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Effect of land use on crime considering exposure and accessibility
Habitat International 89 (2019) 102003
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Habitat International journal homepage: www.elsevier.com/locate/habitatint
Effect of land use on crime considering exposure and accessibility a
b
Soumik Nafis Sadeek , Abu Jar Md. Minhuz Uddin Ahmed , Moinul Hossain a b c
b,∗
, Shinya Hanaoka
T c
International University of Business Agriculture and Technology (IUBAT), Uttara Model Town, Dhaka, 1230, Bangladesh Islamic University of Technology (IUT), Gazipur, 1704, Bangladesh Tokyo Institute of Technology, Japan
A R T I C LE I N FO
A B S T R A C T
Keywords: Crime Land use Accessibility GIS Logistic regression Support vector machine
A substantial number of studies have revealed an association between crime and land use, in some cases by representing land use using socio-economic and demographic data to evaluate their correlation with various types of crimes. The spatial autocorrelation between land use and crime is also often investigated. Many studies focus on data obtained from locations at which a crime has taken place and ignore exposure, i.e., locations where a crime has not taken place. It has also been suggested that transportation accessibility plays a significant role in connecting crime patterns with land use, although this hypothesis requires further study. This paper proposes a new framework to aggregate and synthesize the existing literature based on a geocoding of the association between crimes and land use on a GIS map. The map is broken down into different mesh sizes indicating the occurrence or nonoccurrence of crime within individual mesh cells. An optimal mesh size to best explain the interrelationship between crime and land use with respect to road network accessibility is then developed using logistic regression (LR), and a support vector machine (SVM) is used to identify combinations of land use that are highly susceptible to crime and those that are quite safe. The results of this study can provide insight into reducing crime in cities, allocating law enforcement agency resources, and designing a built environment that can naturally deter crime.
1. Background Land use, road network planning, and green and open space installation form the primary skeleton of city planning and shapes future development patterns. The planning method plays an important role in the quality and efficiency of a city's comprehensive plan and in changing the city's physical structure to reduce the crime rate (Ludin, Aziz, Yusoff, & Razak, 2013; Sayafzadeh & Hassani, 2014). The study of the effect of land use on crime is a relatively new joint sub-field of criminology and urban planning, as classical criminology focused primarily on crime as a result of offender behavior. For example, the Italian criminologist Lombroso (1911) emphasized the connection between individuals' genetic features and propensity to crime in a subsequently refuted theory. Arguably, the first mention of the role of environmental features in crime pattern theory was by the Canadian criminologists Brantingham and Brantingham (1981), who postulated that crime is a complex occurrence that requires the coincidence of many features, including spatial features, in addition to human attributes. According to Greenberg, William, and Williams (1982), the physical characteristics of a neighborhood can significantly affect associated crime scenarios in a manner that can be independent of crime rate. Carter, Carter, and
∗
Dannenberg (2003) used the concept of crime prevention through environmental design to help police in reducing prostitution by identifying specific land uses (hotels, motels, etc.) as targets for highly visible police patrolling. More recent studies have attributed the effects of land use on crime generation and patterns. Minnery and Lim (2005) measured crime prevention in terms of environmental design and proposed a scale based on the results of a social attitude survey to measure the effectiveness of prevention. Their results suggested that effective environmental design can have some effect on reducing victimization, particularly for the residents of groups of houses on a single street. Yirmibesoglu and Ergun (2007) utilized land use and value as variables to reveal that property and personal crime rates increase with land value and the introduction of mixed land use. Kinney, Brantingham, Wuschke, Kirk, and Brantingham (2008) analyzed patterns of assault and motor vehicle theft to identify crime activity nodes and revealed that delinquency tends to occur more often in conjunction with public commercial activities and in parking, shopping, and recreational areas as well as in specific government locations. Stucky and Ottensmann (2009) explored the relationship between violent crime and land use and argued that crimes occur as the result of several socioeconomic factors that are
Corresponding author. Department of Civil and Environmental Engineering, Islamic University of Technology (IUT), Board Bazar, Gazipur, 1704, Bangladesh. E-mail addresses: [email protected], [email protected] (M. Hossain).
https://doi.org/10.1016/j.habitatint.2019.102003 Received 9 March 2018; Received in revised form 25 April 2019; Accepted 17 June 2019 Available online 21 June 2019 0197-3975/ © 2019 Elsevier Ltd. All rights reserved.
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actual crime exposure by investigating what types of land use have the least association with crime. In addition, despite their significant promise, studies on crime, accessibility, and land use are often confined to the identification of vulnerability to crime on individual street segments. This paper synthesizes the lessons learned from preceding and ongoing studies by establishing a GIS-based framework for associating land use and transportation network accessibility with crime type and presents a model describing the relationship between land use characteristics and crime type based on accessibility measures. Our work is distinguished from the existing literature in two respects: first, it considers true exposure by examining locations in which crime is absent and their associated land use; second, rather than relying on spatial auto-correlation alone, it acknowledges the association between crime and accessibility by linking crime and land use with distance measured along the road network rather than Euclidean distance. Our method employs logistic regression (LR) and SVMs to perform modeling in which the study area is divided into meshes of different size. LR-based models are used to produce receiver operating characteristic (ROC) curves to assist in identifying the most suitable mesh size for separating crime from no-crime data and recognizing the influence of various underlying factors, e.g., distance from residential/commercial areas, schools, road networks, etc., on crime occurrence. The SVM provides further insight into crime by considering land use types in various pairs to identify combinations of risky and safe land uses.
determined by land use. Browning et al. (2010) studied the impact of land use patterns on crime in the context of urban regeneration and the role of developers and municipalities in shaping the urban landscape with the goal of reducing crime scenarios in urban areas. Ludin et al. (2013) examined patterns of drug abuse, burglary, and theft in commercial and residential areas using GIS data and found a strong relationship between petty crime, drug abuse, and land use pattern, a finding that supported a linkage between crime density and population density. Song, Andresen, Brantingham, and Spicer (2015) focused on the boundaries between relatively homogeneous neighborhoods under the assumption they would present differing levels of risk for criminal victimization and found that victimization rates are 2–3 times higher at community edges than elsewhere. Nwaogul, Ole, and Pechanec (2016) used GIS-based spatiotemporal analysis to determine that residential and commercial land-use are associated with the highest crime rates, with theft, robbery, and gambling constituting the most common crime types. Zhang (2016) demonstrated that highly populated residential and commercial land use can stimulate the occurrence of crime, thereby reducing public transit ridership. Soon (2016) analyzed the impact of the ratio of commercial to residential land use on burglary and other types of crime and found that residential burglary in commercial area varies with the type of commercial facility and that the proportion of shopping centers in a residential zone is associated with increased residential burglary. The studies discussed above represent the two major trends in crime and land use research. The first approach looks at the socioeconomic and/or demographic characteristics representative of land use and then relate them with various types of crimes. The second approach focuses on the spatial autocorrelation between different land uses and associated crimes. Another, less explored, approach is to relate land use to crime through transportation accessibility based on the hypothesis that the design of street networks influences patterns of movement within a city, which remain relatively unchanged even when the objective of travel is to commit a crime. The pioneering study of Beavon, Brantingham, and Brantingham (1994) is in line with this hypothesis in exploring the relationships among property crime along different road segments with respect to the accessibility of street networks and the concentration of potential targets. Their findings affirmed that accessible and highly used areas experience greater amounts of property crime. Davison and Smith (2003) extended the work of Beavon et al. (1994) using data from the United States and included a guardianship measure and additional land use types and reported similar findings. More recently, Groff and Lockwood (2014) considered the shortest path distances between selected areas and facilities such as bars and street joints in terms of exposure to violent or property crime or disorderly conduct. Adel, Salheen, and Mahmoud (2016) postulated that crime occurs neither equally nor in the same manner in all places. Formulating social factors (e.g., illiteracy, unemployment, and internal migration rates, population density, and average family size) and street network and land use (e.g., commercial, residential, vacant, or industrial) as variables, they concluded that most criminals search for accessible places that are easy to move through and provide high opportunities for escape after committing crimes. In addition to various statistical approaches and spatial analysis methods, recent studies have used data mining techniques to perform crime hotspot detection. Such approaches include the use of support vector machines (SVMs) (Kianmehr & Alhajj, 2008) for spatial and temporal criminal hotspot detection, Bayesian classifiers (Almanie, Mirza, & Lor, 2015) to predict crime type, and the Naïve Bayes algorithm (Vural & Gök, 2016) to predict crime. The literature suggests a considerable association between crime type and land use. A major drawback of previous studies has been the reliance on datasets documenting the connections between various types of crime and land use while overlooking areas in which crimes did not take place during the study period. This is a major shortcoming, as it is also important from a policy perspective to obtain a picture of
2. Study area and data collection Bangladesh is a crime-prone country in which the occurrence of crime has increased by approximately 41% over the 15 years leading up to 2015 (Bangladesh Police Report, 2016). It is believed that the actual situation in the country is far worse, as a substantial portion of crimes go unreported. The high crime rate is attributed to the major resource constraints prevailing in the law enforcement sector: the country has among the lowest police-population ratios (96 per 100,000 people), average officer salaries (US$ 127 per month), and annual budget allocations (US$ 206 million) of any country in the world and compares particularly poorly to developed countries such as the United States, which has 379 officers per 100,000 people, a US$ 2216 monthly per-officer salary, and US$ 4800 million in annual funding. The interdependence between crime prevention and police expenditure (BPO website) helps explain why the performance of the Bangladeshi police has yet to reach standards seen in other countries. In this study, the Uttara model town—one of the eight crime divisions covered by the Dhaka Metropolitan Police—was selected as a representative typical urban neighborhood of Dhaka city, the capital of Bangladesh. Uttara has a total area of 36.91 km2 and a population of 179,907. Its contains many educational institutions, mosques, community centers, street markets, empty plots, slums, and residential areas in which high and low income individuals co-exist. The total police force assigned to maintain law and order is only about 180, corresponding to a low police-population ratio of 100.5:100000. Fig. 1 shows a map of the study area within the greater Dhaka region along with a zoomed-in view. The figure also presents locations in which various types of crimes have taken place and their associated land uses. The data used in this research come from both primary and secondary sources. A GIS map of the Uttara model town was obtained from the City Development Authority (RAJUK). The map contains an updated inventory of land use in Uttara in which nearly all of the infrastructure has been geocoded. Crime data for January 2014–December 2015 were collected from the First Information Report (FIR) books of the Uttara East and Uttara West Police. These include information on type of crime, time and location of occurrence, distance from the nearest police station, etc. As the locational data are based on address, site visits were undertaken to geocode each crime scene with the assistance of the corresponding police stations. Once these records were digitized, another survey team verified the land uses recorded in the 2
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Fig. 1. a) Study area (Uttara) within the Greater Dhaka region; b) Uttara (middle) at a larger scale (Source: RAJUK); c) crimes by type; and d) land use.
