•Inequality in urban greenspace exposure is assessed for 303 cities in China.•Dynamic inequality is characterized using multi-source geospatial data.•Severe inequality in greenspace exposure is ...pervasive in Chinese cities.•Dry cold climate and urban densification contribute to high inequality.
Given the important role of green environments playing in healthy cities, the inequality in urban greenspace exposure has aroused growing attentions. However, few comparative studies are available to quantify this phenomenon for cities with different population sizes across a country, especially for those in the developing world. Besides, commonly used inequality measures are always hindered by the conceptual simplification without accounting for human mobility in greenspace exposure assessments. To fill this knowledge gap, we leverage multi-source geospatial big data and a modified assessment framework to evaluate the inequality in urban greenspace exposure for 303 cities in China. Our findings reveal that the majority of Chinese cities are facing high inequality in greenspace exposure, with 207 cities having a Gini index larger than 0.6. Driven by the spatiotemporal variability of human distribution, the magnitude of inequality varies over different times of the day. We also find that exposure inequality is correlated with low greenspace provision with a statistical significance (p-value < 0.05). The inadequate provision may result from various factors, such as dry cold climate and urbanization patterns. Our study provides evidence and insights for central and local governments in China to implement more effective and sustainable greening programs adjusted to different local circumstances and incorporate the public participatory engagement to achieve a real balance between greenspace supply and demand for developing healthy cities.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Eighty percent of big data are associated with spatial information, and thus are Big Spatial Data (BSD). BSD provides new and great opportunities to rework problems in urban and environmental ...sustainability with advanced BSD analytics. To fully leverage the advantages of BSD, it is integrated with conventional data (e.g. remote sensing images) and improved methods are developed. This paper introduces four case studies: (1) Detection of polycentric urban structures; (2) Evaluation of urban vibrancy; (3) Estimation of population exposure to PM2.5; and (4) Urban land-use classification via deep learning. The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD. Meanwhile, they can also improve the effectiveness of big data itself. Finally, this study makes three key recommendations for the development of BSD with regards to data fusion, data and predicting analytics, and theoretical modeling.
Nitrogen dioxide (NO2) is an important air pollutant that causes direct harms to the environment and human health. Ground NO2 mapping with high spatiotemporal resolution is critical for fine-scale ...air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO2 concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates. This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R2 values of 0.84 and 0.79. The annual mean and standard deviation of ground NO2 concentrations from June 1, 2018 to May 31, 2019 were predicted to be 15.05 ± 7.82 μg/m3, with that in 0.6% of China’s area (10% of the population) exceeding the annual air quality standard (40 μg/m3). The ground NO2 concentrations during the coronavirus disease (COVID-19) period (January and February in 2020) was 14% lower than that during the same period in 2019 and the mean population exposure to ground NO2 was reduced by 25%. This study was the first to use TROPOMI retrievals to map fine-scale daily ground NO2 concentrations across all of China. This was also an early application to use the satellite-estimated ground NO2 data to quantify the impact of the COVID-19 pandemic on the air pollution and population exposures. These newly satellite-derived ground NO2 data with high spatiotemporal resolution have value in advancing environmental and health research in China.
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•Daily 3-km resolution ground NO2 across China was generated with TROPOMI retrievals.•Sample-based and site-based cross-validation R2 of 0.84 and 0.79 were achieved.•Fine-scale spatiotemporal patterns of ground NO2 in China were depicted and assessed.•The impact of the COVID-19 pandemic on ground NO2 was quantitatively evaluated.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Building function labelling plays an important role in understanding human activities inside buildings. This study develops a method of function label classification using integrated features derived ...from remote sensing and crowdsensing data with an extreme gradient boosting tree (XGBoost). The classification framework is verified based on a dataset from Shenzhen, China. An extended label system for six building types (residential, commercial, office, industrial, public facilities, and others) was applied, and various social functions were considered. The overall classification accuracies were 88.15% (kappa index = 0.72) and 85.56% (kappa index = 0.69). The importance of features was evaluated using the occurrence frequency of features at decision nodes. In the six-category classification system, the basic building attributes (22.99%) and POIs (46.74%) contributed most to the classification process; moreover, the building footprint (7.40%) and distance to roads (11.76%) also made notable contributions. The result shows that it is feasible to extract building environments from POI labels and building footprint geometry with a dimensional reduction model using an autoencoder. Additionally, crowdsensing data (e.g., POI and distance to roads) will become increasingly important as classification tasks become more complicated and the importance of basic building attributes declines.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
•The spatial patterns of industrial SO2 emissions of 288 cities were analyzed.•Nighttime Light data were processed and adopted as a proxy of urbanization level.•Urbanization and population size were ...major contributing factors.•Tradeoffs between urbanization and industrial SO2 emission control were discussed.•The impact of urbanization was the highest in the Northeast and Southwest regions.
