Multi-source Spatial Data Research Articles

Retracted: Urban Fine Management of Multisource Spatial Data Fusion Based on Smart City Construction

Retracted: Urban Fine Management of Multisource Spatial Data Fusion Based on Smart City Construction

Towards rainstorm event identification: A transfer learning framework using citizen-report texts and multi-source spatial data

Urban rainstorms are often accompanied by various hazardous events (e.g., water inundation and small-scale debris flow) and subsequent consequential events (e.g., infrastructure failure and trapped citizens). It is imperative to achieve rapid identification of such events for timely and effective rainstorm disaster management. However, due to small-sample event classes, it becomes challenging to achieve the desired identification results. In this paper, a cross-domain transfer learning scheme is proposed to identify rainstorm events by combining citizen-report texts with multi-source spatial data. In the proposed framework, we recommend joint distribution adaptation (JDA) as the key to improving the identification of target events with inadequate samples by transferring knowledge from similar event domains. Meanwhile, multi-source spatial features are extracted and embedded in the text with linear discriminant analysis (LDA) to overcome the feature incompleteness that is caused by short text length. The proposed approach is verified based on the real data in Wuhan, China. Experimental results show that the knowledge transfer between different event classes helps cities to improve the performance of rainstorm event identification, with the accuracy increasing from 81% to 92%. Under data imbalance scenarios, the proposed scheme outperforms the state-of-the-art methods, such as sampling-based methods, ERNIE, and TCA. At the same time, the combination of textual and spatial features results in significant improvement in event identification, as evident by an 8% increase in accuracy. This framework can be followed to build a rainstorm event warning and assistance system to rapidly adapt management responses.

Multisource spatial data integration for use cases applications

AbstractThe reuse and integration of data give big opportunities, supported by the FAIR data principles. Seamless data integration from heterogenous sources has been an interest of the geospatial community for a long time. However, 3D city models, building information models, and information supporting smart cities present higher semantic and geometrical complexity, which pose new challenges never tackled in a comprehensive methodology. Building on previous theories and studies, this article proposes an overarching workflow and framework for multisource (geo)spatial data integration. It starts from the definition of use case‐based requirements for the integrated data, guides the analysis of integrability of the involved datasets, suggesting actions to harmonize them, until data merging and validation. It is finally tested and exemplified in a case study. This approach allows the development of consistent, well‐documented, and inclusive data integration workflows, for the sake of use case automation in various geospatial domains and the production of interoperable and reusable data.

RETRACTED:Pollen risk levels prediction from multi-source historical data

Numerous studies show that meteorological conditions have an impact on the emission, dispersion and suspension of pollens in the air. Several allergenic species permanently threaten the health of millions of people in France and that can be extrapolate that this is the case in most part of the world. Hence, preventive information on the risk of pollen exposure would become a real asset for allergy sufferers. The main objective of this article is to study, through statistical learning techniques exploiting historical data and meteorological parameters of the day ( T ), the ability to predict three-day ahead ( T + 3 ) pollens presence risk levels in the air on a given territory (in metropolitan France). We are interested in the prediction of risk, discretized in four levels for three families of pollens which are among the most allergenic species (ragweed, cupressaceae and grasses). Combining binary logistic regression models for each risk level using a set of ranking rules or a random forest classifier is proposed in this study. The pollen risk level prediction performances reach 70% to more than 90% of auc, precision and recall on the majority of 68 considered sites and especially with a similar prediction capacity on sites with no previous pollen data. The comparative study with some more classical models of the literature shows that the proposed model have a slight performance advantage. • Pollen risk levels prediction from minimal meteorological information. • Aggregation of binary logistic regression models. • Learning with multi-source spatial and temporal data. • Ability to generalize prediction in space

Measuring the coupling of built-up land intensity and use efficiency: An example of the Yangtze River Delta urban agglomeration

Irrational built-up land expansion debilitates urban sustainability and leads to environmental decay, food security, and climate change. Using the Yangtze River Delta (YRD) urban agglomeration as an example, this research identifies the mismatch between the intensity and use efficiency of built-up land at multiple scales. Specifically, the study gauges built-up land intensity (BUI) using horizontal layout and vertical height data. Land use efficiency (LUE) is calculated by fusing multi-source spatial data, including nighttime light (NTL), land surface temperature (LST), population density (PD), gross domestic product (GDP), and points of interest (POI). The coupling index (CI) between BUI and LUE determines the misallocation of built-up land resources. The results indicate that BUI and LUE match well in YRD, whereas well-coupled built-up land with high BUI and LUE is concentrated in the eastern YRD. Based on the coupling findings, built-up land can be divided into three categories: overloaded, well-coupled, or inefficient. When built-up land is overloaded, it is essential to notice the excessive concentration of urban functions. On the other hand, reducing land resource waste should be a priority where built-up land is inefficient. This research helps confine irrational urban expansion, deploy rational urban expansion plans, and foster sustainable cities and communities.

Revealing Annual Crop Type Distribution and Spatiotemporal Changes in Northeast China Based on Google Earth Engine

Northeast China (NEC) produces 1/4 of the grain and 1/3 of the commercial grain in China, and is essential for food security and a sustainable socio-ecological system development. However, long-term annual crop type distribution in this vital area remains largely unknown, compromising the scientific basis for planting structure adjustment and sustainable agriculture management. To this end, we integrated 111-dimensional MOD09A1 features, feature optimization and random forest algorithms on the Google Earth Engine (GEE) platform to classify annual crop types in the NEC during 2000–2020, and adopted multi-source spatial data and geostatistical methods to reveal anthropogenic and natural characteristics of crop type changes. The results demonstrated that sample-based classification accuracies were 84.73–86.93% and statistics-based R2 were 0.81–0.95. From 2000–2020, the sowing area of maize and rice increased by 11.92 × 106 ha (111.05%) and 4.03 × 106 ha (149.28%), whereas that of soybean and other crops decreased by 13.73 × 106 ha (−64.10%) and 1.03 × 106 ha (−50.94%), respectively. Spatially, maize expanded northwestward, rice expanded northeastward, and soybean demonstrated a south-north shrinkage. The soybean-to-maize shift was the main conversion type, and its area largely reduced from 8.68 × 106 ha in 2000–2010 to 4.15 × 106 ha in 2010–2020. Economic comparative benefit and climate change jointly affected crop types in NEC. Higher-benefits maize and rice were mainly planted in more convenient areas with more population and closer to settlements, roads and waterways. The planting of maize and rice required higher temperature and precipitation, and climate change in the NEC provided favorable conditions for their expansion toward high-latitude areas. The crop type changes in the NEC have boosted economic benefits, but increased water–carbon–energy costs. Thus, effective measures such as subsidy policies, ecological compensation, and knowledge-exchange should be implemented to aid crop type and rotation adjustment and ensure food-ecological security.

Spatiotemporal Changes and Driver Analysis of Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands

Ecosystem respiration (RE) plays a critical role in terrestrial carbon cycles, and quantification of RE is important for understanding the interaction between climate change and carbon dynamics. We used a multi-level attention network, Geoman, to identify the relative importance of environmental factors and to simulate spatiotemporal changes in RE in northern China’s grasslands during 2001–2015, based on 18 flux sites and multi-source spatial data. Results indicate that Geoman performed well (R2 = 0.87, RMSE = 0.39 g C m−2 d−1, MAE = 0.28 g C m−2 d−1), and that grassland type and soil texture are the two most important environmental variables for RE estimation. RE in alpine grasslands showed a decreasing gradient from southeast to northwest, and that of temperate grasslands showed a decreasing gradient from northeast to southwest. This can be explained by the enhanced vegetation index (EVI), and soil factors including soil organic carbon density and soil texture. RE in northern China’s grasslands showed a significant increase (1.81 g C m−2 yr−1) during 2001–2015. The increase rate of RE in alpine grassland (2.36 g C m−2 yr−1) was greater than that in temperate grassland (1.28 g C m−2 yr−1). Temperature and EVI contributed to the interannual change of RE in alpine grassland, and precipitation and EVI were the main contributors in temperate grassland. This study provides a key reference for the application of advanced deep learning models in carbon cycle simulation, to reduce uncertainties and improve understanding of the effects of biotic and climatic factors on spatiotemporal changes in RE.

An Accurate and Efficient Quaternion-Based Visualization Approach to 2D/3D Vector Data for the Mobile Augmented Reality Map

Increasingly complex vector map applications and growing multi-source spatial data pose a serious challenge to the accuracy and efficiency of vector map visualization. It is true especially for real-time and dynamic scene visualization in mobile augmented reality, with the dramatic development of spatial data sensing and the emergence of AR-GIS. Such issues can be decomposed into three issues: accurate pose representation, fast and precise topological relationships computation and high-performance acceleration methods. To solve these issues, a novel quaternion-based real-time vector map visualization approach is proposed in this paper. It focuses on precise position and orientation representation, accurate and efficient spatial relationships calculation and acceleration parallel rendering in mobile AR. First, a quaternion-based pose processing method for multi-source spatial data is developed. Then, the complex processing of spatial relationships is mapped into simple and efficient quaternion-based operations. With these mapping methods, spatial relationship operations with large computational volumes can be converted into efficient quaternion calculations, and then the results are returned to respond to the interaction. Finally, an asynchronous rendering acceleration mechanism is also presented in this paper. Experiments demonstrated that the method proposed in this paper can significantly improve vector visualization of the AR map. The new approach, when compared to conventional visualization methods, provides more stable and accurate rendering results, especially when the AR map has strenuous movements and high frequency variations. The smoothness of the user interaction experience is also significantly improved.

Prediction of Urban Street Public Space Art Design Indicators Based on Deep Convolutional Neural Network.

This paper analyzes and studies the structure and parameters of the VGGNet network model and selects the most commonly used and efficient VGG-16 as the prototype of the improved model. A multiscale sampling layer is added at the end of the VGG-16 convolution part so that the model can input images of any size for training and testing while reducing the number of neurons in the fully connected layer. This improves the training speed of the model under the premise of ensuring the accuracy. This paper uses multisource street spatial data combined with geographic information spatial analysis technology to measure and evaluate the spatial quality of streets in the main urban area. From the three dimensions of vitality, safety, and greenness of urban street space quality, a systematic structure for evaluation and analysis of street space quality is constructed. Street vitality includes eight index factors: entrance and exit density, street furniture density, street sketch density, street characteristic landscape density, POI density, POI diversity, commercial POI ratio, and street population density. There are five index factors: degree, roadside parking occupancy ratio, traffic signal system density, sidewalk width proportion, and street facility density. We use ArcGIS to build an index factor information database for statistical analysis and visualization. According to the natural discontinuous point classification method, the safety level of urban street public space is divided into five grades. The sample size of the first four grades has a small fluctuation range. The sample sizes are 153, 172, 153, and 158, respectively, accounting for 21%, 23%, 21%, and 21% of the total street samples, of which the first two grades occupy a total of 44%, so 44% of the streets in the main urban area have a low-quality level of street space. Level 5 has a sample of 102 streets, accounting for 14%, with an average street space quality value of 0.43.

Design and Implementation of Mine Information Management System Based on Wireless Network

With the increasing demand for mineral resources in China, how to manage mineral resources scientifically, efficiently and intelligently has become a practical problem faced by relevant governments, enterprises and academia. As the most advanced technology of GIS in recent years, Web GIS can collect, store, process, analyze and visualize heterogeneous, multi-source and massive spatial geographic data. Therefore, introducing Web GIS technology into mine information management and building an information system for massive, multi-source and heterogeneous mine information management is of great significance for the government and enterprises to improve the management efficiency of mineral resources and realize the accurate and scientific planning and management of mineral resources.

