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The COVID-19 pandemic has dramatically changed human daily life. To mitigate the pandemic’s impacts, different countries and regions implemented various policies to contain COVID-19 and residents showed diverse responses. These human responses in turn shaped the uneven spatial-temporal spread of COVID-19. Such human-pandemic interaction is complex, dynamic, and interconnected. Delineating the reciprocal effects between human society and pandemics is crucial for preparing for and managing future epidemics. Geospatial big data acquired through mobile applications and sensor networks have facilitated near-real-time tracking and assessment of human responses to the pandemic, enabling a surge in researching human-pandemic interactions. However, these investigations involve inconsistent data sources, human activity indicators, relationship detection models, and analysis methods, leading to a fragmented understanding of human-pandemic dynamics. To assess the current state of human-pandemic interactions research using geospatial big data, we conducted a synthesis study based on 67 selected publications between 25 March 2020, and 9 January 2023. We extracted information from each article across six categories, i.e. publication details, research context, research area and time, data, methodological framework, and results and conclusions. Results reveal that the influence of stay-at-home policies on mobility decrease varied regionally, showing limited effectiveness in Europe compared to the US. The positive correlations between human mobility and COVID-19 case rates evolved through time and were highest in the initial outbreak in 2020. Public awareness generally peaked prior to the peaks in COVID-19 cases, with varying intervals of 0 to 19.8 days observed across different countries. This study summarizes the research characteristics of selected articles and highlights the need for future research to spatially and temporally model the long-term, bidirectional causal relationships within human-pandemic systems to inform evidence-based, hyperlocal pandemic mitigation strategies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has dramatically changed human daily life. To mitigate the pandemic’s impacts, different countries and regions implemented various policies to contain COVID-19 and residents showed diverse responses. These human responses in turn shaped the uneven spatial-temporal spread of COVID-19. Such human-pandemic interaction is complex, dynamic, and interconnected. Delineating the reciprocal effects between human society and pandemics is crucial for preparing for and managing future epidemics. Geospatial big data acquired through mobile applications and sensor networks have facilitated near-real-time tracking and assessment of human responses to the pandemic, enabling a surge in researching human-pandemic interactions. However, these investigations involve inconsistent data sources, human activity indicators, relationship detection models, and analysis methods, leading to a fragmented understanding of human-pandemic dynamics. To assess the current state of human-pandemic interactions research using geospatial big data, we conducted a synthesis study based on 67 selected publications between 25 March 2020, and 9 January 2023. We extracted information from each article across six categories, i.e. publication details, research context, research area and time, data, methodological framework, and results and conclusions. Results reveal that the influence of stay-at-home policies on mobility decrease varied regionally, showing limited effectiveness in Europe compared to the US. The positive correlations between human mobility and COVID-19 case rates evolved through time and were highest in the initial outbreak in 2020. Public awareness generally peaked prior to the peaks in COVID-19 cases, with varying intervals of 0 to 19.8 days observed across different countries. This study summarizes the research characteristics of selected articles and highlights the need for future research to spatially and temporally model the long-term, bidirectional causal relationships within human-pandemic systems to inform evidence-based, hyperlocal pandemic mitigation strategies.

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin–destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales—10–100 m and 100–3000 m—to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems. [ABSTRACT FROM AUTHOR]

Urban functional zones (UFZs) serve as the spatial carriers embodying urban economic and social activities, thus making the accurate mapping of UFZs imperative for urban planning, management, and sustainable development. Traditional remote sensing-based methods for mapping UFZs primarily capture the physical attributes of ground objects (such as land cover and spatial patterns) while overlooking the inherent social and economic characteristics, as well as the comprehensiveness, heterogeneity, and scale-dependency. With the rapid development of intelligent sensors, the available geospatial big data, reflecting individual human activities, have greatly increased and enable users to analyze UFZs from both physical and socioeconomic aspects. In this study, we provide a comprehensive review of the existing literature on UFZ mapping using remote sensing and geospatial big data. Specifically, this study summarizes the state of the art from three perspectives: spatial analysis units, representation features derived from multi-source data, and the function classification methods of UFZs. Spatial analysis units encompass regular grids, road blocks, image segmentation units, traffic analysis zones, and buildings. Data features consist of the remote sensing image-derived features (such as visual, spatial pattern, and abstract features) and the geospatial big data-derived features (such as spatial, attribute, and temporal features). For function classification, kernel density estimation, cluster analysis, supervised machine learning, probabilistic topic models, and deep learning methods have been applied. Finally, this study discusses the challenges and limitations of UFZ mapping units, the bias issues of geospatial big data, and the integration of remote sensing and geospatial big data. Meanwhile, future opportunities to these issues and the expansion of functions from 2D to 3D are discussed, in order to formulate an enhanced UFZ mapping framework.

