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Volume 10
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ISPRS Int. J. Geo-Inf., Volume 10, Issue 12 (December 2021) – 43 articles

Cover Story (view full-size image): The heterogeneity of information models of the domains building information modeling (BIM) and urban information modeling (UIM) causes information silos that can be bridged through information integration methods such as instance-level linking. Current information integration efforts are generally limited to a specific situation: they are limited to specific data and to specific use cases. This study discusses the dependency of the link creation on different situational aspects, called contextual linking. Here, an application-oriented perspective on information integration is introduced, contextual linking is analyzed, and the relevance of contextual linking is demonstrated based on three different integration scenarios. The results of the discourse serve as a framework supporting the development of artifacts for linking heterogeneous information models from the domains BIM and UIM. View this paper.
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24 pages, 12218 KiB  
Article
Big Data Spatio-Temporal Correlation Analysis and LRIM Model Based Targeted Poverty Alleviation through Education
by Yue Han, Lin Liu, Qiaoli Sui and Jiaxing Zhou
ISPRS Int. J. Geo-Inf. 2021, 10(12), 837; https://doi.org/10.3390/ijgi10120837 - 20 Dec 2021
Cited by 4 | Viewed by 2881
Abstract
There are many factors affecting poverty, among which education is an important one. Firstly, from the perspective of digital statistics, this research quantitatively analyzes the correlation between average education years (AEY) and Gross Domestic Product per capita (GDP/C), and finds that there is [...] Read more.
There are many factors affecting poverty, among which education is an important one. Firstly, from the perspective of digital statistics, this research quantitatively analyzes the correlation between average education years (AEY) and Gross Domestic Product per capita (GDP/C), and finds that there is a significant positive correlation between AEY and GDP/C in provinces of China. Furthermore, from the perspective of spatial distribution and geostatistics, this research analyzes the correlation between AEY and the distribution of poor counties, revealing the inherent connection between education and poverty. Based on the data processing of nighttime light remote sensing images, this research adopts the machine learning method of random forest to extract the distribution status of spatio-temporal sequences for poor counties. Through the analysis, it is found that poor counties are characterized by centralized distribution and spatial autocorrelation spatially, and the number of poor counties decreases year by year in temporal evolution. On this basis, we analyze the correlation between education levels and the distribution of poor counties. It is found that, on the spatial scale, AEY in poor counties is relatively low, while AEY in non-poor counties is relatively high, showing a significant negative correlation between the two. On the temporal scale, the number of poor counties gradually decreased from 2000 to 2010, and at the same time, the education levels of poor counties also gradually improved. Finally, from the perspective of improving education levels to promote poverty elimination, we analyze the main factors affecting education using Principal Component Analysis (PCA) and other methods and obtain a regression model. This research proposes the Linear and Residual Integration Model (LRIM) to more accurately predict AEY in each province in 2020 based on historical data, and identifies the regions with low AEY as key regions for targeted poverty alleviation through education (TPAE) in the future. This research provides a decision-making basis to achieve TPAE means, helping to achieve the victory of the national education poverty elimination battle. Full article
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18 pages, 20077 KiB  
Article
Analyzing the Contribution of Human Mobility to Changes in Air Pollutants: Insights from the COVID-19 Lockdown in Wuhan
by Jiansheng Wu, Yun Qian, Yuan Wang and Na Wang
ISPRS Int. J. Geo-Inf. 2021, 10(12), 836; https://doi.org/10.3390/ijgi10120836 - 17 Dec 2021
Cited by 2 | Viewed by 3116
Abstract
During the COVID-19 lockdown in Wuhan, transportation, industrial production and other human activities declined significantly, as did the NO2 concentration. In order to assess the relative contributions of different factors to reductions in air pollutants, we implemented sensitivity experiments by Random Forest [...] Read more.
During the COVID-19 lockdown in Wuhan, transportation, industrial production and other human activities declined significantly, as did the NO2 concentration. In order to assess the relative contributions of different factors to reductions in air pollutants, we implemented sensitivity experiments by Random Forest (RF) models, with the comparison of the contributions of meteorological conditions, human mobility, and emissions from industry and households between different periods. In addition, we conducted scenario analyses to suggest an appropriate limit for control of human mobility. Different mechanisms for air pollutants were shown in the pre-pandemic, pre-lockdown, lockdown, and post-pandemic periods. Wind speed and the Within-city Migration index, representing intra-city mobility intensity, were excluded from stepwise multiple linear models in the pre-lockdown and lockdown periods. The results of sensitivity experiments show that, in the COVID-19 lockdown period, 73.3% of the reduction can be attributed to decreased human mobility. In the post-pandemic period, meteorological conditions control about 42.2% of the decrease, and emissions from industry and households control 40.0%, while human mobility only contributes 17.8%. The results of the scenario analysis suggest that the priority of restriction should be given to human mobility within the city than other kinds of human mobility. The reduction in the NO2 concentration tends to be smaller when human mobility within the city decreases by more than 70%. A limit of less than 40% on the control of the human mobility can achieve a better effect, especially in cities with severe traffic pollution. Full article
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15 pages, 2617 KiB  
Article
Risk Assessment of Alpine Skiing Events Based on Knowledge Graph: A Focus on Meteorological Conditions
by Muhua Wang, Xueying Zhang, Deen Feng, Yipeng Wang, Wei Tang and Peng Ye
ISPRS Int. J. Geo-Inf. 2021, 10(12), 835; https://doi.org/10.3390/ijgi10120835 - 15 Dec 2021
Cited by 5 | Viewed by 3670
Abstract
The alpine skiing event is particularly vulnerable to changes in meteorological conditions as a winter sport held outdoors. The commonly used risk assessment methods cannot be inflexible and cannot be dynamically adjusted to combine multiple risk factors and actual conditions. A knowledge graph [...] Read more.
The alpine skiing event is particularly vulnerable to changes in meteorological conditions as a winter sport held outdoors. The commonly used risk assessment methods cannot be inflexible and cannot be dynamically adjusted to combine multiple risk factors and actual conditions. A knowledge graph can organize data resources in the risk domain as structured knowledge systems. This paper combines a knowledge graph and risk assessment to effectively assess the risk status. First of all, we introduce the relevant literature review of sports event risk assessment, combining the characteristics of alpine skiing events. Then, we summarize the risk types of alpine skiing events and related risk knowledge. Secondly, a model is proposed to introduce an event risk assessment model based on the RippleNet framework combined with the characteristics of large-scale sports events. Moreover, the validity of the model is verified. The results show that the RippleNet-based event risk assessment model can be used to assess the risk of alpine skiing events. In order to effectively deal with the large-scale sports events that occur with a variety of risks, the smooth implementation of large-scale sports events provides a strong guarantee. Full article
(This article belongs to the Special Issue Semantic Spatial Web)
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16 pages, 9007 KiB  
Article
Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data
by Feng Gao, Guanping Huang, Shaoying Li, Ziwei Huang and Lei Chai
ISPRS Int. J. Geo-Inf. 2021, 10(12), 834; https://doi.org/10.3390/ijgi10120834 - 13 Dec 2021
Cited by 14 | Viewed by 3828
Abstract
Understanding the relationship between human activity patterns and urban spatial structure planning is one of the core research topics in urban planning. Since a building is the basic spatial unit of the urban spatial structure, identifying building function types, according to human activities, [...] Read more.
