Urban Crime Mapping and Analysis Using GIS

Ristea, Alina; Leitner, Michael

doi:10.3390/ijgi9090511

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…Ristea and Leitner [15] highlighted few research articles having devoted discussions to approaches that can address the question known police patrol route problems, the two main ones being traffic enforcement (the issue here) and population monitoring (the issue being ignored). Crime prevention and law enforcement are also needed because the police would make sure that no criminals are found, but can tend to crimes if they happen.…”

Section: Spatial Interpolation and Autocorrelation In Crime Hotspot Mappingmentioning

confidence: 99%

Geospatial Crime Analytics: A GIS-Based Approach Towards Prediction of Crime Hotspots

Panigrahy¹

2021

Preprint

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GIS has proven its proficiency in many fields but still require some more observations. The spatial interpolation which has seen widespread used in diverse domains with multiple applicability. Currently, some commercial GIS or analytics program packages now provide spatial interpolation technique including inverse distance weighting (IDW), kriging, triangulated irregular networks (TIN) interpolation, kernel density interpolation (KDE). Further, the two spa-tial autocorrelation techniques i.e., also known as LISA indicators that comprised of Moran’s I and Getis-Ord Gi* techniques. Amalgamation of spatial interpolation and spatial autocorrelation are being shown in this paper. For theimplementation, related enabling technologies are summarized. Further, this paper entailed the literature survey of multiple papers comprising the associated works carried out in the domain of spatial interpolation and spatial autocorre-lation areas of GIS. The proposed framework is a generalized framework with four tiers to realize geospatial systems. This paper scrutinized geospatial crime dataset for understanding the proposed framework realistically. Numerous results are produced to prove the efficiency of the proposed model. The paper entailed weighted-overlay analysis, symbological heatmap analysis, choropleth analysis, kernel density interpolation, and Getis-Ord Gi* spatial autocorrelation analysis implemented to practically realize the essence of geospatial crime analytics. Finally, future research directions are recapitulated, where the task of interpolation and autocorrelation might be merged with techniques of artificialintelligence, machine learning, and cloud computing paradigm, serverless com-puting framework and many such in order to enhance the proposed structurewith lessened restrictions.

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Section: Spatial Interpolation and Autocorrelation In Crime Hotspot Mappingmentioning

confidence: 99%

Geospatial Crime Analytics: A GIS-Based Approach Towards Prediction of Crime Hotspots

Panigrahy¹

2021

Preprint

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“…These include promoting crime prevention through social media, conducting social media surveillance, monitoring criminal activity, and regulating social media companies. These measures aim to address privacy and civil liberties concerns, gather valuable information about crimes in progress, and hold social media companies accountable for illegal content [8]. Social media technology revolutionizes interactions through platforms like Facebook, blogs, wikis, and Twitter [9].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid CNN-LSTM and XGBoost Approach for Crime Detection in Tweets Using an Intelligent Dictionary

Abdalrdha,

Al-Bakry,

Farhan

2023

RIA

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As social media grows, recognizing and managing illicit content, including threats, harassment, hate speech, armed robbery, drug smuggling, blackmail, and other crimes, is crucial. The present study uses machine learning and deep learning to create an intelligent lexicon for identifying crime-related material in Twitter tweets. The Aho-Corasick technique effectively creates a dictionary for extensive text corpus search, categorization, and keyword-based action execution. This strategy overcomes the evolution of language dynamics and criminal vocabulary to improve crime-related information detection. This paper aims to gather accurate data to help law enforcement identify and prevent specific crimes. For tweet preprocessing and extraction of relevant information like textual patterns and other distinctive qualities, natural language processing (NLP) technologies are prioritized. The paper describes labeling tweets into crime categories. This dataset trains supervised learning models to categorize tweets as criminal or not. XGBoost and Hybrid CNN-LSTM are combined for this. The suggested technique is assessed using precision, recall, F1-Score, accuracy, and MAP accuracy measures. These metrics measure the model's crime-related tweet identification accuracy. The Arabic tweets dataset, encompassing 18493 tweets and 10 features, is utilized for model testing. After training, the Hybrid CNN-LSTM model demonstrated an accuracy of 99.84% and a macro F1-Score of 98.20%. When the XGBoost method was employed, the traditional machine learning model achieved a peak F1 macro score of 99.36% and a maximum accuracy of 100%. The results suggest that while the deep learning models outperform machine learning models in the F1-Score, XGBoost exhibits superior accuracy. The paper presents a comprehensive strategy for crime detection in tweets, potentially offering a significant tool for law enforcement agencies.

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Performance Evaluation of IoST–Mist–Fog–Cloud Framework for Geospatial Crime Data Visualization: A State Dependent Queueing Approach

Panigrahi,

Goswami,

Mund

et al. 2023

SN COMPUT. SCI.

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Urban Crime Mapping and Analysis Using GIS

Abstract: On 22 April 2018, the authors were invited by the Editor-in-Chief, Prof [...]

Cited by 7 publications

References 20 publications

Geospatial Crime Analytics: A GIS-Based Approach Towards Prediction of Crime Hotspots

Geospatial Crime Analytics: A GIS-Based Approach Towards Prediction of Crime Hotspots

A Hybrid CNN-LSTM and XGBoost Approach for Crime Detection in Tweets Using an Intelligent Dictionary

Performance Evaluation of IoST–Mist–Fog–Cloud Framework for Geospatial Crime Data Visualization: A State Dependent Queueing Approach

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