technique is designed to discover patterns data involving the
location and other spatial related information such as density. The
patterns data consist of valuable traffic density will be used to
simulate routes planning in the patrol car. This paper presents the
approach on how to generate valuable traffic density to accurately
dispatch the patrol cars in a geographic environment as well as to
reduce criminal cases.
CCS Concepts
• Information systems➝
Information systems
applications➝
Spatial-temporal systems➝
Data streaming
Keywords
GIS, GPS, GPRS, traffic density, route, frequency, patrol
1. INTRODUCTION
Nowadays, the big data industry has set a new wave of innovation.
This opportunity is not missed to be utilized by the GIS industry as
a whole. GIS is a framework for gathering, managing, and
analyzing data [1]. In other words, GIS is a world virtually
represented by points, line, polygon, and graph which integrated
many types of data. It helps to visualize the spatial location and
organizes layers of information using maps and 3D scenes. With
this inimitable capability, which able to illustrate deeper insight
into data such as patterns, situations, and relationships, it helps
users make the right wise decisions.
The huge data sets in GIS known as geospatial data is numerical
values in a geographic coordinate system that represents the
location, shape, and size of a physical object. The geospatial data
or spatial data genre consists of vector or raster graphics format.
The raster data referred to as grid cells, express data in pixels. The
raster model is suitable for representing understated changes. For
example, lead contamination and temperature. The vector format is
a two-dimensional data stored in terms of x and y coordinates,
mostly described for a road and a river. It encompassed vertices and
paths with basic symbol types, lines, points, and polygons. The
polygon or nonlinear features mostly stored as a closed loop of
coordinates such as area boundaries. These features are used to
cultivate density mapping which represents by points or lines
estimated in a given area.
Many research practitioners are giving less focus on density
mapping using GIS tools as it is relatively easy to do. However, this
analysis offers precious insight into natural and social phenomena
at an application level. Furthermore, with new forms of big data, it
will generate new avenues of research. Align with that, this paper
aims to generate automate valuable density data by applying the
pairing of linear equations function to generate a frequency of
patrol routes.
In order to that, we extracted qualitative information from global
positioning systems (GPS) and tracking devices of route
enforcement. The information is used to calculate routes frequency
and then is encoded with specified color based density. The route
density is used to propose a new route planning simulation for
enforcer by correlating it with crime hotspot information. It is
important for urban safety research and planning as well as efficient
resource management.
This paper is organized as follows. In Section 2, we discuss the
related work which proposed the traffic density techniques. Then,
we describe our system architecture in Section 3 before explaining
further detail about our method. In Section 4, we present our
method in detail, which deliberate how the data is extracted and
computed for routing frequency, then will be used further analysis
such as correlation. We also present some prototype
implementation on the method. Finally, we conclude the
advantages of our approach and suggest future research directions.
2. RELATED WORK
There is much paper discussed on how to develop an efficient real-
time system for the patrol car allocation to various locations and
implemented variously in many countries. Hussain et al. [2]
presented a few examples of existing patrol car allocation systems
and proposed a new paradigm to develop an efficient real-time
patrol allocation system in the Emirate of Abu Dhabi. Three inputs
are introduced; variables and system parameters, constraints and
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DOI: https://doi.org/10.1145/3395245.3396207

2. objective’s function and expected and unexpected events. The
inputs consist of such as variable exists in the GIS system,
frequency and nature of the incidents, number of patrol cars on duty
and traffic congestion. However, there is no technical explanation
on how to develop the system in detail as well as the discussion on
the density analysis.
Another method to detect the density of traffic is using an infrared
sensor as discussed in Chaitanya et al. [3] paper. The sensor is
installed as part of a traffic light to detect the number and motion
of the vehicle, hence to decide the heavy or normal traffic. The
information will be sent to General Packet Radio Service (GPRS)
device which communicates with the Global Positioning System
(GPS) to provide an alternate traffic route. They also used input
information from police traffic to be integrated into their proposed
application.
A. Badiger et al. [4] in their paper, proposed the Intelligent Traffic
Control Systems (ITCS) using the image processing technique to
detect the traffic density of every road in the junction. It means that
each road is provided with a camera to capture the image for
comparison between a heavily and normal lane. The density
analysis is also used to detect movement of the barricade and
provide proper spacing between the vehicles. They also proposed
an automatic braking system based on the density information and
integrated with the traffic police system to alert for any traffic
broken rules. Their project is not primarily for police patrol
planning but objectively on the accident precaution and facilitation
by providing such as an automatic braking system and the proper
spacing between the vehicles at the traffic, detecting the arrival of
the ambulance and control the barricade movement.