Understanding the relationship between human activity patterns and urban spatial structure planning is one of the core research topics in urban planning. Since a building is the basic spatial unit of the urban spatial structure, identifying building function types, according to human activities, is essential but challenging. This study presented a novel approach that integrated the eigendecomposition method and k-means clustering for inferring building function types according to location-based social media data, Tencent User Density (TUD) data. The eigendecomposition approach was used to extract the effective principal components (PCs) to characterize the temporal patterns of human activities at building level. This was combined with k-means clustering for building function identification. The proposed method was applied to the study area of Tianhe district, Guangzhou, one of the largest cities in China. The building inference results were verified through the random sampling of AOI data and street views in Baidu Maps. The accuracy for all building clusters exceeded 83.00%. The results indicated that the eigendecomposition approach is effective for revealing the temporal structure inherent in human activities, and the proposed eigendecomposition-k-means clustering approach is reliable for building function identification based on social media data. Full article
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21 pages, 11225 KiB  
Article
Virtual Reality-Based Fuzzy Spatial Relation Knowledge Extraction Method for Observer-Centered Vague Location Descriptions
by Jun Xu, Xin Pan, Jian Zhao and Haohai Fu
ISPRS Int. J. Geo-Inf. 2021, 10(12), 833; https://doi.org/10.3390/ijgi10120833 - 13 Dec 2021
Cited by 3 | Viewed by 2583
Abstract
Many documents contain vague location descriptions of observed objects. To represent location information in geographic information systems (GISs), these vague location descriptions need to be transformed into representable fuzzy spatial regions, and knowledge about the location descriptions of observer-to-object spatial relations must serve [...] Read more.
Many documents contain vague location descriptions of observed objects. To represent location information in geographic information systems (GISs), these vague location descriptions need to be transformed into representable fuzzy spatial regions, and knowledge about the location descriptions of observer-to-object spatial relations must serve as the basis for this transformation process. However, a location description from the observer perspective is not a specific fuzzy function, but comes from a subjective viewpoint, which will be different for different individuals, making the corresponding knowledge difficult to represent or obtain. To extract spatial knowledge from such subjective descriptions, this research proposes a virtual reality (VR)-based fuzzy spatial relation knowledge extraction method for observer-centered vague location descriptions (VR-FSRKE). In VR-FSRKE, a VR scene is constructed, and users can interactively determine the fuzzy region corresponding to a location description under the simulated VR observer perspective. Then, a spatial region clustering mechanism is established to summarize the fuzzy regions identified by various individuals into fuzzy spatial relation knowledge. Experiments show that, on the basis of interactive scenes provided through VR, VR-FSRKE can efficiently extract spatial relation knowledge from many individuals and is not restricted by requirements of a certain place or time; furthermore, the knowledge obtained by VR-FSRKE is close to the knowledge obtained from a real scene. Full article
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23 pages, 3797 KiB  
Article
Spatiotemporal RDF Data Query Based on Subgraph Matching
by Xiangfu Meng, Lin Zhu, Qing Li and Xiaoyan Zhang
ISPRS Int. J. Geo-Inf. 2021, 10(12), 832; https://doi.org/10.3390/ijgi10120832 - 12 Dec 2021
Cited by 2 | Viewed by 2607
Abstract
Resource Description Framework (RDF), as a standard metadata description framework proposed by the World Wide Web Consortium (W3C), is suitable for modeling and querying Web data. With the growing importance of RDF data in Web data management, there is an increasing need for [...] Read more.
Resource Description Framework (RDF), as a standard metadata description framework proposed by the World Wide Web Consortium (W3C), is suitable for modeling and querying Web data. With the growing importance of RDF data in Web data management, there is an increasing need for modeling and querying RDF data. Previous approaches mainly focus on querying RDF. However, a large amount of RDF data have spatial and temporal features. Therefore, it is important to study spatiotemporal RDF data query approaches. In this paper, firstly, we formally define spatiotemporal RDF data, and construct a spatiotemporal RDF model st-RDF that is used to represent and manipulate spatiotemporal RDF data. Secondly, we present a spatiotemporal RDF query algorithm stQuery based on subgraph matching. This algorithm can quickly determine whether the query result is empty for queries whose temporal or spatial range exceeds a specific range by adopting a preliminary query filtering mechanism in the query process. Thirdly, we propose a sorting strategy that calculates the matching order of query nodes to speed up the subgraph matching. Finally, we conduct experiments in terms of effect and query efficiency. The experimental results show the performance advantages of our approach. Full article
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19 pages, 10792 KiB  
Article
An Automatic Extraction Method for Hatched Residential Areas in Raster Maps Based on Multi-Scale Feature Fusion
by Jianhua Wu, Jiaqi Xiong, Yu Zhao and Xiang Hu
ISPRS Int. J. Geo-Inf. 2021, 10(12), 831; https://doi.org/10.3390/ijgi10120831 - 10 Dec 2021
Cited by 2 | Viewed by 2339
Abstract
Extracting the residential areas from digital raster maps is beneficial for research on land use change analysis and land quality assessment. In traditional methods for extracting residential areas in raster maps, parameters must be set manually; these methods also suffer from low extraction [...] Read more.
Extracting the residential areas from digital raster maps is beneficial for research on land use change analysis and land quality assessment. In traditional methods for extracting residential areas in raster maps, parameters must be set manually; these methods also suffer from low extraction accuracy and inefficiency. Therefore, we have proposed an automatic method for extracting the hatched residential areas from raster maps based on a multi-scale U-Net and fully connected conditional random fields. The experimental results showed that the model that was based on a multi-scale U-Net with fully connected conditional random fields achieved scores of 97.05% in Dice, 94.26% in Intersection over Union, 94.92% in recall, 93.52% in precision and 99.52% in accuracy. Compared to the FCN-8s, the five metrics increased by 1.47%, 2.72%, 1.07%, 4.56% and 0.26%, respectively and compared to the U-Net, they increased by 0.84%, 1.56%, 3.00%, 0.65% and 0.13%, respectively. Our method also outperformed the Gabor filter-based algorithm in the number of identified objects and the accuracy of object contour locations. Furthermore, we were able to extract all of the hatched residential areas from a sheet of raster map. These results demonstrate that our method has high accuracy in object recognition and contour position, thereby providing a new method with strong potential for the extraction of hatched residential areas. Full article
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27 pages, 7904 KiB  
Article
Urban–Rural Gradients Predict Educational Gaps: Evidence from a Machine Learning Approach Involving Academic Performance and Impervious Surfaces in Ecuador
by Fabián Santos-García, Karina Delgado Valdivieso, Andreas Rienow and Joaquín Gairín
ISPRS Int. J. Geo-Inf. 2021, 10(12), 830; https://doi.org/10.3390/ijgi10120830 - 10 Dec 2021
Cited by 2 | Viewed by 3502
Abstract
Academic performance (AP) is explained by a multitude of factors, principally by those related to socioeconomic, cultural, and educational environments. However, AP is less understood from a spatial perspective. The aim of this study was to investigate a methodology using a machine learning [...] Read more.
