Urban streets exhibit a hierarchical structure. From a network analysis perspective, continuity of streets based on the interior angle between street segments can be used to define ‘natural streets’ or ‘strokes’. The length of these ‘strokes’ can then be used to generate a hierarchy of street network. While researchers have described methods for defining such strokes and released tools that enable such analysis, the existing tools are dependent on proprietary applications and only an outline of the algorithm is available in the literature. This paper addresses these limitations and advances past approaches by (a) describing an efficient algorithm ‘COINS’ for conducting street continuity and hierarchy analysis and (b) releasing the python script and QGIS plugin which will enable users to implement this analysis independent of proprietary software and enable automation to process multiple datasets. The paper demonstrates the application of this tool using street network data from OpenStreetMap for 10 Indian cities and two international ones. Results indicate that our algorithm can detect the skeletal structure of a city which is visually very similar to the OpenStreetMap user-generated hierarchy, both at the city level and at the neighbourhood level. Analysing the results from 10 Indian cities using log–log plots of stroke length and stroke rank, we find that the strokes appear to follow Zipf’s law, but only in the mid-range of stroke lengths. Consistent with existing literature, we see that in our sample of Indian cities also there is a strong deviation from Zipf’s law for strokes that are approximately 100 m or lower.

With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide.