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ISPRS Int. J. Geo-Inf., Volume 10, Issue 6 (June 2021) – 70 articles

Cover Story (view full-size image): The spatial fragmentation in the housing market and the growth of squatter settlements are characteristic for metropolitan areas in developing countries. This study examines the relationship between squatter settlement growth and spatial fragmentation in the housing market of Buenos Aires. GWR analysis of the house prices for the years 2001, 2010, and 2018 shows that while squatter settlements had a strongly negative effect on house prices, the affected areas shifted over time. Our findings indicate that it is not the growth of the squatter settlement that causes spatial fragmentation. However, squatter settlements did determine the spatial demarcation of the fragmentation by attracting low-income households to surrounding low house price areas. View this paper
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Article
Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems
ISPRS Int. J. Geo-Inf. 2021, 10(6), 424; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060424 - 21 Jun 2021
Viewed by 424
Abstract
Many complex problems require a multi-criteria decision, such as the COVID-19 pandemic that affected nearly all activities in the world. In this regard, this study aims to develop a multi-criteria decision support system considering the sustainability, feasibility, and success rate of possible approaches. [...] Read more.
Many complex problems require a multi-criteria decision, such as the COVID-19 pandemic that affected nearly all activities in the world. In this regard, this study aims to develop a multi-criteria decision support system considering the sustainability, feasibility, and success rate of possible approaches. Therefore, two models have been developed: Geo-AHP (applying geo-based data) and BN-Geo-AHP using probabilistic techniques (Bayesian network). The ranking method of Geo-APH is generalized, and the equations are provided in a way that adding new elements and variables would be possible by experts. Then, to improve the ranking, the application of the probabilistic technique of a Bayesian network and the role of machine learning for database and weight of each parameter are explained, and the model of BN-Geo-APH has been developed. In the next step, to show the application of the developed Geo-AHP and BN-Geo-AHP models, we selected the new pandemic of COVID-19 that affected nearly all activities, and we used both models for analysis. For this purpose, we first analyzed the available data about COVID-19 and previous studies about similar virus infections, and then we ranked the main approaches and alternatives in confronting the pandemic of COVID-19. The analysis of approaches with the selected alternatives shows the first ranked approach is massive vaccination and the second ranked is massive swabs or other tests. The third is the use of medical masks and gloves, and the last ranked is the lockdown, mostly due to its major negative impact on the economy and individuals. Full article
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Article
Evaluating Cultural Landscape Remediation Design Based on VR Technology
ISPRS Int. J. Geo-Inf. 2021, 10(6), 423; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060423 - 21 Jun 2021
Viewed by 448
Abstract
Due to the recent excessive pursuit of rapid economic development in China, the cultural heritage resources have been gradually destroyed. This paper proposes cultural recovery and ecological remediation patterns, and adopts virtual reality (VR) technology to evaluate the visual aesthetic effect of the [...] Read more.
Due to the recent excessive pursuit of rapid economic development in China, the cultural heritage resources have been gradually destroyed. This paper proposes cultural recovery and ecological remediation patterns, and adopts virtual reality (VR) technology to evaluate the visual aesthetic effect of the restored landscape. The results show that: (1) the average vegetation coverage increased, providing data support for remediation design evaluation; and (2) the fixation counts and average saccade counts of the subjects increased after the remediation design, indicating that the restored cultural landscape reduced visual fatigue and provided a better visual aesthetic experience. Furthermore, the comparative analysis of the quality of the water environment shows that the remediation design project improved the ecological environment quality of the relics area. The results of this study will contribute to rural revitalization in minority areas in southwest China. Full article
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Article
A Topology-Preserving Simplification Method for 3D Building Models
ISPRS Int. J. Geo-Inf. 2021, 10(6), 422; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060422 - 20 Jun 2021
Viewed by 453
Abstract
Simplification of 3D building models is an important way to improve rendering efficiency. When existing algorithms are directly applied to simplify multi-component models, generally composed of independent components with strong topological dependence, each component is simplified independently. The consequent destruction of topological dependence [...] Read more.
Simplification of 3D building models is an important way to improve rendering efficiency. When existing algorithms are directly applied to simplify multi-component models, generally composed of independent components with strong topological dependence, each component is simplified independently. The consequent destruction of topological dependence can cause unreasonable separation of components and even result in inconsistent conclusions of spatial analysis among different levels of details (LODs). To solve these problems, a novel simplification method, which considers the topological dependence among components as constraints, is proposed. The vertices of building models are divided into boundary vertices, hole vertices, and other ordinary vertices. For the boundary vertex, the angle between the edge and component (E–C angle), denoting the degree of component separation, is introduced to derive an error metric to limit the collapse of the edge located at adjacent areas of neighboring components. An improvement to the quadratic error metric (QEM) algorithm was developed for the hole vertex to address the unexpected error caused by the QEM’s defect. A series of experiments confirmed that the proposed method could effectively maintain the overall appearance features of building models. Compared with the traditional method, the consistency of visibility analysis among different LODs is much better. Full article
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Article
Spatial Distribution of Displaced Population Estimated Using Mobile Phone Data to Support Disaster Response Activities
ISPRS Int. J. Geo-Inf. 2021, 10(6), 421; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060421 - 20 Jun 2021
Viewed by 517
Abstract
Under normal circumstances, people’s homes and work locations are given by their addresses, and this information is used to create a disaster management plan in which there are instructions to individuals on how to evacuate. However, when a disaster strikes, some shelters are [...] Read more.