The spatial distribution and the identification of the influential factors of industrial sulfur dioxide (SO2) emissions have received extensive attention. However, evidence is still lacking on the spatial impacts of urbanization on industrial SO2 emissions at the city scale in China. This work builds a Geography Weighted Regression (GWR) model on a Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) framework to investigate the spatially heterogeneous impacts of potential influencing factors on city-level industrial SO2 emissions from 288 prefecture-level cities in China. The results show that the GWR model significantly improved the goodness-of-fit of the model. The influence of night-time light intensity of the cities, as a proxy of the urbanization level, was calculated to be median (min, max): −0.505(-0.918, −0.413). The highest impacts of urbanization were observed in the Northeast and Southwest regions. Industrial influencing factors had generally promoted the growth of SO2 emissions, with higher positive impacts in western cities. We concluded that urbanization had a significant and negative effect on industrial SO2 emissions in most Chinese cities. It is necessary to formulate emission reduction and city development policies simultaneously based on the trade-offs between urbanization and air pollution control targets.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges to the formulation of preventive interventions, particularly since the effects of physical distancing measures and ...upcoming vaccines on reducing susceptible social contacts and eventually halting transmission remain unclear. Here, using anonymized mobile geolocation data in China, we devise a mobility-associated social contact index to quantify the impact of both physical distancing and vaccination measures in a unified way. Building on this index, our epidemiological model reveals that vaccination combined with physical distancing can contain resurgences without relying on stay-at-home restrictions, whereas a gradual vaccination process alone cannot achieve this. Further, for cities with medium population density, vaccination can reduce the duration of physical distancing by 36% to 78%, whereas for cities with high population density, infection numbers can be well-controlled through moderate physical distancing. These findings improve our understanding of the joint effects of vaccination and physical distancing with respect to a city's population density and social contact patterns.
Recently, transit-oriented development (TOD) projects have begun to prosper around new intercity railway (ICR) stations in China. An important question is whether the ICR-based TOD could perform as ...expected since the regional ICR is different from urban transit on which more TOD projects base. This article utilizes a remote sensing dataset, mobile-phone location-based big data as well as web map portal data and a Geographically and Temporally Weighted Regression (GTWR) model to explore the spatio-temporal relationship between land use and the population distribution in the ICR station area. It adopts the Pearl River Delta region in southern China as a study area, which undergoes rapid urbanization and is a pioneer to adopt TOD to promote the ICR project. The research finds the following. First, the combination of remote sensing, spatial big data, web map portal data and the GTWR model efficiently reveals the underlying spatio-temporal heterogeneities of the relationship between land use and population distribution in the ICR station area. Second, compared with the existing built-up area, the newly developed land after ICR construction has a weaker correlation with population distribution in the ICR station area. Third, the locations of the ICR station areas within the urban-rural system play a significant role in determining the relationship between land use and population distribution. For example, the association of working facilities with population distribution in suburban and town ICR station areas is significantly larger than that in urban and rural ICR station areas.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Understanding the spatio-temporal heterogeneous effects of socioeconomic and meteorological factors on CO2 emissions from combinations of different district heating systems with “Coal-to-Gas” ...transition can contribute to the development of future low-carbon energy systems that are efficient and effective. This work downscales city-level CO2 emissions to a 3 × 3 km2 gridded level in northern China during 2012 to 2018. By employing the Geographically and Temporally Weighted Regression (GTWR) model, nighttime light (NTL) data are adopted as a proxy of the level of urbanization, and the Temperature-Humidity-Wind (THW) Index is used as a proxy of meteorological factors in the downscaling model. The results show that, for more than 85% of the cities, urbanization significantly enhances the CO2 emissions of district heating systems, while the THW Index shows negative impacts on CO2 emissions. Significant spatial and temporal heterogeneity exists. The grids with the highest CO2 emissions from coal-fired boilers (grids with annual variation >0.59 Gg CO2/year) are mainly located in nonurban areas of the two megacities Beijing and Tianjin and also in the capital cities of each province. Urbanization has larger effects on the CO2 emissions of natural gas-fired boilers than of coal-fired boilers and combined heat and power (CHP). The average growth rate of CO2 emissions of gas-fired boilers in the urban areas of the study regions was approximately 4.7 times that of nonurban areas. The spatio-temporal heterogeneous impacts of urbanization on CO2 emissions should therefore be considered in future discussions of clean heating policies and climate response strategies.
