Multi-source Spatial Data Research Articles

Extraction of deterioration and analysis of vegetation impact effects on the south palace wall of Weiyang Palace

Weiyang Palace, the royal palace of the Western Han Dynasty, is a part of the Silk Roads: the Routes Network of Chang’an-Tianshan Corridor on the World Heritage list. The south palace wall of Weiyang Palace is a well-preserved earthen heritage site within the palace, but it is undergoing continuous deterioration due to the influence of vegetation and external environmental factors. This study pioneers the integration of high-resolution three-dimensional LiDAR scanning with multi-source data analysis, including unprecedented on-site botanical surveys, to explore the subtle effects of different vegetation types on the structural integrity of the south palace wall. Through contour line analysis and facade grid analysis, we extracted the deterioration locations of typical sections of the earthen heritage sites. And we classified the overlying vegetation types on the wall using an object-oriented classification algorithm. Our findings reveal a complex interaction between vegetation and earthen structures: paper mulberry exhibits protective qualities against erosion, while Ziziphus jujuba significantly exacerbates structural vulnerabilities. The methodologies applied in this study for extracting deterioration at earthen heritage sites and integrating multi-source spatial data can serve as a technical application model for monitoring and analyzing the driving forces of earthen heritage sites along the entire Silk Road network, thereby better guiding the conservation of earthen heritage sites.

Relationship between Residential Patterns and Socioeconomic Statuses Based on Multi-Source Spatial Data: A Case Study of Nanjing, China

The relationship between residential patterns and socioeconomic statuses highlights the complex interactions between the economic regime, welfare system, and neighborhood effects, which are crucial in urban inequality studies. With the diversification of the housing demand and supply system, the traditional analysis conducted separately from the ethnic or spatial segregation perspective fails to capture the rising inequalities and changing socio-spatial context. Taking Nanjing as an example, based on a multi-source database including the housing price, residential environmental quality, surrounding support facilities, and mobile phone user portrait data, this paper proposed a modified method for discovering the coupling relationship between residential patterns and socioeconomic statuses. It is found that socioeconomic status contributes to residential spatial aggregation and that the relationship between social and spatial dimensions of residential differentiation is tightly coupled and related. The lower socioeconomic strata were displaced to the periphery and the older urban core, while affluent inhabitants were more likely to settle voluntarily in segregated enclaves to isolate themselves from the general population through more flexible housing options. The heterogeneity of the urban socioeconomic dimension is primarily affected by consumption and occupational status, while housing prices mainly determine the divergence of spatial distribution.

Exploring the Configurational Relationships between Urban Heat Island Patterns and the Built Environment: A Case Study of Beijing

The spatial heterogeneity of land surface temperature (LST) within cities is profoundly influenced by the built environment. Although significant progress has been made in the study of the urban thermal environment, there is still a lack of research on how the pattern and structural layout of the built environment affects the thermal environment. In this study, we take the Fifth Ring Road of Beijing as an example, invert the urban LST on the basis of multisource spatial data, characterize the built environment, and use k-means cluster analysis to investigate the main influencing factors of the LST in different functional areas and building patterns within the city, as well as the spatial relationship between the built environment and the urban LST. The results show the following: (1) The urban heat island (UHI) effect occurs to varying degrees over a large part of the study area, and these UHI areas are mainly concentrated in the southwestern part of the city, forming a large contiguous area between the second and fifth ring roads. (2) Class 1 is dominated by transport blocks, Class 3 is dominated by commercial blocks, and Class 5 is dominated by green space blocks, with a clustering index of 0.38. (3) The high-density, high-height class (HH-Class 2) has a greater number of blocks distributed in a ring shape around the periphery of the second ring road. The high-density, low-height class (HL-Class 2) has a relatively small number of blocks but a relatively large area, and the largest blocks are located in the western part of the study area. (4) In the HH and HL building patterns, extreme heat scenarios often occur; from the perspective of functional areas, the probability of extreme heat in the transport block is much higher than that of other functional areas, and except for the HH scenario, the green space functional area plays a very important role in reducing the temperature. This study explores the characteristics of the built environment that influence the urban LST from the perspective of different urban functional zones in cities to provide decision support for quantitative territorial spatial planning, optimization, and management.

