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    Data visualization for social and policy research: A step-by-step approach using R and Python
    (Cambridge University Press, 2022-01-01)
    All social and policy researchers need to synthesize data into a visual representation. Producing good visualizations combines creativity and technique. This book teaches the techniques and basics to produce a variety of visualizations, allowing readers to communicate data and analyses in a creative and effective way. Visuals for tables, time series, maps, text, and networks are carefully explained and organized, showing how to choose the right plot for the type of data being analysed and displayed. Examples are drawn from public policy, public safety, education, political tweets, and public health. The presentation proceeds step by step, starting from the basics, in the programming languages R and Python so that readers learn the coding skills while simultaneously becoming familiar with the advantages and disadvantages of each visualization. No prior knowledge of either Python or R is required. Code for all the visualizations are available from the book's website.
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    An to urban mobility: Data, visualization, artificial intelligent approaches, and its foundations
    (Universidad Simon Bolivar, 2024-01-01)
    In the last years, the scientific community has increasingly studied urban mobility since around 55% of the world population live in urban areas. Thus, individuals living in urban areas have to deal with phenomena like traffic jams, commute time, pollution, among others, which are difficult to understand and solve. Therefore, new innovative approaches such as mobility models, artificial intelligence, or visualization applied to urban mobility analysis problems shed new light on understanding cities’ behavior. In this work, we survey the current state of the mathematical and computational tools we have at our disposal to better understand the current situation of urban areas. Our work presents datasets, discusses relevant artificial intelligence and visualization techniques, and reviews mathematical tools to analyze urban data. We hope our work offers a valuable summary of these ideas and provides the base for future investigations.