摘要：
A quantitative analysis of rural development is required to comprehend the spatial differentiation of a rural area and promote rural sustainable development under the pressure of urbanization and industrialization, especially areas with dramatic changes in rural socioeconomic development of China and other developing countries. Taking Wuhan as the case study, this paper developed an index system including rural settlement, land, industry and human settlement environment for evaluating the level of rural development. Then, using the exploratory spatial data analysis, the principal component analysis and the cluster analysis, this paper analyzes the spatial differentiation and correlation and categorizes the types of rural development. The results are as follows. (1) The spatial differentiation of the level of rural development in Wuhan City’s new urban districts is obvious and the areas with a high level of rural development are mainly distributed at the intersection of the new and central urban areas and gradually decrease outward. (2) There is a significant spatial agglomeration of the developed rural areas and the structure of the spatial change in these areas resembles a certain continuity, specifically a circle of “central heat surrounding cold”. (3) Rural development in the new urban areas can be divided into the following five types: the ecological leisure type, the traditional farming type, the balanced development type, the industrial-and-agricultural mixed type and the industrial promotion type. The corresponding development path is proposed in combination with different types of rural development to provide a theoretical basis and decision-making reference for rural revitalization.
关键词：
city centrality;entropy weight TOPSIS;population mobility;Yangtze River Economic Belt;obstacle degree model
摘要：
Based on statistical data and population flow data for 2016, and using entropy weight TOPSIS and the obstacle degree model, the centrality of cities in the Yangtze River Economic Belt (YREB) together with the factors influencing centrality were measured. In addition, data for the population flow were used to analyze the relationships between cities and to verify centrality. The results showed that: (1) The pattern of centrality conforms closely to the pole-axis theory and the central geography theory. Two axes, corresponding to the Yangtze River and the Shanghai-Kunming railway line, interconnect cities of different classes. On the whole, the downstream cities have higher centrality, well-defined gradients and better development of city infrastructure compared with cities in the middle and upper reaches. (2) The economic scale and size of the population play a fundamental role in the centrality of cities, and other factors reflect differences due to different city classes. For most of the coastal cities or the capital cities in the central and western regions, factors that require long-term development such as industrial facilities, consumption, research and education provide the main competitive advantages. For cities that are lagging behind in development, transportation facilities, construction of infrastructure and fixed asset investment have become the main methods to achieve development and enhance competitiveness. (3) The mobility of city populations has a significant correlation with the centrality score, the correlation coefficients for the relationships between population mobility and centrality are all greater than 0.86 (P<0.01). The population flow is mainly between high-class cities, or high-class and low-class cities, reflecting the high centrality and huge radiating effects of high-class cities. Furthermore, the cities in the YREB are closely linked to Guangdong and Beijing, reflecting the dominant economic status of Guangdong with its geographical proximity to the YREB and Beijing’s enormous influence as the national political and cultural center, respectively.
通讯机构：
[Luo, Jing] C;Cent China Normal Univ, Coll Urban & Environm Sci, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
关键词：
Healthcare inequity;Accessibility;Older adults;Gaussian 2SFCA method;Real-time speed data
摘要：
Introduction: Population aging raises many fundamental questions for public health policymakers, one of which is how to address the inequity of medical services for older adults. Geographic information systems (GIS)-based accessibility has been widely employed to measure the inequity of access to healthcare facilities. However, the focus in literature lies generally on spatial patterns; few accessibility studies have focused on temporal inequity. This study focuses on the disparity of the elderly's healthcare inequity (EHI) in both spatial and temporal dimensions. Methods: EHI is measured by the elderly's medical services accessibility (EMSA) scores. Based on a road network analysis with real-time traffic data acquired by a Python-based web crawler, a Gaussian two-step floating catchment area (2SFCA) method is employed to calculate EMSA scores. Sensitivity analysis is presented for three threshold times (i.e., 5, 15, and 30 min) and different intervals of a time of day (TOD) period. Results: In the temporal patterns, the average EMSA (EMSA-avg) scores of daily variations vary from 0.0262 to 0.0556 at t(0) = 5 min, from 0.0704 to 0.1073 at t(0) = 15 min, and from 0.1734 to 0.2223 at t(0) = 30 min. Meanwhile, three "low value intervals (LVIs)" exist in TOD period. In the spatial patterns, the areas with frequent traffic congestions or no nearby hospitals are characterized by low-level accessibility for the 5 min catchment, while the same level areas for the 30 min are far from the city centre and equipped with inconvenient public transportation. Conclusions: The findings may help the policymakers in planning the medical service resources for the growing aging population and providing caregivers information for a timely and effective medical treatment.
摘要：
Public Earth Observation (EO) data archives, e.g., MODIS, Landsat, and Sentinels, are valuable sources of information for a broad range of applications. For decision-supporting applications used in urban planning, land management, and sustainable development, images covering regions similar to the study area are prerequisites for high-accuracy decision making. These desirable images cannot be quickly searched for in the EO data archives via image metadata alone but can be obtained through content-based image retrieval methods. Land cover (LC) information, traditionally obtained through image segmentation or classification processing, is typically used in existing methods. Image processing is time consuming and has various accuracy levels for heterogeneous images, thus decreasing retrieval efficiency and accuracy. Additionally, the monotemporal LC information used has a limited ability to distinguish among confusable regions with different terrain, e.g., forests located on flatlands or mountains, and to obtain regions, e.g., urban regions, with similar growth rates. In this study, we employ free multiple-year 30 m LC products, a terrain product, and the Google Earth Engine (GEE) platform to accurately and efficiently locate the desired heterogeneous moderate spatial resolution images from various public EO data archives. Regions similar to the query region are detected with two-stage similarity calculations: First, monotemporal pixel-based LC and terrain information are used to filter out the most dissimilar regions; second, object-based LC change and terrain information are used to locate similar regions. Then, the desired images covering these detected similar regions are obtained from EO data archives via image metadata, e.g., geographical location and acquisition time. The experimental results of the two representative query regions show that our method can be used to obtain the desired images within several minutes and has higher accuracy than the LandEx method and a simplified method using only monotemporal LC information. The main contribution of our study is to reveal that LC changes and terrain information are helpful for improving the retrieval accuracy achieved from monotemporal LC information alone. Our method has great operability, with no need to perform EO data acquisition, image processing of raw EO images, or management of computational resources. Our method is conducive to making full use of images in various public EO archives to improve the decision making quality of decision-supporting applications.
