Crime-prone Areas Research Articles

Application of Fuzzy C-Means and Borda in Clustering Crime–Prone Areas and Predicting Crime Rates Using Long Short Term Memory in Northern Aceh Regency

North Aceh is a district with diverse geographical conditions, ranging from vast lowland areas in the north stretching from west to east, to mountainous areas in the south. The average altitude in North Aceh is 125 meters. The district covers an area of 2,694.66 km² with a population of 614,640 people in 2022. The issue of crime in North Aceh District has caused significant discomfort among the community. According to data from the Central Bureau of Statistics (BPS) of Aceh Province, the number of criminal cases increased from 6,651 cases in 2022 to 10,137 cases in 2023. Using the Fuzzy C-Means clustering method, the data was grouped into three clusters: cluster 1 represents safe areas, cluster 2 represents moderately vulnerable areas, and cluster 3 represents vulnerable areas. For ranking using the Borda method, the Dewantara Police Sector ranked first for the physical aspect, while the Muara Batu Police Sector ranked first for the item aspect. As for predictions using the LSTM model, almost all subdistricts achieved MAPE values below 20%, indicating that the LSTM model is quite effective in predicting crime-prone areas. For example, Baktiya District recorded a MAPE value of 15.85% for the physical aspect, while the best result was achieved by Simpang Keramat District for the item aspect with a MAPE value of 0.00%. However, in Syamtalira Bayu District, the item aspect reached a MAPE value of 20.07%. Although the MAPE value for the item aspect in Syamtalira Bayu is relatively high, it is still considered acceptable as it remains below 50%.

Secure Route: Enhancing Route Safety Through Real-Time Updates and User Feedback

The increasing prevalence of crime in urban areas necessitates innovative solutions to enhance public safety during navigation. This study presents the "Secure Route" system, a community-driven web application designed to provide real-time safe route navigation and crime reporting capabilities. Drawing insights from existing frameworks, the application integrates user feedback, crowdsourced data, and geolocation-based updates to assess route safety. Leveraging techniques such as risk assessment, heatmap visualization, and collaborative data filtering, the system enables users to identify safer routes while avoiding crime-prone areas. This research highlights the importance of community engagement and real-time data in creating a responsive and adaptive safety navigation platform. SecureWalk offers a novel approach to urban safety by fostering collaboration between users and leveraging GIS technologies for informed decision-making. The integration of AI- driven models for analyzing crime patterns and predicting future risks further enhances the accuracy and reliability of the system’s recommendations. By combining collaborative filtering, user feedback, and advanced geospatial analytics, SecureWalk aims to improve both individual safety and community awareness, fostering a safer environment for urban commuters. Keywords: Secure Route, Community-driven, Real- time navigation, Crime reporting Geolocation-based updates, Heatmap visualization

Web-Based Mapping of Crime-Prone Areas in Samarinda Seberang and Loa Janan Ilir Districts, Samarinda Citys

Web-Based Mapping of Crime-Prone Areas in Samarinda Seberang and Loa Janan Ilir Districts, Samarinda Citys

Klasterisasi Daerah Rawan Kriminalitas di Sulawesi Tenggara Menggunakan Metode K-Means Clustering

Crime is anything that relates to unlawful behavior. In every society, the presence of crime affects social dynamics, levels of security, and general well-being. Southeast Sulawesi in the midst of its diversity is facing a significant increase in crime, this is evident from the 3,000 cases that have been reported. The purpose of this research is to categorize crime-prone areas in Southeast Sulawesi using the K-Means Clustering method. K-Means is a research data analysis method or a data mining method that will carry out modeling without supervision (unsupervised). The data from this research was taken from the sultra.bps.go.id web page. The data taken in the form of data on the number of crimes and the percentage of crime victims in the regions of Southeast Sulawesi. The results of this study obtained 3 clusters of crime-prone areas in Southeast Sulawesi. The clustering evaluation results if calculated by the Daviees Bouilden Index method is as much as 0.5904 index.

