This paper examines the former location-based social medium Brightkite, over its three-year life span, based on the concept of natural cities. The term 'natural cities' refers to spatially clustered geographic events, such as the agglomerated patches aggregated from individual social media users' locations. We applied the head/tail division rule to derive natural cities, based on the fact that there are far more low-density locations than high-density locations on the earth surface. More specifically, we generated a triangulated irregular network, made up of individual unique user locations, and then categorized small triangles (smaller than an average) as natural cities for the United States (mainland) on a monthly basis. The concept of natural cities provides a powerful means to develop new insights into the evolution of real cities, because there are virtually no data available to track the history of cities across their entire life spans and at very fine spatial and temporal scales. Therefore, natural cities can act as a good proxy of real cities, in the sense of understanding underlying interactions, at a global level, rather than of predicting cities, at an individual level. Apart from the data produced and the contributed methods, we established new insights into the structure and dynamics of natural cities, e.g., the idea that natural cities evolve in nonlinear manners at both spatial and temporal dimensions.

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To better support urban designers in planning sustainable, resilient, and livable urban environments, new methods and tools are needed. A variety of computational approaches have been proposed, including different forms of spatial analysis to evaluate the performance of design proposals, or the automated generation of urban design proposals based on specific parameters. However, most of these propositions have produced separate tools and disconnected workflows. In the context of urban design optimization procedures, one of the main challenges of integrating urban analytics and generative methods is a suitable computational representation of the urban design problem. To overcome this difficulty, we present a holistic data representation for urban fabrics, including the layout of street networks, parcels, and buildings, which can be used efficiently with evolutionary optimization algorithms. We demonstrate the use of the data structure implemented for the software Grasshopper for Rhino3D as part of a flexible, modular, and extensible optimization system that can be used for a variety of urban design problems and is able to reconcile potentially contradicting design goals in a semi-automated design process. The proposed optimization system aims to assist a designer by populating the design space with options for more detailed exploration. We demonstrate the functionality of our system using the example of an urban master-design project for the city of Weimar.

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This paper is motivated by the fact that in Cape Town, South Africa, approximately 7.5 million people live in informal settlements and focuses on potential upgrading strategies for such sites. To this end, we developed a computational method for rapid urban design prototyping. The corresponding planning tool generates urban layouts including street network, blocks, parcels and buildings based on an urban designer's specific requirements. It can be used to scale and replicate a developed urban planning concept to fit different sites. To facilitate the layout generation process computationally, we developed a new data structure to represent street networks, land parcellation and the relationship between the two. We also introduced a nested parcellation strategy to reduce the number of irregular shapes generated due to algorithmic limitations. Network analysis methods are applied to control the distribution of buildings in the communities so that preferred neighborhood relationships can be considered in the design process. Finally, we demonstrate how to compare designs based on various urban analysis measures and discuss the limitations that arise when we apply our method in practice, especially when dealing with more complex urban design scenarios.