Under normal circumstances, people’s homes and work locations are given by their addresses, and this information is used to create a disaster management plan in which there are instructions to individuals on how to evacuate. However, when a disaster strikes, some shelters are destroyed, or in some cases, distance from affected areas to the closest shelter is not reasonable, or people have no possibility to act rationally as a natural response to physical danger, and hence, the evacuation plan is not followed. In each of these situations, people tend to find alternative places to stay, and the evacuees in shelters do not represent the total number of the displaced population. Knowing the spatial distribution of total displaced people (including people in shelters and other places) is very important for the success of the response activities which, among other measures, aims to provide for the basic humanitarian needs of affected people. Traditional methods of people displacement estimation are based on population surveys in the shelters. However, conducting a survey is infeasible to perform at scale and provides low coverage, i.e., can only cover the numbers for the population that are at the shelters, and the information cannot be delivered in a timely fashion. Therefore, in this research, anonymized mobile Call Detail Records (CDRs) are proposed as a source of information to infer the spatial distribution of the displaced population by analyzing the variation of home cell-tower for each anonymized mobile phone subscriber before and after a disaster. The effectiveness of the proposed method is evaluated using remote-sensing-based building damage assessment data and Displacement Tracking Matrix (DTM) from an individual’s questionnaire survey conducted after a severe cyclone in Beira city, central Mozambique, in March 2019. The results show an encouraging correlation coefficient (over 70%) between the number of arrivals in each neighborhood estimated using CDRs and from DTM. In addition to this, CDRs derive spatial distribution of displaced populations with high coverage of people, i.e., including not only people in the shelter but everyone who used a mobile phone before and after the disaster. Moreover, results suggest that if CDRs data are available right after a disaster, population displacement can be estimated, and this information can be used for response activities and hence contribute to reducing waterborne diseases (e.g., diarrheal disease) and diseases associated with crowding (e.g., acute respiratory infections) in shelters and host communities. Full article
(This article belongs to the Special Issue Disaster Management and Geospatial Information)
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Article
Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images
ISPRS Int. J. Geo-Inf. 2021, 10(6), 420; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060420 - 20 Jun 2021
Viewed by 291
Abstract
Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization [...] Read more.
Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects. Full article
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Article
Tree Height Growth Modelling Using LiDAR-Derived Topography Information
ISPRS Int. J. Geo-Inf. 2021, 10(6), 419; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060419 - 19 Jun 2021
Viewed by 496
Abstract
The concepts of ecotopes and forest sites are used to describe the correlative complexes defined by landform, vegetation structure, forest stand characteristics and the relationship between soil and physiography. Physically heterogeneous landscapes such as karst, which is characterized by abundant sinkholes and outcrops, [...] Read more.
The concepts of ecotopes and forest sites are used to describe the correlative complexes defined by landform, vegetation structure, forest stand characteristics and the relationship between soil and physiography. Physically heterogeneous landscapes such as karst, which is characterized by abundant sinkholes and outcrops, exhibit diverse microtopography. Understanding the variation in the growth of trees in a heterogeneous topography is important for sustainable forest management. An R script for detailed stem analysis was used to reconstruct the height growth histories of individual trees (steam analysis). The results of this study reveal that the topographic factors influencing the height growth of silver fir trees can be detected within forest stands. Using topography modelling, we classified silver fir trees into groups with significant differences in height growth. This study provides a sound basis for the comparison of forest site differences and may be useful in the calibration of models for various tree species. Full article
(This article belongs to the Special Issue The Use of Geo-Spatial Tools in Forestry)
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Article
A Spatially Highly Resolved Ground Mounted and Rooftop Potential Analysis for Photovoltaics in Austria
ISPRS Int. J. Geo-Inf. 2021, 10(6), 418; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060418 - 16 Jun 2021
Viewed by 547
Abstract
Austria aims to meet 100% of its electricity demand from domestic renewable sources by 2030 which means, that an additional 27 TWh/a of renewable electricity generation are required, thereof 11 TWh/a from photovoltaic. While some federal states and municipalities released a solar rooftop cadastre, there is lacking knowledge on the estimation of the potential of both, ground mounted installations and rooftop modules, on a national level with a high spatial resolution. As a first, in this work data on agricultural land-use is combined with highly resolved data on buildings on a national level. Our results show significant differences between urban and rural areas, as well as between the Alpine regions and the Prealpine- and Easter Plain areas. Rooftop potential concentrates in the big urban areas, but also in densely populated areas in Lower- and Upper Austria, Styria and the Rhine valley of Vorarlberg. The ground mounted photovoltaic potential is highest in Eastern Austria. This potential is geographically consistent with the demand and allows for a production close to the consumer. In theory, the goal of meeting 11 TWh/a in 2030 can be achieved solely with the rooftop PV potential. However, considering the necessary installation efforts, the associated costs of small and dispersed production units and finally the inherent uncertainty with respect to the willingness of tens of thousands of individual households to install PV systems, installing the necessary solar PV on buildings alone is constrained. Full article
(This article belongs to the Collection Spatial and Temporal Modelling of Renewable Energy Systems)
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Article
Rurality and Origin–Destination Trajectories of Medical School Application and Matriculation in the United States
ISPRS Int. J. Geo-Inf. 2021, 10(6), 417; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060417 - 16 Jun 2021
Viewed by 321
Abstract
Physician shortages are more pronounced in rural than in urban areas. The geography of medical school application and matriculation could provide insights into geographic differences in physician availability. Using data from the Association of American Medical Colleges (AAMC), we conducted geospatial analyses, and [...] Read more.
Physician shortages are more pronounced in rural than in urban areas. The geography of medical school application and matriculation could provide insights into geographic differences in physician availability. Using data from the Association of American Medical Colleges (AAMC), we conducted geospatial analyses, and developed origin–destination (O–D) trajectories and conceptual graphs to understand the root cause of rural physician shortages. Geographic disparities exist at a significant level in medical school applications in the US. The total number of medical school applications increased by 38% from 2001 to 2015, but the number had decreased by 2% in completely rural counties. Most counties with no medical school applicants were in rural areas (88%). Rurality had a significant negative association with the application rate and explained 15.3% of the variation at the county level. The number of medical school applications in a county was disproportional to the population by rurality. Applicants from completely rural counties (2% of the US population) represented less than 1% of the total medical school applications. Our results can inform recruitment strategies for new medical school students, elucidate location decisions of new medical schools, provide recommendations to close the rural–urban gap in medical school applications, and reduce physician shortages in rural areas. Full article
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Article
The Use of Land Cover Indices for Rapid Surface Urban Heat Island Detection from Multi-Temporal Landsat Imageries
ISPRS Int. J. Geo-Inf. 2021, 10(6), 416; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060416 - 16 Jun 2021
Viewed by 328
Abstract
The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. [...] Read more.
The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period. Full article
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Article
Monitoring the Spatiotemporal Trajectory of Urban Area Hotspots Using the SVM Regression Method Based on NPP-VIIRS Imagery
ISPRS Int. J. Geo-Inf. 2021, 10(6), 415; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060415 - 16 Jun 2021
Viewed by 405
Abstract
Urban area hotspots are considered to be an ideal proxy for spatial heterogeneity of human activity, which is vulnerable to urban expansion. Nighttime light (NTL) images have been extensively employed in monitoring current urbanization dynamics. However, the existing studies related to NTL images [...] Read more.
Urban area hotspots are considered to be an ideal proxy for spatial heterogeneity of human activity, which is vulnerable to urban expansion. Nighttime light (NTL) images have been extensively employed in monitoring current urbanization dynamics. However, the existing studies related to NTL images mainly concern detection of urban areas, leaving inner spatial differences in urban NTL luminosity poorly explored. In this study, we propose an innovative approach to explore the spatiotemporal trajectory of urban area hotspots using monthly Visible Infrared Imaging Radiometer Suite (VIIRS) NTL images. Firstly, multi-temporal VIIRS NTL intensity was decomposed by time-series analysis to obtain annual stable components after data preprocessing. Secondly, the support vector machine (SVM) regression model was utilized to identify urban area hotspots. In order to ensure the model accuracy, the grid search and cross-validation method was integrated to achieve the optimized model parameters. Finally, we analyzed the spatiotemporal migration trajectory of urban area hotspots by the center of gravity method (i.e., shift distance and angle of urban area hotspot centroid). The results indicate that our method successfully captured urban area hotspots with a regression coefficient over 0.8. Meanwhile, the findings give an intuitive understanding of coupling interaction between urban area hotspots and socioeconomic indicators. This study provides important insights for further decision-making regarding sustainable urban planning. Full article
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Article
Integrating Network Centrality and Node-Place Model to Evaluate and Classify Station Areas in Shanghai
ISPRS Int. J. Geo-Inf. 2021, 10(6), 414; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060414 - 16 Jun 2021
Viewed by 323
Abstract
Transit-oriented development (TOD) is generally understood as an effective urban design model for encouraging the use of public transportation. Inspired by TOD, the node-place (NP) model was developed to investigate the relationship between transport stations and land use. However, existing studies construct the [...] Read more.
Transit-oriented development (TOD) is generally understood as an effective urban design model for encouraging the use of public transportation. Inspired by TOD, the node-place (NP) model was developed to investigate the relationship between transport stations and land use. However, existing studies construct the NP model based on the statistical attributes, while the importance of travel characteristics is ignored, which arguably cannot capture the complete picture of the stations. In this study, we aim to integrate the NP model and travel characteristics with systematic insights derived from network theory to classify stations. A node-place-network (NPN) model is developed by considering three aspects: land use, transportation, and travel network. Moreover, the carrying pressure is proposed to quantify the transport service pressure of the station. Taking Shanghai as a case study, our results show that the travel network affects the station classification and highlights the imbalance between the built environment and travel characteristics. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Improving Geographically Weighted Regression Considering Directional Nonstationary for Ground-Level PM2.5 Estimation
ISPRS Int. J. Geo-Inf. 2021, 10(6), 413; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060413 - 15 Jun 2021
Viewed by 403
Abstract
The increase in atmospheric pollution dominated by particles with an aerodynamic diameter smaller than 2.5 μm (PM2.5) has become one of the most serious environmental hazards worldwide. The geographically weighted regression (GWR) model is a vital method to estimate the spatial [...] Read more.
The increase in atmospheric pollution dominated by particles with an aerodynamic diameter smaller than 2.5 μm (PM2.5) has become one of the most serious environmental hazards worldwide. The geographically weighted regression (GWR) model is a vital method to estimate the spatial distribution of the ground-level PM2.5 concentration. Wind information reflects the directional dependence of the spatial distribution, which can be abstracted as a combination of spatial and directional non-stationarity components. In this paper, a GWR model considering directional non-stationarity (GDWR) is proposed. To assess the efficacy of our method, monthly PM2.5 concentration estimation was carried out as a case study from March 2015 to February 2016 in the Yangtze River Delta region. The results indicate that the GDWR model attained the best fitting effect (0.79) and the smallest error fluctuation, the ordinary least squares (OLS) (0.589) fitting effect was the worst, and the GWR (0.72) and directionally weighted regression (DWR) (0.74) fitting effects were moderate. A non-stationarity hypothesis test was performed to confirm directional non-stationarity. The distribution of the PM2.5 concentration in the Yangtze River Delta is also discussed here. Full article
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Article
A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics
ISPRS Int. J. Geo-Inf. 2021, 10(6), 412; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060412 - 15 Jun 2021
Viewed by 372
Abstract
With the recent increase in the use of sea transportation, the importance of maritime surveillance for detecting unusual vessel behavior related to several illegal activities has also risen. Unfortunately, the data collected by surveillance systems are often incomplete, creating a need for the [...] Read more.