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•The 3×3 km2 gridded CO2 emission inventory of district heating systems are presented.•Temperature-Humidity-Wind Index contributes to the spatial heterogeneity of CO2 emissions.•The spatio-temporal variations of the effects of urbanization and meteorological variables on CO2 emissions are discussed.•Accelerating the clean energy transition in the most heavily urbanized areas would have the greatest effects on CO2 reductions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Understanding the different impacts of urbanization on sectorial carbon dioxide (CO2) emissions at different spatial scales is of great importance for the evaluation of energy transition policies and ...reduction of environmental inequality. However, how urbanization affects the CO2 emissions of central heating systems at high spatial resolution in China has not been fully studied before. Based on satellite-observed NPP-VIIRS nighttime light (NTL) data, we develop a 5 km × 5 km annual CO2 emission inventory for coal boilers, thermal power plants (TPPs), and natural gas boilers in China’s central heating systems for the period 2012–2017 by using the geographical and temporally weighted regression (GTWR) model. It is observed that nonurban areas generated 2–4 times the CO2 emissions of coal boilers in urban areas. The largest increments of CO2 emissions of gas boilers are observed in urban areas of the eastern (6.80 times) and central regions (2.86 times) in 2013–2014, due to the clean heating policy in the “2 + 26” cities in China. The effects of urbanization on CO2 emissions from natural gas boilers are approximately 2–3 times those of coal boilers, and the differences are largest in western cities with only minor differences in northeastern cities. Our results will aid in designing low-carbon development goals and provide micro-level information on central heating facilities in urbanized and less developed regions.
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•Gridded CO2 emissions inventory of central heating systems in 2012–2017 is developed.•The nighttime light data is used to downscale the prefecture-city level emissions.•A GTWR model is built to explore the spatio-temporal heterogeneity among cities.•City development has larger impacts on CO2 emissions from gas-than coal-boilers.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Urban growth comes with significant warming impacts and related increases in air pollution concentrations, so many cities have implemented growth management to minimize “sprawl” and its environmental ...consequences. However, controlling the amount of growth is costly. Therefore, in this Article, we focus on urban warming and investigate whether climate-conscious urban growth planning (CUGP), that is, urban growth with the same magnitude but optimized spatial arrangements, brings significant mitigation effects. First, the classical spatial multiobjective land-use optimization (SMOLA) model is improved by integrating the spatially, diurnally, and compositionally varying associations between land-use and their warming impacts. We then solve the improved model using the nondominated genetic algorithm (NSGA-II) to generate urban growth plans with minimal warming impacts and minimal cost of change without reducing the amount of urban growth. Results show that climate-conscious urban growth brings 33.3 ± 4.6% less warming impacts as compared to unplanned urban growth in Shenzhen, China, and suggest a compact and spatially equalized development pattern. This study provides evidence that spatial planning tools such as the CUGP can help mitigate human impacts on the environment. Meanwhile, the improved SMOLA model could be applied to balance urban development and other environmental consequences such as air pollution.
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IJS, KILJ, NUK, PNG, UL, UM
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