Identification of Priority Areas for Improving Urban Ecological Carrying Capacity: Based on Supply–Demand Matching of Ecosystem Services

As the most concentrated area of human activities, cities consume many natural resources and discharge a large amount of waste into the natural environment, which has a huge environmental impact. Most of the ecological and environmental problems, such as environmental pollution, global climate change, and loss of biodiversity, are related to urban systems. How to coordinate urban development with the urban ecological carrying capacity is related to the destiny of the city itself, and also to whether its surrounding areas can successfully achieve the goal of high environmental quality and sustainable development. At present, the theory and methods of urban ecological carrying capacity research are relatively new, which has caused problems for policy makers in practical applications. This paper proposes a theoretical framework for urban ecological carrying capacity assessment based on the analysis of ecosystem services supply and demand. Combined with multi-source spatial data and spatial model methods, the supply and demand of ecosystem services were spatially quantified. The capital city of China, Beijing, was the case study area for this research. The spatial differentiation of the supply–demand relationship of ecosystem services is formed. The priority areas for ecological carrying capacity improvement at pixel scale and at the administrative level are obtained, respectively. The results show that the first priority area is concentrated in the center of the urban area, accounting for 31.11% of the total area of Beijing. According to the secondary zone and the specific ecosystem service type, the ecological carrying capacity improvement strategy of different zones is proposed. This study provides a new perspective for investigating urban ecological carrying capacity and for identifying the priority areas for ecological carrying capacity improvement, and helps the policy-makers to design tailored policy actions.

Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data

Lightning is an important cause of casualties, and of the interruption of power supply and distribution facilities. Monitoring lightning locations is essential in disaster prevention and mitigation. Although there are many ways to obtain lightning information, there are still substantial problems in intelligent lightning monitoring. Deep learning combined with weather radar data and land attribute data can lay the foundation for future monitoring of lightning locations. Therefore, based on the residual network, the Lightning Monitoring Residual Network (LM-ResNet) is proposed in this paper to monitor lightning location. Furthermore, comparisons with GoogLeNet and DenseNet were also conducted to evaluate the proposed model. The results show that the LM-ResNet model has significant potential in monitoring lightning locations. In this study, we converted the lightning monitoring problem into a binary classification problem and then obtained weather radar product data (including the plan position indicator (PPI), composite reflectance (CR), echo top (ET), vertical integral liquid water (VIL), and average radial velocity (V)) and land attribute data (including aspect, slope, land use, and NDVI) to establish a lightning feature dataset. During model training, the focal loss function was adopted as a loss function to address the constructed imbalanced lightning feature dataset. Moreover, we conducted stepwise sensitivity analysis and single factor sensitivity analysis. The results of stepwise sensitivity analysis show that the best performance can be achieved using all the data, followed by the combination of PPI, CR, ET, and VIL. The single factor sensitivity analysis results show that the ET radar product data are very important for the monitoring of lightning locations, and the NDVI land attribute data also make significant contributions.

Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China

Investigating urban vibrancy and factors that impact urban vibrancy aids the understanding of urban land use policies, provides solid foundation for scientific urban planning. The boom in information and communication technologies and the advancement of big data extraction provides new sources of data and make it possible to measure and analyze urban vibrancy at a finer spatial and temporal scale. This study aims to portray the spatiotemporal variation patterns of urban vibrancy in 24 h and investigate the potential influence mechanism of it. The central districts of Beijing consisting of 135 subdistricts are selected as the study area. Massive and spontaneous geo-tagged check-in data released from social media platforms has attracted increasing attentions in urban vibrancy studies because it reflects well people’s activities at a certain time, which is a good proxy for urban vibrancy. This study hence uses the check-in data from Weibo, the largest microblogging platform in China, to proxy urban vibrancy. We also extract from multisource spatial big data to explore potential determinants of urban vibrancy. This study seeks to reveal the global and local varying impacts of different factors on urban vibrancy by employ spatial lag model (SLM) and multiscale geographically weighted regression (MGWR) model. Results show that the increase in the number of different point of interests (POIs) improves urban vibrancy. Their effects on vibrancy vary at different times but have no obvious spatial scale variation. Splitting effect and attraction effect of land use diversity are introduced to explain its significantly negative effect on the intensity and fluctuation of urban vibrancy. It requires the wisdom of urban planners to balance these two effects of land use diversity in the process of urban construction. The guidance strategy of “highlighting the main functions and enriching the auxiliary functions” is helpful to build vibrant cities. Socioeconomical conditions, location and accessibility have different spatial scale effects on urban vibrancy at subdistrict level. These findings enable us to have a deeper understanding of the variation patterns and influence mechanism of urban vibrancy in China’s megacities and benefit the urban land use policy research and management community.

