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Data Science for Transport A Self-Study Guide with Computer Exercises

Title
Data Science for Transport [electronic resource] : A Self-Study Guide with Computer Exercises / by Charles Fox.
ISBN
9783319729534
Publication
Cham : Springer International Publishing : Imprint: Springer, 2018.
Physical Description
1 online resource (XVII, 185 p.) 77 illus., 49 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
This book offers a unique introduction to the application of data science for transport professionals and students of transport studies. Based on a course taught by the Leeds Institute for Transport Studies, the world’s leading center for training transport professionals, it represents the first textbook in this new area. As transportation planning has become increasingly data-driven, all graduate students and transport professionals urgently need to update their skills to include databases, machine learning, Bayesian statistics, geographic information system (GIS), and big data tools. Similarly, transport professionals including national and local government planners, transport consultants, and car company engineers are called upon to integrate these disparate areas with a specific focus on transportation issues, such as maps. The textbook also features a downloadable software package with all of the open source tools and libraries used in code examples throughout the book, including Python, Spyder, PostGIS, PyMC and GPy installations. As such, it offers a unique resource for graduate/advanced undergraduate students and instructors in transportation studies, urban and regional planning, engineering and geography, as well as transportation professionals.
Variant and related titles
Springer ebooks.
Other formats
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
May 07, 2018
Series
Springer Textbooks in Earth Sciences, Geography and Environment.
Springer Textbooks in Earth Sciences, Geography and Environment,
Contents
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.
Citation

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