{"title":"扫描统计在识别和分析印度拉贾斯坦邦侵害妇女犯罪热点中的应用","authors":"Poonam K. Saravag, Rushi Kumar B.","doi":"10.1007/s12061-024-09572-z","DOIUrl":null,"url":null,"abstract":"<div><p>Crime against women (CAW) is not a present-day problem but has been prevalent in the world through the ages and since the beginning of civilizations. The cases of CAW have been increasing in almost all parts of the world and India is no exception. The distribution of CAW cases has not been found uniform across the country. The evidence of heterogeneity of cases has been a concern. Rajasthan, the largest state in India, has witnessed a very high surge in CAW in recent years. Therefore, there arises a need to study and analyze the pattern of CAW to identify the areas with high intensity for prevention and control. The CAW data from the National Crime Records Bureau (NCRB) website for the period 2014 to 2021 and the population census data of 2011 are used for the analysis. The Statistical analysis software, SaTScan, is employed for hotspot (areas with a high concentration of crimes) detection. Python programming is used to compute the data’s trend or pattern through visualization and descriptive statistics. In addition, the simple exponential smoothing method is applied for predicting the CAW for the year 2021. Our work elucidates Jhalawar, Baran, Kota, Bundi, Sawai Madhopur, and Chittorgarh districts as consistently occurring hotspots of CAW in the state. A comparative study of the hotspots found is made with the result obtained from the descriptive analysis. The trend in the data explains the years 2017 and 2019 as trough and crest of CAW cases. The hotspot detected using the forecast value of 2021 appears to be the same districts as for the period 2014 to 2020. Our work concludes that the consistency and the most likely cluster of CAW are distributed distinctly. We also found that the hotspot of CAW is not by chance but has certain man-made reasons. Most of the clusters have been identified as districts sharing boundaries with adjacent states. This further implies that if sincere efforts to collaborate with the government of the adjacent states like Madhya Pradesh, Haryana, Punjab, Uttar Pradesh, and Gujarat, the incidences resulting in detrimental effects due to CAW could reduce effectively and significantly. Thus, our study may help the government, law enforcement agencies, police organizations, judiciaries, and other stakeholders to optimize their scarce resources most effectively to curb such incidents.</p></div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"17 3","pages":"963 - 982"},"PeriodicalIF":2.0000,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Application of Scan Statistics in Identification and Analysis of Hotspot of Crime against Women in Rajasthan, India\",\"authors\":\"Poonam K. Saravag, Rushi Kumar B.\",\"doi\":\"10.1007/s12061-024-09572-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Crime against women (CAW) is not a present-day problem but has been prevalent in the world through the ages and since the beginning of civilizations. The cases of CAW have been increasing in almost all parts of the world and India is no exception. The distribution of CAW cases has not been found uniform across the country. The evidence of heterogeneity of cases has been a concern. Rajasthan, the largest state in India, has witnessed a very high surge in CAW in recent years. Therefore, there arises a need to study and analyze the pattern of CAW to identify the areas with high intensity for prevention and control. The CAW data from the National Crime Records Bureau (NCRB) website for the period 2014 to 2021 and the population census data of 2011 are used for the analysis. The Statistical analysis software, SaTScan, is employed for hotspot (areas with a high concentration of crimes) detection. Python programming is used to compute the data’s trend or pattern through visualization and descriptive statistics. In addition, the simple exponential smoothing method is applied for predicting the CAW for the year 2021. Our work elucidates Jhalawar, Baran, Kota, Bundi, Sawai Madhopur, and Chittorgarh districts as consistently occurring hotspots of CAW in the state. A comparative study of the hotspots found is made with the result obtained from the descriptive analysis. The trend in the data explains the years 2017 and 2019 as trough and crest of CAW cases. The hotspot detected using the forecast value of 2021 appears to be the same districts as for the period 2014 to 2020. Our work concludes that the consistency and the most likely cluster of CAW are distributed distinctly. We also found that the hotspot of CAW is not by chance but has certain man-made reasons. Most of the clusters have been identified as districts sharing boundaries with adjacent states. This further implies that if sincere efforts to collaborate with the government of the adjacent states like Madhya Pradesh, Haryana, Punjab, Uttar Pradesh, and Gujarat, the incidences resulting in detrimental effects due to CAW could reduce effectively and significantly. Thus, our study may help the government, law enforcement agencies, police organizations, judiciaries, and other stakeholders to optimize their scarce resources most effectively to curb such incidents.</p></div>\",\"PeriodicalId\":46392,\"journal\":{\"name\":\"Applied Spatial Analysis and Policy\",\"volume\":\"17 3\",\"pages\":\"963 - 982\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Spatial Analysis and Policy\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12061-024-09572-z\",\"RegionNum\":4,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Spatial Analysis and Policy","FirstCategoryId":"90","ListUrlMain":"https://link.springer.com/article/10.1007/s12061-024-09572-z","RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