Academic performance (AP) is explained by a multitude of factors, principally by those related to socioeconomic, cultural, and educational environments. However, AP is less understood from a spatial perspective. The aim of this study was to investigate a methodology using a machine learning approach to determine which answers from a questionnaire-based survey were relevant for explaining the high AP of secondary school students across urban–rural gradients in Ecuador. We used high school locations to construct individual datasets and stratify them according to the AP scores. Using the Boruta algorithm and backward elimination, we identified the best predictors, classified them using random forest, and mapped the AP classification probabilities. We summarized these results as frequent answers observed for each natural region in Ecuador and used their probability outputs to formulate hypotheses with respect to the urban–rural gradient derived from annual maps of impervious surfaces. Our approach resulted in a cartographic analysis of AP probabilities with overall accuracies around 0.83–0.84% and Kappa values of 0.65–0.67%. High AP was primarily related to answers regarding the academic environment and cognitive skills. These identified answers varied depending on the region, which allowed for different interpretations of the driving factors of AP in Ecuador. A rural-to-urban transition ranging 8–17 years was found to be the timespan correlated with achievement of high AP. Full article
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15 pages, 997 KiB  
Article
Exploring the Effects of Urban Built Environment on Road Travel Speed Variability with a Spatial Panel Data Model
by Guangyue Nian, Jian Sun and Jianyun Huang
ISPRS Int. J. Geo-Inf. 2021, 10(12), 829; https://doi.org/10.3390/ijgi10120829 - 10 Dec 2021
Cited by 6 | Viewed by 3367
Abstract
Road traffic congestion is a common problem in most large cities, and exploring the root causes is essential to alleviate traffic congestion. Travel behavior is closely related to the built environment, and affects road travel speed. This paper investigated the direct effect of [...] Read more.
Road traffic congestion is a common problem in most large cities, and exploring the root causes is essential to alleviate traffic congestion. Travel behavior is closely related to the built environment, and affects road travel speed. This paper investigated the direct effect of built environment on the average travel speed of road traffic. Taxi trajectories were divided into 30 min time slot (48 time slots throughout the day) and matched to the road network to obtain the average travel speed of road segments. The Points of Interest (POIs) in the buffer zone on both sides of the road segment were used to calculate the built environment indicators corresponding to the road segment, and then a spatial panel data model was proposed to assess the influence of the built environment adjacent to the road segment on the average travel speed of the road segment. The results demonstrated that the bus stop density, healthcare service density, sports and leisure service density, and parking entrance and exit density are the key factors that positively affect the average road travel speed. The residential community density and business building density are the key factors that negatively affect the average travel speed. Built environments have spatial correlation and spatial heterogeneity in their influence on the average travel speed of road segments. Findings of this study may provide useful insights for understanding the correlation between road travel speed and built environment, which would have important implications for urban planning and governance, traffic demand forecasting and traffic system optimization. Full article
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15 pages, 10255 KiB  
Article
Automatic Extraction of Indoor Spatial Information from Floor Plan Image: A Patch-Based Deep Learning Methodology Application on Large-Scale Complex Buildings
by Hyunjung Kim, Seongyong Kim and Kiyun Yu
ISPRS Int. J. Geo-Inf. 2021, 10(12), 828; https://doi.org/10.3390/ijgi10120828 - 10 Dec 2021
Cited by 13 | Viewed by 9904
Abstract
Automatic floor plan analysis has gained increased attention in recent research. However, numerous studies related to this area are mainly experiments conducted with a simplified floor plan dataset with low resolution and a small housing scale due to the suitability for a data-driven [...] Read more.
Automatic floor plan analysis has gained increased attention in recent research. However, numerous studies related to this area are mainly experiments conducted with a simplified floor plan dataset with low resolution and a small housing scale due to the suitability for a data-driven model. For practical use, it is necessary to focus more on large-scale complex buildings to utilize indoor structures, such as reconstructing multi-use buildings for indoor navigation. This study aimed to build a framework using CNN (Convolution Neural Networks) for analyzing a floor plan with various scales of complex buildings. By dividing a floor plan into a set of normalized patches, the framework enables the proposed CNN model to process varied scale or high-resolution inputs, which is a barrier for existing methods. The model detected building objects per patch and assembled them into one result by multiplying the corresponding translation matrix. Finally, the detected building objects were vectorized, considering their compatibility in 3D modeling. As a result, our framework exhibited similar performance in detection rate (87.77%) and recognition accuracy (85.53%) to that of existing studies, despite the complexity of the data used. Through our study, the practical aspects of automatic floor plan analysis can be expanded. Full article
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
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23 pages, 9831 KiB  
Article
Estimating the Photovoltaic Potential of Building Facades and Roofs Using the Industry Foundation Classes
by Xiu Lu, Guannan Li, Andong Wang, Qingqin Xiong, Bingxian Lin and Guonian Lv
ISPRS Int. J. Geo-Inf. 2021, 10(12), 827; https://doi.org/10.3390/ijgi10120827 - 9 Dec 2021
Cited by 2 | Viewed by 3779
Abstract
Photovoltaic energy generation has gained wide attention owing to its efficiency and environmental benefits. Therefore, it has become important to accurately evaluate the photovoltaic energy generation potential of building surfaces. As the number of building floors increases, the area of the facades becomes [...] Read more.
Photovoltaic energy generation has gained wide attention owing to its efficiency and environmental benefits. Therefore, it has become important to accurately evaluate the photovoltaic energy generation potential of building surfaces. As the number of building floors increases, the area of the facades becomes much larger than that of the roof, providing improved potential for photovoltaic equipment installation. Conventional urban solar potential evaluation methods are usually based on light detection and ranging (LiDAR). However, LiDAR can only be used in existing buildings, and the lack of semantic information in the point cloud data generated by LiDAR makes it impossible to evaluate the photovoltaic potential of facades (including details such as windows) in detail and with accuracy. In this study, we developed a method to accurately extract facades and roofs in order to evaluate photovoltaic potential based on the Industry Foundation Classes. To verify the feasibility of this approach, we used a building from Xuzhou city, Jiangsu province, China. The simulation results indicate that, out of the total building photovoltaic installable area (8995 m2), that of the facade is 8240 m2. The photovoltaic potential of the simulated building could reach 1054.69 MWh/year. The sensitivity studies of the grid resolution, the time interval and the computation time confirmed the reasonability of the determined conditions. The method proposed offers great potential for energy planning departments and the improved utilization of buildings. Full article
(This article belongs to the Collection Spatial and Temporal Modelling of Renewable Energy Systems)
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26 pages, 4849 KiB  
Article
A Novel Parallel Algorithm with Map Segmentation for Multiple Geographical Feature Label Placement Problem
by Mohammad Naser Lessani, Jiqiu Deng and Zhiyong Guo
ISPRS Int. J. Geo-Inf. 2021, 10(12), 826; https://doi.org/10.3390/ijgi10120826 - 6 Dec 2021
Cited by 2 | Viewed by 3607
Abstract
Multiple geographical feature label placement (MGFLP) is an NP-hard problem that can negatively influence label position accuracy and the computational time of the algorithm. The complexity of such a problem is compounded as the number of features for labeling increases, causing the execution [...] Read more.