Another approach of density analysis suggested by S. Deepika and
R. Saradha [5] is by calculating the ANN index for hotspots and
cold spots of crash occurrences. It means that the data consist of
location and density analysis is clustering and used to calculate the
frequency of occurrences and risk density of each crash. The crash
data is collected from related segments such as road, busy cities,
growing towns, rural pathways and motorway segments which are
unmonitored currently. The result of the ANN index will assist
them to identify crash hotspots’ severity rates. The maximum rate
of the severity for each particular route can be used during police
dispatch. Hence, their method is claimed to be able to improve the
existing dispatch pattern of police enforcement by identifying areas
of higher crash severity.
X. Li et al. [6], proposes a density-based algorithm based on
clustering moving objects and clusters road segments of the
common traffic density. The traffic sensors were located at the
intersection or edge of the road and distance was calculated
between edges to define the relationship between traffic edges. The
system is said to be both effective and efficient at determining hot
routes. Similar to our approach, the density is shown by
highlighting (encode with color) on the road network but using
dimension reduction (feature vector) technique to describe the
traffic density.
Other prominent method namely kernel density estimation is used
to direct patrol routes in real-time by targeting high crime areas at
the same time as maximizing demand coverage. It is discussed in
Leigh et al. [7]. They developed an algorithm aims to direct patrols
in a better position to react to emergency calls and to reduce crimes.
The algorithm involves historical crime data and performing kernel
density after filtering unrelated data to generate hotspots. They use
weighting systems to improve the accuracy of hotspot predictions.
The highest potential to prevent crime is identified based on the
hotspots area were areas with the most effective of patrolling. To
solve the police positioning problem, researchers decided to use a
version of the maximum coverage location problem (MCLP). This
mechanism is adopted due to limited resources whereby there is a
hotspot not patrolled at once. The solution is a combination of
hotspots to patrol with the available resources that providing the
maximum coverage of predicted demand.
Most of the papers as discussed earlier were using the hotspot in
their study. Hotspot defines in [8] is a visible location on a screen
that may be linked to perform a specified task. Cheng [9] defines a
hotspot as “location that has underlying correctable safety
problems, and whose effect will be revealed through higher crash
frequencies on similar locations”. Elliott et al. [10] characterizing
the hotspot as correctable safety problems involving crash
frequencies based on this definition, because their study on optimal
patrol involving crashes of motor vehicles in transport on roadway
and crime data.
3. DENSITY OF ROUTE FREQUENCY
3.1 System Architecture Overview
A proper technique choice to produce valuable density is an
important consideration when working with Geographical
Information (GI) and urban safety, including route planning. For
example, in the case of determining route planning for a patrol car,
although the method is not so limited, routes have been
conventionally planned by looking at crime indexes using classical
frequency-based density methods.
However, the classical frequency-based method can be defined as
an open set, which might fall within a range [0, ∞]. Furthermore,
classical frequency based analyses focus on each road (one road
specifically) but do not cover all roads for a specific or defined area.
It is therefore statistically difficult to distribute between places with
a high crime index and lower crime index because there is a
possibility the level of crime is not static to a particular road. B.
Jennifer [11] elaborated in her paper, that hotspot is a perceptual
construct and somewhat arbitrary. So, there is necessarily an
analysis of the hotspot attributes such as the weighting scheme. The
related issue to show the level of crime index was also studied by
P. D. Allemar Jhone [12]. He used a K-Means algorithm to predict
the volume of crime for each cluster represents a city or
municipality in the province of Surigao del Norte.
In our context, we focus on route frequencies integrating with crime
hotspots, areas on a map that have high crime density. Then, we
proposed a new route planning simulation for enforcer based on the
route frequencies and crime hotspots correlation. The correlation
information is producing quality GI results to simulate route
planning for each patrol car to tackle unsafe places in the crime
area. Based on the simulation, the management can use it to decide
in more detail a route planning by providing aggregated traffic
patterns over time.
Similar to most of the existing routing enforcement systems, we
also propose a system for extracting route and traffic density consist
of three main modules; a geolocation recorder module, analysis
module and decision-maker module. Figure 1 illustrates an
exemplary overview of the system showing its interaction over a
network. The system includes a geolocation recorder module
adapted to gather traffic information from a source. For example,
the source may include Automated Vehicle Locator Systems
(AVLS). Satellites receive requests for geolocation information
from vehicles and then send the geolocation information to the

3. vehicles making the requests via radio or wireless communication.
Thereafter, the information is then resent via radio or wireless
communication, from the in-vehicle system to an analysis server to
process the information. The analysis server includes a map
extractor, route Extract Transfer Load (ETL) module, analysis
components, and related databases to analyze traffic data
comprising the geolocation information. The client computer
displays the results from the analysis’s server as a layer on the map.
A decision-maker module (i.e. decision support system) with some
simulation, prediction and an analytic tool is provided to simplify
the client analysis, such as route planning management.
