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Volume 11
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ISPRS Int. J. Geo-Inf., Volume 11, Issue 1 (January 2022) – 71 articles

Cover Story (view full-size image): A comprehensive information system of the historic Vltava River valley contains numbers of resources, either spatial or non-spatial. Old maps and their georeferencing and potential problems in creating seamless mosaics are the core issues. Other sources of data such as old photographs are localized and stored in the system along with the spatial definition point. The vectorization of data was undertaken for area features used for the analysis of land-use changes, and for contours used for the creation of historic DEM. Vectorized footprints of buildings and vectors of other functional areas subsequently serve as a basis for the procedural modeling of the virtual 3D landscape. The aim of creating such a complex and broad information system is to draw attention to a possible approach to the presentation and visualization of the historical landscape, along with links to important documents. View this paper
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14 pages, 3062 KiB  
Article
Indoor Positioning Algorithm Based on Reconstructed Observation Model and Particle Filter
by Li Ma, Ning Cao, Xiaoliang Feng, Jianping Zhang and Jingjing Yan
ISPRS Int. J. Geo-Inf. 2022, 11(1), 71; https://doi.org/10.3390/ijgi11010071 - 17 Jan 2022
Cited by 4 | Viewed by 3007
Abstract
In a complex indoor environment, wireless signals are affected by multiple factors such as reflection, scattering or diffuse reflection of electromagnetic waves from indoor walls and other objects, and the signal strength will fluctuate significantly. For the signal strength and the distance between [...] Read more.
In a complex indoor environment, wireless signals are affected by multiple factors such as reflection, scattering or diffuse reflection of electromagnetic waves from indoor walls and other objects, and the signal strength will fluctuate significantly. For the signal strength and the distance between the unknown nodes and the known nodes are a typical nonlinear estimation problem, and the unknown nodes cannot receive all Access Points (APs) signal strength data, this paper proposes a Particle Filter (PF) indoor position algorithm based on the Kernel Extreme Learning Machine (KELM) reconstruction observation model. Firstly, on the basis of establishing a fingerprint database of wireless signal strength and unknown node position, we use KELM to convert the fingerprint location problem into a machine learning problem and establish the mapping relationship between the location of the unknown node and the wireless signal strength, thereby refocusing construct an observation model of the indoor positioning system. Secondly, according to the measured values obtained by KELM, PF algorithm is adopted to obtain the predicted value of the unknown nodes. Thirdly, the predicted value is fused with the measured value obtained by KELM to locate the position of the unknown nodes. Moreover, a novel control strategy is proposed by introducing a reception factor to deal with the situation that unknown nodes in the system cannot receive all of the AP data, i.e., data loss occurs. This indoor positioning experimental results show that the accuracy of the method is significantly improved contrasted with commonly used PF, GP-PF and other positioning algorithms. Full article
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<p>The basic structure of ELM.</p> Full article ">Figure 2
<p>Location principle of fingerprint positioning. (The offline fingerprint database is established by collecting the wireless signal strength of the reference points in the offline stage. The positioning algorithm is the research method in this paper, and the position coordinates are the coordinates of unknown nodes).</p> Full article ">Figure 3
<p>Nodes distribution map of indoor localization experiment area. (<span class="html-fig-inline" id="ijgi-11-00071-i001"> <img alt="Ijgi 11 00071 i001" src="/ijgi/ijgi-11-00071/article_deploy/html/images/ijgi-11-00071-i001.png"/></span> AP1~AP8 are the 8 APs deployed; <span class="html-fig-inline" id="ijgi-11-00071-i002"> <img alt="Ijgi 11 00071 i002" src="/ijgi/ijgi-11-00071/article_deploy/html/images/ijgi-11-00071-i002.png"/></span> is the reference point for establishing the fingerprint database; 516~521 are the laboratory room numbers for indoor positioning).</p> Full article ">Figure 4
<p>Positioning accuracies of different algorithms in room 521.</p> Full article ">Figure 5
<p>Positioning accuracies of different algorithms in room 520.</p> Full article ">Figure 6
<p>Positioning accuracies of different algorithms in the corridor.</p> Full article ">Figure 7
<p>The position results of PF, GP-PF and KELM-PF.</p> Full article ">Figure 8
<p>The RMSEs of PF, GP-PF and KELM-PF.</p> Full article ">
12 pages, 15939 KiB  
Article
Analyzing the Behaviors of OpenStreetMap Volunteers in Mapping Building Polygons Using a Machine Learning Approach
by Müslüm Hacar
ISPRS Int. J. Geo-Inf. 2022, 11(1), 70; https://doi.org/10.3390/ijgi11010070 - 17 Jan 2022
Cited by 7 | Viewed by 3761
Abstract
Mapping as an action in volunteered geographic information is complex in light of the human diversity within the volunteer community. There is no integrated solution that models and fixes all data heterogeneity. Instead, researchers are attempting to assess and understand crowdsourced data. Approaches [...] Read more.
