Discovering spatial interaction patterns of near repeat crime by spatial association rules mining

He, Zhanjun; Tao, Liufeng; Xie, Zhen; Xu, Chong Hai

doi:10.1038/s41598-020-74248-w

Cited by 15 publications

(6 citation statements)

References 46 publications

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“…Previous studies have shown that pain and discomfort and neuroendocrine changes caused by chronic diseases cause changes in the transmitters of the central nervous system, thereby inducing the depression. Basically all patients with chronic diseases are accompanied by varying degrees of mental dysfunction, and the most frequent occurrence is depression [24][25][26]. All in all, mental illnesses and CKD recovery interact with each other.…”

Section: Factors Affecting Mental Health Of Patientsmentioning

confidence: 99%

Diagnosis and Treatment Rules of Chronic Kidney Disease and Nursing Intervention Models of Related Mental Diseases Using Electronic Medical Records and Data Mining

Wang

Sun

et al. 2021

Journal of Healthcare Engineering

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Objective. On the basis of electronic medical records, the data mining technology was adopted to explore the law of chronic kidney disease (CKD) and the intervention mode of mental health of patients. Methods. Based on the electronic medical records, the corresponding data extraction, database establishment, and data cleaning of CKD were performed. After that, the related data analysis, frequency analysis, cluster analysis, and nonparametric analysis were used to explore the laws of CKD diagnosis and treatment and nursing intervention mode of mental illness. The most common causes of CKD were chronic glomerulonephritis (43.76%), aristolochic acid nephritis (16.34%), diabetic nephritis (12.87%), and hypertensive nephritis (11.58%). The major treatment method for end-stage patients was alternative therapies, accounting for 46%. Compared with the depression score before intervention, that of the patients after the mindfulness therapy (50.99 ± 9.77 vs. 47.01 ± 9.33, P = 0.024 < 0.5 ) and target behaviour nursing intervention (52.21 ± 8.12 vs. 48.01 ± 9.33, P = 0.032 < 0.05 ) was obviously decreased. Conclusion. The data mining technology based on electronic records showed a good application prospect in the analysis of the diagnosis and treatment of CKD; and target behaviour nursing and mindfulness intervention were effective psychological intervention models.

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Section: Factors Affecting Mental Health Of Patientsmentioning

confidence: 99%

Diagnosis and Treatment Rules of Chronic Kidney Disease and Nursing Intervention Models of Related Mental Diseases Using Electronic Medical Records and Data Mining

Wang

Sun

et al. 2021

Journal of Healthcare Engineering

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“…It is widely used in the identification of near-repeat crimes, but the selection of spatial and temporal scales is relatively arbitrary without clear criteria. Recently, the spatial association rules mining has been innovatively applied to analyze the spatial interactions of near-repeat crimes [41]. It can delineate the anisotropic features of the spatial interaction, while it focuses more on the spatial than the temporal dimension.…”

Section: Spatial-temporal Analysis Methods In Place-based Crime Researchmentioning

confidence: 99%

Discovering Spatial-Temporal Indication of Crime Association (STICA)

Jiang

Liu

Qin

et al. 2021

IJGI

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The importance of combining spatial and temporal aspects has been increasingly recognized over recent years, yet pertinent pattern analysis methods in place-based crime research still need further development to explicitly indicate spatial-temporal localities of pertinent factors’ influence ranges. This paper proposes an approach, Spatial-Temporal Indication of Crime Association (STICA), to facilitate identifying the main contributing factors of crime, which are operated at diverse spatial-temporal scales. The method’s rationale is to progressively discern the spatial zones with diverse temporal crime patterns. A specific implementation of the STICA approach, by combining kernel density estimation, k-median-centers clustering, and thematic mapping, is applied to understand the burglary in an urban peninsula, China. The empirical findings include: (1) both the main time-stable and time-varying factors of crime can be indicated with the disparities of temporal crime patterns for different spatial zones based on the STICA results. (2) The spatial range of these factors can enlighten the understanding of interactions for generating crime patterns, especially with regards to how temporally transient and spatially global factors can produce a locally crime-ridden zone through the mediation of stable factors. (3) The STICA results can reveal the spatially contextual effects of stable factors, which are of great value to improve modeling crime patterns. As demonstrated, the STICA approach is effective in exploring contributing factors of crime and has shown great potential for providing a new vision in place-based crime research.

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“…Understanding the spatial pattern of crime is the first step for crime analysis and crime prevention. The basic objective of spatial crime pattern analysis is to find spatial patterns of crime distribution and then use these patterns to help identify the root causes of the crimes and generate strategies for crime prevention [21,22]. One aim of crime distribution pattern analysis is to figure out the spatial dependence structure of crime occurrence.…”

Section: Crime Distribution Patternmentioning

confidence: 99%

Measuring the Influence of Multiscale Geographic Space on the Heterogeneity of Crime Distribution

He,

Wang,

et al. 2023

IJGI

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Urban crimes are not homogeneously distributed but exhibit spatial heterogeneity across a range of spatial scales. Meanwhile, while geographic space shapes human activities, it is also closely related to multiscale characteristics. Previous studies have explored the influence of underlying geographic space on crime occurrence from the mechanistic perspective, treating geographic space as a collection of points or lines, neglecting the multiscale nature of the spatial heterogeneity of crime and underlying geographic space. Therefore, inspired by the recent concept of “living structure” in geographic information science, this study applied a multiscale analysis method to explore the association between underlying geographic space and crime distribution. Firstly, the multiscale heterogeneity is described while simultaneously considering both the statistical and geometrical characteristics. Then, the spatial association rule mining approach is adopted to quantitatively measure the association between crime occurrence and geographic space at multiple scales. Finally, the effectiveness of the proposed methods is evaluated by crime incidents in the city of Philadelphia. Experimental results show that crime heterogeneity is indeed closely related with the spatial scales. It is also proven that the influence of underlying geographic space on crime heterogeneity varies with the spatial scales. This study may enrich the methodology in crime pattern and crime explanation analysis, and it provides useful insights for effective crime prevention.

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Discovering spatial interaction patterns of near repeat crime by spatial association rules mining

Cited by 15 publications

References 46 publications

Diagnosis and Treatment Rules of Chronic Kidney Disease and Nursing Intervention Models of Related Mental Diseases Using Electronic Medical Records and Data Mining

Diagnosis and Treatment Rules of Chronic Kidney Disease and Nursing Intervention Models of Related Mental Diseases Using Electronic Medical Records and Data Mining

Discovering Spatial-Temporal Indication of Crime Association (STICA)

Measuring the Influence of Multiscale Geographic Space on the Heterogeneity of Crime Distribution

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