ISSN 2319-8885
Vol.03, Issue.03,
March-2014,
Pages:
www.semargroup.org,
www.ijsetr.com
Assessment of crime & its mapping using Remote Sensing & 3D Geo-spatial
model for Chennai city
D.LENIN BARATH KUMAR1, K.SELVAVINAYAGAM 2, S.SURESH BABU
3
PG Scholar1,Associate Professor2, Dean –R&D3, Department of Civil Engineering, Adhiyamaan College of
Engineering, Hosur, Tamilnadu, India, E-mail: leninbarath@gmail.com.
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 display of 3D city model are
achieved through Google Sketch up using Google satellite image and hotspot regions are generated based on reliable crime
record from (CCRB) Chennai police department. The relationship between crime clusters and their spatial neighborhood are
analysed through kernel density estimation function this led to hotspot crime incidents. The intensity of property crimes in
higher rate and in lower rate are differentiated through 3D modelling. 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 some region of
city display the intensity of crime incident 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 characteristic of these
areas that contribute to localized criminal activity.
Keywords: GIS, Remote Sensing, 3D Modeling, Kernel Density Estimation, Chennai Crime Record Bureau (CCRB).
I.
INTRODUCTION
This Crime or criminal offence is a harmful act not only to an
individual but also to the community, such acts are
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 and age-old system of criminal record
maintenance has failed to live 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 rapid advancement of technology, computer –
based techniques for exploring, visualizing and explaining
the occurrence of criminal activity have been essential. 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 for integrating and accessing
massive amounts of location-based information. 2D crime
visualization deals with the issue of how to define adequate
threshold values for Choropleth maps regarding certain
hotspots. Often it is rather ambiguous from what value
precisely a certain hotspot can be considered as to be “hot”.
The three dimensional Geo-visualization information provide
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
occurrence in Chennai city, India the researcher succeeded in
identifying the Hotspots of crime, proximity of crime with
police station and displacement of crimes through crime stat
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 the high 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
International Journal of Scientific Engineering and Technology Research
Volume.03, IssueNo.03, March-2014, Pages:
records thematic maps generated, which results in decrease
of crime incident report.
II.
OBJECTIVE
•
To analyse 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 study area.
•
Creation of 3D model to infer spatial pattern using
Google satellite image.
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 city in India. The urban and suburban
agglomeration of the city has a combined population of 8.9
million. The Economy of city has a broad industrial base in
the automobile, Information technology Business process
outsourcing and Hardware manufacturing. The Greater
Chennai police are the main law enforcement agency in the
city, as of 2011 the city covers jurisdiction over 745 km2.
The city was divided into four police zones North, East West
& South with three district of each and forty eight ranges
with 172 police stations.
Fig.1 Study Area
International Journal of Scientific Engineering and Technology Research
Volume.03, IssueNo.03, March-2014, Pages:
Fig.3 Methodology flow chart
V.
Fig.2 Google Satellite Image of Study Area.
IV.
M ETHODOLOGY AND M ETHODS
The methodology approach includes primary and
secondary data, primary defines the Satellite image of study
area is collected from Google Earth Map and secondary is
collection of crime details from 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 are main data used to generate 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 information of property
crimes on police record, which are created and generated
using Arcmap and 3D model represent the density of
buildings and spatial pattern distribution in crime intense
region. The relationship between crime clusters and their
spatial neighbourhood are analysed by kernel density
estimation function this led to hotspot crime incidents. The
intensity of property crimes in higher rate and in lower rate
are differentiated through 3D modelling. Although, the
creation and display of 3D city models for large regions is
difficult it is vital for planning and designing safer cities, as
well as public places. The some part of city display the
intensity of crime incident whereas, this led to nefarious
activity over the region.
RESULTS AND DISCUSSION
The present study is based on reliable data from Chennai
crime record bureau (CCRB) of Chennai police department
over the year 2010,2011 and 2012.The criminology detail
include property crimes such as Robbery, Snatching, Dacoity,
Grave Major Theft, Grave Burglary Day & Night, Automobile
Theft, Pick pocket, murder for gain & other thefts.
A. Analysation of Hotspots
The dataset which contain 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 offence. To identify areas that
are characterised by a higher crime density than other areas,
hotspot analysis is evaluated. A hotspot is an area that has a
greater than average value of crime events, therefore the
Hotspot is defined by Sherman(1995) “as small places in
which the occurrence of crime is so frequent that it is highly
predictable, at least over one year period of time”. Hotspot
analysis is achieved 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 crimes scenes per
square kilometre). For this purpose KDE-algorithms overlay a
study area with a grid of user definable cell size. In a second
step, density values for each cell are calculated – depending
on the implemented kernel density function. Here KDE is
implemented with a quadratic kernel density function.
International Journal of Scientific Engineering and Technology Research
Volume.03, IssueNo.03, March-2014, Pages:
Assessment of crime & its mapping using Remote Sensing & 3D Geo-spatial model for Chennai city
D.Lenin Barath Kumar, K.Selvavinayagam, S.Suresh Babu
Fig.4 Chennai Police Jurisdiction level
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
help the police with information on the location 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 on 2010-2012 in Chennai city. Fig 7 & Fig 8
gives an interesting representation of Grave Burglary Day
(GBD) & Grave Burglary Night (GBN) respectively. The
zones such as Saidapet, Madipakkam, perungudi, Sankar
nagar, Chormpet, Ambattur, villivakkam and its
neighbourhood 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 are distributed over all major part of
city. The region of high intensity of GBN are Ennore,
Thiruvotriyur, Madavaram, Washermenpet, Flower bazar,
Broadway, Central, Villivakkam, Kolathur, perambaur,
pulianthope,
Vyasarpadi,
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.
