Modeling Polycentric Urbanization Using Multisource Big Geospatial Data

Understanding the dynamics of polycentric urbanization is important for urban studies and management. This paper proposes an analytical model that uses multisource big geospatial data to characterize such dynamics to facilitate policy making. There are four main steps: (1) main centers and subcenters are identified using spatial cluster analysis and geographically weighted regression (GWR) based on Visible Infrared Imaging Radiometer Suite (VIIRS)/NPP and social media check-in data; (2) the built-up areas are extracted by using Defense Meteorological Satellite Program—Operational Linescan System (DMSP/OLS) gradient images; (3) the economic corridors that connect the main center and subcenters are constructed using road network data from Open Street Map (OSM) with the least-cost distance method; and (4) the major urban development direction is identified by analyzing the changes in built-up areas within the economic corridors. The model is applied to three major cities in northeastern, central, and northwestern China (Shenyang, Wuhan, and Xi’an) from 1992 to 2012.

[1]  D. Wheeler,et al.  The Road to Growth: Measuring the Tradeoffs between Economic Growth and Ecological Destruction , 2018 .

[2]  Josef Kittler,et al.  On the accuracy of the Sobel edge detector , 1983, Image Vis. Comput..

[3]  N. Phelps,et al.  Contrasts in agglomeration: proto-industrial, industrial and post-industrial forms compared , 2003 .

[4]  B. Johnson,et al.  Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping: Case study of the Laguna de Bay area of the Philippines , 2016 .

[5]  X. Bai,et al.  Society: Realizing China's urban dream , 2014, Nature.

[6]  D. Stathakis,et al.  Urbanization in European regions based on night lights , 2015 .

[7]  Linlin Li,et al.  Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach , 2017, Remote. Sens..

[8]  Saro Lee,et al.  Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model , 2012, Hydrogeology Journal.

[9]  P. Gong,et al.  Validation of urban boundaries derived from global night-time satellite imagery , 2003 .

[10]  Sebastian Aleksandrowicz,et al.  Impervious surface detection with nighttime photography from the International Space Station , 2016 .

[11]  Qihao Weng,et al.  Global Urban Monitoring and Assessment through Earth Observation , 2014 .

[12]  Chenghu Zhou,et al.  Assessing Spatiotemporal Characteristics of Urbanization Dynamics in Southeast Asia Using Time Series of DMSP/OLS Nighttime Light Data , 2018, Remote. Sens..

[13]  Zhifeng Liu,et al.  Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008 , 2012 .

[14]  Bo Huang,et al.  Using multi-source geospatial big data to identify the structure of polycentric cities , 2017 .

[15]  J. Friedmann.  Four Theses in the Study of China’s Urbanization , 2006 .

[16]  Wei Song,et al.  A New Approach for Detecting Urban Centers and Their Spatial Structure With Nighttime Light Remote Sensing , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Ke Wang,et al.  Analysis of the Spatio-Temporal Dynamic of Polycentric City Using Night-Time Light Remote Sensing Imagery , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[18]  Yuan Wang,et al.  Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data , 2018, Comput. Geosci..

[19]  K. Seto,et al.  The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity , 2013 .

[20]  B. Derudder,et al.  Polycentric urban development in China: A multi-scale analysis , 2018 .

[21]  P. Sutton,et al.  Radiance Calibration of DMSP-OLS Low-Light Imaging Data of Human Settlements , 1999 .

[22]  Zhen Liu,et al.  Polycentric Development and the Role of Urban Polycentric Planning in China’s Mega Cities: An Examination of Beijing’s Metropolitan Area , 2018 .

[23]  Duong H. Nong,et al.  Mapping Urban Transitions Using Multi-Temporal Landsat and DMSP-OLS Night-Time Lights Imagery of the Red River Delta in Vietnam , 2014 .

[24]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[25]  Sasanka Ghosh,et al.  Exploring the lateral expansion dynamics of four metropolitan cities of India using DMSP/OLS night time image , 2017, Spatial Information Research.

[26]  Jacinto Estima,et al.  Investigating the Potential of OpenStreetMap for Land Use/Land Cover Production: A Case Study for Continental Portugal , 2015, OpenStreetMap in GIScience.

[27]  Chunyang He,et al.  The urbanization process of Bohai Rim in the 1990s by using DMSP/OLS data , 2006 .

[28]  Matthew Zook,et al.  Using Geotagged Digital Social Data in Geographic Research , 2014 .

[29]  Arie Romein,et al.  Polycentric Structures in Latin American Metropolitan Areas: Identifying Employment Sub-centres , 2014 .

[30]  K. Seto,et al.  Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data , 2011 .

[31]  R. Slotow,et al.  Social Media Data Can Be Used to Understand Tourists’ Preferences for Nature‐Based Experiences in Protected Areas , 2018 .

[32]  Huadong Guo,et al.  Monitoring Urban Dynamics in the Southeast U.S.A. Using Time-Series DMSP/OLS Nightlight Imagery , 2016, Remote. Sens..

[33]  Saro Lee,et al.  GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea , 2011 .

[34]  Mingshu Wang,et al.  How polycentric is urban China and why? A case study of 318 cities , 2016 .

[35]  Patrick Weber,et al.  OpenStreetMap: User-Generated Street Maps , 2008, IEEE Pervasive Computing.

[36]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[37]  Xi Shao,et al.  Suomi NPP VIIRS sensor data record verification, validation, and long‐term performance monitoring , 2013 .

[38]  E. Adam,et al.  Use of Landsat multi-temporal imagery to assess secondary growth Miombo woodlands in Luanshya, Zambia , 2018, Southern Forests: a Journal of Forest Science.

[39]  D. McMillen.  Nonparametric Employment Subcenter Identification , 2001 .