Pub Date : 2023-06-17DOI: 10.1007/s10109-023-00416-x
K. Iles, S. Kedzior
{"title":"Operationalizing participation: experiences and perspectives of participatory GIS program coordinators","authors":"K. Iles, S. Kedzior","doi":"10.1007/s10109-023-00416-x","DOIUrl":"https://doi.org/10.1007/s10109-023-00416-x","url":null,"abstract":"","PeriodicalId":47245,"journal":{"name":"Journal of Geographical Systems","volume":"16 1","pages":"539 - 565"},"PeriodicalIF":2.9,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81588371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-15DOI: 10.1007/s10109-023-00418-9
Daniela Arias-Molinares, J. García-Palomares, Gustavo Romanillos, J. Gutiérrez
{"title":"Uncovering spatiotemporal micromobility patterns through the lens of space–time cubes and GIS tools","authors":"Daniela Arias-Molinares, J. García-Palomares, Gustavo Romanillos, J. Gutiérrez","doi":"10.1007/s10109-023-00418-9","DOIUrl":"https://doi.org/10.1007/s10109-023-00418-9","url":null,"abstract":"","PeriodicalId":47245,"journal":{"name":"Journal of Geographical Systems","volume":"18 1","pages":"403 - 427"},"PeriodicalIF":2.9,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85336702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-14DOI: 10.1007/s10109-023-00413-0
Kevin Credit, Matthew Lehnert
{"title":"A structured comparison of causal machine learning methods to assess heterogeneous treatment effects in spatial data","authors":"Kevin Credit, Matthew Lehnert","doi":"10.1007/s10109-023-00413-0","DOIUrl":"https://doi.org/10.1007/s10109-023-00413-0","url":null,"abstract":"","PeriodicalId":47245,"journal":{"name":"Journal of Geographical Systems","volume":"2 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87662566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-14DOI: 10.1007/s10109-023-00412-1
Umut Türk, John Östh
{"title":"Introducing a spatially explicit Gini measure for spatial segregation","authors":"Umut Türk, John Östh","doi":"10.1007/s10109-023-00412-1","DOIUrl":"https://doi.org/10.1007/s10109-023-00412-1","url":null,"abstract":"","PeriodicalId":47245,"journal":{"name":"Journal of Geographical Systems","volume":"63 1","pages":"469 - 488"},"PeriodicalIF":2.9,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84933997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-05DOI: 10.1007/s10109-023-00415-y
Aynaz Lotfata, Stefanos Georganos
The increase in physical inactivity prevalence in the USA has been associated with neighborhood characteristics. While several studies have found an association between neighborhood and health, the relative importance of each component related to physical inactivity or how this value varies geographically (i.e., across different neighborhoods) remains unexplored. This study ranks the contribution of seven socioecological neighborhood factors to physical inactivity prevalence in Chicago, Illinois, using machine learning models at the census tract level, and evaluates their predictive capabilities. First, we use geographical random forest (GRF), a recently proposed nonlinear machine learning regression method that assesses each predictive factor's spatial variation and contribution to physical inactivity prevalence. Then, we compare the predictive performance of GRF to geographically weighted artificial neural networks, another recently proposed spatial machine learning algorithm. Our results suggest that poverty is the most important determinant in the Chicago tracts, while on the other hand, green space is the least important determinant in the rise of physical inactivity prevalence. As a result, interventions can be designed and implemented based on specific local circumstances rather than broad concepts that apply to Chicago and other large cities.
Supplementary information: The online version contains supplementary material available at 10.1007/s10109-023-00415-y.