Identifying the Driving Factors of Urban Land Surface Temperature

Land surface temperature (LST) has a profound impact on urban climate and ecology, and is widely used to quantify surface urban heat islands. The spatial heterogeneity of LST is affected by natural and human factors, with seasonal differences. This study selected Dongguan, a rapidly urbanizing city in China, as an example to analyze the relationship between the spatial heterogeneity of LST in different seasons and influencing factors in six dimensions. Multi-source spatial data were combined, including Landsat images, meteorological data, digital elevation models, National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite nighttime light, and points of interest. The results show that spatial patterns of LST across different seasons were consistent, although there were local differences. Based on the GeoDetector model, the result indicated differences between separate effects and interactive effects, and identified the high temperature risk areas.

The use of multisource spatial data for determining the proliferation of stingless bees in Kenya

ABSTRACT Stingless/meliponine bees are eusocial insects whose polylactic nature enables interaction with a wide variety of wild plants and crops that enhance pollination and, hence, support ecosystem services. However, their true potential regarding pollination services and honey production is yet to be fully recognized. Worldwide, there are over 800 species of meliponine bees, with over 20 species documented on the African continent. Out of these, only 12 species have been well documented in Kenya. Moreover, interest on meliponine bees has increased amid climate change, agricultural intensification, and other anthropogenic effects. Generally, stingless bees are under-researched, with no previous documented evidence of their ecological niche (EN) distribution in most African countries. Hence, this study sought to establish the influence of bioclimatic, topographic, and vegetation phenology on their spatial distribution and change patterns. Stingless response variables from 490 sample points were collected and used in conjunction with 11 non-conflating features to build stingless ecological niche models. Six machine learning-based EN models were used to predict the distribution of seven stingless bees’ species combined. The results from the EN models showed that annual precipitation was the most influential variable to stingless bee distribution (contributing 43.09% logit), while potential evapotranspiration and temperature seasonality contributed 21.18% of the information needed to predict the spatial distribution of stingless bees. Vegetation phenology (21.36%) and topography (14.36%) had moderate effect on stingless bees’ distribution. On the other hand, high seasonality in precipitation and temperature indicated high stingless niche variability in the future (i.e. 2055). The performance of six EN algorithms used to predict distribution of stingless bees was found to be “excellent” for random forest (true skills statistics (TSS) = 0.91) and ranger (TSS = 0.90) and “good” for generalized additive models (TSS = 0.87), multivariate adaptive regression spline (TSS = 0.80), and boosted regression trees (TSS = 0.80), while they were “fair” for recursive portioning and regression trees (TSS = 0.79). These EN models could be utilized to inform stingless bee farming and insects pollinated crops by highlighting regions that provide highly suitable conditions for stingless bees. Additionally, the findings could be harnessed to increase both bee and agricultural productivity and forest conservation efforts through supplementary pollination services.

