مدل‌سازی و تحلیل عوامل مؤثر بر وقوع جرم در مناطق حاشیه‌نشینِ شهر کاشان

فصلنامه علمی برنامه ریزی منطقه ای

مقاله 7، دوره 10، شماره 37، اردیبهشت 1399، صفحه 91-108   

نوع مقاله: مقاله پژوهشی

نویسندگان

محسن شاطریان^{* 1}؛ رسول حیدری²؛ محمود شاطریان³؛ کامران دولتیاران⁴

¹استاد گروه جغرافیا و اکوتوریسم، دانشگاه کاشان، کاشان، ایران

²استادیار گروه جغرافیا و اکوتوریسم، دانشگاه کاشان، کاشان، ایران

³استادیار گروه حقوق، دانشکده علوم انسانی، دانشگاه کاشان، کاشان، ایران

⁴کارشناس ارشد جغرافیا و برنامه ریزی شهری، دانشگاه کاشان، کاشان، ایران

تاریخ دریافت: 25 مهر 1397،  تاریخ بازنگری: 10 آذر 1398،  تاریخ پذیرش: 26 مهر 1398 

چکیده

       امروزه عوامل و عناصر متنوعی در بروز آسیب‌های اجتماعی و جرائم در شهرها اثرگذارند، حاشیه‌نشینی و اسکان غیررسمی از چالش‌های مهم ناشی از فقر فرهنگی و اقتصادی و عصر جهانی‌شدن می‌باشد که درشدت بخشیدن به آسیب‌های اجتماعی اهمیت بسزایی دارد، به‌طوری‌که می‌توان گفت حاشیه‌نشینی و جرم علت و معلول یکدیگرند. هدف پژوهش حاضر بررسی و تحلیل عوامل مؤثر بر وقوع جرائم در مناطق حاشیه‌نشین شهر کاشان می‌باشد. روش تحقیق توصیفی- تحلیلی و دارای ماهیتی کاربردی می‌باشد. حجم نمونه با استفاده از نرم افزار Sample Power 160 پرسش‌نامه برآورد گردید. برای تجزیه‌وتحلیل توصیفی و استنباطی داده‌ها از آزمون آماری T-Test در نرم‌افزار SPSS و جهت تبیین و مدل‌سازی اثرات نیز از مدل‌سازی معادلات ساختاری در نرم‌افزار AMOS استفاده شد. نتایج حاصل از آزمون T تک نمونه‌ای نمایانگر معنی‌دار بودن اثرات اجتماعی، اقتصادی، فرهنگی و کالبدی با وقوع جرم در محلات حاشیه‌نشین دارد. تحلیل یافته‌های منتج از مدل‌سازی معادلات ساختاری بیانگر این است که از بین شاخص‌های موردسنجش شاخص اجتماعی بیشترین بار عاملی را با وزن رگرسیونی (87/0) و مقدار خطای (000/0) P value = در وقوع جرائم به خود اختصاص داده است.

کلیدواژه‌ها

حاشیه‌نشینی؛ جرم؛ معادلات ساختاری؛ آزمون تی؛ شهر کاشان

عنوان مقاله [English]

Modeling and analyzing the factors affecting the occurrence of crime in informal settlements ( case study: kashan informal settlement

نویسندگان [English]

mohsen shaterian¹؛ rasoul heidary²؛ Mahmood shaterian³؛ Kamran Dolatyaran⁴

¹Professor, geography and urban planning, University of kashan, Kashan, Iran

²Assistance Professor, geography and urban planning, University of kashan, Kashan, Iran

³Assistance Professor, Human Coleg, University of kashan, Kashan, Iran

⁴MS, geography and urban planning,University of kashan, Kashan, Iran

چکیده [English]

Nowadays, various factors and elements affect social damage and crime in the cities, informal settlement and informal settlement are one of the important challenges of cultural and economic poverty and the age of globalization, which is important in the heightening of social harm, so that it can be said that the marginalization and crime of the cause and Disabled are different. The purpose of this study was to investigate and analyze the factors affecting the occurrence of crimes in peripheral areas of Kashan. The research method is descriptive-analytic and has a practical nature. The sample size was estimated using Sample Power 160 questionnaire. For analyzing the descriptive and inferential statistics, t-test was used in SPSS software. Structural equation modeling in AMOS software was used to explain and model the effects. The results of single-sample t-test showed significant social, economic, cultural and physical effects of crime in marginalized neighborhoods. Analysis of the results of structural equation modeling shows that among the indexed indicators, the social index has the highest factor load with regression weight (0.87) (p value = 000 / p value =) in the occurrence of crimes.
Extended Abstract
 
