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Mapping and monitoring suburban crimes using high resolution data
O M Murali, R Pari
WTI Advanced Technology Ltd,
98 Peters Road, Chennai 600 086
Tel: 91-44-8260178/8214442
Fax: 91-44-8272876
Email: wtiatl@eth.net, murali_om@hotmail.com
O M Murali, R Pari
WTI Advanced Technology Ltd,
98 Peters Road, Chennai 600 086
Tel: 91-44-8260178/8214442
Fax: 91-44-8272876
Email: wtiatl@eth.net, murali_om@hotmail.com
Abstract
In recent years, the crime pattern, which is occurring in Chennai, mostly confined to suburban areas, rather than the city core. In other words, majority of burglary, forcible entry, theft, house loot, murder, etc. were committed more in suburbs. This situation had put tremendous pressure on police force to find new ways of improving the safety to suburbs which remained cut-off from well connected roads, accessibility, and isolation from the urban centers. Also, sub-urban areas were peculiar in its activity which favoured range of human activities because of its vastness, emptiness, and well suited for prolonged activity without notice have really encouraged certain crimes to flourish.
This way, pornography was going on unnoticed without trace for many years in isolated bunglow at suburban Anna Nagar in Chennai. It was actually serving the patients in the day time with active engagement in porn related activities during late hours by a prominent doctor. Yet another incidence received wider attention was suburban Ambattur where 5 Rupee coin was illegally manufactured in isolated locality. This way, certain crimes go unnoticed for years together due to their favourable location in suburbs which motivated us to handle this type of crime with technologies like GIS to better understand the situation.
These criminal incidences helped us to understand the geography of Chennai and its various social activities across the city, particularly in the suburbs. Initially for our own understanding, entire Chennai was captured in digital form with existing road networks. Extensive knowledge of the city helped in easy demarcation of different areas which segregated the CBD (Central Business District) and the target area – suburban. Information gathered from various sources ranging from daily newspapers to unreported crimes which took place in suburban areas had been carefully analysed. This helped us in distinguishing between crimes committed in urban core areas and that of suburban. Based on this, a GIS based map was prepared to know the maximum occurrence of crimes and it was found to be the suburban which outnumbered the CBD in certain crime types.
Crimes varied to a level and became fresher, well organized and often untimely, in the sense, that even house locked for 3 hours became the ideal target house for criminals to attack during such hours also. This way, criminals started monitoring every individual houses in suburbs with geographic precision. This level of attack by criminals has really brainstormed our attention from simple mapping of suburban crime locations to individual house level details which is possible only with high resolution data. This high resolution mapping actually helps in collecting, storing and monitoring even individual house level. This house level information alone can help better understand and monitor the increasing suburban crimes. This approach has proved to be very much useful and capable enough to map individual houses to improve safety measures, increase security personnel to isolated and long-locked houses and to increase the day time patrol in vulnerable areas.
As a sample study, small area was taken with demographical data like no. of persons in a house, house type, duration of lock, securities and other details were collected and integrated into a database linked to a geographical data represented as polygon (houses) for further analysis.
High resolution orthophoto was used for capturing the individual houses in AutoCAD Map. Upon conversion into vector form, relevant demographical data of individual houses were added. Only with such high resolution aerial photo or satellite data like IKONOS, can this suburban crime be better mapped, monitored for safety and to know the future vulnerable areas. It also aids in easy retrieval and manipulation of data, to update new houses, its population type, etc. to enable periodic report generation to check the crimes, its changing pattern and to improve the ultimate safety of suburbanites.
Such high resolution data can be very much useful to police, social scientists, urban planners, and NGOs to better understand the dynamics of suburban areas. This study is the first of its kind in India to highlight the use Orthophoto for suburban crimes. It can really help improve the law and order situation, particularly in the metros of India.
