Jizhe Xia

1 Rapid urbanisation has become one of the most prominent features of global change in the 21 st century (Zhang et al. 2018). Currently, the global rate of urban expansion is approximately twice th...

Ensuring equitable access to health services is crucial for public welfare and social equity, and is a key objective of the United Nations’ Sustainable Development Goals (SDGs). However, existing datasets often define hospital accessibility using travel time to hospitals in geographic dimension only, without considering the supply (hospital capacity) and demand (population distribution) dynamics. To overcome this limitation, we developed and validated a national-scale 1 km map of both hospital travel time and hospital accessibility in China. We used the Gaussian two-step floating catchment area (Ga2SFCA) model to calculate hospital accessibility, incorporating hospital capacity and service population. Various file types and statistical indicators are provided, making the dataset highly accessible for non-specialists. The dataset fills the gap in publicly available nationwide hospital accessibility data for China and can serve as a critical tool in optimizing resource allocation and developing targeted strategies to improve healthcare equity.

Air temperature (Ta) is essential for studying surface processes and human activities, particularly agricultural cultivation, which is strongly influenced by temperature. Remote sensing techniques that integrate multi-source data can estimate Ta with a high degree of accuracy, overcoming the shortcomings of traditional measurements due to spatial heterogeneity. Based on in situ measurements in Guangdong Province from 2012 to 2018, this study applied three machine learning (ML) models and fused multi-source datasets to evaluate the performance of four data combinations in Ta estimation. Correlations of covariates were compared, focusing on rice planting areas (RA). The results showed that (1) The fusion of multi-source data improved the accuracy of model estimations, where the best performance was achieved by the random forest (RF) model combined with the ERA5 combination, with the highest R² reaching 0.956, the MAE value of 0.996 °C, and the RMSE of 1.365 °C; (2) total precipitation (TP), wind speed (WD), normalized difference vegetation index (NDVI), and land surface temperature (LST) were significant covariates for long-term Ta estimations; (3) Rice planting improved the model performance in estimating Ta, and model accuracy decreased during the crop rotation in summer. This study provides a reference for the selection of temperature estimation models and covariate datasets. It offers a case for subsequent ML studies on remote sensing of temperatures over agricultural areas and the impact of agricultural cultivation on global warming.

The bus stop layout and route deployment may influence the efficiency of bus services. Evaluating the supply of bus service requires the consideration of demand from various urban activities, such as residential and job-related activities. Although various evaluation methods have been explored from different perspectives, it remains a challenging issue. This study proposes a spatial statistical approach by comparing the density of the potential demand and supply of bus services at bus stops. The potential demand takes jobs-housing locations into account, and the supply of bus services considers bus stops and their associated total number of daily bus arrivals. The kernel density estimation (KDE) and spatial autocorrelation analyses are employed to investigate the coupling relationship between the demand and supply densities at global and local scales. A coupling degree index (CDI) is constructed to standardize the measurement of demand-supply balance. A case study in Wuhan, China demonstrated that: (1) the spatial distribution of bus stops is reasonable at global level, (2) Seriously unbalanced locations for bus services have been detected at several stops. Related adjustments that can improve these defects are highly recommended.