Big data is increasingly regarded as a new approach for understanding urban informatics and complex systems. Today, there is unprecedented data availability, with detailed remote-sensed data on the built environment and rich mineable web-based sources in the form of social media, web mapping, information services and other sources of unstructured "big data".
This book brings together a group of international contributors to consider the geographical implications of mobility, wellbeing and development within and across Chinese cities through location-based big data perspectives. The degree of urban sprawl, productive density and vibrancy can be reflected from location-based social media big data. The challenge is to identify, map and model these relationships to develop cities at different places in the urban hierarchical system that are more sustainable. This edited book aims to tackle these issues through two inter-related geographical scales: inter-city level and intra-city level.
The text is designed for graduate courses in planning, geography, public policy and administration, and for international researchers who are involved in urban and regional economics and economic geography.
"Big data have come to dominate how we rethink about economies and the applications of big data offer new areas of research and new insights into urban science. Geographers and economists point to the increasing heterogeneity of mobility, wellbeing and development within and between cities, and their importance of local and regional prosperity. This book provides useful evidence that has correspondingly directed attention to the interactions of places and people as the foci of urban management and planning policies."
Professor Tao Sun, Dean of Zhou Enlai School of Government, Nan Kai University, China
"New forms of data can enrich our understanding of cities. This book offers valuable insights into understanding the significant transformations taking place in Chinese cities through analytics of novel data sources. This is a helpful approach as traditional data sources in this context are relatively limited at the fine temporal and spatial scales needed to derive such understanding. The lessons learned with big data provides crucial understanding of what drives the dynamics of urban development, activity patterns, human mobility and wellbeing."
Piyushimita (Vonu) Thakuriah, Distinguished Professor and Dean, Edward J. Bloustein School of Planning and Public Policy, Rutgers University, USA
Foreword I *
Foreword II *
Chapter 1 Introduction *
Chapter 2 Mining China's Urban Social Interaction Footprint Patterns Using Big Data *
Chapter 3 Is China’s airline network similar to its long-distance mobility network? A comparative analysis *
Chapter 4 Spatially weighted interaction model of traffic flows on motorway networks *
Chapter 5 Profiling Rapid Urban Transformation through Urban Mobility Data in Shenzhen *
Chapter 6 Modeling Land Development: Heterogeneity in Space, Time and Context *
Chapter 7 A Big Data-based Characterisation of the Residential Rental Market in Shanghai *
Chapter 8 Evaluating Polycentric Spatial Strategy of Megacities *
Chapter 9 Multi-criteria locational analysis for retail development in small towns *
Chapter 10 Profiling PM2.5 Pollution Patterns and Policy Development *
Chapter 11 Conclusion *