Planning Assessment
Interrelationships Between Crime and Demographic Factors of Bhopal City in India
2023 Volume 11 Issue 1 Pages 226-252
Details
2023 Volume 11 Issue 1 Pages 226-252
Demographic factors strongly connect to the spatial distribution of crime type and crime rate. Therefore, it is of cardinal importance to analyse these demographic factors and make precise outcomes to minimise crime. This study focuses on understanding the spatial interrelationships between crime and demographic factors in the municipal corporation of Bhopal city in India. The study involved the collection of data and its statistical analysis. GIS mapping of the crime and demographic factors and correlation analysis showed a robust linkage between the factors. The findings of this study represent how crimes are affected by the spatial distribution of demographic factors. This research's further scope has emphasised the optimum distribution of demographic factors for minimising crime.
Crime has been an integral part of human settlements since ancient times. Since the eighteenth century, crime and its pattern were investigated considering multi-disciplinary positions such as criminology, philosophy, sociology, and anthropology. Theoretically, crime has always been a subject of investigation in criminology (Mueller, 1962). Classical criminology studies have always perceived crime in terms of offender behaviours. In sociological aspects, Durkheim, X. E. and Lunden (1958) has studied crime in its purpose. Crime is analysed in the light of anthropological aspects based on evidence (Horswell, 2004). Various research has been conducted to understand the direct and indirect relationships between crime and urban planning factors. The first of these relations was given by Canadian criminologists (Brantingham and Brantingham, 1981), who explained the role of environmental features in an urban setting in their crime pattern theory. UN-HABITAT report on Enhancing Urban Safety and Security states that “the rapid pace of urbanisation coupled with the growth in city size and density is associated with increased crime and violence”. According to NCRB (National Crime Records Bureau), urban areas are generally considered the breeding ground for crime. Poor urban planning, urban design, and city management also play a significant role in shaping urban environments that might put citizens and property at risk (UN-Habitat, 2012). Kinney, Brantingham et al. (2008), in the case of urban planning and crime, has revealed the relationships between land use and crime. Sadeek, Ahmed et al. (2019), further articulated the relation between crime and land use, incorporating transport accessibility factors in their study of crime analysis. Kang, Kim et al. (2014) have demonstrated crime assessment in Nonhyeon-dong a ward of Gangnam-gu in Seoul, South Korea considering multiple factors of land use, street, configuration and crime using Multi-Layered Risk Assessment (MLRA). (Ceccato, 2016), has contributed significantly to this field by adopting remote sensing and Geographic Information Sensing (GIS) tools in analysing urban crime. Figure 1 shows the conceptual understanding of how crime is understood and examined from different ontological positions and perspectives.
From the above-described ontological positions, crime and its pattern have become a subject of scientific and institutionalised debates, primarily focused on the integrated aspects of land use, urban planning, and urban design.
Recent literature studies reveal that inflation in crime rates is a consequence of social and economic problems like unemployment, particularly youth unemployment, migration, and increasing income inequality in western countries. In case of analysing socioeconomic, demographic factors and crime major contributions were made by Entorf and Spengler (2000). Urban areas are considered one of the most vulnerable places to experience criminal activities. Urban planning targets making the cities and human settlements inclusive, safe, resilient, and sustainable. All the current approaches in urban planning talk about making cities smart or resilient. Smart city and resilient city approaches focus on making cities digital, resilient, and safe from disaster (vulnerable situations), improving road safety and sustainable human settlement planning.
In India, the seventh schedule of the Indian constitution defines police and public order through IPC (Indian Penal Code) but does not directly indicate the national policy for security. There is a lack of integration of crime prevention planning and policy at the national, state, and city (urban planning) level (Clifford, 1986). However, the NCRB under the MHA (Ministry of Home Affairs) complies and informs about the crime scenario in India through yearly publications. IPC does provide provisions for legal actions against the offence/crime committed but not merely for any effective preventive policy. The current approaches in urban planning often lag in dealing with crime.
URDPFI- Urban and Regional Development Plans Formulation and Implementation states that “disparities in the socioeconomic and demographic conditions impact the crime cases in an area”. Also, URDPFI, in its vol-1 pg-142, says, “Increased rate of crime is primarily due to socioeconomic and demographic disparities. These disparities include the segregation of population income classes, lack of recreational activities, lack of educational facilities for low-income class, male workers living without families, and many bachelor populations”. However, all such safety concerns and crime rates have been overlooked in the preparation of physical plans. URDPFI, even though providing the apex guidelines for the preparation of urban and rural plans in India, has not sufficiently incorporated significant safety approaches towards crime.
Crime around the world is defined in various aspects, such as “violation of law” by (ICPSR, 2021) or “offence against an individual or state “by the oxford dictionary. Also, various sociologists (Durkheim, E., 1984) and criminologists (Mueller, 1962) have enumerated crime as an act against human societies. Crime being multifaceted, theorists have tried to put together various theories to understand crime. Most crime definitions focus on the offender or the victim, omitting the environmental factors that enable the crime. Considering the importance of environmental features in crime pattern theory, Brantingham and Brantingham (1981) postulated that crime is an event of complex occurrence that requires the coincidence of many spatial features and human attributes. Environmental criminology shares a common interest in criminal events and the immediate circumstances in which they occur (Wortley and Townsley, 2016). Environmental criminologists look for crime patterns and seek to explain them in terms of environmental influences. These explanations derive rules that enable predictions about emerging crime problems, ultimately informing the development of strategies that might be employed to prevent crime. This environmental perspective has three premises: first, based on criminal behaviour; secondly, the distribution of crime in time and space; and the third domain is understanding the role of criminogenic environments (Wortley and Townsley, 2016). Across these three domains of theory, analysis and practice, the environmental perspective is multi-disciplinary in its foundations, empirical in its methods, and utilitarian in its mission (Wortley and Townsley, 2016). Such theories have focused on crime exploration and defining crime in planning studies. According to EU COST, “crime is a multi-dimensional event that occurs when an offender and target converge in time and place (such as a street, corner, address, building or street segment) (Cardia, 2013).” The environmental and sociological perspective draws on the ideas and expertise of sociologists, psychologists, geographers, architects, town planners, industrial designers, computer scientists, demographers, political scientists and economists.
Crime analysis is defined as a law enforcement function involving systematic analysis to identify and analyse patterns and trends in crime and disorder. The root cause of crime depends upon two factors; first, the mindset of the criminal and second, the opportunity he is getting in the surrounding built environment.
Crime analysis is an investigative tool, defined as ‘the set of systematic, analytical processes that provide timely, pertinent information about crime patterns and crime-trend correlations. It uses crime data and police reports to study crime problems, including the characteristics of crime scenes, offenders, and victims. Crime pattern analysis depicts the crime rate, density, and temporal and spatial qualities and visually represents graphs, tables, and maps. Using these findings, crime analysts provide tactical advice to police on criminal investigations, deployment of resources, planning, evaluation, and crime prevention (Wortley and Townsley, 2016).
Various terms related to crime analysis are crime rate and crime mapping. Crime rate per 1,00,000 populations is calculated as = (Number of crime incidences/population) x 1,00,000. Furthermore, crime mapping creates an overview of the crime scene maps for any particular area, enables the viewer to visualise crime patterns, and helps in crime trend analysis. Hence all such insights from different subject domains have provided a theoretical framework for identifying the interrelationship between crime and demographic factors.
Research gapThis research has reviewed various articles related to crime and demographic factors covering global scenarios ranging from cities to regions. A good amount of literature on crime, demographic factors and urban planning is evident from the studies of Ackerman and Murray (2004), Ludin, Aziz et al. (2013), Brantingham and Brantingham (1981), Cardia (2013), Ceccato (2016), Durkheim, E. (1984), Entorf and Spengler (2000), Greenberg, Rohe et al. (1982), Groff and Lockwood (2014), Kinney, Brantingham et al. (2008), Mueller (1962), Roth, Ross et al. (2013), Handy, Boarnet et al. (2002), Wadsworth (2001), Wortley and Townsley (2016), and Yirmibesoglu and Ergun (2007). Many of these studies are relevant to the first-world countries. A very limited literature studies were found in the context of developing or third-world countries, for example, Appiahene-Gyamfi (2003), Adeyemi, Mayaki et al. (2021), Kunnuji (2016), Sadeek, Ahmed et al. (2019), Nolan and James (2004) and Mallubhotla (2013). When we search the online databases with the keywords ‘India, IPC crime, demographic and urban planning results has shown limited articles on these keywords. These articles are mostly covered on regional or state scale, whereas this study looks at the city and police boundary level. However, the crime scenario in the developing counties context is crucial as poverty, employment and socio-cultural and demographic factors play an important role. The reason for such limited studies is probably due to the complexity of the census and crime-related data which is not easily accessible in the required formats. Moreover, the existing studies have employed either correlation or regression to identify the relationship between crime and influencing factors. This research attempts to extend the efforts made by the previous studies by applying the correlation analysis along with spatially mapping the crime and demographic factors for visual and spatially analysis.
Crime scenario in IndiaAccording to the International Crime Victimization Survey 2016 and 2018, India’s crime rate and ranking are 46.59 (rank 53) and 43.15 (rank 61). According to NCRB, based on the rank of different states and their ranking on average, states like Madhya Pradesh, Maharashtra, and Uttar Pradesh have had the highest crime rates throughout the years, as mentioned in Figure 2.
Indian Penal Code (IPC) defines crime in India as an activity that involves breaking the law and enforcement. It further classifies all the crimes into two categories, Cognisable crime - Sec.2(C)(Cr.P.C) - Criminal Procedure Code (immediate arrest without warrant) and non-cognisable crime - Sec2(I)(Cr.P.C) – Criminal Procedure Code (arrest warrant from a judge or magistrate is required). In India, the major share of IPC cognisable crime is theft, including general theft and automobile theft (Figure 3).
Source: 2016 report of NCRB (National Crime Records Bureau), Ministry of Home Affairs, Government of India
Source: 2016 report of NCRB (National Crime Records Bureau), Ministry of Home Affairs, Government of India
The conventional studies discussed in the literature represent various approaches to crime analysis and the factors influencing it. However, much of the research conducted is on individual factors affecting the rate and type of crime that occurs from place to place at different scales statistically. However, using GIS analysis, consolidated research exploring multiple factors’ impacts on crime in urban settings is nascent. From a methodological position, this research study stands unique from the existing literature in two aspects: first, it considers the exposure of crime in an area and its associated demographic factors. Second, rather than just relying on the statistics, it acknowledges the spatial overlay of these factors, emphasising correlation analysis. This research employs choropleth mapping and overlay analysis as a part of spatial analysis and Correlation analysis (CA) as statistical analysis.
Methods commonly adopted in this study of crime analysis include creating crime patterns through choropleth mapping, overlay analysis and crime density (hotspot) analysis. Crime patterns created from the spatial analysis tool in GIS have helped in crime’s visual and spatial analysis. The level of analysis achieved using GIS could determine even the crime density within every spatial scale. GIS in crime analysis provides an alternative tool that can aid policymakers, urban planners, decision-makers, and police officials/security operatives to ensure peace, security, and proper development in society. The integration of spatial and statistical techniques with GIS can help local authorities and police departments identify hotspots for crime incidences and reframe the policies and guidelines of development to avoid crime.
Choropleth mapping is a technique for mapping and representing geocoded data spatially with different colour shades. In this research, Choropleth mapping is used to map the crime rates and demographic factors in the form of polygons in GIS by dividing the data of class range into different intervals. This technique is preferred over other analysis techniques when data available is for an area instead of some particular point or location. Overlay analysis utilises the layers of choropleth maps created for crime, land use and demographic factors and stacks them over. It will help check the spatial divergence and convergence of the relationship between crime and demographic factors represented through correlation analysis.
Correlation analysis represents the statistical relationship between two random variables or bivariate data, whether causal. This study has adopted the following steps to investigate the spatial and statistical relationship between crime and demographic factors in different urban settings.
Study area selectionStep One of the study was selecting the study area. The reason for taking up Bhopal is that according to the NCRB report, Bhopal lies among the top five cities with the highest crime rate on an average from 2012 to 2015. Also, Bhopal’s data procurement was more manageable than any other city since acquiring crime data is tedious and cannot be acquired without proper reference personnel.
On average, in megacities, from 2008 to 2015, Bhopal, Indore, and Kochi have been the cities with the highest crime rates, as mentioned in Table 1. Crime rate means the number of crimes per one lakh population.
| Year | Bhopal | Indore | Kochi |
|---|---|---|---|
| 2008 | 791.4 | 941.4 | 783.9 |
| 2009 | 836.4 | 860.3 | 646.3 |
| 2011 | 667.6 | 669.3 | 1636.4 |
| 2013 | 598.7 | 809.9 | 636.3 |
| 2014 | 688.4 | 885.9 | 806.8 |
| 2015 | 789 | 852 | 650.7 |
Source: NCRB report 2008,2009,2011,2013,2014,2015
Bhopal is the administrative capital of Madhya Pradesh state in India and the second-largest populated city in M.P, as shown in Figure 4.
As for the 2011 Census, the population of Bhopal municipal corporation (area shown in Table 2) is 1,795,648; out of this, males and females are 939,560 and 856,088, respectively. The sex ratio of Bhopal city is 911 females per 1000 males. The average literacy rate of Bhopal city is 85.24 per cent, of which male and female literacy is 89.19 and 80.90 percent.
Source: Bhopal City Development Plan 2005, digitised by author
| Indicator | City Municipal Corporation | State (Urban) |
|
|---|---|---|---|
| Total Population | 1,798,218 | 20,069,405 |
|
| Total Population of UA (if) | 1,886,100 | ||
| Population rank in India 2021 | 22 | ||
| Share of ULB population in District Urban population (%) | 93.80 | ||
| Population Growth Rate (AEGR) 2001-11 | 2.24 | 2.29 |
|
| Area (km2)* | 285.88 | ||
| Share of ULB area in district (%) * # | 10.31 | ||
| Municipal performance index 2021 rank in India (Ministry of housing and urban affairs) | 3 | ||
| Density of population (person per km2) * | 6290 | ||
| Literacy Rate (%) | 83.47 | 82.85 |
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| Schedule Caste (%) | 13.46 | 15.32 |
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| Schedule Tribes (%) | 2.56 | 5.18 |
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| Youth, 15 - 24 years (%) | 21.30 | 20.61 |
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| Slum Population (%) | 26.68 | 8.43 |
|
| Working Age Group, 15-59 years (%) | 65.22 | 63.80 |
|
| Per Capita Income (Rs.) at 2004-05 constant price * | Rs. 47214 | Rs. 23539 |
|
Source: Census of India, 2011, * District Census Handbook, Census of India, 2011, # The ULB is spread in more than one district
Step two was to find the correct data set for analysis. The crime data collected was from Bhopal Police headquarters (PHQ) and Bhopal SP (Superintendent of Police) office for 2012-2015 and demographic factors data from the census-2011 development plan. The study consists of analysing crime and demographic factors for Bhopal city. The data used in the analyses is procured from the Bhopal SP office (Urban). The 27 police stations falling under the Bhopal municipal corporation are used for analysis. The crime records obtained were mainly IPC crime counts for the cognisable crimes.
The primary census abstract of Bhopal Municipal Corporation (2011) provided the demographic factors data. The final compiled demographic factors to be analysed with crime rate are population, population density, literate population and working population (these all Census population enumeration of 2011 is projected till 2015 using growth rate from the development plan). Each police station of Bhopal comprises one or more wards, and to bring uniformity in police station data and wards, all the wards were tabulated and aggregated to their respective police boundary to obtain uniform information for each police station, as mentioned in Appendix 1. This procedure was necessary since population information provided in the criminal records for each police station was outdated compared to the 2011 Bhopal census.
