Spatial Distribution and Morphological Identification of Regional Urban Settlements Based on Road Intersections

Abstract

To measure and present urban size urban spatial forms, in solving problems in the rapid urbanization of China, urban territorial scope identification is essential. Although current commonly used methods can quantitatively identify urban territorial scopes to a certain extent, the results are displayed using a continuous and closed curve with medium- and low-resolution images. This makes the acquisition and interpretation of data challenging. In this paper, by extracting discretely distributed urban settlements, road intersections in OpenStreetMap (OSM), electronic maps, and urban expansion curve based on fractal thoughts have been used to present urban territorial scope and spatial form. Guangzhou, Chengdu, Nanjing, and Shijiazhuang cities were chosen as the identification targets. The results showed that the distance threshold corresponding to the principal curvature point of the urban expansion curve plays a vital role in the extraction of urban settlements. Moreover, from the analysis, the optimal distance thresholds of urban settlements in Guangzhou, Chengdu, Nanjing, and Shijiazhuang were 132 m, 204 m, 157 m, and 124 m, respectively, and the corresponding areas of urban territorial scopes were 1099.36 km2, 1076.78 km2, 803.07 km2, and 353.62 km2, respectively. These metrics are consistent with those for the built-up areas.

Assessing Regional Development Balance Based on Zipf’s Law: The Case of Chinese Urban Agglomerations

“…First, GUB data identifies the geographical area of the city based on GAIA data. In the urban periphery, GUB data include a large number of non-urban parcels, and the addition of these parcels will make the whole city area larger [15]. Second, in the Pearl River Delta region, the GUB data ignore the influence of topography and identify multiple cities as one urban agglomeration, merging otherwise discrete urban agglomerations (Figure 13e,f).…”

“…The accuracy of the obtained critical distance threshold and urban agglomeration results based on OSM data depends on the precision of the OSM data. However, data timeliness is poorer in urban fringe areas [15]. Furthermore, on the northern plains of China, rural settlements exhibit strong agglomeration tendencies and often integrate with adjacent large urban agglomerations, reflecting greater subjective dependence on urban agglomeration choices away from urban areas.…”

“…However, neither stage represents the main period of urban expansion. Therefore, the curvature extremal points of the second stage (100-1000 m) are selected as the main curvature points [15]. From 2015 to 2022, both the main curvature points and critical distance thresholds increase annually and tend to stabilize.…”

“…Some cities are expanding faster than the population growth [14]. Therefore, delineating urban territories is a prerequisite for studying the power-law distribution of cities, but there are differences in the urban territories obtained by different methods [15]. Urban territorial boundaries can be divided into three categories, namely administrative, functional, and physical territories.…”

“…For the above reasons, this paper is based on the fractal concept and starts from within the city by selecting road intersection data that is highly accurate and easily obtainable. Using the urban agglomerations approach avoids predefined critical distance thresholds and identifies the geographic extent of cities nationwide, away from the limits of administrative districts [15,41], to explore the power-law distribution law of urban agglomerations and reasonably quantify the unevenness of development in different regions, so as to provide a reference for solving the intensification of regional development differences and maintain coordinated regional development in the future.…”