Introduction:
        Attention to the crime and its roots in the major cities is one of the Interests in various fields such as urban geography, urban sociology, social ecology, environmental criminology, and criminology. More criminal events occurring in areas where land features, amenities and possess a certain population. The Industrial Revolution led to the influx of urban migrants in the world, the group attacked the outskirts of marginalization provides field built and it has become a problem for everyone in the world. Investigation shows that the marginalization of these areas has always been the main center of deviations, the incidence of social ills and crime and those that threaten the security of cities. Marginalization is end modern industrial civilization and is one of the manifestations of rapid urbanization. Marshall Klynard in defining cultural marginalization says with a set of values ​​and norms Khrdh marginalization in neighborhoods with poor hygiene, social deviations and other features such as social isolation is associated. The first time the relationship between crime and the built environment design and urban planning in the field of book Death and Life of Great America Cities by Jane Jacobs was raised. He stressed that the diversity of land use and a high level of pedestrian activity is an opportunity to monitor natural (unofficial) are the most important feature is neighborhood safety. Shaw and McKay believe that the high levels of instability, ethnic diversity and economic losses with low levels of social cohesion and informal social control there is a positive correlation neighborhood. In other words, social stigma about it discusses how the inhabitants of a neighborhood, to maintain order in society organize. This theory holds that crime happens to cluster in areas that have three key characteristics including low socio-economic base, ethnic heterogeneity, and is residential mobility and high population density. Density and instability, population, social network Urban impair makes ethnic diversity leads to a lack of effective communication community members together and ultimately poor resident’s sources of capital and little to change their environment to the instability help and this high rate of crime and delinquency lead. In other words, neighborhoods with high crime rates and cultural socio-economic base of those who are not in good condition, demographics are not so homogenous distribution and high density.The city of Kashan, consequently the rapid expansion of urbanization phenomenon of marginalization in the old neighborhoods and surrounding areas faced. This study seeks to answer the question of the relationship between socio-economic, cultural and physical characteristics of marginalized and commit crimes there.
Methodology:
          This research is a descriptive and analytical survey. Collection tools in this study, library, athletics, and use of scientific resources related organizations. Random sampling. The data collected by means of a questionnaire that forms the background check and the related literature and related Parameters collection centers and marginalized Kashan crime is regulated.
The results of each test are shown in separate tables. The population of the research areas covered by the marginal areas, including households located in neighborhoods Lthr, pumps and Gazrgah Farmhouse, Arab neighborhoods and towns that have a population of 9,500 people in February twenty-two picks. The sample power of software to determine sample size used with regard to alpha 0.5, 0.95and be 0.82 confidence level and one-sided test sample size of 160 was calculated. To analyze the data, descriptive and inferential statistical tests T-Test in SPSS software and to explain and model the effects of structural equation modeling was used in AMOS software.
Results and discussion:
         The first hypothesis social factors of crime in the marginalized areas assessed, in order to measure meaningful social effects of crime in the marginalized regions of one sample T-test was used. As is evident in all social indicators on crime in urban areas and increase the effectiveness of a variety of crimes. The results of tests T Table 2 shows the elements of the crime of statements of unemployment, population density, friendships, bad, violence, dropping out of school, social, family, education, harassment, divorce and lack of educational services affecting many according to the charges, and higher mean of mediocrity, the indicators are generally different underlying crimes. The second hypothesis examined the economic effects of crime in Kashan is excluded. The results of T-test one sample Table (3) to measure economic variables and their role in the crime shows that if you specify all subtypes of these items, including lack of investment, lack of income, nutrition inaccurate, lack of expertise, burgundy infrastructure, buying allow 5% significant relationship between economic factors and crime. The third hypothesis to investigate the effects of body mass in squatter areas of Kashan's results of T-test one sample for Table (4) shows that inadequate housing lack of lighting, lack of recreation, tissue aging, lack of sanitation, poor design of buildings lack Fencing, no warning signs, no buildings, all the crime and the high mean effective due to the number of mediocrity (3) represents the hypothesis of the study, the significance of the relationship between physical and crime. The results obtained show that the second-order factor model of social factors accounted for the highest load factor weighing 0.87and most of the other factors that affect crime. After the cause of social, physical and cultural factors are the common factor loadings vary from 850 in the second and third. Finally, the economic factor load factor affecting crime indices 0.78 in fourth place in Kashan were excluded; So it can be said that among the four factors of social crime in urban areas most affected elements of the crime. The burden of the social index marker hypothesis is confirmed.
Conclusion:
       This study examines the relationship between marginalization and crime in high crime neighborhoods in the city of Kashan in relation to social, economic, cultural and physical. In this regard, four hypotheses were tested. The first hypothesis of the role of social factors on crime in the city of Kashan analyzed that due to the higher index (3.66) of the standard value (3) indicates the significance of the relationship between crime and marginalization and social factors the first hypothesis was accepted. The second hypothesis to evaluate the economic indicators relating to the offenses and according to the comparison with a significance level of 5% results showed the significant alpha level of crime is related to marginalization and economic factors and the second hypothesis is confirmed. The third hypothesis regarding marginalization and physical factors of the crime and marginalization test results showed a significant relationship between body mass index is associated with the hypothesis was confirmed. The fourth hypothesis cultural indices of crime examined the results of T-test one sample showed that due to the significant level (sig) in comparison with 95% and the amount of error of 5% cultural factors averaging 3.61 on crime in the area affected and the significance of the relationship between marginalization and crime fourth hypothesis was confirmed. Overall, one-sample T-test results indicated that a direct relationship between crime and marginalization are four indices And the average of all indices studied is equal to 62/3 from the middle (number 3) was higher, indicating a high crime situation in the studied areas. Also, structural equation modeling was used to identify the variables and factors affecting the occurrence of different crimes. According to the findings of the second-order factor model, the results showed that the social factor had the highest factor load (0.87) and then physical factors. And cultural, with regression weights of 0.85 in the second and third, and economic variable with the factor of 0.76 in fourth.