1. Introduction
Last year at suburban Anna Nagar in Chennai, the production and illegal distribution of pornography related activities was flashed across and upsetting the general public. The surprising fact was that the person himself was a prominent doctor who was heading this operation and sending to foreign countries. Such illegal activities were going on for quite long time without any trace or notice even to the neighbouring houses because that independent house was actually serving the patients and registered as clinic. In yet another incident, at suburban Tambaram Sanatorium, a house was looted by criminals who forcibly entered at midnight breaking the doors and escaped without trace with jewels and money worth around a lakh. The victims were unaware of this, sleeping in the adjacent room. In this looting case, the jewels were brought home just a day before from the bank locker to the house for a function, and this indicated that the victim was being closely monitored by criminals prior to the attempt. These were few incidences, particularly happening in suburban parts of Chennai where houses of independent nature, depending upon the duration of day and night time lock, presence of aged people and other related social factors make the houses ideal hunting ground (suburban) for the notorious criminals who show their hands at frequent intervals across these vast areas of suburban Chennai. Keeping this in mind, this attempt has been made to highlight the potential application of high-resolution data in mapping and monitoring the crimes taking place in suburban parts of the major cities of India. This in turn, help periodically monitor the status of individual houses to increase security personnel at vulnerable locations and to long locked houses, and help to initiate community participation which is the key to check such incidences.
2. Methodology
In order to understand the suburban geography and the occurrence of crimes, initially, the complete Chennai city was captured with different areas. Prominent areas have been delineated with other areas merged with the existing well known areas. Data obtained from various sources have been used to populate the polygons and from that it was found that the suburban areas came under the grip of criminals for various crimes like robbery, theft, loot, murder, etc. It was also noticed that the primary areas frequented by criminals include Velachery, Tambaram, Pallavaram, Chrompet, Pammal, Madippakkam, Nanganallur, Ennore, Anna Nagar, Vyasarpadi, Thiruvottiyur, Pulianthope, Madhavaram, Manali, Avadi, Adampakkam and Ambattur. Interestingly, these areas fall under suburban category in Chennai as shown in the Figure 1 in blue color (dark color). Of these areas, few of them are industrial belts.
Many social and geographical reasons have been responsible for such increasing crimes in Chennai and other major metros of India. Changing lifestyles, migration from nearby villages and towns to major cities in search of prosperity, poor standard of living, unemployment, poverty, declining joint family system, cheap availability of land in suburban areas, etc. directly or indirectly reflect in terms of increasing crimes.
Figure 1 shows suburban crime prone areas shown in Blue (dark color) in Chennai
This simple mapping of suburban areas have not helped in better understanding the trend owing to the shifting methods adopted by the criminals. Also, they started targeting houses, which remain locked during day or night or occupied by aged people.
These incidences across the suburban Chennai helped to focus our attention to high-resolution data which alone raised the hope of mapping and monitoring individual houses at greater details with possible solution.
3. Data Used
In order to handle the suburban crimes, sample data of unknown area covering the suburban type houses have been used to replicate the Indian scenario. From the orthophoto, roads, individual houses and other relevant features have been captured in AutoCAD. All the houses have been captured as polygon and after necessary editing; topology is built to populate the statistical details. This sample data covers 584 individual houses in suburban area as shown in Figure 2.
Figure 2 showing a sample data of unknown area captured from Orthophoto showing the suburban houses
4. Results & Discussion
In this study, the use of high-resolution orthophoto has clearly indicated the immense potential of such data to know the pattern of suburban crime. It also helps to find out the social set up of individual houses and its peculiar activities. The key factor in identifying the vulnerable houses comes from the demographical data which is unique to every house in suburban areas in India. From the query based analysis in GIS environment, it is possible to identify the following set of social factors which come under the grip of criminals:
Independent houses
Nighttime locked houses
Houses locked for more than 3 hours in daytime
Aged people living in the houses
Active during night for illegal activities
Lack of security
The above set of social pattern existing in suburbs is incorporated into the GIS environment. Query based analysis help to identify the potential vulnerable locations and houses. When such studies are initiated at suburban areas of major Indian cities, then it can really help control the crimes. Furthermore, it helps for better user interaction when the data is maintained in GIS environment. With other tabular information like the suburban crimes, its locations, crime type, types of goods stolen, criminal records, etc. then, it gives clue to solve some of the unsolved crimes. This helps to improve the security at large and to have a balanced socio-economic development of any city.
