Spatial-Temporal Analysis of Residential Burglary Repeat Victimization: Case Study of Chennai City Promoters Apartments, INDIA

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1 Spatial-Temporal Analysis of Residential Burglary Repeat Victimization: Case Study of Chennai City Promoters Apartments, INDIA M.Vijaya Kumar Assistant Professor K.S.R.College of Engineering Tiruchengode Dr.C.Chandrasekar Associate Professor Periyar University Salem Abstract One of the most important roles of government is to protect its citizens from crime and unsafe situations. The use of Geographic Information Systems (GIS) to understand spatial and temporal patterns of crime offences has become more prevalent in recent years; GIS help to optimize effectiveness in the reduction of crime & to increase the safety of residents. Important process offered through GIS is the identification of hot spots, or locations with a high crime rates. The identification of hot spots in time may even be very important; help in better understanding of crime pattern to create a crime reduction plan & allowing for the strategic deployment of resources sometimes & places when they can make the greatest difference. Spatial-Temporal information analysis plays a central role in lots of security-related applications. This study carried out to inquire in to and evaluate the effectiveness of associating spatial and temporal factors for repeated events in residential housebreaking. To demonstrate the application of spatial statistics, this approach can be a viable analysis alternative in security informatics. In this paper used Chennai City Promoters Apartments in India as a case study. Key Words Crime hot spots, repeat victimisation, Geographic Information System (GIS), Spatial-Temporal Analysis of Crime, Residential Burglary.. 1. Introduction It is often helpful to look at how spatial patterns of events modify over a specific Space - Time unit. Space-Time analysis is an important part of crime analysis since location and time are critical aspects of most crime related events (Ned Levin, 2002). The outputs of such analyses can provide useful information to guide the activities aimed at stopping, detecting, and responding to security issues. Obvious high crime areas have long been identified based on location, but with changes in the way urban residents live & work, a temporal part becomes more important. Areas that are filled with the hot spot crime events in the coursework of the normal workday may modify dramatically in the evenings or on weekends. Suburban neighbourhoods may be all but deserted in the coursework of the middle of the day. Areas with convention centres or sports arenas may be filled with people day & empty the next. Space-Time hotspot analyses provide information to fight crime and to increase the effectiveness of the available resources. when able to foretell the time & place of hot 1177

2 M.vijaya kumar et al, Int. J. Comp. Tech. Appl., Vol 2 (5), spots, or high crime areas, can help Police department for better prepared to either reduce the intensity of the hot spot or be able to more quickly mobilize officers to handle the increased crime rate (Boba, 2005).. For this study, knowledge from the Chennai City Police Department is used to demonstrate a process to identify hot spot times for crime in the city of Chennai City Promoters Apartments. This technique is then used to decide time windows within spatial hot spots. 1.1 Objective The objective of this study is to explores a combined approach of the spatial and temporal pattern of repeat burglaries and discuss the interpretation of the Spatialtemporal map, through analyzing data from the Chennai City Police, for residential burglaries from January 2005 to December 2008.And to demonstrate the application of spatial statistics in GIS to problems in criminology. Study used GIS to explore spatial patterns of the Residential Burglary repeated offences. Explores a combined approach of the spatial and temporal patterns of repeat burglaries. Discuss the interpretation of the Spatial-Temporal map. 1.2Study Area South Chennai is Tamil Nadu eighth largest Town, and is second largest Town in North Chennai with a total population of residents on 31 December 2006.North Chennai one of the largest and most Historical modern Baltic Sea ports. Fig 1.Chennai city South Chennai and North Chennai Apartments in Tamil Nadu. Residential Burglary in South and North Chennai Promoter Apartments, based on reported burglaries from Chennai City Police from Jan 2005 to Dec 2006: Total Residential Burglary Count: 1,235 One Time Addresses for Residential Burglary: 534 Repeat Residential Burglaries Count: 701 (56.76%) Number of Properties affected by Residential Burglary: 762 Number of Properties Revictimized: 228 (29.92% from the total reported residential burglary offences in South Chennai Promoter Apartments are revictimized more than one). 2. Literature Review 2.1 Spatial-Temporal Analysis: Crime is a dynamic event. The incidents of crime are neither stationary over space nor stationary over time. Spatial-temporal analysis is an important component of crime analysis since location and time are two critical aspects of most crime related events. The outputs of such analyses can provide useful information to 1178