Mapping of Crime-Prone Areas in The Ulu Area of Palembang City Based on Public Service Availability Factors

Crimes often occur in various places in Palembang City; the Ulu area is the most dominant area, especially the Seberang Ulu I District. Different places and times make it difficult to determine which areas have the highest level of crime vulnerability. This information is needed by the public and law enforcement. Most of the crimes that arise are caused by economic influences. This study tries to link two variables, namely the crime rate variable and the variable availability of public services. The method used is descriptive-quantitative with spatial analysis. Conclusions in this study: (1) Several areas in Seberang Ulu I District have different levels of vulnerability to crime. Areas that are categorized as prone to crime are areas that have public services, including Tuan Kentang, 7 Ulu, and 15 Ulu. (2) Crimes most often occur at night between 18.00 and 05.59 WIB, with as many as 19 cases, and in the afternoon between 06.00 and 17.59 WIB, with as many as 11 cases.

자치경찰제의 현황과 개선방안

In order to analyze the operation status of the autonomous police system, we tried to analyze the basic ideology and current status of the Sejong Autonomous Police based on the ideology (necessity) of the autonomous police and suggest the direction of development based on this. First, in terms of democracy (citizen participation), it is implemented in many areas, such as the local police chief's recommendation system, the formation and operation of the “Uteummaru”, the conduct of a circuit meeting of the residents' council, and the operation of the website, but it is necessary to find ways for citizens to actively participate in the operation of the website. Second, in terms of decentralization (providing security services reflecting the characteristics of the region), it operates projects such as building balanced security by improving the environment of crime-prone areas and proactively responding to the changing school violence environment, but it is necessary to develop security service projects that reflect the characteristics of the region. Police statistics show that the rate of special economic crimes is higher than that of other local governments. Population growth is higher than in other regions. Sejong Special Self-Governing City provides security services to respond to relatively young people (Date violence, juvenile crimes, etc.) Third, in terms of political neutrality (representativeness), it is necessary to play a role as a local security service provider that reflects expertise and representativene1ss more by fully considering occupational and career aspects in the composition of the next autonomous police committee.

Comparative Soil Analysis from Different Crime Prone Areas of Kottayam and Kollam Districts of Kerala for Forensic Characterization

Soil is the loose surface material that covers land on which plants grow. It consists of a mixture of organic remains, clay, and rock particles, which gives it its uniqueness from place to place. Because of the uniqueness of soil, it has a high evidential value in the field of forensic science. In the present study, research has been conducted to check the range of variations in the soil collected from different crime-prone areas of Kottayam and Kollam districts of Kerala by physical, chemical, microscopic, and instrumental examination. In physical examination, soil colour, texture, consistency, and density were measured, and in chemical examination, pH and total soluble sulphate of the soil were measured. In instrumental examination, Heavy metal analysis was done by using Atomic Absorption Spectrometry (AAS), which helps to measure the amount of trace elements present in the soil. The study found that each soil has some significant variations that make it unique. In the field of forensic science, this can be highly helpful in determining the origin of soil by examining its colour, texture, structure, content, etc.

Review on the concentration of police stations in the Accra Metropolitan Area and its implications on security and social control

The increasing urban crime rate in Ghana is causing the development of police stations in the Accra metropolitan area. The location of a police station depends on factors influencing crime-prone areas. In Accra, 16 departments report crime situations to the metro coordinator director, who then organizes meetings with the presiding member. Crime rates are typically high in densely populated areas. This study aims to reveal the density of police stations and their impact on security and social control in the Accra metropolitan area. GIS spatial data and tools were used to observe the concentration of police stations, while Arc Software was used to determine the level of concentration and its impact on crime response and social control. Hotspots were also used to collect crime data for better monitoring of police station concentration in Accra and number of crime cases observed in each police station and more police stations presents with its impact on response to crime and social control.

Z-Score and Floyd Warshall Algorithms for Determining Alternative Routes of Mugging-Prone Areas in Medan City, Indonesia

This study analyzes and implements the Floyd Warshall algorithm using Z-Score to track alternative routes to areas in Medan City, Indonesia that are prone to mugging. The data was obtained from Porlestabes (Police station) Medan-Indonesia. This study employed the Z-Score rank method to rank specific values and determine the levels of crime-prone areas. The highest and lowest levels of crime-proneness were identified using the Z-Score method, and the Floyd Warshall Algorithm is used to identify alternative routes to avoid such areas. The language used in this study adheres to objective and formal writing principles, with value-neutral and clear terminology employed throughout. The results of this analysis showed that out of 99 roads across 18 districts, 4.04% of them were classified as very high prone, 9.09% as high prone, 11.11% as prone, and 75.76% as low prone. The search results from conducting alternative route analysis with the Floyd Warshall algorithm on Perintis Kemerdekaan street indicate the identification of the safest routes.