Mapping as an action in volunteered geographic information is complex in light of the human diversity within the volunteer community. There is no integrated solution that models and fixes all data heterogeneity. Instead, researchers are attempting to assess and understand crowdsourced data. Approaches based on statistics are helpful to comprehend trends in crowd-drawing behaviors. This study examines trends in contributors’ first decisions when drawing OpenStreetMap (OSM) buildings. The proposed approach evaluates how important the properties of a point are in determining the first point of building drawings. It classifies the adjacency types of the buildings using a random forest classifier for the properties and aids in inferring drawing trends from the relative impact of each property. To test the approach, detached and attached building groups in Istanbul and Izmir, Turkey, were used. The result had an 83% F-score. In summary, the volunteers tended to choose as first points those further away from the street and building centroid and provided lower point density in the detached buildings than the attached ones. This means that OSM volunteers paid more attention to open spaces when drawing the first points of the detached buildings in the study areas. The study reveals common drawing trends in building-mapping actions. Full article
(This article belongs to the Special Issue Cartographic Communication of Big Data)
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<p>Location of attached (red points) and detached (green points) building groups in Istanbul and Izmir (map data taken from the Global Administrative Areas [<a href="#B31-ijgi-11-00070" class="html-bibr">31</a>] database).</p> Full article ">Figure 2
<p>Attached (<b>a</b>) and detached (<b>b</b>) buildings.</p> Full article ">Figure 3
<p>The workflow of the proposed approach.</p> Full article ">Figure 4
<p>The computation schema of the measures.</p> Full article ">Figure 5
<p>Buildings (black continuous), point sets (red), centroids (blue), <math display="inline"><semantics> <mrow> <mi>D</mi> <mi>i</mi> <mi>s</mi> <msub> <mi>t</mi> <mi>C</mi> </msub> </mrow> </semantics></math> (grey dashed), and streets (grey continuous).</p> Full article ">Figure 6
<p>Buildings (black continuous), point sets (red), <math display="inline"><semantics> <mrow> <mi>D</mi> <mi>i</mi> <mi>s</mi> <msub> <mi>t</mi> <mi>S</mi> </msub> </mrow> </semantics></math> (grey dashed), and streets (grey continuous).</p> Full article ">Figure 7
<p>The importance of measures on predicting the adjacency types: PP as prediction process, PD as predicting the detached buildings, and PA as predicting the attached buildings.</p> Full article ">
25 pages, 7139 KiB  
Article
A Fast and Accurate Spatial Target Snapping Method for 3D Scene Modeling and Mapping in Mobile Augmented Reality
by Kejia Huang, Chenliang Wang, Runying Liu and Guoxiong Chen
ISPRS Int. J. Geo-Inf. 2022, 11(1), 69; https://doi.org/10.3390/ijgi11010069 - 17 Jan 2022
Cited by 3 | Viewed by 4169
Abstract
High-performance spatial target snapping is an essential function in 3D scene modeling and mapping that is widely used in mobile augmented reality (MAR). Spatial data snapping in a MAR system must be quick and accurate, while real-time human–computer interaction and drawing smoothness must [...] Read more.
High-performance spatial target snapping is an essential function in 3D scene modeling and mapping that is widely used in mobile augmented reality (MAR). Spatial data snapping in a MAR system must be quick and accurate, while real-time human–computer interaction and drawing smoothness must also be ensured. In this paper, we analyze the advantages and disadvantages of several spatial data snapping algorithms, such as the 2D computational geometry method and the absolute distance calculation method. To address the issues that existing algorithms do not adequately support 3D data snapping and real-time snapping of high data volumes, we present a new adaptive dynamic snapping algorithm based on the spatial and graphical characteristics of augmented reality (AR) data snapping. Finally, the algorithm is experimented with by an AR modeling system, including the evaluation of snapping efficiency and snapping accuracy. Through the experimental comparison, we found that the algorithm proposed in this paper is substantially improved in terms of shortening the snapping time, enhancing the snapping stability, and improving the snapping accuracy of vector points, lines, faces, bodies, etc. The snapping efficiency of the algorithm proposed in this paper is 1.6 times higher than that of the traditional algorithm on average, while the data acquisition accuracy based on the algorithm in this paper is more than 6 times higher than that of the traditional algorithm on average under the same conditions, and its data accuracy is improved from the decimeter level to the centimeter level. Full article
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<p>Snapping algorithm (ARSnap) system architecture.</p> Full article ">Figure 2
<p>The pipeline of accurate 3D modeling in MAR.</p> Full article ">Figure 3
<p>Calculation process of the intersection point between plane p and ray r.</p> Full article ">Figure 4
<p>Adaptive octree model.</p> Full article ">Figure 5
<p>Real-time capture results of the camera’s six degrees of freedom (6-DOF) positions on different vertical planes: (<b>a</b>) 6-DOF projection points in vertical plane a; (<b>b</b>) 6-DOF projection points in vertical plane b; (<b>c</b>) 6-DOF projection points in vertical plane c.</p> Full article ">Figure 6
<p>Real-time capture results of virtual marker information from different perspectives: (<b>a</b>) the capture results from a top-down perspective; (<b>b</b>) the capture results from a square perspective; (<b>c</b>) the capture results from an upward view.</p> Full article ">Figure 7
<p>The process of capturing the virtual elevation between the connection point and any 3D plane in space.</p> Full article ">Figure 8
