International Journal of Health Geographics最新文献

{"title":"The population randomization observation process (PROP) assessment method: using systematic habitation observations of street segments to establish household-level epidemiologic population samples","authors":"M. Smart, R. Sadler, Alan Harris, Z. Buchalski, A. Pearson, C. Debra Furr-Holden","doi":"10.1186/s12942-019-0190-z","DOIUrl":"https://doi.org/10.1186/s12942-019-0190-z","url":null,"abstract":"","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12942-019-0190-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43960169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Google Earth™ and Geographical Information System data as method to delineate sample domains for an urban household surveys: the case of Maroua (Far North Region-Cameroon)","authors":"Ronald R. B. Ngom Vougat, Steven Chouto, Sylvain Aoudou Doua, R. Garabed, André Zoli Pagnah, B. Gonne","doi":"10.1186/s12942-019-0186-8","DOIUrl":"https://doi.org/10.1186/s12942-019-0186-8","url":null,"abstract":"","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2019-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12942-019-0186-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42121797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of the Australian Cancer Atlas: spatial modelling, visualisation, and reporting of estimates","authors":"Earl W. Duncan, S. Cramb, J. Aitken, K. Mengersen, P. Baade","doi":"10.1186/s12942-019-0185-9","DOIUrl":"https://doi.org/10.1186/s12942-019-0185-9","url":null,"abstract":"","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12942-019-0185-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41882150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A statistical downscaling approach for generating high spatial resolution health risk maps: a case study of road noise and ischemic heart disease mortality in Melbourne, Australia","authors":"I. Hanigan, T. Chaston, B. Hinze, M. Dennekamp, B. Jalaludin, Y. Kinfu, G. Morgan","doi":"10.1186/s12942-019-0184-x","DOIUrl":"https://doi.org/10.1186/s12942-019-0184-x","url":null,"abstract":"","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2019-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12942-019-0184-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42164101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The use of GPS data loggers to describe the impact of spatio-temporal movement patterns on malaria control in a high-transmission area of northern Zambia","authors":"M. Hast, K. Searle, M. Chaponda, J. Lupiya, J. Lubinda, J. Sikalima, Tamaki Kobayashi, T. Shields, M. Mulenga, J. Lessler, W. Moss","doi":"10.1186/s12942-019-0183-y","DOIUrl":"https://doi.org/10.1186/s12942-019-0183-y","url":null,"abstract":"","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2019-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12942-019-0183-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47545914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The linkage between the perception of neighbourhood and physical activity in Guangzhou, China: using street view imagery with deep learning techniques.","authors":"Ruoyu Wang,&nbsp;Ye Liu,&nbsp;Yi Lu,&nbsp;Yuan Yuan,&nbsp;Jinbao Zhang,&nbsp;Penghua Liu,&nbsp;Yao Yao","doi":"10.1186/s12942-019-0182-z","DOIUrl":"https://doi.org/10.1186/s12942-019-0182-z","url":null,"abstract":"<p><strong>Background: </strong>Neighbourhood environment characteristics have been found to be associated with residents' willingness to conduct physical activity (PA). Traditional methods to assess perceived neighbourhood environment characteristics are often subjective, costly, and time-consuming, and can be applied only on a small scale. Recent developments in deep learning algorithms and the recent availability of street view images enable researchers to assess multiple aspects of neighbourhood environment perceptions more efficiently on a large scale. This study aims to examine the relationship between each of six neighbourhood environment perceptual indicators-namely, wealthy, safe, lively, depressing, boring and beautiful-and residents' time spent on PA in Guangzhou, China.</p><p><strong>Methods: </strong>A human-machine adversarial scoring system was developed to predict perceptions of neighbourhood environments based on Tencent Street View imagery and deep learning techniques. Image segmentation was conducted using a fully convolutional neural network (FCN-8s) and annotated ADE20k data. A human-machine adversarial scoring system was constructed based on a random forest model and image ratings by 30 volunteers. Multilevel linear regressions were used to examine the association between each of the six indicators and time spent on PA among 808 residents living in 35 neighbourhoods.</p><p><strong>Results: </strong>Total PA time was positively associated with the scores for \"safe\" [Coef. = 1.495, SE = 0.558], \"lively\" [1.635, 0.789] and \"beautiful\" [1.009, 0.404]. It was negatively associated with the scores for \"depressing\" [- 1.232, 0.588] and \"boring\" [- 1.227, 0.603]. No significant linkage was found between total PA time and the \"wealthy\" score. PA was further categorised into three intensity levels. More neighbourhood perceptual indicators were associated with higher intensity PA. The scores for \"safe\" and \"depressing\" were significantly related to all three intensity levels of PA.