In India, GIS based community policing is catching up and at this juncture, the use of high-resolution data can be really useful to the police across the major Indian cities to tackle this increasing crime. It helps the police to have a regular monitoring of houses in the given area which remains as a base camp of criminals for discussion, exchange of illegal goods, etc. This high-resolution mapping and analysis in GIS environment can greatly help nab the criminals before they carry out some disastrous work.
It also helps to initiate an integrated approach among various other user agencies like police, builders, urban planners, NGOs engaged in community development activities. This helps to plan for better security arrangements in vulnerable areas.
The property lost and sometimes the murder of beloved ones will be priceless. But little investment to capture, store and monitor this data in GIS environment can save lakhs and possibly the life of an individual. The data can be updated and manipulated at frequent intervals to increase security and police patrol to specific areas. This untimely loss of property and lives can be greatly minimized provided technological approach is given priority to control this suburban crime in Indian cities.
5. Conclusion
It is recommended and highly suggested to use high-resolution data to map, monitor and check, specially the suburban crimes in major cities. Also, the cost involved in procuring and installing such GIS data will be less when compared with the amount of distress people are put to after being attacked and killed by the criminals. It is high time now that such study be carried out in Indian cities, particularly the major metros which have been transforming as the base for criminals.
Acknowledgements
Authors wish to express their thanks to Shri. T.R. Srinivasan, President and Shri. P. Siva Kumar, Senior Manager, WTI Advanced Technology Ltd. for their support to carry out this work.
Authors grateful to Dr. Krishna Prasad, Vice President, WTI Advanced Technology Ltd. for his critical suggestion during this work.
Thanks to Shri. S. Vivek, Shri. S. Basker, Shri. S. Saravanan, Shri. G. Sri Ramajayam, Shri. M. Sudhakar for their technical support.
The following parameters have been used to understand and monitor the individual houses across the given suburban area:
Table 1 shows the social parameters used to identify the vulnerable areas in suburban parts of the city (applicable to Indian cities)
Figure 3 showing the independent houses with lock duration more than 4 hours (vulnerable under suburbs of Chennai)
Figure 4 shows the houses vulnerable to varieties of crimes which are independent and having aged people who stay alone in day time as highlighted in yellow (light color) (Typical of suburban crime in Chennai)
In recent years, the crime pattern, which is occurring in Chennai, mostly confined to suburban areas, rather than the city core. In other words, majority of burglary, forcible entry, theft, house loot, murder, etc. were committed more in suburbs. This situation had put tremendous pressure on police force to find new ways of improving the safety to suburbs which remained cut-off from well connected roads, accessibility, and isolation from the urban centers. Also, sub-urban areas were peculiar in its activity which favoured range of human activities because of its vastness, emptiness, and well suited for prolonged activity without notice have really encouraged certain crimes to flourish.
This way, pornography was going on unnoticed without trace for many years in isolated bunglow at suburban Anna Nagar in Chennai. It was actually serving the patients in the day time with active engagement in porn related activities during late hours by a prominent doctor. Yet another incidence received wider attention was suburban Ambattur where 5 Rupee coin was illegally manufactured in isolated locality. This way, certain crimes go unnoticed for years together due to their favourable location in suburbs which motivated us to handle this type of crime with technologies like GIS to better understand the situation.
These criminal incidences helped us to understand the geography of Chennai and its various social activities across the city, particularly in the suburbs. Initially for our own understanding, entire Chennai was captured in digital form with existing road networks. Extensive knowledge of the city helped in easy demarcation of different areas which segregated the CBD (Central Business District) and the target area – suburban. Information gathered from various sources ranging from daily newspapers to unreported crimes which took place in suburban areas had been carefully analysed. This helped us in distinguishing between crimes committed in urban core areas and that of suburban. Based on this, a GIS based map was prepared to know the maximum occurrence of crimes and it was found to be the suburban which outnumbered the CBD in certain crime types.