3 guide the activities aimed at preventing, detecting, and responding to security problems. GIS provide remarkable tool for mapping crime over space and time. The combination of spatial and temporal techniques allows of establishing a typology of spatial-temporal characteristics of hotspots, as the spatial features of the crime patterns within the hotspot can be established (Ratcliffe, 2002). While there are theoretically infinite spatial arrangements of crime events within a crime hotspot, analytical work in this area over the last few years has established three broad categories of identified spatial patterns and temporal patterns. Maps have traditionally been represented time in multiple ways (Vasiliev, 1996, p. 138), including: Moments. Providing times of events in geographic space. When did crime incidents occurred and where? Duration. How long did an event or process continue in a specific space? For example, how long did a crime rate remain above or below a certain level in a particular area? How long did a hot spot (an area of high crime) persist? Structured time. Space standardized by time (for example, shift-based patrol areas, precincts, and posts). Distance as time. We often express distance as time. "How far is it?" "About 20 minutes." More to the point, perhaps, is concern with response times. In practice, a fixed, maximum permissible response time corresponds to the maximum distance feasible for patrol units to drive. Another application would be an investigation to see whether a suspect could have travelled from the last place he was seen to the location of the crime within a certain time period. 2.2 Spatial Categories: There are three categories for spatial patterns: Dispersed This is a type of crime hotspot where the points that generate the hotspot are spread throughout the hotspot area. They are still more concentrated than in other areas of the study (or else they would not be a hotspot) but do not cluster or congregate in any particular part of the hotspot region. An example might be a housing Apartments where burglary events are spread throughout the Apartments, due to poor design of the properties. The events within the hotspot do not cluster as each property is as vulnerable as the next (Chainey2005). Clustered This is a type of hotspot where the events that make the hotspot tend to cluster at one or more particular areas within the hotspot region. An example of this might be a hotspot region that includes a Apartments Colony. While the Apartments Colony may be the focus of a number of vehicle crimes, it does not prevent the possibility that other areas in the neighbourhood are also victimized by auto crime. Hotpoint A Hotpoint is a crime hotspot that is caused by the repeat victimization of a single location. An example could be the generation of a crime hotspot due to repeat burglaries at school. This differs from the clustered hotspot in that clustered events still have a high concentration in one or more areas, but can also have numerous crime events happen elsewhere in the hotspot Figure2: Shows Three Categories for spatial patterns of Hotspot: Dispersed (A), Clustered (B), and Hotpoint (C). 2.3 Temporal Categories: As well as spatial hotspot types, there are three general temporal patterns to crime hotspots. 1179

4 Diffused These are crime hotspots where the crime events could happen at any time over the 24- hour period of a day, or because the time span of events is so large that it is impossible to select any significant peaks of activity Figure2. This does not prevent the diffused hotspot from having some peaks and troughs, but it does mean that none are significant to be thought about useful from a crime prevention point of view. Fig.3: Diffused Temporal Hotspot. Focused With a focused temporal pattern, there is a time or a block of time, where criminal activity is significantly more focused than at others, Figure4 (Chainey, 2005). Alternatively it could be a block of a few hours in the coursework of the day when residential burglaries occur (Bottoms and Wiles, 2002). The operational value of a focused crime series is that it is significant to permit police resources to be targeted more effectively to the best deployment times. Fig.4: Focused Temporal Hotspot Acute Acute is temporal equivalent of a Hotpoint hotspot, the acute temporal pattern has a significant clustering of crime events into one short period of time (Chainey, 2005). Such as a block of three or four hours, Figure5. Fig.5: Acute Temporal Hotspot. 2.4 Spatial- Temporal Hotspot A spatial-temporal hotspot is a geographic area with an unusually high concentration of criminal incidents. There are three defining criteria of Spatial-Temporal Hotspot: 1. Number of Incidents. 2. Spatial Proximity. 3. Temporal Proximity 2.5 Research on Repeat Victimisation Repeat victimization, where a place or person experiences over criminal offence within a given time period, (Sorenson S.B et al 1991), has been a recognized phenomenon since the early study by Johnson et al. (1973) and the seminal publications by Sparks et al. (1977) and Hindelang et al. (1978), but it's only been in the 1990s that extensive research has been conducted. Since the early seventies, repeat victimization has developed from an idea of purely academic interest to the point where some police services now organize their responses to specific crimes on the basis of repeat victim s data (Pease, 1998). 2.6 Repeat Places Hot Spots The most basic kind of a hot spot is a place that has lots of crimes. A place can be an address, street corner, store, house, or any other tiny location, most of which can be seen by a person standing at its middle (Sherman et al., 1989). Places usually have a single owner and a specific function s residence, retail sales, recreation, school (Eck and Weisburd, 1995). Crime often is concentrated at a few places, even in high-crime areas. Although hot places often are concentrated within areas, they often are separated by other places with few or no crimes. Because such hot spots are 1180