Multiple geographical feature label placement (MGFLP) is an NP-hard problem that can negatively influence label position accuracy and the computational time of the algorithm. The complexity of such a problem is compounded as the number of features for labeling increases, causing the execution time of the algorithms to grow exponentially. Additionally, in large-scale solutions, the algorithm possibly gets trapped in local minima, which imposes significant challenges in automatic label placement. To address the mentioned challenges, this paper proposes a novel parallel algorithm with the concept of map segmentation which decomposes the problem of multiple geographical feature label placement (MGFLP) to achieve a more intuitive solution. Parallel computing is then utilized to handle each decomposed problem simultaneously on a separate central processing unit (CPU) to speed up the process of label placement. The optimization component of the proposed algorithm is designed based on the hybrid of discrete differential evolution and genetic algorithms. Our results based on real-world datasets confirm the usability and scalability of the algorithm and illustrate its excellent performance. Moreover, the algorithm gained superlinear speedup compared to the previous studies that applied this hybrid algorithm. Full article
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30 pages, 1140 KiB  
Article
Towards the Semantic Enrichment of Trajectories Using Spatial Data Infrastructures
by Jarbas Nunes Vidal-Filho, Valéria Cesário Times, Jugurta Lisboa-Filho and Chiara Renso
ISPRS Int. J. Geo-Inf. 2021, 10(12), 825; https://doi.org/10.3390/ijgi10120825 - 6 Dec 2021
Cited by 3 | Viewed by 2939
Abstract
The term Semantic Trajectories of Moving Objects (STMO) corresponds to a sequence of spatial-temporal points with associated semantic information (for example, annotations about locations visited by the user or types of transportation used). However, the growth of Big Data generated by users, such [...] Read more.
The term Semantic Trajectories of Moving Objects (STMO) corresponds to a sequence of spatial-temporal points with associated semantic information (for example, annotations about locations visited by the user or types of transportation used). However, the growth of Big Data generated by users, such as data produced by social networks or collected by an electronic equipment with embedded sensors, causes the STMO to require services and standards for enabling data documentation and ensuring the quality of STMOs. Spatial Data Infrastructures (SDI), on the other hand, provide a shared interoperable and integrated environment for data documentation. The main challenge is how to lead traditional SDIs to evolve to an STMO document due to the lack of specific metadata standards and services for semantic annotation. This paper presents a new concept of SDI for STMO, named SDI4Trajectory, which supports the documentation of different types of STMO—holistic trajectories, for example. The SDI4Trajectory allows us to propose semi-automatic and manual semantic enrichment processes, which are efficient in supporting semantic annotations and STMO documentation as well. These processes are hardly found in traditional SDIs and have been developed through Web and semantic micro-services. To validate the SDI4Trajectory, we used a dataset collected by voluntary users through the MyTracks application for the following purposes: (i) comparing the semi-automatic and manual semantic enrichment processes in the SDI4Trajectory; (ii) investigating the viability of the documentation processes carried out by the SDI4Trajectory, which was able to document all the collected trajectories. Full article
(This article belongs to the Special Issue Geospatial Semantics Applications)
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23 pages, 26122 KiB  
Article
Functional Classification of Urban Parks Based on Urban Functional Zone and Crowd-Sourced Geographical Data
by Su Cao, Shihong Du, Shuwen Yang and Shouhang Du
ISPRS Int. J. Geo-Inf. 2021, 10(12), 824; https://doi.org/10.3390/ijgi10120824 - 6 Dec 2021
Cited by 12 | Viewed by 5097
Abstract
Urban parks have important impacts on urban ecosystems and in disaster prevention. They also have diverse social functions that are important to the living conditions and spatial structures of cities. Identifying and classifying the different types of urban parks are important for analyzing [...] Read more.
Urban parks have important impacts on urban ecosystems and in disaster prevention. They also have diverse social functions that are important to the living conditions and spatial structures of cities. Identifying and classifying the different types of urban parks are important for analyzing the sustainable development and the greening progress in cities. Existing studies have predominantly focused on the data extraction of urban green spaces as a whole, while there have been relatively few studies that have considered different categories of urban parks and their impact, which makes it difficult to characterize or predict the spatial distribution and structures of urban parks and limits further refinement of urban research. At present, the classification of urban parks relies on the physical features observed in remote sensing images, but these methods are limited when mapping the diverse functions and attributes of urban parks. Crowd-sourced geographic data may more accurately express the social functions of points of interest (POIs) in cities, and, therefore, employing open data sources may assist in data extraction and the classification of different types of urban parks. This paper proposed a multi-source data fusion approach for urban park classification including POI and urban functional zone (UFZ) data. First, the POI data were automatically reclassified using improved natural language processing (NLP) (i.e., text similarity measurements and topic modeling) to establish the links between urban park green-space types and POIs. The reclassified POI data as well as the UFZ data were then subjected to scene-based data fusion, and various types of urban parks were extracted using data attribute analysis and social attribute recognition for urban park mapping. Experimental analysis was conducted across Beijing and Hangzhou to verify the effectiveness of the proposed method, which had an overall classification accuracy of 82.8%. Finally, the urban park types of the two cities were compared and analyzed to obtain the characteristics of urban park types and structures in the two cities, which have different climates and urban structures. Full article
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23 pages, 871 KiB  
Article
People, Projects, Organizations, and Products: Designing a Knowledge Graph to Support Multi-Stakeholder Environmental Planning and Design
by Sean N. Gordon, Philip J. Murphy, John A. Gallo, Patrick Huber, Allan Hollander, Ann Edwards and Piotr Jankowski
ISPRS Int. J. Geo-Inf. 2021, 10(12), 823; https://doi.org/10.3390/ijgi10120823 - 6 Dec 2021
Cited by 10 | Viewed by 4030
Abstract
As the need for more broad-scale solutions to environmental problems is increasingly recognized, traditional hierarchical, government-led models of coordination are being supplemented by or transformed into more collaborative inter-organizational networks (i.e., collaboratives, coalitions, partnerships). As diffuse networks, such regional environmental planning and design [...] Read more.