<p>The combination of marker bar and ground circle to deal with snapping results at varying distances that impose visual limitations: (<b>a</b>) close-up snapping of the office area; (<b>b</b>) close-up snapping result of the conference room; (<b>c</b>) long-distance snapping of the office area; (<b>d</b>) long-range snapping of the conference room.</p> Full article ">Figure 9
<p>AR snapping model based on adaptive decomposition.</p> Full article ">Figure 10
<p>Modeling visual view of buildings in ARSnap: (<b>a</b>) Display the elevation line snap results of the real scene, (<b>b</b>) display the vector snap results of the virtual scene; (<b>c</b>) display the snap results of the irregular sofa; (d) display the plane snap results of the real scene; (<b>e</b>) display the wall snap results of the virtual scene; (<b>f</b>) display the snap results of the regular vending machine.</p> Full article ">Figure 11
<p>Capturing points on the specified 3D plane: (<b>a</b>) snapping to a point in a 3D plane (short distance); (<b>b</b>) snapping to a point in a 3D plane (far distance).</p> Full article ">Figure 12
<p>Capturing the extended lines and horizonal lines in 3D space: (<b>a</b>) before snapping the extended lines; (<b>b</b>) after snapping the extended lines; (<b>c</b>) before snapping the horizonal lines; (<b>d</b>) after snapping the horizonal lines.</p> Full article ">Figure 13
<p>Capturing geometric nodes in 3D space: (<b>a</b>) 3D geometry node before snapping; (<b>b</b>) 3D geometry nodes in snapping; (<b>c</b>) 3D geometry node after snapping.</p> Full article ">Figure 14
<p>Capture of interior building doors and window based on 3D-GIS Semantic Constraints: (<b>a</b>) gate snapping based on 3D-GIS semantics; (<b>b</b>) the result of gate snapping, (<b>c</b>) window snapping based on 3D-GIS semantics; (<b>d</b>) the result of window snapping.</p> Full article ">Figure 15
<p>Side-by-side comparison results of the same building: (<b>A</b>) RoomScan; (<b>B</b>) ARSnap; (<b>C</b>) ARPlan3D; (<b>D</b>) Magicplan.</p> Full article ">Figure 16
<p>The modeling results of Indoor 3D model obtained based on ARSnap in unity: (<b>a</b>) modeling results after capturing the first-floor indoor model; (<b>b</b>) modeling results after capturing the second-floor indoor model; (<b>c</b>) details of modeling results of indoor model on the first floor; (<b>d</b>) details of modeling results of indoor model on the second floor.</p> Full article ">
27 pages, 5592 KiB  
Article
Positioning Localities for Vague Spatial Location Description: A Supervaluation Semantics Approach
by Peng Ye, Xueying Zhang, Chunju Zhang and Yulong Dang
ISPRS Int. J. Geo-Inf. 2022, 11(1), 68; https://doi.org/10.3390/ijgi11010068 - 15 Jan 2022
Cited by 3 | Viewed by 4222
Abstract
In the big data era, spatial positioning based on location description is the foundation to the intelligent transformation of location-based-services. To solve the problem of vagueness in location description in different contexts, this paper proposes a positioning method based on supervaluation semantics. Firstly, [...] Read more.
In the big data era, spatial positioning based on location description is the foundation to the intelligent transformation of location-based-services. To solve the problem of vagueness in location description in different contexts, this paper proposes a positioning method based on supervaluation semantics. Firstly, through combing the laws of human spatial cognition, the types of elements that people pay attention to in location description are clarified. On this basis, the source of vagueness in the location description and its embodiment in the expression form of each element are analyzed from multiple levels. Secondly, the positioning model is constructed from the following three aspects: spatial object, distance relation and direction relation. The contexts of multiple location description are super-valued, respectively, while the threshold of observations is obtained from the context semantics. Thus, the precisification of location description is realized for positioning. Thirdly, a question-answering system is designed to the collect contexts of location description, and a case study on the method is conducted. The case can verify the transformation of a set of users’ viewpoints on spatial cognition into the real-world spatial scope, to realize the representation of vague location description in the geographic information system. The result shows that the method proposed in the paper breaks through the traditional vagueness modeling, which only focuses on spatial relationship, and enhances the interpretability of semantics of vague location description. Moreover, supervaluation semantics can obtain the precisification results of vague location description in different situations, and the positioning localities are more suitable to individual subjective cognition. Full article
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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<p>Different stages of spatial cognition.</p> Full article ">Figure 2
<p>Source of vagueness in location description.</p> Full article ">Figure 3
<p>Threshold of observations of vague spatial object.</p> Full article ">Figure 4
<p>Threshold of observations of vague distance relation.</p> Full article ">Figure 5
<p>Threshold of observations of vague direction relation.</p> Full article ">Figure 6
<p>Interface diagram of back-stage management module in Q&amp;A system.</p> Full article ">Figure 7
<p>Nanjing Road Walkway and some landmarks.</p> Full article ">Figure 8
<p>The contexts of single spatial assertion in Q&amp;A system.</p> Full article ">Figure 9
<p>Positioning results of single spatial assertion. (<b>a</b>) is the schematic diagram of the path between every McDonald’s storefront and the metro station. (<b>b</b>) is a schematic diagram of screening paths that meet the threshold according to the distance relation.</p> Full article ">Figure 9 Cont.
<p>Positioning results of single spatial assertion. (<b>a</b>) is the schematic diagram of the path between every McDonald’s storefront and the metro station. (<b>b</b>) is a schematic diagram of screening paths that meet the threshold according to the distance relation.</p> Full article ">Figure 10
<p>The contexts of compound spatial assertion in Q&amp;A system.</p> Full article ">Figure 11
<p>Positioning results of each compound spatial assertion. (<b>a</b>) is the schematic diagram of the direction relation with Hubei road intersection as the reference. (<b>b</b>) is the schematic diagram of the distance relation with Hubei road intersection as the reference. (<b>c</b>) is the schematic diagram of the distance and direction relations with Sunshine commercial building as the reference. (<b>d</b>) is the schematic diagram of the direction relation with Hyland hotel as the reference.</p> Full article ">Figure 12
<p>Positioning results of all compound spatial assertions. (<b>a</b>) shows all candidate location in <a href="#ijgi-11-00068-f011" class="html-fig">Figure 11</a>. (<b>b</b>) is the overlap of all candidate location. Besides, <span class="html-italic">D</span> is the location of Century Square determined by the candidate location, and <span class="html-italic">G</span> is the real location of Century Square.</p> Full article ">
21 pages, 11959 KiB  
Article
Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China
by Meijie Chen, Yumin Chen, John P. Wilson, Huangyuan Tan and Tianyou Chu
ISPRS Int. J. Geo-Inf. 2022, 11(1), 67; https://doi.org/10.3390/ijgi11010067 - 15 Jan 2022
Cited by 5 | Viewed by 3433
Abstract
The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this [...] Read more.
The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies. Full article
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<p>Two study areas (mainland China and Hubei province).</p> Full article ">Figure 2
<p>Flowchart of research method and experimental procedures.</p> Full article ">Figure 3
<p>Residual maps for model fitted in Hubei province.</p> Full article ">Figure 4
<p>Residual maps for model fitted in Mainland China.</p> Full article ">Figure 5
<p>ESF-SVC spatially varying coefficient maps of health risk factors in mainland China, each column represents a risk factor (MS, BD, TEMP, DEM) and each line (<b>a</b>–<b>g</b>) represents a week.</p> Full article ">
18 pages, 3287 KiB  
Article
Indoor Emergency Path Planning Based on the Q-Learning Optimization Algorithm
by Shenghua Xu, Yang Gu, Xiaoyan Li, Cai Chen, Yingyi Hu, Yu Sang and Wenxing Jiang
ISPRS Int. J. Geo-Inf. 2022, 11(1), 66; https://doi.org/10.3390/ijgi11010066 - 14 Jan 2022
Cited by 10 | Viewed by 3989
Abstract
The internal structure of buildings is becoming increasingly complex. Providing a scientific and reasonable evacuation route for trapped persons in a complex indoor environment is important for reducing casualties and property losses. In emergency and disaster relief environments, indoor path planning has great [...] Read more.
The internal structure of buildings is becoming increasingly complex. Providing a scientific and reasonable evacuation route for trapped persons in a complex indoor environment is important for reducing casualties and property losses. In emergency and disaster relief environments, indoor path planning has great uncertainty and higher safety requirements. Q-learning is a value-based reinforcement learning algorithm that can complete path planning tasks through autonomous learning without establishing mathematical models and environmental maps. Therefore, we propose an indoor emergency path planning method based on the Q-learning optimization algorithm. First, a grid environment model is established. The discount rate of the exploration factor is used to optimize the Q-learning algorithm, and the exploration factor in the ε-greedy strategy is dynamically adjusted before selecting random actions to accelerate the convergence of the Q-learning algorithm in a large-scale grid environment. An indoor emergency path planning experiment based on the Q-learning optimization algorithm was carried out using simulated data and real indoor environment data. The proposed Q-learning optimization algorithm basically converges after 500 iterative learning rounds, which is nearly 2000 rounds higher than the convergence rate of the Q-learning algorithm. The SASRA algorithm has no obvious convergence trend in 5000 iterations of learning. The results show that the proposed Q-learning optimization algorithm is superior to the SARSA algorithm and the classic Q-learning algorithm in terms of solving time and convergence speed when planning the shortest path in a grid environment. The convergence speed of the proposed Q- learning optimization algorithm is approximately five times faster than that of the classic Q- learning algorithm. The proposed Q-learning optimization algorithm in the grid environment can successfully plan the shortest path to avoid obstacle areas in a short time. Full article
(This article belongs to the Special Issue Large-Scale Evacuation Planning and Spatial Optimization with Connected Vehicles and Big Data Sources)
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<p>Raster map.</p> Full article ">Figure 2
<p>Reinforcement learning basic model.</p> Full article ">Figure 3
<p>Flow chart of the Q-learning optimization algorithm in the grid environment.</p> Full article ">Figure 4
<p>The 25 × 25 simulation grid obstacle environment.</p> Full article ">Figure 5
<p>Simulation results of path planning under a 25 × 25 environment. (<b>a</b>) Path planning of the SARSA algorithm; (<b>b</b>) Path planning of the Q-learning algorithm; (<b>c</b>) Path planning of the proposed Q-learning optimization algorithm.</p> Full article ">Figure 6
<p>Graph of step changes during training. (<b>a</b>) Steps of SARSA algorithm; (<b>b</b>) Steps of Q-learning algorithm; (<b>c</b>) Steps of the proposed Q-learning optimization algorithm.</p> Full article ">Figure 7
<p>Change graph of cumulative rewards during training. (<b>a</b>) Cumulative reward of the SARSA algorithm; (<b>b</b>) Cumulative reward of the Q-learning algorithm; (<b>c</b>) Cumulative reward of the proposed Q-learning optimization algorithm.</p> Full article ">Figure 8
<p>3D virtual scene map of an office building fire.</p> Full article ">Figure 9
<p>No fire simulation environment.</p> Full article ">Figure 10
