Y. Nevertheless, a compact spatial scale ordinarily reflects only the partial/individual traits of an region but not its overall/common characteristics embedded into the transportation network at a bigger scale. Moreover, existing studies are generally restricted to applying complex network theory [22], fractal theory [23], and space syntax [24] when measuring urban street network complexity. Boeing proposed a street network complexity evaluation process primarily based on OSMnx, a Python package developed by his team [25]. This method used a unified OpenStreetMap data source and optimized network topology. Street networks are complex analysis objects; therefore, the introduction of OSMnx solves the following complications, which existed in prior studies on street networks: (1) network oversimplification as well as the inconsistency of simplified models exert fundamental effects around the research final results [26], and (2) the lack of totally free downloadable and easy-to-handle tools [27]. OSMnx enables the measurement of urban street network complexity by means of street grain, connectedness, street network Triflusal-d3 supplier orientation entropy, and circuity. In (R)-Citalopram-d4 supplier current years, some research on urban street networks have been conducted by using OSMnx. Yen et al. used circuity as among the metrics to analyze 3 street network patterns, namely, walkable, bikeable, and drivable, in Phnom Penh, Cambodia [28]. Their benefits recommended that urban central locations are more favorable for walking and biking than peripheral districts. Boeing applied OSMnx as a data-access tool and the street network of 100 cities as the study subject. He integrated street orientation entropy as a metric for quantifying street network analysis and identified that US cities tended to be much more grid-oriented than other cities [29]. Moreover, the huge sample of an urban street network may be collected by using OSMnx, considerably facilitating the study of urban street networks. Zhao et al. compared the network traits from the 26 pilot cities from the ASEAN Clever City Network by downloading the drivable and walkable road networks, working with OSMnx with numerous network metrics [30]. Boeing utilised OSMnx and OpenStreetMap to analyze a street network with 27,000 urban street networks inside the US and shared the large-scale data he collected within a public database [31]. Zhou et al. obtained a large sample of street network patterns by utilizing OSMnx and located that comparable street network patterns exhibit a clustered form in spatial distribution [32]. The influence of topography on a street network is one of the most important indicators of transportation expenses and car driving functionality [33,34]. Nevertheless, current research haven’t but explored in detail how topography impacts the distribution of street networks. In our study, we utilized OSMnx to extract the city street networks of China and quantitatively analyze the closeness with the connection involving topography and street networks by the Pearson correlation coefficient. This study enriches and complements current investigation around the complexity of Chinese street networks inside the theoretical and applied aspects. It contributes towards the understanding of your layout and improvement of street networkISPRS Int. J. Geo-Inf. 2021, ten,3 ofpatterns and their related urban forms in China, and could also play a higher role in future urban planning. 2. Study Location and Data 2.1. Overview of Study Region In this study, China was chosen as the study location for the following reasons. Initially of all, Chinese territory is vast and s.