ارائه یک روش سیستماتیک به منظور شناسایی و نمایش کانون های مکانی و زمانی جرم خیزی - مطالعه موردی: سرقت از منازل

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی سیستم‌های اطلاعات‌مکانی، دانشکده مهندسی عمران و حمل‌ونقل، دانشگاه اصفهان، اصفهان، ایران

2 استادیار گروه مهندسی نقشه‌برداری، دانشکده مهندسی عمران و حمل و نقل، دانشگاه اصفهان، اصفهان، ایران

3 دانشیار گروه مهندسی نقشه‌برداری، دانشکده مهندسی عمران و حمل و نقل، دانشگاه اصفهان، اصفهان، ایران

10.22131/sepehr.2022.252766

چکیده

بزهکاری مانند دیگر رفتارهای بشر دارای ظرف زمان و مکان است و اعمال مجرمانه میتوانند در کانونهای جرمخیز در یک مکان و زمان واحد قرار گیرند. هدف این مطالعه، ارائه یک روش سیستماتیک برای شناسایی و نمایش کانونهای مکانی و زمانی جرمخیزی است. این روش، اطلاعات مکانی و زمانی را به­گونه­ای ترکیب میکند تا بهطور شهودی پروفایل زمانی کانونهای جرمخیز در سطح خرد و کلان (ساعت، سال) قابل ارزیابی باشد. بدین منظور تعداد 5573 فقره سرقت از منازل مسکونی در شهر بوستون آمریکا در بازه زمانی سال 2015 تا 2018 بهعنوان جامعه آماری مورد مطالعه قرار گرفت و از قابلیت‌‌های GIS برای انجام آزمونهای آماری و گرافیکی بهمنظور شناسایی و نمایش کانونهای مکانی و زمانی جرمخیزی استفاده شد. در این تحقیق چهار کانون جرمخیز در خصوص سرقت از منازل مسکونی با استفاده از آزمون تراکم کرنل شناسایی شد. نتایج نشان داد که 78% سرقت از منازل مسکونی در این چهار کانون مکانی جرمخیزی رخ میدهد که تنها 25% از کل مساحت منطقه مورد مطالعه را در برمیگیرند. یافته­های تحقیق نشان داد که ترکیب کانون مکانی زمانی جرمخیزی با تحلیل زمانی به صورت یکجا و بدون در نظر گرفتن کانونهای مکانی جرمخیز در بازه­های ماهانه، روزانه و ساعتی تفاوتهای قابل ملاحظه دارد؛ اما نتایج تحلیل سالیانه هر چهار کانون در چهار سال مورد بررسی نشان داد که بیشترین میزان وقوع سرقت در سال 2016 و کم­ترین میزان سرقت در سال 2018 بوده است. همچنین نتایج نشان داد که بیشترین فراوانی وقوع سرقت از منازل مسکونی در کانونی رخ داده است که کوچک‌ترین مساحت را در بین کانونها داشته است.

کلیدواژه‌ها


عنوان مقاله [English]

Presentation a systematic method for identifying and displaying spatial and temporal of hot crime spots: A case study of residential burglary

نویسندگان [English]

  • Sara Haghbayan 1
  • Behnam Tashayo 2
  • Mehdi Momeni 3
1 PhD student,Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan
2 Assistant Professor, Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan
3 Associate Professor, Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan
چکیده [English]

Extended Abstract
Introduction
Today, one of the most complex issues in most countries is the high crime rate and the increase in social anomalies in them. One of these anomalies is residential burglary, which is one of the most widespread crimes in most countries of the world. Because spatial and time play a very important and undeniable role in the formation of hot crime spots such as residential burglary therefore, by identifying the spatial and temporal of hot crime spots can be largely prevented. Previous studies have focused more on identifying and analyzing spatial crime hotspots and performing temporal analysis of crimes independently of spatial crime hotspots. However, in order to prevent the occurrence of these crimes in the future, a combination of time and spatial hot crime spots is needed to provide a more complete and accurate analysis. The aim of this study is to provide a systematic method for combining spatial and temporal information of residential burglary. The proposed method is based on spatial analysis and allows investigating the temporal distribution of events in hot crime spots. For this purpose, GIS capabilities have been used to perform statistical and graphical tests to identify and display crime hotspots. The results showed that hotspots follow a spatially clustered and temporally focused pattern. The research findings showed that the highest frequency of burglary is in hot spot No.4 in 2016 August, on Wednesday at 8 am, and the lowest frequency of burglary is in hot spot No.1 in 2018 January, on Sunday at 4 am.
 