The analysis consists of two parts:
a. Choropleth mapping creates an overview of the crime scenario, enabling us to visualise crime patterns and helps in crime trend analysis. For choropleth mapping, the crime rate is mapped in the form of polygons in GIS according to the different class intervals.
b. Overlay analysis is used to overlap the crime maps with demographic factors.
Crime pattern and crime scenario analysisThe Choropleth maps from 2012 to 2013 show crime in core and CBD areas are increasing, but from 2013 to 2015, crime in periphery areas started to grow, as shown in Figure 5. This rise in crime rate in the periphery areas (shown in Figure 6) is due to new developments along NH-12 near Misrod towards Hoshangabad and along Vidhisha road. The city development majorly concentrates around the M P Nagar, TT Nagar, Kamla Nagar, KoheFiza, Govindpura, and Habibganj. So does the crime concentration in these areas.
Source: Crime data Bhopal PHQ, 2015 digitized by author.
Crime trend analysis in the study area shows a consistent rise in the crime rate of some police stations like Hanumanganj, MP Nagar, Kohefiza, TT Nagar, Govindpura, Piplani, Habibganj, Jahangirbad and Kamla Nagar. Hanumanganj and MP Nagar have a crime rate higher than the city’s average crime rate of 855 (Figure 7).
Source: Crime data Bhopal PHQ, 2015.
A peculiar trend observed from the data of 2012-2015 is the reflection of high crime in one of the city’s major CBD area that is TT Nagar, old city area that is Hanumanganj and one of city’s outgrowth industrial area that is Misrod (Figure 8).
Source: Crime data Bhopal PHQ,2015.
An effort was made to select independent variables that were not highly correlated with one another. Nevertheless, the variables appear negatively associated with the crime rate with low multiple correlations. Table 3 demonstrates the acronym used in the correlation analysis.
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Descriptive statistics were calculated for each data set to determine the minimum, maximum, mean, standard deviation and coefficient of variation of each variable. The coefficient of variation shows a greater variability in the variables among 27 police stations used in the study (Table 4).
| Mean | Std. Deviation | Coefficient of Variation | |
|---|---|---|---|
| CR | 1316.612 | 1396.608 | 1.06 |
| Area km2 | 8.3163 | 7.90664 | 0.95 |
| Pop | 62081.04 | 32356.71 | 0.52 |
| TOT_M | 32093.11 | 16852.86 | 0.53 |
| TOT_F | 29987.93 | 15507.56 | 0.52 |
| PD | 18427.81 | 23027.18 | 1.25 |
| No_HH | 13204.11 | 7199.921 | 0.55 |
| Literates | 45219.89 | 24354.39 | 0.54 |
| M_LIT | 24471.74 | 13319.19 | 0.54 |
| F_LIT | 20748.15 | 11048.82 | 0.53 |
| WP | 21581.63 | 11344.14 | 0.53 |
| TOT_W_M | 16714.85 | 8692.771 | 0.52 |
| TOT_W_F | 4866.78 | 2735.712 | 0.56 |
| EI | 1 | 1.209 | 1.21 |
| B&L | 0.7 | 1.265 | 1.81 |
The correlation analysis between crime rate and demographic factors demonstrates a range of factors associated with these variables, either positively or negatively and strong or weak correlation. The table signifies that there appears to be a weak and negative statistical association between crime and demographic factors in Bhopal. The socioeconomic and demographic factors depicted a negative but strong correlation. Since crime itself is not a positive variable, its association with most socioeconomic and demographic variables is not favourable, even though they are positively associated with each other.
The Pearson's Correlation analysis is done by using SPSS (Statistical Package for Social Sciences). This method determines whether there is an association between the variables and measure the association's strength and direction. Pearson's correlation coefficient is denoted by (r), which measures of the strength of the association between two variables. The formula for Pearson correlation coefficient r is given by:
where n is the sample size, xi & yi are the ith sample points and x̄ & ȳ are the sample means for the random variables X and Y respectively.
The present study's sample size is 27, the dependent variable X is the crime rate (CR), and the independent variables are the demographic factors.
Table 5 shows the results of the correlation analysis between the crime rate and selected variables. The correlation coefficient have been summarised in Table 5. The correlation coefficient have been tested at 5% significance level (p value≤0.05). As evident from the table total female population, total population, total working males and total male population are found statistically significant at 5% level of significance. Hence these factors are inversely related and have effect on the crime rate and in the study area. Whereas factors like area, population density, educational institutes, bar and liquor shops are found statistically insignificant at 5% level of significance indicating no effect of these factors on crime rate in the study area. Although population is found to be highly correlated to the all the factors used in the study except educational institutes, bar and liquor shops.