With the recent increase in the use of sea transportation, the importance of maritime surveillance for detecting unusual vessel behavior related to several illegal activities has also risen. Unfortunately, the data collected by surveillance systems are often incomplete, creating a need for the data gaps to be filled using techniques such as interpolation methods. However, such approaches do not decrease the uncertainty of ship activities. Depending on the frequency of the data generated, they may even confuse operators, inducing errors when evaluating ship activities and tagging them as unusual. Using domain knowledge to classify activities as anomalous is essential in the maritime navigation environment since there is a well-known lack of labeled data in this domain. In an area where identifying anomalous trips is a challenging task using solely automatic approaches, we use visual analytics to bridge this gap by utilizing users’ reasoning and perception abilities. In this work, we propose a visual analytics tool that uses spatial segmentation to divide trips into subtrajectories and score them. These scores are displayed in a tabular visualization where users can rank trips by segment to find local anomalies. The amount of interpolation in subtrajectories is displayed together with scores so that users can use both their insight and the trip displayed on the map to determine if the score is reliable. Full article
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Perspective
Eye Tracking Research in Cartography: Looking into the Future
ISPRS Int. J. Geo-Inf. 2021, 10(6), 411; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060411 - 14 Jun 2021
Viewed by 500
Abstract
Eye tracking has been served as one of the most objective and valuable tools towards the examination of both map perceptual and cognitive processes. The aim of the present article is to concisely present the contribution of eye tracking research in cartography, indicating [...] Read more.
Eye tracking has been served as one of the most objective and valuable tools towards the examination of both map perceptual and cognitive processes. The aim of the present article is to concisely present the contribution of eye tracking research in cartography, indicating the existing literature, as well as the current research trends in the examined domain. The authors discuss the existing challenges and provide their perspectives about the future outlook of cartographic eye tracking experimentation by reporting specific key approaches that could be integrated. Full article
(This article belongs to the Special Issue Eye-Tracking in Cartography)
Article
Socioeconomic and Environmental Impacts on Regional Tourism across Chinese Cities: A Spatiotemporal Heterogeneous Perspective
ISPRS Int. J. Geo-Inf. 2021, 10(6), 410; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060410 - 14 Jun 2021
Viewed by 577
Abstract
Understanding geospatial impacts of multi-sourced drivers on the tourism industry is of great significance for formulating tourism development policies tailored to regional-specific needs. To date, no research in China has explored the combined impacts of socioeconomic and environmental drivers on city-level tourism from [...] Read more.
Understanding geospatial impacts of multi-sourced drivers on the tourism industry is of great significance for formulating tourism development policies tailored to regional-specific needs. To date, no research in China has explored the combined impacts of socioeconomic and environmental drivers on city-level tourism from a spatiotemporal heterogeneous perspective. We collected the total tourism revenue indicator and 30 potential influencing factors from 343 cities across China during 2008–2017. Three mainstream regressions and an emerging local spatiotemporal regression named the Bayesian spatiotemporally varying coefficients (Bayesian STVC) model were constructed to investigate the global-scale stationary and local-scale spatiotemporal nonstationary relationships between city-level tourism and various vital drivers. The Bayesian STVC model achieved the best model performance. Globally, eight socioeconomic and environmental factors, average wage (coefficient: 0.47, 95% credible intervals: 0.43–0.51), employed population (−0.14, −0.17–−0.11), GDP per capita (0.47, 0.42–0.52), population density (0.21, 0.16–0.27), night-time light index (−0.01, −0.08–0.05), slope (0.10, 0.06–0.14), vegetation index (0.66, 0.63–0.70), and road network density (0.34, 0.29–0.38), were identified to have nonlinear effects on tourism. Temporally, the main drivers might have gradually changed from the local macro-economic level, population density, and natural environment conditions to the individual economic level over the last decade. Spatially, city-specific dynamic maps of tourism development and geographically clustered influencing maps for eight drivers were produced. In 2017, China formed four significant city-level tourism industry clusters (hot spots, 90% confidence), the locations of which coincide with China’s top four urban agglomerations. Our local spatiotemporal analysis framework for geographical tourism data is expected to provide insights into adjusting regional measures to local conditions and temporal variations in broader social and natural sciences. Full article
(This article belongs to the Special Issue Geo Data Science for Tourism)
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Article
Heterogeneity of Spatial Distribution and Factors Influencing Unattended Locker Points in Guangzhou, China: The Case of Hive Box
ISPRS Int. J. Geo-Inf. 2021, 10(6), 409; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060409 - 14 Jun 2021
Viewed by 410
Abstract
Hive Box is a company that operates a network of express unattended collection and delivery points (UCDPs) in China. Hive Box distribution enhances community-based end-to-end delivery services and low-carbon city logistics. It is argued that UCDPs compared with attended collection and delivery points [...] Read more.
