International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 8 - Mar 2014
Assessment of Crime & its Mapping Using Remote
Sensing & 3D Geo-Spatial Model for Chennai City
Lenin Barath Kumar.D#1, Selvavinayagam.k#2, SureshBabu.S#3
1
2
PG Scholar, Department of Civil Engineering, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India.
Associate Professor, Department of Civil Engineering, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India.
3
Dean, R&D, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India.
Abstract— This study integrates a combined set of methods and
techniques for 3D Geo-spatial virtual environment and mapping
property crimes in Chennai city for a period of three years
(2010-2012). The creation and presentation of 3D city model are
achieved through Google Sketch up using Google satellite image
and hotspot areas are generated based on reliable crime record
from (CCRB) Chennai police department. The relationship
between crime clusters and their spatial neighborhood are
analyzed through kernel density estimation function this lead to
hotspot crime incidents. The intensity of property crimes in
higher rate and in lower rate is differentiated through 3D
modelling and hotspot mapping. Although, the creation and
display of 3D city models for large, wide areas is difficult, it is
vital for planning and designing safer cities and as well as public
places. The particular region of a city display the intensity of
crime whereas, this lead to nefarious activity over the regions.
The study has provided valuable information concerning
property crimes in Chennai city, including the social and
infrastructural characteristics of these areas that contribute to
the localized criminal activity.
Keywords— GIS, Remote Sensing, 3D Modeling, Kernel Density
Estimation, Chennai Crime Record Bureau (CCRB).
I.
INTRODUCTION
The Crime or criminal offense is a harmful act not only to
an individual, but as well as to the community, such acts are a
phenomenon which is universal in its varying forms in all
cultures and societies, at all stages of organization. The rate of
crime events is increasing in all developing countries due to
transform of capability and majestic lifestyle and also due to
poor social, political, and environmental conditions. Crime
assessment is the measurement of the impact(s) of the
responses on the targeted crime/disorder problem using
information collected from various sources, both before and
after the responses has been implemented. The Manual Pin
maps were traditional methodology and age-old system of
criminal record maintenance has failed to exist up to the
requirements of the existing crime scenario. The solution to
this ever-increasing problem lies in effective use of
Information Technology. Today, with the rapid progress of
applied science, computer – based techniques for exploring,
visualizing and explaining the natural event of criminal
activity have been indispensable. One of the most influential
explorations of spatial distribution of crime mapping has been
GIS. Geographic Information System(GIS) and Remote
sensing(RS) uses geography and digital maps as an interface
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for integrating and accessing massive amounts of locationbased information. 2D crime visualization deals with the issue
of how to define adequate threshold values for Choropleth
maps regarding certain hot spots. Oftentimes, it is quite
equivocal about what value exactly a certain hotspot can be
considered as to be “hot”. The three dimensional Geovisualization, information provides a spatial relation between
the building level in easy to comprehend way and allow police
personnel to plan effectively for emergency response,
determine mitigation priorities and predict future events.
Markus Wolff el al approached crime assessment in cologne
city, Germany the researcher focused on analysis, integration,
visualization of crime data into three dimensional Geo-virtual
environments and the kernel density estimation technique
were implemented through ArcGIS to identify the robbery
clusters and burglary Hotspots. Jaishankar et al analyzed and
mapped property crime occurrences in Chennai city, India the
researcher succeeded in identifying the Hotspots of crime,
proximity of crime with police stations and a displacement of
crimes through crime stats and ArcGIS software. Ahmed et al
implemented the temporal distribution of crime pattern and
high crime rated region through computerized crime mapping
in Dala LGA of Kano State, Nigeria. Based on spatial and
temporal information of incidents through law and order
department Akpinar et al emphasized higher rates of burglary,
auto theft incidents in Cankaya district, Ankara province,
Turkey. Thangavelu et al assessed spatial distribution of rural
crime mapping in Coimbatore district of Tamilnadu, India
based on the crime records thematic map generated which
results in reduction of crime incident report.
II.
OBJECTIVE
•
To analyze and identify the hot spots and thematic
maps based on crime incidence using Geo-spatial
pattern recognition for the study area.
•
To Prepare Geo-spatial database for the subject
region.
•
Creation of 3D model to infer spatial pattern using
Google satellite image.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 8 - Mar 2014
III.
STUDY AREA
Chennai is the capital city of Tamilnadu, India. It covers over
464 sq.km and geographically lies between 13˚14' 30''N 12˚52' 30'' N Latitude and 80˚01‘15’’ E - 80˚15' 45 '' E
Longitude. It is the fourth largest metropolitan and sixth
densely populated urban center in India. The urban and
suburban agglomeration of the metropolis holds a combined
population of 8.9 million. The Economy of the metropolis
holds a wide industrial base in the automobile, Information
technology Business process outsourcing and Hardware
manufacturing. The Greater Chennai police are the primary
law enforcement agency in the city, as of 2011 the city covers
jurisdiction over 745 km2. The city was divided into four
policing zones North, East, West & South with three districts
of each (12 districts) and forty eight ranges with 172 police
stations.
