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Spatial Analysis Using Big Data: Methods and Urban Applications helps readers understand the most powerful, state-of-the-art spatial econometric methods, focusing particularly on urban research problems. The methods represent a cluster of potentially transformational socio-economic modeling tools that allow researchers to capture real-time and high-resolution information to potentially reveal new socioeconomic dynamics within urban populations. Each method, written by leading exponents of the discipline, uses real-time urban big data to solve research problems in spatial science. Urban applications of these methods are provided in unsurpassed depth, with chapters on surface temperature mapping, view value analysis, community clustering and spatial-social networks, among many others.
- 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
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
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 tools
Part 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
- No. of pages:
- © Academic Press 2020
- 1st November 2019
- Academic Press
- Paperback ISBN:
Yoshiki Yamagata is the Head of Global Carbon Project International Office at the Center for Global Environmental Research, National Institute for Environmental Studies.He received his Ph.D. degree in System Science from the University of Tokyo, Japan.His research interests include Urban Resilience, Urban Analytics, and Urban Systems.
Head of Global Carbon Project International Office at the Center for Global Environmental Research, National Institute for Environmental Studies, Japan
Hajime Seya received his Ph.D. degree in engineering from University of Tsukuba. His research interests include urban transportation planning, regional science, geographical information science, integrated land-use-transport modeling, and spatial statistics/econometrics. Seya has published 33 papers.
Associate Professor, Department of Civil Engineering, Faculty of Engineering, Graduate School of Engineering, Kobe University, Japan