5 M.vijaya kumar et al, Int. J. Comp. Tech. Appl., Vol 2 (5), best depicted by dots, they have a dimension of zero. 3. Data Description Generally, data required for any GIS could be grouped into spatial and attribute (non spatial) data and there are various methods for obtaining these data. 3.1 The Attribute Data Offences Data The attribute data for residential burglary was obtained from the Chennai City Police, for the two years. And some crime statistics obtained from the Tamil Nadu Council for Crime Prevention, for the years Also there are some Interviews with many employees of the Chennai City Police. The detailed description of data used in this report is provided in Table1. Table1. List of Attribute: Data and the Data Source Characteristics of the Dataset In Chennai City police records there are different codes for the residential burglary used to classify the offences according to the type of target, Table2. Describe the residential burglary different codes used in Chennai. Data and Data Quality The quality of recorded crime data available is a key factor in determining the level and effectiveness of analysis. The application of GIS in particular relies on particular and accurate geographic information about crimes. Table2. Characteristics of Dataset Attribute Data Limitations There are some problems for the residential burglary data obtained from Chennai City Police, and this problem appeared during the data preparation stage: 1. Some fields are missing in the police records for the residential burglary data during the study period. 2. Duplication, this is other problems of residential burglary data quality that arise during the process of recording residential burglary. To solve this problem need more efforts during the data preparation stage to clean the data before to be ready for the analysis. 3. The police records do not represent the accurate statistics for residential burglary Type of Offences: Two years data for residential burglary offences are used in this study for repeat victimization spatial-temporal analysis. Residential burglaries have been chosen for the following reasons: 1. More than 30% from the total residential burglaries reported in the South Chennai city from Jan 2005 to Dec 2006 are repeated offences. 2. Data for residential burglary are quite accurate in police records in Tamil Nadu since insurance companies require police records before paying compensation. 3. There are rich literatures for the 1181

6 M.vijaya kumar et al, Int. J. Comp. Tech. Appl., Vol 2 (5), residential burglary about their spatial patterns in different countries. 4. Burglary is a crime against property. Properties are easier to manage than people for prevention purposes. In this study, the specific address of the residential burglary offence was used as the unit of analysis. 3.2 The Spatial Data Some of the spatial data layers used to construct the study map for South Chennai City was obtained from the GIS data source in Google maps.. And the other layers obtained from the North Chennai city municipality. 4. Methodology and Implementation There are different methodologies and steps done in order to understand and to study the relation between spatial and temporal profile of residential burglary in South Chennai city, this include but not all: Thematic Mapping. Location Quotients. Mode & Fuzzy Mode. Knox Index Method. Spatial & Temporal Analysis of Crime. K-Means Clustering. Kernal density Smoothing. 4.1 The Spatial Data Preparation During this stage, the map constructed using different layers to represent South Chennai city, urban areas, water bodies, commune boundaries, land use, road network, properties and land use 4.2 The Temporal Data Preparation After constricted the study area using ArcMap, during this stage developed the time profile of the residential burglary in South Chennai City in the study period, and reclassify the temporal data for residential burglary according to different time format, Classifying according to, Day Hours, week days, Months and seasonal variation. Methods of Time Profiling 1. Residential burglary usually have a much shorter time span than other offences therefore residential burglary offences is more suitable for time profiling than other offences, 2. The original dataset analyzed for this profile had 1,235 records. 3. 4% of records without a proper time stamp. 4. 6% of records contained some missing fields, and these records discarded from analysis % of records had an accurate time stamp % records to map (1,120). Time Profiling Steps: 1. Data cleaning, during this step of time profiling, cleaned the residential burglary data to avoid the record duplication and other attribute data problem, this step done using Excel work sheet and Access database. 2. Reclassified the data to make your time frames easy to search (Sorting and rounding the time format). 3. Counts the data for each time frame, to know how much data available for each time frame and is it enough to map. 4.3 Spatial-Temporal Profiling: Creating a Time-Series Map: After constructing the spatial profile for South Chennai City, and residential burglary time profile, during this stage of mapping a time series, associate the spatial profile with the constructed time profile to produce the time-series map for residential burglary There are many steps followed to create a time-series map: 1. Geo-coded data of a residential burglary time profile, the resolution of the map is dependent upon the accuracy of the geo-coding. 1182

