Urban systems include many closely related components that have become more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Transforming these data into knowledge to facilitate planning efforts in addressing the current
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Urban systems include many closely related components that have become more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Transforming these data into knowledge to facilitate planning efforts in addressing the current challenges of complex urban systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. However, by applying a purely data-driven approach, it is very easy to get lost in the "forest" of data, and miss the "tree" of successful and livable cities that are the ultimate goal of city planning. This paper describes how geospatial data from urban analysis, using hybrid methods, can contribute to a better understanding of urban dynamics and human behavior, and how it can enhance planning efforts to improve livability. Based on a review of the latest research, this paper goes a step further and also addresses the potential as well as the limitations of new data sources in urban analytics to get a better overview of the entire "jungle" of these new data sources. The present work is a qualitative review based on research conducted with analytical methods about the reliability of using big data from social media platforms or sensors, and how to extract information from massive amounts of data through new analytical methods, such as learning. The machine is for more informed decision-making with the aim of improving urban livability. The purpose of this article is to review some of the synergies and challenges of the interdisciplinary approach (dissection and urban analysis based on GIS in improving the livability of cities, by examining the key findings and questions obtained from the advanced literature).
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