| CR | Pop | Area km2 | PD | No_HH | Literates | WP | ||
|---|---|---|---|---|---|---|---|---|
| CR | PC | 1.00 | -.678** | -0.19 | -0.15 | -.648** | -.660** | -.664** |
| Sl | 0.00 | 0.35 | 0.44 | 0.00 | 0.00 | 0.00 | ||
| Pop | PC | -.678** | 1.00 | 0.13 | 0.09 | .995** | .995** | .998** |
| Sl | 0.00 | 0.51 | 0.66 | 0.00 | 0.00 | 0.00 | ||
|
Area km2 |
PC | -0.19 | 0.13 | 1.00 | -.520** | 0.10 | 0.09 | 0.13 |
| Sl | 0.35 | 0.51 | 0.01 | 0.61 | 0.64 | 0.53 | ||
| PD | PC | -0.15 | 0.09 | -.520** | 1.00 | 0.09 | 0.10 | 0.08 |
| Sl | 0.44 | 0.66 | 0.01 | 0.65 | 0.62 | 0.71 | ||
| No_HH | PC | -.648** | .995** | 0.10 | 0.09 | 1.00 | .997** | .998** |
| Sl | 0.00 | 0.00 | 0.61 | 0.65 | 0.00 | 0.00 | ||
|
Literates |
PC | -.660** | .995** | 0.09 | 0.10 | .997** | 1.00 | .997** |
| Sl | 0.00 | 0.00 | 0.64 | 0.62 | 0.00 | 0.00 | ||
| WP | PC | -.664** | .998** | 0.13 | 0.08 | .998** | .997** | 1.00 |
| Sl | 0.00 | 0.00 | 0.53 | 0.71 | 0.00 | 0.00 |
(b)
| CR | TOT_M | TOT_F | M_LIT | F_LIT | TOT_W_M | TOT_W_F | EI | B&L | ||
|---|---|---|---|---|---|---|---|---|---|---|
| CR | PC | 1 | -.675** | -.681** | -.656** | -.664** | -.678** | -.601** | -.242 | .103 |
| Sl | .000 | .000 | .000 | .000 | .000 | .001 | .223 | .610 | ||
| TOT_M | PC | -.675** | 1 | 1.000** | .995** | .993** | .999** | .962** | .088 | -.125 |
| Sl | .000 | .000 | .000 | .000 | .000 | .000 | .663 | .535 | ||
| TOT_F | PC | -.681** | 1.000** | 1 | .995** | .994** | .999** | .964** | .099 | -.123 |
| Sl | .000 | .000 | .000 | .000 | .000 | .000 | .622 | .540 | ||
| M_LIT | PC | -.656** | .995** | .995** | 1 | .998** | .994** | .973** | .054 | -.117 |
| Sl | .000 | .000 | .000 | .000 | .000 | .000 | .791 | .560 | ||
| F_LIT | PC | -.664** | .993** | .994** | .998** | 1 | .993** | .973** | .074 | -.116 |
| Sl | .000 | .000 | .000 | .000 | .000 | .000 | .714 | .563 | ||
| TOT_ | PC | -.678** | .999** | .999** | .994** | .993** | 1 | .960** | .104 | -.115 |
| W_M | Sl | .000 | .000 | .000 | .000 | .000 | .000 | .605 | .567 | |
| TOT_ | PC | -.601** | .962** | .964** | .973** | .973** | .960** | 1 | .074 | -.167 |
| W_F | Sl | .001 | .000 | .000 | .000 | .000 | .000 | .715 | .404 | |
| EI | PC | -.242 | .088 | .099 | .054 | .074 | .104 | .074 | 1 | .176 |
| Sl | .223 | .663 | .622 | .791 | .714 | .605 | .715 | .380 | ||
| B&L | PC | -0.12 | -0.12 | -0.12 | -0.12 | -0.12 | -0.17 | 0.18 | 1.00 | |
| Sl | 0.54 | 0.54 | 0.56 | 0.56 | 0.57 | 0.40 | 0.38 |
Note:
PC-principal component analysis
Sl-significance level 2 tailed
All Census population enumeration of 2011 is projected till 2015 using growth rate from development plan
For the analysis, Crime rate and population density are calculated. Table 4 in Appendix 3 shows the descriptive statistics for the total crime incidents and four census variables. The table indicates that all crime variables have low coefficients of variation (the standard deviation divided by the mean), indicating less significant dispersion of individual values around the mean.
Analysis of demographic factors and crime rateAs per the census 2011, Bhopal falls under class 1 city, a million plus populated city. The population size, including the total population and male and female population in Bhopal, showed a negative correlation with crime, which shows that the crime rate decreases when the population size increases in the area of Bhopal. The findings in the case of population size are similar to those presented by Nolan and James (2004), who enumerated a negative correlation of population size and crime by the gender-based analysis, where crime rate analysis with male and female populations showed a negative correlation. Also, in his other population study, he explained that higher populated cities reported higher crime rates with a positive correlation and a negative correlation for midrange populated cities (Nolan and James, 2004) which is not the same in the case of Bhopal. The charts and maps of crime rate and population are mentioned in Appendix 4. The crime rate analysis with population density showed that areas ranging from 1876 to 40000 persons/sq km accumulate most of the crime. As the population density increases above 40000, there is a fall in the crime rate (for charts and maps of crime rate and population density, refer to Appendix 5).
