Analyzing the Spatially Heterogeneous Relationships between Nighttime Light Intensity and Human Activities across Chongqing, China

Abstract

Nighttime light (NTL) intensity is highly associated with the unique footprint of human activities, reflecting the development of socioeconomic and urbanization. Therefore, better understanding of the relationship between NTL intensity and human activities can help extend the applications of NTL remote sensing data. Different from the global effect of human activities on NTL intensity discussed in previous studies, we focused more attention to the local effect caused by the spatial heterogeneity of human activities with the support of the multiscale geographically weighted regression (MGWR) model in this study. In particular, the Suomi National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) NTL data within Chongqing, China were taken as example, and the point of interest (POI) data and road network data were adopted to characterize the intensity of human activity type. Our results show that there is significant spatial variation in the effect of human activities to the NTL intensity, since the accuracy of fitted MGWR (adj.R2: 0.86 and 0.87 in 2018 and 2020, respectively; AICc: 4844.63 and 4623.27 in 2018 and 2020, respectively) is better than that of both the traditional ordinary least squares (OLS) model and the geographically weighted regression (GWR) model. Moreover, we found that almost all human activity features show strong spatial heterogeneity and their contribution to NTL intensity varies widely across different regions. For instance, the contribution of road network density is more homogeneous, while residential areas have an obviously heterogeneous distribution which is associated with house vacancy. In addition, the contributions of the commercial event and business also have a significant spatial heterogeneity distribution, but show a distinct decrement when facing the COVID-19 pandemic. Our study successfully explores the relationship between NTL intensity and human activity features considering the spatial heterogeneity, which aims to provide further insights into the future applications of NTL data.

The Assessment of Industrial Agglomeration in China Based on NPP-VIIRS Nighttime Light Imagery and POI Data

“…Road network data were obtained from OpenStreetMap (http://www.openstreetmap. org) accessed on 23 December 2023 and can reflect the intensity of economic activity and the level of development of infrastructure in a city [3]. This study used primary road, secondary road, tertiary road, motorway, and trunk road as auxiliary variables to calculate the road network density and length for each city.…”

“…In previous studies, NTL data became a vital proxy in many socioeconomic fields. For example, Wu, et al [3] analyzed the spatially heterogeneous relationships between NTL intensity and human activities by using NTL and points-of-interest (POI) data. Wang, et al [4] improved population mapping by using NTL and location-based social media data and the results showed higher accuracy than ones acquired with WorldPop data.…”

Measurement of 2D and 3D Fractal Features of Urban Morphology from an Architectural View and Its Influencing Factors

“…MGWR is derived from GWR; both are typical local regression models that allow the parameters to vary spatially and are different from the conventional global regression models, which assume that the coefficients to be estimated are constant over space [38,39].…”

Comprehensive Evaluation and Analysis of Human Settlements’ Suitability in the Yangtze River Delta Based on Multi-Source Data

“…As a comprehensive index of human factors, the NLI can reflect the intensity of human activities, the level of urbanization, and the form structure of urban construction land [49,50]. This can better reflect the main characteristics of the human environment [33].…”