{"title":"探索童工及其相关因素的空间非平稳性:印度多尺度地理加权回归研究","authors":"Tom Cunningham , Wendy Olsen , Nuno Pinto","doi":"10.1016/j.apgeog.2024.103363","DOIUrl":null,"url":null,"abstract":"<div><p>The relationships between various factors and child labour have been explored in the literature but, despite findings that suggest the predictive factors of child labour can vary according to context, there has been little research that has used spatial methods of analysis or attempted to estimate local relationships between covariates and the prevalence of child labour. This paper seeks to address this knowledge gap by using geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models. Using India 2011 Census Data as a case study, the findings show that GWR and MGWR models both perform significantly better than a global regression model across the whole of India. The study also finds significant spatial non-stationarity in the relationships between child labour and its covariates, with the association between district-level child labour rates and both the Muslim population and the child sex ratio found to have opposite directions in different parts of the country. Using MGWR, it was also possible to demonstrate that different covariates interact with child labour at various spatial scales, suggesting that interventions aiming to address varying aspects of the child labour problem may need to be deployed at different administrative levels to maximise their efficacy.</p></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0143622824001681/pdfft?md5=918fc3443d1dee029f1ffd9b2fae5ecf&pid=1-s2.0-S0143622824001681-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Exploring spatial non-stationarity of child labour and its related factors: A multiscale geographically weighted regression study of India\",\"authors\":\"Tom Cunningham , Wendy Olsen , Nuno Pinto\",\"doi\":\"10.1016/j.apgeog.2024.103363\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The relationships between various factors and child labour have been explored in the literature but, despite findings that suggest the predictive factors of child labour can vary according to context, there has been little research that has used spatial methods of analysis or attempted to estimate local relationships between covariates and the prevalence of child labour. This paper seeks to address this knowledge gap by using geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models. Using India 2011 Census Data as a case study, the findings show that GWR and MGWR models both perform significantly better than a global regression model across the whole of India. The study also finds significant spatial non-stationarity in the relationships between child labour and its covariates, with the association between district-level child labour rates and both the Muslim population and the child sex ratio found to have opposite directions in different parts of the country. Using MGWR, it was also possible to demonstrate that different covariates interact with child labour at various spatial scales, suggesting that interventions aiming to address varying aspects of the child labour problem may need to be deployed at different administrative levels to maximise their efficacy.</p></div>\",\"PeriodicalId\":48396,\"journal\":{\"name\":\"Applied Geography\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0143622824001681/pdfft?md5=918fc3443d1dee029f1ffd9b2fae5ecf&pid=1-s2.0-S0143622824001681-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Geography\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0143622824001681\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Geography","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0143622824001681","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY","Score":null,"Total":0}
Exploring spatial non-stationarity of child labour and its related factors: A multiscale geographically weighted regression study of India
The relationships between various factors and child labour have been explored in the literature but, despite findings that suggest the predictive factors of child labour can vary according to context, there has been little research that has used spatial methods of analysis or attempted to estimate local relationships between covariates and the prevalence of child labour. This paper seeks to address this knowledge gap by using geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models. Using India 2011 Census Data as a case study, the findings show that GWR and MGWR models both perform significantly better than a global regression model across the whole of India. The study also finds significant spatial non-stationarity in the relationships between child labour and its covariates, with the association between district-level child labour rates and both the Muslim population and the child sex ratio found to have opposite directions in different parts of the country. Using MGWR, it was also possible to demonstrate that different covariates interact with child labour at various spatial scales, suggesting that interventions aiming to address varying aspects of the child labour problem may need to be deployed at different administrative levels to maximise their efficacy.
期刊介绍:
Applied Geography is a journal devoted to the publication of research which utilizes geographic approaches (human, physical, nature-society and GIScience) to resolve human problems that have a spatial dimension. These problems may be related to the assessment, management and allocation of the world physical and/or human resources. The underlying rationale of the journal is that only through a clear understanding of the relevant societal, physical, and coupled natural-humans systems can we resolve such problems. Papers are invited on any theme involving the application of geographical theory and methodology in the resolution of human problems.