Fig.1 Study Area
IV.
METHODOLOGY A ND METHODS
The methodological approach includes primary and
secondary information, primary determine the Satellite data of
the study area, which is accumulated from the Google Earth
Map and secondary is the aggregation of crime details from
the Chennai police department. The digital maps are generated
using ArcGIS software and the police jurisdiction level is
extracted from ward map of study area along with Police
District map. The Google satellite image is primary data used
to get three dimensional Geo-spatial model using Google
sketch up pro. The Crime analysis outcome is based on
hotspot maps and digital Choropleth maps from reliable
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information about property crimes on police record, which are
produced and generated using Arcmap and 3D model
represents the density of buildings and spatial pattern
distribution in the crime intense region. The relationship
between crime clusters and their spatial neighborhood are
analyzed by kernel density estimation function this led to
hotspot crime incidents. The volume of property crimes in
higher rate and in lower rate is differentiated through 3D
modelling. Although, the creation and showing of 3D city
models for large regions is difficult, it is vital for designing
and designing safer cities, as well as public spaces. The some
part of city display the intensity of crime incident whereas,
this led to nefarious activity over the region.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 8 - Mar 2014
Fig.2 Methodology flow chart
V.
Fig.3 Google Satellite Image of Study Area.
RESULTS AND D ISCUSSION
The present study is based on authentic data from the
Chennai crime record bureau (CCRB) of the Chennai police
department over the year 2010,2011 and 2012.The
criminology detail includes property crimes such as Robbery,
Snatching, Dacoity, Grave Major Theft, Grave Burglary Day
& Night, Automobile Theft, Pickpocket, murder for gain &
otherthefts.
Fig.4 Chennai Police Jurisdiction level
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 8 - Mar 2014
A. Analyzation of Hotspots
The dataset which contains property crime details is
represented as an individual point, geocoded by X and Y Coordinates. Beyond these Co-ordinates each point has further
attributes describing the time of crime. To identify areas that
are characterized by a higher crime density than other areas,
hotspot analysis is evaluated. A hot spot is an arena that
receives a greater than ordinary value of crime issues,
therefore the Hotspot is defined by Sherman (1995) “as small
places in which the occurrence of criminal offense is so
frequent that it is highly predictable, at least over one year
period of time”. Hot spot analysis is accomplished by
transforming the discrete point distribution of crime scenes to
a continuous surface of crime scene density. The hotspots are
identified and visualized using kernel density estimation
through ArcGIS version10. Based on a given point dataset,
this technique calculates a grid whose cell values represent
density values related to a certain surface measure (for
instance number of crime scenes per square kilometre).
For this purpose KDE-algorithms overlay a study area with
a grid of user definable cell size. In a second tone, density
values for each cell are calculated – depending on the
implemented kernel density function. Here KDE is
implemented with a quadratic kernel density function.
Fig.5 Robbery crime scene over period of 2010-2012
With t = d ij / h , h as bandwidth, i as crime scene position.
The kernel density estimation (KDE) is defined to reflect the
belief that there is a greater probability of an incident
occurring in a given location the closer it is to the location of a
known incident. The representation of point based clusters
helps the police with information on the locations affected by
frequent crime incidents and high crime rates. With heedful
and deliberate planning the hotspots could be turned into
lesser crime prone zones.
The grave burglaries are one of the major types of crime
occurrence of 2010-2012 in Chennai city. Fig 7 & Fig 8
presents an interesting representation of the Grave Burglary
Day (GBD) & Grave Burglary Night (GBN) respectively. The
zones such as Saidapet, Madipakkam, perungudi, Sankar
nagar, Chormpet, Ambattur, villivakkam and its neighboring
areas are more prone to daytime burglary. In this area, the
residents may be more working professionals, and both
spouses may be going for work.The impression of GBD is
lower as compared to GBN, whereas the hotspots of Grave
Burglary Night is distributed over all major part of city. The
region of high intensity of GBN are in Ennore, Thiruvotriyur,
Madavaram, Washermenpet, Flower bazar, Broadway, Central,
Villivakkam, Kolathur, perambaur, pulianthope, Vyasarpadi,
Fig.6 Auto theft crime scene over period of 2010-2012
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 8 - Mar 2014
MKB nagar, Ambattur, Maduravoyal, Thiruverkadu,
Ponamalle, Mankadu, Tambaram, Slaiyur, pallikaranai,
Thoraipakkam, Neelankarai. The zone of core region of city
like T.nagar, Vadapalani, Thirumangalam, Anna nagar,
Nungambakkam, kodambakkam, Saidapet, valasaravakkam,
virugambakkam, Mylapore, Teynampet, Triplicane, Foreshore
Estate and some south zone of city as Thiruvanmiyur,
Peerkankaranai, and Madipakkam has very high intensity
GBN crime prone. The outcome shows that the affected
region is Commercial zones, Upper middle class and middle
class Residential zones which can be controlled by
displacement of intensive police patrolling in those areas. The
area where criminals resided in Chennai city is the areas that
are disorganized. This finding corroborates with social
Disorganization theory (Shaw and McKay 1942) suggests that
the “Economic composition of a local community is related to
crime rates”. When a community is not unified, lack the
values and lack the interest in protecting the neighborhood, an
arena is considered socially disorganized. Indicators of this
include high unemployment rates, high school dropout rates
and low income level, economically poor and transient.