Hive Box is a company that operates a network of express unattended collection and delivery points (UCDPs) in China. Hive Box distribution enhances community-based end-to-end delivery services and low-carbon city logistics. It is argued that UCDPs compared with attended collection and delivery points (ACDPs) should be considered for further investigation. Therefore, the present study employed kernel density estimation, spatial autocorrelation analysis, and geographically weighted regression to investigate the spatial heterogeneity of Hive Box distribution across Guangzhou. Hive Box location data were collected from smartphone apps. The results were as follows: (1) the kernel density declined from the city center toward the outskirts, and showed point-like spatial agglomerations in the city center; (2) the Moran’s I index analysis showed that Hive Box distribution exhibited spatial agglomeration from a global perspective and geographic variations in locality in space; the heterogeneity of urban–rural differences implies the uneven development of Hive Box distribution in Guangzhou; and (3) the factors influencing Hive Box distribution were multilevel, and their effects were complex and varied across regions. These results shed light on the agglomeration and heterogeneity characteristics of the spatial distribution and influencing factors of Hive Boxes. For an enhanced community-based end-to-end delivery service, this study suggested the identification of the geographic variations of Hive Box distribution and the combined effects of multiple factors in intensifying the infrastructure of unattended locker points. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
How Image Acquisition Geometry of UAV Campaigns Affects the Derived Products and Their Accuracy in Areas with Complex Geomorphology
ISPRS Int. J. Geo-Inf. 2021, 10(6), 408; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060408 - 13 Jun 2021
Viewed by 619
Abstract
The detailed and accurate mapping of landscapes and their geomorphological characteristics is a key issue in hazard management. The current study examines whether the image acquisition geometry of unmanned aerial vehicle (UAV) campaigns affects the accuracy of the derived products, i.e., orthophotos, digital [...] Read more.
The detailed and accurate mapping of landscapes and their geomorphological characteristics is a key issue in hazard management. The current study examines whether the image acquisition geometry of unmanned aerial vehicle (UAV) campaigns affects the accuracy of the derived products, i.e., orthophotos, digital surface models (DSMs) and photogrammetric point clouds, while performing a detailed geomorphological mapping of a landslide area. UAV flights were executed and the collected imagery was organized into three subcategories based on the viewing angle of the UAV camera. The first subcategory consists of the nadir imagery, the second is composed of the oblique imagery and the third category blends both nadir and oblique imagery. UAV imagery processing was carried out using structure-from-motion photogrammetry (SfM). High-resolution products were generated, consisting of orthophotos, DSMs and photogrammetric-based point clouds. Their accuracy was evaluated utilizing statistical approaches such as the estimation of the root mean square error (RMSE), calculation of the geometric mean of a feature, length measurement, calculation of cloud-to-cloud distances as well as qualitive criteria. All the quantitative and qualitative results were taken into account for the impact assessment. It was demonstrated that the oblique-viewing geometry as well as the combination of nadir and oblique imagery could be used effectively for geomorphological mapping in areas with complex topography and steep slopes that overpass 60 degrees. Moreover, the accuracy assessment revealed that those acquisition geometries contribute to the creation of significantly better products compared to the corresponding one arising from nadir-viewing imagery. Full article
(This article belongs to the Special Issue GIS and Remote Sensing Applications in Geomorphology)
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Article
Emojis as Contextual Indicants in Location-Based Social Media Posts
ISPRS Int. J. Geo-Inf. 2021, 10(6), 407; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060407 - 12 Jun 2021
Viewed by 538
Abstract
The presented study aims to investigate the relationship between the use of emojis in location-based social media and the location of the corresponding post in terms of perceived objects and conducted activities connected to this place. The basis for this is not a [...] Read more.
The presented study aims to investigate the relationship between the use of emojis in location-based social media and the location of the corresponding post in terms of perceived objects and conducted activities connected to this place. The basis for this is not a purely frequency-based assessment, but a specifically introduced measure called typicality. To evaluate the typicality measure and examine the assumption that emojis are contextual indicants, a dataset of worldwide geotagged posts from Instagram relating to sunset and sunrise events is used, converted to a privacy-aware version based on a Hyperloglog approach. Results suggest that emojis can often provide more nuanced information about user activities and the surrounding environment than is possible with hashtags. Thus, emojis may be suitable for identifying less obvious characteristics and the sense of a place. Emojis are already explored in research, but mainly for sentiment analysis, for semantic studies or as part of emoji prediction. In contrast, this work provides novel insights into the user’s spatial or activity context by applying the typicality measure and therefore considers emojis contextual indicants. Full article
(This article belongs to the Special Issue Social Computing for Geographic Information Science)
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Article
Comprehensive Evaluation of the GF-4 Satellite Image Quality from 2015 to 2020
ISPRS Int. J. Geo-Inf. 2021, 10(6), 406; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060406 - 12 Jun 2021
Viewed by 391
Abstract
GaoFen-4(GF-4) is the highest spatial resolution Earth observation satellite operating in geosynchronous orbit. Its fixed Earth observation location, rapid responsiveness, and wide observation range make it popular in disaster and emergency monitoring. To evaluate the GF-4 image quality in detail on a long-term [...] Read more.
GaoFen-4(GF-4) is the highest spatial resolution Earth observation satellite operating in geosynchronous orbit. Its fixed Earth observation location, rapid responsiveness, and wide observation range make it popular in disaster and emergency monitoring. To evaluate the GF-4 image quality in detail on a long-term basis, this study analyzes the image quality after the commissioning phase by focusing on ground sample distance (GSD) and geometric and radiometric quality. The theoretical calculation, geometric and radiometric measurements, and on-site experiments results show that (1) the GSD of the GF-4 image is ~50 m at the nadir point and increases gradually with the distance away from the nadir point, (2) most external geometric errors are within the design requirements of 4 km despite some exceeding the limit, and the internal geometric errors are tested within 1 pixel, and (3) image sharpness is generally stable but varies with the atmosphere condition and imaging time, and the radiometric response gradually degrades at the rate of less than 5.5% per year. Full article
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Article
KMapper: A Field Geological Survey System
ISPRS Int. J. Geo-Inf. 2021, 10(6), 405; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060405 - 12 Jun 2021
Viewed by 353
Abstract
The computing power of smart mobile devices has evolved as much as the power of desktop personal computers (PCs). Accordingly, a field geological survey system capable of utilizing the performance of smart devices is needed. Thus, the objective of this paper is to [...] Read more.