کلیدواژه‌ها [English]

informal settlements, mass, Structural Equations, Single Sample T Test, Kashan City

مراجع

1. Adel, H., Salheen, M., & Mahmoud, R. A. (2015): Crime in relation to urban design. Case study: The Greater Cairo Region. Ain Shams Engineering Journal.
2. Alavi, Moussa (2013): "Structural Equation Modeling in Health Sciences Education Research Introduction and its Application", Iranian Journal of Medical Education, Volume 13, Number 6, pp. 530-519. (In persian)
3. Amaldam, S. (2011): Housing in third world; Annual Report of World Bank. P 4.
4. Anderson, J. and J.M. MacDonald, and R. Bluthenthal, and S Ashwood, (2013): Reducing crime by shaping the built environment with zoning: An empirical study of Los Angeles. University of Pennsylvania Law Review, 161, 699–756.
5. Bane.R.and Rawal. A. slums. A case study of Anand city. (2002): Available. at; www. I boro. as uk /wedc /papers
6.  Barzegar, Sadegh, Heidari, Taghi, Anbarloo Alireza (1396): "Analysis of Informal Settlements with a Biodiversity Approach", Case Study of Zanjan Informal Settlements, Regional Planning Research, Ninth Year, No. 33, pp. 137-152(In persian) .
7.  Bayat, Bahram (2015): >> Analysis of Damages and Crimes in Suburbs of Tehran <<, Social Security Studies Quarterly, No. 4, pp. 20-1. (In persian)
8. Boshaq, Mohammad Reza, (2015): << Structural Equation Modeling in the Humanities >> 22, AMOS, Tehran, Sociology Publications. (In persian)
9. Buonanno, P., Fergusson, L. and Vargas, F. J. (2014): The Crime Kuznets Curve, Serie Documentos De Trabajo, No. 155.
10. Ceccato, V., & Lukyte, N. (2011): Safety and sustainability in a city in transition: the case of Vilnius, Lithuania. Cities, 28 (1), 83-94.
11. Dougherty, R. G. (2015): Social Disorganization, Extra-Curricular Activities, and Delinquency.Electronic Theses and Dissertations.Paper 2476. http://dc.etsu.edu/etd/2476.
12.  Hatami Nejad, Hossein, Mehdi, Ali, Bahmani Mahdian, Masoumeh (2014): "Attitude toward crime and committing crime in the suburbs of Shad Gholi Khan neighborhood of Qom", Geographical Research Quarterly, Vol. , Pp. 240-221. (In persian)
13.  Hatami Nejad, Hossein, Hatami Nejad, Hojjat, Farabi Asl, Nir, Sabet Koushaki Nian, Mojtaba, Fawadi, Fatemeh (2012): "Geographical Analysis on the Physical Impact of Urban Areas on Crime", Case Study: Informal Settlements Mashhad, Journal of Regional Planning, Second Year, No. 7, pp. 65-75. (In persian)
14. Hillier, Bill and Ozlem Sahbaz, (2010): High Resolution Analysis of Crime Patterns in Urban Street Networks: an initial statistical sketch from an ongoing study of a London borough, University College London, UK.
15.  Esfajir Abbasi, Ali Asghar, Victim of the Principle of Valiyallah (2016): << The Role of Social Components in Prevention of Urban Crime (Robbery and Drugs) >>, Case Study of Sari District Three Cities, Journal of Urban Sociological Studies, Volume 6, Number 20 Pp. 186-157.(In persian)