Figure 1. Architecture diagram
The general process flow of our approach is illustrated in Figure 2.
Figure 2. General Process Flow
The process starts with getting traffic information from any
geolocation device. The information consisting of geographic data,
time capture and device identity for each predefined interval. The
next step is to compute the region of interests (ROI) and defining
routes as discussed in subsection 3.2. The routes information
involved extract, transfer, and load (ETL) process from the db_A.
The route frequency computation module has elaborated more in
subsection 3.3 to generate a route patrol hotspot. The last step is the
process of route color encodes module as described in subsection
3.4.
3.2 Map Extraction and Route ETL
The map extraction module is to define a region of interests (ROIs)
for roads within a defined area. The process starts by checking
whether all the roads within the defined area have been completely
processed or not. If not, longitude and latitude for every
intersection, road end and turn within the defined area are marked
with a unique identifier 𝑡𝑎, 𝑡𝑏, 𝑡𝑐, 𝑡𝑑, 𝑡𝑒. Then, the process
continues with computing a linear equation given two connected
intersections or road ends or turns or any combination thereof. The
equation values are saved as paired list A:
𝐴 = {(𝑡a, 𝑡𝑏), (𝑡𝑏, 𝑡𝑐), (𝑡𝑏, 𝑡𝑑), (𝑡𝑐, 𝑡𝑒)}
where 𝐴 is the starting point, 𝑡𝑏 is an intersection, 𝑡𝑐 and 𝑡𝑒 are on
the same road, and 𝑡𝑑 is an endpoint. If some of the elements in the
paired list (𝐴) have not been processed, two locations are paired to
get a linear relationship in terms of slope and y-intercept values.
The values are then indicated as a new identity (𝑡′ab, 𝑡′𝑏𝑐, 𝑡′𝑎𝑐 … )
and the values saved or written into a new paired list (A’). The
procedure is repeated until all the pairs have been processed. Then,
the paired values (A’) are saved into the Geospatial database
(db_B).
Figure 3 depicts the sample of extracting the map equation. Next,
the geolocation information from the db_B is extracted and
processed. Processing comprises searching the nearest on-road
location of the sorted information (by time) along with other
properties (such as region) and storing this into the new list
(list_B’). The transformed information is then saved into the
Transposed database (db_A’).
Figure 3. Sample of extracting map linear equation
3.3 Route Frequency
The main process in our system is route frequency computation in
the calculating route frequency module as illustrated in more detail
in Figure 4. The process starts by getting the transformed
geolocation information from the db_A’. A determination is then
made as to whether all the geolocation information has been
processed. If not, it will be saved as a temporary list. Then, the
nearest on-road location of the sorting information along with other
properties are determined and stored into new list C.
Road ID information (ID_R_1) is then read and extracted from the
geospatial database. The process then involves checking if the data
information of list C already exists in the road ID information
Automated Vehicle Locator
(AVLS)
Geolocation
Recorder
Analysis Center
Map Extractor
Route ETL
Analyst Component
Database
User (Analyst)
Decision
Maker
Simulation
Prediction
Analytic
Internet
Identifier Location (with identifier)
Jalan PJS 1/29 • 3.080437,101.646699 (Jalan PJS 1/29 intersection 1)
• 3.080577,101.645561 (Jalan PJS 1/29 intersection 2)
• 3.080705,101.644574 (Jalan PJS 1/29 intersection 3)
= , ( , , )
3.080577,101.645561
3.080705,101.644574
3.080437,101.646699

4. (ID_R_1). If not, the process involves creating new frequency data
of the road information along with its properties and saving the data
into the Frequency database (db_D). Otherwise, the process
involves checking if the geolocation (list C) is located on the same
road (ID_R_1) or not. If the geolocation is not on the same road,
the information is saved as frequency data into the db_D. The
process is then repeated until all the information in the Transposed
database (db_A’) have been processed.
Figure 4. Process flow of calculating route frequency
3.4 Route Color Encoding
After the process of generating route frequency information, the
data is then sorted in matrix order, in which columns denote roads
and properties such as a vehicle, date/time or region sorted into
rows. Dimension reduction techniques are used to obtain
percentages for each road (column). Then, the matrix along with
the road information (selected vehicle, date/time or region) and its
percentage is saved in the Route Statistic database (db_E). The last
execution module is to update the route statistic information with a
color encoded equation. A range of colors to be encoded is then
defined at least using color space as RGB (Red, Green, and Blue)
or CMYK (Cyan, Magenta, Yellow and Key (Black)) components.
Finally, the new layer is displayed using the encoded equation
based on all generated information overlaying it with a predefined
map.
3.5 Prototype Implementation
We built a prototype implementation based on the technique
discussed earlier, using AVLS sample data. The area with the
highest crime and population is selected for this prototype. The
ALVS data is plotted and shown as illustrated in Figure 5.