Geospatial Location of Crime-Prone Areas in Igangan, Tapa and Ayete Community in Ibarapa North Local Government Area, Oyo State, Nigeria

Due to insecurity, Ibarapa land is believed to be the most challenging zone in Oyo State. Ibarapa axis of Oyo has been the hotbed of several violent clashes between the bandits and the indigenous farmers where farmers can no longer go on their daily faming activities due to kidnapping by the herdsman. This study aimed at depicting the geographical locations of crime prone areas in Ibarapa North Local Government Areas of Oyo State, Nigeria. The study adopted field survey methods which involved field surveying method. IKONOS satellite imagery was employed as a tool to depict the geographical location of crime prone areas. The field surveying method involved the use of Handheld GPS (Garmin 78s) for data ground truth and kidnapping crime hotspots in Igangan, Tapa and Ayete. The study was able to visited Eight (8) crime hotspots in Igangan, five (5) in Tapa community and none in Ayete as all crime areas are in the/along farmland, and due to security reasons, the study cannot further to all the locations. Further processing was done using ArcGIS 10.4 (Arcmap 10.4 version). The results from the study were presented in form of tables, map and map queries. However, proximity of police stations to the crime areas was done to determine how close/near of the crime hotspots to the nearest police station through buffer analysis. Therefore, the study recommends the establishment of more security agents by the government, community vigilante be established also and empower them and as well create more police stations in the study areas.

K-Means and AHC Methods for Classifying Crime Victims by Indonesian Provinces: A Comparative Analysis

Crime is a common phenomenon that often occurs in society and has a negative impact both individually and collectively. Gaining a deeper understanding of crime can help us tackle the problem more efficiently. In an era that is increasingly complex and globally connected as it is now, crime has undergone significant developments and changes. Crime remains a serious threat to our security, integrity, and well-being. Some common types of crime include theft, robbery, fraud, physical abuse, and murder. Crime can happen anytime and anywhere. To tackle crime, data mining techniques can be used to analyze the surrounding situation and gain new knowledge. One approach is to classify provinces based on crime data from previous years so that crime-prone areas can be identified and security measures can be increased. In this study, two grouping methods were used, namely K-Means and AHC using the complete linkage mode. There are 34 provinces in Indonesia which are grouped based on the number of victims of crime from 2019 to 2021. The grouping results using the K-Means method yield two groups with 17 provinces each. However, using the AHC complete linkage method, there is a difference in the number of provinces between cluster 0 and cluster 1 compared to the K-Means results. In addition, there are differences in the location of the province in the cluster between the two methods. In the K-Means method, provincial data is located in cluster 0, while in the AHC method, the province's data is in cluster 1. Thus, this study provides insight into crime in Indonesia and provides information about the grouping of provinces based on crime rates using the K-Means method. Means and AHC

Soil Analysis from Crime-Prone Areas of Sultanpur City for Forensic Aspect: A Research

The soil is a crucial resource in many criminal trials and the same has received a recent research approach inrecent years, with numerous groups researching with objectives that allow for improved differentiation of soilprofiles. This investigation was conducted in 2022-2023 with the objective of assessment of soil characterizationfrom several areas in Sultanpur City, Uttar Pradesh. The primary goal of this study was to conduct a quantitativeanalysis of the samples using physical, chemical, microscopic and instrumental examination techniques. The soilsamples were collected from a depth of 0-15 cm and after analysing the samples, it was found that the colour of thesoil varies according to its composition. Soil features like texture, stickiness, plasticity, density, pH, and percentageof soluble sulphate were examined using different physical and chemical tests. Soil texture was classified underthe sandy clay loam category and stickiness and plasticity were measured to be less, medium or more. The soildensity ranged from 1.794 g/cm3 to 2.479 g/cm3. The pH of the soil samples was overall acidic, ranging from6.27 - 7.77. The percentage of soluble sulphate in soil varied from 0.11184 - 0.0864.A stereo microscope was used toobserve the granular and prismatic structures of soil samples. Atomic Absorption Spectrometry (AAS) was usedfor quantitative analysis of soil samples to measure the amount of trace elements present in the soil. The studyresults showed that each soil sample varied from another and the parameters used for soil profiling helped in thedifferentiation of these soil samples. The results from this study can be helpful in preparing soil which can furtherassist the forensic scientists in their investigations related to soil as evidence.

Rectified DenseNet169-based automated criminal recognition system for the prediction of crime prone areas using face recognition

With the increase in the crime rate, it becomes difficult for police officials to catch criminals. Face recognition is a technique that helps police officials to catch suspects easily with the help of CCTV footage. The constant checking of surveillance video is a tiresome approach that involves substantial visual consideration and is not mentally captivating, making it increasingly inclined to errors. Therefore, the authors proposed an automated criminal recognition system in the present paper. The present paper comprised three phases; the initial phase investigated the performance of various existing face detection algorithms. The second phase proposed a rectified fine-tuned DenseNet169 model for face recognition. In the third phase, the authors proposed an automated criminal recognition system in which the identification of crime-prone areas is made, based on the data collected where most of the criminals are identified. The proposed rectified model outperformed 1.02% to DeepID, 1% to deep convolutional neural network (CNN), and 1.05% to self-learning CNN on the LFW dataset, 1.09% to VGGFace2, 1.13% to SphereFace, and 1.03% to lightweight CNN on the CPLFW dataset, and achieved 88.7% recognition accuracy on a self-created dataset by meticulously selecting the hyperparameters and customizing the layers of the model.

Geographic Information System Mapping Of Criminality Villed Areas In Lhokseumawe Using K-Means Method

Crime is a serious problem that can have a wide impact on all levels of society. Every day criminal acts can occur anywhere, especially in big cities, including Lhokseumawe, it is difficult for the community to determine the locations of areas prone to crime and the locations of safe areas. As a solution to the problem, a map of crime-prone areas is needed to show and display the location of these areas. To assist the mapping process of areas prone to crime, a Geographical Information System is needed with criminal data in the form of fraud, theft, gambling, torture and rape obtained from the Lhokseumawe Police, for the classification process of the level of vulnerability of an area the K-Means method is used, and Openstreetmap and QuantumGis are used for mapping of the classification of areas prone to crime. The results of this study are a Geographical Information System which can display the results of an interactive classification of crime-prone areas in the form of a map and can be accessed by related parties to determine the level of crime vulnerability, then from the public's side can find out the location of crime-prone areas in lhokseumawe so that it becomes an initial reference for action anticipation.

EKSISTENSI PENDIDIKAN ISLAM DALAM UPAYA PENURUNAN TINGKAT KRIMINALITAS PADA DAERAH RAWAN KRIMINAL DI LAMPUNG TIMUR

East Lampung as one of the crime-prone areas has shown a very rapid decline in crime rates in the last three years, followed by an increase in community enthusiasm and participation in Islamic education. This study aims to determine the extent of Islamic education in reducing crime rates in crime-prone areas in East Lampung. The research method used is descriptive qualitative with a case study approach by selecting the four most crime-prone areas in East Lampung. The sources of data of this research are several religious counselors, local religious leaders, and the surrounding community by selecting samples by purposively using the interview as the main instrument, observation and documentation as secondary intrument. The results of the study found out that East Lampung experienced a drastic reduction in the crime rate due to the development of Islamic informal education, namely Majlis Taklim, Qur’anic schools, Madrasah Diniyah and Islamic Boarding Schools that encourage the drastic changes in attitudes and behavior to be more religious and better. Besides, that able to strengthen community relations and can be a medium for various conflict resolutions between residents so that crime rates can be optimally reduced and the quality of social life in the community can be continually optimized.Keywords: Islamic Education, Criminalism, Crime-Prone Areas