<p>Fire simulation environment.</p> Full article ">Figure 11
<p>Fire-free environment path planning.</p> Full article ">Figure 12
<p>Fire environment path planning.</p> Full article ">Figure 13
<p>Graph of step changes during training. (<b>a</b>) No change in steps in the fire environment; (<b>b</b>) Change in steps in the fire environment.</p> Full article ">Figure 14
<p>Change graph of cumulative rewards during training. (<b>a</b>) Cumulative reward for the no-fire environment; (<b>b</b>) Fire environment cumulative reward.</p> Full article ">
16 pages, 807 KiB  
Article
Spatial Variability of the ‘Airbnb Effect’: A Spatially Explicit Analysis of Airbnb’s Impact on Housing Prices in Sydney
by William Thomas Thackway, Matthew Kok Ming Ng, Chyi-Lin Lee, Vivien Shi and Christopher James Pettit
ISPRS Int. J. Geo-Inf. 2022, 11(1), 65; https://doi.org/10.3390/ijgi11010065 - 14 Jan 2022
Cited by 15 | Viewed by 6210
Abstract
Over the last decade, the emergence and significant growth of home-sharing platforms, such as Airbnb, has coincided with rising housing unaffordability in many global cities. It is in this context that we look to empirically assess the impact of Airbnb on housing prices [...] Read more.
Over the last decade, the emergence and significant growth of home-sharing platforms, such as Airbnb, has coincided with rising housing unaffordability in many global cities. It is in this context that we look to empirically assess the impact of Airbnb on housing prices in Sydney—one of the least affordable cities in the world. Employing a hedonic property valuation model, our results indicate that Airbnb’s overall effect is positive. A 1% increase in Airbnb density is associated with approximately a 2% increase in property sales price. However, recognizing that Airbnb’s effect is geographically uneven and given the fragmented nature of Sydney’s housing market, we also employ a GWR to account for the spatial variation in Airbnb activity. The findings confirm that Airbnb’s influence on housing prices is varied across the city. Sydney’s northern beaches and parts of western Sydney experience a statistically significant value uplift attributable to Airbnb activity. However, traditional tourist locations focused around Sydney’s CBD and the eastern suburbs experience insignificant or negative property price impacts. The results highlight the need for policymakers to consider local Airbnb and housing market contexts when deciding the appropriate level and design of Airbnb regulation. Full article
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<p>Distribution of Airbnb activity throughout Sydney.</p> Full article ">Figure 2
<p>Spatial distribution of GWR parameter estimates for Airbnb density variable with 190 nearest neighbours.</p> Full article ">Figure A1
<p>Spatial kernel types (Fotheringham et al., 2003).</p> Full article ">
13 pages, 2819 KiB  
Article
Where Maps Lie: Visualization of Perceptual Fallacy in Choropleth Maps at Different Levels of Aggregation
by Giedrė Beconytė, Andrius Balčiūnas, Aurelija Šturaitė and Rita Viliuvienė
ISPRS Int. J. Geo-Inf. 2022, 11(1), 64; https://doi.org/10.3390/ijgi11010064 - 14 Jan 2022
Cited by 5 | Viewed by 4179
Abstract
This paper proposes a method for quantitative evaluation of perception deviations due to generalization in choropleth maps. The method proposed is based on comparison of class values assigned to different aggregation units chosen for representing the same dataset. It is illustrated by the [...] Read more.
This paper proposes a method for quantitative evaluation of perception deviations due to generalization in choropleth maps. The method proposed is based on comparison of class values assigned to different aggregation units chosen for representing the same dataset. It is illustrated by the results of application of the method to population density maps of Lithuania. Three spatial aggregation levels were chosen for comparison: the 1 × 1 km statistical grid, elderships (NUTS3), and municipalities (NUTS2). Differences in density class values between the reference grid map and the other two maps were calculated. It is demonstrated that a perceptual fallacy on the municipality level population map of Lithuania leads to a misinterpretation of data that makes such maps frankly useless. The eldership level map is, moreover, also largely misleading, especially in sparsely populated areas. The method proposed is easy to use and transferable to any other field where spatially aggregated data are mapped. It can be used for visual analysis of the degree to which a generalized choropleth map is liable to mislead the user in particular areas. Full article
(This article belongs to the Special Issue Cartographic Communication of Big Data)
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<p>Population represented at different levels of detail: 1 × 1 km cells and administrative units, similar pattern (<b>a</b>); 1 × 1 km cells and administrative units, different pattern (<b>b</b>).</p> Full article ">Figure 2
<p>Calculation of perceptual fallacies.</p> Full article ">Figure 3
<p>Fragment of map of (<b>a</b>) class values for benchmark 1 km<sup>2</sup> grid cells; (<b>b</b>) class values for elderships (NUTS3 units of ca.100 km<sup>2</sup>); (<b>c</b>) PF values; published on <a href="http://carto.com" target="_blank">carto.com</a> (accessed on 28 November 2021).</p> Full article ">Figure 4
<p>Results of survey concerning assumptions about population density (<span class="html-italic">n</span> = 140).</p> Full article ">Figure 5
<p>Rural population density of Lithuania represented at municipality level.</p> Full article ">Figure 6
<p>Interactive map of perceptual fallacies.</p> Full article ">
12 pages, 22201 KiB  
Article
The Influence of Landscape Structure on Wildlife–Vehicle Collisions: Geostatistical Analysis on Hot Spot and Habitat Proximity Relations
by Lina Galinskaitė, Alius Ulevičius, Vaidotas Valskys, Arūnas Samas, Peter E. Busher and Gytautas Ignatavičius
ISPRS Int. J. Geo-Inf. 2022, 11(1), 63; https://doi.org/10.3390/ijgi11010063 - 14 Jan 2022
Cited by 6 | Viewed by 3390
Abstract
Vehicle collisions with animals pose serious issues in countries with well-developed highway networks. Both expanding wildlife populations and the development of urbanised areas reduce the potential contact distance between wildlife species and vehicles. Many recent studies have been conducted to better understand the [...] Read more.