between land use and crime with respect to accessibility is divided into several components. First, a GIS map is geocoded with infrastructures of interest and crime occurrences by type. The map is then divided into successively smaller meshes (grids), with each grid cell associated with a land use type and the presence or absence of crime. LR is then employed to identify the ideal mesh size that most accurately distinguishes between crime-prone and non-crime-prone mesh cells. Finally, an SVM is used to reveal the associations between crime and land use. The process involves the following primary steps:
GIS data, specially identifying community centers, educational institutions, mosques, open spaces, shopping complexes and street markets, and the highway network. The FIR data contained 12 separate crime types, which were re-classified into the following five categories based on the severity of punishment described in the Bangladesh Penal Code: 1) theft, 2) robbery, 3) drug infractions, 4) grievous harm, and 5) murder. Crime types such as “unnatural death” were ignored as the related reports do not identify any offenders. The number of samples for each category for which locations could be properly identified and the corresponding FIR data were complete were 290, 72, 238, 61, and 111, respectively.
Step 1 – Data collection and processing: collection, geocoding, verifying, and linking among land use, road network pattern, and crime data. Step 2 – Mesh preparation: the GIS map of the study area is divided into small cells through the formation of square meshes of various
3. Methodology Our replicable framework for evaluating the interrelationship 3
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The fundamental equation for the LR shows that, when the value of an independent variable increases by one unit and all other variables are held constant, the new probability ratio is given by
dimensions. Eight mesh sizes, ranging from 50 m × 50 m–400 m × 400 m in 50-m steps, are applied. Step 3 – Assigning land use, crime status, and distance to cells: each cell in the attribute table of the GIS file is assigned a land use (community center, educational institution, mosque, open space, or shopping complex/street markets), crime status, and distance. The land use is assigned by evaluating which of the five land use types covers most of the cell. If a crime took place on the cell, it is coded with a value corresponding to the crime; if no crime took place at that location, the cell is coded as “0.” Step 4 – Finding the optimum mesh size: ROC curves are drawn for each model using an LR developed for each mesh size (50 m × 50 m, 100 m × 100 m, 150 m × 150 m, 200 m × 200 m, 250 m × 250 m, 300 m × 300 m, 350 m × 350 m, and 400 m × 400 m) with crime type set as a dependent variable with a dichotomous outcome and distances from various land uses set as independent variables. Step 5 – Assessing the interrelationship among land use, transportation networks, and crime: LR and an SVM analyses are used to uncover the interrelationship among land use, the transportation network connected with the land, and different types of crimes. Step 6 – Model output assessment: the SVM outputs a confusion matrix for each combination of land use and crime type with corresponding accuracy, sensitivity, specificity, and F-scores. The accuracy measure reflects each model's overall ability to associate both crime and non-crime situations with specific land use for each crime type; the sensitivity reflects the classification accuracy with respect to crime; the specificity presents each classification in terms of the “no crime” condition; the F-score is a measure of the discrimination between two compared sets of land use, where a higher value corresponds to a postulate of more discriminative features. In the analysis, a minimum threshold value of 75% is set for all four indicators to identify the combinations of land uses that can be classified with high accuracy. As neither land uses nor crimes are equally distributed, the number of crimes and the number of cells belonging to each combination of land use are determined for the combinations of land use that can be classified with high accuracy and the number of crimes per cell is calculated. The values are then weighted with the distribution of various crime types, with higher values corresponding to higher likelihoods of crime. Finally, the land use combinations associated with high and low crime rates are identified to enable a discussion of the policy implications of the findings.
Pi Pi ⎞ β = e βo e βi (Xi + 1) = e βo e βi Xi e βi = ⎛ e i 1 − Pi ⎝ 1 − Pi ⎠ ⎜
1 − Pi
the factor e βi , which is known as the odds ratio (OR) and ranges from 0 to ∞ (Gujarati, Porter, & Gunasekar, 2012).
3.2. Support vector machine (SVM) The proposed method involves a binary classification in which crime-prone and non-prone areas are distinguished based on their land uses and associated road networks. An SVM—a hyperplane-based classification technique—is used to perform this classification as the tool provides highly accurate results and is less prone to overfitting. The purpose of SVM learning is to develop a model that classifies or accurately predicts future, unseen values of a variable. To briefly explain the SVM method, we consider a data set S = (x1, y1), …, (x|S|, y|S|), where the Xi are training tuples and the yi are the associated class of tuples, where. yi ∈ {1,0} i. e . yi ∈ {crime , nocrime} There are an infinite number of hyperplanes separating the two classes, and the objective of the SVM is to find the one that best separates them. To do this, the SVM searches for the hyperplane with the largest margin, i.e., the maximum marginal hyperplane (MMH). A separating hyperplane can be represented as
where P = {p1 , p2 , p3 , …, pn } is a weight vector and c is a scalar (bias), which can be written as p0. In this case, P represents the orientation of the hyperplane within the space and c finds the position of the hyperplane within the space. For a 2-D plane, Equation (4) can be expressed as
p0 + p1 x1 + p2 x2 = 0
(5)
The hyperplane defining the sides of the margin are
H1: p0 + p1 x1 + p2 x2 ≥ 1,
for ti = 1 (crime), and
H2: p0 + p1 x1 + p2 x2 < 1,
for t1 = 0 (no crime)
Any training dataset that falls on or over H1 is classified as crimeprone; any training data that falls on or below H2 is classified as nocrime. Combining these two inequalities yields ti (po + p1 x1 + p2 x2) ≥ 1; ∀ i , and any data points falling on H1 or H2 are regarded as support vectors to be solved using a constrained optimization problem. The optimization is performed on Equation (4) with the objective of finding a maximum separation of the hyperplanes (H1 and H2) for correctly classifying crime and no-crime zones. To obtain an optimized hyperplane, it is necessary to minimize ||P|| and maximize c, which is done by partially differentiating the Lagrange multiplier, L (||P||, c) with respect to P and c to derive minimum and maximum values, respectively (Han, Kamber, & Pei, 2012). After optimizing Equation (4), the maximum separating hyperplanes for classifying crime and no-crime points under various land use combinations are determined. To analyze the data, an open source statistical software source, “R,” was used to perform LR and SVM analysis. The “e1071” package was used to develop the SVM (Meyer, Dimitriadou, Hornik, Weingessel, & Leisch, 2017).
LR is widely used to model phenomena with dichotomous outcomes with the goal of finding the best-fit model to describe the relationship between the dichotomous characteristic of interest (dependent variable) and a set of independent variables. In this model, the logit is the natural logarithm of the odds or the likelihood ratio that the dependent variable is Y = 1 for crime in a mesh net cell rather than Y = 0 for no crime in the cell. The probability P of crime occurrence is ⎟
(1)
where Pi is the probability of crime occurrence and (1 – Pi) is the probability of no crime occurrence, βo is the model constant, and the βi are the parameter estimates for the independent variables. A simple transformation of Equation (1) yields
Pi = e βo + βi Xo = e βo e βi Xi 1 − Pi
(4)
PX + c = 0
3.1. Logistic regression (LR)
⎜
(3)
From Equation (3), we see that, when a predictor Xi increases by one unit with all other factors remaining constant, the odds Pi increase by
The following sub-sections provide a short introduction to the LR and SVM approaches.
Pi ⎞ y = log it (p) = ln ⎛ = βo + βi Xi 1 − Pi ⎠ ⎝
⎟
(2) 4
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Table 1 Odds Ratio of Different types of Crimes and Land use. Crime Land use 50 × 50 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 100 × 100 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 150 × 150 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 200 × 200 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 250 × 250 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 300 × 300 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 350 × 350 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 400 × 400 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial
All crime (Odds Ratio)
Theft (Odds Ratio)
Robbery (Odds Ratio)
Drug (Odds Ratio)
Grievous Hurt (Odds Ratio)
Murder (Odds Ratio)
1.50 (0.03*) 0.33 (0.008)** – 0.41 (0.0004***)
1.75 0.24 0.21 0.32
4.48 – – – 0.30 0.18 2.53 –
2.07 (0.009**) 0.22 (0.02*) – – – 0.42 (0.0006***) – –
– – – – – 0.30 (0.001**) –
– – 0.23 (0.007**) – – 0.023 (0.06.) – –
5.10 – – – 0.32 0.27 0.52
– – – – – – –
(0.03*) (0.02*) (0.06.) (0.001**)
(0.005**)
(0.02*) (0.0002***) (0.04*)
0.57 (0.0006***) 0.81 (0.001**) 2.10 (0.002**)
0.34 (0.0005***) 0.465 (0.000***) –
5.00 0.18 0.38 0.41 0.39 0.18 0.60 2.58
(0.000***) (0.013*) (0.08.) (0.03*) (0.000***) (0.000***) (0.027*) (0.029*)
5.61 0.09 – – 0.29 0.12 0.49
3.53 0.05 – 0.36 0.42 0.51 0.45 –
(0.005**) (0.003**)
3.88 (0.009**) 0.014 (0.000***) 0.18 (0.09.) – 0.24 (0.000***) 0.21 (0.001**) 0.49 (0.07.) –
– – – 3.39 (0.08.) 0.17 (0.001**) 0.26 (0.03*) – –
4.49 (0.003**) 0.081 (0.003*) – 0.33 (0.05.) – 0.31 (0.01*) 0.34 (0.007**) –
– 0.045 (0.04*)
0.14 (0.006**) 0.194 (0.001**) 0.81 (0.000***) 7.93 (0.000***) – –
7.82 (0.006**) 0.023 (0.02*) – 0.16 (0.06.) 0.06 (0.000***) 0.11 (0.001**) 4.35 (0.005**) –
4.98 (0.07.) 0.014 (0.01*) – – 0.06 (0.000***) 0.14 (0.018*) – 2.89 (0.014*)
8.39 – 0.12 0.07 0.43 0.13 – –
– – – – – – – 2.16 (0.05.)
