{"title":"利用地理加权回归分析加拿大安大略省的社会人口因素与 COVID-19 限制导致的流动性变化之间的关系","authors":"Ben Klar, Jason A. Gilliland, Jed A. Long","doi":"10.1111/cag.12879","DOIUrl":null,"url":null,"abstract":"<p>Transportation research has shown that socio-demographic factors impact people's mobility patterns. During the COVID-19 pandemic, some of these effects have changed in accordance with changing mobility needs adapting to the pandemic, including restrictions on in-person gatherings, closure of in-person businesses, and working from home. We investigate two gaps in current knowledge in this area of transportation research: to what extent the associations between socio-demographic factors and mobility metrics have changed, and how these associations vary across geographic space. We used aggregate deidentified cell tower location data to measure two mobility metrics—movement time and radius of gyration—and socio-demographic data from the 2016 Canadian Census to model these associations across Ontario, Canada in 2020 using a linear model and a geographically weighted regression model. We find that certain associations between socio-demographics and mobility have changed from what we previously observed before the pandemic, and we can see the variation of these associations across space. These findings will improve our understanding of how socio-demographic factors affect mobility patterns in different communities and demonstrate the importance of measuring these associations at a more fine-grained level using models that consider spatial variation to best reflect the nature of these associations.</p>","PeriodicalId":47619,"journal":{"name":"Canadian Geographer-Geographe Canadien","volume":"68 2","pages":"256-275"},"PeriodicalIF":1.4000,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cag.12879","citationCount":"0","resultStr":"{\"title\":\"Associations between socio-demographic factors and change in mobility due to COVID-19 restrictions in Ontario, Canada using geographically weighted regression\",\"authors\":\"Ben Klar, Jason A. Gilliland, Jed A. Long\",\"doi\":\"10.1111/cag.12879\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Transportation research has shown that socio-demographic factors impact people's mobility patterns. During the COVID-19 pandemic, some of these effects have changed in accordance with changing mobility needs adapting to the pandemic, including restrictions on in-person gatherings, closure of in-person businesses, and working from home. We investigate two gaps in current knowledge in this area of transportation research: to what extent the associations between socio-demographic factors and mobility metrics have changed, and how these associations vary across geographic space. We used aggregate deidentified cell tower location data to measure two mobility metrics—movement time and radius of gyration—and socio-demographic data from the 2016 Canadian Census to model these associations across Ontario, Canada in 2020 using a linear model and a geographically weighted regression model. We find that certain associations between socio-demographics and mobility have changed from what we previously observed before the pandemic, and we can see the variation of these associations across space. These findings will improve our understanding of how socio-demographic factors affect mobility patterns in different communities and demonstrate the importance of measuring these associations at a more fine-grained level using models that consider spatial variation to best reflect the nature of these associations.</p>\",\"PeriodicalId\":47619,\"journal\":{\"name\":\"Canadian Geographer-Geographe Canadien\",\"volume\":\"68 2\",\"pages\":\"256-275\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2023-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cag.12879\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Canadian Geographer-Geographe Canadien\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/cag.12879\",\"RegionNum\":4,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Geographer-Geographe Canadien","FirstCategoryId":"90","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/cag.12879","RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOGRAPHY","Score":null,"Total":0}
Associations between socio-demographic factors and change in mobility due to COVID-19 restrictions in Ontario, Canada using geographically weighted regression
Transportation research has shown that socio-demographic factors impact people's mobility patterns. During the COVID-19 pandemic, some of these effects have changed in accordance with changing mobility needs adapting to the pandemic, including restrictions on in-person gatherings, closure of in-person businesses, and working from home. We investigate two gaps in current knowledge in this area of transportation research: to what extent the associations between socio-demographic factors and mobility metrics have changed, and how these associations vary across geographic space. We used aggregate deidentified cell tower location data to measure two mobility metrics—movement time and radius of gyration—and socio-demographic data from the 2016 Canadian Census to model these associations across Ontario, Canada in 2020 using a linear model and a geographically weighted regression model. We find that certain associations between socio-demographics and mobility have changed from what we previously observed before the pandemic, and we can see the variation of these associations across space. These findings will improve our understanding of how socio-demographic factors affect mobility patterns in different communities and demonstrate the importance of measuring these associations at a more fine-grained level using models that consider spatial variation to best reflect the nature of these associations.