A data-driven approach for understanding human behavior in urban studies

As digital tools and methods continue to alter the development and growth patterns of the cities, the relationship of the urbanites with the city and also the designers' tools and methods used to design the city; the use of data has become an essential focus in understanding and designing urban environments. Although cities have experienced several technological paradigm shifts, the current transformation defined with digitalization of information and related methods, approaches and practices have a remarkable impact on the cities, urbanites and designers. Consequently, the cities become defined, equipped, supported and represented with data and they have turned into major generators of data. In recent years, the rise of digital tools and methods has generated vast amounts of data that offer insights into the behavior of urbanites and the design of cities. Therefore, the notion of Big-Data, a key output of digitalization, has become a decisive spotlight in our efforts to comprehend cities. The emphasis of the thesis is to investigate the potential of Big Data as a valuable resource for urban studies and proposes a data-driven approach to better understand the human behavior. By introducing innovative model for leveraging Big Data within the frame of Urban Informatics, it examines the use of "Topic Modeling" to analyze user-generated data and seamlessly integrate interdisciplinary approaches. This model also aims to make a significant contribution to urban studies and emphasize the crucial role of data in shaping our cities concentrating on analyzing human behavior in urban environments. Keywords: big-data, urban informatics, data-driven urban studies, topic modelling