Urban-rural disparities in spatiotemporal and multimodal accessibility to pharmacy care in Tartu County, Estonia

Abstract

Pharmacies are vital frontline healthcare providers, yet equitable access is often hindered by spatial and temporal disparities along the urban-rural continuum. Despite increasing research into healthcare access, few studies comprehensively examine the combined influence of multimodal transport and temporal variations across different settlement contexts. This thesis addresses this gap by assessing spatiotemporal and multimodal pharmacy accessibility in Tartu County, Estonia, specifically focusing on identifying urban-rural disparities. The thesis evaluates pharmacy accessibility across 24 hours in two-hour intervals, focusing on typical workdays and Sundays. Using Python for advanced geospatial processing, the research integrates pedestrian networks, public transport schedules, and population register data to model walking and public transit modes. Through descriptive statistics and spatial analysis, the study quantifies travel-time dynamics and identifies geographic disparities across the urban, peri-urban, and rural areas of Tartu County. Results show that static proximity measures underestimate accessibility inequalities because they ignore temporal and multimodal differences. Urban areas in Tartu generally maintain high accessibility through walking and public transport, whereas peri-urban and rural areas experience significant transit shortages, with the disparity becoming even greater on Sundays. Importantly, during late-night and early-morning hours, public transport is largely unavailable across the county, leaving peri-urban and rural residents especially underserved, while walkbased accessibility remains limited to the urban core. This study offers important insights for regional planners and policymakers aiming to improve equitable access to pharmacy services across different settlement types. Future research could examine winter conditions, additional transport modes such as cycling and private car use, and additional socio-demographic factors, such as age and income, to provide a more comprehensive understanding of urban–rural healthcare accessibility disparities.