Fuzzy-Based Spatiotemporal Hot Spot Intensity and Propagation—An Application in Crime Analysis

Cardone, Barbara; Martino, Ferdinando Di

doi:10.3390/electronics11030370

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…Unlike the study, our research employs a dataset that spans more than one year. In [16], the article introduces a technique for examining the strength and spatiotemporal progression of hotspots identified by the EFCM algorithm [17] for spatiotemporal hotspot detection. The proposed method in the article introduces a novel approach for analyzing the spatiotemporal evolution of hot spots in a specific area by calculating the hot spot strength index, which measures the percentage of time a selected area is affected by hot spots.…”

Section: Related Studiesmentioning

confidence: 99%

Model for Spatiotemporal Crime Prediction with Improved Deep Learning

Angbera,

Chan

2023

cai

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proposed an improved deep learning crime prediction model using convolutional neural networks and the xgboost algorithm to predict crime. The major goal of this research is to provide an improved crime prediction model based on previous criminal records. Using the Boston crime dataset, where our larceny crime dataset was extracted, exploratory data analysis (EDA) is used to uncover patterns and explain trends in crimes. The performance of the proposed model on the basis of accuracy, recall, and f-measure was 100 % outperforming the other models used in this study. The analysis of the proposed model and prediction can aid security services in making better use of their resources, anticipating crime at a certain time, and serving the society better.

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Section: Related Studiesmentioning

confidence: 99%

Model for Spatiotemporal Crime Prediction with Improved Deep Learning

Angbera,

Chan

2023

cai

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“…The reliability of a fuzzy set is measured in [12,23] to evaluate the reliability of fuzzy clusters detected in hot spot analysis. It is given by the formula:…”

Section: Fuzzy Entropy Function and Fuzziness Of A Fuzzy Setmentioning

confidence: 99%

A Fuzzy Entropy-Based Thematic Classification Method Aimed at Improving the Reliability of Thematic Maps in GIS Environments

Cardone

Martino

2022

Electronics

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Thematic maps of spatial data are constructed by using standard thematic classification methods that do not allow management of the uncertainty of classification and, consequently, evaluation of the reliability of the resulting thematic map. We propose a novel fuzzy-based thematic classification method applied to construct thematic maps in Geographical Information Systems. An initial fuzzy partition of the domain of the features of the spatial dataset is constructed using triangular fuzzy numbers; our method finds an optimal fuzzy partition evaluating the fuzziness of the fuzzy sets by using a fuzzy entropy measure. An assessment of the reliability of the final thematic map is performed according to the fuzziness of the fuzzy sets. We implement our method on a GIS framework, testing it on various vector and image spatial datasets. The results of these tests confirm that our thematic classification method provide thematic maps with a higher reliability with respect to that obtained through fuzzy partitions constructed by expert users.

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“…These urban areas are identified as hotspots, where, in spatial analysis, a hotspot is an area of greater intensification of phenomena and exposure to risks. Recently, many studies focused on the detection of hotspots in urban analysis problems inherent to different problems in which the population is exposed to risks, such as soil pollution [7], earthquake disasters [8], crime analysis during pandemic periods [9], and traffic incident analysis [10].…”

Section: Introductionmentioning

confidence: 99%

A GIS-Based Fuzzy Model to Detect Critical Polluted Urban Areas in Presence of Heatwave Scenarios

Cardone,

Di Martino,

Miraglia

2024

Computers

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This research presents a new method for detecting urban areas critical for the presence of air pollutants during periods of heatwaves. The proposed method uses a geospatial model based on the construction of Thiessen polygons and a fuzzy model based on assessing, starting from air quality control unit measurement data, how concentrations of air pollutants are distributed in the urban study area during periods of heatwaves and determine the most critical areas as hotspots. The proposed method represents an optimal trade-off between the accuracy of the detection of critical areas and the computational speed; the use of fuzzy techniques for assessing the intensity of concentrations of air pollutants allows evaluators to model the assessments of critical areas more naturally. The method is implemented in a GIS-based platform and has been tested in the city of Bologna, Italy. The resulting criticality maps of PM10, NO2, and PM2.5 pollutants during a heatwave period that occurred from 10 to 14 July 2023 revealed highly critical hotspots with high pollutant concentrations in densely populated areas. This framework provides a portable and easily interpretable decision support tool which allows you to evaluate which urban areas are most affected by air pollution during heatwaves, potentially posing health risks to the exposed population.

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Fuzzy-Based Spatiotemporal Hot Spot Intensity and Propagation—An Application in Crime Analysis

Cited by 7 publications

References 28 publications

Model for Spatiotemporal Crime Prediction with Improved Deep Learning

Model for Spatiotemporal Crime Prediction with Improved Deep Learning

A Fuzzy Entropy-Based Thematic Classification Method Aimed at Improving the Reliability of Thematic Maps in GIS Environments

A GIS-Based Fuzzy Model to Detect Critical Polluted Urban Areas in Presence of Heatwave Scenarios

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