Crimes varied to a level and became fresher, well organized and often untimely, in the sense, that even house locked for 3 hours became the ideal target house for criminals to attack during such hours also. This way, criminals started monitoring every individual houses in suburbs with geographic precision. This level of attack by criminals has really brainstormed our attention from simple mapping of suburban crime locations to individual house level details which is possible only with high resolution data. This high resolution mapping actually helps in collecting, storing and monitoring even individual house level. This house level information alone can help better understand and monitor the increasing suburban crimes. This approach has proved to be very much useful and capable enough to map individual houses to improve safety measures, increase security personnel to isolated and long-locked houses and to increase the day time patrol in vulnerable areas.
As a sample study, small area was taken with demographical data like no. of persons in a house, house type, duration of lock, securities and other details were collected and integrated into a database linked to a geographical data represented as polygon (houses) for further analysis.
High resolution orthophoto was used for capturing the individual houses in AutoCAD Map. Upon conversion into vector form, relevant demographical data of individual houses were added. Only with such high resolution aerial photo or satellite data like IKONOS, can this suburban crime be better mapped, monitored for safety and to know the future vulnerable areas. It also aids in easy retrieval and manipulation of data, to update new houses, its population type, etc. to enable periodic report generation to check the crimes, its changing pattern and to improve the ultimate safety of suburbanites.
Such high resolution data can be very much useful to police, social scientists, urban planners, and NGOs to better understand the dynamics of suburban areas. This study is the first of its kind in India to highlight the use Orthophoto for suburban crimes. It can really help improve the law and order situation, particularly in the metros of India.
1. Introduction
Last year at suburban Anna Nagar in Chennai, the production and illegal distribution of pornography related activities was flashed across and upsetting the general public. The surprising fact was that the person himself was a prominent doctor who was heading this operation and sending to foreign countries. Such illegal activities were going on for quite long time without any trace or notice even to the neighbouring houses because that independent house was actually serving the patients and registered as clinic. In yet another incident, at suburban Tambaram Sanatorium, a house was looted by criminals who forcibly entered at midnight breaking the doors and escaped without trace with jewels and money worth around a lakh. The victims were unaware of this, sleeping in the adjacent room. In this looting case, the jewels were brought home just a day before from the bank locker to the house for a function, and this indicated that the victim was being closely monitored by criminals prior to the attempt. These were few incidences, particularly happening in suburban parts of Chennai where houses of independent nature, depending upon the duration of day and night time lock, presence of aged people and other related social factors make the houses ideal hunting ground (suburban) for the notorious criminals who show their hands at frequent intervals across these vast areas of suburban Chennai. Keeping this in mind, this attempt has been made to highlight the potential application of high-resolution data in mapping and monitoring the crimes taking place in suburban parts of the major cities of India. This in turn, help periodically monitor the status of individual houses to increase security personnel at vulnerable locations and to long locked houses, and help to initiate community participation which is the key to check such incidences.
2. Methodology
In order to understand the suburban geography and the occurrence of crimes, initially, the complete Chennai city was captured with different areas. Prominent areas have been delineated with other areas merged with the existing well known areas. Data obtained from various sources have been used to populate the polygons and from that it was found that the suburban areas came under the grip of criminals for various crimes like robbery, theft, loot, murder, etc. It was also noticed that the primary areas frequented by criminals include Velachery, Tambaram, Pallavaram, Chrompet, Pammal, Madippakkam, Nanganallur, Ennore, Anna Nagar, Vyasarpadi, Thiruvottiyur, Pulianthope, Madhavaram, Manali, Avadi, Adampakkam and Ambattur. Interestingly, these areas fall under suburban category in Chennai as shown in the Figure 1 in blue color (dark color). Of these areas, few of them are industrial belts.
Many social and geographical reasons have been responsible for such increasing crimes in Chennai and other major metros of India. Changing lifestyles, migration from nearby villages and towns to major cities in search of prosperity, poor standard of living, unemployment, poverty, declining joint family system, cheap availability of land in suburban areas, etc. directly or indirectly reflect in terms of increasing crimes.
Figure 1 shows suburban crime prone areas shown in Blue (dark color) in Chennai
This simple mapping of suburban areas have not helped in better understanding the trend owing to the shifting methods adopted by the criminals. Also, they started targeting houses, which remain locked during day or night or occupied by aged people.