7 2. Time coded data, how many time periods will the map show, e.g. (hourly and daily). 3. Named each time series file in the sequence in the order in which it appears. 4. Use the same map extent for each frame in the time-series (create a bookmark in ArcGIS). 5. A time-series map is made up of a sequence of individual maps linked together to describe the change of offences hourly and daily. 6. Keep a record of all the variables involved in making the map. 4.4 Determining Repeats Once the dataset had been cleaned as much as possible, a set of distinct victimized locations was compiled and a victimization count for each location was calculated. This was performed using Hot Spot Analysis I and Hot Spot Analysis II routine, in the CrimeStatIII, (on the appropriate X, Y field). In this study, the specific address of the residential burglary offence was used as the unit of analysis. Repeat victimization, for this study, is when a property has been recorded in South Chennai City police record as burgled on more than one time during a given time period. Individual addresses location in properties such as multifamily houses; villas and summer houses are identified. This means specific address of the residential burglary offence was used as the unit of analysis. 5. Results and Analysis The use of geographical displays for viewing crime data makes the information more understandable. This part of the study displayed the result of analyzing the repeat Victimization in residential burglary focusing on the associating the space with a time profile. Many statistical formulas and quires are used to identify and analyze the spatialtemporal patterns of residential burglaries repeat victimization. South Chennai City Residential Burglary Residential Burglary in South Chennai, based on reported burglaries from Chennai City police from Jan 2005 to Dec 2006: Total Residential Burglary Count: 1,235 One Time Addresses for Residential Burglary: 534 Repeat Residential Burglaries Count: 701 (56.76%) Number of Properties affected by Residential Burglary: 762 Number of Properties Revictimized: 228 (29.92% from the total reported residential burglary offences in South Chennai city are revictimized more than one). Figure6: shows details of the study area. The symbol represents the locations of all reported burglaries in the South Chennai city from Jan 2005 to Dec The number of burglaries at each location is not shown, each dot representing an address that has been burglared at some point, irrespective of number of burglaries at that point. Another technique used to display the density of residential burglary to show the area of highest offences is to use Kernal Smoothing to Identify Point Clusters of residential burglary Figure

8 5.1 Determining the Repeat Locations There are different statistical techniques used to Identifying residential burglary repeat address, many, but not all, of the techniques are typically known under the general statistical label of cluster analysis. The Mode and Fuzzy mode The Mode and Fuzzy Mode technique focuses on Point locations; this is the most intuitive type of cluster involving the number of offences occurring at different locations. Locations with the most number of offences are defined as hot spots. The CrimeStat Mode routine calculates the frequency of incidents occurring at each unique location (a point with a unique X and Y coordinate), sorts the list, and outputs the results in rank order from the most frequent to the least frequent, Table3. Fig.7. Residential Burglaries in South Chennai, Jan 2005 to Dec 2006, Using Kernal Smoothing to Identify Point Clusters. Only locations that are represented in the primary file are identified. The routine outputs a dbf file that includes four variables: 1.The rank order of the location with 1 being the location with the most incidents, 2 being the location with the next most incidents, 3 being the location with the third most incidents, and so forth until those locations that have only one incident each; 2.The frequency of incidents at the location. This is the number of incidents occurring at that location; 3.The X coordinate of the location; and 4.The Y coordinate of the location. Table3.Sample Result, Frequency of victimization for all burglaries in the study area during study period. (Table result from Crime Stat III). Time Residential Burgled Affected Offences Offences (%) Total Table4. Shows the distribution of victimization for all burglaries across the study area from January 2005 to December The data examined covered the whole of the time period. 5.2 Mapping the Repeat The first step towards identifying repeat locations is to isolate the addresses which have been burglared more than once during the study period this can be seen in Figure 1184

