Potentiality of Using Luojia1-01 Night-Time Light Imagery to Estimate Urban Community Housing Price—A Case Study in Wuhan, China

The first professional night-time light remote sensing satellite in China, Luojia1-01, has raised the resolution of night-time light data to 130 m, which provides a possibility for the study of small-scale night-time light. This paper is the first research on spatial analysis and quantitative modeling between night-time light intensity (NTLI) and community housing price (CHP) on a small scale by using the Luojia1-01 night-time light imagery. This paper takes Wuhan as the research area, CHP data obtained by web-crawler technology as the research object, combines Luojia1-01 data, and carries out spatial correlation analysis and quantitative modeling on a small scale for them. The experimental results show that there is a strong linear positive correlation between the NTLI and CHP based on geographically weighted regression (GWR), and the CHP data in Wuhan have obvious spatial non-stationarity. Moreover, the coupling mechanism between the NTLI and CHP is also revealed. We can conclude that there is potential for estimating the CHP by using Luojia1-01 night-time light imagery.

[1]  N. Oden.  Book Review:Spatial Processes: Models & Applications. A. D. Cliff, J. K. Ord , 1982 .

[2]  S. Fotheringham,et al.  Geographically weighted summary statistics — aframework for localised exploratory data analysis , 2002 .

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

[4]  F. Ren,et al.  Evaluating the Effects of Landscape on Housing Prices in Urban China , 2018 .

[5]  Ting Ma,et al.  Quantitative Responses of Satellite-Derived Nighttime Lighting Signals to Anthropogenic Land-Use and Land-Cover Changes across China , 2018, Remote. Sens..

[6]  Suman Chakraborti,et al.  Estimation of Spatial Association Between Housing Price and Local Environmental Amenities in Kolkata, India Using Hedonic Local Regression , 2018 .

[7]  Yuyu Zhou,et al.  Remote sensing of night-time light , 2017 .

[8]  Wei Song,et al.  Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China , 2014, Int. J. Geogr. Inf. Sci..

[9]  Jianping Wu,et al.  Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data , 2014, Remote. Sens..

[10]  Christopher Bitter,et al.  Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method , 2007, J. Geogr. Syst..

[11]  Albert Saiz,et al.  Housing Supply and Housing Bubbles , 2008 .

[12]  A. Stewart Fotheringham,et al.  Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity , 2010 .

[13]  Xi Li,et al.  Mapping Urban Extent Using Luojia 1-01 Nighttime Light Imagery , 2018, Sensors.

[14]  Guo Zhang,et al.  Evaluating the Potential of LJ1-01 Nighttime Light Data for Modeling Socio-Economic Parameters , 2019, Sensors.

[15]  L. Anselin.  Local Indicators of Spatial Association—LISA , 2010 .

[16]  Clifford M. Hurvich,et al.  Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion , 1998 .

[17]  Guo Zhang,et al.  Analysis and Reduction of Solar Stray Light in the Nighttime Imaging Camera of Luojia-1 Satellite , 2019, Sensors.

[18]  Chen Peng,et al.  Urban Built-Up Area Extraction From Log- Transformed NPP-VIIRS Nighttime Light Composite Data , 2018, IEEE Geoscience and Remote Sensing Letters.

[19]  Xi Li,et al.  Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China , 2013, Remote. Sens..

[20]  Yuyu Zhou,et al.  Urban mapping using DMSP/OLS stable night-time light: a review , 2017, Remote Sensing of Night-time Light.

[21]  Eric R. Ziegel,et al.  Geographically Weighted Regression , 2006, Technometrics.

[22]  M. Charlton,et al.  Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis , 1998 .

[23]  Yijin Wu,et al.  DMSP/OLS night-time light intensity as an innovative indicator of regional sustainable development , 2018, International Journal of Remote Sensing.

[24]  Keith Ord,et al.  Spatial Autocorrelation: A Review of Existing and New Measures with Applications , 1970 .

[25]  W. Wheaton,et al.  Real Estate 'Cycles': Some Fundamentals , 1999 .

[26]  John M. Quigley,et al.  Real Estate Prices and Economic Cycles , 2002 .

[27]  Chang Li,et al.  Study on radiometric intercalibration methods for DMSP-OLS night-time light imagery , 2016 .

[28]  Greg T. Smersh,et al.  The Influence of Canadian Investment on U.S. Residential Property Values , 1997 .

[29]  Jianping Wu,et al.  Estimating House Vacancy Rate in Metropolitan Areas Using NPP-VIIRS Nighttime Light Composite Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  Jianping Wu,et al.  Integration of nighttime light remote sensing images and taxi GPS tracking data for population surface enhancement , 2018, Int. J. Geogr. Inf. Sci..

[31]  Zhi Wei,et al.  Time-Aware Latent Hierarchical Model for Predicting House Prices , 2017, 2017 IEEE International Conference on Data Mining (ICDM).

[32]  Michael Jendryke,et al.  A preliminary investigation of Luojia-1 night-time light imagery , 2019, Remote Sensing Letters.

[33]  M. Charlton,et al.  Some Notes on Parametric Significance Tests for Geographically Weighted Regression , 1999 .

[34]  J. Muller,et al.  Mapping regional economic activity from night-time light satellite imagery , 2006 .

[35]  C. Elvidge,et al.  Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption , 1997 .

[36]  Chang Li,et al.  Port economics comprehensive scores for major cities in the Yangtze Valley, China using the DMSP-OLS night-time light imagery , 2017, Remote Sensing of Night-time Light.

[37]  Steven Farber,et al.  A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations , 2007, J. Geogr. Syst..

[38]  Daniel A. Griffith,et al.  Spatial-Filtering-Based Contributions to a Critique of Geographically Weighted Regression (GWR) , 2008 .

[39]  K. Chau,et al.  Determinants and Sustainability of House Prices: The Case of Shanghai, China , 2015 .