Joon-Seok Kim
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Abstract
The common trend in the scientific inquiry of urban areas and their populations is to use real-world geographic and population data to understand, explain, and predict urban phenomena. We argue that this trend limits our understanding of urban areas as dealing with arbitrarily collected geographic data requires technical expertise to process; moreover, population data is often aggregated, sparsified, or anonymized for privacy reasons. We believe synthetic urban areas generated via procedural city generation, which is a technique mostly used in the gaming area, could help improve the state-of-the-art in many disciplines which study urban areas. In this paper, we describe a selection of research areas that could benefit from such synthetic urban data and show that the current research in procedurally generated cities needs to address specific issues (e.g., plausibility) to sufficiently capture real-world cities and thus take such data beyond gaming.
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