The emergence of the Digital Twin of Earth (DTE) signifies a pivotal advancement in the field of the Digital Earth, which includes observations, simulations, and predictions regarding the state of the Earth system and its temporal evolution. While the Geospatial Data Cubes (GDCs) hold promise in probing the DTE across time, existing GDC approaches are not well-suited for real-time data ingestion and processing. This paper proposes the design and implementation of a real-time ingestion and processing approach in Geospatial Data Cube (RTGDC), achieved by ingesting real-time observation streams into data cubes. The methodology employs a publish/subscribe model for efficient observation ingestion. To optimize observation processing within the data cube, a distributed streaming computing framework is utilized in the implementation of the RTGDC. This approach significantly enhances the real-time capabilities of GDCs, providing an efficient solution that brings the realization of the DTE concept within closer reach. In RTGDC, two cases involving local and global scales were implemented, and their real-time performance was evaluated based on latency, throughput, and parallel efficiency.

The development of urban underground space (UUS) is an important approach to the sustainable development of modern cities. This means that assessing potential demand differences in undeveloped UUS can inform the areas that need to be prioritized for development in urban planning and development. The recent emergence of geospatial big data provides fine-grained support for capturing changes in the development of various urban subjects. However, to date, research applying geospatial big data to UUS assessment has been limited. In this study, UUS evaluation indicators were constructed using geospatial Big Data, and the weights of the indicators driving the existing UUS demand were determined based on the Geodetector model, avoiding the drawbacks of human subjective influence and the lack of reference of general methods. It also combined the linear weighting method to quantify the UUS demand in undeveloped areas within the grid based on the attribute values and weights of each indicator and used the magnitude of the resulting demand values to provide a grid-scale judgment of priority areas for future underground space development. Taking the Qiantang District of Hangzhou, China, as an example, it was verified that the high-demand areas are consistent with the existing UUS distribution, indicating that the UUS demand evaluation model based on geospatial big data established in this study is feasible and accurate, and in this way, new high-demand areas were circled in the undeveloped areas as a direction for future urban underground development. [ABSTRACT FROM AUTHOR]

Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.

There is a growing interest in the optimization of vehicle fleets management in urban environments. However, limited attention has been paid to the integrated optimization of electric taxi fleets accounting for different operations as well as complex spatiotemporal demand dynamics. To this end, this study develops a real-time recommendation framework based on deep reinforcement learning (DRL) for electric taxis (E-taxis) to improve their system performance with explicit modeling of multiple vehicle actions and varying travel demand across space and over time. Spatiotemporal patterns of urban taxi travels are extracted from large-scale taxi trajectories. Spatiotemporal strategies are proposed to coordinate E-taxis' repositioning and recharging with optimized recommendation for next destinations and charging stations. A spatiotemporal double deep Q-network (ST-DDQN) is embedded in the DRL framework to maximize the daily profit. A prototype real-time recommendation system for E-taxis is implemented for the decision-making of E-taxi drivers and sensitivity analyses are carried out. The experimental results in Shenzhen, China suggest that the proposed framework could improve the overall performance. This study will benefit the promotion of connected E-taxis and the development of clean and smart transportation. [ABSTRACT FROM AUTHOR]

The study reviews a broad body of published literature on Geospatial big data or big geospatial data to analyse the nature of the research context, identify trends in the research area, and provide future research directions. Web of Science and Scopus databases were used to collect and convert data to information through an analysis conducted using the Bibliometrix R package and Biblioshiny. 1114 articles were retrieved after de-duplication and the analysis shows that the leading term, Geospatial big data, was first seen in the year 2012. Since then, it has captured the attention of researchers with a yearly increase in articles on the topic. The percentage decline from 2017 to 2018 was 6%, while that between 2020 and 2021 was 17%. On the other hand, the overall increase in annual scientific production between 2012 and 2021 was about 95%, which indicates a massive growth of interest in Geospatial big data. The results identify the country with the largest number of published articles, the most globally influential author, and the author with the highest average number of citations per year, while research collaborations among countries and the top leading institutions were also identified. Furthermore, the study compared author keywords plus outputs, using co-word analysis. Finally, the study identified gaps and proffered future research directions along which scholarly investigations could expand. The paper suggests that future research should focus on Geospatial big data applications to social media, urban planning, disaster management, and incorporation of artificial intelligence in addressing sustainable development challenges. [ABSTRACT FROM AUTHOR]

With the rapid pace of urbanization, enhancing the quality of life has become an urgent demand for the general public in both developed and developing countries. This study addresses the pressing need to understand the spatial distribution and underlying mechanisms of urban leisure activity participation. To achieve this, we propose a novel methodological framework that integrates diverse big data sources, including mobile phone signaling data, urban geospatial data, and web-crawled data. By applying this framework to the urban area of Nanjing, our study reveals both the temporal and spatial patterns of urban leisure activity participation in the city. Notably, leisure activity participation is significantly higher on weekends, with distinctive daily peaks. Moreover, we identify spatial heterogeneity in leisure activity participation across the study area. Leveraging the OLS regression model, we design and quantify a comprehensive set of 12 internal and external indicators to explore the formation mechanisms of leisure participation for different leisure activity types. Our findings offer valuable guidance for urban planners and policymakers to optimize the allocation of resources, enhance urban street environments, and develop leisure resources in a rational and inclusive manner. Ultimately, this study contributes to the ongoing efforts to improve the quality of urban life and foster vibrant and sustainable cities. [ABSTRACT FROM AUTHOR]

