Decoding city squares with big data: A method for urban analytics

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2022-5-12

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Özen, Aslıhan

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The responsive city nowadays is considered as an assemblage of a large number of complex characteristics. Similar to cities, people's social behavior is a complex system. Seeing cities with Big Data allows architects and urban designers to understand social networks of cities. This study outlines a computational tool to uncover latent characteristics of cities by combining Machine Learning and Social Media Analytics so that it may be possible to render an “image” and visualize a dense web of a city. The thesis aims to reveal visual characteristics of city squares and create a model that can learn the unique features of squares to support urban design processes that integrate big data-informed predictions. In order to achieve these goals of the research, the following questions were formulated. 1) Can we analyze social media data as a knowledge discovery tool to uncover city squares’ visual characteristics? 2) How can we map social communities by using visual social media data visualization and deciding points of interest in city squares?

Subject Keywords

Responsive City, Social Sensing, Social Media Analytics, Big-Data Informed Urban Characteristics, Machine Learning

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https://hdl.handle.net/11511/97752

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

A. Özen, “Decoding city squares with big data: A method for urban analytics,” M.Arch. - Master of Architecture, Middle East Technical University, 2022.