0.31 (0.03*) – –
18.35 (0.004**) – – 0.02 (0.004**) 0.12 (0.000***) 0.20 (0.04*) – –
29.16 (0.000***) – – – 0.015 (0.000***) 0.07 (0.002**) – –
18.56 (0.002**) 0.03 (0.08.) – – 0.058 (0.001**) 0.066 (0.002**) – –
7.11 (0.01*) – – – 0.12 (0.001**) 0.11(0.004**) – –
0.04 (0.06.) – – 0.42 (0.06.) – – –
– – – – – – –
4.48 (0.05.)
6.90 (0.05.)
19.65 (0.007**)
6.08 (0.059.) 0.024 (0.06.)
6.17 (0.04*)
0.054 (0.05.)
0.016 (0.02*)
(0.08.) (0.01*) (0.09.) (0.06.)
1.36 (0.003**)
(0.000***) (0.004**)
(0.000***) (0.000***) (0.004**)
9.52 (0.006**) – – – 0.14 (0.000***) 0.06 (0.000***)
9.18 (0.000***) 0.14 (0.02*) 0.19 (0.000***) 0.09 (0.000***)
(0.04*)
(0.03*) (0.09.) (0.06.)
4.2 (0.003**)
(0.001**) (0.05.) (0.001**) (0.05.) (0.000***)
– – – – –
– – 0.16 (0.04*) – – – – – – – 0.12 (0.04*)
0.07 (0.09.) 0.04 (0.03*)
0.095 (0.004**) 0.047 (0.005**) – –
0.29 (0.001**) 0.58 (0.013*) – –
0.09 (0.007**) 0.11 (0.02*) – –
13.6 (0.03*) – – 0.011 (0.01*) 0.44 (0.04*) 0.24 (0.02*) – –
– – – 0.064 (0.02*) 0.074 (0.002**) 0.11 (0.003**) – –
– – – – 0.18 (0.09.) 0.12 (0.06.) –
– – – – – – – –
– – – – 1.67 (0.01*) 0.14 (0.08.) – –
– – – – 0.02 (0.017*) 0.04 (0.06.) – 7.41 (0.05.)
(Significance levels: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1).
5
0.024 (0.000***) – –
0.22 (0.07.) – –
0.29 (0.09.) 0.15 (0.03*) – –
– – – 0.58 (0.06.)
– – – – – – – –
– – – 0.06 (0.09.) 0.10 (0.03*) 0.16 (0.09.) – –
0.12 (0.06.) 0.028 (0.04*) – – – – – – 0.056 (0.06.) – 17.28 (0.04*)
34.43 (0.05.)
0.37 (0.08.) 0.067 (0.05.) 0.068 (0.08.) 7.76 (0.03*)
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Fig. 2. (a) Heat map of AUC values at different mesh sizes obtained from ROC curves; (b) ROC curve of optimum mesh size (300 m × 300 m).
interpreted is obtained by noting, for example, that the OR of “community center” is 1.50 for all crimes, which indicates that the OR of crime occurrence increases by 50% when the distance between the crime and a community center increases by one additional unit. Similarly, the “educational institution”/theft OR of 0.24 indicates that the OR of theft decreases by 76% if the distance between that crime and an educational institution increases by one additional unit, which in turn indicates that theft is frequent around educational institutions.
4. Results and discussion 4.1. Result from logistic regression Table 1 lists the outcomes of the LR model in terms of the OR for different mesh sizes. The ROC curves at eight different mesh sizes, ranging from 50 m × 50 m up to 400 m × 400 m in 50-m steps, were assessed to obtain the optimal mesh size, as illustrated by Fig. 2. The ROC curve results suggested that the 300 m × 300 m mesh size yields the best performance with the highest area under curve (AUC) value of 97.3% for all crimes and 96.6, 92.2, 93.2, 91.0, and 92.6% for theft, robbery, drug, grievous harm, and murder cases, respectively. The lower AUC values at the 350 m × 350 m and 400 m × 400 m mesh sizes confirmed that peak performance occurred at 300 m × 300 m. Table 1 lists OR values showing the relationship between various crimes and land uses for different mesh sizes at significance levels of 0, 0.1, 1, 5, and 10%. A better understanding of how the results can be
4.1.1. Community center It is seen in Table 1 that all significant OR values for all crime/ community center combinations are all greater than one, which means that a unit distance increase (here, 1 km) from a community center can result in an increase in occurrence of any type of crime. Theft can be classified correctly with mesh sizes ranging from 50 m × 50 m through 250 m × 250 m. Drug use can be significantly identified for all cell sizes less than 350 m. Robbery is significant at all mesh sizes up to 6
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except for 400 m × 400 m, with theft and robbery following a similar pattern. Drug abuse is significant at 200 m × 200 m and 250 m × 250 m, grievous harm is significant at 100 m × 100 m, 250 m × 250 m, and 400 m × 400 m, and murder shows similar results at 300 m × 300 m and 350 m × 350 m. It can be concluded that street markets are substantially crime-prone areas and that robbery, theft, and drug abuse can occur frequently in their vicinities. Many cash transactions occur in such places, which are also normally quite crowded, making it easier for petty crimes to occur. Murders are less likely near street markets and are significant only at larger mesh sizes, i.e., at 300 m × 300 m and 350 m × 350 m.
300 m × 300 m except for 150 m × 150 m. Overall, 100 m × 100 m and 300 m × 300 m mesh sizes can classify most crime types accurately, with the latter having a greater level of accuracy. These results indicate that the environments surrounding community centers in Bangladesh are less crime prone. Such spaces are primarily used to stage various public or family events, such as marriage ceremonies, birthday parties, etc. In most cases, paid security guards are on hand to ensure the safety of the visitors and the area is under surveillance from CCTV cameras. In addition, grievous harm and crimes involving murder were found to be statistically insignificant at almost all mesh sizes, suggesting that serious crimes such as these do not exhibit any specific patterns in the neighborhoods surrounding community centers.
4.1.6. Highway The overall output from the LR model identifies “highway” as one of the more significant variables correlated to the occurrence of crime. Aggregated crimes are statistically significant at all mesh sizes except 400 m × 400 m. At all mesh sizes, the output patterns of theft, robbery, and drug abuse are similar to those of aggregated crimes. Grievous harm is significant at 100 m × 100 m, 250 m × 250 m, and 400 m × 400 m, while murder is significant at 50 m × 50 m, 200 m × 200 m, 300 m × 300 m, and 350 m × 350 m. At 300 m × 300 m, all types of crimes, i.e., theft, robbery, drugs, grievous harm, and murder, are significant. These results are quite understandable given that criminals would be expected to prefer locations for committing crime that offer a high degree of mobility and a route to post-crime escape.
4.1.2. Educational institution The ORs of all individual crimes in the vicinity of educational institutions are all less than one, indicating that crime frequently occurs near educational institutions. This result is consistent across all categories of crimes at all mesh sizes whenever it is statistically significant. It is seen from Table 1 that theft, drug abuse, and aggregated crimes are significant from 50 m × 50 m–150 m × 150 m. Robbery and grievous harm become significant at mesh sizes greater than 150 m × 150 m and up to 250 m × 250 m for robbery and 300 m × 300 m for grievous harm. Murder is insignificant at all mesh sizes. Overall, 50 m × 50 m, 100 m × 100 m, and 150 m × 150 m mesh sizes can classify half of the crime types accurately. Grievous harm crimes become significant only at the optimal mesh size of 300 m × 300 m. Recently, print and electronic media have voiced concern regarding crimes near educational institutes as several youth gangs involved in different types of crimes have been active in these areas.
4.1.7. Residential and commercial area It is seen from Table 1 that aggregated crimes and theft are significant in residential areas with an OR of less than one at mesh sizes of 50 m × 50 m–150 m × 150 m. Drug abuse shows the same pattern at 150 m × 150 m and 350 m × 350 m, while grievous harm is significant at 50 m × 50 m and 100 m × 100 m. By contrast, OR values of greater than one are found for theft and robbery at 200 m × 200 m and 50 m × 50 m, respectively. Murder is statistically insignificant at all mesh sizes. Like community centers, all statistically significant crimes have OR > 1 in the vicinity of commercial spaces. Aggregated crimes are significant at 50 m × 50 m and 100 m × 100 m; robbery is significant at 100 m × 100 m, 200 m × 200 m, and 400 m × 400 m; and drug abuse is significant at 200 m × 200 m and 400 m × 400 m. Theft is insignificant around commercial spaces at all mesh sizes. Murder is insignificant at all mesh sizes around both residential and commercial spaces. These findings make sense as, like community centers, commercial places are generally under continual surveillance.
4.1.3. Mosque The OR values for “mosques” suggest that the likelihood of crime increases in the vicinity of mosques, i.e., that the areas close to mosques within the study area are crime prone. It is seen from Table 1 that aggregated crimes are significant at 100 m × 100 m, 200 m × 200 m, and 300 m × 300 m mesh sizes. The individual crimes theft and grievous harm are both significant at 50 m × 50 m and 150 m × 150 m mesh sizes. Crimes involving drug abuse and murder are significant at 200 m × 200 m and both robberies and murders are significant at 300 m × 300 m. All types of crime are insignificant at 350 m × 350 m and 400 m × 400 m. Overall, both 200 m × 200 m and 300 m × 300 m mesh sizes can classify most crime types accurately. Although these findings contradict the conventional belief that religious places are less prone to crime, it should be noted that such places have high numbers of parked vehicles and many small vendors. Furthermore, the probability that a crime near a mosque will be reported to the police is high owing to the high visibility of such areas.