As the need for more broad-scale solutions to environmental problems is increasingly recognized, traditional hierarchical, government-led models of coordination are being supplemented by or transformed into more collaborative inter-organizational networks (i.e., collaboratives, coalitions, partnerships). As diffuse networks, such regional environmental planning and design (REPD) efforts often face challenges in sharing and using spatial and other types of information. Recent advances in semantic knowledge management technologies, such as knowledge graphs, have the potential to address these challenges. In this paper, we first describe the information needs of three multi-stakeholder REPD initiatives in the western USA using a list of 80 need-to-know questions and concerns. The top needs expressed were for help in tracking the participants, institutions, and information products relevant to the REDP’s focus. To address these needs, we developed a prototype knowledge graph based on RDF and GeoSPARQL standards. This semantic approach provided a more flexible data structure than traditional relational databases and also functionality to query information across different providers; however, the lack of semantic data expertise, the complexity of existing software solutions, and limited online hosting options are significant barriers to adoption. These same barriers are more acute for geospatial data, which also faces the added challenge of maintaining and synchronizing both semantic and traditional geospatial datastores. Full article
(This article belongs to the Special Issue Geospatial Open Systems)
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24 pages, 2177 KiB  
Article
A Bayesian Approach to Estimate the Spatial Distribution of Crowdsourced Radiation Measurements around Fukushima
by Carolynne Hultquist, Zita Oravecz and Guido Cervone
ISPRS Int. J. Geo-Inf. 2021, 10(12), 822; https://doi.org/10.3390/ijgi10120822 - 6 Dec 2021
Cited by 2 | Viewed by 2532
Abstract
Citizen-led movements producing spatio-temporal big data are potential sources of useful information during hazards. Yet, the sampling of crowdsourced data is often opportunistic and the statistical variations in the datasets are not typically assessed. There is a scientific need to understand the characteristics [...] Read more.
Citizen-led movements producing spatio-temporal big data are potential sources of useful information during hazards. Yet, the sampling of crowdsourced data is often opportunistic and the statistical variations in the datasets are not typically assessed. There is a scientific need to understand the characteristics and geostatistical variability of big spatial data from these diverse sources if they are to be used for decision making. Crowdsourced radiation measurements can be visualized as raw, often overlapping, points or processed for an aggregated comparison with traditional sources to confirm patterns of elevated radiation levels. However, crowdsourced data from citizen-led projects do not typically use a spatial sampling method so classical geostatistical techniques may not seamlessly be applied. Standard aggregation and interpolation methods were adapted to represent variance, sampling patterns, and the reliability of modeled trends. Finally, a Bayesian approach was used to model the spatial distribution of crowdsourced radiation measurements around Fukushima and quantify uncertainty introduced by the spatial data characteristics. Bayesian kriging of the crowdsourced data captures hotspots and the probabilistic approach could provide timely contextualized information that can improve situational awareness during hazards. This paper calls for the development of methods and metrics to clearly communicate spatial uncertainty by evaluating data characteristics, representing observational gaps and model error, and providing probabilistic outputs for decision making. Full article
(This article belongs to the Special Issue Mapping, Modeling and Prediction with VGI)
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20 pages, 4349 KiB  
Article
A Continuous Taxi Pickup Path Recommendation under The Carbon Neutrality Context
by Mengmeng Chang, Yuanying Chi, Zhiming Ding, Jing Tian and Yuhao Zheng
ISPRS Int. J. Geo-Inf. 2021, 10(12), 821; https://doi.org/10.3390/ijgi10120821 - 4 Dec 2021
Cited by 1 | Viewed by 2786
Abstract
In the context of the carbon neutrality target, carbon reduction in the daily operation of the transportation system is more important than that in productive activities. There are few travel services that can quantify low-carbon travel, with a lack of effective low-carbon travel [...] Read more.
In the context of the carbon neutrality target, carbon reduction in the daily operation of the transportation system is more important than that in productive activities. There are few travel services that can quantify low-carbon travel, with a lack of effective low-carbon travel tools to guide transportation behavior. On-demand access to taxi services can effectively reduce the additional carbon emissions caused by cruising, which in turn increases efficiency in urban mobility with a reduced taxi fleet scale. For individual taxis, they lack macroscopic horizon in their choice of passenger pickup paths. The selected travel path based on personal operational experience or real-time location is limited by local optimization when making path decisions. In this work, we proposed a macro-path recommendation method to assist the taxi pickup path selection to accelerate the transformation of the taxi system towards low-carbon sharing. First, an adaptive learning spatiotemporal neural network was used to predict the coarse-grained distribution of potential trips. Next, the trajectory sharing graph was constructed based on the potential trips distribution to reallocate the taxi orders for the continuous pickup path optimization. As a result, the continuous pickup path balanced the relation between travel demands and taxi supply, improving the economic and environmental benefits of taxi operation and contributing to the goal of carbon neutrality. We conducted experiments on the Chengdu city ride-hailing dataset. Compared with the current status of taxi operations, the solution shows improvements in both the scale of taxi services and order gain. Full article
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17 pages, 9273 KiB  
Article
Impact of the Cartographer’s Position and Topographic Accessibility on the Accuracy of Historical Land Use Information: Case of the Second Military Survey Maps of the Habsburg Empire
by Krzysztof Ostafin, Małgorzata Pietrzak and Dominik Kaim
ISPRS Int. J. Geo-Inf. 2021, 10(12), 820; https://doi.org/10.3390/ijgi10120820 - 4 Dec 2021
Cited by 6 | Viewed by 3162
Abstract
Historical maps are critical for long-term land use reconstructions; however, quantifying the uncertainty involved in comparing historical maps with recent data remains a considerable challenge. To date, many works have focused on the technical aspects of comparing historical and contemporary materials, but the [...] Read more.
Historical maps are critical for long-term land use reconstructions; however, quantifying the uncertainty involved in comparing historical maps with recent data remains a considerable challenge. To date, many works have focused on the technical aspects of comparing historical and contemporary materials, but the potential sources of uncertainty inherent in historical data remain poorly understood. In this paper, we analyze the impacts of the topographic accessibility and cartographer’s field position on the content quality of historical Austrian second military survey maps by referring to independent census data. Our results show that the topographic accessibility and visibility from the cartographer’s surveying table points had very little impact on the map content quality and that the surveying table point locations were uniformly distributed throughout the area, regardless of the landscape conditions. These findings demonstrate that the second military survey maps can be seen as valuable and consistent historical data sources, making them especially useful for long-term land use research in Central Europe. Full article
(This article belongs to the Special Issue Geographic Information Science (GIScience) and Geospatial Approaches for the Analysis of Historical Visual Sources and Cartographic Material)
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15 pages, 30660 KiB  
Article
Combining Global Geopotential Models, Digital Elevation Models, and GNSS/Leveling for Precise Local Geoid Determination in Some Mexico Urban Areas: Case Study
by Norberto Alcantar-Elizondo, Ramon Victorino Garcia-Lopez, Xochitl Guadalupe Torres-Carillo and Guadalupe Esteban Vazquez-Becerra
ISPRS Int. J. Geo-Inf. 2021, 10(12), 819; https://doi.org/10.3390/ijgi10120819 - 4 Dec 2021
Cited by 5 | Viewed by 3473
Abstract
This work shows improvements of geoid undulation values obtained from a high-resolution Global Geopotential Model (GGM), applied to local urban areas. The methodology employed made use of a Residual Terrain Model (RTM) to account for the topographic masses effect on the geoid. This [...] Read more.
