Geographers play important roles in public health research, particularly in understanding healthcare accessibility, utilisation, and individual healthcare experiences. Most accessibility studies have benefited from the increased sophistication of geographic information systems (GIS). Some studies have been enhanced with semi-structured in-depth interviews to understand individual experiences of people as they access healthcare. However, few accessibility studies have explicitly utilised individual in-depth interview data in the construction of new GIS accessibility measures. Using mixed methods including GIS analysis and individual data from semi-structured in-depth interviews, we offer satisfaction-adjusted distance as a new way of conceptualising accessibility in GIS. Based on fieldwork in a predominantly lower-income community in Columbus, Ohio (USA), we find many residents felt neighbourhood healthcare facilities offered low-quality care, which suggested an added perceived distance as they attempt to access high-quality healthcare facilities. The satisfaction-adjusted distance measure accounts for the perceived distance some residents feel as they search for high-quality healthcare in lower-income urban neighbourhoods. In moving beyond conventional GIS and re-conceptualising accessibility in this way, we offer a more realistic portrayal of the issues lower-income urban residents face as they attempt to access high-quality healthcare facilities. The work has theoretical implications for conceptualising healthcare accessibility, advances the mixed-methodologies literature, and argues for a more equitable distribution of high-quality healthcare in urban neighbourhoods.

Ocean warming endangers coastal ecosystems through increased risk of infectious disease, yet detection, surveillance, and forecasting of marine diseases remain limited. Eelgrass (Zostera marina) meadows provide essential coastal habitat and are vulnerable to a temperature-sensitive wasting disease caused by the protist Labyrinthula zosterae. We assessed wasting disease sensitivity to warming temperatures across a 3500 km study range by combining long-term satellite remote sensing of ocean temperature with field surveys from 32 meadows along the Pacific coast of North America in 2019. Between 11% and 99% of plants were infected in individual meadows, with up to 35% of plant tissue damaged. Disease prevalence was 3Â higher in locations with warm temperature anomalies in summer, indicating that the risk of wasting disease will increase with climate warming throughout the geographic range for eelgrass. Large-scale surveys were made possible for the first time by the Eelgrass Lesion Image Segmentation Application, an artificial intelligence (AI) system that quantifies eelgrass wasting disease 5000Â faster and with comparable accuracy to a human expert. This study highlights the value of AI in marine biological observing specifically for detecting widespread climate-driven disease outbreaks.Disease outbreaks frequently cause rapid declines of host populations, transforming community structure and ecosystem functioning. Outbreaks that affect foundation or keystone species have particularly widespread and long-lasting consequences. Prominent examples include the ecological extinction of chestnut trees in eastern U.S. forests from chestnut blight (Ellison et al. 2005); decimation of at least 20 species of sea-stars in the eastern Pacific due to sea-star wasting disease

We utilized a participatory mapping approach to collect point locations, photographs, and descriptive data about select built environment stressors identified and prioritized by community residents living in the Proctor Creek Watershed, a degraded, urban watershed in Northwest Atlanta, Georgia. Residents (watershed researchers) used an indicator identification framework to select three watershed stressors that influence urban livability: standing water, illegal dumping on land and in surface water, and faulty stormwater infrastructure. Through a community–university partnership and using Geographic Information Systems and digital mapping tools, watershed researchers and university students designed a mobile application (app) that enabled them to collect data associated with these stressors to create a spatial narrative, informed by local community knowledge, that offers visual documentation and representation of community conditions that negatively influence the environment, health, and quality of life in urban areas. By elevating the local knowledge and lived experience of community residents and codeveloping a relevant data collection tool, community residents generated fine-grained, street-level, actionable data. This process helped to fill gaps in publicly available datasets about environmental hazards in their watershed and helped residents initiate solution-oriented dialogue with government officials to address problem areas. We demonstrate that community-based knowledge can contribute to and extend scientific inquiry, as well as help communities to advance environmental justice and leverage opportunities for remediation and policy change.

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