The crime rate analysis with the number of households showed that areas with more households tend to have lesser crime rates than areas with fewer households (for charts and maps of crime rate and the number of households, refer to Appendix 6). Several studies have shown that criminals tend to be less educated and from poorer economic backgrounds than non-criminals (Ehrlich, 1973). The same can be seen in the case of Bhopal, where areas with a literate population showed a negative correlation. Hence, it is observed in the case of Bhopal that the areas with higher literacy among the population showcased a decreased crime rate. The relationship between male and female literate population analysis also showed a negative correlation, where crime rate decreases with an increase in the male and female literate population (for charts and maps of crime rate and literate population, refer to Appendix 7).
Employment correlates negatively to crime. Assuming that this relationship is causal, this suggests for increasing employment leads to lower crime rates (Mallubhotla, 2013). The crime rate analysis with the working population showed a negative correlation, showing that the working population increases in an area with the crime rate decrease. The relationship between male and female working population analysis also showed a negative correlation, where crime rate decreases with an increase in the male and female working population (for charts and maps of crime rate and working population, refer to Appendix 8).
Educational institutes correlate negatively to crime. Assuming that this relationship is casual, this suggests that an increasing number of educational institutes leads to lower crime rates (for charts and maps of crime rates and educational institutes, refer to Appendix 9). Bars and liquor shops correlate positively to crime. Assuming that this relationship is casual, this suggests that increasing the number of bars and liquor shops in an area leads to higher crime rates in that area (for charts and maps of crime rates and bars and liquor shops, refer to Appendix 10).
This study explores a Pearson correlation between crime rate and demographic factors. The spatial analyses in Section 3.3 confirm the results of Charron (2011), indicating that crime is not randomly distributed in space but rather concentrated in neighbourhoods that share particular characteristics. Similar studies on crimes have examined a peculiar relation between demographic factors and crime either by correlation (Shaw, 1949) or regression (Nolan and James, 2004). This study reveals that a large population size leads to a reduction in crimes. A large population will reduce crime rates in the area as the criminal acts can be prevented by the fear of close and easier street monitoring by people in a largely populated street or community. However, this study's findings on population density were not in complete agreement with the results from the study conducted by Adeyemi, Mayaki et al. (2021). They adopted a generalised linear mixed model (GLMM) and observed that the high population density neighbourhood reflects a reduced crime rate. As the study focuses on one of the class-1 cities (million plus-population 1000000 above) i.e., Bhopal, the population size and density findings may be relevant in other similar Class-1 cities in India.
The findings in our study indicate that the crime rate does not have any correlation with area size. Results also demonstrated that areas with more literates in case of total and as well as female and male literates show an inverse correlation with the crime rate. This indicates that improving the quality of education can reduce crimes (Becker and Mulligan, 1997). A similar inverse correlation is observed between the working population and the crime rate as the increase in working activities /employment reduces the crime rate. Findings of the working population with crime rate are similar to the results of Lochner (2004), who emphasises the role of education as a human capital investment that increases future legitimate work opportunities, which discourages participation in the crime. The presence of educational institutes and the location of bars and liquors shops showed a negligent correlation between the factors. However, these demographic factors are highly correlated with each other.
The study has a few unavoidable limitations. First, only the data aggregated at the ward level has been made available to ensure confidentiality. Point-based data has not been accessible for analysis. Hence, the method of choropleth mapping has been adopted, despite its limitation of mapping only area-based data and not point-based entities. Unlike a few European countries' census data formats, data from the Census of India and other corresponding agencies restrict sensitive data such as ethnicity, immigration status, and income; therefore, these have not been accessible for the study for various security or official reasons. Thus, the technique of analysing a wider range of crime characteristics using data from similar available factors was adopted for a more accurate analysis of factors associated with crime. Studies on the relationships between crime and demographic factors in the Indian scenario are nascent. So far, few or no such studies have attempted to look at such a relationship between crime and demographic factors in Bhopal or any similar city in the state of Madhya Pradesh or in the Indian context.
Furthermore, although the study has analysed crime occurrences from 2012 to 2015 and provided a detailed analysis for 2015, it has not been able to include data spanning a large number of years, or current data, due to confidentiality and sensitivity reasons.
This study has addressed the components of Brantingham’s crime pattern theory and ideas of crime analysis from a positivist perspective (Brantingham and Brantingham, 1981). The study has relied upon statistical and spatial data related to crime and demographic factors that have been treated as empirical evidence. Statistical data has been analysed with the help of correlation analysis and spatial data using overlay analysis. As explained in the correlation matrix in Table 5, this study reveals the pattern through which society functions concerning to crime and its demographic conditions. This study's findings reflect Brantingham’s position on crime, which pointed out that crime is a complex occurrence requiring the coincidence of many features, including spatial features and human attributes (Brantingham and Brantingham, 1981). From Bhopal's empirical case, this study makes the inference that crime is influenced by its socioeconomic and demographic conditions, but the converse needs to be verified; i.e., whether the continuous occurrence of crime has influenced an area’s socioeconomic and demographic conditions. The image of crime and the locational setting may, perhaps, influence the locations that people would prefer to reside in a city, affecting the development of the area and those areas’ circle value. The city’s core areas have a denser concentration of crimes and are closely linked to city planning and development mishaps.
Although the use of correlation in the measure of crime is underutilised in various scientific studies, this study has adopted a correlation matrix that has shown a light on why crimes occur in particular places. This is carried out through a thorough analysis of demographic characteristics that have attracted criminal activities.