</p><p><strong>Conclusions: </strong>People living in perceived safe, lively and beautiful neighbourhoods were more likely to engage in PA, and people living in perceived boring and depressing neighbourhoods were less likely to engage in PA. Additionally, the relationship between neighbourhood perception and PA varies across different PA intensity levels. A combination of Tencent Street View imagery and deep learning techniques provides an accurate tool to automatically assess neighbourhood environment exposure for Chinese large cities.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"18 1","pages":"18"},"PeriodicalIF":4.9,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12942-019-0182-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41216749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a comprehensive set of GPS-based indicators reflecting the multidimensional nature of daily mobility for applications in health and aging research.","authors":"Michelle Pasquale Fillekes,&nbsp;Eleftheria Giannouli,&nbsp;Eun-Kyeong Kim,&nbsp;Wiebren Zijlstra,&nbsp;Robert Weibel","doi":"10.1186/s12942-019-0181-0","DOIUrl":"https://doi.org/10.1186/s12942-019-0181-0","url":null,"abstract":"<p><strong>Background: </strong>GPS tracking is increasingly used in health and aging research to objectively and unobtrusively assess individuals' daily-life mobility. However, mobility is a complex concept and its thorough description based on GPS-derived mobility indicators remains challenging.</p><p><strong>Methods: </strong>With the aim of reflecting the breadth of aspects incorporated in daily mobility, we propose a conceptual framework to classify GPS-derived mobility indicators based on their characteristic and analytical properties for application in health and aging research. In order to demonstrate how the classification framework can be applied, existing mobility indicators as used in existing studies are classified according to the proposed framework. Then, we propose and compute a set of selected mobility indicators based on real-life GPS data of 95 older adults that reflects diverse aspects of individuals' daily mobility. To explore latent dimensions that underlie the mobility indicators, we conduct a factor analysis.</p><p><strong>Results: </strong>The proposed framework enables a conceptual classification of mobility indicators based on the characteristic and analytical aspects they reflect. Characteristic aspects inform about the content of the mobility indicator and comprise categories related to space, time, movement scope, and attribute. Analytical aspects inform how a mobility indicator is aggregated with respect to temporal scale and statistical property. The proposed categories complement existing studies that often underrepresent mobility indicators involving timing, temporal distributions, and stop-move segmentations of movements. The factor analysis uncovers the following six dimensions required to obtain a comprehensive view of an older adult's daily mobility: extent of life space, quantity of out-of-home activities, time spent in active transport modes, stability of life space, elongation of life space, and timing of mobility.</p><p><strong>Conclusion: </strong>This research advocates incorporating GPS-based mobility indicators that reflect the multi-dimensional nature of individuals' daily mobility in future health- and aging-related research. This will foster a better understanding of what aspects of mobility are key to healthy aging.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"18 1","pages":"17"},"PeriodicalIF":4.9,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12942-019-0181-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41216750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating the size of urban populations using Landsat images: a case study of Bo, Sierra Leone, West Africa.","authors":"Roger Hillson,&nbsp;Austin Coates,&nbsp;Joel D Alejandre,&nbsp;Kathryn H Jacobsen,&nbsp;Rashid Ansumana,&nbsp;Alfred S Bockarie,&nbsp;Umaru Bangura,&nbsp;Joseph M Lamin,&nbsp;David A Stenger","doi":"10.1186/s12942-019-0180-1","DOIUrl":"https://doi.org/10.1186/s12942-019-0180-1","url":null,"abstract":"<p><strong>Background: </strong>This is the third paper in a 3-paper series evaluating alternative models for rapidly estimating neighborhood populations using limited survey data, augmented with aerial imagery.</p><p><strong>Methods: </strong>Bayesian methods were used to sample the large solution space of candidate regression models for estimating population density.</p><p><strong>Results: </strong>We accurately estimated the population densities and counts of 20 neighborhoods in the city of Bo, Sierra Leone, using statistical measures derived from Landsat multi-band satellite imagery. The best regression model proposed estimated the latter with an absolute median proportional error of 8.0%, while the total population of the 20 neighborhoods was estimated with an error of less than 1.0%. We also compare our results with those obtained using an empirical Bayes approach.</p><p><strong>Conclusions: </strong>Our approach provides a rapid and effective method for constructing predictive models for population densities and counts utilizing remote sensing imagery. Our results, including cross-validation analysis, suggest that masking non-urban areas in the Landsat section images prior to computing the candidate covariate regressors should further improve model generality.