Introduction: advances in geospatial analysis for health research

In the past decade, studies and practices of applying geospatial analysis and GIS-based technology to epidemiology and public health studies have flourished. This special issue intends to sample some recent advances in this interdisciplinary domain, from theories to applications. A majority of papers in this collection are from a forum titled ‘Advances in Geospatial Analysis for Health Research’ held in Jiangxi Normal University in the summer of 2019, along with a few from other authors.

Compared with a previous special issue of Annals of GIS with a similar theme published in 2015, while the topics covered by current issue still fall into the four general aspects: communicable diseases, environmental health, healthcare services, and data infrastructure (Shi and Kwan 2015), some highlights of current issue reflect new waves in the domain. First, while it is unfortunate that due to the original planning and time limitation, this special issue does not include papers that directly cover the ongoing COVID-19 pandemic, two out of the eight papers in this issue are about communicable diseases, and they can be generally inspiring to researches on such diseases. Li, Shi, and Li in their ‘Integration of spatialization and individualization: the future of epidemic modelling for communicable diseases’ lay out a fundamental statement that the integration of spatialization and individualization is where the geospatial conceptualization, methodology, and technique, and conventional epidemic modelling really meet, and is at least one major future direction of this interdisciplinary exploration. In ‘Spatial prediction of flea index of transmitting plague based on environmental similarity’, Du, Zhu, and Wang demonstrate how to apply the Third Law of Geography (Zhu et al. 2018) to the prediction of risk factors in the transmission of a communicable disease. The profoundness of this innovative application, especially its contribution to the theoretical construction of spatial epidemiology as a research field, perhaps can be fully appreciated only after years when we look back. This latter paper is undoubtedly also highly relevant to environmental health studies.

Then, in the environmental health aspect, where the geospatial analysis has a long tradition, we have four papers and each represents a new trend in the area. In ‘Spatiotemporally Varying Coefficients (STVC) model: a Bayesian local regression to detect spatial and temporal nonstationarity in variables relationships’, Song, Shi, and Wang presents a case study of a new modelling framework recently proposed by the authors (Song et al. 2019). The framework comprehensively and sophistically handles spatiotemporal information in environmental health studies. Such a framework comes out in a good timing. On the one hand, researchers have increasingly realized that to achieve accurate estimation of the environmental exposure, both spatial information and temporal information are critical, and on the other hand, spatiotemporal data of patients, population, and environment become increasingly available. This situation is represented by the ongoing US National Institutes of Health (NIH) programme, ‘Integration of Individual Residential Histories into Cancer Research’ (National Institutes of Health 2017). However, currently methodological options for handling spatiotemporal data are still few, and a framework like STVC is in high demand. Another trend in the environmental health research area, and actually a trend in the entire spatial epidemiology and spatial public health research area, is the expansion of research target from physical health to mental health. This is represented by Persad’s paper ‘Spatiotemporal analysis of mental illness and the impact of marginalization-based factors: a case study of Ontario, Canada’. Besides going deeper in methodology and going wider to include more health conditions, using new technologies to convey environmental health information to policymakers and public is another big topic demanding much research, which is covered by the paper of Delmelle and his colleagues, ‘A web-based spatial decision support system for monitoring the risk of water contamination in private wells’. The culmination of this kind of innovation in this special issue is ‘Urban physical environment sensing using street view imagery for public health studies’, authored by Kang and his colleagues. The paper introduces the novel idea and techniques of extracting health-related environmental information, e.g., greenness, condition of the built environment, and mental feeling, from the street view data, such as those of the Google Street View. The sheer details and amount of the data, augmented particularly by the data’s human perspective (unlike the satellite imagery's view from above), should make such data and techniques to have a great potential in the environmental health area.

Jia’s paper, ‘Evaluating the effectiveness of the Hospital Referral Region (HRR) boundaries: a pilot study in Florida’, updates readers about healthcare service zoning, one of the most forefront and debatable topics in healthcare service research that deeply involves geography and geospatial analysis. Last but not least, the paper of Chen et al., ‘Enhancing the U.S. TBI data infrastructure: geospatial perspective’, brings about new ideas on the construction of data infrastructure, a unique but critical issue in applying geospatial analysis to health research.

While the collection of papers in this special issue feature a great diversity, again, they are a small sample of a rapidly growing field, but we do hope readers find them seminal and inspiring. Finally, we want to extend our sincere gratitude and appreciation to the School of Geography and Environment, Jiangxi Normal University, for its support, including financial support, which eventually made the forum in 2019 summer and this special issue possible.

Disclosure statement

No potential conflict of interest was reported by the authors.