Based on the analysis and findings, this study makes a few recommendations that can inform the planning policy and practice towards crime reduction in the city planning process. The population factor itself being strongly and positively related to other demographic factors; change in population can affect the relationship with crime. It is recommended that any strategic planning framework focused on similar city demographic conditions as that of Bhopal should consider an optimal population distribution over the wards.
Planning authorities must consider the ward boundaries' reconstitution for optimal population distribution. This reconstitution of the ward boundary should be an administrative integration in cognisance with the police authorities for ideal distribution of population and significant convergence of ward boundary and police boundary. This can help avoid many administrative and bureaucratic issues and helps in taking relevant measures in crime prevention at a particular place and time.
Investment in youth education and in policies that promote schooling would reduce the occurrence of most types of street crimes among adults. The analysis in this paper has provided many clues about the demographic array but has given rise to many new questions. Transparency in the availability of point-based data is crucial for any decision-making process. The information in point-based data can open up more cues for in-depth analysis of crime and demographic factors.
Future research should pay more attention to the inclusion of multiple factors, such as land use influencing crime and its types. Even land-use itself as an individual factor can influence crime rate and types of crime. As a way forward, several issues should be considered together to analyse crime and criminogenic factors. The impact of factors like transport nodes, population density, vacant land, streetlights, surveillance area, liquor shops' location, literacy and employment might also influence this relationship. Articles have mentioned that the change in land use itself brings about an exorbitant change in the distribution of the types of crime (Nuissl and Siedentop, 2021). So, there is ample scope for further investigations into crime analysis considering multiple influencing factors, which will address the root cause of crime. The practicability of this crime analysis lies in statistical analysis and incorporating spatial/geographical analysis, which determines the ‘strength’ of the relationship between crime and criminogenic factors.
Conceptualization, B. M. and P. R. S.; methodology, B. M.; software, B. M.; investigation, B. M.; resources, B. M.; data curation, B. M.; writing—original draft preparation, B. M and P. R. S.; writing—review and editing, B. M. and P. R. S.; supervision, P. R. S. All authors have read and agreed to the published version of the manuscript.
The authors declare that they have no conflicts of interest regarding the publication of the paper.
| Police station | Ward No. |
|---|---|
| Hanumanganj | 57 |
| Habibganj | 12,13,17,19 |
| M P Nagar | 24,33 |
| Piplani | 30,58,60 |
| Kohe Fiza | 7 |
| TT Nagar | 20,26,25,27 |
| Jahangirbad | 28,34,35,40,43 |
| Govindpura | 21,23,31,32 |
| Nishantpura | 9,6 |
| Kamla nagar | 11,16,18 |
| Aishbag | 46,42,36 |
| Ashoka Garden | 44,49,52,68 |
| Bag Sevania | 4 |
| Bairagarh | 70,61,53 |
| Bajariya | 62 |
| Bilkhiria | 14,38 |
| Gandhi Nagar | 70 |
| Gautam Nagar | 65,67 |
| Kolar Road | 15 |
| Kotwali | 45,47 |
| Mangalwara | 48 |
| Misrod | 1 |
| Ratibarh | was not in municipal area in 2011 |
| Shahpura | 5,8 |
| Shahjahanabad | 51,55,64,69 |
| Shyamla Hills | 22,29 |
| Sukhi Sevania | was not in municipal area in 2011 |
| Talliya | 39,41 |
| Teelajamalpura | 63,66 |
(a)
| Police station | Total Crime incidents | Population | Crime rate all | Area km 2 | Population density | No_HH | Literates | Working pop. |
|---|---|---|---|---|---|---|---|---|
| Aishbag | 192 | 70225 | 831.61 | 2.11 | 33283.32 | 14972 | 49697 | 23982 |
