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Advanced 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 article draws on recent studies to argue that researchers need to be attentive to the limits of the neighborhood effect as conventionally understood. It highlights the complexities of contextual influences and the challenges in accurately representing and measuring individual exposures to those influences. Specifically, it discusses the idiosyncratic and multidimensional nature of contextual effects, the temporal complexities of contextual influences, the frame dependence of exposure measures, selective mobility bias, and publication bias in neighborhood effects research. It also discusses how contextual uncertainties could be mitigated in future research (e.g., through collecting and using high-resolution space-time data and moving toward frame-independent exposure measures with results that are not affected by how data are organized with respect to space and time). [ABSTRACT FROM AUTHOR]

The "mobility turn" in environmental health studies promoted the integrated 3S (GIS, GPS, and remote sensing) approach in the study of urban residents' exposure to green space and corresponding health outcomes. However, few studies have examined the uncertainty induced by contextual settings when measuring people's exposure to green space using the conventional and integrated 3S approaches. In this paper, we compared the differences in green space exposure obtained from different geographic contexts using residence-based and mobility-based methods, multiple spatial resolutions, and buffer zones. We collected 7-day GPS trajectories from 208 participants at the 1-minute temporal resolution in Hong Kong. Entire Hong Kong's green space was delineated using multiple remote sensing data sources at 3 m, 10 m, and 30 m spatial resolutions. Lastly, the residence-based and mobility-based measurements of exposure to green space were calculated for each participant using 100 m, 300 m, and 500 m buffer zones at three spatial resolutions. Descriptive analyses, t-tests, and logistic regression were employed to examine the influence of different contextual settings on different measurements of green space exposure and their health effects. The results indicate multiple significant disparities. The most striking difference is that mobility-based measurements of exposure to green space are significantly higher than those of residence-based measurements, manifesting the uncertain geographic context problem (UGCoP). For future studies, we suggest using mobility-based measurements of exposure to green space, smaller buffer zones, and finer spatial resolutions, which enable more accurate measurements of green space exposure for the study of green space's health effects. [ABSTRACT FROM AUTHOR]

The uncertain geographic context problem (UGCoP), first articulated by Kwan (2012; The uncertain geographic context problem. Annals of the Association of American Geographers, 102 (5), 958–968), refers to the problem that findings about the effects of area-based contextual variables on individual behaviors or outcomes may be affected by how contextual units (e.g., neighborhoods) are geographically delineated and the extent to which these areal units deviate from the true geographic context. It is a significant methodological problem because it means that analytical results can differ for different delineations of contextual units even if everything else is the same. Drawing upon Kwan (2012) and recent social science studies (especially environmental health and neighborhood effects research), this article further elaborates on the nature of the UGCoP and explores how recent advances in geographical information system (GIS) and geospatial technologies can help address the problem. It discusses possible means for mitigating the UGCoP, especially with regard to the collection of detailed individual space–time data with global positioning systems, construction of individual activity spaces, and the use of qualitative and web-based GIS to capture people's activity locations and everyday experiences. It also discusses the challenges for future research that seeks to address the UGCoP. [ABSTRACT FROM AUTHOR]

Any study that examines the effects of area-based attributes on individual behaviors or outcomes faces another fundamental methodological problem besides the modifiable areal unit problem (MAUP). It is the problem that results about these effects can be affected by how contextual units or neighborhoods are geographically delineated and the extent to which these areal units deviate from the true geographic context. The problem arises because of the spatial uncertainty in the actual areas that exert the contextual influences under study and the temporal uncertainty in the timing and duration in which individuals experienced these contextual influences. Using neighborhood effects and environmental health research as a point of departure, this article clarifies the nature and sources of this problem, which is referred to as the uncertain geographic context problem (UGCoP). It highlights some of the inferential errors that the UGCoP might cause and discusses some means for mitigating the problem. It reviews recent studies to show that both contextual variables and research findings are sensitive to different delineations of contextual units. The article argues that the UGCoP is a problem as fundamental as the MAUP but is a different kind of problem. Future research needs to pay explicit attention to its potential confounding effects on research results and to methods for mitigating the problem. [ABSTRACT FROM AUTHOR]

Ignoring people's daily mobility and exposures to nonresidential contexts may lead to erroneous results in epidemiological studies of people's exposures to and the health impact of environmental factors. This paper identifies and describes a phenomenon called neighborhood effect averaging, which may significantly confound the neighborhood effect as a result of such neglect when examining the health impact of mobility-dependent exposures (e.g., air pollution). Several recent studies that provide strong evidence for the neighborhood effect averaging problem (NEAP) are discussed. The paper concludes that, due to the observed attenuation of the neighborhood effect associated with people's daily mobility, increasing the mobility of those who live in disadvantaged neighborhoods may be helpful for improving their health outcomes.

Traditionally, static units of analysis such as administrative units are used when studying obesity. However, using these fixed contextual units ignores environmental influences experienced by individuals in areas beyond their residential neighborhood and may render the results unreliable. This problem has been articulated as the uncertain geographic context problem (UGCoP). This study investigates the UGCoP through exploring the relationships between the built environment and obesity based on individuals' activity space. First, a survey was conducted to collect individuals' daily activity and weight information in Guangzhou in January 2016. Then, the data were used to calculate and compare the values of several built environment variables based on seven activity space delineations, including home buffers, workplace buffers (WPB), fitness place buffers (FPB), the standard deviational ellipse at two standard deviations (SDE2), the weighted standard deviational ellipse at two standard deviations (WSDE2), the minimum convex polygon (MCP), and road network buffers (RNB). Lastly, we conducted comparative analysis and regression analysis based on different activity space measures. The results indicate that significant differences exist between variables obtained with different activity space delineations. Further, regression analyses show that the activity space delineations used in the analysis have a significant influence on the results concerning the relationships between the built environment and obesity. The study sheds light on the UGCoP in analyzing the relationships between obesity and the built environment., Competing Interests: The authors declare no conflicts of interest.