ConvFormer-KDE: A Long-Term Point-Interval Prediction Framework for PM2.5 Based on Multi-Source Spatial and Temporal Data.

Accurate long-term PM2.5 prediction is crucial for environmental management and public health. However, previous studies have mainly focused on short-term air quality point predictions, neglecting the importance of accurately predicting the long-term trends of PM2.5 and studying the uncertainty of PM2.5 concentration changes. The traditional approaches have limitations in capturing nonlinear relationships and complex dynamic patterns in time series, and they often overlook the credibility of prediction results in practical applications. Therefore, there is still much room for improvement in long-term prediction of PM2.5. This study proposes a novel long-term point and interval prediction framework for urban air quality based on multi-source spatial and temporal data, which further quantifies the uncertainty and volatility of the prediction based on the accurate PM2.5 point prediction. In this model, firstly, multi-source datasets from multiple monitoring stations are preprocessed. Subsequently, spatial clustering of stations based on POI data is performed to filter out strongly correlated stations, and feature selection is performed to eliminate redundant features. In this paper, the ConvFormer-KDE model is presented, whereby local patterns and short-term dependencies among multivariate variables are mined through a convolutional neural network (CNN), long-term dependencies among time-series data are extracted using the Transformer model, and a direct multi-output strategy is employed to realize the long-term point prediction of PM2.5 concentration. KDE is utilized to derive prediction intervals for PM2.5 concentration at confidence levels of 85%, 90%, and 95%, respectively, reflecting the uncertainty inherent in long-term trends of PM2.5. The performance of ConvFormer-KDE was compared with a list of advanced models. Experimental results showed that ConvFormer-KDE outperformed baseline models in long-term point- and interval-prediction tasks for PM2.5. The ConvFormer-KDE can provide a valuable early warning basis for future PM2.5 changes from the aspects of point and interval prediction.

Integrated Risk Assessment of Mountainous Long-Distance Oil and Gas Pipelines Based on Multisource Spatial Data.

Pipeline risk assessment is crucial for pipeline safety management and operation. The aim of this study is to develop a comprehensive assessment model that accurately evaluates pipeline risks and ensures the safe and reliable operation of the pipeline system. The model is based on multisource spatial data and is primarily applicable to long-distance oil and gas pipelines that traverse complex geological conditions in mountainous areas. The research is conducted using the example of the Jinliwen natural gas pipeline in Zhejiang Province, China. By analyzing the geological data of the study area and the potential risks that the pipeline may encounter, a comprehensive risk assessment indicator system for the pipeline was developed using slope units to divide pipeline sections. The pipeline risk levels are classified using the K-means clustering-entropy weighted-random forest algorithm. The model is evaluated using accuracy (Acc), precision (Pre), recall (R), F1-score, and the ROC curve. The results show that the model has an accuracy of 0.917, a precision of 0.92, a recall of 0.916, an F1-score of 0.914, and an AUC (Area Under Curve) of 0.93, indicating its strong predictive capability. The risk assessment results demonstrate a strong consistency when compared with actual incident events. This indicates that the constructed model effectively reflects the influencing factors of pipeline risk, providing a basis for pipeline risk assessment and disaster prevention and mitigation efforts in similar regions.

