Abstract:The nighttime light data records artificial light on the Earth's surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were released by the Earth Observation Group of National Oceanic and Atmospheric Administration's National Geophysical Data Center (NOAA/NGDC). As new-generation data, NPP-VIIRS data have a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. This study aims to investigate the potential of NPP-VIIRS data in modeling GDP and EPC at multiple scales through a case study of China. A series of preprocessing procedures are proposed to reduce the background noise of original data and to generate corrected NPP-VIIRS nighttime light images. Subsequently, linear regression is used to fit the correlation between the total nighttime light (TNL) (which is extracted from corrected NPP-VIIRS data and DMSP-OLS data) and the OPEN ACCESSRemote Sens. 2014, 6 1706 GDP and EPC (which is from the country's statistical data) at provincial-and prefectural-level divisions of mainland China. The result of the linear regression shows that R 2 values of TNL from NPP-VIIRS with GDP and EPC at multiple scales are all higher than those from DMSP-OLS data. This study reveals that the NPP-VIIRS data can be a powerful tool for modeling socioeconomic indicators; such as GDP and EPC.

Whereas monthly and annual nighttime light (NTL) composite datasets are being increasingly used to estimate socioeconomic status, use of the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data has been limited for detecting and assessing the impact of short-term disastrous events. This study explores the application of daily NPP-VIIRS DNB data in assessing the impact of three types of natural disasters: earthquakes, floods, and storms. Daily DNB images one month prior to and 10 days after a disastrous event were collected and a Percent of Normal Light (PNL) image was produced as the ratio of the mean DNB radiance of the pre- and post-disaster images. Areas with a PNL value lower than one were considered as being affected by the event. The results were compared with the damaged proxy map and the flood proxy map generated using synthetic aperture radar data as well as the reported power outage rates. Our analyses show that overall NPP-VIIRS DNB daily data are useful for detecting damages and power outages caused by earthquake, storm, and flood events. Cloud coverage was identified as a major limitation in using the DNB daily data; rescue activities, traffic, and socioeconomic status of the areas also affect the use of DNB daily data in assessing the impact of natural disasters. Our findings offer new insight into the use of the daily DNB data and provide a practical guide for researchers and practitioners who may consider using such data in different situations or regions.

Diverse urban environmental elements provide health and amenity value for residents. People are willing to pay a premium for a better environment. Thus, it is essential to assess the benefits and values of these environmental elements. However, limited by the interpretability of the machine learning model, existing studies cannot fully excavate the complex nonlinear relationships between housing prices and environmental elements, as well as the spatial variations of impacts of urban environmental elements on housing prices. This study explored the impacts of urban environmental elements on residential housing prices based on multisource data in Shanghai. A SHapley Additive exPlanations (SHAP) method was introduced to explain the impacts of urban environmental elements on housing prices. By combining the ensemble learning model and SHAP, the contributions of environmental characteristics derived from street view data and remote sensing data were computed and mapped. The experimental results show that all the urban environmental characteristics account for 16 percent of housing prices in Shanghai. The relationships between housing prices and two green characteristics (green view index from street view data and urban green coverage rate from remote sensing) are both nonlinear. Shanghai's homebuyers are willing to pay a premium for green only when the green view index or urban green coverage rate are of higher value. However, there are significant differences between the impacts of the green view index and urban green coverage rate on housing prices. The sky view index has a negative influence on housing prices, which is probably because the high-density and high-rise residential area often has better living facilities. Residents in Shanghai are willing to pay a premium for high urban water coverage. The case of Shanghai shows that the proposed framework is practical and efficient. This framework is believed to provide a tool to inform the decisions of housing buyers, property developers and policies concerning land-selling and buying, property development and urban environment improvement.