16.  Golchin, Masoud, Sabaheddin, Mafakheri (1396): "Studying the crime rate in the neighborhoods of Tehran and its effect on the trust of the interpersonal relationships of citizens", Urban Sociological Studies Quarterly, Eighth Year, No. 25, pp. 140- 117. (In persian)
17.  Goodarzi, Majid, Malaei, Maryam, Abdollahi, Masood (2016): << An Analysis of Urban Tourism Spaces in New Cities with a Sense of Women's Security >> Case Study: Baharestan New City, Regional Planning Journal, Sixth Year , No. 22, pp. 97- 108. (In persian)
18. Hirschfield, A. and M. Birkin, C. Brunsdon, and N. Malleson, and A. Newton, (2014): How Places Influence Crime: The Impact of Surrounding Areas on Neighborhood Burglary Rates in a British City, urban studies, 51 (5), pp.1057-1072.
19. Jonathan G. Allen, B.A. (2010): Assessing the Effects of Social Disorganization on Crime in Texas Border Counties: A Time-Series Cross-Sectional Analysis Fom 1990 to 2007, Master of Science, Graduate Council of Texas State University-San Marcos, San Marcos, Texas.
20. Kajalo, S. and A. Lindblom, (2015): Creating a safe and pleasant shopping environment: A retailer's view. Property Management, 33(3), 275–286.
21. Keri B. Burchfield (2009): Attachment as a source of informal social control in urban neighborhoods, Journal of Criminal Justice 37 :45 –54.
22.  Kiani Salmi, Sedigheh, Boshaq, Mohammadreza (2016): "Explaining the Effects of the Waterfowl Festival from the Perspective of Local Residents", Journal of Tourism Management Studies, Volume 11, Number 34, pp. 92-65. (In persian)
23. Kirschkamp, A. (2008): Contingency-Based View of Chief Executive Officers Early Warning Behavior. Gabler (GWV).
24. Kruger, T. (2012): Safer by design- towards effective crime prevention through environmental design in South Africa, CSIR, Pretoria, Vol, 12, PP 211-294
25. Lahmian, Reza (1396): "Investigating the Relationship between Design and Redesign of Urban Spaces and Crime Occurrence", Case Study: Babol, Journal of Regional Planning, Seventh Year, No. 25, pp. 166-155. (In persian)
26. Lockwood, B.; Groff, E.B.; Rengert, G.; Grunwald, H.E. (2014): The relationship between social distance and treatment attrition for juvenile offenders, The Journal of Urban Affairs, Vol. 37, No. 4, 462–477.
27. Louderback, E. R. (2015): Social Disorganization, Institutional Anomie and the Geographic Patterning of Instrumental Crime: ProgressTowards an Integrated Theory.Open Access Theses. Vol, 16, NO. 21, 510-549.
28. Martınez, Javier, Gora Mboupb, Richard Sliuzasa and Alfred Steina. (2008): Trends in urban and slum indicators across developing world cities, 1990–2003. Habitat International, Vol 32, pp 86–108.
29. Mohseni, Reza Ali, Nosrati Payani, Ezzatollah (2014): >> The Relationship of City to Crime and Social Injuries <<, Gorgan City Law Enforcement Affairs Second Year, No. 7. Pages 138-113. (In persian)
30.  Mohsin, Shahriar, Esfidani, Mohammad Rahim (1396): << Structural Equations Based on Partial Least Squares Approach with CD (Educational and Practical Software) with CD >> Second Edition, Tehran, Mehraban. (In persian)