Vehicle collisions with animals pose serious issues in countries with well-developed highway networks. Both expanding wildlife populations and the development of urbanised areas reduce the potential contact distance between wildlife species and vehicles. Many recent studies have been conducted to better understand the factors that influence wildlife–vehicle collisions (WVCs) and provide mitigation methods. Most of these studies examined road density, traffic volume, seasonal fluctuations, etc. However, in analysing the distribution of WVC, few studies have considered a spatial and significant distance geostatistical analysis approach that includes how different land-use categories are associated with the distance to WVCs. Our study investigated the spatial distribution of agricultural land, meadows and pastures, forests, built-up areas, rivers, lakes, and ponds, to highlight the most dangerous sections of roadways where WVCs occur. We examined six potential ‘hot spot’ distances (5–10–25–50–100–200 m) to evaluate the role different landscape elements play in the occurrence of WVC. The near analysis tool showed that a distance of 10–25 m to different landscape elements provided the most sensitive results. Hot spots associated with agricultural land, forests, as well as meadows and pastures, peaked on roadways in close proximity (10 m), while hot spots associated with built-up areas, rivers, lakes, and ponds peaked on roadways farther (200 m) from these land-use types. We found that the order of habitat importance in WVC hot spots was agricultural land < forests < meadows and pastures < built-up areas < rivers < lakes and ponds. This methodological approach includes general hot-spot analysis as well as differentiated distance analysis which helps to better reveal the influence of landscape structure on WVCs. Full article
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<p>Total number of hot spots and distance to different land-use types: (<b>A</b>) short-range sensitive land-use types and (<b>B</b>) long-range sensitive land-use types.</p> Full article ">Figure 2
<p>Distribution of species-specific WVC hot spots at different distances for each land-use type: (<b>A</b>) forests, (<b>B</b>) agricultural land, (<b>C</b>) meadows and pastures, (<b>D</b>) built-up areas, (<b>E</b>) rivers, (<b>F</b>) lakes and ponds. * Includes 23 species of wild and domestic animals that comprise less than five percent of total accidents in any separate year. ** Includes cases in database with record ‘animal’.</p> Full article ">Figure 3
<p>Distribution of hot spots (red) and cold spots (blue) of WVCs in Lithuania combining all land-use types and distances (large map) and by land-use type at three distance categories—10 m, 50 m, and 200 m (small maps).</p> Full article ">Figure 3 Cont.
<p>Distribution of hot spots (red) and cold spots (blue) of WVCs in Lithuania combining all land-use types and distances (large map) and by land-use type at three distance categories—10 m, 50 m, and 200 m (small maps).</p> Full article ">
20 pages, 7705 KiB  
Article
Network Patterns of Zhongyuan Urban Agglomeration in China Based on Baidu Migration Data
by Zhenkai Yang, Yixin Hua, Yibing Cao, Xinke Zhao and Minjie Chen
ISPRS Int. J. Geo-Inf. 2022, 11(1), 62; https://doi.org/10.3390/ijgi11010062 - 14 Jan 2022
Cited by 18 | Viewed by 3456
Abstract
As a new product of the Internet and big data era, migration data are of great significance for the revealing of the complex dynamic network patterns of urban agglomerations and for studying the relations between cities by using the “space of flows” model. [...] Read more.
