Cluster analysis for reducing city crime rates

Alkhaibari, Adel Ali; Chung, Ping-Tsai

doi:10.1109/lisat.2017.8001983

Cited by 4 publications

(2 citation statements)

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“…K-Mean clustering is common in traditional hotspots analysis [3][4] [5]. Alkhaibari and Chung [4] performed clustering in NYC with same dataset to identify hotspots of Graffiti Crime, and to investigate reasons for stop and search by police in each cluster. Authors picked 2015 and time between 8p.m.…”

Section: State Of the Artmentioning

confidence: 99%

Discovering Spatio-Temporal Pattern of City Crime – Visual Analysis on Felony Crime in New York

Lau¹

2021

Preprint

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Pattern recognition has long been regarded as key role for crime prevention and reduction. Crime analysts and policy makers can formulate effective strategies and allocate resources with reference to spatial and temporal pattern of crime. In order the combat and prevent severe crime in New York City (NYC), this study analyzed Felony Crime data of NYC in previous 5 years (2015 2020) and discovered criminal hotspots pattern and temporal patterns with open criminal complaint data provided by New York Police Department (NYPD). This study adapt a human computer interactive appraoch to draw patterns from crime data, whereas computations and visualization are performed by Python libraries, and human to inform the decision of visualization methods, computational parameters and direction of this exploratary analysis. Density based clustering algorithms, Grid Thematic Mapping and Density Heatmap are displayed to identify hotspots and demonstrates their associations with spatial features. Timeline analysis on moments of crime occurance demonstrates seasonality where crimes are mostly commited, while aoristic analysis showed hours of day when crime is mostly committed considering their timespan. Lastly, 3D visualization improved recognition of the displacement of hotspot over time, and suggested long term hotspots in NYC in 3 D visualization. This inform strategic plans for police deployment.

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Section: State Of the Artmentioning

confidence: 99%

Discovering Spatio-Temporal Pattern of City Crime – Visual Analysis on Felony Crime in New York

Lau¹

2021

Preprint

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“…The different procedures utilized for various violations have been talked about with a prologue to the concerned crime. The sorts of wrongdoing are as referenced beneath [1]. 1) Misrepresentation Detection: Extortion is misleading or exploiting another.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Crime Detection And Prediction Techniques

Gendre¹

2022

IJRASET

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Crime is a preeminent issue where the main concern has been worried by individual, the local area and government. Wrongdoing forecast utilizes past information and in the wake of investigating information, anticipate the future wrongdoing with area and time. In present days sequential criminal cases quickly happen so it is a provoking assignment to anticipate future wrongdoing precisely with better execution. This paper examines about various wrongdoing expectation and location. A productive wrongdoing forecast framework speeds up the method involved with addressing violations.. Wrongdoing Prediction framework utilizes recorded information and examinations the information utilizing a few dissecting strategies and later can anticipate the examples and patterns of wrongdoing utilizing any of the underneath referenced methodologies. Keywords: Crime Analysis, Data Mining, Classifiaction , Clustering

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A Prognostic Approach to Crime Analysis

Mathew

Asha²

2022

Advances in Machine Learning for Big Data Analysis

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Cluster analysis for reducing city crime rates

Cited by 4 publications

References 1 publication

Discovering Spatio-Temporal Pattern of City Crime – Visual Analysis on Felony Crime in New York

Discovering Spatio-Temporal Pattern of City Crime – Visual Analysis on Felony Crime in New York

A Survey on Crime Detection And Prediction Techniques

A Prognostic Approach to Crime Analysis

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