International Journal of Scientific Engineering and Technology Research
Volume.03, IssueNo.03, March-2014, Pages:
Assessment of crime & its mapping using Remote Sensing & 3D Geo-spatial model for Chennai city
D.Lenin Barath Kumar, K.Selvavinayagam, S.Suresh Babu
Fig.5 Robbery crime scene over period of 2010-2012
Fig.6 Auto theft crime scene over period of 2010-2012
Fig.7: Grave Burglary Day Crime scene over period of
2010-2012
Fig.8: Grave Burglary Night Crime scene over period of
2010-2012
International Journal of Scientific Engineering and Technology Research
Volume.03, IssueNo.03, March-2014, Pages:
Assessment of crime & its mapping using Remote Sensing & 3D Geo-spatial model for Chennai city
D.Lenin Barath Kumar, K.Selvavinayagam, S.Suresh Babu
The result shows that the affected region are 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 area is
considered socially disorganized. Indicators of this include
high unemployment rates, high school dropout rates and
low income level, economically poor and transient.
In general perception that crimes will occur well away from
the Police stations. However, when the data is plotted a
different picture 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 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
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 followed for the
development of 3d model is in fig 3. Initially, the High
resolution satellite image is downloaded from Google Earth
satellite map, it is then Geo rectified through Arc Map. To
provide a basic 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 crime prone area and low intensity of crime
prone area.
Fig.9 3d model representation of Chennai city – Flower bazaar & Washermenpet Police station range.
International Journal of Scientific Engineering and Technology Research
Volume.03, IssueNo.03, March-2014, Pages:
Assessment of crime & its mapping using Remote Sensing & 3D Geo-spatial model for Chennai city
D.Lenin Barath Kumar, K.Selvavinayagam, S.Suresh Babu
Fig.10 3d Model Represent Clustered buildings with High Intensity Property Crime.
160
140
No. of crime occurence
Robbery
120
Grave Burgalary day
Grave Burgalary night
100
Grave Majore Theft
80
Ordinary Burgalary Day
60
Ordinary Bugalary Night
Other Theft
40
Snatching
20
Pick pocket
Auto Mobile
0
flower bazzar
washermenpet
Meenambakkam
Area of crime occurence
Fig.11 Chart represents high intensity and low intensity of property crime over three year period of time
International Journal of Scientific Engineering and Technology Research
Volume.03, IssueNo.03, March-2014, Pages:
Assessment of crime & its mapping using Remote Sensing & 3D Geo-spatial model for Chennai city
D.Lenin Barath Kumar, K.Selvavinayagam, S.Suresh Babu
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.
The Geo-spatial nature of the area in the form of
undulations
and
hilly
terrain
surrounding
the
Meenambakkam has led 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,
Pick pocket and other theft as shown in fig 11. Due to large
open spaces for manoeuvring aircrafts, it makes the area
more secured in terms of increased surveillance with the
help of cameras and security personnel. 3d city model are
increasingly used for the presentation, exploration and
evaluation of urban designs. 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 visualisation of existing spatial
entities, but also serve as a means of planning future
infrastructural designs and forecasting changes in spatial
entities.
C. GIS Analysis
The outcome of this study integrates assessment of crime
mapping with collateral information pertaining to crimes
obtained from Chennai Crime Record Bureau (CCRB). This
study has provided valuable information concerning
property crimes in Chennai city, including the social and
International Journal of Scientific Engineering and Technology Research
Volume.03, IssueNo.03, March-2014, Pages:
Assessment of crime & its mapping using Remote Sensing & 3D Geo-spatial model for Chennai city
D.Lenin Barath Kumar, K.Selvavinayagam, S.Suresh Babu
infrastructural characteristic of the particular area that
contributes to localized criminal activity. The major areas of
crime scene were analysed through Kernel density
estimation technique, this lead to Hotspot identification.
The 3D geo spatial information and the maps will serve as
guide for crime affairs / CBI officers / Police Department /
Ambulances / Surveyors / in identifying the proper study of
environmental assistance for the population who would be
benefitted from the new interventions.
VI.
CONCLUSION
The outcome of this study integrates assessment of crime
mapping with collateral information pertaining to crimes
obtained from Chennai Crime Record Bureau (CCRB). This
study has provided valuable information concerning
property crimes in Chennai city, including the social and
infrastructural characteristic of the particular area that
contributes to localized criminal activity. The major areas of
crime scene were analysed through Kernel density
estimation technique, this lead to Hotspot identification.
The 3D geo spatial information and the maps will serve as
guide for crime affairs / CBI officers / Police Department /
Ambulances / Surveyors / in identifying the proper study of
environmental assistance for the population who would be
benefitted from the new interventions.
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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Volume.03, IssueNo.03, March-2014, Pages: