Nighttime Light Intensity Research Articles

Spatiotemporal Dynamics of Ecological Vulnerability and Its Influencing Factors in Shenyang City of China: Based on SRP Model.

For Shenyang, the central city of Northeast China, its municipal-level Territorial Spatial Planning is of great significance to the whole of Northeast China. Territorial Spatial Planning is an essential carrier of China's ecological civilization construction. The demarcation of "three districts and three lines" defines the scope of ecological protection areas, which is of guiding significance to the future development of ecological civilization construction. The regional ecological vulnerability assessment can provide reference for ecological pattern planning and the demarcation of ecological red lines in "three districts and three lines". In order to explore the spatial distribution pattern of ecological vulnerability in Shenyang, predict the development trend of ecological vulnerability in the future and guide the construction of ecological civilization in Shenyang and provide certain basis for Shenyang's Territorial Spatial Planning and the delineation of "three districts and three lines". This paper based on the "sensitivity-resilience-pressure" model selected 13 indexes, to evaluate the ecological vulnerability of Shenyang from 2010 to 2020. Furthermore, the spatial distribution characteristics and influencing factors of ecological vulnerability in Shenyang are summarized using spatial autocorrelation analysis and geographic detector model, and the future development trend of ecological vulnerability in Shenyang in 2025 is predicted by using CA-Markov model. The results show that: (1) In 2010, 2015 and 2020, the total area of slightly vulnerable areas in Shenyang was large, and the ecological vulnerability showed a gradually vulnerable spatial change trend from south to north and from west to east. (2) The results of geographical detectors show that normalized difference vegetation index, economic density and nighttime light intensity are the main driving factors of ecological vulnerability in Shenyang. (3) The forecast result of CA-Markov model is reliable. In 2025, the ecological vulnerability of Shenyang will be mainly light and extreme vulnerability areas, and the areas of light and extreme vulnerability areas will increase in 2025. The research results can provide some reference for the delineation of "three districts and three lines" and ecological protection in Shenyang's Territorial Spatial Planning, and have certain significance for promoting regional sustainable development and balancing ecological protection and economic development.

Coupling relationship between urbanization and water-related ecosystem services in China’s Yangtze River economic Belt and its socio-ecological driving forces: A county-level perspective

“Taking the county as an important carrier to promote urbanization” has been identified as China’s most significant strategic planning for urban development. Exploring the level of coordination among both water-related ecosystem services (WES) and urbanization and analyzing their social and ecological drivers at the county-level can benefit in tackling various environmental challenges that arise as a result of the urbanization. This research examined the spatial and temporal variation of WES (i.e., water provision, soil conservation, and water purification services) in the Yangtze River Economic Belt (YREB) from 2000 to 2018 through the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and a quantitative assessment of urbanization level in terms of population, economy, and urban area increase. Then, coupling coordination degree (CCD) model was then used to assess the extent of coordinated growth of WES and urbanization. Finally, the GeoDetector model and the regionally weighted regression model were used to investigate the influence of multiple factors on the CCD The findings indicate that: 1) Each county unit in the YREB had a significantly higher level of urbanization from 2000 to 2018, with hotspots located primarily in the Shanghai, Jiangsu, Zhejiang and in the vicinity of Wuhan and Chengdu. 2) The WES in the YREB varied considerably between years, but exhibited an upward tendency. 3) The CCD of WES and urbanization in the eastern portion of the YREB is higher than that in the western portion. The level of CCD was increasing but overall low. 4) Land use intensity, nighttime light intensity, and NDVI were the primary determinants of the CCD between WES and urbanization, with considerable heterogeneity in the driving forces between locations. To improve the coordination level between WES and urbanization, differentiated urbanization development schemes and ecological protection measures were proposed based on the findings.

A Vegetation Nighttime Condition Index Derived From the Triangular Feature Space Between Nighttime Light Intensity and Vegetation Index

A Vegetation Nighttime Condition Index Derived From the Triangular Feature Space Between Nighttime Light Intensity and Vegetation Index

Using Remote Sensing Data and Graph Theory to Identify Polycentric Urban Structure

Polycentric urban structures determine the combination and correlation of urban resources. In the past, nighttime light data were often used to identify the center locations, but the borders of polycentric urban regions (PURs) could not be obtained. Using multisource remote sensing data and graph, this research proposes an effective method for polycentric structure identification. First, we regard nighttime light data as a continuous mathematical surface, which can be constructed as nighttime light intensity graphs (NLIGs). Then, the space-optimized Girvan–Newman (SGN) method is proposed to detect the communities, and the eigenvector centrality (EC) and gray value are used to discover the central node of each community. Finally, the geographical location mapping (GLM) between Landsat 8 data segmentation objects and nodes is established, and the PURs and centers can be mapped to the communities and central nodes. This study took Shenyang, Chengdu, and Xi’an as study areas and used monthly Visible Infrared Imaging Radiometer-National Polar-orbiting Partnership (NPP-VIIRS) data in April 2019 and Landsat 8 data in January and August 2019. The average accuracies of PURs and centers identified by the proposed method were 86.24% and 72.5%, respectively. The developed method can provide technical support and data support for urban planning.

Identifying Ecological Security Patterns Based on Ecosystem Service Supply and Demand Using Remote Sensing Products (Case Study: The Fujian Delta Urban Agglomeration, China)

As the global population increases and cities expand, increasing social needs and ecosystem degradation generally coexist, especially in China’s urban agglomerations. Identifying ecological security patterns (ESPs) for urban agglomerations serves as an effective way to sustain regional ecological security and promote harmonious ecological conservation and economic development. Focusing on the Fujian Delta Urban Agglomeration (FDUA) as an example, this study aims to present a framework for linking the supply and demand of ecosystem services (ESs) to identify ESPs in 2020. First, the ecological sources are delimited by coupling the supply and demand of four critical ESs (carbon storage, water provision, grain production, and outdoor recreation). Afterward, the resistance coefficient is modified using nighttime light intensity data and the ecological risk index, the second of which combines the effects of the soil erosion sensitivity index, the geological disaster risk index, and the land desertification risk index. Then, ecological corridors are determined by employing the minimum cumulative resistance method. With the integration of ecological sources and corridors, the ESPs of the FDUA can be identified. The results show a distinct supply–demand mismatch for ESs, with supply exhibiting an upward gradient from coastal cities to inland mountain cities and demand showing the opposite trend. The ESPs consist of 8359 km2 of ecological sources that are predominantly forests, 171 ecological corridors with a total length of 789.04 km, 34 pinch points, 26 barriers, and 48 break points. This paper presents a realizable approach for constructing ESPs for urban agglomerations, which will help decision makers optimize ecological sources and ecological protection policies.

Dynamic threats of nighttime light-represented human activities to giant pandas and their habitat

Human activities are one of the main factors threatening the long-term survival of wild giant pandas. Long-term and large-scale human activities can be quickly assessed by night light remote sensing data, which has been more and more widely used. In this paper, we analyzed the spatial patterns of nighttime light intensity and their dynamic changes from 2000 to 2020 in the entire giant panda distribution area using long-term nighttime light remote sensing data, and evaluated the dynamic threats of nighttime light-represented human activities to giant panda populations by proximity of light source and its intensity. We also assessed the relationships between the nighttime light dynamics and the trends of habitat fragmentation. Results showed that the intensity and range of the nighttime light around panda habitat had increased significantly from 2000 to 2020. The nighttime light intensity inside the natural reserves is significantly lower than outside, and it becomes more obvious over time. The intensity and range of nighttime light inside the natural reserves first increased and then decreased during the two decades, indicating that the protection inside the natural reserves effectively reversed the rapid increase in human activities in the previous decade. From 2000 to 2020, nighttime light sources became closer to local panda populations. In particular, they approached the pandas in the first decade, and stayed away in the second decade. This is mainly reflected in the weakened threats of nighttime light-represented human activities on the core large populations of the Qinling, Minshan and Qionglai Mountains from 2010 to 2020, but the threats on the peripheral populations continued to strengthen, where nighttime light became not only closer but also more intensified. The increase in nighttime light intensity and range were also significantly positively correlated with the increase in habitat fragmentation, especially during 2000–2010. Our study reveals the dynamic changes of nighttime light-represented human activities’ threats to wild giant panda and its habitat, gives advices for effectively protecting giant pandas and their habitats from human activities, and has implications for the assessment of human activities’ threats to other species in the world.