Spatial optimization is an interdisciplinary field dedicated to the scientific and rational allocation of resources spatially, which has received tremendous attention across various disciplines including geography, operations research, management science, and computer science. Spatial optimization provides important theoretical foundations and solutions for determining optimal spatial arrangements or configurations of entities, resources, or goods. However, the complexity of spatial optimization problems poses critical challenges in spatial optimization problems modeling, and efficiently solving. Recently, the surge of multi-source geospatial big data, the emerging technologies such as geospatial artificial intelligence (GeoAI), and the advancements of computing technologies along with the ever-expanding capabilities of computer and data storage resources, have created significant opportunities to the effective and efficient addressing of spatial optimization issues, even though numerous challenges still exist. Therefore, this paper aims to revisit the existing literature of spatial optimization quantitatively and qualitatively, as well as reflect on the opportunities and challenges, especially posed by geospatial big data and GeoAI. Through these efforts, we seek to stimulate greater engagement in spatial optimization research and practices, accelerate the integration of novel technologies and methods, as well as collectively advance the development of the field.

The economic viability of an urban area in terms of trade and shopping significantly impacts its residents’ quality of life and is crucial for any sustainable development initiative. Geographic information systems (GIS) are well established, but the use of GIS technology within finance and trade analysis is still in its infancy. In this article, we highlighted the potential of GIS technology and big data analytics and demonstrated the importance of thinking in spatial terms for analysing patterns within the trade and finance industries. We studied spatiotemporal trade and shopping patterns in the city of Tabriz using data generated by customer purchase transactions obtained from 5200 stores, shopping, business and service centres. We employed time series transaction data collected from the points of sale in stores, shopping, service and business centres located in different areas of the city. We applied four well known geospatial big data driven approaches including machine learning nearest neighbour, kernel density estimation, space–time pattern mining and spatiotemporal coupling tele-coupling for detecting and mapping of spatial trade hotspot patterns. The results of this study indicated the potential of GIScience methods for the explicit spatial mapping of trade and shopping patterns. The results revealed that the city centre, particularly the Bazaar of Tabriz, acts as the city’s heart of trade, and we identify additional major business hotspots. Furthermore, the results allow for studying the impacts of unbalanced urban development in Tabriz, where the wealthy suburbs with high quality of life, such as Valiasr and Elguli, host the major shopping hotspots. The spatial patterns obtained enable local stakeholders, decision makers and authorities to develop strategic plans for urban sustainable development in Tabriz. The geospatial big data approach used can stimulate novel and progressive research. Results of this study demonstrate methodological advancements in GIScience by ’spatializing’ individual purchase data and therefor proposing an explicit geospatial big data analysis approach.

Abstract The development of high-precision location tracking devices and advancements in data collection, storage, transmission technologies, and data mining algorithms have led to the availability of large datasets with high spatiotemporal resolution. These geospatial big data can be used to identify human movement patterns in urban areas. However, identifying human movement patterns may yield different results depending on the scale size used. In this paper, we employed first and second order texture analysis algorithms to identify spatial patterns of human movement for various scale sizes based on taxi trajectory data from Nanjing, China. The results demonstrated that texture analysis can quantify changes in human movement patterns for different scale sizes in an urban area. Furthermore, the results may differ based on the location of the study area. This study contributed both methodologically and empirically. Methodologically, we used texture analysis to examine the impact of different scale sizes on the extraction of aggregated human travel patterns. Empirically, we quantified the effects of different scale sizes on extracting aggregated travel patterns of an urban area. Overall, the findings of this study can have significant implications for urban planning and policy-making, as understanding human movement patterns at different scales can provide valuable insights for optimizing transportation systems and enhancing overall urban mobility.

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin–destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales—10–100 m and 100–3000 m—to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems.

The human spatial perception of urban streets has a high complexity and traditional research methods often focus on access surveys of human perception. Urban streets serve as both a direct conduit for pedestrians' impressions of a city and a reflection of the spatial quality of that city. Street-view images can provide a large amount of primary data for the image semantic segmentation technique. Deep learning techniques were used in this study to collect the boring, beautiful, depressing, lively, safe, and wealthy perception scores of street spaces based on these images. Then, the spatial pattern of urban street-space quality perception was analyzed by global Moran's I and GIS hotspot analyses. The findings demonstrate that various urban facilities affect street quality perception in different ways and that the strength of an influencing factor's influence varies depending on its geographical location. The results of the influencing factors reveal the difference in the degree of influence of positive and negative influencing factors on various perceptions of the visual dimension of pedestrians. The primary contribution of this study is that it reduces the potential bias of a single data source by using multi-dimensional impact analysis to explain the relationship between urban street perception and urban facilities and visual elements. The study's findings offer direction for high-quality urban development as well as advice for urban planning and enhanced design. [ABSTRACT FROM AUTHOR]