9 8, and this process based on point locations for residential burglary, the output from the fuzzy mod routine exported as a DBF to map the repeat location in ArcGIS. It then becomes apparent that the distribution of repeat burglaries is different to the distribution of all burglaries in figure8. A large number of repeats appear concentrated around main road. Fig.8. Location where more than one burglary took place over the study period. 5.3 The Size of Repeat Figure 9. Shows the same detail of the study area and the same locations where repeat burglaries have occurred. This map shows the size of repeat burglaries at each address over the two years period. The larger the circle, the more offences have been reported to the police as occurring at the location. Fig.9. Repeat and Non-Repeat Residential Burglary Offences in South Chennai City during the Period of Study. Repeat Time Series Analysis: Temporal analysis is the analysis of data in relation to unit of time, in general, tactical crime analysis examine tiny numbers of case in comparatively short time periods (Using a method known as Time Series Analysis), such as hours and days. Among the most important characteristics of crime offences, both for identifying patterns and for describing patterns are the times of day, days of week, etc., and the order in which they occur. In the work of this stage of analysis of residential housebreaking repeat victimization, examined the knowledge by a different time series, 1. Hours. 2. Days. 3. Months. 4. Seasons. The time analysis of residential housebreaking offences has to be treated with caution, as usually there is no direct contact between the offender and the victim. This presents a window of opportunity in the work of which the offence might have been committed, often spanning the length of a working day or a weekend. During the daytime, the lack of a car on the drive is an obvious indication that a property may be left unattended and knocking will confirm any burglar s suspicions with little risk of being seen. Going on later into the evening and the state of occupancy of the premises will again become fairly simple to gauge through lighting, especially during the months of less daylight. Exact Time: Exact time series analysis is the examination of offences that have an exact time of occurrence. It is common practice in crime analysis to use 24 hours time and to round time variables to the nearest hour. Rounding makes the information slightly less accurate Table 5, displays a list of cases in Residential Burglary repeat victimization 1185

10 M.vijaya kumar et al, Int. J. Comp. Tech. Appl., Vol 2 (5), pattern in South Chennai City, showing the exact times at which they occurred and their rounded times. This necessary compromise to produce clear, understandable results. For example, the time of a crime that occurred at 10:35 A.M. would round to be with in 10:00 to 10:59, and the time of a crime that took place at 10:35 P.M. would be rounded to be with in 22:00 to 22:59. Table6. List of Offences: Residential Burglary pattern per Hour. Table5. List of Offences: Rounded Time. Even though it may be interesting to know the exact times of the offences, the purpose of analysis is to organize the information so that a pattern can emerge. Table 6 shows the number of residential burglary per hour, which is a useful way of displaying information about the time events in a pattern. Although this information is an improvement over the list in Table 5, the results are even easier to understand when they are displayed in the form of a chart, as in Figure 10, which clearly shows that most of the Residential burglaries have occurred in the day time hours. Fig.10. Chart: Number of Residential Burglary per Hour. Exact Time Distinct Categories: To analyze exact time of residential burglary have divided the time services of offences into four distinct categories to help eliminate 24 hours for day, to four day time range: 1. Morning 7. A.M To 11 A.M 2. Afternoon 12 P.M To 5 P.M 3. Evening 5 P.M To 10 P.M 4. Night 10 P.M To 7 A.M In South Chennai city, the afternoon period, i. e from 12 p.m to 5 p.m faced a large number of offences rather than the other day period; Figure 11 represent the number of offences at each exact distinct category. 1186