4.1.8. ROC curves The AUC values obtained from the ROC curves for each crime type at each mesh size are presented in the form of a heat map in Fig. 2(a); Fig. 2(b) shows the ROC curves for each crime type at 300 m × 300 m. The results from Fig. 2(a) suggest that the accuracy of the LR models increases from 50 m × 50 m through 200 m × 200 m, decreases at the 250 m × 250 m mesh scale, increases again from the 300 m × 300 m through 350 m × 350 m scales, and finally decreases up to 400 m × 400 m. This pattern suggests an optimal mesh size at 300 m × 300 m, which was therefore selected for further SVM analysis.
4.1.4. Open space The significant OR values for “open space” are all less than one at all distances and in all crime cases except robbery. It is seen from Table 1 that aggregated crimes are significant from 50 m × 50 m–350 m × 350 m, with theft significant at 50 m × 50 m, 200 m × 200 m, and 350 m × 350 m. Drug abuse is significant from 100 m × 100 m–350 m × 350 m, and murders are significant at 350 m × 350 m and 400 m × 400 m. Grievous harm is insignificant at all mesh sizes. It should be noted that robbery, with an OR of 3.39, is significant at 150 m × 150 m, suggesting that this type of crime is not common near open spaces, which is understandable given that robbery is expected to be associated with commercial or residential land use.
4.2. Support vector machine results The LR-based analysis facilitated the identification of the optimal mesh size and revealed the relationship between various land uses and crime types. As the analysis focused on investigating each land use independently, a subsequent SVM-based analysis at the 300 m × 300 m optimal mesh size was conducted to further investigate the interrelationship between land use and different types of crime. After retrieving the separating hyper plane, the plausible affinity of crimes with land use type with accessibility measures (highway [HW], community
4.1.5. Street markets The OR results for “street market” are consistently lower than one at all mesh sizes, with the exception of theft at 400 m × 400 m. This suggests that the likelihood of crime increases in the vicinity of street markets. Aggregated crimes are statistically significant at all mesh sizes 7
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Fig. 3. Heat map of accuracy, sensitivity, specificity, and F-score produced by SVM analysis.
might have been an irrelevant exercise as the distribution of land use was highly clustered within the study area, as shown by Fig. 1(d). The outcomes of the SVM analysis are shown in Fig. 3. The combinations of land use that had accuracy, sensitivity, and specificity values and F-
center [CC], educational institution [ED], mosque [M], open space [OS], street market [SM], residential area [RA], and commercial area [CA]) were assessed and explained two variables at a time (although it was possible to consider more than two types of land use at a time, it 8
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connection between transit and crime. Finally, the developed framework is replicable for use with other developing-world cities to identify their respective crime potentials with respect to land use. As it is unlikely that police force funding will increase to sufficiency levels in developing-world cities within a short time horizon, having a framework to better understand the underlying relationship between crime and land use and road networks should help promote smarter resource allocation and urban planning. The methodological contribution of this study is the development of a tool to equip authorities with a framework that can be used to map and investigate the crime patterns in their cities. This can guide the authorities in increasing their vigilance in areas found conducive to crime. In addition, urban planners can use the framework to design land use and associated road networks that inherently deter criminal activities. The study has some limitations and several paths for future research. As it was conducted in a model town within Dhaka city, the results might not fully represent the relationship between crime and land use for the overall city. In addition, the scope of the study was limited to presenting a framework to identify the association between land use and crime and to demonstrate it using data from Uttara. However, understanding why and how these land uses are associated with various crimes will require further in-depth study involving criminology experts. It is also important to note that the Uttara road network is primarily in a grid pattern, and a future study should include other locations within the city with other road network patterns such as lollipops on a stick, loops and lollipops, warped parallel, fragmented parallel, etc. Finally, socio-economic and demographic characteristics should be incorporated within a future study.
Table 2 High and low crime rate associations by land use combination. Crime Type
High
Low
Land Use
Weight
Land Use
Weight
Drug
SM vs. RA OS vs. RA ED vs. CA
0.31 0.17 0.04
M vs. RA
0.024
Grievous Hurt
ED vs. CC SM vs. CA SM vs. RA
0.91 0.32 0.25
M vs. RA M vs. CC ED vs. SM
0.017 0.063 0.089
Murder
M vs. OS OS vs. CC OS vs. RA
0.16 0.14 0.12
HW vs. OS M vs. CA
0.000 0.026
Robbery
SM vs. CA OS vs. CA M vs. CA
0.48 0.46 0.31
HW vs. RA M vs. RA M vs. CC
0.000 0.037 0.054
Theft
SM vs CA OS vs. CA SM vs. RA
0.51 0.43 0.41
M vs. CC M vs. RA
0.067 0.094
scores all higher than 75% were then identified and the corresponding numbers of cells and crimes for each combination were determined to calculate the weighted crimes per cell (see Step 6 in the methodology section). The primary land use combinations for high and low crime scenarios of each crime type are listed in Table 2. The findings suggest several interesting patterns. For example, the low association with crimes for open spaces and residential areas next to highways is attributable to the fact that, in general, there are very few residential areas or open spaces along the highways. Furthermore, although mosques had an overall high association with crime in the LR analysis, in which crime frequency was not weighted for the number of cells and the distribution of crime type, the SVM results revealed that mosques situated within residential areas are the least crime-prone locations. Aside from murders, all types of crimes were predominant in the commercial areas, which are expected to have high opportunity factors. At the same time, street markets, which are mostly operated illegally in Uttara and encroach the public roads, invite various types of crime. Specifically, drug-related crimes are predominant near street markets situated within residential areas, while grievous harm, theft, and robbery-related crimes take place near street markets located in the vicinity of residential and commercial areas. It is also seen that open spaces, which are in general not monitored by law enforcement agencies, provide an environment favorable to the commission of crimes.
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.habitatint.2019.102003. References Adel, H., Salheen, M., & Mahmoud, R. A. (2016). Crime in relation to urban design. Case Study : The greater cairo region. Ain Shams Engineering Journal, 7(3), 925–938. http://doi.org/10.1016/j.asej.2015.08.009. Almanie, T., Mirza, R., & Lor, E. (2015). Crime prediction based on crime types and using spatial and temporal criminal hotspots. International Journal of Data Mining and Knowledge Management Process, 5(4), 1–19. Bangladesh police report. Beavon, D., Brantingham, P. L., & Brantingham, P. J. (1994). The influence of street networks on the patterning of property offenses. In Ronald V. Clarke (Vol. Ed.), Crime prevention studies: Vol. 2, (pp. 149–163). New York: Willow Tree Press. Brantingham, P. L., & Brantingham, P. J. (1981). Environmental criminology. Beverly Hills: Sage Publications. Browning, C. R., Byron, R. A., Calder, C. A., Krivo, L. J., Kwan, M., Lee, J. Y., et al. (2010). Commercial density, residential concentration and Crime : Land use patterns and violence in neighborhood context. Journal of Research in Crime and Delinquency, 47(3), 329–357. http://doi.org/10.1177/0022427810365906. Carter, S. P., Carter, S. L., & Dannenberg, A. L. (2003). Zoning out crime and improving community health in sarasota, Florida: Crime prevention through environmental design. American Journal of Public Health, 93(9), 1442–1445. Davison, E. L., & Smith, W. R. (2003). Exploring accessibility versus Opportunity crime factors. Sociation Today, 1(1). link: http://www.ncsociology.org/sociationtoday/ raleigh.htm, Accessed date: 2 March 2018. Greenberg, S. W., William, M. R., & Williams, J. R. (1982). Safety inurban neighborhoods: A comparison of physical characteristics and informal territorial control in high and low crime neighborhoods. Population and Environment, 5(3), 141–165. Groff, E. R., & Lockwood, B. (2014). Criminogenic facilities and crime across street segments in philadelphia: Uncovering evidence about the spatial extent of facility influence. Journal of Research in Crime and Delinquency, 51(3) 277–214. Gujarati, D. N., Porter, D. C., & Gunasekar, S. (2012). Basic econometrics (5th ed.). New Delhi: Tata McGraw-Hill, 7 West Patel Nagar. Han, J., Kamber, M., & Pei, J. (2012). (3rd ed.). Data mining concepts and techniquesVol. 225. Wyman Street, Waltham, USA: Morgan Kaufmann Publishers (Elsevier). Kianmehr, K., & Alhajj, R. (2008). Effectiveness of support vector machine for crime hotspots prediction. Applied Artificial Intelligence: International Journal, 22(5), 433–458. Kinney, J. B., Brantingham, P. L., Wuschke, K., Kirk, M. G., & Brantingham, P. J. (2008). Crime attractors , generators and detractors: Land use and urban crime opportunities. Built Environment, 34(1), 62–74. Lombroso, C. (1911). Crime: Its causes and remedies, Vol. 3. Little, Brown and Company. Ludin, A. N. M., Aziz, N. A., Yusoff, N. H., & Razak, W. J. W. A. (2013). Impacts of urban
5. Conclusion This paper presented a framework in which an attempt was made to derive relationships among different types of crime and land use in terms of their transportation accessibility. The results were presented in the context of a city in a developing country in which there is scant resource allocation for law enforcement. A novel methodology in which an area is divided into provisional meshes of different sizes, with each mesh associated with a land use and crime status, was introduced. LR analysis was then used to identify the mesh size that can best distinguish between crime-prone and safe land use areas, and the chosen optimal mesh size was then subjected to SVM analysis to discriminate combinations of land use that experience high crime rates from those that do not. We believe that the results of this study make three major contributions. First, our approach adopts an exposure-based approach instead of a frequency-based approach by considering both crime-prone and non-prone areas. Second, it contextualizes the proximity of land use to crime location in terms of transportation accessibility to uncover the 9
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Patterns of crime and land use change. Cartography and Geographic Information Science. Soon, D. W. (2016). Do all commercial land uses deteriorate neighborhood safety?: Examining the relationship between commercial land-use mix and residential burglary. Habitat International, 55, 148–158. Stucky, T. D., & Ottensmann, J. R. (2009). Land use and violant crime. Journal of Criminology, 47(2009), 1223–1264. Vural, M. S., & Gök, M. (2016). Criminal prediction using naive Bayes theory. Neural computing and applications. 1–12. http://doi.org/10.1007/s00521-016-2205. Yirmibesoglu, F., & Ergun, N. (2007). Property and personal crime in istanbul. European Planning Studies, 15(3), 37–41. http://doi.org/10.1080/09654310601017067. Zhang, W. (2016). Does compact land use trigger a rise in crime and a fall in Ridership ? A role for crime in the land use – travel connection. Journal of Urban Studies, 53(14), 3007–3026. http://doi.org/10.1177/0042098015605222.