The development of high-precision location tracking devices and advancements in data collection, storage, transmission technologies, and data mining algorithms have led to the availability of large datasets with high spatiotemporal resolution. These geospatial big data can be used to identify human movement patterns in urban areas. However, identifying human movement patterns may yield different results depending on the scale size used. In this paper, we employed first and second order texture analysis algorithms to identify spatial patterns of human movement for various scale sizes based on taxi trajectory data from Nanjing, China. The results demonstrated that texture analysis can quantify changes in human movement patterns for different scale sizes in an urban area. Furthermore, the results may differ based on the location of the study area. This study contributed both methodologically and empirically. Methodologically, we used texture analysis to examine the impact of different scale sizes on the extraction of aggregated human travel patterns. Empirically, we quantified the effects of different scale sizes on extracting aggregated travel patterns of an urban area. Overall, the findings of this study can have significant implications for urban planning and policy-making, as understanding human movement patterns at different scales can provide valuable insights for optimizing transportation systems and enhancing overall urban mobility. [ABSTRACT FROM AUTHOR]

Enormous amount of earth information, gathered from satellite sensors, simulations, and other resources, are collectively referred to as Big Earth Observation Data (BEOD). The data contains remarkable insights and spatio-temporal stamps of pertinent Earth phenomena for enhancing our knowledge, responding, and addressing demanding situations of earth sciences and observations. However, However, traditional data mining algorithms are generally time-inefficient, making it difficult to process and analyze BEOD. To address this challenge, we explore two ways to enhance scalability: 1) improving the algorithm with specific parameters or data modifications when run on a single machine, and 2) making the algorithm parallel through distributed execution on multiple machines, such as with cluster-based implementations. We also suggest improvements for existing techniques and widely used algorithms for processing BEOD. In this review, we conduct a systematic review of data mining techniques for classification, clustering, prediction, regression, association rules, pattern mining, and anomaly detection to determine their scalability and performance on various Earth observation use cases. We explored advanced mining techniques, including statistical, machine learning, and deep learning approaches, for handling BEOD at scale. We also identified potential challenges, open research issues, and provide future directions for the development of scalable algorithms for mining BEOD. We observed that applying data mining techniques on BEOD introduces serious concerns since the data has both spatial and temporal components. Statistical and machine learning models such as ARIMA, SARIMA, Naive Bayes, Bayesian networks, KNN, and K-means, as well as SVM, are not suitable for working with the volume and heterogeneity present in the data, but it can be improved by employing big data environments. Nowadays, deep learning-based techniques are popularly used for working with large amounts of data, but it requires specialized systems and upgrades as the data volume increases. This issue can be addressed through a big data platform. A unified deep learning architecture can be employed to handle both the spatial and temporal components of BEOD, and performance can be improved by deploying the architecture on a big data environment. Therefore, this study also reveals that, although deep learning architectures are efficient and trending, traditional statistical methods and machine learning can achieve competitive or sometimes improved performance with the involvement of big data technologies and/or internal data representation. [ABSTRACT FROM AUTHOR]

Evidence suggests city grouping is an important way to implement urbanization in China. However, the Chengdu-Chongqing Economic Circle (CCEC) is a typical dual-core structure, and the development level of each city is different. If we do not focus on the key directions for urban development, it will not be conducive to the new-type urbanization process. Therefore, we use spatial analysis techniques and geographic big data sets to construct an approach for urban system layout optimization from a global perspective. It mainly includes urban extended trend analysis based on night light, multi-modal traffic network analysis, and spatial economic density analysis using Open Street Map (OSM) and Point of Interest (POI) data. The research results show the following interesting findings. Firstly, the historical relationship of cities has a significant impact on city grouping, and efficient transportation connections and prosperous enterprise distribution are key conditions for urban grouping during the acceleration period of urbanization. Secondly, the development of urban grouping should break through administrative restrictions and achieve a moderate separation of administrative divisions and economic divisions. It is beneficial to the rapid growth of the city group and the improvement of the internal structure. Thirdly, the urban group of Southern Sichuan and Western Chongqing (SSWC) is the region with the most potential for growth in CCEC. The urban expansion index (UEI) of the Yibin-Luzhou area is 2.16, and the spatial economic density has increased by 130/km2 in the past decade. Providing flexible development authority and focus on the construction of the Yibin-Luzhou Urban Belt along the Yangtze River is an important way to integrate southern Sichuan. Moreover, we found the UEI has a good universality and it can be used for studying urban expansion trends and city relationships in rapidly developing regions, especially in metropolitan areas or urban agglomeration.