A new approach to peri-urban area land use efficiency identification using multi-source datasets: A case study in 36 Chinese metropolitan areas

Land use efficiency (LUE) is considered a significant yardstick for guiding the practice toward sustainable urban development for metropolitan areas. However, existing evaluation methods on LUE ignore the urban function carrying capacity of the urban construction land. This study proposes a theoretical approach for measuring LUE from a vacancy-and-utilization perspective. Accordingly, the land use efficiency index (LUEI) measurement is built up by incorporating nighttime light intensity, population density, and facility density of built-up land. Furthermore, the spatial pattern of the peri-urban areas' LUEI of 36 metropolitan areas in China is investigated. There are three significant findings. First, the LUEIs of the peri-urban area in eastern and western areas are 0.102 and 0.116, respectively, which are almost twice higher than that of the central area, which is only 0.067. Second, the balance between the contributions of LUEI's indicators is conducive to promoting LUE, indicating a bucket effect for achieving high LUE. Third, the average LUEI of county units with fewer facilities is 1.27 and 2.65 times lower than county units with less population and human activities. This new method not only provides a practical way to monitor LUE but would also be an essential basis for land use management.

Spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland China, 2006–2020: a Bayesian modeling study of national mortality registries

BackgroundCardiovascular disease (CVD) is the leading cause of death in China. No previous study has reported CVD mortality at county-level, and little was known about the nonmedical ecological factors of CVD mortality at such small scale in mainland China. Understanding the spatiotemporal variations of CVD mortality and examining its nonmedical ecological factors would be of great importance to tailor local public health policies.MethodsBy using national mortality registration data in China, this study used hierarchical spatiotemporal Bayesian model to demonstrate spatiotemporal distribution of CVD mortality in 2844 counties during 2006 to 2020 and investigate how nonmedical ecological determinants have affected CVD mortality inequities from the spatial perspectives.ResultsDuring 2006–2020, the age-standardized mortality rate (ASMR) of CVD decreased from 284.77 per 100,000 in 2006 to 241.34 per 100,000 in 2020. Among 2844 counties, 1144 (40.22%) were hot spots counties with a higher CVD mortality risk compared to the national average and located mostly in northeast, north central, and westernmost regions; on the contrary, 1551 (54.53%) were cold spots counties and located mostly in south and southeast coastal counties. CVD mortality risk decreased from 2006 to 2020 was larger in counties where CVD mortality rate had been higher in 2006 in most of the counties, vice versa. Nationwide, nighttime light intensity (NTL) was the major influencing factor of CVD mortality, a higher NTL appeared to be negatively associated with a lower CVD mortality, with one unit increase in NTL, and the CVD mortality risk will decrease 11% (relative risk of NTL was estimated as 0.89 with 95% confidence interval of 0.83–0.94).ConclusionsSubstantial between-county discrepancies of CVD mortality distribution were observed during past 15 years in mainland China. Nonmedical ecological determinants were estimated to significantly explain the overall and local spatiotemporal patterns of this CVD mortality risk. Targeted considerations are needed to integrate primary care with clinical care through intensifying further strategies to narrow unequally distribution of CVD mortality at local scale. The approach to county-level analysis with small area models has the potential to provide novel insights into Chinese disease-specific mortality burden.

Multi-source satellite imagery and point of interest data for poverty mapping in East Java, Indonesia: Machine learning and deep learning approaches

This study proposes a novel approach to provide a more granular poverty map in terms of coverage (up to a grid level with the spatial resolution of 1.5 km) with less cost and time to update to support better poverty monitoring. Two poverty estimation model development scenarios using machine learning and deep learning were evaluated in this study. First, the model is constructed using multisource satellite imagery and geospatial point of interest location of economic infrastructures using zonal statistics feature extraction, which is the proposed approach in this study. The specific multisource satellite imagery to be used as poverty indicators are night-time light intensity (NTL) as an approach to economic activity, normalized difference vegetation index (NDVI) as an approach to detect rural areas based on vegetation, built-up index (BUI) as an approach to detect urban areas based on building distribution, normalized difference water index (NDWI) to detect land cover, land surface temperature (LST) as an approach to detecting urban areas based on surface temperature, carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) as an approach to detecting economic activities based on pollution, as well as point of interest (POI) density, and POI distance which show the accessibility of an area. Second, the model is constructed from daytime multiband and nighttime light intensity satellite imagery using the deep learning architecture of Resnet-34 transfer learning feature extraction. In each scenario, we compared the performance of different machine learning (support vector regression/SVR, decision tree regression/DTR, and random forest regression/RFR) algorithms along with deep learning (multiple layer perceptron/MLP and one dimensional-convolutional neural network/CNN-1D) algorithms. Based on the results of the model development evaluation, the CNN-1D model was chosen as the best model in the first scenario, and the Resnet-34 + MLP model was chosen as the best model in the second scenario. The two best models from the first and second scenarios are then used to predict poverty at the grid level with a spatial resolution of 1.5 km. Based on official poverty data, the estimated poverty mapping built using the CNN-1D model in the first scenario was chosen as the best map with the value of root mean squared error (RMSE) 1.95 and adjusted R2 0.84 at the district level. The results of visual identification revealed that high estimates of poverty are typically found in sparsely inhabited areas surrounded by unoccupied land, which is usually an agricultural area. Otherwise, low poverty estimates, on the other hand, are more likely to be found in highly populated regions with convenient access. Our finding is aligned with the official report of Statistics Indonesia, which claims that poverty in rural regions is higher than in urban areas.

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

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.

Study on spatio-temporal modelling between NPP-VIIRS night-time light intensity and GDP for major urban agglomerations in China

ABSTRACT Currently, most of the studies establish the relationship between night-time light intensity (NTLI) and gross domestic product (GDP) only in the temporal dimension or spatial dimension, without combining both of them or considering real spatio-temporal modelling. Moreover, few studies verify the spatio-temporal heterogeneity of the model. To solve the aforementioned problems, this paper is the first to propose using the geographically and temporally weighted regression model (GTWR) for coupling NTLI and GDP. The NTLI derived from NPP-VIIRS satellites and GDP statistics for 14 urban agglomerations in China from 2013 to 2020 were systematically studied by comparing four methods, including OLS (ordinary least squares), GWR (geographically weighted regression model), TWR (time-weighted regression model), and GTWR. It is found that the GTWR model has the highest coefficient of determination. This finding proves that ‘the spatio-temporal nature of material movement is inseparable from each other’ and verifies the superiority, correctness, and scientific validity of the GTWR model. The implications of this paper include 1) demonstrating the superiority of the GTWR model (i.e. three-dimensional spatio-temporal model) in fitting the relationship between NTLI and GDP of urban agglomerations; 2) solving the problem of insufficient sample size of the single dimension of NPP-VIIRS through the spatio-temporal model; 3) quantitatively detecting the spatio-temporal heterogeneity of urban agglomerations and analysing the high non-stationarity urban agglomerations by GTWR and spatio-temporal cube with spatio-temporal hot spot analysis.

Evaluation of Urban Spatial Structure from the Perspective of Socioeconomic Benefits Based on 3D Urban Landscape Measurements: A Case Study of Beijing, China

Urban spatial structures (USS) play an essential role in urbanization. Understanding the impact of USS patterns on their socioeconomic benefits is crucial to evaluating urban structure quality. Previous studies have, primarily, relied on statistical data and have significant temporal consistency and spatial accuracy limitations. Moreover, previous evaluation methods mainly determined the weight of indicators based on subjective assessments, such as the Delphi method, without integrating the actual socioeconomic benefits of complex urban systems. By measuring the two-dimensional (2D) urban functional landscape patterns and three-dimensional (3D) building forms of the city and considering the level of urban socioeconomic vitality as revealed by nighttime light intensity (NTLI), this study explores the influence of urban spatial structure on socioeconomic vitality. It provides a new perspective for evaluating the USS level. Furthermore, a comprehensive index, namely the Spatial Structure Socioeconomic Benefit Index (SSSBI), was constructed to quantify the socioeconomic benefits of USS. The results showed that (1) the impact of spatial structure on NTLI differs significantly with the distribution of urban functional landscape patterns and building forms. (2) The combined effect of any two spatial structure factors on NTLI was higher than the effect of each factor separately, indicating that multiple dimensions can improve urban spatial construction. (3) This study quantitatively extracts the characteristics of USS from multiple scales, which helps to find the optimal evaluation scale and build a scientific and objective evaluation model. The results showed that the USS assessment based on the SSSBI index is practical. This study could provide a reference for the government’s urban planning and land-use decisions.

The impact of cyclones on local economic growth: Evidence from local projections

We shed new light on the short-term dynamic effects of cyclones on local economic growth in India. We proxy local GDP growth with night-time light intensity data and construct a cyclone index that varies across months and districts depending on wind speed exposures. Using local projections on highly granular data for the period 1993M1-2011M12, we find that yearly estimations hide large short-term differential impacts and that the negative impact of cyclones is the largest between 4 and 8 months after the event. • We study the short-term dynamic effect of storms on local economic growth in India. • We proxy economic growth with the district-level growth of night-light intensity. • We use wind-based exposure to storms and local projections over 1993M1-2011M12. • Yearly estimations hide large short-term differential impacts. • We find the largest negative effect between 4 and 8 months after the cyclone.

A building volume adjusted nighttime light index for characterizing the relationship between urban population and nighttime light intensity

The brightness of nighttime lights (NTL) has been proven to be strongly related to population density and thus has been widely used for population estimation from national to county levels. However, the limited availability of fine-grained census data makes an accurate assessment of the pixel-level relationship between NTL intensity and population a challenge. Consequently, pixel-level population estimation bias based on NTL intensity has been rarely investigated. Using fine-grained census data, we quantitively evaluated the correlation between urban population and NTL intensity over the core areas in Shanghai city, China. We also proposed a simple index called building volume adjusted nighttime light index (BVANI) for better characterizition of the relationship between urban population and NTL intensity. Our results found that pixel-level NTL intensity has a minimal correlation with population and the exclusive use of NTL intensity will not improve our ability to model population. While the assessment of BVANI shows that BVANI has an inverse relationship with pupation and the relationship between BVANI and population follows a power-law distribution. The relationship strength with population can be significantly improved by using BVANI with a correlation coefficient of 0.60. We believe that BVANI can be used as an important modeling factor for mapping fine-scale urban populations.

Impact of COVID-19 on convergence in Indian districts

PurposeThe purpose of this paper is to estimate the impact of the COVID-19 pandemic on the patterns of convergence/divergence among the districts in India. Specifically, this paper investigates if the impact is heterogeneous among different cohorts of districts (based on income distribution). The differential impact may lead to heterogeneous long-run growth paths, resulting in unbalanced development across regions within the country. A study of convergence can ascertain the possible trajectory of such development across regions. Investigation of this phenomenon is the primary aim of this study.Design/methodology/approachThis paper uses the panel regression method for estimation. This paper uses high-frequency nighttime light intensity data as a proxy for aggregate output.FindingsThe authors observe a significant reduction in the convergence rate as a result of the pandemic. Across the cluster of districts, the drop in ß-convergence rate, compared to the pre-pandemic period, varied from approximately 33% for the poorer districts to close to zero for the richest group of districts. These findings suggest that the pandemic may lead to a wider disparity among different regions within the country.Originality/valueThis paper contributes to the literature in the following ways. First, to the best of the authors’ knowledge, this is the first paper to investigate the impact of COVID-19 on the convergence rate. A detailed look into the possible disparity in convergence among various regions is critical because a larger drop in convergence, especially among the poorer regions, may call for policy attention to attain long-term equitable development. The authors perform this exercise by dividing the districts into four quantile groups based on the distribution of night-light intensity. Second, while previous studies on convergence using nighttime light data have used a cross-sectional approach, this study is possibly the first attempt to use the panel regression method on this data. The application of this method can be useful in tackling district-level omitted variables bias. Finally, the heterogeneity analysis using different quantiles of the distribution of night-light intensity may help in designing targeted policies to mitigate the disparity across districts due to the shock.

Link Ecological and Social Composite Systems to Construct Sustainable Landscape Patterns: A New Framework Based on Ecosystem Service Flows

Integrating the flow of supply and demand of ecosystem services (ESs) into the ecological security pattern (ESP) of coastal ecosystems with extremely fragile ecological backgrounds and contradictory human–land relationships is beneficial to the coordinated development of human–land systems. However, existing studies ignore the issue of scales of supply–demand linkages, making the ESP not properly guide sustainable development. Based on ESs delivery chain theory and landscape ecology approaches, we developed a sustainable development framework consisting of coupled microscopic natural–social systems. The method was tested using data from the Liao River Delta. In this study area, the natural supply potential and demand mapping distribution of key ESs were assessed to identify ecological sources in the Liao River Delta, a typical coastal zone in northern China. The resistance surface based on land use type assignment was modified using hydrological connectivity frequency and nighttime light intensity. Ecological corridors were extracted and optimized using a minimum cumulative resistance model and connectivity evaluation. The study found that the high supply area and the high demand reflection area are not consistent in location and supply level. Ecological source areas are evenly distributed, accounting for 12% of the total area. The ecological corridors are mainly concentrated in the west and southeast and do not cross the built-up areas in the east. This ESP framework safeguards the local demand for natural products and the natural potential to maintain services over the longer term and to a larger scale while informing the development of environmental management measures.

Regional Inequality in ASEAN Countries: Evidence from an Outer Space Perspective

ABSTRACT The ASEAN countries are in a golden stage of development, although the uneven regional development remains a prominent challenge to integrated growth. The study tries to investigate regional inequality in ASEAN countries from an outer space perspective. It first estimates the relationship between nighttime light intensity and GDP per capita for the 10 ASEAN countries at the national level, based on which it predicts regional incomes at the subnational level to assess regional inequality and explores the affecting factors. The results indicate that regional inequality in the ASEAN region and economic development present an inverted N-shaped relationship. The overall inequality of the region is largely attributed to the uneven development between countries. It is also found that transportation, urbanization, openness, mineral rents, and tax revenue are all significantly relevant to regional inequality.

Prediction of Urban Thermal Environment Based on Multi-Dimensional Nature and Urban Form Factors

The urban thermal environment is affected by multiple urban form and natural environment factors; research on the accurate prediction of the urban thermal environment, considering the interaction among different urban environmental factors, is still lacking. The development of a machine learning model provides a good means of solving complex problems. This study aims to clarify the relationship between urban environmental variables and the urban thermal environment through high-precision machine learning models as well as provide scenarios of future urban thermal environment developments. We defined an urban thermal environment index (UTEI), considering twelve urban form and natural indicators sourced from the remote sensing data of 150 cities in the Jing-Jin-Ji region from 2000 to 2015. We achieved accurate predictions of UTEI through training a gradient-boosted regression trees model. By unpacking the model, we found that the contribution rate of elevation (ELEV) was the highest. Among all the urban form indicators, the elongation index (ELONG), urban population (POP), nighttime light intensity (NLI), urban area size (AREA), and urban shape index (SHAPE) also had high contributions. We set up five scenarios to simulate the possible impact of different urban form factors on the overall urban thermal environment quality in the region. Under extremely deteriorated patterns that do not control urban expansion and vegetation reduction, the average UTEI could be as high as 0.55–0.76 °C in summer and 0.24–0.29 °C in winter, yet in the extremely optimized situation, UTEI decreased by 0.69 °C in summer and 0.56 °C in winter. Results showed that better urban form improves the quality of urban environments and can provide important insights for urban planners to mitigate urban heat island problems.

Threshold regression with nonparametric sample splitting

This paper develops a threshold regression model where an unknown relationship between two variables nonparametrically determines the threshold. We allow the observations to be cross-sectionally dependent so that the model can be applied to determine an unknown spatial border for sample splitting over a random field. We derive the uniform rate of convergence and the nonstandard limiting distribution of the nonparametric threshold estimator. We also obtain the root-n consistency and the asymptotic normality of the regression coefficient estimator. We illustrate empirical relevance of this new model by estimating the tipping point in social segregation problems as a function of demographic characteristics; and determining metropolitan area boundaries using nighttime light intensity collected from satellite imagery.

Correlation Analysis of CO2 Concentration Based on DMSP-OLS and NPP-VIIRS Integrated Data

In view of global warming, caused by the increase in the concentration of greenhouse gases, China has proposed a series of carbon emission reduction policies. It is necessary to obtain the spatiotemporal distribution of carbon emissions accurately. Nighttime light data is recognized as an important basis for carbon emission estimation. A large number of research results show that there is a positive correlation between nighttime light intensity and carbon emission. However, in the current context of China’s industrial reforms, this positive relationship may not be entirely correct. First, we correct the nighttime light data from different satellites and established a long-term series data set. Then, we verify the positive correlation between nighttime light and carbon emission. However, the time scale of emission data often lags, and the carbon concentration data are released earlier and are more accurate than emission data. Therefore, we propose to investigate the relationship between nighttime light and carbon concentration. It is found that there may be different correlations between nighttime light and the carbon concentration, due to different urban industrial structure and development planning. Therefore, by exploring the relationship between nighttime light and the carbon concentration, the existing carbon emission estimation model can be modified to improve the accuracy of the emission model.