基于多维城乡差异化人地特征的城区范围识别方法 ——面向中国多样本城市的实践探索

研究目的：基于概念辨析与方法演进分析，从城乡差异化人地特征入手，构建指标量化下的城区范围识别方法并应用于107个样本城市进行准确度校验。研究方法：文献资料法、归纳演绎法、实证分析法。研究结果：（1）城区概念中的实体地域认知日益深化，现有多类指标筛选法、遥感影像识别法、大数据与人工智能识别法三类识别方法且具有差异化应用特征；（2）可从人口、土地、产业、环境4个维度选取差异化特征要素并确定指标条件，构建以实体地域为基础的城区范围识别方法；（3）借助影像数据、矢量数据和统计数据，依次通过初步筛选获得城区初始范围，通过图斑功能、连接度、边界核查等判断获得城区实体地域范围，通过图斑占比、市政公用和公共服务设施等分析获得城区范围；（4）在方法准确度校验中，105个样本城市的城区实体地域范围面积与同年度建成区面积平均偏差为21.2%，趋势线斜率为0.825，判定方法有效。研究结论：本方法可应用于全国各区域城市的城区范围识别，未来可在人口指标增补、统计精度提升等方面进行优化并开展镇区、乡村空间识别的延续性研究。

Urban Built-up Area Identification Method Based on Multi-dimensional Human-land Characteristics of Urban-Rural Differentiation: Based on the Practical Exploration of Various Cities in China

The purposes of this paper are to construct the urban built-up area identification method under index quantification and to apply the method in 107 sample cities for accuracy verification, based on concept discrimination and method evolution analysis, starting from the human-land characteristics of urban and rural differentiation. The research methods include literature review, induction and deduction, and empirical analysis. The results show that: 1) the cognition of physical region in urban built-up area concept is deepening day by day. There are three kinds of recognition methods, namely multi-category index screening method, remote sensing image recognition method and big data and artificial intelligence recognition method, which have differentiated application characteristics. 2) The differentiated characteristic elements are selected from the four dimensions of population, land, industry and environment, and the index conditions are determined to construct an urban built-up area identification method based on the physical region. 3) With the help of image data, vector data and statistical data, the preliminary urban built-up area is obtained through screening, the physical urban built-up area is obtained through the judgment of land patch function, connectivity, boundary verification, and the urban built-up area is obtained through the analysis of land patch proportion, municipal and public service facilities. 4) As for the accuracy verification of the method, the average deviation between the physical urban built-up area and the corresponding built-up area of the 105 sample cities is 21.2%, and the slope of the trend line is 0.825. The preliminary determination of the method is effective. In conclusion, this method can be applied to the urban built-up area identification of cities in various regions. It can be optimized in the future in terms of population index supplement and statistical accuracy improvement. The follow-up research is expected to focus on the spatial identification of towns and villages.