A Novel Approach to Identify Spatio-Temporal Crime Pattern in Dhaka City

Parvez, Rizwan; Mosharraf, Turash; Ali, Mohammed Eunus

doi:10.1145/2909609.2909624

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…Human behavior is often the result of a socialization process that occurs within the context of a specific ethnic and cultural environment (Parvez et al, 2016). The socio-cultural environment has an impact that can affect crime rates in various ways.…”

Section: Discussionmentioning

confidence: 99%

Analysis Model of Socio-Cultural Environment on Criminality Rate: A Rural Case Study

Nirmalasary,

Rejo,

Windusari

et al. 2024

J. of Law and Sust. Develop.

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Objective: Criminality in Indonesia is still a serious problem that threatens public safety and order. A study was conducted to analyze how improving the socio-cultural environment can reduce crime in Indonesia. Method: This study uses primary data obtained from 100 respondents who are detainees in several police stations, and uses logistic regression analysis to test the influence of several socio-cultural environmental factors. Results: The results of the analysis show that the social environment in the form of increased employment, family relationships, and ethnicity can reduce the incidence of repeat criminality in Indonesia. This finding highlights that it is necessary to pay attention to socio-cultural environmental factors in designing crime prevention and response policies. Conclusion: The conclusion of this study is that special attention should be paid to factors such as employment, family relations, and ethnicity in crime prevention and control efforts. Research Implications: A well-integrated policy strategy can also help reduce crime rates in an area.

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Section: Discussionmentioning

confidence: 99%

Analysis Model of Socio-Cultural Environment on Criminality Rate: A Rural Case Study

Nirmalasary,

Rejo,

Windusari

et al. 2024

J. of Law and Sust. Develop.

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“…Bangladesh: In underdeveloped nations such as Bangladesh, street crime is a frequent issue. Although this issue has been acknowledged for quite some time, there is no apparent solution to the problem of street crime in Dhaka (Parvez et al, 2016). The street children's population is particularly large in Bangladesh.…”

Section: Countries With a Medium Hdimentioning

confidence: 99%

Analytical Study of Street Crimes in Developed and Developing Countries

Rehman¹

2022

pjia

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The intent to conduct street crimes results from cultural standards that promote and encourage violence. Often, street crimes are exacerbated by the proliferation and formation of gangs that perpetuate and reinforce streetculture habits. Other causes of street crime include poverty and unemployment. It is believed that locations with higher poverty rates and unemployment have higher crime rates than other, more developed ones. In addition, those with poor self-control are more likely to perpetrate street crimes. The victims of street criminals become prey to moveable properties, i.e., mobile phones, cash, credit and debit cards, and vehicles. Victims' precious lives are sometimes taken during the occurrence of these incidents. Because street crimes are getting worse and people feel less safe, and because law enforcement agencies haven't been able to stop street crimes, this issue will stay. The researchers aspire that this study will open a venue for society to understand street crimes and their causes, the reasons for low conviction rates, and suggestions regarding the increase of conviction rates.

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“…A predictive model is proposed by Zhang and Yuan [21] for air quality monitoring by analysis of real-time meteorology data from Beijing city. Some of the work attempted for detecting, and monitoring of the crimes are forecasting crime trends in urban areas by Cesario et al [22], spectral analysis of crimes in the city by Venturini and Baralis [23], Parvez et al [24] and an intelligent solution for the smart city using real-time crime analysis by Ghosh et al [25].…”

Section: Related Workmentioning

confidence: 99%

Real-time Big Data Analytics Framework with Data Blending Approach for Multiple Data sources in Smart City Applications

Manjunatha

Annappa

2020

SCPE

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Advancement in Information Communication Technology (ICT) and the Internet of Things (IoT) has to lead tothe continuous generation of a large amount of data. Smart city projects are being implemented in various parts of the world where analysis of public data helps in providing a better quality of life. Data analytics plays a vital role in many such data-driven applications. Real-time analytics for finding valuable insights at the right time using smart city data is crucial in making appropriate decisions for city administration. It is essential to use multiple data sources as input for the analysis to achieve better and more accurate data-driven solutions. It helps in finding more accurate solutions and making appropriate decisions. Public safety is one of the major concerns in any smart city project in which real-time analytics is much useful in the early detection of valuable data patterns. It is crucial to find early predictions of crime-related incidents and generating emergency alerts for making appropriate decisions to provide security to the people and safety of the city infrastructure. This paper discusses the proposed real-time big data analytics framework with data blending approach using multiple data sources for smart city applications. Analytics using multiple data sources for a specific data-driven solution helps in finding more data patterns, which in turn increases the accuracy of analytics results. The data preprocessing phase is a challenging task in data analytics when data being ingested continuously in real-time into the analytics system. The proposed system helps in the preprocessing of real-time data with data blending of multiple data sources used in the analytics. The proposed framework is beneficial when data from multiple sources are ingested in real-time as input data and is also flexible to use any additional data source of interest. The experimental work carried out with the proposed framework using multiple data sources to find the crime-related insights in real-time helps the public safety solutions in the smart city. The experimental outcome shows that there is a significant increase in the number of identified useful data patterns as the number of data sources increases. A real-time based emergency alert system to help the public safety solution is implementedusing a machine learning-based classification algorithm with the proposed framework. The experiment is carried out with different classification algorithms, and the results show that Naive Bayes classification performs better in generating emergency alerts.

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A Novel Approach to Identify Spatio-Temporal Crime Pattern in Dhaka City

Cited by 10 publications

References 4 publications

Analysis Model of Socio-Cultural Environment on Criminality Rate: A Rural Case Study

Analysis Model of Socio-Cultural Environment on Criminality Rate: A Rural Case Study

Analytical Study of Street Crimes in Developed and Developing Countries

Real-time Big Data Analytics Framework with Data Blending Approach for Multiple Data sources in Smart City Applications

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