Spatial-temporal patterns and influencing factors of the Building Green View Index: A new approach for quantifying 3D urban greenery visibility

The vertical expansion of urbanization has increased the morphological heterogeneity of the urban landscape, affecting the physical and emotional wellbeing of urban dwellers by obstructing the view of greenery. In this study, multisource spatial data was used to calculate the Building Green View Index (BGVI). Baidu Street View (BSV) images were collected for comparison with the corresponding BGVI results. A random forest model was used to analyze the contributions and marginal effects of multiple influencing factors on BGVI. The results indicated that approximately 76.10 % of the sampled sites had a higher BGVI than the street view green view index, indicating buildings' superiority of visible greenery in height. The western edge of the research region frequently had the highest BGVI. Meanwhile, the hotspot regions were primarily located in the west, which was more consistent with the distribution of high value zones. The green area within the maximum visible distance, the maximum visible distance, and the average height were the most influential factors of BGVI according to marginal effects analysis with the highest IncNodePurity. As a quantitative measure of urban dwellers’ visual accessibility to green space, the BGVI will contribute to urban green planning and the development of landscape architecture.

Spatial Identification and Distribution Pattern of the Complexity of Rural Poverty in China Using Multisource Spatial Data

Regional poverty is one of the most serious challenges facing the world today. Poverty, antipoverty, and poverty alleviation are the focus of the attention of scholars and the public. This paper takes China’s counties as the research unit, selects the influencing factors of poverty from natural and socio-economic factors, establishes an evaluation index system, simulates the natural poverty index and socio-economic poverty eradication index of each county, and clarifies the distribution characteristics of spatial poverty using GIS spatial analysis and BP artificial neural network. The results indicate that natural factors are the main cause of poverty in Chinese counties, with 710 counties having a high natural poverty index, accounting for nearly 30% of the total number of counties in the country. The national county-level natural poverty index shows a clear strip distribution pattern along latitude and longitude, with a strip distribution from north to south and from west to east; socio-economic factors have played a certain role in poverty alleviation, with as many as 1521 counties with low socio-economic poverty alleviation indices, accounting for approximately 64% of the total number of counties in the country. The spatial distribution of the county-level socio-economic poverty alleviation index is relatively fragmented. Through spatial scanning statistics, a total of 44 county poverty pressure index risk clusters reached a statistical significance level, involving 243 counties and districts. In poverty reduction practice, the internal counties and districts of contiguous poverty-stricken areas should strengthen cooperation and exchange. In the process of poverty alleviation and development, targeted poverty alleviation and economic development should be carried out based on the poverty-dominant type and self-development ability of the county, in order to improve efficiency. Regions that are relatively prosperous and have taken the lead in poverty reduction should play a leading and exemplary role in strengthening the radiation power of regional central cities. The prominent feature of this study is the comprehensive utilization of multisource data and the use of new spatial analysis methods (flexible spatial scanning method is widely used in the field of infectious disease prevention and control research). By constructing a multidimensional poverty measurement system that includes natural and social factors, it distinguishes the differences between the factors that cause poverty and the factors that eliminate poverty in regional poverty. At the same time, the flexible spatial scanning detection method was used to detect the differentiation mechanism of poverty spatial patterns.

The identification and efficiency evaluation of industrial parcels by integrating multi-source spatial data in Shenzhen, South China

ABSTRACT Industrial land status, encompassing quantity, distribution and efficiency, is pivotal in urban planning and sustainable development. However, obtaining detailed and up-to-date industrial land maps poses a significant challenge in many developing countries due to rapid urban expansion. Furthermore, the identification and efficiency assessment of industrial land at the parcel scale are rarely undertaken. To address these challenges, our study proposes an innovative approach to map industrial land, categorize its types and assess its efficiency at the parcel scale. We select Shenzhen, one of China’s largest industrial cities, as a case study to demonstrate the effectiveness of our method. Our results unveil several key findings: (1) Shenzhen’s urban area was meticulously segmented into 15,591 multi-size socio-economic parcels, of which 5,738 were identified as industrial parcels (IPs); (2) IPs were further categorized into three distinct types: office industrial parcels at 69.47%, manufacturing industrial parcels at (6.08%) and comprehensive industrial parcels at 24.45%; (3) Their efficiency was classified into four levels: low (20.30%), middle (35.73%), fine (35.97%) and high (8.00%). Efficient IPs were predominately concentrated in the southwest, whereas other IPs clustered in the northwest. Our findings can provide valuable insights into industrial land identification, classification and efficiency evaluation at parcel scale.

Modeling the Spatial Distribution of Population Based on Random Forest and Parameter Optimization Methods: A Case Study of Sichuan, China

Spatial population distribution data is the discretization of demographic data into spatial grids, which has vital reference significance for disaster emergency response, disaster assessment, emergency rescue resource allocation, and post-disaster reconstruction. The random forest (RF) model, as a prominent method for modeling the spatial distribution of population, has been studied by many scholars, both domestically and abroad. Specifically, research has focused on aspects such as multi-source data fusion, feature selection, and data accuracy evaluation within the modeling process. However, discussions about parameter optimization methods during the modeling process and the impact of different optimization methods on modeling accuracy are relatively limited. In light of the above circumstances, this paper employs the RF model to conduct research on population spatialization with multi-source spatial information data. The study primarily explores the differences in model parameter optimization achieved through random search algorithms, grid search algorithms, genetic algorithms, simulated annealing algorithms, Bayesian optimization based on Gaussian process algorithms, and Bayesian optimization based on gradient boosting regression tree algorithms. Additionally, the study investigates the influence of different optimization algorithms on the accuracy of population spatialization modeling. Subsequently, the model with the highest accuracy is selected as the prediction model for population spatialization. Based on this model, a spatial population distribution dataset of Sichuan Province at a 1 km resolution is generated. Finally, the population dataset created in this paper is compared and validated with open datasets such as GPW, LandScan, and WorldPop. Experimental results indicate that the spatial population distribution dataset produced by the Bayesian optimization-based random forest model proposed in this paper exhibits a higher fitting accuracy with real data. The Coefficient of Determination (R2) is 0.6628, the Mean Absolute Error (MAE) is 12,459, and the Root Mean Squared Error (RMSE) is 25,037. Compared to publicly available international datasets, the dataset generated in this paper more accurately represents the spatial distribution of the population.

Exploring the Relationships between Tradeoffs and Synergies among Island Ecosystem Service Bundles: A Study on Zhoushan Archipelago Located on the Southeast Coast of China

Due to the rapid rise of China’s coastal economic zone, the urbanization of the surrounding islands has accelerated. Intensive disturbance caused by human activities and frequent changes in land types have resulted in the continuous erosion of ecological sources and the degradation of ecosystem services on the islands year by year. It is particularly important to explore sustainable development strategies for the islands to achieve a balance between conservation and development, which is particularly important for the islands in the process of urbanization. Taking Zhoushan Archipelago as an example, this paper uses multi-source spatial data and employs InVEST models and USLE to quantify island ecosystem services. Furthermore, using principal component analysis and cluster analysis, the study aims to identify clusters of island ecosystem services and investigate their tradeoffs, synergistic mechanisms, and regional heterogeneity using spatial analysis. In addition to providing island urban planners with effective zoning governance recommendations and assistance in spatial planning to promote coordinated and sustainable development, the findings of this study can assist in the development of appropriate management plans for each ecological functional service cluster on islands.

Threshold effect of data amount and grid size on urban land use type identification using multi-source data fusion

Reliable urban land use maps are important for sustainable development and planning. Currently, the effects of different data source combinations and grid sizes on mapping results have rarely been studied. To reduce subjectivity in data selection, 10 collected multi-source spatial data were combined by traversal to create 1013 simulated combination schemes. Considering the size range of these data sources, 10 fusion grid sizes were selected. Then, a multi-source data learning model for urban land use classification (ULUC) was established by combining convolutional neural networks and long short-term memory. By taking Jinshui District (Zhengzhou, China) as an example, 10130 ULUC mappings were obtained. The maximum accuracy (82.9%) was achieved in the combination scheme D1D2D3D5D6D7D8D9D10 at a grid size of 30 m. The optimal solution among simulation 10130 schemes had an accuracy of 82.9%, a 14.7% improvement compared to the average accuracy of 67.6%. It is found that (1) The maximum accuracy showed a tendency to increase and then decrease with the increase in the variety of multi-source data combinations;(2) As the grid size decreases, the maximum accuracy also exhibited a tendency to increase and then decrease; (3) There was a significant threshold effect for both data combination types and grid sizes.

Identification and Optimization Strategy for the Ecological Security Pattern in Henan Province Based on Matching the Supply and Demand of Ecosystem Services

Ecological security pattern construction is a fundamental approach to ensuring regional ecological security and enhancing human welfare. Taking Henan Province, a typical region of China’s main agricultural production area, as the study area, we use multi-source spatial data to calculate the high-value areas of ecosystem services and identify ecological source sites. On the basis of calculating the degree of land use development and GDP per land and population density to determine high-demand areas of the ecosystem, the ecological resistance surface coefficient is modified with nighttime lighting data, and the ecological corridor between the source site and the high-demand area is extracted using the minimum cumulative resistance model, so as to construct and optimize the regional ecological security pattern. The following results are presented. (1) The total area of ecological source sites in Henan Province is 3.02 × 104 km2, accounting for 18.12% of the total study area, which is concentrated in the mountainous areas of East Henan and South Henan. (2) The high-demand area of ecosystem services has a total area of 4.1 × 104 km2, accounting for 24.73% of the total study area, mainly concentrated in the central and eastern regions of Henan, with poor spatial matching of ecosystem service supply and demand. (3) The total length of ecological corridors is 1062.3 km, and the overall pattern forms the main corridor axes. The identification of ecological corridors focuses on the ecological demand space and puts forward suggestions for the optimization of the regional ecological security pattern based on it.

Study on Land Subsidence Simulation Based on a Back-Propagation Neural Network Combined with the Sparrow Search Algorithm

Rapid simulation of land subsidence can provide an effective means of facilitating its management and control. This paper innovatively introduced a back-propagation (BP) neural network subsidence simulation model which was optimized by the sparrow search algorithm (SSA), hereinafter referred to as the SSA-BP model, to simulate land subsidence in Tongzhou District, Beijing. First, based on the time series interferometric synthetic aperture radar (InSAR) monitoring, different technologies such as spatial analysis, Google Earth Engine (GEE), and machine learning were utilized together with a variety of multi-source spatial data, including groundwater level, compressible layer thickness, Visible Infrared Imager Radiometer Suite (VIIRS) nighttime light images, and the OpenStreetMap (OSM) road distribution. Furthermore, we determined the optimal grid scale for land subsidence research and worked out a multifactor-driven SSA-BP land subsidence simulation model for which sensitivity analysis was performed with great care. Main conclusions: (1) From November 2010 to January 2020, the average annual surface displacement rate in Beijing’s subcentre, Tongzhou, ranged from −133.9 to +3.9 mm/year. (2) Our SSA-BP land subsidence simulation model allows for a relatively high overall accuracy. The average root mean square error (RMSE) is 5.00 mm/year, the average mean absolute error (MAE) is 3.80 mm/year, and the average coefficient of determination (R2) is 0.83. (3) Sensitivity analysis shows that the SSA-BP model driven by multiple factors has a far better simulation performance than the model driven by any single weighting factor, and displays the highest sensitivity to the groundwater level factor among all the weighting factors. In terms of subdividing the study area, our SSA-BP land subsidence model runs with multifunctional zones displayed a higher accuracy than that without. This paper made use of a machine learning model to simulate land subsidence in Beijing’s Tongzhou District and explored the sensitivity of different factors to land subsidence, which is helpful for its scientific prevention.

