基于夜光遥感影像与百度POI数据的中国城市群空间范围识别方法

提出基于夜间灯光遥感影像、电子地图兴趣点和社会经济统计数据等,以经济地理学中的“点-轴”理论为基础,以“点-轴集聚区”的识别为核心,利用迭代自组织聚类、聚合分析、指标阈值筛选等方法,识别中国城市群及其空间范围的技术方法。通过该方法识别出中国14个城市群,其中8个城市群的空间范围与规划范围接近;与规划范围不一致的则表现为三种情况,分别揭示出规划中需要考虑的不同问题。研究结果表明,本文提出的方法能突破行政边界限制,科学反映城市群辐射范围,客观反映城市之间的社会经济联系强度,并基于“现状-动态”视角有利于深入发掘潜在的城市群对象。研究结果可以为城市群规划和管理提供参考。     

基于VIIRS夜间灯光的中国城市中心的分异特征及其影响因素

城市多中心是现代城市发展的重要趋势,精准识别城市中心和理解城市中心形成机制对推进以人为核心的新型城镇化具有十分重要的意义。基于VIIRS夜间灯光数据设计出城市中心识别方法,对全国286个地级以上城市的城市中心进行识别,从城市中心数和中心发展的均衡度两个方面对城市中心的分异特征及影响因素进行分析。结果表明：① 在全国286个城市中识别出181个多中心城市,所识别的城市中心与城市规划中心具有很大程度一致性,识别结果具有一定的可靠性。② 城市中心数表现为“东多西少”的空间格局,并随城市规模等级的增加呈现“S”型递增趋势。③ 城市中心发展的均衡度呈现“东高西低”的空间格局,并随城市规模等级的增加呈现稳定递增,到特大型城市基本保持稳定。④ 河流对市区的分割、市区是否临海以及市区平地占比等自然地理因素对城市中心发展具有影响,但是社会经济因素影响更大;社会经济因素中,轨道交通站点数、市辖区人口数、城市规模等级、地区生产总值、第三产业占地区生产总值比例、固定资产投资对城市中心发展具有显著影响。     

Integration of nighttime light remote sensing images and taxi GPS tracking data for population surface enhancement

The population distribution grid at fine scales better reflects the distribution of residents and plays an important role in investigating urban systems. The recent years have witnessed a growing trend of applying the nighttime light data to the estimation of population at micro levels. However, using the nighttime light data alone to estimate population may cause the overestimation problem due to excessively high light radiance in specific types of areas such as commercial zones and transportation hubs. In dealing with this issue, this study used taxi trajectory data that delineate people’s movements, and explored the utility of integrating the nighttime light and taxi trajectory data in the estimation of population in Shanghai at the spatial resolution of 500 m. First, the initial population distribution grid was generated based on the NPP-VIIRS nighttime light data. Then, a calibration grid was created with taxi trajectory data, whereby the initial population grid was optimized. The accuracy of the resultant population grid was assessed by comparing it with the refined survey data. The result indicates that the final population distribution grid performed better than the initial population grid, which reflects the effectiveness of the proposed calibration process.     

Similarities and differences of city-size distributions in three main urban agglomerations of China from 1992 to 2015: A comparative study based on nighttime light data

Comparing the city-size distribution at the urban agglomeration (UA) scale is important for understanding the processes of urban development. However, comparative studies of city-size distribution among China’s three largest UAs, the Beijing-Tianjin-Hebei agglomeration (BTHA), the Yangtze River Delta agglomeration (YRDA), and the Pearl River Delta agglomeration (PRDA), remain inadequate due to the limitation of data availability. Therefore, using urban data derived from time-series nighttime light data, the common characteristics and distinctive features of city-size distribution among the three UAs from 1992 to 2015 were compared by the Pareto regression and the rank clock method. We identified two common features. First, the city-size distribution became more even. The Pareto exponents increased by 0.17, 0.12, and 0.01 in the YRDA, BTHA, and PRDA, respectively. Second, the average ranks of small cities ascended, being 0.55, 0.08 and 0.04 in the three UAs, respectively. However, the average ranks of large and medium cities in the three UAs experienced different trajectories, which are closely related to the similarities and differences in the driving forces for the development of UAs. Place-based measures are encouraged to promote a coordinated development among cities of differing sizes in the three UAs.