The computing power of smart mobile devices has evolved as much as the power of desktop personal computers (PCs). Accordingly, a field geological survey system capable of utilizing the performance of smart devices is needed. Thus, the objective of this paper is to introduce a system with functions to take advantage of the performance of smart devices while meeting the various requirements of a geological survey. The system integrates geographic information system functions and smart sensors to execute field geological surveys effectively and can express various collections on a map. It also includes a map editing function that allows users to edit geological boundaries and areas on a map from the touch-based interface of a smart device. The records collected can be exported for editing of the geological map on a desktop PC. The developed app can replace traditional recording media used in field geological surveying and exploration work. It can be used to acquire location-referenced measurements with smart sensors, making field work more effective. Full article
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Article
Three-Dimensional Measurement and Three-Dimensional Printing of Giant Coastal Rocks
ISPRS Int. J. Geo-Inf. 2021, 10(6), 404; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060404 - 11 Jun 2021
Viewed by 474
Abstract
In recent years, the use of three-dimensional (3D) measurement and printing technologies has become an effective means of analyzing and reproducing both physical and natural objects, regardless of size. However, in some complex environments, such as coastal environments, it is difficult to obtain [...] Read more.
In recent years, the use of three-dimensional (3D) measurement and printing technologies has become an effective means of analyzing and reproducing both physical and natural objects, regardless of size. However, in some complex environments, such as coastal environments, it is difficult to obtain the required data by conventional measurement methods. In this paper, we describe our efforts to archive and digitally reproduce a giant coastal rock formation known as Sanouiwa, a famous site off the coast of Miyako City, Iwate Prefecture, Japan. We used two different 3D measurement techniques. The first involved taking pictures using a drone-mounted camera, and the second involved the use of global navigation satellite system data. The point cloud data generated from the high-resolution camera images were integrated using 3D shape reconstruction software, and 3D digital models were created for use in tourism promotion and environmental protection awareness initiatives. Finally, we fabricated the 3D digital models of the rocks with 3D printers for use as museum exhibitions, school curriculum materials, and related applications. Full article
(This article belongs to the Special Issue Virtual 3D City Models)
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Article
Coupling Fuzzy Multi-Criteria Decision-Making and Clustering Algorithm for MSW Landfill Site Selection (Case Study: Lanzhou, China)
ISPRS Int. J. Geo-Inf. 2021, 10(6), 403; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060403 - 11 Jun 2021
Cited by 1 | Viewed by 410
Abstract
The siting of Municipal Solid Waste (MSW) landfills is a complex decision process. Existing siting methods utilize expert scores to determine criteria weights, however, they ignore the uncertainty of data and criterion weights and the efficacy of results. In this study, a coupled [...] Read more.
The siting of Municipal Solid Waste (MSW) landfills is a complex decision process. Existing siting methods utilize expert scores to determine criteria weights, however, they ignore the uncertainty of data and criterion weights and the efficacy of results. In this study, a coupled fuzzy Multi-Criteria Decision-Making (MCDM) approach was employed to site landfills in Lanzhou, a semi-arid valley basin city in China, to enhance the spatial decision-making process. Primarily, 21 criteria were identified in five groups through the Delphi method at 30 m resolution, then criteria weights were obtained by DEMATEL and ANP, and the optimal fuzzy membership function was determined for each evaluation criterion. Combined with GIS spatial analysis and the clustering algorithm, candidate sites that satisfied the landfill conditions were identified, and the spatial distribution characteristics were analyzed. These sites were subsequently ranked utilizing the MOORA, WASPAS, COPRAS, and TOPSIS methods to verify the reliability of the results by conducting sensitivity analysis. This study is different from the previous research that applied the MCDM approach in that fuzzy MCDM for weighting criteria is more reliable compared to the other common methods. Full article
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Article
Research on Feature Extraction Method of Indoor Visual Positioning Image Based on Area Division of Foreground and Background
ISPRS Int. J. Geo-Inf. 2021, 10(6), 402; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060402 - 11 Jun 2021
Viewed by 413
Abstract
In the process of indoor visual positioning and navigation, difficult points often exist in corridors, stairwells, and other scenes that contain large areas of white walls, strong consistent background, and sparse feature points. Aiming at the problem of positioning and navigation in the [...] Read more.
In the process of indoor visual positioning and navigation, difficult points often exist in corridors, stairwells, and other scenes that contain large areas of white walls, strong consistent background, and sparse feature points. Aiming at the problem of positioning and navigation in the real physical world where the walls with sparse feature points are difficult to be filled with pictures, this paper designs a feature extraction method, ARAC (Adaptive Region Adjustment based on Consistency) using Free and Open-Source Software and tools. It divides the image into foreground and background and extracts their features respectively, to achieve not only retain positioning information but also focus more energy on the foreground area which is favourable for navigation. In the test phase, under the combined conditions of illumination, scale and affine changes, the feature matching maps by the feature extraction algorithm proposed in this paper are compared with those by SIFT and SURF. Experiments show that the number of correctly matched feature pairs obtained by ARAC is better than SIFT and SURF, and whose time of feature extraction and matching is comparable to SURF, which verifies the accuracy and efficiency of the ARAC feature extraction method. Full article
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Article
Yearly and Daily Relationship Assessment between Air Pollution and Early-Stage COVID-19 Incidence: Evidence from 231 Countries and Regions
ISPRS Int. J. Geo-Inf. 2021, 10(6), 401; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060401 - 10 Jun 2021
Viewed by 497
Abstract
The novel coronavirus disease 2019 (COVID-19) has caused significantly changes in worldwide environmental and socioeconomics, especially in the early stage. Previous research has found that air pollution is potentially affected by these unprecedented changes and it affects COVID-19 infections. This study aims to [...] Read more.