land use on crime patterns through GIS application. Geospatial Analysis in Urban Planning, 2, 1–22. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. (2017). e1071: Misc Functions of the Department of Statistics. Probability Theory Group, TU Wien. Rpackage version 1.6–8 https://CRAN.R-project.org/package=e1071 2017 . Minnery, J. R., & Lim, B. (2005). Measuring crime prevention through environmental design. Journal fo Architechtural and Planning Research, 22(4), 330–341. Nwaogul, C., Ole, T., & Pechanec, V. (2016). Land-use and crime relationships in subsahara Africa : A spatio- temporal assessment using GIS in apapa council area, Nigeria. International Journal of Social Research Methodology, 5(1), 79–90. Sayafzadeh, A., & Hassani, M. J. (2014). The effects of land use in urban crime commitment in the unofficial settlement of Islam Abad , zanjan. International Journal of Academic Research in Business and Social Sciences, 4(1), 474–489. http://doi.org/10. 6007/IJARBSS/v4-i1/554. Song, J., Andresen, M. A., Brantingham, P. L., & Spicer, V. (2015). Crime on the edges:
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Effect of land use on crime considering exposure and accessibility a
b
Soumik Nafis Sadeek , Abu Jar Md. Minhuz Uddin Ahmed , Moinul Hossain a b c
b,∗
, Shinya Hanaoka
T c
International University of Business Agriculture and Technology (IUBAT), Uttara Model Town, Dhaka, 1230, Bangladesh Islamic University of Technology (IUT), Gazipur, 1704, Bangladesh Tokyo Institute of Technology, Japan
A R T I C LE I N FO
A B S T R A C T
Keywords: Crime Land use Accessibility GIS Logistic regression Support vector machine
A substantial number of studies have revealed an association between crime and land use, in some cases by representing land use using socio-economic and demographic data to evaluate their correlation with various types of crimes. The spatial autocorrelation between land use and crime is also often investigated. Many studies focus on data obtained from locations at which a crime has taken place and ignore exposure, i.e., locations where a crime has not taken place. It has also been suggested that transportation accessibility plays a significant role in connecting crime patterns with land use, although this hypothesis requires further study. This paper proposes a new framework to aggregate and synthesize the existing literature based on a geocoding of the association between crimes and land use on a GIS map. The map is broken down into different mesh sizes indicating the occurrence or nonoccurrence of crime within individual mesh cells. An optimal mesh size to best explain the interrelationship between crime and land use with respect to road network accessibility is then developed using logistic regression (LR), and a support vector machine (SVM) is used to identify combinations of land use that are highly susceptible to crime and those that are quite safe. The results of this study can provide insight into reducing crime in cities, allocating law enforcement agency resources, and designing a built environment that can naturally deter crime.
1. Background Land use, road network planning, and green and open space installation form the primary skeleton of city planning and shapes future development patterns. The planning method plays an important role in the quality and efficiency of a city's comprehensive plan and in changing the city's physical structure to reduce the crime rate (Ludin, Aziz, Yusoff, & Razak, 2013; Sayafzadeh & Hassani, 2014). The study of the effect of land use on crime is a relatively new joint sub-field of criminology and urban planning, as classical criminology focused primarily on crime as a result of offender behavior. For example, the Italian criminologist Lombroso (1911) emphasized the connection between individuals' genetic features and propensity to crime in a subsequently refuted theory. Arguably, the first mention of the role of environmental features in crime pattern theory was by the Canadian criminologists Brantingham and Brantingham (1981), who postulated that crime is a complex occurrence that requires the coincidence of many features, including spatial features, in addition to human attributes. According to Greenberg, William, and Williams (1982), the physical characteristics of a neighborhood can significantly affect associated crime scenarios in a manner that can be independent of crime rate. Carter, Carter, and
∗
Dannenberg (2003) used the concept of crime prevention through environmental design to help police in reducing prostitution by identifying specific land uses (hotels, motels, etc.) as targets for highly visible police patrolling. More recent studies have attributed the effects of land use on crime generation and patterns. Minnery and Lim (2005) measured crime prevention in terms of environmental design and proposed a scale based on the results of a social attitude survey to measure the effectiveness of prevention. Their results suggested that effective environmental design can have some effect on reducing victimization, particularly for the residents of groups of houses on a single street. Yirmibesoglu and Ergun (2007) utilized land use and value as variables to reveal that property and personal crime rates increase with land value and the introduction of mixed land use. Kinney, Brantingham, Wuschke, Kirk, and Brantingham (2008) analyzed patterns of assault and motor vehicle theft to identify crime activity nodes and revealed that delinquency tends to occur more often in conjunction with public commercial activities and in parking, shopping, and recreational areas as well as in specific government locations. Stucky and Ottensmann (2009) explored the relationship between violent crime and land use and argued that crimes occur as the result of several socioeconomic factors that are
Corresponding author. Department of Civil and Environmental Engineering, Islamic University of Technology (IUT), Board Bazar, Gazipur, 1704, Bangladesh. E-mail addresses: [email protected], [email protected] (M. Hossain).
https://doi.org/10.1016/j.habitatint.2019.102003 Received 9 March 2018; Received in revised form 25 April 2019; Accepted 17 June 2019 Available online 21 June 2019 0197-3975/ © 2019 Elsevier Ltd. All rights reserved.
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actual crime exposure by investigating what types of land use have the least association with crime. In addition, despite their significant promise, studies on crime, accessibility, and land use are often confined to the identification of vulnerability to crime on individual street segments. This paper synthesizes the lessons learned from preceding and ongoing studies by establishing a GIS-based framework for associating land use and transportation network accessibility with crime type and presents a model describing the relationship between land use characteristics and crime type based on accessibility measures. Our work is distinguished from the existing literature in two respects: first, it considers true exposure by examining locations in which crime is absent and their associated land use; second, rather than relying on spatial auto-correlation alone, it acknowledges the association between crime and accessibility by linking crime and land use with distance measured along the road network rather than Euclidean distance. Our method employs logistic regression (LR) and SVMs to perform modeling in which the study area is divided into meshes of different size. LR-based models are used to produce receiver operating characteristic (ROC) curves to assist in identifying the most suitable mesh size for separating crime from no-crime data and recognizing the influence of various underlying factors, e.g., distance from residential/commercial areas, schools, road networks, etc., on crime occurrence. The SVM provides further insight into crime by considering land use types in various pairs to identify combinations of risky and safe land uses.