Figure 5. Sample for police patrol car captured from AVLS
The figure 5 shows a sample for police patrol car captured for every
3 seconds for 10 days. The total size for this data is around 10 GB
raw text files. As stated in the previous section, AVLS only
provides the location for the patrol car where all tracks are shown
in the red dot.
Based on this example, let say 1,2 is the equation for segment
connecting location 𝑡𝑎 and 𝑡𝑏, constrained by 1,2 and 1,2, and let
say 𝑊𝐴 is the set of segments for road identified by ID 1, where
𝑊1 { 1,2, 2,3, 2,4, 3,5} from db_B. We define 𝐹(𝑊
𝑛) and
𝐹( 𝑖,𝑗) as the frequency function for road 𝑊
𝑛 and segment 𝑖,𝑗
respectively. Let say 𝑊 is the collection of all roads, i.e. 𝑊
{𝑊1, 𝑊2, … , 𝑊𝑁}. These collections are stored in db_A’. For any
given patrol car location 𝑃 = (𝑢, 𝑣) from db_A, the minimum
distance for 𝑃 for any road, min 𝑑(𝑃, 𝑎,𝑏) will be calculated and
assigned to set as 𝑊
𝑛 . The canonical frequency 𝑊
𝑛 will be
increased by one and stored into db_D. This calculation is for an
aggregated 1-day time span. We need to normalize all the frequency
to apply a color-coded function. Based on all daily collection of 𝑊,
we define the distribution of 𝑊 as 𝑉~𝑁(𝜇, 𝜎) and stored it in
db_E. Now we can apply color-coded for visualization. For
example, used in Figure 6, we defined red for 𝐹(𝑊
𝑛) ≤ −2𝜎,
yellow for −2𝜎 < 𝐹(𝑊
𝑛) ≤ −𝜎 and green for otherwise.
Figure 6. Applying route frequency method on a police patrol
car
By using the same technique, it can also be applied for crime data
by creating new db_A, db_A’ and db_D. The db_B will be reusable
as a reference to capture the nearest road segment for each criminal
case as shown in Figure 7.

5. Figure 7. Applying route frequency using crime data
As an extension of this approach, we used correlation to see the
pattern between police patrolling and crimes. Let say 𝐹(𝑊
𝑛) is the
frequency of patrol car for road ID 𝑛, and 𝐹(𝑈𝑛) is the frequency
of crime case at road ID 𝑛, the correlation between frequency of
patrol car and criminal cases is calculated using Pearson
Correlation Coefficient equation for particular road ID 𝑛, i.e.,
𝜌𝑊𝑛,𝑈𝑛 = cov(𝑊
𝑛, 𝑈𝑛) (𝜎𝑊𝑛𝜎𝑈𝑛)
⁄ , with a difference that all sets
of 𝑊
𝑛 is based on 30 minutes interval. This is for creating rows to
compute the correlation. Figure 8 shows the correlation between
traffic density of police patrol car and crime frequency, based on
the road. This shows that the green is sufficient for police
patrolling, but for red or yellow road segment shows that there is a
crime happening in that area but police are patrolling less in the said
road.
Figure 8. Example of correlation of crime data and police
patrol car, populated using route frequency
Our prototype system simulated crime hotspot and route
frequencies correlation over time that can be used for patrol
allocation planning. The data also can be manipulated in many
ways using any existing analytical tools as a part of predictive and
analytics policing and management. The various correlation and
analytical data such as route frequencies over an area, time and
crime index is the best way to predict the pattern of criminal time
activities for short-term decision making. The other data such as
the number of enforcement resources and skills and population
density can be added to predict more criminal patterns that
beneficial for long-term policies related to urban safety.
4. CONCLUSION
We have presented an approach to discuss the issue faced by police
forces of inefficient patrolling. The approach aims to offer a system
for planning patrols by directing them to problem areas to reduce
crime levels. The density information is digitally coded on any
transmission channel and used to propose a new route planning
simulation for an enforcer. The route frequency output of our
approach also can be used for further analysis of prediction and
analytics such as the police patrol car and crime data correlation as
shown in our prototype. Based on the route frequency simulation,
police forces can plan the resources to be assigned in a dedicated
area at a specific time.
Future work in this area includes the accident frequency and
allocation resources of police forces in patrol and population
density. We hope to extend the analysis of density to allow
enforcement agencies to optimize their resources in route planning
of crime areas and traffic events. The collected data also may not
only from AVLS but from any GPRS tool such as body-worn
cameras for law enforcement.
5. ACKNOWLEDGMENTS
The cooperation of the PDRM is gracefully acknowledged as
without this support, the prototype project would not be possible.
This work was also supported by our Director Head Lab, Azhar bin
Abu Talib from National Security Lab, MIMOS Berhad.
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