Our call for papers on Geoprocessing in Public and Environmental Health is open!

This special issue is a collaboration between Mei-Po Kwan, Derek Karssenberg, Simon Scheider and the NEEDS team.

Since the introduction of geographic information systems (GIS) a few decades ago, this technology has radically altered numerous scientific domains with public and environmental health standing out. The emerging opportunities to integrate and process spatial information into health research provide a rich ground for understanding how physical and mental health outcomes are interwoven with place (Richardson et al. 2013). Some common themes in past (Rushton 2003) and recent research agendas (Kirby et al. 2017) can be recognized, such as the visualization of spatial distributions of diseases, the detection of disease clusters, the determination of spatial accessibility of health care facilities, and the identification of disease risk across space and over time.

Despite significant progress, however, numerous challenges remain in public and environmental health research (Kwan 2012, Kirby et al. 2017, Helbich 2018). First, we progressed from a data-poor to a data-rich society. While offering a great potential to develop new hypotheses, the volume and variety of spatial data—originating in databases with different spatial resolution, temporal granularity, and different semantics—make it possible to scale-up analysis across these sources, but at the same time make geospatial health research more challenging than ever. Second, driven by progress in data collection (e.g., tracking through Global Positioning Systems, GPS) in tandem with advances in information and communications technology (e.g., smartphone-based sensing), mobility-based approaches gain momentum as well as enable mobile computation and analysis with a high spatiotemporal resolution, complementing traditional place-based approaches to assessing environmental exposures. While the latter relies on aggregated data to model environmental exposures based on people’s residential neighborhood, the former considers exposures dynamically along people`s daily travel routes and over people`s life course. Third, to handle large amounts of data and to perform computationally intensive geospatial analyses, a tight integration between databases, GIS and statistical computing that utilizes high-performance computing approaches, such as parallelization and distributed computing, are needed for efficient and scalable geoprocessing. Addressing these and many other challenges might lead to significant progress in the fields of geographic information science, public and environmental health.

This Special Issue aims to stimulate discussions on the development and application of the latest GIS and data-driven methodologies to better understand health outcomes, the underlying mechanisms, and their dynamics over time and across space. It seeks to publish original research, review papers, methodology-oriented papers, and innovative applications. Through the combination of geographic information science with public and environmental health, significant contributions are expected from transdisciplinary approaches integrating health (register) data, increasingly available environmental data together with geospatial technologies (e.g., GIS, GPS) and data analytics (e.g., machine learning, Bayesian spatial and space-time models). Research opportunities also exist in health data linkage, integration, and in GIS-based exposure modeling and locational privacy for health studies. We hope that the contributions will support evidence-based public health policies in the long term. Our interest is in papers that cover a wide spectrum of methodological and domain-specific topics on physical and mental health, including, but not limited to, the following:

• Novel geoprocessing algorithms for health data
• GIS-based environmental exposure modeling
• Accessibility of health care facilities
• Disease clustering and surveillance
• Space-time personal environmental exposure and risk assessments
• System science approaches through spatial simulation modeling
• Space-time disease mapping
• High-performance computing for disease and environment data
• Geocoding and locational privacy
• Data linkage and integration of health data
• Uncertainty and the uncertain geographic context problem