An Application of Scan Statistics in Identification and Analysis of Hotspot of Crime against Women in Rajasthan, India
Crime against women (CAW) is not a present-day problem but has been prevalent in the world through the ages and since the beginning of civilizations. The cases of CAW have been increasing in almost all parts of the world and India is no exception. The distribution of CAW cases has not been found uniform across the country. The evidence of heterogeneity of cases has been a concern. Rajasthan, the largest state in India, has witnessed a very high surge in CAW in recent years. Therefore, there arises a need to study and analyze the pattern of CAW to identify the areas with high intensity for prevention and control. The CAW data from the National Crime Records Bureau (NCRB) website for the period 2014 to 2021 and the population census data of 2011 are used for the analysis. The Statistical analysis software, SaTScan, is employed for hotspot (areas with a high concentration of crimes) detection. Python programming is used to compute the data’s trend or pattern through visualization and descriptive statistics. In addition, the simple exponential smoothing method is applied for predicting the CAW for the year 2021. Our work elucidates Jhalawar, Baran, Kota, Bundi, Sawai Madhopur, and Chittorgarh districts as consistently occurring hotspots of CAW in the state. A comparative study of the hotspots found is made with the result obtained from the descriptive analysis. The trend in the data explains the years 2017 and 2019 as trough and crest of CAW cases. The hotspot detected using the forecast value of 2021 appears to be the same districts as for the period 2014 to 2020. Our work concludes that the consistency and the most likely cluster of CAW are distributed distinctly. We also found that the hotspot of CAW is not by chance but has certain man-made reasons. Most of the clusters have been identified as districts sharing boundaries with adjacent states. This further implies that if sincere efforts to collaborate with the government of the adjacent states like Madhya Pradesh, Haryana, Punjab, Uttar Pradesh, and Gujarat, the incidences resulting in detrimental effects due to CAW could reduce effectively and significantly. Thus, our study may help the government, law enforcement agencies, police organizations, judiciaries, and other stakeholders to optimize their scarce resources most effectively to curb such incidents.
期刊介绍：
Description
The journal has an applied focus: it actively promotes the importance of geographical research in real world settings
It is policy-relevant: it seeks both a readership and contributions from practitioners as well as academics
The substantive foundation is spatial analysis: the use of quantitative techniques to identify patterns and processes within geographic environments
The combination of these points, which are fully reflected in the naming of the journal, establishes a unique position in the marketplace.
RationaleA geographical perspective has always been crucial to the understanding of the social and physical organisation of the world around us. The techniques of spatial analysis provide a powerful means for the assembly and interpretation of evidence, and thus to address critical questions about issues such as crime and deprivation, immigration and demographic restructuring, retailing activity and employment change, resource management and environmental improvement. Many of these issues are equally important to academic research as they are to policy makers and Applied Spatial Analysis and Policy aims to close the gap between these two perspectives by providing a forum for discussion of applied research in a range of different contexts
Topical and interdisciplinaryIncreasingly government organisations, administrative agencies and private businesses are requiring research to support their ‘evidence-based’ strategies or policies. Geographical location is critical in much of this work which extends across a wide range of disciplines including demography, actuarial sciences, statistics, public sector planning, business planning, economics, epidemiology, sociology, social policy, health research, environmental management.
FocusApplied Spatial Analysis and Policy will draw on applied research from diverse problem domains, such as transport, policing, education, health, environment and leisure, in different international contexts. The journal will therefore provide insights into the variations in phenomena that exist across space, it will provide evidence for comparative policy analysis between domains and between locations, and stimulate ideas about the translation of spatial analysis methods and techniques across varied policy contexts. It is essential to know how to measure, monitor and understand spatial distributions, many of which have implications for those with responsibility to plan and enhance the society and the environment in which we all exist.
Readership and Editorial BoardAs a journal focused on applications of methods of spatial analysis, Applied Spatial Analysis and Policy will be of interest to scholars and students in a wide range of academic fields, to practitioners in government and administrative agencies and to consultants in private sector organisations. The Editorial Board reflects the international and multidisciplinary nature of the journal.
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