Accurate knowledge of the spatiotemporal distribution of gross domestic product (GDP) is critical for achieving sustainable development goals (SDGs). However, there are rarely continuous multitemporal gridded GDP datasets for China in small geographies, and less is known about the variable importance of GDP mapping. Based on remotely sensed and point-of-interest (POI) data, a geographical random forest model was employed to map China's multitemporal GDP distribution from 2010 to 2020 and to explore the regional differences in the importance of auxiliary variables to GDP modeling. Our new GDP density maps showed that the areas with a GDP density higher than 0.1 million CNY/km2 account for half of China, mainly distributed on the southeast side of the Hu-line. The proportion of the areas with GDP density lower than 0.05 million CNY/km2 has decreased by 11.38% over the past decade and the areas with an increase of 0.01 million CNY/km2 account for 70.73% of China. Our maps also showed that the GDP density of most nonurban areas in northeast China declined, especially during 2015–2020, and the barycenter of China's GDP moved 128.80 km to the southwest. These results indicate China's achievements in alleviating poverty and the widening gaps between the South and the North. Meanwhile, the number of counties with the highest importance score for POI density, population density, and nighttime lights in GDP mapping accounts for 52.76%, 23.66%, and 23.56%, respectively, which suggests that they play a crucial role in GDP mapping. Moreover, the relationship between GDP and auxiliary variables displayed obvious regional differences. Our results provide a reference for the formulation of a sustainable development strategy. [ABSTRACT FROM AUTHOR]

Rapid urbanization brings a series of dilemmas to the development of human society. To address urban sustainability, Sustainable Development Goal 11 (SDG 11) is formulated by the United Nations (UN). Quantifying progress and interactions toward SDG 11 indicators is essential to achieving Sustainable Development Goals (SDGs). However, it is limited by a lack of data in many countries, particularly at small scales. To address the gap, this study used systematic methods to calculate the integrated index of SDG 11 at prefecture-level cities with different economic groups in the Yellow River Basin based on Big Earth Data and statistical data, analyzed its spatial aggregation characteristics using spatial statistical analysis methods, and quantified synergies and trade-offs among indicators under SDG 11. We found the following results: (1) except for SDG 11.1.1, the performance of the integrated index and seven indicators improved from 2015 to 2020. (2) In GDP and disposable income groups, the top 10 cities had higher values, whereas the bottom 10 cities experienced greater growth rates in the integrated index. However, the indicators' values and growth rates varied between the two groups. (3) There were four pairs of indicators with trade-offs that were required to overcome and eight pairs with synergies that were crucial to be reinforced and cross-leveraged in the future within SDG 11 at a 0.05 significance level. Our study identified indicators that urgently paid attention to the urban development of the Yellow River Basin and laid the foundation for local decision-makers to more effectively implement the 2030 Agenda for Sustainable Development (the 2030 Agenda). [ABSTRACT FROM AUTHOR]

With the rapid pace of urbanization, enhancing the quality of life has become an urgent demand for the general public in both developed and developing countries. This study addresses the pressing need to understand the spatial distribution and underlying mechanisms of urban leisure activity participation. To achieve this, we propose a novel methodological framework that integrates diverse big data sources, including mobile phone signaling data, urban geospatial data, and web-crawled data. By applying this framework to the urban area of Nanjing, our study reveals both the temporal and spatial patterns of urban leisure activity participation in the city. Notably, leisure activity participation is significantly higher on weekends, with distinctive daily peaks. Moreover, we identify spatial heterogeneity in leisure activity participation across the study area. Leveraging the OLS regression model, we design and quantify a comprehensive set of 12 internal and external indicators to explore the formation mechanisms of leisure participation for different leisure activity types. Our findings offer valuable guidance for urban planners and policymakers to optimize the allocation of resources, enhance urban street environments, and develop leisure resources in a rational and inclusive manner. Ultimately, this study contributes to the ongoing efforts to improve the quality of urban life and foster vibrant and sustainable cities.

Improving the travel ratio of public transportation (PTR) is important for realizing low-carbon transportation and sustainable city development. However, limited by data resolution and model accuracy, existing research rarely involves the spatially refined calculation of PTR and the quantitative analysis of its influencing factors. In this study, based on multi-source geospatial big data, we propose a novel computational framework to solve the above problems. Specifically, we first design a linear programming-based three-step method, which realizes the calculation of PTR at 500-meter grid-pair scale for the first time; secondly, we develop a Beta-binomial model for regression analysis, which improves by more than 50% compared with traditional generalized linear models. The case of Wangjing area in Beijing shows that: the overall PTR in Wangjing is only 16%, which is much lower than the official expectation (45%), and less than 20% of origin–destination (OD) pairs meet the standard; among the influencing factors, the travel duration gap between public transportation and private cars, walking distance, number of transfers, and residential parking density have significant negative effects on PTR. Finally, this paper provides an implication of the proposed computational framework, i.e., the accurate detection of public transportation (PT) supply–demand imbalance areas, which proves its great potential in refined transportation optimization and sustainable urban planning.