determined by land use. Browning et al. (2010) studied the impact of land use patterns on crime in the context of urban regeneration and the role of developers and municipalities in shaping the urban landscape with the goal of reducing crime scenarios in urban areas. Ludin et al. (2013) examined patterns of drug abuse, burglary, and theft in commercial and residential areas using GIS data and found a strong relationship between petty crime, drug abuse, and land use pattern, a finding that supported a linkage between crime density and population density. Song, Andresen, Brantingham, and Spicer (2015) focused on the boundaries between relatively homogeneous neighborhoods under the assumption they would present differing levels of risk for criminal victimization and found that victimization rates are 2–3 times higher at community edges than elsewhere. Nwaogul, Ole, and Pechanec (2016) used GIS-based spatiotemporal analysis to determine that residential and commercial land-use are associated with the highest crime rates, with theft, robbery, and gambling constituting the most common crime types. Zhang (2016) demonstrated that highly populated residential and commercial land use can stimulate the occurrence of crime, thereby reducing public transit ridership. Soon (2016) analyzed the impact of the ratio of commercial to residential land use on burglary and other types of crime and found that residential burglary in commercial area varies with the type of commercial facility and that the proportion of shopping centers in a residential zone is associated with increased residential burglary. The studies discussed above represent the two major trends in crime and land use research. The first approach looks at the socioeconomic and/or demographic characteristics representative of land use and then relate them with various types of crimes. The second approach focuses on the spatial autocorrelation between different land uses and associated crimes. Another, less explored, approach is to relate land use to crime through transportation accessibility based on the hypothesis that the design of street networks influences patterns of movement within a city, which remain relatively unchanged even when the objective of travel is to commit a crime. The pioneering study of Beavon, Brantingham, and Brantingham (1994) is in line with this hypothesis in exploring the relationships among property crime along different road segments with respect to the accessibility of street networks and the concentration of potential targets. Their findings affirmed that accessible and highly used areas experience greater amounts of property crime. Davison and Smith (2003) extended the work of Beavon et al. (1994) using data from the United States and included a guardianship measure and additional land use types and reported similar findings. More recently, Groff and Lockwood (2014) considered the shortest path distances between selected areas and facilities such as bars and street joints in terms of exposure to violent or property crime or disorderly conduct. Adel, Salheen, and Mahmoud (2016) postulated that crime occurs neither equally nor in the same manner in all places. Formulating social factors (e.g., illiteracy, unemployment, and internal migration rates, population density, and average family size) and street network and land use (e.g., commercial, residential, vacant, or industrial) as variables, they concluded that most criminals search for accessible places that are easy to move through and provide high opportunities for escape after committing crimes. In addition to various statistical approaches and spatial analysis methods, recent studies have used data mining techniques to perform crime hotspot detection. Such approaches include the use of support vector machines (SVMs) (Kianmehr & Alhajj, 2008) for spatial and temporal criminal hotspot detection, Bayesian classifiers (Almanie, Mirza, & Lor, 2015) to predict crime type, and the Naïve Bayes algorithm (Vural & Gök, 2016) to predict crime. The literature suggests a considerable association between crime type and land use. A major drawback of previous studies has been the reliance on datasets documenting the connections between various types of crime and land use while overlooking areas in which crimes did not take place during the study period. This is a major shortcoming, as it is also important from a policy perspective to obtain a picture of
2. Study area and data collection Bangladesh is a crime-prone country in which the occurrence of crime has increased by approximately 41% over the 15 years leading up to 2015 (Bangladesh Police Report, 2016). It is believed that the actual situation in the country is far worse, as a substantial portion of crimes go unreported. The high crime rate is attributed to the major resource constraints prevailing in the law enforcement sector: the country has among the lowest police-population ratios (96 per 100,000 people), average officer salaries (US$ 127 per month), and annual budget allocations (US$ 206 million) of any country in the world and compares particularly poorly to developed countries such as the United States, which has 379 officers per 100,000 people, a US$ 2216 monthly per-officer salary, and US$ 4800 million in annual funding. The interdependence between crime prevention and police expenditure (BPO website) helps explain why the performance of the Bangladeshi police has yet to reach standards seen in other countries. In this study, the Uttara model town—one of the eight crime divisions covered by the Dhaka Metropolitan Police—was selected as a representative typical urban neighborhood of Dhaka city, the capital of Bangladesh. Uttara has a total area of 36.91 km2 and a population of 179,907. Its contains many educational institutions, mosques, community centers, street markets, empty plots, slums, and residential areas in which high and low income individuals co-exist. The total police force assigned to maintain law and order is only about 180, corresponding to a low police-population ratio of 100.5:100000. Fig. 1 shows a map of the study area within the greater Dhaka region along with a zoomed-in view. The figure also presents locations in which various types of crimes have taken place and their associated land uses. The data used in this research come from both primary and secondary sources. A GIS map of the Uttara model town was obtained from the City Development Authority (RAJUK). The map contains an updated inventory of land use in Uttara in which nearly all of the infrastructure has been geocoded. Crime data for January 2014–December 2015 were collected from the First Information Report (FIR) books of the Uttara East and Uttara West Police. These include information on type of crime, time and location of occurrence, distance from the nearest police station, etc. As the locational data are based on address, site visits were undertaken to geocode each crime scene with the assistance of the corresponding police stations. Once these records were digitized, another survey team verified the land uses recorded in the 2
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Fig. 1. a) Study area (Uttara) within the Greater Dhaka region; b) Uttara (middle) at a larger scale (Source: RAJUK); c) crimes by type; and d) land use.
between land use and crime with respect to accessibility is divided into several components. First, a GIS map is geocoded with infrastructures of interest and crime occurrences by type. The map is then divided into successively smaller meshes (grids), with each grid cell associated with a land use type and the presence or absence of crime. LR is then employed to identify the ideal mesh size that most accurately distinguishes between crime-prone and non-crime-prone mesh cells. Finally, an SVM is used to reveal the associations between crime and land use. The process involves the following primary steps:
GIS data, specially identifying community centers, educational institutions, mosques, open spaces, shopping complexes and street markets, and the highway network. The FIR data contained 12 separate crime types, which were re-classified into the following five categories based on the severity of punishment described in the Bangladesh Penal Code: 1) theft, 2) robbery, 3) drug infractions, 4) grievous harm, and 5) murder. Crime types such as “unnatural death” were ignored as the related reports do not identify any offenders. The number of samples for each category for which locations could be properly identified and the corresponding FIR data were complete were 290, 72, 238, 61, and 111, respectively.
Step 1 – Data collection and processing: collection, geocoding, verifying, and linking among land use, road network pattern, and crime data. Step 2 – Mesh preparation: the GIS map of the study area is divided into small cells through the formation of square meshes of various
3. Methodology Our replicable framework for evaluating the interrelationship 3
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The fundamental equation for the LR shows that, when the value of an independent variable increases by one unit and all other variables are held constant, the new probability ratio is given by
dimensions. Eight mesh sizes, ranging from 50 m × 50 m–400 m × 400 m in 50-m steps, are applied. Step 3 – Assigning land use, crime status, and distance to cells: each cell in the attribute table of the GIS file is assigned a land use (community center, educational institution, mosque, open space, or shopping complex/street markets), crime status, and distance. The land use is assigned by evaluating which of the five land use types covers most of the cell. If a crime took place on the cell, it is coded with a value corresponding to the crime; if no crime took place at that location, the cell is coded as “0.” Step 4 – Finding the optimum mesh size: ROC curves are drawn for each model using an LR developed for each mesh size (50 m × 50 m, 100 m × 100 m, 150 m × 150 m, 200 m × 200 m, 250 m × 250 m, 300 m × 300 m, 350 m × 350 m, and 400 m × 400 m) with crime type set as a dependent variable with a dichotomous outcome and distances from various land uses set as independent variables. Step 5 – Assessing the interrelationship among land use, transportation networks, and crime: LR and an SVM analyses are used to uncover the interrelationship among land use, the transportation network connected with the land, and different types of crimes. Step 6 – Model output assessment: the SVM outputs a confusion matrix for each combination of land use and crime type with corresponding accuracy, sensitivity, specificity, and F-scores. The accuracy measure reflects each model's overall ability to associate both crime and non-crime situations with specific land use for each crime type; the sensitivity reflects the classification accuracy with respect to crime; the specificity presents each classification in terms of the “no crime” condition; the F-score is a measure of the discrimination between two compared sets of land use, where a higher value corresponds to a postulate of more discriminative features. In the analysis, a minimum threshold value of 75% is set for all four indicators to identify the combinations of land uses that can be classified with high accuracy. As neither land uses nor crimes are equally distributed, the number of crimes and the number of cells belonging to each combination of land use are determined for the combinations of land use that can be classified with high accuracy and the number of crimes per cell is calculated. The values are then weighted with the distribution of various crime types, with higher values corresponding to higher likelihoods of crime. Finally, the land use combinations associated with high and low crime rates are identified to enable a discussion of the policy implications of the findings.
Pi Pi ⎞ β = e βo e βi (Xi + 1) = e βo e βi Xi e βi = ⎛ e i 1 − Pi ⎝ 1 − Pi ⎠ ⎜
1 − Pi
the factor e βi , which is known as the odds ratio (OR) and ranges from 0 to ∞ (Gujarati, Porter, & Gunasekar, 2012).
3.2. Support vector machine (SVM) The proposed method involves a binary classification in which crime-prone and non-prone areas are distinguished based on their land uses and associated road networks. An SVM—a hyperplane-based classification technique—is used to perform this classification as the tool provides highly accurate results and is less prone to overfitting. The purpose of SVM learning is to develop a model that classifies or accurately predicts future, unseen values of a variable. To briefly explain the SVM method, we consider a data set S = (x1, y1), …, (x|S|, y|S|), where the Xi are training tuples and the yi are the associated class of tuples, where. yi ∈ {1,0} i. e . yi ∈ {crime , nocrime} There are an infinite number of hyperplanes separating the two classes, and the objective of the SVM is to find the one that best separates them. To do this, the SVM searches for the hyperplane with the largest margin, i.e., the maximum marginal hyperplane (MMH). A separating hyperplane can be represented as
where P = {p1 , p2 , p3 , …, pn } is a weight vector and c is a scalar (bias), which can be written as p0. In this case, P represents the orientation of the hyperplane within the space and c finds the position of the hyperplane within the space. For a 2-D plane, Equation (4) can be expressed as
p0 + p1 x1 + p2 x2 = 0
(5)
The hyperplane defining the sides of the margin are
H1: p0 + p1 x1 + p2 x2 ≥ 1,
for ti = 1 (crime), and
H2: p0 + p1 x1 + p2 x2 < 1,
for t1 = 0 (no crime)
Any training dataset that falls on or over H1 is classified as crimeprone; any training data that falls on or below H2 is classified as nocrime. Combining these two inequalities yields ti (po + p1 x1 + p2 x2) ≥ 1; ∀ i , and any data points falling on H1 or H2 are regarded as support vectors to be solved using a constrained optimization problem. The optimization is performed on Equation (4) with the objective of finding a maximum separation of the hyperplanes (H1 and H2) for correctly classifying crime and no-crime zones. To obtain an optimized hyperplane, it is necessary to minimize ||P|| and maximize c, which is done by partially differentiating the Lagrange multiplier, L (||P||, c) with respect to P and c to derive minimum and maximum values, respectively (Han, Kamber, & Pei, 2012). After optimizing Equation (4), the maximum separating hyperplanes for classifying crime and no-crime points under various land use combinations are determined. To analyze the data, an open source statistical software source, “R,” was used to perform LR and SVM analysis. The “e1071” package was used to develop the SVM (Meyer, Dimitriadou, Hornik, Weingessel, & Leisch, 2017).
LR is widely used to model phenomena with dichotomous outcomes with the goal of finding the best-fit model to describe the relationship between the dichotomous characteristic of interest (dependent variable) and a set of independent variables. In this model, the logit is the natural logarithm of the odds or the likelihood ratio that the dependent variable is Y = 1 for crime in a mesh net cell rather than Y = 0 for no crime in the cell. The probability P of crime occurrence is ⎟
(1)
where Pi is the probability of crime occurrence and (1 – Pi) is the probability of no crime occurrence, βo is the model constant, and the βi are the parameter estimates for the independent variables. A simple transformation of Equation (1) yields
Pi = e βo + βi Xo = e βo e βi Xi 1 − Pi
(4)
PX + c = 0
3.1. Logistic regression (LR)
⎜
(3)
From Equation (3), we see that, when a predictor Xi increases by one unit with all other factors remaining constant, the odds Pi increase by
The following sub-sections provide a short introduction to the LR and SVM approaches.
