International Journal of Scientific Engineering and Technology Research
Volume.03, IssueNo.03, March-2014, Pages:
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ISSN 2319-8885
Vol.03, Issue.03, March-2014, Pages:
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 Kumar1, K.Selvavinayagam2, S.Suresh Babu3
PG Scholar1,Associate Professor2, Dean –R&D3, Department of Civil Engineering, Adhiyamaan College of Engineering, Hosur, Tamilnadu, India, E-mail: .
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).
International Journal of Scientific Engineering and Technology Research
Volume.03, IssueNo.03, March-2014, Pages:
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 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.
International Journal of Scientific Engineering and Technology Research
Volume.03, IssueNo.03, March-2014, Pages:
Fig.1 Study Area
International Journal of Scientific Engineering and Technology Research
Volume.03, IssueNo.03, March-2014, Pages:
Fig.2 Google Satellite Image of Study Area.
IV. Methodology And Methods
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.
Fig.3 Methodology flow chart
V. 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 Co-ordinates. 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:
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.
Fig.5 Robbery crime scene over period of 2010-2012Fig.7: Grave Burglary Day Crime scene over period of 2010-2012
Fig.6 Auto theft crime scene over period of 2010-2012
Fig.8: Grave Burglary Night Crime scene over period of 2010-2012
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.
Fig.10 3d Model Represent Clustered buildings with High Intensity Property Crime.
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.
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.