The novel coronavirus disease 2019 (COVID-19) has caused significantly changes in worldwide environmental and socioeconomics, especially in the early stage. Previous research has found that air pollution is potentially affected by these unprecedented changes and it affects COVID-19 infections. This study aims to explore the non-linear association between yearly and daily global air pollution and the confirmed cases of COVID-19. The concentrations of tropospheric air pollution (CO, NO2, O3, and SO2) and the daily confirmed cases between 23 January 2020 and 31 May 2020 were collected at the global scale. The yearly discrepancies of air pollutions and daily air pollution are associated with total and daily confirmed cases, respectively, based on the generalized additive model. We observed that there are significant spatially and temporally non-stationary variations between air pollution and confirmed cases of COVID-19. For the yearly assessment, the number of confirmed cases is associated with the positive fluctuation of CO, O3, and SO2 discrepancies, while the increasing NO2 discrepancies leads to the significant peak of confirmed cases variation. For the daily assessment, among the selected countries, positive linear or non-linear relationships are found between CO and SO2 concentrations and the daily confirmed cases, whereas NO2 concentrations are negatively correlated with the daily confirmed cases; variations in the ascending/declining associations are identified from the relationship of the O3-confirmed cases. The findings indicate that the non-linear relationships between global air pollution and the confirmed cases of COVID-19 are varied, which implicates the needs as well as the incorporation of our findings in the risk monitoring of public health on local, regional, and global scales. Full article
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Article
A New Data-Enabled Intelligence Framework for Evaluating Urban Space Perception
ISPRS Int. J. Geo-Inf. 2021, 10(6), 400; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060400 - 09 Jun 2021
Viewed by 481
Abstract
The urban environment has a great impact on the wellbeing of citizens and it is of great significance to understand how citizens perceive and evaluate places in a large scale urban region and to provide scientific evidence to support human-centered urban planning with [...] Read more.
The urban environment has a great impact on the wellbeing of citizens and it is of great significance to understand how citizens perceive and evaluate places in a large scale urban region and to provide scientific evidence to support human-centered urban planning with a better urban environment. Existing studies for assessing urban perception have primarily relied on low efficiency methods, which also result in low evaluation accuracy. Furthermore, there lacks a sophisticated understanding on how to correlate the urban perception with the built environment and other socio-economic data, which limits their applications in supporting urban planning. In this study, a new data-enabled intelligence framework for evaluating human perceptions of urban space is proposed. Specifically, a novel classification-then-regression strategy based on a deep convolutional neural network and a random-forest algorithm is proposed. The proposed approach has been applied to evaluate the perceptions of Beijing and Chengdu against six perceptual criteria. Meanwhile, multi-source data were employed to investigate the associations between human perceptions and the indicators for the built environment and socio-economic data including visual elements, facility attributes and socio-economic indicators. Experimental results show that the proposed framework can effectively evaluate urban perceptions. The associations between urban perceptions and the visual elements, facility attributes and a socio-economic dimension have also been identified, which can provide substantial inputs to guide the urban planning for a better urban space. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Geometric Accuracy of 3D Reality Mesh Utilization for BIM-Based Earthwork Quantity Estimation Workflows
ISPRS Int. J. Geo-Inf. 2021, 10(6), 399; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060399 - 09 Jun 2021
Viewed by 551
Abstract
Current surveying techniques are typically applied to survey the as-is condition of buildings, brownfield sites and infrastructure prior to design. However, within the past decade, these techniques evolved significantly, and their applications can be enhanced by adopting unmanned aerial vehicles (UAVs) for data [...] Read more.
Current surveying techniques are typically applied to survey the as-is condition of buildings, brownfield sites and infrastructure prior to design. However, within the past decade, these techniques evolved significantly, and their applications can be enhanced by adopting unmanned aerial vehicles (UAVs) for data acquisition, up-to-date software for creating 3D reality mesh, which in turn opens new possibilities for much more efficient construction site surveying and constant updating and process management. In this study the workflows of three UAV-based photogrammetry techniques: Real Time Kinematic (RTK), Post-Processing Kinematic (PPK) and Global Positioning System (GPS) based on control points were analyzed, described, and compared to conventional surveying method with Global Navigation Satellite System (GNSS) receiver. Tests were performed under realistic conditions in 36 ha quarry in Lithuania. The results of the relationship between ground sample distance (GSD) and the comparison of volume measurements under each technique, including conventional method were analyzed. The deviation of data collected on field vs. generated in reality mesh, including ground control points (GCPs) and check points (CHPs) with different configurations, was investigated. The research provides observations on each workflow in the terms of efficiency and reliability for earthwork quantity estimations and explains processing schemes with advanced commercial software tools. Full article
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Article
Cycling Trajectory-Based Navigation Independent of Road Network Data Support
ISPRS Int. J. Geo-Inf. 2021, 10(6), 398; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060398 - 09 Jun 2021
Viewed by 423
Abstract
The popularization of smart phones and the large-scale application of location-based services (e.g., exercises, traveling and food delivery via cycling) have resulted in the emergence of massive amounts of personalized cycling trajectory data, spurring the demand for map navigation based on cycling trajectories. [...] Read more.
