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Spatial lifecourse epidemiology is an interdisciplinary field that utilizes advanced spatial, location-based, and artificial intelligence technologies to investigate the long-term effects of environmental, behavioural, psychosocial, and biological factors on health-related states and events and the underlying mechanisms. With the growing number of studies reporting findings from this field and the critical need for public health and policy decisions to be based on the strongest science possible, transparency and clarity in reporting in spatial lifecourse epidemiologic studies is essential. A task force supported by the International Initiative on Spatial Lifecourse Epidemiology (ISLE) identified a need for guidance in this area and developed a Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) Statement. The aim is to provide a checklist of recommendations to improve and make more consistent reporting of spatial lifecourse epidemiologic studies. The STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement for cohort studies was identified as an appropriate starting point to provide initial items to consider for inclusion. Reporting standards for spatial data and methods were then integrated to form a single comprehensive checklist of reporting recommendations. The strength of our approach has been our international and multidisciplinary team of content experts and contributors who represent a wide range of relevant scientific conventions, and our adherence to international norms for the development of reporting guidelines. As spatial, location-based, and artificial intelligence technologies used in spatial lifecourse epidemiology continue to evolve at a rapid pace, it will be necessary to revisit and adapt the ISLE-ReSt at least every 2-3 years from its release.

Sex work around the world takes place under conditions of structural violence and vulnerability. The México-U.S. border region is characterized by the presence of factors that increase the risk for health harms among female sex workers (FSW); located in this context, the risk environments of Tijuana and Ciudad Juárez have similar yet distinct characteristics that influence how risk is produced and experienced among FSWs. Exploring the ways in which FSWs enact agency in risk environments can illustrate how environmental characteristics shape perceived risks and the strategies that FSWs develop to manage them. This approach also identifies the limits that are placed by environmental characteristics over the capacity for harm reduction and prevention practices among FSWs. We analyzed the role of agency in the work environments of female sex workers and its relationship with risk perception and management in the cities of Tijuana and Cd. Juárez.

Community dependence reflects individuals' daily needs for community space and facilities and is closely related to the accurate allocation of social resources and human quality of life. This study examines the differences in community use and dependence among different income groups at the overall and community levels from the perspective of the microscale individual activity space. Compared with non‐low‐income groups, low‐income groups are generally more dependent on their communities during their daily lives. However, these differences are diverse among different communities; they are more moderate among different income groups in communities located in central city areas and significant among different income groups in suburban affordable housing communities. The regression models reveal that in addition to income, the characteristics of activities and built environment attributes also affect individuals' community dependence. Social policies should prioritize the quality and quantity of community resources for vulnerable suburban groups. [ABSTRACT FROM AUTHOR]

AbstractAdvanced techniques in Geographic Information Systems (GIS) currently provide one of the most promising approaches to investigating the health impacts of green space. The GIS solution of deriving causally relevant green space exposures still faces challenges from the arbitrary determination of the contextual unit size. This paper presents an in-depth and rigorously defined analytical framework to illustrate the effect of buffer and to find the optimal buffer radius. We employed a cross-sectional study with 980 participants in Hong Kong to validate our analytics. The home locations, socio-demographic attributes, and self-reported health statuses were collected from questionnaires. Residence-based green space exposures were derived using an exhaustive range of buffer radii and fine-grained remote sensing data. Participants’ overall health was modeled through logistic regression to validate our analytics. Our results clearly indicate the U-shaped p-value curves along the gradient of buffer radii, which illustrates the optimal buffer sizes in pertinent geographic contexts. We also observed two independent ranges of optimal buffer sizes. Our work elicited the effect of buffer size on green space exposure measures and essential implications for a range of health geography and environmental health studies that require accurate green space exposure measures. [ABSTRACT FROM AUTHOR]

This study seeks to examine the possible impacts of sociodemographic factors, individual mobility patterns, and daily activities on individual exposure to COVID-19 risk when assessed by different risk measures. Taking Hong Kong as the study area, we first model the risk of COVID-19 using a density-based approach and a network-based approach and reveal the differences in the spatial distributions of COVID-19 transmission risk they obtained. Then, using two-day individual Global Positioning System trajectory data and travel diaries collected from individuals in two communities, we measure individual exposure to COVID-19 transmission risk based on their mobility patterns and reveal the disparities in COVID-19 risk exposure among residents of different demographic groups and neighborhoods when conducting different activities. This study reveals the differences in the spatial patterns of COVID-19 transmission risk when the risk is conceptualized by density and network. It demonstrates the power of a spatial network approach in enhancing our understanding of individual exposure to infectious diseases. This study also advances the existing literature by exploring COVID-19 risk exposure, considering individual daily mobility and addressing the uncertain geographic context problem in analyzing individual COVID-19 risk exposure. [ABSTRACT FROM AUTHOR]

Developing walkable urban environments is crucial for enhancing urban liveability and sustainability. To evaluate the chrono‐urbanism status, this study combines conventional census and mapping data with social media check‐in big data. A composite index is proposed, which assesses the accessibility of essential urban functions under the 5/10/15‐minute chrono‐thresholds. This index incorporates five categories of urban functions (public transit, living, education, health care and entertainment) in Macau. Utilising check‐in information from Sina Weibo, the level of attraction for different urban functions is integrated into the calculation. The findings highlight significant disparities in chrono‐urbanism status, with regions inhabited by lower socio‐economic status residents being more vulnerable than the affluent institutional and commercial centre of Macau. Leveraging social media big data, this study provides valuable insights to urban planners and policy‐makers, enabling them to understand the overall chrono‐urbanism status and target interventions for developing a more sustainable and liveable urban environment in Macau. [ABSTRACT FROM AUTHOR]

Rapid morphological and socioeconomic changes have accelerated the urbanization process and urban land use transformation in China. Megacities comprise clusters of urban cities and exhibit both newly formed and well-developed urban land use development beyond administrative boundaries. It is necessary to distinguish the changing effects of spatial-varying driving factors on newly formed urban land uses from well-developed built-up areas in megacities. This study proposed a multi-spatial urbanization framework to quantify region-level socioeconomics, cluster-level ecological morphologies, and grid-level urban functional morphologies. A three-level Bayesian hierarchical model was developed to investigate the impacts of multi-spatial driving factors on urban land use transformation in megacities. The study period focused on the urbanization process between 2000 and 2018 in Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Results revealed that compared with well-developed urban built-up land, changing impacts of three-level driving factors in urban land use transformation could be captured based on the proposed Bayesian hierarchical model. The region-level total population was associated with increasing possibilities in forming new residential land than the well-developed ones in 35 districts/counties/cities in GBA. Cluster-level ecological attributes with higher proportion, lower edge density of urban built areas, and lower-degree ecological complexity showed increasing probability on newly formed industrial and public land. Grid-level urban functional factors including public transportation density and shopping/dining distribution exhibited significantly decreasing probability (coefficients: −2.12 to −0.51) on contributing newly formed land uses compared with the well-developed areas, whereas business/industry distribution represented higher (coefficients: 0.99 and 0.15) and lower probabilities (coefficient: −0.22) of forming industrial/public land and residential land separately. This research shows a new attempt to distinguish multi-spatial morphological and socioeconomic effects in urban land use transformation in megacities. [ABSTRACT FROM AUTHOR]