Quality Evaluation of Park Green Space Based on Multi-Source Spatial Data in Shenyang

Park green spaces serve as essential urban public recreation spaces and represent significant components of urban construction land. Investigating the quality of park green spaces is a vital requirement for the establishment of a high-quality urban living environment and the enhancement of land use efficiency. This study developed a quality evaluation index system for park green spaces in Shenyang, considering the constituent elements of recreational spaces. By categorizing 168 park spaces into four size grades and employing the entropy-TOPSIS method with multiple data sources, the research assessed and analyzed the generally quality of park green spaces in Shenyang. Additionally, the study analyzed factors limiting park green spaces and introduced the Recreational Quality Index (RQI) to represent the distribution of park green space resources within the study area. The results demonstrated that 46 low-quality park green spaces require urgent improvement, and the riverside area and the vicinity of the second ring road possess high-quality park green space resources. Barriers of the same scale in park green spaces exhibited similar characteristics. Park green spaces in Huanggu District were allocated recreational resources optimally. The analysis determined that system construction and deep cultural heritage are crucial factors. This study can offer a scientific basis and recommendations for the development of park green spaces and the enhancement of efficient urban land use.

Identification of Urban Spatial Structure of Pearl River Delta Urban Agglomeration Based on Multisource Spatial Data

Identifying urban center structure and urban centers is an important component of urban geography research. This study aimed to propose an urban center identification method, which was constructed by combining nighttime light data, point-of-interest data, and population density data. The method involved constructing the Main Center Score Index and the Mean Subcenter Score Index and using the existing Getis-Ord Gi* model, natural discontinuity method, and other models. This study used this method to identify the urban center structure of the Pearl River Delta urban agglomeration. The results of this study are as follows: A total of 69 urban centers in the Pearl River Delta urban agglomeration were identified in this study, including 9 main centers and 60 subcenters. However, the urban center of the Pearl River Delta urban agglomeration exhibits the phenomenon of uneven spatial distribution, and the urban center is concentrated in the central area of the urban agglomeration. In the urban center structure, Guangzhou, Foshan, Zhaoqing, Huizhou, Dongguan, Shenzhen, Zhongshan, Jiangmen, and Zhuhai were polycentric structures. Among the dominant factors affecting the spatial distribution of urban centers, the dominant factor in Guangzhou and Foshan was the government-led spatial development model, the dominant factor in Dongguan and Zhongshan was the administrative division system, the dominant factors in Shenzhen and Zhuhai were the special economic zone policy and government industry guidance policy, and the dominant factors in Huizhou, Jiangmen, and Zhaoqing were geographical proximity, industrial transfer, and transportation infrastructure. In addition, this study compared the urban center identification results with three data sets and Chinese government urban planning documents to test the accuracy of the method for identifying urban centers. The results showed that the overall identification accuracy of urban centers in this study was 85.48%, among which Foshan exhibited the highest identification accuracy of 90.9%.

Exploring the Relationships between Land Surface Temperature and Its Influencing Factors Using Multisource Spatial Big Data: A Case Study in Beijing, China

A better understanding of the relationship between land surface temperature (LST) and its influencing factors is important to the livable, healthy, and sustainable development of cities. In this study, we focused on the potential effect of human daily activities on LST from a short-term perspective. Beijing was selected as a case city, and Weibo check-in data were employed to measure the intensity of human daily activities. MODIS data were analyzed and used for urban LST measurement. We adopted spatial autocorrelation analysis, Pearson correlation analysis, and spatial autoregressive model to explore the influence mechanism of LST, and the study was performed at both the pixel scale and subdistrict scale. The results show that there is a significant and positive spatial autocorrelation between LSTs, and urban landscape components are strong explainers of LST. A significant and positive effect of human daily activities on LST is captured at night, and this effect can last and accumulate over a few hours. The variables of land use functions and building forms show varying impacts on LST from daytime to nighttime. Moreover, the comparison between results at different scales indicates that the relationships between LST and some explainers are sensitive to the study scale. The current study enriches the literature on LST and offers meaningful and practical suggestions for the monitoring, early warning, and management of urban thermal environment with remote sensing technology and spatial big data sources.

