Data Science for Transport

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.