
Spatial Analysis Using Big Data
Methods and Urban Applications
Description
Key Features
- Reviews some of the most powerful and challenging modern methods to study big data problems in spatial science
- Provides computer codes written in R, MATLAB and Python to help implement methods
- Applies these methods to common problems observed in urban and regional economics
Readership
Graduate and PhD students and other early career researchers who seek to conduct research on urban communities using spatial econometric methods, obviously including spatial statistics and spatial econometrics, but also GIS, computer science, environmental science, and transportation
Table of Contents
Part 1. Introduction
Part 2. Methods for big spatial data analysis
1. Spatial statistics and data assimilation
2. Spatial and temporal statistical models
3. Spatial econometrics and social interaction models
4. Spatial clustering models
5. Complex network models
6. Spatial mobility data models
7. Land use and transport models
8. Land use scenario visualization toolsPart 3. Urban applications of big spatial data analysis
9. Surface temperature mapping for heat wave risk management
10. Spatial heat-wave assessments using Geo-tagged Twitter data
11. Assimilation of cell phone mobility data for agent based simulation
12. Spatial-social network analysis of the patent data
13. CO2 emission mapping using human sensor data
14. Optimal community clustering for sharing economy
15. View value analysis using 3D urban structure data
16. Big Spatial Data Analysis: case studies in New York
17. Big Spatial Data Analysis: case studies in London
Product details
- No. of pages: 302
- Language: English
- Copyright: © Academic Press 2019
- Published: November 2, 2019
- Imprint: Academic Press
- Paperback ISBN: 9780128131275
- eBook ISBN: 9780128131329
About the Editors
Yoshiki Yamagata
Affiliations and Expertise
Hajime Seya
Affiliations and Expertise
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