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"18 1","pages":"16"},"PeriodicalIF":4.9,"publicationDate":"2019-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12942-019-0180-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37414832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Access to and availability of exercise facilities in Madrid: an equity perspective.","authors":"Luis Cereijo, Pedro Gullón, Alba Cebrecos, Usama Bilal, Jose Antonio Santacruz, Hannah Badland, Manuel Franco","doi":"10.1186/s12942-019-0179-7","DOIUrl":"10.1186/s12942-019-0179-7","url":null,"abstract":"<p><strong>Background: </strong>Identifying socioeconomic determinants that are associated with access to and availability of exercise facilities is fundamental to supporting physical activity engagement in urban populations, which in turn, may reduce health inequities. This study analysed the relationship between area-level socioeconomic status (SES) and access to, and availability of, exercise facilities in Madrid, Spain.</p><p><strong>Methods: </strong>Area-level SES was measured using a composite index based on seven sociodemographic indicators. Exercise facilities were geocoded using Google Maps and classified into four types: public, private, low-cost and sessional. Accessibility was operationalized as the street network distance to the nearest exercise facility from each of the 125,427 residential building entrances (i.e. portals) in Madrid. Availability was defined as the count of exercise facilities in a 1000 m street network buffer around each portal. We used a multilevel linear regression and a zero inflated Poisson regression analyses to assess the association between area-level SES and exercise facility accessibility and availability.</p><p><strong>Results: </strong>Lower SES areas had a lower average distance to the closest facility, especially for public and low-cost facilities. Higher SES areas had higher availability of exercise facilities, especially for private and seasonal facilities.</p><p><strong>Conclusion: </strong>Public and low-cost exercise facilities were more proximate in low SES areas, but the overall number of facilities was lower in these areas compared with higher SES areas. Increasing the number of exercise facilities in lower SES areas may be an intervention to improve health equity.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"18 1","pages":"15"},"PeriodicalIF":3.0,"publicationDate":"2019-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6604462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37112000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Urban Liveability Index: developing a policy-relevant urban liveability composite measure and evaluating associations with transport mode choice.","authors":"Carl Higgs, Hannah Badland, Koen Simons, Luke D Knibbs, Billie Giles-Corti","doi":"10.1186/s12942-019-0178-8","DOIUrl":"10.1186/s12942-019-0178-8","url":null,"abstract":"<p><strong>Background: </strong>Designing healthy, liveable cities is a global priority. Current liveability indices are aggregated at the city-level, do not reflect spatial variation within cities, and are often not aligned to policy or health.</p><p><strong>Objectives: </strong>To combine policy-relevant liveability indicators associated with health into a spatial Urban Liveability Index (ULI) and examine its association with adult travel behaviours.</p><p><strong>Methods: </strong>We developed methods to calculate spatial liveability indicators and the ULI for all residential addresses in Melbourne, Australia. Associations between the address-level ULI and adult travel behaviours from the 2012-2014 Victorian Integrated Survey of Travel and Activity (VISTA) (n = 12,323) were analysed using multilevel logistic regression. Sensitivity analyses to evaluate impact of methodological choices on distribution of liveability as assessed by the ULI and associations with travel mode choice were also conducted.</p><p><strong>Results: </strong>Liveability estimates were calculated for 1,550,641 residential addresses. ULI scores were positively associated with active transport behaviour: for each unit increase in the ULI score the estimated adjusted odds ratio (OR) for: walking increased by 12% (95% Credible Interval: 9%, 15%); cycling increased by 10% (4%, 17%); public transport increased by 15% (11%, 19%); and private vehicle transport decreased by 12% (- 9%, - 15%).</p><p><strong>Conclusions: </strong>The ULI provides an evidence-informed and policy-relevant measure of urban liveability, that is significantly and approximately linearly associated with adult travel behaviours in the Melbourne context. The ULI can be used to evaluate progress towards implementing policies designed to achieve more liveable cities, identify spatial inequities, and examine relationships with health and wellbeing.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"18 1","pages":"14"},"PeriodicalIF":3.0,"publicationDate":"2019-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6558748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37323945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}