As a new product of the Internet and big data era, migration data are of great significance for the revealing of the complex dynamic network patterns of urban agglomerations and for studying the relations between cities by using the “space of flows” model. Based on Baidu migration data of one week in 2021, this paper constructs a 30 × 30 rational data matrix for cities in Zhongyuan Urban Agglomeration and depicts the network pattern from static and dynamic perspectives by using social network analysis and dynamic network visualization. The results show that the network of Zhongyuan Urban Agglomeration is characterized by a circular structure with Zhengzhou as the center, a city belt around Zhengzhou as the connection, subcentral cities as the support and peripheral cities as the extension. Zhengzhou is the core city of the entire network, related to which the central and backbone networks divided in this paper account for nearly 40% of the total migration. Shangqiu, Luoyang, Zhoukou and Handan also play an important role in the structure of the migration network as subcentral cities. For a single city, the migration scale generally peaks on weekends and reaches its minimum during Tuesday to Thursday. Regarding the relations between cities, the migration variation can be divided into four types: peaking on Monday, peaking on weekends, bimodal and stable, and there are obvious phenomena of weekly commuting. In general, the links between cities outside Henan Province and other cities in the urban agglomeration are relatively weak, and the constraints of administrative regionalization on intercity migration are presumed to still exist. According to the results, the location advantage for multi-layer development and construction of Zhongyuan Urban Agglomeration should be made use of. In addition, the status as the core city and the radiation range should be strengthened, and the connections between the peripheral cities and the other cities should be improved, so as to promote the integrated and efficient development of the whole urban agglomeration. Full article
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<p>Work flow.</p> Full article ">Figure 2
<p>Location of Zhongyuan Urban Agglomeration in China.</p> Full article ">Figure 3
<p>Line chart of migration scale index (taking Zhengzhou as an example).</p> Full article ">Figure 4
<p>Representation of nodes and edges based on TimeCell.</p> Full article ">Figure 5
<p>Migration network levels of Zhongyuan Urban Agglomeration.</p> Full article ">Figure 6
<p>City point weight.</p> Full article ">Figure 7
<p>Cities of the largest scale of immigration to 30 cities.</p> Full article ">Figure 8
<p>Single-link clustering based on cohesive subgroups.</p> Full article ">Figure 9
<p>Variation in cluster centers based on immigration.</p> Full article ">Figure 10
<p>Variation in cluster centers based on emigration.</p> Full article ">Figure 11
<p>Migration variation visualization based on graded TimeCell.</p> Full article ">
27 pages, 17502 KiB  
Article
Long and Short-Term Coastal Changes Assessment Using Earth Observation Data and GIS Analysis: The Case of Sperchios River Delta
by Emmanouil Psomiadis
ISPRS Int. J. Geo-Inf. 2022, 11(1), 61; https://doi.org/10.3390/ijgi11010061 - 14 Jan 2022
Cited by 3 | Viewed by 3576
Abstract
The present study provides information about the evolution of the Sperchios River deltaic area over the last 6500 years. Coastal changes, due to natural phenomena and anthropogenic activities, were analyzed utilizing a variety of geospatial data such as historic records, topographic maps, aerial [...] Read more.
The present study provides information about the evolution of the Sperchios River deltaic area over the last 6500 years. Coastal changes, due to natural phenomena and anthropogenic activities, were analyzed utilizing a variety of geospatial data such as historic records, topographic maps, aerial photos, and satellite images, covering a period from 4500 BC to 2020. A qualitative approach for the period, from 4500 BC to 1852, and a quantitative analysis, from 1852 to the present day, were employed. Considering their scale and overall quality, the data were processed and georeferenced in detail based on the very high-resolution orthophoto datasets of the area. Then, the multitemporal shorelines were delineated in a geographical information system platform. Two different methods were utilized for the estimation of the shoreline changes and trends, namely the coastal change area method and the cross-section analysis, by implementing the digital shoreline analysis system with two statistical approaches, the end point rate and the linear regression rate. Significant river flow and coastline changes were observed with the overall increase in the delta area throughout the study period reaching 135 km2 (mean annual growth of 0.02 km2/yr) and the higher accretion rates to be detected during the periods 1805–1852, 1908–1945 and 1960–1986, especially at the central and north part of the gulf. During the last three decades, the coastline has remained relatively stable with a decreasing tendency, which, along with the expected sea-level rise due to climate change, can infer significant threats for the coastal zone in the near future. Full article
(This article belongs to the Special Issue GIS and Remote Sensing Applications in Geomorphology)
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<p>(<b>a</b>) The Sperchios River basin and its geomorphological characteristics (red rectangle demonstrates the study area); (<b>b</b>) The study area, covering the coastal delta part of the basin as it appears nowadays.</p> Full article ">Figure 2
<p>(<b>a</b>) Part of Riga’s Charta (number 2, 1797 AD) [<a href="#B56-ijgi-11-00061" class="html-bibr">56</a>] demonstrating the southern part of the Sperchios river estuary (at the northern part) and Maliakos gulf, along with the area of Thermopylae showing also the narrow path that the famous battlefield took place at 480 BC; (<b>b</b>) The Declassified satellite photograph of 1975 acquired from the USGS portal (<a href="https://earthexplorer.usgs.gov/" target="_blank">https://earthexplorer.usgs.gov/</a>, accessed on 1 January 2021), which is a new and significant freely available dataset from the last quarter of the 20th century.</p> Full article ">Figure 3
<p>Methodological approach flowchart showing the type of geospatial data employed in the present study and the steps of their processing and analysis.</p> Full article ">Figure 4
<p>Images derived from EO data revealing the shallow waters and other characteristics of the delta area (<b>a</b>) The MNDWI spectral index derived from the process of Sentinel-2 data; and (<b>b</b>) temporal differentiate image derived from the SAR.PRI radar data.</p> Full article ">Figure 5
<p>Temporal distribution of the time points where the shoreline analysis of changes and trends was made. The datasets were divided into two time periods, from 4500 BC to 1852, by considering historical data, and from 1852 to 2020, based on geospatial data of high accuracy with a precise coordinate system.</p> Full article ">Figure 6
<p>The spatial location of the sea-bottom depth measurements in Maliakos Gulf, showing with the red labels the sample number and with the white labels the depth in meters for each point, which can be compared to the depth isolines derived from the topographic maps.</p> Full article ">Figure 7
<p>The shoreline position and coastal drainage network development in the qualitative period from 4500 BC to 1805 (<b>a</b>) 4500 BC; (<b>b</b>) 480 BC; (<b>c</b>) 1805; and (<b>d</b>) the combination of the three shorelines in these three time-points.</p> Full article ">Figure 8
<p>The shoreline position and coastal drainage network development in the second qualitative and quantitative period that more precise measurement was accomplished due to the high accuracy and spatial analysis of the available geospatial data (<b>a</b>) 1852; (<b>b</b>) 1908; (<b>c</b>) 1945; (<b>d</b>) 1960; (<b>e</b>) 1986; (<b>f</b>) 1997; (<b>g</b>) 2007; and (<b>h</b>) 2020.</p> Full article ">Figure 8 Cont.
