Urban and rural disparities in general hospital accessibility within a Chinese metropolis.

Accessibility is one of the crucial criteria for measuring the ease of access to public services in a region. Given China's historical rural-urban dualism and rapid urbanization process, there exist significant disparities in public services within metropolises. This study selects Nanjing as a representative metropolis and employs the Gaussian two-step floating catchment area method and an improved potential model to calculate the accessibility of comprehensive hospitals. Spatial autocorrelation and urban-rural disparities are analyzed, confirming spatial variations in healthcare service equity. The results show that: ①The spatial variability of accessibility to comprehensive hospitals is significant. The Gaussian two-step floating catchment method overestimates overall accessibility, and for Nanjing, the improved potential model with ß = 1.5 proves more suitable. ②Accessibility exhibits pronounced clustering characteristics. Healthcare conditions for residents in the northern part of Liuhe District, eastern part of Qixia District, western part of Pukou District, peripheral areas of Jiangning District, eastern part of Gaochun District, and residents in Lishui District need improvement. ③Comprehensive healthcare services are relatively lacking in nearly 60% of rural areas. Our research findings provide valuable insights for improving spatial justice in public infrastructure in metropolises of developing countries.

Impacts of climate change on suitability zonation for potato cultivation in Jilin Province, Northeast China.

Global climate change is causing notable shifts in the environmental suitability of the main regions involved in potato cultivation and has, thus, changed the production potential of potatoes. These shifts can be mapped at fine scales to better understand climate change within areas of potato cultivation and to find infrastructural- and breeding-based solutions. As a case study, we have identified and mapped the structural and spatial shifts that occurred in areas suitable for potato cultivation in Jilin Province, China. We identified a discontinuity in climate change trends between 1961 and 2018 based on data for Jilin Province, and analyzed the averages and linear trends for six important climatic parameters. We used the averages of these climatic parameters to establish climate models for the province and determined cultivation using a multi-criteria, decision-based model that integrates Analytical Hierarchy Process Weighted Principal Component Analysis (AHP-PCA) and Geographic Information System (GIS). We mapped the environmentally suitable areas for potato cultivation at a 3-km resolution based on the geo-climate model for each time period and analyzed differences between them. We found that "Most suitable" areas for potato cultivation were mainly distributed in the central area of Jilin Province, "Suitable" areas were located in the northwestern plains, and "Sub-suitable" areas were located in the eastern mountainous areas. In contrast, "Not suitable" areas occur mainly in the high-altitude areas in the east. The areas of "Most suitable" and "Suitable" areas for potato cultivation in Jilin Province were increasing, with increasing rates of 0.37 × 1,000 km2 decade-1 (R2 = 0.58, P < 0.01) and 0.20 × 1,000 km2 decade-1 (R2 = 0.28, P < 0.01), respectively, while the extent of "Sub-suitable" areas is decreasing, with a decreasing rate of 0.58 × 1,000 km2 decade-1 (R2 = 0.53, P < 0.05). The area of "Not suitable" areas had undergone little change. "Most suitable" and "Suitable" areas for potato cultivation showed a trend towards northward expansion. Overall, our results suggest that global climate change has had a positive impact on potato cultivation in Jilin Province over the past 58 years.

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage.

BACKGROUND: The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. METHODS: In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. RESULTS: The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. CONCLUSION: This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.