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Previous studies have frequently taken urban density as a surrogate for economic efficiency and applied composite scores such as urban intensity to assess the existing physical forms of towns. However, measures of intensity might nuance crude calculations of density while considering how such measures relate to future plans and the process of transformations. This paper used open source geospatial data and regulatory detailed planning to measure urban intensity of the existing and the planned. Spatial analytical techniques were applied to compute accessibility to destinations, building density, compactness of development, and diversity of land use function of sixteen specialty towns in China. The results showed that the regulatory detailed planning of these cases might not improve town planning measured by intensity in terms of spatial arrangement in the time horizon of 15–20 years. We found that well-integrated monocentric forms with the finer street network could potentially provide a more suitable spatial distribution for mid-sized towns measured by urban intensity, no matter whether the urban form is linear or center-spreading. Moreover, the comparison between the existing and the planned implies that the poly-centric urban form for mid-sized towns in China could end up with a lower score of urban intensity. We outline a framework of urban intensity measures that include analytical consideration of what is there now, planned to what is forthcoming, and conceptual in terms of time and space. • Differentiating urban intensity from density • Using urban block as the spatial unit of study is more accurate than a synthetic spatial grid. • Comparing existing and planned conditions of medium-sized towns • Evaluation and intervention of town planning policies [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]