Development of Crime Reporting System to Identify Patterns of Crime in Laguna (original) (raw)
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International Journal of Advanced Trends in Computer Science and Engineering, 2020
In the past years, crime becomes one of the main concerns in the Philippines for it affects drastically in the economic growth of the country. Awareness was one of the key factors that a police officer must possessed to effectively reduced crime in particular location. Many criminologists study on the number or occurrence of a crime to resolve the problem, however, number vagueness and possible source are often encountered that compromises the possible real effects or pattern. Machine learning is well-known to produce new knowledge and discover hidden pattern intelligently in particular database which can be used to produce data-driven reasoning or policy recommendation. The key objective of this research is to develop a predictive model in investigating crime records in the province of Laguna. Following the famous concept of knowledge discovery in databases, the researchers found out that decision tree algorithm is the best machine learning algorithm in classifying crime occurrence. Furthermore, date, time and place have a significant correlation in crime occurrence. Also shown in this paper, that the bigger district in the province of Laguna is more vulnerable in different crime.
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The primary goal of this study is to successfully develop a Geographic Crime Information Reporting System at a station in Mandaue City, an electronic blotter that uses Geographical Information System (GIS) in recording crime information. The "Geographic Crime Information Reporting System" contains a Geo Spatial Crime Visualization module that loads and displays the maps from shape files. The Crime Management Module to assign coordinates in the map given by the user to be stored in a Database. The Geographic Crime Information Analyzer analyzes the data and creates a report which is a list of possible suspects. The system is then evaluated by randomly selected Philippine National Police Officers (PNP Officer) and Philippine National Police Information Technology personnel (PNP IT Personnel) of Mandaue City. The Results show that 75% of the respondents are familiar with the Electronic-blotter (E-Blotter), with only 26.7% of them who are currently using it. 85% are familiar wi...
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The spatial distribution of crime has been extensively examined at both the point and areal levels, often without regard to environmental factors. However, the emergence of community policing demands a more holistic approach to crime analysis. The systemic nature of crime, requires that information systems supply data about the social and physical environment surrounding a crime event, not just information about the crime event itself. Urban form, design components, mobility and 'incivilities' have been identified as contributing factors in the crime equation. Information about these influencing factors is collected by various governmental agencies and stored in a myriad of separate databases on multiple computer systems. Data sharing among these departments is often a difficult, multi-step process. This study demonstrates how a Geographic Information System can be utilized to provide an integrated information source that will be able to support community policing activities in neighborhoods.
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With the continuous rise in crimes in some big cities of the world like Karachi and the increasing complexity of these crimes, the difficulties the law enforcing agencies are facing in tracking down and taking out culprits have increased manifold. To help cut back the crime rate, a Crime Forecasting System (CFS) can be used which uses historical information maintained by the local Police to help them predict crime patterns with the support of a huge and self-updating database. This system operates to prevent crime, helps in apprehending criminals, and to reduce disorder. This system is also vital in helping the law enforcers in forming a proactive approach by helping them in identifying early warning signs, take timely and necessary actions, and eventually help stop crime before it actually happens. It will also be beneficial in maintaining an up to date database of criminal suspects includes information on arrest records, communication with police department, associations with othe...
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This paper reviews modern crime analysis with regard to the research and educational challenges outlined by the University Consortium for Geographic Information Science. In the context of Geographical Information Systems (GIS), attention is devoted to the role that crime analysis currently and potentially plays in reducing crime and improving the efficiency of police activity. It is our aim to stimulate interest in advancing crime analysis in the areas of crime mapping and visualization. It is hoped that an outcome of this effort will be the attention that granting agencies may give to this rich and productive mixture of state-of-the-art technology and social responsibility.
Geographic Information Systems for Crime Prone Areas Clustering
JOIN: Jurnal Online Informatika, 2020
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.