31. Noonan, George A. (2017): A spatial analysis of the relationship between violent neighborhood crime rates and alternative gentrification indicators in Louisville, KY (2010-2016). (2017): Electronic Theses and Dissertations.Paper: 26-44 https://doi.org/10.18297/etd/2644
32. Ortiz, R. (2016). War on Drugs: Examining the Effects on Social Disorganization and Crime in Cities. In BS Master’s Theses and Projects.Item 42.
33. Salimi, Zahra (2016): "Assessment and Evaluation of Physical Resilience of Wasted Textures Against Earthquake", (Case Study: Central Tissue Locations of Bushehr City) M.Sc., Faculty of Natural Resources and Earth Sciences, Kashan University. (In persian)
34. Sanati Sharghi, Nader, Rouhollah Vahidi, Hamidi, Soheil (2016): "Structural Analysis of the Relationship between Suburbia and Crime", Ninth Year Social Discipline Research Quarterly, No. 3, pp. 82-58. (In persian)
35. Swatt, M. Varano, S. Uchida, C. Solomon, S. (2013): "Fear of Crime, Incivilities, and Collective Efficacy in four Miami", Journal of Criminal Justice, 41:1-11.
36. Sohn, Dong-Wook. (2016): Residential crimes and neighborhood built environment: Assessing the effectiveness of crime prevention through environmental design (CPTED), cities journal,52, pp.86-93.
37. Sypion-Dutkowska, N. & Leitner, M. (2017): Land Use Influencing the Spatial Distribution of Urban Crime: A Case Study of Szczecin, Poland, ISPRS Int. J. Geo-Inf. 2017, 6, 74; doi:10.3390/ijgi6030074.
38. Van Patten, Isaac T. Mc Keldin-Coner, Jennifer & Cox, Deana. (2010): A Micro-Spatial Analysis of Robbery: Prospective Hot Spotting in a Small City. Department of Criminal Justice, Radford University.19: 35-49
39. Wang, D., Ding, W., Lo, H., Morabito, M., Chen, P., Salazar, J, & Stepinski, T. (2013): Understanding the spatial distribution of crime based geospatial discriminative patterns. Computers, Environment and Urban Systems, 39: 93-106.
40. Yamamura, E. (2009): Formal and informal deterrents of crime in Japan: Roles of police and social capital revisited, The Journal of Socio-Economics, Vol. 38, pp. 611–621.
41. Zangi Abadi, Ali, Mubarak, Omid (2012): << Investigating the Factors Affecting the Suburbia Formation, Tabriz City and Its Consequences >> Case Study (Ahmadabad, Koi Beheshti, Khalilabad), Journal of Geography and Environmental Planning, Twenty-third Year , No. 1, pp. 67-80. (In persian)
42. Zakaria, S, Abdul Rahman, N, (2016): the mapping of spatial patterns of property crime in Malaysia: normal mixture model approach, journal of Business and social Development Volum11-1 :2016 , (1)4.
43. Zhong, H., Yin, J., Wu, J., Yao, S., Wang, Z., Lv, Z., & Yu, B. (2011): Spatial analysis for crime pattern of metropolis in transition using police records and GIS: a case study of Shanghai, China. International Journal of Digital Content Technology and its Applications, 5 (2): 93-1.