범죄발생 위험지역의 공간분포와 도시쇠퇴 특성과의 관계 : GIS기반 공간통계기법을 통한 범죄발생 위험지표를 중심으로

Purpose: In general, urban decline is caused by physical aging, population decline, and population aging. In order to promote the vitality of the declined region, Korea diagnoses the degree of urban decline through various indicators and provides the basis for selecting a target site for urban regeneration projects. Urban crime is known to be caused by defects in various socio-demographic and cultural factors from the perspective of Environmental Criminology. In particular, the crime problem in the original city center, which is accelerating urban decline, is an important task to be solved along with urban regeneration projects. Therefore, this study aims to analyze the characteristics of urban decline on crime-prone areas, and to empirically analyze whether the current legal urban decline index for selecting urban regeneration projects reflects urban problems such as crime-prone areas. Method: First, the spatial distribution characteristics of crime occurrence risk indicators are analyzed using spatial autocorrelation analysis for administrative districts in Seoul. Second, the characteristic factors of urban decline on crime-prone areas are analyzed using the spatial regression model. Result: As a result of this study, it was found that the characteristics of urban decline affecting the crime-prone area were the areas where the socially vulnerable lived and the areas where the declining small-scale food and lodging businesses were located. In addition, according to the legal standards, areas that met the criteria for urban decline indicators were found to have a negative (-) effect on the crime-prone area. This means that it is necessary to consider crime and safety factors in projects in urban regeneration areas.

Citizenguard: A Watchdog for Unsafe-Area Detection to Increase the Safety of Citizens

Increasing numbers of crime cases over the years in Mumbai have caused concerns on the safety of citizens living in Mumbai City. There are some areas which can be called as a crime hotspot and the citizens can be roaming in such areas with no idea about how much danger they are in. To curb this problem there should be a solution feasible and accessible to each person. The proposed system may serve as a crime deterrent to increase security that enables the user to be notified in various ways and may transform their mobile devices into smart personal emergency phones. The proposed system gives valuable information on crime prone areas in a densely populated city. The proposed project can be summarized as a safety app for citizens of Mumbai, or tourists travelling from other states or countries. The project can assist these users by providing Risk alert notification, Nearby police stations, Nearby Hospitals, Emergency panic button and Emergency helpline contact numbers. With the help of these features it makes it easy for the citizens and tourists to be aware of any danger they might be in.

COMPARATIVE ANALYSIS OF CRIME TYPE PREDICTION

Crime analysis and prediction is a logical approach for analysing and identifying different patterns, relationships and trends in crime. The system can predict the area which have high probability for crime occurrence and indicate crime prone areas. It will be useful for the law enforcement agencies to speed up the process of solving crimes with the increasing computerized systems and with the help of crime data analysts. Here we have an approach between computer science and criminal justice to develop a data mining procedure that can help solve crimes faster. Analysis of police data createawareness that lets officers track criminal activities, predict the incidents, effectively deploy resources and solve cases faster. In this paper crime analysis is done by k-means,Spectral clustering and Agglomerative clustering on the crime dataset.

Geographic Information Systems for Crime Prone Areas Clustering

Crime is one of the problems that is quite complicated and very disturbing to the community. Crimes can occur at different times and places, making it difficult to track which areas are prone to such actions. K-means algorithm is used to cluster prone areas and Geographic Information System is used to map crime-prone areas. Web-based application is developed with the PHP programming language. The data used is quantitative data in the form of the number of crimes committed and the coordinates of the cases. The attributes of the crime used consist of five parameters: theft, mistreatment, rape, women and child protection cases and fraud. The results of this study are clustering areas into 3 cluster and mapping prone areas that is safe area, safe enough area and prone area. From the overall crime data for 2019 in Purwakarta district, it was found that 68.75% was safe, 18.75% was quite safe and 12.5% was prone area.

The influence of location, planning and design features on residents’ satisfaction with security in public housing estates in Lagos, Nigeria

Location, planning and design of the built environment are known to have influence on the security of lives and property in residential neighbourhoods. However, there is a limited understanding of the extent to which these aspects contribute to security in public housing environments in a developing country like Nigeria. This study investigated the influence of locational, planning and design features on residents’ perception of security in public housing estates in Lagos Metropolis, Nigeria. The data used were derived from a questionnaire survey of 1036 residents in 12 Lagos State Development and Property Corporation (LSDPC) housing estates in the study area. Results of the descriptive statistics, factor and regression analyses revealed that the residents were generally satisfied with security of lives and property in the housing estates. Provisions for communal living, layouts of the estates and passive security measures and location of the estates close to neighbourhood facilities and in crime-prone areas of the city contributed most to residents’ perception of security in the estates. This study implies that to enhance security in public housing environments, priority should be given to location, planning and design strategies that promote communal living and residents’ participation in the security of their homes.