Pi ⎞ y = log it (p) = ln ⎛ = βo + βi Xi 1 − Pi ⎠ ⎝
⎟
(2) 4
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Table 1 Odds Ratio of Different types of Crimes and Land use. Crime Land use 50 × 50 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 100 × 100 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 150 × 150 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 200 × 200 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 250 × 250 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 300 × 300 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 350 × 350 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial 400 × 400 Mesh Community center Educational institution Mosque Open space Street market Highway Residential Commercial
All crime (Odds Ratio)
Theft (Odds Ratio)
Robbery (Odds Ratio)
Drug (Odds Ratio)
Grievous Hurt (Odds Ratio)
Murder (Odds Ratio)
1.50 (0.03*) 0.33 (0.008)** – 0.41 (0.0004***)
1.75 0.24 0.21 0.32
4.48 – – – 0.30 0.18 2.53 –
2.07 (0.009**) 0.22 (0.02*) – – – 0.42 (0.0006***) – –
– – – – – 0.30 (0.001**) –
– – 0.23 (0.007**) – – 0.023 (0.06.) – –
5.10 – – – 0.32 0.27 0.52
– – – – – – –
(0.03*) (0.02*) (0.06.) (0.001**)
(0.005**)
(0.02*) (0.0002***) (0.04*)
0.57 (0.0006***) 0.81 (0.001**) 2.10 (0.002**)
0.34 (0.0005***) 0.465 (0.000***) –
5.00 0.18 0.38 0.41 0.39 0.18 0.60 2.58
(0.000***) (0.013*) (0.08.) (0.03*) (0.000***) (0.000***) (0.027*) (0.029*)
5.61 0.09 – – 0.29 0.12 0.49
3.53 0.05 – 0.36 0.42 0.51 0.45 –
(0.005**) (0.003**)
3.88 (0.009**) 0.014 (0.000***) 0.18 (0.09.) – 0.24 (0.000***) 0.21 (0.001**) 0.49 (0.07.) –
– – – 3.39 (0.08.) 0.17 (0.001**) 0.26 (0.03*) – –
4.49 (0.003**) 0.081 (0.003*) – 0.33 (0.05.) – 0.31 (0.01*) 0.34 (0.007**) –
– 0.045 (0.04*)
0.14 (0.006**) 0.194 (0.001**) 0.81 (0.000***) 7.93 (0.000***) – –
7.82 (0.006**) 0.023 (0.02*) – 0.16 (0.06.) 0.06 (0.000***) 0.11 (0.001**) 4.35 (0.005**) –
4.98 (0.07.) 0.014 (0.01*) – – 0.06 (0.000***) 0.14 (0.018*) – 2.89 (0.014*)
8.39 – 0.12 0.07 0.43 0.13 – –
– – – – – – – 2.16 (0.05.)
0.31 (0.03*) – –
18.35 (0.004**) – – 0.02 (0.004**) 0.12 (0.000***) 0.20 (0.04*) – –
29.16 (0.000***) – – – 0.015 (0.000***) 0.07 (0.002**) – –
18.56 (0.002**) 0.03 (0.08.) – – 0.058 (0.001**) 0.066 (0.002**) – –
7.11 (0.01*) – – – 0.12 (0.001**) 0.11(0.004**) – –
0.04 (0.06.) – – 0.42 (0.06.) – – –
– – – – – – –
4.48 (0.05.)
6.90 (0.05.)
19.65 (0.007**)
6.08 (0.059.) 0.024 (0.06.)
6.17 (0.04*)
0.054 (0.05.)
0.016 (0.02*)
(0.08.) (0.01*) (0.09.) (0.06.)
1.36 (0.003**)
(0.000***) (0.004**)
(0.000***) (0.000***) (0.004**)
9.52 (0.006**) – – – 0.14 (0.000***) 0.06 (0.000***)
9.18 (0.000***) 0.14 (0.02*) 0.19 (0.000***) 0.09 (0.000***)
(0.04*)
(0.03*) (0.09.) (0.06.)
4.2 (0.003**)
(0.001**) (0.05.) (0.001**) (0.05.) (0.000***)
– – – – –
– – 0.16 (0.04*) – – – – – – – 0.12 (0.04*)
0.07 (0.09.) 0.04 (0.03*)
0.095 (0.004**) 0.047 (0.005**) – –
0.29 (0.001**) 0.58 (0.013*) – –
0.09 (0.007**) 0.11 (0.02*) – –
13.6 (0.03*) – – 0.011 (0.01*) 0.44 (0.04*) 0.24 (0.02*) – –
– – – 0.064 (0.02*) 0.074 (0.002**) 0.11 (0.003**) – –
– – – – 0.18 (0.09.) 0.12 (0.06.) –
– – – – – – – –
– – – – 1.67 (0.01*) 0.14 (0.08.) – –
– – – – 0.02 (0.017*) 0.04 (0.06.) – 7.41 (0.05.)
(Significance levels: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1).
5
0.024 (0.000***) – –
0.22 (0.07.) – –
0.29 (0.09.) 0.15 (0.03*) – –
– – – 0.58 (0.06.)
– – – – – – – –
– – – 0.06 (0.09.) 0.10 (0.03*) 0.16 (0.09.) – –
0.12 (0.06.) 0.028 (0.04*) – – – – – – 0.056 (0.06.) – 17.28 (0.04*)
34.43 (0.05.)
0.37 (0.08.) 0.067 (0.05.) 0.068 (0.08.) 7.76 (0.03*)
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Fig. 2. (a) Heat map of AUC values at different mesh sizes obtained from ROC curves; (b) ROC curve of optimum mesh size (300 m × 300 m).
interpreted is obtained by noting, for example, that the OR of “community center” is 1.50 for all crimes, which indicates that the OR of crime occurrence increases by 50% when the distance between the crime and a community center increases by one additional unit. Similarly, the “educational institution”/theft OR of 0.24 indicates that the OR of theft decreases by 76% if the distance between that crime and an educational institution increases by one additional unit, which in turn indicates that theft is frequent around educational institutions.
4. Results and discussion 4.1. Result from logistic regression Table 1 lists the outcomes of the LR model in terms of the OR for different mesh sizes. The ROC curves at eight different mesh sizes, ranging from 50 m × 50 m up to 400 m × 400 m in 50-m steps, were assessed to obtain the optimal mesh size, as illustrated by Fig. 2. The ROC curve results suggested that the 300 m × 300 m mesh size yields the best performance with the highest area under curve (AUC) value of 97.3% for all crimes and 96.6, 92.2, 93.2, 91.0, and 92.6% for theft, robbery, drug, grievous harm, and murder cases, respectively. The lower AUC values at the 350 m × 350 m and 400 m × 400 m mesh sizes confirmed that peak performance occurred at 300 m × 300 m. Table 1 lists OR values showing the relationship between various crimes and land uses for different mesh sizes at significance levels of 0, 0.1, 1, 5, and 10%. A better understanding of how the results can be
4.1.1. Community center It is seen in Table 1 that all significant OR values for all crime/ community center combinations are all greater than one, which means that a unit distance increase (here, 1 km) from a community center can result in an increase in occurrence of any type of crime. Theft can be classified correctly with mesh sizes ranging from 50 m × 50 m through 250 m × 250 m. Drug use can be significantly identified for all cell sizes less than 350 m. Robbery is significant at all mesh sizes up to 6
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except for 400 m × 400 m, with theft and robbery following a similar pattern. Drug abuse is significant at 200 m × 200 m and 250 m × 250 m, grievous harm is significant at 100 m × 100 m, 250 m × 250 m, and 400 m × 400 m, and murder shows similar results at 300 m × 300 m and 350 m × 350 m. It can be concluded that street markets are substantially crime-prone areas and that robbery, theft, and drug abuse can occur frequently in their vicinities. Many cash transactions occur in such places, which are also normally quite crowded, making it easier for petty crimes to occur. Murders are less likely near street markets and are significant only at larger mesh sizes, i.e., at 300 m × 300 m and 350 m × 350 m.
300 m × 300 m except for 150 m × 150 m. Overall, 100 m × 100 m and 300 m × 300 m mesh sizes can classify most crime types accurately, with the latter having a greater level of accuracy. These results indicate that the environments surrounding community centers in Bangladesh are less crime prone. Such spaces are primarily used to stage various public or family events, such as marriage ceremonies, birthday parties, etc. In most cases, paid security guards are on hand to ensure the safety of the visitors and the area is under surveillance from CCTV cameras. In addition, grievous harm and crimes involving murder were found to be statistically insignificant at almost all mesh sizes, suggesting that serious crimes such as these do not exhibit any specific patterns in the neighborhoods surrounding community centers.
4.1.6. Highway The overall output from the LR model identifies “highway” as one of the more significant variables correlated to the occurrence of crime. Aggregated crimes are statistically significant at all mesh sizes except 400 m × 400 m. At all mesh sizes, the output patterns of theft, robbery, and drug abuse are similar to those of aggregated crimes. Grievous harm is significant at 100 m × 100 m, 250 m × 250 m, and 400 m × 400 m, while murder is significant at 50 m × 50 m, 200 m × 200 m, 300 m × 300 m, and 350 m × 350 m. At 300 m × 300 m, all types of crimes, i.e., theft, robbery, drugs, grievous harm, and murder, are significant. These results are quite understandable given that criminals would be expected to prefer locations for committing crime that offer a high degree of mobility and a route to post-crime escape.