Kernel density estimation (KDE) is a commonly used method for spatial point pattern analysis, but it is computationally demanding when analyzing large datasets. GPU-based parallel computing has been adopted to address such computational challenges. The existing GPU-parallel KDE method, however, utilizes only one GPU for parallel computing. Additionally, it assumes that the input data can be held in GPU memory all at once for computation, which is unrealistic when conducting KDE analysis over large geographic areas at high resolution. This study develops a multi-GPU-parallel and tile-based KDE algorithm to overcome these limitations. It exploits multiple GPUs to speedup complex KDE computation by distributing computation across GPUs, and approaches density estimation with a tile-based strategy to bypass the memory bottleneck. Experiment results show that the parallel KDE algorithm running on multiple GPUs achieves significant speedups over running on a single GPU, and higher speedups are achieved on KDE tasks of a larger problem size. The tile-based strategy renders it feasible to estimate high-resolution density surfaces over large areas even on GPUs with only limited memory. Multi-GPU parallel computing and tile-based density estimation, while incurring very little computational overhead, effectively enable conducting KDE for large-scale spatial point pattern analysis on geospatial big data. [ABSTRACT FROM AUTHOR]

Black communities in the United States have been disproportionately affected by the COVID-19 pandemic; however, few empirical studies have been conducted to examine the conditions of Black-owned businesses in the United States during this challenging time. In this article, we assess the circumstances of Black-owned restaurants during the entire year of 2020 through a longitudinal quantitative analysis of restaurant patronage. Using multiple sources of geospatial big data, the analysis reveals that most Black-owned restaurants in this study are disproportionately affected by the COVID-19 pandemic among different cities in the United States over time. The finding reveals the need for a more in-depth understanding of Black-owned restaurants' situations during the pandemic and further indicates the significance of carrying out place-based relief strategies. Our findings also urge big tech companies to improve existing Black-owned business campaigns to enable sustainable support. As the first to systematically examine the racialization of locational information, this article implies that geographic information systems (GIS) development should not be detached from human experience, especially that of minorities. A humanistic rewiring of GIS is envisioned to achieve a more racially equitable world. [ABSTRACT FROM AUTHOR]

This paper is the first in a series that aims to highlight the potential of satellite data obtained by remote sensing in the massive geospatial data processing stage. With the help of advanced open source analytics tools, the issue of deforestation is targeted, i.e. the detection of forest changes at ground level. Six techniques for visualizing forest losses are presented here, using Hansen maps and a technique for quantifying forest losses found in Romania in the last twenty years, using two datasets: the Hansen dataset and the dataset provided by the Department of United States (USDOS).

Mapping the distribution, pattern, and composition of urban land use categories plays a valuable role in understanding urban environmental dynamics and facilitating sustainable development. Decades of effort in land use mapping have accumulated a series of mapping approaches and land use products. New trends characterized by open big data and advanced artificial intelligence, especially deep learning, offer unprecedented opportunities for mapping land use patterns from regional to global scales. Combined with large amounts of geospatial big data, deep learning has the potential to promote land use mapping to higher levels of scale, accuracy, efficiency, and automation. Here, we comprehensively review the advances in deep learning based urban land use mapping research and practices from the aspects of data sources, classification units, and mapping approaches. More specifically, delving into different settings on deep learning-based land use mapping, we design eight experiments in Shenzhen, China to investigate their impacts on mapping performance in terms of data, sample, and model. For each investigated setting, we provide quantitative evaluations of the discussed approaches to inform more convincing comparisons. Based on the historical retrospection and experimental evaluation, we identify the prevailing limitations and challenges of urban land use classification and suggest prospective directions that could further facilitate the exploitation of deep learning techniques in urban land use mapping using remote sensing and other spatial data across various scales. • A review in the field of deep learning for urban land use classification • A comprehensive overview of various deep learning model architectures • Eight comparative experiments considering data, samples, and model variations • Challenges and opportunities in deep learning-driven urban land use classification [ABSTRACT FROM AUTHOR]

The human spatial perception of urban streets has a high complexity and traditional research methods often focus on access surveys of human perception. Urban streets serve as both a direct conduit for pedestrians’ impressions of a city and a reflection of the spatial quality of that city. Street-view images can provide a large amount of primary data for the image semantic segmentation technique. Deep learning techniques were used in this study to collect the boring, beautiful, depressing, lively, safe, and wealthy perception scores of street spaces based on these images. Then, the spatial pattern of urban street-space quality perception was analyzed by global Moran’s I and GIS hotspot analyses. The findings demonstrate that various urban facilities affect street quality perception in different ways and that the strength of an influencing factor’s influence varies depending on its geographical location. The results of the influencing factors reveal the difference in the degree of influence of positive and negative influencing factors on various perceptions of the visual dimension of pedestrians. The primary contribution of this study is that it reduces the potential bias of a single data source by using multi-dimensional impact analysis to explain the relationship between urban street perception and urban facilities and visual elements. The study’s findings offer direction for high-quality urban development as well as advice for urban planning and enhanced design.

