Jing Yao

Background: Population demographic structures have become increasingly complex worldwide, posing new challenges for planning Emergency Medical Services (EMS) in ways to improve equality. Existing geographical studies have mainly focused on EMS equality in geographic accessibility or coverage, while demographic equality has received less attention. This study develops spatial optimization models that incorporate demographic equality into facility location planning. Methods: Two bi-objective spatial optimization models are developed to improve demographic equality, while maintaining efficiency provision of EMS. In detail, the two models have the same efficiency objective that is to maximize the service coverage ( Z ₁ ). Then, the first model (M1) addresses coverage-based demographic equality by minimizing disparities in coverage rates among population groups ( Z ₂ ). The second model (M2) focused on accessibility-based demographic equality by minimizing the maximum average travel time among demographic groups ( Z ₃ ). The models were applied to Wuhan, China, using age groups as the demographic representation and historical emergency medical service records to estimate spatially varying age-specific risks. Results: The empirical results showed that both models were effective in improving demographic equality, although clear trade-offs were observed between equality enhancement and service efficiency. For M1, the nine Pareto-optimal solutions indicated that coverage-based demographic inequality, measured by Z ₂ , decreased from 7.64% to 0.61%. This improvement in demographic equality, however, required an efficiency loss of up to 3.94% of total coverage. For M2, improving accessibility for the worst-served demographic group by 1.4 min ( Z ₃ ) required a remarkable trade-off. The equality-oriented solution requires a 3.98% of efficiency loss compared with the efficiency-oriented solution. Both models consistently identified southern Hongshan and northern Jiangxia as priority areas for new EMS stations. Conclusions: The proposed framework provides a transferable approach for integrating population-group equality into healthcare planning and supports more equitable allocation of EMS resources.

The Canterbury Earthquake Sequence (CES), which includes the 2010 and 2011 Christchurch earthquakes, is one of the deadliest disasters in New Zealand history. Following the CES, displacement of the affected population occurred, leading to an out‐migration from affected areas and changes to places of residence. This paper investigates the spatial changes in population following the CES, using a multiscale geographically weighted regression (MGWR) analysis approach to examine if there is a relationship between population change within the Canterbury region, and potential driving forces across two time periods: 2006–2013 and 2013–2018. The findings of this study could assist in informing future decision making and planning for earthquake events and to increase the effectiveness of land use policy decisions for post‐disaster recovery in New Zealand.

Background: Access as a primary indicator of Emergency Medical Service (EMS) efficiency has been widely studied over the last few decades. Most previous studies considered one-way trips, either getting ambulances to patients or transporting patients to hospitals. This research assesses spatiotemporal access to EMS at the shequ (the smallest administrative unit) level in Wuhan, China, attempting to fill a gap in literature by considering and comparing both trips in the evaluation of EMS access. Methods: Two spatiotemporal access measures are adopted here: the proximity-based travel time obtained from online map services and the enhanced two-step floating catchment area (E-2SFCA) which is a gravity-based model. First, the travel time is calculated for the two trips involved in one EMS journey: one is from the nearest EMS station to the scene (i.e. scene time interval (STI)) and the other is from the scene to the nearest hospital (i.e. transport time interval (TTI)). Then, the predicted travel time is incorporated into the E-2SFCA model to calculate the access measure considering the availability of the service provider as well as the population in need. For both access measures, the calculation is implemented for peak hours and off-peak hours. Results: Both methods showed a marked decrease in EMS access during peak traffic hours, and differences in spatial patterns of ambulance and hospital access. About 73.9% of shequs can receive an ambulance or get to the nearest hospital within 10 min during off-peak periods, and this proportion decreases to about 45.5% for peak periods. Most shequs with good ambulance access but poor hospital access are in the south of the study area. In general, the central areas have better ambulance, hospital and overall access than peripheral areas, particularly during off-peak periods. Conclusions: In addition to the impact of peak traffic periods on EMS access, we found that good ambulance access does not necessarily guarantee good hospital access nor the overall access, and vice versa.