Multi-scenario simulation of land use and land cover based on shared socioeconomic pathways: The case of coastal special economic zones in China

Urban land-use change simulations without considering the sustainable planning policies, especially in special economic park highly concerned by planners, might lack the reliability and availability. Thus, this study proposes a novel planning support systems integrating the Cellular Automata Markov chain model and Shared Socioeconomic Pathways (CA-Markov-SSPs) for predicting the changing of land use and land cover (LULC) at the local and system level by using a novel machine learning-driven, multi-source spatial data modelling framework. Using multi-source satellite data of coastal special economic zones from 2000 to 2020 as a sample, calibration validation based on the kappa indicates a highest average reliability above 0.96 from 2015 to 2020, and the cultivated land and built-up land classes of LULC is the most significant changes in 2030 by using the transition matrix of probabilities, the other classes except water bodies continue to increase. And the non-sustainable development scenario can be prevented by the multiple level collaboration of socio-economic factors. This research intended to help decision makers to confine irrational urban expansion and achieve the sustainable development.

Aggravated multi-source air pollution exposure caused by open fires in China

Open fires produce pollutants that critically harm human health. Differences in emission characteristics and toxic substances from the burning of different biomasses lead to distinct exposure patterns. However, the details of these exposure patterns are still poorly known due to the uncertainties regarding cross-region transport. In this study, we investigated the particulate matters (PM) and ozone (O3) pollution produced by open fires from different vegetation types and locations, and assessed their exposure risks throughout China, by using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model combined with multi-source spatial data. Results revealed that open fires significantly enhanced the pollutant concentrations in China, which aggravated the risks of PM2.5 and O3 exposures for 303 million and 405 million people, respectively. Compared with cropland fires, forest and grassland fires had greater impacts on PM2.5 exposure, significantly increasing exposure risk for 208 million people in southern China. Cropland fires contribute more to O3 exposure in central and eastern China, which could increase the risk for 341 million people. For the location factor, the fires outside China, mainly forest and grassland burning, affected more than 74 million people, while domestic fires, mainly cropland burning, affected 354 million people. Therefore, the results suggest that the burning vegetation type and cross-region transport of pollutants should be considered in the formulation of national and regional pollution control measures, especially in densely populated areas.

Near-real-time global gridded daily CO2 emissions 2021

We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from the near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies’ effectiveness and make adjustments quickly.

Assessment of Influence Mechanisms of Built Environment on Street Vitality Using Multisource Spatial Data: A Case Study in Qingdao, China

Street vitality is a significant indicator of a city’s capacity for sustainable development. Significant progress has been made on the basis of measurements of a single indicator of street vitality, but few studies have used multisource data to measure street vitality in a comprehensive way. In this study, in order to explore the multidimensional vitality characteristics of streets, streets were taken as the analysis unit, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) evaluation model with combined weights was used to identify the spatial pattern of streets vitality from social, economic, and cultural dimensions using multisource spatial data such as Baidu heat map, Meituan store rating, and cultural facilities points of interest in the main urban area of Qingdao City, China. Using a Multiscale Geographically Weighted Regression (MGWR) model, the spatial correlations and differences between street built environment components and multidimensional street vitality were examined, to reveal the influence mechanism of street vitality creation in each street. The study found that the comprehensive vitality of the streets in the main urban area of Qingdao City exhibits the spatial differentiation features of “weak east–west, strong central, multicenter, cluster type”. Furthermore, although commercial and public services are essential for enhancing street vitality and attracting crowds, a very high degree of functional mix has not resulted in a high degree of street vitality. Lastly, high spatial heterogeneity between built environment factors and street vitality necessitates considering the functional positioning and development basis of the street, tailoring to local conditions and policies, considering the street’s vitality development status and development needs, complementing strengths, promoting coordinated development, and releasing and enhancing the street’s vitality. Therefore, it is essential to explore street vitality and its influencing mechanisms to improve people’s quality of life and promote sustainable urban development.