This work shows improvements of geoid undulation values obtained from a high-resolution Global Geopotential Model (GGM), applied to local urban areas. The methodology employed made use of a Residual Terrain Model (RTM) to account for the topographic masses effect on the geoid. This effect was computed applying the spherical tesseroids approach for mass discretization. The required numerical integration was performed by 2-D integration with 1DFFT technique that combines DFT along parallels with direct numerical integration along meridians. In order to eliminate the GGM commission error, independent geoid undulations values obtained from a set of GNSS/leveling stations are employed. A corrector surface from the associated geoid undulation differences at the stations was generated through a polynomial regression model. The corrector surface, in addition to the GGM commission error, also absorbs the GNSS/leveling errors as well as datum inconsistencies and systematic errors of the data. The procedure was applied to five Mexican urban areas that have a geodetic network of GNSS/leveling points, which range from 166 to 811. Two GGM were evaluated: EGM2008 and XGM2019e_2159. EGM2008 was the model that showed relatively better agreement with the GNSS/leveling stations having differences with RMSE values in the range of 8–60 cm and standard deviations of 5–8 cm in four of the networks and 17 cm in one of them. The computed topographic masses contribution to the geoid were relatively small, having standard deviations on the range 1–24 mm. With respect to corrector surface estimations, they turned out to be fairly smooth yielding similar residuals values for two geoid models. This was also the case for the most recent Mexican gravity geoid GGM10. For the three geoid models, the second order polynomial regression model performed slightly better than the first order with differences up to 1 cm. These two models produced geoid correction residuals with a standard deviation in one test area of 14 cm while for the others it was of about 4–7 cm. However, the kriging method that was applied for comparison purposes produced slightly smaller values: 8 cm for one area and 4–6 cm for the others. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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16 pages, 2526 KiB  
Article
Deep Learning for Toponym Resolution: Geocoding Based on Pairs of Toponyms
by Jacques Fize, Ludovic Moncla and Bruno Martins
ISPRS Int. J. Geo-Inf. 2021, 10(12), 818; https://doi.org/10.3390/ijgi10120818 - 2 Dec 2021
Cited by 14 | Viewed by 4657
Abstract
Geocoding aims to assign unambiguous locations (i.e., geographic coordinates) to place names (i.e., toponyms) referenced within documents (e.g., within spreadsheet tables or textual paragraphs). This task comes with multiple challenges, such as dealing with referent ambiguity (multiple places with a same name) or [...] Read more.
Geocoding aims to assign unambiguous locations (i.e., geographic coordinates) to place names (i.e., toponyms) referenced within documents (e.g., within spreadsheet tables or textual paragraphs). This task comes with multiple challenges, such as dealing with referent ambiguity (multiple places with a same name) or reference database completeness. In this work, we propose a geocoding approach based on modeling pairs of toponyms, which returns latitude-longitude coordinates. One of the input toponyms will be geocoded, and the second one is used as context to reduce ambiguities. The proposed approach is based on a deep neural network that uses Long Short-Term Memory (LSTM) units to produce representations from sequences of character n-grams. To train our model, we use toponym co-occurrences collected from different contexts, namely textual (i.e., co-occurrences of toponyms in Wikipedia articles) and geographical (i.e., inclusion and proximity of places based on Geonames data). Experiments based on multiple geographical areas of interest—France, United States, Great-Britain, Nigeria, Argentina and Japan—were conducted. Results show that models trained with co-occurrence data obtained a higher geocoding accuracy, and that proximity relations in combination with co-occurrences can help to obtain a slightly higher accuracy in geographical areas with fewer places in the data sources. Full article
(This article belongs to the Special Issue Deep Learning Meets GIR: Recent Advances in Geographic Information Retrieval)
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21 pages, 4985 KiB  
Article
PSOTSC: A Global-Oriented Trajectory Segmentation and Compression Algorithm Based on Swarm Intelligence
by Zhihong Ouyang, Lei Xue, Feng Ding and Da Li
ISPRS Int. J. Geo-Inf. 2021, 10(12), 817; https://doi.org/10.3390/ijgi10120817 - 2 Dec 2021
Cited by 5 | Viewed by 2610
Abstract
Linear approximate segmentation and data compression of moving target spatio-temporal trajectory can reduce data storage pressure and improve the efficiency of target motion pattern mining. High quality segmentation and compression need to accurately select and store as few points as possible that can [...] Read more.
Linear approximate segmentation and data compression of moving target spatio-temporal trajectory can reduce data storage pressure and improve the efficiency of target motion pattern mining. High quality segmentation and compression need to accurately select and store as few points as possible that can reflect the characteristics of the original trajectory, while the existing methods still have room for improvement in segmentation accuracy, reduction of compression rate and simplification of algorithm parameter setting. A trajectory segmentation and compression algorithm based on particle swarm optimization is proposed. First, the trajectory segmentation problem is transformed into a global intelligent optimization problem of segmented feature points, which makes the selection of segmented points more accurate; then, a particle update strategy combining neighborhood adjustment and random jump is established to improve the efficiency of segmentation and compression. Through experiments on a real data set and a maneuvering target simulation trajectory set, the results show that compared with the existing typical methods, this method has advantages in segmentation accuracy and compression rate. Full article
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18 pages, 3458 KiB  
Article
Spatial Data Sequence Selection Based on a User-Defined Condition Using GPGPU
by Driss En-Nejjary, François Pinet and Myoung-Ah Kang
ISPRS Int. J. Geo-Inf. 2021, 10(12), 816; https://doi.org/10.3390/ijgi10120816 - 2 Dec 2021
Viewed by 2395
Abstract
The size of spatial data is growing intensively due to the emergence of and the tremendous advances in technology such as sensors and the internet of things. Supporting high-performance queries on this large volume of data becomes essential in several data- and compute-intensive [...] Read more.
The size of spatial data is growing intensively due to the emergence of and the tremendous advances in technology such as sensors and the internet of things. Supporting high-performance queries on this large volume of data becomes essential in several data- and compute-intensive applications. Unfortunately, most of the existing methods and approaches are based on a traditional computing framework (uniprocessors) which makes them not scalable and not adequate to deal with large-scale data. In this work, we present a high-performance query for massive spatio–temporal data. The query consists of selecting fixed size raster subsequences, based on the average of their region of interest, from a spatio–temporal raster sequence satisfying a user threshold condition. In our paper, for the purpose of simplification, we consider that the region of interest is the entire raster and not only a subregion. Our aim is to speed up the execution using parallel primitives and pure CUDA. Furthermore, we propose a new method based on a sorting step to save computations and boost the speed of the query execution. The test results show that the proposed methods are faster and good performance is achieved even with large-scale rasters and data. Full article
(This article belongs to the Special Issue Large Scale Geospatial Data Management, Processing and Mining)
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16 pages, 3743 KiB  
Article
The Societal Echo of Severe Weather Events: Ambient Geospatial Information (AGI) on a Storm Event
by Rafael Hologa and Rüdiger Glaser
ISPRS Int. J. Geo-Inf. 2021, 10(12), 815; https://doi.org/10.3390/ijgi10120815 - 2 Dec 2021
Viewed by 3138
Abstract
The given article focuses on the benefit of harvested Ambient Geographic Information (AGI) as complementary data sources for severe weather events and provides methodical approaches for the spatio-temporal analysis of such data. The perceptions and awareness of Twitter users posting about severe weather [...] Read more.