11 M.vijaya kumar et al, Int. J. Comp. Tech. Appl., Vol 2 (5), Fig11. Chart: Number of Residential Categorized by four different time range. Burglary Mapping the Exact Time series Kernel Density KD is used to map the result of exact time series analysis, in recent years KD has proven to be an indispensable tool in spatial analysis because of its effectiveness in pinpointing hot spots in the point data, which are locations of comparatively elevated density compared to the rest of the study area. O Sullivan et al believe that KD is one of the most useful applications in applied Geographic Information System Analysis (O Sullivan et al., 2003). Kernel density KD is a particularly useful method as it helps meet a number of aims of creating hotspot maps. The method, 1. helps to more precisely identifying the location, the spatial extent and intensity of crime hotspots 2. Is visually attractive, so helps to invoke further enquiry and exploring the reasoning behind why crime and disorder is concentrated in some areas. Below is an output of a kernel density KD time series for residential burglaries committed by distinct time category of the day, help to avoid 24 day hours mapping, (Figure 12 15) describe the change of residential burglaries offences over the day times. Fig.12,13. Density of Residential Burglary Density of Residential Burglary Offences During the Morning Time, After Noon. Offences During the Afternoon Time. Fig.14, 15. Density of Residential Burglary Offences during the Evening and Night Time. Exact Day Exact day series analysis is the examination of offences over the week days; Used the same method for counting crimes by the day hours, Table 8 illustrates the frequency of residential burglary and the percentage of the total by day. Figure 16 depicts the counts of residential burglary by day of the week. It can be seen from figure, that residential burglaries are likely to be committed over the weekend night especially on a Friday and Saturday night. Table8. List of Offences: Number and Percentage of Residential Burglary per Day of the Week 1187

12 M.vijaya kumar et al, Int. J. Comp. Tech. Appl., Vol 2 (5), Fig.18. Chart: Months between Repeat in Residential Burglary Fig16.Chart: Number of Residential Burglary per Day Below is an output of a kernel density time series for residential burglaries committed by days of the week, (Fig 17) describe the change of residential burglaries offences over the week days. Fig: 17. One Week(Mon-Sun) Burglaries Offences. Exact Month The database of residential burglary was broken down in to twenty four separate monthly subsets to Analyze repeat in residential burglary on monthly basis Figure 18, the result shows that the repeat victimization occurred within a relatively short time frame, that is within the one to two months, Exact Day and Time Results In South Chennai, 65% of Residential Burglary occurred during the working hours, especially between 12:00 p.m. to 5:00 p.m. 10% occur during the evening, from 5:00 p.m. to 10:00 p.m., and 25% occur overnight between 10:00 p.m. to 7:00 a.m. Each time period features a different type of burglary: Day time burglars count on the fact that the residents are not at home. They spend more time in the residence and steal more valuables. They are most likely to strike large apartment buildings in densely residential areas where they will be more anonymous in the visible daylight hours. Night time burglars count on the fact that their residents are asleep. They are quieter, entering through an unlocked door or window, or by prying or jimmying a window. They spend a short amount of time in the residence and steal only property that they can carry in a single trip usually lone item, like a laptop computer. They are more likely to target houses as well as apartments. Evening burglars have the most anger of all. They enter homes knowing that the residents are likely to be at home and awake. They creep through unlocked windows or doors, target cash or other small valuables, and get out quick. Almost all evening burglaries target houses, which typically more rooms, most of which are vacant, and more points of entry. Day time burglaries are most common on Friday, Saturday, and Sunday. Night time breaks favour Friday and Saturday. Seasonal Variations Seasonal dependency (seasonality) is another general component of the time series pattern. Seasonal variations reflect local factors, including the weather and how it affects, and the period of day light. Residential burglary does not typically reflect large seasonal variations, from analyzed data for residential burglary in 1188

13 South Chennai showed that, the traditional summer time (June, July, and August) had a high burglary rate. In 2005 and 2006, for instance, this is due to expect the absence of residents on vacation during the summer months, Figure 19. From December to February (Winter Season) had a low burglaries rate than the summer months, and across the whole year months. points is also divided in to two groups - Close in time and Not close in time. The definitions of Near and Not Near are left as the variable 3. A simple 2 x 2 table is produced that compares closeness in distance with closeness in time. The numbers of pairs that fall in each of the four cells are compared (Table9). Fig.19. Chart: Seasonal variation in Residential Burglary 5.4 Space-Time Cluster After the result of counting the victimized location irrespective of time profile, In this stage of analysis conducted a test for the locations that are close in time with close in distance based on a nearest neighbour analysis technique, There are many statistics methods developed for testing the spatial patterns of distribution of near repeat, the Knox index, the Mantel index, the Spatialtemporal moving average, and Correlated Walk Analysis. During this study I used the Knox Index method to analyze the spatial distribution of repeat residential burglary. Knox Index method The Knox Index is a simple comparison of the relationship between incidents in terms of distance (space) and (time) (Knox, 1963; 1964). That is, each individual pair is compared in terms of distance and in terms of time interval. Since each pair of point s is being compared, there are N*(N-1)/2 pairs. The distance between points is divided in to two groups Close in distance and Not close in distance, and the time interval between Table9. Logical Instructor of Knox Index CrimeStat III software package documentation). Where N = O1 + O2 + O3 + O4 S1 = O1 + O2 S2 = O3 + O4 S3 = O1 + O3 S4 = O2 + O4 N: Number of total offences in the test. S1: Total number of offences: close in distance with close in time, and offences close in distance and not close in time. S2: Total number of offences not close in distance with close in time, and offences not close in distance and also not close in time. S3: Total numbers of offences close in time with close in distance, and offences close in time and not close in distance. S4: Total number of offences not close in time with close in distance, and offences not close in time and also not close in distance. 1189

