Introduces data science for students of transport studies, geography and the geosciences, as well as transport professionals The only book to integrate the whole stack of transport data analysis Addresses a range of aspects, from setting up practical database software and scraping web data, to Bayesian analysis, dashboard visualization, big data, and privacy ethics Comes with an easy-to-use software installer that sets up the complete software stack to run the Python, GIS, machine learning, and big data examples

Autorentext

Dr. Charles Fox is a University Academic Fellow in Vehicle and Road Automation at the Institute for Transport Studies, University of Leeds. He researches autonomous off-road, on-road, and road-side perception, control and data analytics systems, using primarily Bayesian methods. Recent projects include IBEX2 off-road autonomous agricultural vehicles, featured in The Times and on the Discovery Channel; INTERACT pedestrian detection analytics for autonomous vehicles, with BMW; UDRIVE data mining of manual car driving big data to identify causes of dangerous driving, with Volvo; and Automated Number Plate Recognition analytics for Mouchel and the Highways Agency. Dr. Fox holds a first class MA degree in Computer Science from the University of Cambridge, MSc with Distinction in Informatics from the University of Edinburgh, and a DPhil in Pattern Analysis and Machine Learning from the Robotics Research Group at the University of Oxford. He has workedas a researcher in robotics and data-driven speech recognition at the University of Sheffield for users including the BBC, NHS and GCHQ, and as a high frequency data-driven trader for London hedge fund Algometrics Ltd. He has published 50 conference and journal papers cited 800 times and has h-index 15. He is a director of Ibex Automation Ltd which advises hedge fund and venture capital clients and is available for consultancy and R&D work in Data Science and Robotics.

Inhalt
Preface/Foreword (professional public transport analyst.- Introduction.- What is Data Science?.- Introduction to Python programming.- Database Design.- Data Munging.- Spatial Data.- Bayesian Interference.- Discriminative Classification.- Spatial Analysis.- Data Visualisation.- Database Scaling.- Professional Issues.- Appendix.- Index.