Interpretation of Discrepancies between Cities in the Transmission of COVID-19: Evidence from China in the First Weeks of the Pandemic

ObjectivesThis study aims to examine and explain the differences at city level in cumulative COVID-19 cases and time from first to last infection during the first 6 weeks of the epidemic in China. MethodsA quantitative study is conducted in China based on the multisource spatial data of 315 Chinese cities. Firstly, the spatial discrepancy of COVID-19 transmission was examined based on spatial autocorrelation analysis and hot pot analysis. Next, a comprehensive indicator framework was established by including a wide range of factors such as human mobility, geographical features, public health measures, and residents’ awareness. Finally, multivariate regression models using these variables were constructed to identify the determinants of COVID-19 transmission. ResultsSignificant spatial discrepancy of transmission was proved, and 10 determinants were identified. ConclusionsThe transmission consequence (measured by the number of cumulative cases) was mostly correlated with the migration scale from Wuhan, followed by socioeconomic factors. Transmission duration (measured as the time from the first to last case within the city) was mostly determined by total migration scale and lockdown speed, which suggests that timely implementation of public health measures facilitated fast control of transmission. Residents’ attention to COVID-19 was proved to be not only helpful for reducing confirmed cases, but also in favor of rapid transmission control. Altitude produced slight but significant effect on transmission duration. These conclusions are expected to provide decision support for the local governments of China and other jurisdictions.

Evaluation and Improvement of Cultivated Land Leisure Service Function Based on Multisource Spatial Data

The leisure service function is an important component of the derivative function and non-market function of cultivated land. Therefore, exploring the strength of the cultivated land leisure service function with the help of spatial information technology is significant in guiding the proper utilization and protection of cultivated land resources. This paper constructed an evaluation system based on the three dimensions of ecological landscape, social activities, and economic performance, explored the spatial difference of the cultivated land leisure service function in Yuanyang County, the major grain-producing area along the Yellow River through spatial weighted overlay, classified the hot spots of leisure services and presented suggestions for improvement. Results show the following: (1) the landscape resources in the northern part are relatively monotonous, while those in the southern part are rich and evenly distributed. Spatial accessibility presents a distribution of “one core with multiple subcores”. The distribution of leisure service supply capacity is characterized by “multiple cores and multiple circles.” (2) The hot spots of the cultivated land leisure service function are the Urban Agricultural Central Area and the Ecological Agriculture Core Area in the middle of the county, and the Suburban Agritourism Development Area, the Yellow River Agritourism Transitional Area, and the Leisure Agriculture Connection Area on the periphery of the county. (3) The agricultural landscape should be fully protected and utilized in the Urban Agricultural Central Area. The spatial accessibility and regional reputation of the Ecological Agriculture Core Area need to be improved. The landscape diversity and landscape quality should be improved in the Suburban Agritourism Development Area. The Yellow River Agritourism Transitional Area needs to overcome the loss of tourists. The Leisure Agriculture Connection Area should increase the number of leisure and tourism facilities.

Spatial Prioritization for Wildfire Mitigation by Integrating Heterogeneous Spatial Data: A New Multi-Dimensional Approach for Tropical Rainforests

Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.

Supply-Demand Assessment of Urban Parks Based on Social Media and Multi-Source Spatial Data: A Case Study in Shanghai, China

Supply-Demand Assessment of Urban Parks Based on Social Media and Multi-Source Spatial Data: A Case Study in Shanghai, China

Accessibility Assessment of Buildings Based on Multi-Source Spatial Data: Taking Wuhan as a Case Study

The question of whether each building of housing estate has equal access to nearby social service resources (e.g., public transportation service, catering, entertainment, etc.) is a major concern of citizens. This paper takes Wuhan as a case to explore the equality in social service resource sharing of the housing estate at a microscopic level by analyzing the accessibility of each building under different travel patterns. To estimate the accessibility of each building, we developed a novel model with multi-travel modes and residential suitability evaluation of residents. The specific values of the parameters involved in the proposed model were extracted from the multi-source spatial data such as social media data, census data, point of interest, and road network data. These data were acquired from multiple platforms, e.g., Gaode map, OSM (OpenStreetMap), and GeoQ. We chose three types of districts in the city of Wuhan, including the old central district, new central district, and suburban district. We applied the proposed model to assess the accessibility of communities in these districts. Based on the results, we further analyzed whether and to what extent the distribution of each building in urban communities is equitable for social service resource sharing in China.