4.1.2. Educational institution The ORs of all individual crimes in the vicinity of educational institutions are all less than one, indicating that crime frequently occurs near educational institutions. This result is consistent across all categories of crimes at all mesh sizes whenever it is statistically significant. It is seen from Table 1 that theft, drug abuse, and aggregated crimes are significant from 50 m × 50 m–150 m × 150 m. Robbery and grievous harm become significant at mesh sizes greater than 150 m × 150 m and up to 250 m × 250 m for robbery and 300 m × 300 m for grievous harm. Murder is insignificant at all mesh sizes. Overall, 50 m × 50 m, 100 m × 100 m, and 150 m × 150 m mesh sizes can classify half of the crime types accurately. Grievous harm crimes become significant only at the optimal mesh size of 300 m × 300 m. Recently, print and electronic media have voiced concern regarding crimes near educational institutes as several youth gangs involved in different types of crimes have been active in these areas.
4.1.7. Residential and commercial area It is seen from Table 1 that aggregated crimes and theft are significant in residential areas with an OR of less than one at mesh sizes of 50 m × 50 m–150 m × 150 m. Drug abuse shows the same pattern at 150 m × 150 m and 350 m × 350 m, while grievous harm is significant at 50 m × 50 m and 100 m × 100 m. By contrast, OR values of greater than one are found for theft and robbery at 200 m × 200 m and 50 m × 50 m, respectively. Murder is statistically insignificant at all mesh sizes. Like community centers, all statistically significant crimes have OR > 1 in the vicinity of commercial spaces. Aggregated crimes are significant at 50 m × 50 m and 100 m × 100 m; robbery is significant at 100 m × 100 m, 200 m × 200 m, and 400 m × 400 m; and drug abuse is significant at 200 m × 200 m and 400 m × 400 m. Theft is insignificant around commercial spaces at all mesh sizes. Murder is insignificant at all mesh sizes around both residential and commercial spaces. These findings make sense as, like community centers, commercial places are generally under continual surveillance.
4.1.3. Mosque The OR values for “mosques” suggest that the likelihood of crime increases in the vicinity of mosques, i.e., that the areas close to mosques within the study area are crime prone. It is seen from Table 1 that aggregated crimes are significant at 100 m × 100 m, 200 m × 200 m, and 300 m × 300 m mesh sizes. The individual crimes theft and grievous harm are both significant at 50 m × 50 m and 150 m × 150 m mesh sizes. Crimes involving drug abuse and murder are significant at 200 m × 200 m and both robberies and murders are significant at 300 m × 300 m. All types of crime are insignificant at 350 m × 350 m and 400 m × 400 m. Overall, both 200 m × 200 m and 300 m × 300 m mesh sizes can classify most crime types accurately. Although these findings contradict the conventional belief that religious places are less prone to crime, it should be noted that such places have high numbers of parked vehicles and many small vendors. Furthermore, the probability that a crime near a mosque will be reported to the police is high owing to the high visibility of such areas.
4.1.8. ROC curves The AUC values obtained from the ROC curves for each crime type at each mesh size are presented in the form of a heat map in Fig. 2(a); Fig. 2(b) shows the ROC curves for each crime type at 300 m × 300 m. The results from Fig. 2(a) suggest that the accuracy of the LR models increases from 50 m × 50 m through 200 m × 200 m, decreases at the 250 m × 250 m mesh scale, increases again from the 300 m × 300 m through 350 m × 350 m scales, and finally decreases up to 400 m × 400 m. This pattern suggests an optimal mesh size at 300 m × 300 m, which was therefore selected for further SVM analysis.
4.1.4. Open space The significant OR values for “open space” are all less than one at all distances and in all crime cases except robbery. It is seen from Table 1 that aggregated crimes are significant from 50 m × 50 m–350 m × 350 m, with theft significant at 50 m × 50 m, 200 m × 200 m, and 350 m × 350 m. Drug abuse is significant from 100 m × 100 m–350 m × 350 m, and murders are significant at 350 m × 350 m and 400 m × 400 m. Grievous harm is insignificant at all mesh sizes. It should be noted that robbery, with an OR of 3.39, is significant at 150 m × 150 m, suggesting that this type of crime is not common near open spaces, which is understandable given that robbery is expected to be associated with commercial or residential land use.
4.2. Support vector machine results The LR-based analysis facilitated the identification of the optimal mesh size and revealed the relationship between various land uses and crime types. As the analysis focused on investigating each land use independently, a subsequent SVM-based analysis at the 300 m × 300 m optimal mesh size was conducted to further investigate the interrelationship between land use and different types of crime. After retrieving the separating hyper plane, the plausible affinity of crimes with land use type with accessibility measures (highway [HW], community
4.1.5. Street markets The OR results for “street market” are consistently lower than one at all mesh sizes, with the exception of theft at 400 m × 400 m. This suggests that the likelihood of crime increases in the vicinity of street markets. Aggregated crimes are statistically significant at all mesh sizes 7
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Fig. 3. Heat map of accuracy, sensitivity, specificity, and F-score produced by SVM analysis.
might have been an irrelevant exercise as the distribution of land use was highly clustered within the study area, as shown by Fig. 1(d). The outcomes of the SVM analysis are shown in Fig. 3. The combinations of land use that had accuracy, sensitivity, and specificity values and F-
center [CC], educational institution [ED], mosque [M], open space [OS], street market [SM], residential area [RA], and commercial area [CA]) were assessed and explained two variables at a time (although it was possible to consider more than two types of land use at a time, it 8
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connection between transit and crime. Finally, the developed framework is replicable for use with other developing-world cities to identify their respective crime potentials with respect to land use. As it is unlikely that police force funding will increase to sufficiency levels in developing-world cities within a short time horizon, having a framework to better understand the underlying relationship between crime and land use and road networks should help promote smarter resource allocation and urban planning. The methodological contribution of this study is the development of a tool to equip authorities with a framework that can be used to map and investigate the crime patterns in their cities. This can guide the authorities in increasing their vigilance in areas found conducive to crime. In addition, urban planners can use the framework to design land use and associated road networks that inherently deter criminal activities. The study has some limitations and several paths for future research. As it was conducted in a model town within Dhaka city, the results might not fully represent the relationship between crime and land use for the overall city. In addition, the scope of the study was limited to presenting a framework to identify the association between land use and crime and to demonstrate it using data from Uttara. However, understanding why and how these land uses are associated with various crimes will require further in-depth study involving criminology experts. It is also important to note that the Uttara road network is primarily in a grid pattern, and a future study should include other locations within the city with other road network patterns such as lollipops on a stick, loops and lollipops, warped parallel, fragmented parallel, etc. Finally, socio-economic and demographic characteristics should be incorporated within a future study.
Table 2 High and low crime rate associations by land use combination. Crime Type
High
Low
Land Use
Weight
Land Use
Weight
Drug
SM vs. RA OS vs. RA ED vs. CA
0.31 0.17 0.04
M vs. RA
0.024
Grievous Hurt
ED vs. CC SM vs. CA SM vs. RA
0.91 0.32 0.25
M vs. RA M vs. CC ED vs. SM
0.017 0.063 0.089
Murder
M vs. OS OS vs. CC OS vs. RA
0.16 0.14 0.12
HW vs. OS M vs. CA
0.000 0.026
Robbery
SM vs. CA OS vs. CA M vs. CA
0.48 0.46 0.31
HW vs. RA M vs. RA M vs. CC
0.000 0.037 0.054
Theft
SM vs CA OS vs. CA SM vs. RA
0.51 0.43 0.41
M vs. CC M vs. RA
0.067 0.094
scores all higher than 75% were then identified and the corresponding numbers of cells and crimes for each combination were determined to calculate the weighted crimes per cell (see Step 6 in the methodology section). The primary land use combinations for high and low crime scenarios of each crime type are listed in Table 2. The findings suggest several interesting patterns. For example, the low association with crimes for open spaces and residential areas next to highways is attributable to the fact that, in general, there are very few residential areas or open spaces along the highways. Furthermore, although mosques had an overall high association with crime in the LR analysis, in which crime frequency was not weighted for the number of cells and the distribution of crime type, the SVM results revealed that mosques situated within residential areas are the least crime-prone locations. Aside from murders, all types of crimes were predominant in the commercial areas, which are expected to have high opportunity factors. At the same time, street markets, which are mostly operated illegally in Uttara and encroach the public roads, invite various types of crime. Specifically, drug-related crimes are predominant near street markets situated within residential areas, while grievous harm, theft, and robbery-related crimes take place near street markets located in the vicinity of residential and commercial areas. It is also seen that open spaces, which are in general not monitored by law enforcement agencies, provide an environment favorable to the commission of crimes.
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5. Conclusion This paper presented a framework in which an attempt was made to derive relationships among different types of crime and land use in terms of their transportation accessibility. The results were presented in the context of a city in a developing country in which there is scant resource allocation for law enforcement. A novel methodology in which an area is divided into provisional meshes of different sizes, with each mesh associated with a land use and crime status, was introduced. LR analysis was then used to identify the mesh size that can best distinguish between crime-prone and safe land use areas, and the chosen optimal mesh size was then subjected to SVM analysis to discriminate combinations of land use that experience high crime rates from those that do not. We believe that the results of this study make three major contributions. First, our approach adopts an exposure-based approach instead of a frequency-based approach by considering both crime-prone and non-prone areas. Second, it contextualizes the proximity of land use to crime location in terms of transportation accessibility to uncover the 9
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Patterns of crime and land use change. Cartography and Geographic Information Science. Soon, D. W. (2016). Do all commercial land uses deteriorate neighborhood safety?: Examining the relationship between commercial land-use mix and residential burglary. Habitat International, 55, 148–158. Stucky, T. D., & Ottensmann, J. R. (2009). Land use and violant crime. Journal of Criminology, 47(2009), 1223–1264. Vural, M. S., & Gök, M. (2016). Criminal prediction using naive Bayes theory. Neural computing and applications. 1–12. http://doi.org/10.1007/s00521-016-2205. Yirmibesoglu, F., & Ergun, N. (2007). Property and personal crime in istanbul. European Planning Studies, 15(3), 37–41. http://doi.org/10.1080/09654310601017067. Zhang, W. (2016). Does compact land use trigger a rise in crime and a fall in Ridership ? A role for crime in the land use – travel connection. Journal of Urban Studies, 53(14), 3007–3026. http://doi.org/10.1177/0042098015605222.
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