The given article focuses on the benefit of harvested Ambient Geographic Information (AGI) as complementary data sources for severe weather events and provides methodical approaches for the spatio-temporal analysis of such data. The perceptions and awareness of Twitter users posting about severe weather patterns were explored as there were aspects not documented by official damage reports or derived from official weather data. We analysed Tweets regarding the severe storm event Friederike to map their spatio-temporal patterns. More than 50% of the retrieved >23.000 tweets were geocoded by applying supervised information retrievals, text mining, and geospatial analysis methods. Complementary, central topics were clustered and linked to official weather data for cross-evaluation. The data confirmed (1) a scale-dependent relationship between the wind speed and the societal echo. In addition, the study proved that (2) reporting activity is moderated by population distribution. An in-depth analysis of the crowds’ central topic clusters in response to the storm Friederike (3) revealed a plausible sequence of dominant communication contents during the severe weather event. In particular, the merge of the studied AGI and other environmental datasets at different spatio-temporal scales shows how such user-generated content can be a useful complementary data source to study severe weather events and the ensuing societal echo. Full article
(This article belongs to the Special Issue Mapping, Modeling and Prediction with VGI)
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20 pages, 18905 KiB  
Article
A Hierarchical Spatial Network Index for Arbitrarily Distributed Spatial Objects
by Xiangqiang Min, Dieter Pfoser, Andreas Züfle and Yehua Sheng
ISPRS Int. J. Geo-Inf. 2021, 10(12), 814; https://doi.org/10.3390/ijgi10120814 - 1 Dec 2021
Cited by 4 | Viewed by 2853
Abstract
The range query is one of the most important query types in spatial data processing. Geographic information systems use it to find spatial objects within a user-specified range, and it supports data mining tasks, such as density-based clustering. In many applications, ranges are [...] Read more.
The range query is one of the most important query types in spatial data processing. Geographic information systems use it to find spatial objects within a user-specified range, and it supports data mining tasks, such as density-based clustering. In many applications, ranges are not computed in unrestricted Euclidean space, but on a network. While the majority of access methods cannot trivially be extended to network space, existing network index structures partition the network space without considering the data distribution. This potentially results in inefficiency due to a very skewed node distribution. To improve range query processing on networks, this paper proposes a balanced Hierarchical Network index (HN-tree) to query spatial objects on networks. The main idea is to recursively partition the data on the network such that each partition has a similar number of spatial objects. Leveraging the HN-tree, we present an efficient range query algorithm, which is empirically evaluated using three different road networks and several baselines and state-of-the-art network indices. The experimental evaluation shows that the HN-tree substantially outperforms existing methods. Full article
(This article belongs to the Special Issue Geo-Enriched Data Modeling & Mining)
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18 pages, 4397 KiB  
Article
Instance Segmentation for Governmental Inspection of Small Touristic Infrastructure in Beach Zones Using Multispectral High-Resolution WorldView-3 Imagery
by Osmar Luiz Ferreira de Carvalho, Rebeca dos Santos de Moura, Anesmar Olino de Albuquerque, Pablo Pozzobon de Bem, Rubens de Castro Pereira, Li Weigang, Dibio Leandro Borges, Renato Fontes Guimarães, Roberto Arnaldo Trancoso Gomes and Osmar Abílio de Carvalho Júnior
ISPRS Int. J. Geo-Inf. 2021, 10(12), 813; https://doi.org/10.3390/ijgi10120813 - 30 Nov 2021
Cited by 7 | Viewed by 3155
Abstract
Misappropriation of public lands is an ongoing government concern. In Brazil, the beach zone is public property, but many private establishments use it for economic purposes, requiring constant inspection. Among the undue targets, the individual mapping of straw beach umbrellas (SBUs) attached to [...] Read more.
Misappropriation of public lands is an ongoing government concern. In Brazil, the beach zone is public property, but many private establishments use it for economic purposes, requiring constant inspection. Among the undue targets, the individual mapping of straw beach umbrellas (SBUs) attached to the sand is a great challenge due to their small size, high presence, and agglutinated appearance. This study aims to automatically detect and count SBUs on public beaches using high-resolution images and instance segmentation, obtaining pixel-wise semantic information and individual object detection. This study is the first instance segmentation application on coastal areas and the first using WorldView-3 (WV-3) images. We used the Mask-RCNN with some modifications: (a) multispectral input for the WorldView3 imagery (eight channels), (b) improved the sliding window algorithm for large image classification, and (c) comparison of different image resizing ratios to improve small object detection since the SBUs are small objects (<322 pixels) even using high-resolution images (31 cm). The accuracy analysis used standard COCO metrics considering the original image and three scale ratios (2×, 4×, and 8× resolution increase). The average precision (AP) results increased proportionally to the image resolution: 30.49% (original image), 48.24% (2×), 53.45% (4×), and 58.11% (8×). The 8× model presented 94% AP50, classifying nearly all SBUs correctly. Moreover, the improved sliding window approach enables the classification of large areas providing automatic counting and estimating the size of the objects, proving to be effective for inspecting large coastal areas and providing insightful information for public managers. This remote sensing application impacts the inspection cost, tribute, and environmental conditions. Full article
(This article belongs to the Special Issue Geomatic Applications to Coastal Research: Challenges and New Developments)
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13 pages, 2826 KiB  
Article
Spatiotemporal Distribution Patterns and Local Driving Factors of Regional Development in Java
by Andrea Emma Pravitasari, Ernan Rustiadi, Rista Ardy Priatama, Alfin Murtadho, Adib Ahmad Kurnia, Setyardi Pratika Mulya, Izuru Saizen, Candraningratri Ekaputri Widodo and Siti Wulandari
ISPRS Int. J. Geo-Inf. 2021, 10(12), 812; https://doi.org/10.3390/ijgi10120812 - 30 Nov 2021
Cited by 12 | Viewed by 4268
Abstract
Although uneven regional development has long been an issue in Java, most parts of the territory experienced an increased level of development over the last two decades. Due to the variance in local background and spatial heterogeneity, the driving factors of the development [...] Read more.