14 Table10. Expected Frequencies for Knox Index (Ned Levine s, (2003) CrimeStat III software package documentation). Where E1 = S1 * S3 / N E2 = S1 * S4 / N E3 = S2 * S3 / N E4 = S2 * S4 / N Results In South Chennai city, after analyzing the residential burglary using Knox Index, Figure 20 observed there are clustering between space and time in residential burglary in some areas, Figure 9. Approximately, 59% of the repeat burglary offences were both close in distance and close in time. However, when individual months are examined, six months show relationships: January, April, July, August, November and December. During these months, there is an interaction between space and time. Offences that cluster together spatially tend also to cluster together temporally. Fig.27. Close Time-Space Address of Residential Burglaries in South Chennai, Jan 2005 to Dec 2006, Using Knox Index Method for Time-Space Analysis by Using CrimeStat III. 6. Findings 1.Most of the Residential burglaries repeat victimization has occurred in the day time hours (Working Hours). 2.In many areas in South Chennai there are many repeat in residential burglary, was occurred with very close in distance and time, presence of apace time clusters, (Near Repeat). 3.Repeat victimization has also been found to occur within a relatively short timeframe, That is, within the one to two months. 4.Repeat victimization in residential burglary occurs mainly on the area of single family houses like villas. 5.Residential burglaries repeat victimization are likely to be committed over the weekend night especially on a Friday and Saturday night, and this is likely to be directly linked to the absence of the potential victim from the property. 6. From analyzed data for residential burglary in South Chennai showed that, the traditional summer time (June, July, and August) had a high burglary rate. In 2005 and 2006, for instance, from December to February (Winter Season) had a low burglaries rate than the summer months, and across the whole year months. 7. The lowest number of offences was associated with time, but not space. 8. Examination of hour of day by day of week and season showed that the 1190

15 evening peaks for burglary on weekdays are only present during the darker months. References 1. Alex, Hirschfield, Bowers Kate. (2001) Mapping and Analyzing Crime Data, Lessons From Research and Practice, Taylor and Francis, New Fetter Lane, London EC4P 4EE 2. Boba, Rachel.(2005) Crime Analysis and Crime Mapping. Sage Publications, Inc New Delhi India 10. Tamil Nadu Police Website Crime Mapping in India: A GIS implementation in Chennai City Policing, Geographic Information Sciences, Vol.10, No1, June Jaishankar K., and Debarati. 12. Chennai City Map: amilnadu/chennai-map.htm 3. Chainey, Spencer. (2005) GIS and Crime Mapping, Wiley, UK. 4. Pease, Ken.(2004) Repeat Victimisation: Taking Stock (Home Office Police Research Group Briefing Note, Crime Detection and Prevention Series 90) 5. Polvi N et al (1991) The Time Course of Repeat Burglary Victimisation, British Journal of Criminology, HOME DEPARTMENT TAMIL NADU POLICE POLICY NOTE FOR Tamil Nadu Police Service Annual Statistical Report Statistical Summary. 7. Impact Assessment of Modernization of Police Forces (MPF) (From 2000 to 2010). Bureau of Police Research and Development Ministry of Home Affairs EXECUTIVE SUMMARY of Tamil Nadu Police Statistical Report Statistical Summary. 9. CRIME REVIEW TAMIL NADU State Crime Records Bureau Crime CID, Chennai Tamil Nadu. 1191