Fig.8. Grave Burglary Night Crime scene over period of 2010-2012
In general perception that crimes will occur well away from
the Police stations. Yet, when the data are plotted a different
image was obtained, crimes occur anywhere in the city
irrespective of whether a Police station is present or not in the
locality. It is to be noted that police need mobility, and the
police officer of a particular police station will always be
away from their own area. Therefore, those station areas
become a more vulnerable target for the criminals. This
necessitates the police to ensure that sufficient protection is
given to the people, as the criminals seem to take advantage of
any loopholes in the scene of the security.
B. 3D Models for Crime scene analysis
Fig.7. Grave Burglary Day Crime scene over period of 2010-2012
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The growing interest in construction of 3d models of urban
environment may increase the supply of remotely sensed data
concerning the 3d visualization of model more feasible and
popular. The methodology adopted for the development of 3d
model is in fig 2. Initially, the High resolution satellite image
is downloaded from Google Earth satellite map, it is then geo
rectified through Arc Map. To provide a basis for subsequent
urban crime data visualization, a three dimensional Geo
virtual environment is created for some part of Chennai city to
measure high intensity of a crime prone area and low intensity
of crime prone area.
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Fig.9. 3d model representation of Chennai city – Flower bazaar & Washermenpet Police station range
The fig.10 below represents a high crime intensity zone. The
clustered arrangement of buildings in the area makes it highly
vulnerable to occurrence of property crimes. The narrow roads
between the buildings make it difficult to monitor traffic and
pedestrian movement in and around flower bazaar and
Washermenpet. This leads to property crimes such as robbery,
pick-pocketing, grave burglaries, ordinary burglaries,
snatching and Auto-theft in this area. The congested
environment makes it difficult for the police to trace and
capture the law-breakers in time. Thus, surveillance cameras
may be mounted at suitable locations for providing aid to
police in monitoring such clustered environments.
Fig.10. 3d Model Represent Clustered buildings with High Intensity Property Crime
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Fig.11. Chart represents high intensity and low intensity of property crime over three year period of time
Fig.12. Table shows high & Low intensity of property crime over three year period of time.
Fig.13 3d model representation of Chennai airport - Meenambakkam police station range with low intensity of crime.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 8 - Mar 2014
The Geo-spatial nature of the area in the form of
undulations and hilly terrain surrounding the Meenambakkam
(fig.13) has lead to poor settlements and low urban
agglomeration. This makes it less vulnerable to property
crimes such as Robbery, grave burglary Day and Night,
Ordinary Burglary Day and Night, Snatching, Auto theft,
Pickpocket and other theft which shown in fig 11. Due to
large open spaces for maneuvering aircrafts, it makes the area
more secured in terms of increased surveillance with the help
of cameras and security force.
The 3d city model is increasingly used for the
demonstration, exploration and evaluation of urban patterns.
Real time 3D Geo-visualization and interactive exploration of
models help decision makers and planning authorities for
obtaining better solutions. 3d models not only provide
visualization of existing spatial entities, but also serve as a
means of planning future infrastructural designs and
forecasting changes in spatial entities.
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C. GIS Analysis
By integrating types of crime maps the intensity of the crime
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section of Chennai has poor spatial distribution planning
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better analyzation and to curb crime.
VI.
CONCLUSION
The outcome of this study integrates assessment of crime
mapping with collateral information pertaining to crimes
obtained from the Chennai Crime Record Bureau (CCRB).
This study has provided valuable information concerning
property crimes in Chennai city, including the social and
infrastructural characteristics of the particular area that
contributes to the localized criminal activity. The major areas
of crime scene were analyzed through Kernel density
estimation technique, this lead to Hotspot identification. The
3D geo spatial information and the maps will serve as a guide
for crime affairs / CBI officers / Police Department /
Ambulances / Surveyors in identifying the proper study of
environmental assistance to the population who would be
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ACKNOWLEDGMENT
It is our privilege to express our gratitude to Mr. Inba
Kumar, Assistant commissioner of police, Head of Chennai
Crime Record Bureau and we extend our thanks to Mr.
Narashiman, Sub-inspector of police, CCRB and Mr.
Jeganathan, Sub-inspector of police, Social and Justice
Welfare.
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