Abordagens para análise de dados geográficos em transportes urbanos

Abstract

The proposal of this work is to apply techniques and methodologies in data science for analysis of geographic data in urban transport. The paper describes the process of acquisition and loading of the database, the process of cleaning and reorganizing the data, and the process of visualizing this information. A special focus is given to taxis in New York, USA, between the years 2009 to 2014 and the proposed theme was inspired by a competition proposed by Kaggle’s website in conjunction with Google Could and Coursera. Python was the language used throughout the development of this work, as well as several of its manipulation and visualization libraries. In conjunction with the basic libraries, more advanced ones were also used to visualize and process spatial data.