Materials & Methods
The statistical tests used in this study include mean center, standard deviation ellipse test for clustering. The first step in identifying crime hotspots is to use the tests for clustering. For this purpose, in this study, the method of the average nearest neighbor is used. The results of residential burglary test for clustering showed that this crime is a cluster pattern in the study area. After proving to be clustered, graphical methods including point map display and kernel density have been used to display the hot crime spots. The results of the kernel density test cause to the identification and display of four spatial the hot crime spots in the study area.
The data used in this research include information on the time, place and type of crimes in the years 2015, 2016, 2017, 2018. The total number of crimes is 319073, of which 5573 were related to residential burglary, which was used as a statistical population in this study.
 
Results & Discussion
Statistical analysis was performed over a period of four years, which is equivalent to 48 months and 35064 around the clock for each hot crime spot. The results show that the highest incidence of crime in hot spot No.4 is equivalent to 1172 cases of residential burglary, which of all these four hot spot has a smaller area equivalent to 1117 hectares. Temporal analyzes of hot crime spots were performed annually, monthly, weekly and hourly. The results of the annual analysis of all four hot spots show that the highest rate of residential burglary is in 2016 and the lowest rate is in 2018.
The findings of this study show that the combination of spatial and temporal of hot crime spots analysis lump-sum by temporal analysis regardless of the spatial hot spots in monthly, daily and hourly intervals is significantly different. The combination of spatial and temporal of hot crime spots in the monthly interval shows that the maximum and minimum rates of residential burglary per month are different in these four hot spots.  The highest number of residential burglary respectively occurred in hot spot No. 1 in October, in hot spot No. 2 in August, in hot spot No. 3 in June and in hot spot No. 4 in August. However, the results of the statistical analysis of time without considering the spatial hot crime spots show that August is the highest and April is the lowest. Daily statistical analysis shows that the highest number of residential burglary occurs in hot spot No. 1 and hot spot No. 3 on Friday, while in hot spot No. 2 it is Thursday and in hot spot No. 4 it is Wednesday. This analysis is different with a general daily analysis that shows Friday as the highest number of occurrences. Hourly analysis also shows that the peak of residential burglary in all four centers is at different hours; Thus, the peak of residential burglary areas in the study area is in the hot spot No. 1 hour 22, in the hot spot No. 2 hours 17, in hot spot No. 3 hours 12, in the hot spot No. 4 hours 8. However, statistical analysis of the time without considering the spatial hot spot shows the peak of residential burglary at 12 noon.
 
Conclusion
In this study, a new framework for the simultaneously displaying the pattern of crimes in two dimensions of spatial and time was presented, which can be used to identify the pattern of distribution of spatial and temporal of hot crime spots. The results of kernel density estimation analysis are four spatial-temporal crime hotspots where the spatial hotspot distribution pattern is clustered and the temporal of hot crime spots distribution pattern is focused. The results show that 78% of burglaries occur in these four crime hotspot, which cover only 25% of the total area of the study area. Therefore, by identifying the spatial and temporal of hot spots, crime can be largely prevented. This method is used to identify and display any type of crime in each study area and allows the identification and display of the combination of spatial and temporal hot crime spots.

کلیدواژه‌ها [English]

  • Spatial and temporal of hot crime spots
  • GIS
  • Residential burglary
  • Kernel Density Estimation (KDE)
  • Average Nearest Neighbor (ANN)
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