Timely griddedgross domestic product (GDP) data is a fundamental indicator in many applications. It is critical to characterize the complex relationship between GDP and its auxiliary information for accurately estimating gridded GDP. However, few knowledge is available about the performance of deep learning approaches for learning this complex relationship. This article develops a novel convolutional neural network based GDP downscaling approach (GDPnet) to transform the statistical GDP data into GDP grids by integrating various geospatial big data. An existing autoencoder-based downscaling approach (Resautonet) is employed to compare with GDPnet. The latest county-level GDP data of China and the multiple geospatial big data are adopted to generate the 1-km gridded GDP data in 2019. Due to the different related auxiliary data of each GDP sector, the two downscaling approaches are first separately built for each GDP sector and then the results are merged to the gridded total GDP data. Experimental results show that the two deep learning approaches had good predictive power with R2 over 0.8, 0.9, and 0.92 for the three sectors tested by county-level GDP data. Meanwhile, the proposed GDPnet outperformed the existing Resautonet. The average R2 of GDPnet was 0.034 higher than that of Resautonet in terms of county-level GDP test data. Furthermore, GDPnet had higher accuracy (R2 = 0.739) than Resautonet (R2 = 0.704) assessed by town-level GDP data. In addition, the proposed GDPnet is faster (about 78% running time) than the Resautonet. Hence, the proposed approach provides a valuable option for generating gridded GDP data.

Due to the challenges in data acquisition, especially for developing countries and at local levels, spatiotemporal evaluation for SDG11 indicators was still lacking. The availability of big data and earth observation technology can play an important role to facilitate the monitoring of urban sustainable development. Taking Guilin, a sustainable development agenda innovation demonstration area in China as a case study, we developed an assessment framework for SDG indicators 11.2.1, 11.3.1, and 11.7.1 at the neighborhood level using high-resolution (HR) satellite images, gridded population data, and other geospatial big data (e.g., road network and point of interest data). The findings showed that the proportion of the population with convenient access to public transport in the functional urban area gradually improved from 42% in 2013 to 52% in 2020. The increase in built-up land was much faster than the increase in population. The areal proportion of public open space decreased from 56% in 2013 to 24% in 2020, and the proportion of the population within the 400 m service areas of open public space decreased from 73% to 59%. The township-level results indicated that low-density land sprawling should be strictly managed, and open space and transportation facilities should be improved in the three fast-growing towns, Lingui, Lingchuan, and Dingjiang. The evaluation results of this study confirmed the applicability of SDG11 indicators to neighborhood-level assessment and local urban governance and planning practices. The evaluation framework of the SDG11 indicators based on HR satellite images and geospatial big data showed great promise to apply to other cities for targeted planning and assessment. [ABSTRACT FROM AUTHOR]

We are currently living in the era of big data. The volume of collected or archived geospatial data for land use and land cover (LULC) mapping including remotely sensed satellite imagery and auxiliary geospatial datasets is increasing. Innovative machine learning, deep learning algorithms, and cutting-edge cloud computing have also recently been developed. While new opportunities are provided by these geospatial big data and advanced computer technologies for LULC mapping, challenges also emerge for LULC mapping from using these geospatial big data. This article summarizes the review studies and research progress in remote sensing, machine learning, deep learning, and geospatial big data for LULC mapping since 2015. We identified the opportunities, challenges, and future directions of using geospatial big data for LULC mapping. More research needs to be performed for improved LULC mapping at large scales. [ABSTRACT FROM AUTHOR]

Clustering of multi-source geospatial big data provides opportunities to comprehensively describe urban structures. Most existing studies focus only on the clustering of a single type of geospatial big data, which leads to biased results. Although multi-view subspace clustering methods are advantageous for fusing multi-source geospatial big data, exploiting a robust shared subspace in high-dimensional, non-uniform, and noisy geospatial big data remains a challenge. Therefore, we developed a method with adaptive graphs to constrain multi-view subspace clustering of multi-source geospatial big data (agc2msc). First, for each type of data, high-dimensional and noisy original features were projected into a low-dimensional latent representation using autoencoder networks. Then, adaptive graph constraints were used to fuse the latent representations of multi-source data into a shared subspace representation, which preserved the neighboring relationships of data points. Finally, the shared subspace representation was used to obtain the clustering results by employing a spectral clustering algorithm. Experiments on four benchmark datasets showed that agc2msc outperformed nine state-of-the-art methods. agc2msc was applied to infer urban land use types in Beijing using the taxi GPS trajectory, bus smart card transaction, and points of interest datasets. The clustering results may provide useful calibration and reference for urban planning. [ABSTRACT FROM AUTHOR]