| Ashoka Garden | 213 | 121619 | 432.50 | 8.58 | 14168.62 | 28108 | 95063 | 44233 |
| Bag Sevania | 116 | 53178 | 812.37 | 8.36 | 6361.21 | 10941 | 34905 | 18399 |
| Bairagarh | 148 | 115847 | 341.83 | 9.14 | 12675.52 | 24604 | 83620 | 39759 |
| Bajariya | 69 | 44437 | 1435.74 | 3.49 | 12727.78 | 10308 | 35603 | 15857 |
| Bilkhiria | 39 | 62154 | 769.06 | 33.12 | 1876.44 | 12122 | 40836 | 20673 |
| Gandhi Nagar | 44 | 55840 | 515.76 | 23.38 | 2388.12 | 11058 | 38194 | 19037 |
| Gautam Nagar | 97 | 68378 | 1014.95 | 2.83 | 24160.07 | 14392 | 50035 | 23431 |
| Govindpura | 289 | 84062 | 687.59 | 16.33 | 5146.85 | 17997 | 58389 | 28220 |
| Habibganj | 236 | 76014 | 1015.60 | 7.68 | 9902.93 | 14987 | 54958 | 25310 |
| Hanumanganj | 222 | 16549 | 6308.54 | 1.34 | 12379.49 | 3766 | 11694 | 6332 |
| Jahangirbad | 247 | 103525 | 573.77 | 4.44 | 23294.73 | 22074 | 76341 | 36861 |
| Kamla nagar | 173 | 61569 | 724.39 | 8.92 | 6900.20 | 12700 | 45428 | 21841 |
| Kohe Fiza | 325 | 24972 | 2602.92 | 5.80 | 4302.22 | 4511 | 17555 | 8456 |
| Kolar Road | 197 | 29476 | 1336.68 | 2.05 | 14406.93 | 5658 | 19902 | 9766 |
| Kotwali | 111 | 29848 | 2083.89 | 0.65 | 45601.97 | 7118 | 24627 | 11170 |
| M P Nagar | 511 | 36031 | 1071.30 | 0.50 | 72409.72 | 7512 | 26834 | 12487 |
| Mangalwara | 43 | 7880 | 4949.24 | 13.55 | 581.52 | 1797 | 6645 | 2804 |
| Misrod | 95 | 27615 | 2614.52 | 2.37 | 11646.51 | 5654 | 15834 | 9349 |
| Nishantpura | 254 | 51255 | 991.12 | 20.95 | 2446.57 | 10343 | 36556 | 18280 |
| Piplani | 255 | 79554 | 892.48 | 15.44 | 5153.01 | 18238 | 60368 | 29526 |
| Shahjahanabad | 129 | 123170 | 290.66 | 8.49 | 14504.15 | 27024 | 89448 | 42786 |
| Shahpura | 105 | 58795 | 527.26 | 10.49 | 5602.39 | 12381 | 43304 | 20746 |
| Shyamla Hills | 44 | 58795 | 600.39 | 7.44 | 7901.66 | 12381 | 43304 | 20746 |
| Talliya | 105 | 26834 | 1155.25 | 1.07 | 24966.06 | 5339 | 19882 | 8588 |
| Teelajamalpura | 39 | 92573 | 300.30 | 0.89 | 104057.44 | 19463 | 66698 | 31184 |
| TT Nagar | 321 | 95993 | 668.80 | 5.13 | 18705.32849 | 21063 | 75217 | 32881 |
(b)
| Police station | TOT_Males | TOT_Females | M_LIT | F_LIT | TOT_WORK_M | TOT_WORK_F | Educatioanl Institutes | Bars and liquor shop |
|---|---|---|---|---|---|---|---|---|
| Aishbag | 36440 | 33785 | 27487 | 22210 | 18720 | 5262 | 0 | 2 |
| Ashoka Garden | 63053 | 58566 | 51626 | 43437 | 33181 | 11052 | 1 | 0 |
| Bag Sevania | 27392 | 25786 | 18880 | 16025 | 14103 | 4296 | 4 | 0 |
| Bairagarh | 60183 | 55664 | 45701 | 37919 | 30422 | 9337 | 0 | 0 |
| Bajariya | 23055 | 21382 | 19146 | 16457 | 12091 | 3766 | 0 | 1 |
| Bilkhiria | 31979 | 30175 | 21725 | 19111 | 17258 | 3415 | 2 | 0 |
| Gandhi Nagar | 28963 | 26877 | 21125 | 17069 | 14929 | 4108 | 1 | 0 |
| Gautam Nagar | 35875 | 32503 | 27953 | 22082 | 18781 | 4650 | 0 | 1 |
| Govindpura | 43875 | 40187 | 32339 | 26050 | 22399 | 5821 | 1 | 0 |
| Habibganj | 39025 | 36989 | 29028 | 25930 | 20933 | 4377 | 3 | 5 |
| Hanumanganj | 8551 | 7998 | 6495 | 5199 | 4598 | 1734 | 0 | 1 |
| Jahangirbad | 53644 | 49881 | 41283 | 35058 | 28254 | 8607 | 0 | 0 |
| Kamla nagar | 32124 | 29445 | 24647 | 20781 | 17667 | 4174 | 2 | 0 |
| Kohe Fiza | 12580 | 12392 | 8942 | 8613 | 6632 | 1824 | 0 | 0 |
| Kolar Road | 15174 | 14302 | 10445 | 9457 | 8315 | 1451 | 1 | 0 |
| Kotwali | 15268 | 14580 | 13038 | 11589 | 8107 | 3063 | 0 | 0 |
| M P Nagar | 18744 | 17287 | 14767 | 12067 | 9666 | 2821 | 0 | 4 |
| Mangalwara | 3985 | 3895 | 3434 | 3211 | 1974 | 830 | 0 | 1 |
| Misrod | 14247 | 13368 | 8717 | 7117 | 7039 | 2310 | 2 | 1 |
| Nishantpura | 26129 | 25126 | 19293 | 17263 | 13700 | 4580 | 2 | 0 |
| Piplani | 41024 | 38530 | 32893 | 27475 | 21958 | 7568 | 3 | 2 |
| Shahjahanabad | 64067 | 59103 | 48623 | 40825 | 33154 | 9632 | 1 | 0 |
| Shahpura | 29831 | 28964 | 22782 | 20522 | 15463 | 5283 | 0 | 0 |
| Shyamla Hills | 29831 | 28964 | 22782 | 20522 | 15463 | 5283 | 3 | 1 |
| Talliya | 13760 | 13074 | 10574 | 9308 | 7166 | 1422 | 0 | 0 |
| Teelajamalpura | 47946 | 44627 | 36396 | 30302 | 24303 | 6881 | 0 | 0 |
| TT Nagar | 49769 | 46224 | 40616 | 34601 | 25025 | 7856 | 1 | 0 |
