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Risk Areas for Property-related Crimes in Nan Municipality, Nan Province |
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| รหัสดีโอไอ | |
| Creator | Suphachot Rotrungrueang |
| Title | Risk Areas for Property-related Crimes in Nan Municipality, Nan Province |
| Contributor | Boonsiri Sukpromsun,Arthit Limpiyakorn |
| Publisher | School of Information and Communication Technology, University of Phayao |
| Publication Year | 2567 |
| Journal Title | The Journal of Spatial Innovation Development |
| Journal Vol. | 5 |
| Journal No. | 2 |
| Page no. | 105-122 |
| Keyword | Risk area of crime, Property theft, Spatial distribution |
| URL Website | https://ph01.tci-thaijo.org/index.php/jsid/index |
| Website title | The Journal of Spatial Innovation Development |
| ISSN | 2730-1494 |
| Abstract | Crime is a significant problem in Thailand, leading to damages to both life and property. The highest incidence of criminal cases occurs in the Nan municipality, Nan Province, particularly in cases of theft. The objectives of this research are as follows: 1) to study the spatial distribution patterns of areas at risk for property-related crimes, 2) to analyze the patterns of relationships contributing to property-related crimes and 3) to examine the directional trends in the spatial distribution of property-related crimes. The study aimed to examine property crime incidents from the year 2018 to 2022 using Kernel Density estimation to illustrate the risk levels of property crime occurrences. Additionally, it aimed to analyze the relationship between high-risk areas and physical factors such as population density, land use, road networks, and police stations using the Getis-Ord Gi* method. Furthermore, the study investigated the trends and directions of crime distribution using the Standard Deviational Ellipse (SDE) method.The study found that property crime incidents exhibited a dispersed pattern with varying risk levels. Areas at risk of property crimes are divided into areas with high, medium, low risk and no risk areas. which can be displayed in the colors on the map as red, orange, yellow and green, respectively. Green areas indicated low or no risk, where property crime incidents were absent. Analysis using the Getis-Ord Gi* method revealed that areas with high population density, urban areas, and secondary road networks posed higher risks for incidents. The findings of this study can be utilized to inform planning and surveillance efforts by law enforcement agencies or relevant organizations in preventing property crime occurrences. |