{"title":"Spatial machine learning for predicting physical inactivity prevalence from socioecological determinants in Chicago, Illinois, USA.","authors":"Aynaz Lotfata, Stefanos Georganos","doi":"10.1007/s10109-023-00415-y","DOIUrl":"10.1007/s10109-023-00415-y","url":null,"abstract":"<p><p>The increase in physical inactivity prevalence in the USA has been associated with neighborhood characteristics. While several studies have found an association between neighborhood and health, the relative importance of each component related to physical inactivity or how this value varies geographically (i.e., across different neighborhoods) remains unexplored. This study ranks the contribution of seven socioecological neighborhood factors to physical inactivity prevalence in Chicago, Illinois, using machine learning models at the census tract level, and evaluates their predictive capabilities. First, we use geographical random forest (GRF), a recently proposed nonlinear machine learning regression method that assesses each predictive factor's spatial variation and contribution to physical inactivity prevalence. Then, we compare the predictive performance of GRF to geographically weighted artificial neural networks, another recently proposed spatial machine learning algorithm. Our results suggest that poverty is the most important determinant in the Chicago tracts, while on the other hand, green space is the least important determinant in the rise of physical inactivity prevalence. As a result, interventions can be designed and implemented based on specific local circumstances rather than broad concepts that apply to Chicago and other large cities.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s10109-023-00415-y.</p>","PeriodicalId":47245,"journal":{"name":"Journal of Geographical Systems","volume":" ","pages":"1-21"},"PeriodicalIF":2.9,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10241140/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10090783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-28DOI: 10.1007/s10109-023-00407-y
Shih-Lung Shaw
Time geography was conceptualized in the 1960s when the technology was very different from what we have today. Conventional time-geographic concepts therefore were developed with a focus on human activities and interactions in physical space. We now live in a smart, connected, and dynamic world with human activities and interactions increasingly taking place in virtual space enabled by modern information and communications technology. Coupled with recent advances in sensing and mobile technologies, it is now feasible to collect human dynamics data in both physical and virtual spaces with unprecedented spatial and temporal details in the so-called Big Data era. The Big Data era brings both opportunities and challenges to time geography. While the unprecedented data collected in the Big Data era can serve as useful data sources to time-geographic research, we also notice that some classical concepts in time geography are insufficient to properly handle human dynamics in today's hybrid physical-virtual world in many cases. This paper first discusses the evolving human dynamics enabled by technological advances to illustrate different types of hybrid physical-virtual space performed through internet applications, digital twins, and augmented reality/virtual reality/metaverse. We then review the classical time-geographic concepts of constraints, space-time path, space-time prism, bundle, project/situation, and diorama in a hybrid physical-virtual world to discuss potential extensions of some classical time-geographic concepts to bolster human dynamics research in today's hybrid physical-virtual world.
{"title":"Time geography in a hybrid physical-virtual world.","authors":"Shih-Lung Shaw","doi":"10.1007/s10109-023-00407-y","DOIUrl":"10.1007/s10109-023-00407-y","url":null,"abstract":"<p><p>Time geography was conceptualized in the 1960s when the technology was very different from what we have today. Conventional time-geographic concepts therefore were developed with a focus on human activities and interactions in physical space. We now live in a smart, connected, and dynamic world with human activities and interactions increasingly taking place in virtual space enabled by modern information and communications technology. Coupled with recent advances in sensing and mobile technologies, it is now feasible to collect human dynamics data in both physical and virtual spaces with unprecedented spatial and temporal details in the so-called Big Data era. The Big Data era brings both opportunities and challenges to time geography. While the unprecedented data collected in the Big Data era can serve as useful data sources to time-geographic research, we also notice that some classical concepts in time geography are insufficient to properly handle human dynamics in today's hybrid physical-virtual world in many cases. This paper first discusses the evolving human dynamics enabled by technological advances to illustrate different types of hybrid physical-virtual space performed through internet applications, digital twins, and augmented reality/virtual reality/metaverse. We then review the classical time-geographic concepts of constraints, space-time path, space-time prism, bundle, project/situation, and diorama in a hybrid physical-virtual world to discuss potential extensions of some classical time-geographic concepts to bolster human dynamics research in today's hybrid physical-virtual world.</p>","PeriodicalId":47245,"journal":{"name":"Journal of Geographical Systems","volume":" ","pages":"1-18"},"PeriodicalIF":2.9,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10139915/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9692277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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