The popularization of smart phones and the large-scale application of location-based services (e.g., exercises, traveling and food delivery via cycling) have resulted in the emergence of massive amounts of personalized cycling trajectory data, spurring the demand for map navigation based on cycling trajectories. Therefore, in the current paper, we propose a cycling trajectory-based navigation algorithm without the need for road network data support. The proposed algorithm focuses on extracting navigation information from a given trajectory and then guiding others to the destination along the original trajectory. In particular, the algorithm analyzes the coordinate and azimuth angle data collected by the built-in positioning and direction sensors of mobile smart phones to identify several turning modes from the provider’s cycling trajectory. In addition, the interference of the traffic conditions during data collection is considered in order to improve the recognition accuracy of the turning modes. The turning modes in the trajectory are subsequently transformed into navigation information and shared with users, so as to realize the shared navigation of the cycling trajectory. Experimental results indicate that the algorithm can accurately extract the turning feature points from cycling trajectory data, recognize various turning modes and generate correct navigation messages, thereby guiding users to arrive at the destination safely and accurately along the original trajectory. The algorithm is independent of electronic map platforms and does not require road network data support. Full article
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Article
The Importance of Indigenous Cartography and Toponymy to Historical Land Tenure and Contributions to Euro/American/Canadian Cartography
ISPRS Int. J. Geo-Inf. 2021, 10(6), 397; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060397 - 08 Jun 2021
Viewed by 525
Abstract
Indigenous maps are critical in understanding the historic and current land tenure of Indigenous groups. Furthermore, Indigenous claims to land can be seen in their connections via toponymy. European concepts of territory and political boundaries did not coincide with First Nation/American Indian views, [...] Read more.
Indigenous maps are critical in understanding the historic and current land tenure of Indigenous groups. Furthermore, Indigenous claims to land can be seen in their connections via toponymy. European concepts of territory and political boundaries did not coincide with First Nation/American Indian views, resulting in the mistaken view that Natives did not have formal concepts of their territories. And Tribes/First Nations with cross-border territory have special jurisdictional problems. This paper illustrates how many Native residents were very spatially aware of their own lands, as well as neighboring nations’ lands, overlaps between groups, hunting territories, populations, and trade networks. Finally, the Sinixt First Nation serve as a perfect example of a case study on how an Aboriginal people are currently inputting and using a GIS representation of their territory with proper toponymy and use areas. Full article
(This article belongs to the Special Issue Mapping Indigenous Knowledge in the Digital Age)
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Article
Identification of Groundwater Potential Zones Using GIS and Multi-Criteria Decision-Making Techniques: A Case Study Upper Coruh River Basin (NE Turkey)
ISPRS Int. J. Geo-Inf. 2021, 10(6), 396; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060396 - 08 Jun 2021
Viewed by 588
Abstract
In this study, geographic information system (GIS)-based, analytic hierarchy process (AHP) techniques were used to identify groundwater potential zones to provide insight to decisionmakers and local authorities for present and future planning. Ten different geo-environmental factors, such as slope, topographic wetness index, geomorphology, [...] Read more.
In this study, geographic information system (GIS)-based, analytic hierarchy process (AHP) techniques were used to identify groundwater potential zones to provide insight to decisionmakers and local authorities for present and future planning. Ten different geo-environmental factors, such as slope, topographic wetness index, geomorphology, drainage density, lithology, lineament density, rainfall, soil type, soil thickness, and land-use classes were selected as the decision criteria, and related GIS tools were used for creating, analysing and standardising the layers. The final groundwater potential zones map was delineated, using the weighted linear combination (WLC) aggregation method. The map was spatially classified into very high potential, high potential, moderate potential, low potential, and very low potential. The results showed that 21.5% of the basin area is characterised by high to very high groundwater potential. In comparison, the very low to low groundwater potential occupies 57.15%, and the moderate groundwater potential covers 21.4% of the basin area. Finally, the GWPZs map was investigated to validate the model, using discharges and depth to groundwater data related to 22 wells scattered over the basin. The validation results showed that GWPZs classes strongly overlap with the well discharges and groundwater depth located in the given area. Full article
(This article belongs to the Special Issue Advances in GIS Hydrological Modeling)
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Article
Population Mobility and the Transmission Risk of the COVID-19 in Wuhan, China
ISPRS Int. J. Geo-Inf. 2021, 10(6), 395; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060395 - 07 Jun 2021
Viewed by 510
Abstract
At the beginning of 2020, a suddenly appearing novel coronavirus (COVID-19) rapidly spread around the world. The outbreak of the COVID-19 pandemic in China occurred during the Spring Festival when a large number of migrants traveled between cities, which greatly increased the infection [...] Read more.
At the beginning of 2020, a suddenly appearing novel coronavirus (COVID-19) rapidly spread around the world. The outbreak of the COVID-19 pandemic in China occurred during the Spring Festival when a large number of migrants traveled between cities, which greatly increased the infection risk of COVID-19 across the country. Financially supported by the Wuhan government, and based on cellphone signaling data from Unicom (a mobile phone carrier) and Baidu location-based data, this paper analyzed the effects that city dwellers, non-commuters, commuters, and people seeking medical services had on the transmission risk of COVID-19 in the early days of the pandemic in Wuhan. The paper also evaluated the effects of the city lockdown policy on the spread of the pandemic outside and inside Wuhan. The results show that although the daily business activities in the South China Seafood Wholesale Market and nearby commuters’ travel behaviors concentrated in the Hankou area, a certain proportion of these people were distributed in the Wuchang and Hanyang areas. The areas with relatively high infection risks of COVID-19 were scattered across Wuhan during the early outbreak of the pandemic. The lockdown in Wuhan closed the passageways of external transport at the very beginning, largely decreasing migrant population and effectively preventing the spread of the pandemic to the outside. However, the Wuhan lockdown had little effect on preventing the spread of the pandemic within Wuhan at that time. During this period, a large amount of patients who went to hospitals for medical services were exposed to a high risk of cross-infection without precaution awareness. The pandemic kept dispersing in three towns until the improvement of the capacity of medical treatment, the management of closed communities, the national support to Wuhan, and the implementation of a series of emergency responses at the same time. The findings in this paper reveal the spatiotemporal features of the dispersal of infection risk of COVID-19 and the effects of the prevention and control measures during the early days of the pandemic. The findings were adopted by the Wuhan government to make corresponding policies and could also provide supports to the control of the pandemic in the other regions and countries. Full article
(This article belongs to the Collection Spatial Components of COVID-19 Pandemic)
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