These incidences across the suburban Chennai helped to focus our attention to high-resolution data which alone raised the hope of mapping and monitoring individual houses at greater details with possible solution.
3. Data Used
In order to handle the suburban crimes, sample data of unknown area covering the suburban type houses have been used to replicate the Indian scenario. From the orthophoto, roads, individual houses and other relevant features have been captured in AutoCAD. All the houses have been captured as polygon and after necessary editing; topology is built to populate the statistical details. This sample data covers 584 individual houses in suburban area as shown in Figure 2.
Figure 2 showing a sample data of unknown area captured from Orthophoto showing the suburban houses
4. Results & Discussion
In this study, the use of high-resolution orthophoto has clearly indicated the immense potential of such data to know the pattern of suburban crime. It also helps to find out the social set up of individual houses and its peculiar activities. The key factor in identifying the vulnerable houses comes from the demographical data which is unique to every house in suburban areas in India. From the query based analysis in GIS environment, it is possible to identify the following set of social factors which come under the grip of criminals:
Independent houses
Nighttime locked houses
Houses locked for more than 3 hours in daytime
Aged people living in the houses
Active during night for illegal activities
Lack of security
The above set of social pattern existing in suburbs is incorporated into the GIS environment. Query based analysis help to identify the potential vulnerable locations and houses. When such studies are initiated at suburban areas of major Indian cities, then it can really help control the crimes. Furthermore, it helps for better user interaction when the data is maintained in GIS environment. With other tabular information like the suburban crimes, its locations, crime type, types of goods stolen, criminal records, etc. then, it gives clue to solve some of the unsolved crimes. This helps to improve the security at large and to have a balanced socio-economic development of any city.
In India, GIS based community policing is catching up and at this juncture, the use of high-resolution data can be really useful to the police across the major Indian cities to tackle this increasing crime. It helps the police to have a regular monitoring of houses in the given area which remains as a base camp of criminals for discussion, exchange of illegal goods, etc. This high-resolution mapping and analysis in GIS environment can greatly help nab the criminals before they carry out some disastrous work.
It also helps to initiate an integrated approach among various other user agencies like police, builders, urban planners, NGOs engaged in community development activities. This helps to plan for better security arrangements in vulnerable areas.
The property lost and sometimes the murder of beloved ones will be priceless. But little investment to capture, store and monitor this data in GIS environment can save lakhs and possibly the life of an individual. The data can be updated and manipulated at frequent intervals to increase security and police patrol to specific areas. This untimely loss of property and lives can be greatly minimized provided technological approach is given priority to control this suburban crime in Indian cities.
5. Conclusion
It is recommended and highly suggested to use high-resolution data to map, monitor and check, specially the suburban crimes in major cities. Also, the cost involved in procuring and installing such GIS data will be less when compared with the amount of distress people are put to after being attacked and killed by the criminals. It is high time now that such study be carried out in Indian cities, particularly the major metros which have been transforming as the base for criminals.
Acknowledgements
Authors wish to express their thanks to Shri. T.R. Srinivasan, President and Shri. P. Siva Kumar, Senior Manager, WTI Advanced Technology Ltd. for their support to carry out this work.
Authors grateful to Dr. Krishna Prasad, Vice President, WTI Advanced Technology Ltd. for his critical suggestion during this work.
Thanks to Shri. S. Vivek, Shri. S. Basker, Shri. S. Saravanan, Shri. G. Sri Ramajayam, Shri. M. Sudhakar for their technical support.
The following parameters have been used to understand and monitor the individual houses across the given suburban area:
| S.No | No. of Persons | Occupation | House Type | Lock Time | Lock Duration | Security | Aged Person | Night activity |
| 1 | 2 | Business | Indepe. | Day | 0800 | - | - | - |
| 2 | 4 | Office | Indepe | - | - | 1 | - | |
| 3 | 7 | Business | Indepe. | Day | 0700 | - | 2 | Manufac. |
Figure 3 showing the independent houses with lock duration more than 4 hours (vulnerable under suburbs of Chennai)
Figure 4 shows the houses vulnerable to varieties of crimes which are independent and having aged people who stay alone in day time as highlighted in yellow (light color) (Typical of suburban crime in Chennai)