Although uneven regional development has long been an issue in Java, most parts of the territory experienced an increased level of development over the last two decades. Due to the variance in local background and spatial heterogeneity, the driving factors of the development level should, theoretically, vary over space. Therefore, in this study, we aim to investigate the local factors that influence the development level of Java’s regions. We used the spatiotemporal pattern analysis, ordinary least squares (OLS) regression, and geographically weighted regression (GWR), utilizing the regional development index as the predicted variable, and the social level, economy, infrastructure, land use, and environmental barriers as predictors. As per our results, it was found that the level of development in Java has improved over the past two decades. Metropolitan areas continued to lead this improvement. All the predictors that we examined significantly affected regional development. However, the spatial pattern of the local regression coefficients of Human Development Index (HDI), landslide, paddy conversion, and crime shifted due to changes in the spatial concentration of development activities. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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21 pages, 4918 KiB  
Article
Evaluation of the Optimal Topic Classification for Social Media Data Combined with Text Semantics: A Case Study of Public Opinion Analysis Related to COVID-19 with Microblogs
by Qin Liang, Chunchun Hu and Si Chen
ISPRS Int. J. Geo-Inf. 2021, 10(12), 811; https://doi.org/10.3390/ijgi10120811 - 30 Nov 2021
Cited by 6 | Viewed by 3492
Abstract
Online public opinion reflects social conditions and public attitudes regarding special social events. Therefore, analyzing the temporal and spatial distributions of online public opinion topics can contribute to understanding issues of public concern, grasping and guiding the developing trend of public opinion. However, [...] Read more.
Online public opinion reflects social conditions and public attitudes regarding special social events. Therefore, analyzing the temporal and spatial distributions of online public opinion topics can contribute to understanding issues of public concern, grasping and guiding the developing trend of public opinion. However, how to evaluate the validity of classification of online public opinion remains a challenging task in the topic mining field. By combining a Bidirectional Encoder Representations from Transformers (BERT) pre-training model with the Latent Dirichlet Allocation (LDA) topic model, we propose an evaluation method to determine the optimal classification number of topics from the perspective of semantic similarity. The effectiveness of the proposed method was verified based on the standard Chinese corpus THUCNews. Taking Coronavirus Disease 2019 (COVID-19)-related geotagged posts on Weibo in Wuhan city as an example, we used the proposed method to generate five categories of public opinion topics. Combining spatial and temporal information with the classification results, we analyze the spatial and temporal distribution patterns of the five optimal public opinion topics, which are found to be consistent with the epidemic development, demonstrating the feasibility of our method when applied to practical cases. Full article
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21 pages, 12182 KiB  
Article
Air Humidity Characteristics in “Local Climate Zones” of Novi Sad (Serbia) Based on Long-Term Data
by Jelena Dunjić, Dragan Milošević, Milena Kojić, Stevan Savić, Zorana Lužanin, Ivan Šećerov and Daniela Arsenović
ISPRS Int. J. Geo-Inf. 2021, 10(12), 810; https://doi.org/10.3390/ijgi10120810 - 30 Nov 2021
Cited by 11 | Viewed by 4100
Abstract
This study aims to investigate spatial and temporal dynamics and relationship between air temperature and five air humidity parameters (relative humidity, water vapor pressure, absolute humidity, specific humidity, and vapor pressure deficit) in Novi Sad, Serbia, based on two-year data (December 2015–December 2017). [...] Read more.
This study aims to investigate spatial and temporal dynamics and relationship between air temperature and five air humidity parameters (relative humidity, water vapor pressure, absolute humidity, specific humidity, and vapor pressure deficit) in Novi Sad, Serbia, based on two-year data (December 2015–December 2017). The analysis includes different urban areas of Novi Sad, which are delineated in five built (urban) types of local climate zones (LCZ) (LCZ 2, LCZ 5, LCZ 6, LCZ 8, and LCZ 9), and one land cover (natural) local climate zone (LCZ A) located outside the urban area. Temporal analysis included annual, seasonal, and monthly dynamics of air temperature and air humidity parameters, as well as their patterns during the extreme periods (heat and cold wave). The results showed that urban dry island (UDI) occurs in densely urbanized LCZ 2 from February to October, unlike other urban LCZs. The analysis of the air humidity dynamics during the heat wave shows that UDI intensity is most pronounced during the daytime, but also in the evening (approximately until midnight) in LCZ 2. However, lower UDI intensity is observed in the afternoon, in other urban LCZs (LCZ 6, LCZ 8, and LCZ 9) and occasionally in the later afternoon in LCZ 5. Regression analysis confirms the relationship between air temperature and each of the analyzed air humidity parameters. Full article
(This article belongs to the Special Issue Application of GIS-Based Mapping of Local Climate Zones in Urban Areas)
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32 pages, 17162 KiB  
Article
Impact of Multitemporal Land Use and Land Cover Change on Land Surface Temperature Due to Urbanization in Hefei City, China
by Jing Sun and Suwit Ongsomwang
ISPRS Int. J. Geo-Inf. 2021, 10(12), 809; https://doi.org/10.3390/ijgi10120809 - 30 Nov 2021
Cited by 7 | Viewed by 2968
Abstract
Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, [...] Read more.
Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature. Full article
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19 pages, 3596 KiB  
Article
Investigating Carnivore Guild Structure: Spatial and Temporal Relationships amongst Threatened Felids in Myanmar
by Pyae Phyoe Kyaw, David W. Macdonald, Ugyen Penjor, Saw Htun, Hla Naing, Dawn Burnham, Żaneta Kaszta and Samuel A. Cushman
ISPRS Int. J. Geo-Inf. 2021, 10(12), 808; https://doi.org/10.3390/ijgi10120808 - 30 Nov 2021
Cited by 6 | Viewed by 3762
Abstract
The co-occurrence of felid species in Southeast Asia provides an unusual opportunity to investigate guild structure and the factors controlling it. Using camera-trap data, we quantified the space use, temporal activity, and multi-dimensional niche overlap of the tiger, clouded leopard, Asiatic golden cat, [...] Read more.
The co-occurrence of felid species in Southeast Asia provides an unusual opportunity to investigate guild structure and the factors controlling it. Using camera-trap data, we quantified the space use, temporal activity, and multi-dimensional niche overlap of the tiger, clouded leopard, Asiatic golden cat, marbled cat, and leopard cat in the Htamanthi Wildlife Sanctuary, Myanmar. We hypothesised that the spatio-temporal behaviour of smaller cats would reflect the avoidance of the larger cats, and similar-sized guild members would partition their niches in space or time to reduce resource competition. Our approach involved modelling single-species occupancy, pairwise spatial overlap using Bayesian inference, activity overlap with kernel density estimation, and multivariate analyses. The felid assembly appeared to be partitioned mainly on a spatial rather than temporal dimension, and no significant evidence of mesopredator release was observed. Nonetheless, the temporal association between the three mesopredators was inversely related to the similarity in their body sizes. The largest niche differences in the use of space and time occurred between the three smallest species. This study offers new insight into carnivore guild assembly and adds substantially to knowledge of five of the least known felids of conservation concern. Full article
(This article belongs to the Special Issue Geospatial Data and Services for Wildlife Management and Conservation)
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