The visualization of geographic vector data is an important premise for spatial analysis and spatial cognition. Traditional geographic vector data visualization methods are data-driven, and their computational costs have increased rapidly with the growth of the scale of data used. Even if the distributed parallel strategy is used, it is still difficult to achieve a real-time response when dealing with big geographic vector data (BGVD). To solve this problem, this paper proposes a viewport generalization model and a visualization method for the online interactive visualization of BGVD. The method takes the viewport display pixel as the analysis unit and synthesizes the existence or quantity results of geographic vector data in the corresponding spatial range of each viewport display pixel into the display value of this display pixel; thus, it converts traditional computational complexity, dependent on the data scale, into computational complexity dependent on the number of pixels in the viewport. When the number of pixels in the viewport is much smaller than that of the geographic vector data, the visualization efficiency is greatly improved. In order to realize the above conversion, the pixel quadtree index (VPQ) structure and the real-time visualization algorithm of geographic vector data based on VPQ are proposed. Experiments show that the proposed method can achieve the near-real-time interactive visualization of BGVD, and provides more than a tenfold performance improvement over the best existing methods. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review. [ABSTRACT FROM AUTHOR]

Background: Often combined with other traditional and non-traditional types of data, geospatial sensing data have a crucial role in public health studies. We conducted a systematic narrative review to broaden our understanding of the usage of big geospatial sensing, ancillary data, and related spatial data infrastructures in public health studies. Methods: English-written, original research articles published during the last ten years were examined using three leading bibliographic databases (i.e., PubMed, Scopus, and Web of Science) in April 2022. Study quality was assessed by following well-established practices in the literature. Results: A total of thirty-two articles were identified through the literature search. We observed the included studies used various data-driven approaches to make better use of geospatial big data focusing on a range of health and health-related topics. We found the terms 'big' geospatial data and geospatial 'big data' have been inconsistently used in the existing geospatial sensing studies focusing on public health. We also learned that the existing research made good use of spatial data infrastructures (SDIs) for geospatial sensing data but did not fully use health SDIs for research. Conclusions: This study reiterates the importance of interdisciplinary collaboration as a prerequisite to fully taking advantage of geospatial big data for future public health studies. [ABSTRACT FROM AUTHOR]

A parallel real-time visualization software tool HiVecMap is proposed here for large-scale geographic vector data mapping. HiVecMap calculates the mapping value of each display cell instead of the value of each vector feature. The mapping value of each display cell is determined by the number of geographic vector features in the corresponding spatial range. Thereby, the computational complexity is depends on the number of the cells rather than the number of features of the whole geographic dataset. When the number of the cells is much smaller than the number of geographic features, the visualization efficiency can be greatly improved. HiVecMap is implemented with the MPI+OpenMP parallel strategy, and has been extensively tested. Results show that large-scale geographic vector data can be online mapped in real-time, which has a performance improvement of more than ten times compared with the existing best methods.

Detailed and precise urban green spaces (UGS) maps provide essential data for the sustainable urban development and related studies (e.g. heatwave events, heat related health risk, urban flooding, urban biodiversity and ecosystem services). However, remote sensing of mapping UGS is challenging due to the existence of mixed pixels and the cost and difficulty of collecting quality training data. This study presents a neural network-based automatic mapping method of UGS that integrates the use of Sentinel-2A satellite images and crowdsourced geospatial big data. The proposed neural network consists of three parts: (i) a multi-scale feature extraction module; (ii) a multi-modal information fuse module; and (iii) and a boundary enhancement module. The results showed that the proposed method achieved a high overall classification accuracy of 94.6%, which presents a clear UGS structure of a large scale. This study provides a fresh insight into how remote-sensing and crowdsourced geospatial big data can be integrated to improve urban mapping of green spaces through neural network.

In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry. • This study examines the impact of high-dimensional features variables on urban solar energy development. • Over 70% of solar PV installation capacity is concentrated in Northern and Northwest parts of China. • Water resources, solar radiation, population density have significant impact on solar PV distribution. • Solar PV distribution in China should shift from resource orientation to the comprehensive orientation. [ABSTRACT FROM AUTHOR]

Rapid urbanization brings a series of dilemmas to the development of human society. To address urban sustainability, Sustainable Development Goal 11 (SDG 11) is formulated by the United Nations (UN). Quantifying progress and interactions toward SDG 11 indicators is essential to achieving Sustainable Development Goals (SDGs). However, it is limited by a lack of data in many countries, particularly at small scales. To address the gap, this study used systematic methods to calculate the integrated index of SDG 11 at prefecture-level cities with different economic groups in the Yellow River Basin based on Big Earth Data and statistical data, analyzed its spatial aggregation characteristics using spatial statistical analysis methods, and quantified synergies and trade-offs among indicators under SDG 11. We found the following results: (1) except for SDG 11.1.1, the performance of the integrated index and seven indicators improved from 2015 to 2020. (2) In GDP and disposable income groups, the top 10 cities had higher values, whereas the bottom 10 cities experienced greater growth rates in the integrated index. However, the indicators’ values and growth rates varied between the two groups. (3) There were four pairs of indicators with trade-offs that were required to overcome and eight pairs with synergies that were crucial to be reinforced and cross-leveraged in the future within SDG 11 at a 0.05 significance level. Our study identified indicators that urgently paid attention to the urban development of the Yellow River Basin and laid the foundation for local decision-makers to more effectively implement the 2030 Agenda for Sustainable Development (the 2030 Agenda).