<p>The shoreline position and coastal drainage network development in the second qualitative and quantitative period that more precise measurement was accomplished due to the high accuracy and spatial analysis of the available geospatial data (<b>a</b>) 1852; (<b>b</b>) 1908; (<b>c</b>) 1945; (<b>d</b>) 1960; (<b>e</b>) 1986; (<b>f</b>) 1997; (<b>g</b>) 2007; and (<b>h</b>) 2020.</p> Full article ">Figure 9
<p>Schematic representation of the calculated in <a href="#ijgi-11-00061-t002" class="html-table">Table 2</a> for (<b>a</b>) the changing area for each period (km<sup>2</sup>); (<b>b</b>) the annual change rate for each period.</p> Full article ">Figure 10
<p>Schematic representation of erosion accretion changes using coastline pairs between two periods, each time (<b>a</b>) 4500–480 BC; (<b>b</b>) 480 BC–1805; (<b>c</b>) 1805–1852; (<b>d</b>) 1852–1908; (<b>e</b>) 1908–1945; (<b>f</b>) 1945–1960; (<b>g</b>) 9960–1986; (<b>h</b>) 1986–1997; (<b>i</b>) 1997–2007; and (<b>j</b>) 2007–2020.</p> Full article ">Figure 10 Cont.
<p>Schematic representation of erosion accretion changes using coastline pairs between two periods, each time (<b>a</b>) 4500–480 BC; (<b>b</b>) 480 BC–1805; (<b>c</b>) 1805–1852; (<b>d</b>) 1852–1908; (<b>e</b>) 1908–1945; (<b>f</b>) 1945–1960; (<b>g</b>) 9960–1986; (<b>h</b>) 1986–1997; (<b>i</b>) 1997–2007; and (<b>j</b>) 2007–2020.</p> Full article ">Figure 11
<p>The implementation of the cross-section analysis (CSA) method by using transects perpendicular to the baseline and applying two statistical approaches, (<b>a</b>) LRR and (<b>b</b>) EPR.</p> Full article ">
15 pages, 23283 KiB  
Article
Modelling the Risk of Imported COVID-19 Infections at Maritime Ports Based on the Mobility of International-Going Ships
by Zhihuan Wang, Chenguang Meng, Mengyuan Yao and Christophe Claramunt
ISPRS Int. J. Geo-Inf. 2022, 11(1), 60; https://doi.org/10.3390/ijgi11010060 - 14 Jan 2022
Cited by 4 | Viewed by 3183
Abstract
Maritime ports are critical logistics hubs that play an important role when preventing the transmission of COVID-19-imported infections from incoming international-going ships. This study introduces a data-driven method to dynamically model infection risks of international ports from imported COVID-19 cases. The approach is [...] Read more.
Maritime ports are critical logistics hubs that play an important role when preventing the transmission of COVID-19-imported infections from incoming international-going ships. This study introduces a data-driven method to dynamically model infection risks of international ports from imported COVID-19 cases. The approach is based on global Automatic Identification System (AIS) data and a spatio-temporal clustering algorithm that both automatically identifies ports and countries approached by ships and correlates them with country COVID-19 statistics and stopover dates. The infection risk of an individual ship is firstly modeled by considering the current number of COVID-19 cases of the approached countries, increase rate of the new cases, and ship capacity. The infection risk of a maritime port is mainly calculated as the aggregation of the risks of all of the ships stopovering at a specific date. This method is applied to track the risk of the imported COVID-19 of the main cruise ports worldwide. The results show that the proposed method dynamically estimates the risk level of the overseas imported COVID-19 of cruise ports and has the potential to provide valuable support to improve prevention measures and reduce the risk of imported COVID-19 cases in seaports. Full article
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<p>Overall framework: modeling the risk of overseas imported COVID-19 in maritime ports.</p> Full article ">Figure 2
<p>The overall framework to identify the approached ports and countries.</p> Full article ">Figure 3
<p>Gross tonnage distribution of 421 cruise ships.</p> Full article ">Figure 4
<p>Global port-level network of 421 cruise ships in the year 2020 extracted from AIS data.</p> Full article ">Figure 5
<p>Cruise ship movement network in western Europe.</p> Full article ">Figure 6
<p>Histogram of the annual COVID-19 risk index for all cruise ports.</p> Full article ">Figure 7
<p>Comparison of overseas imported COVID-19 risk of global cruise ports on 16 November 2020.</p> Full article ">Figure 8
<p>Comparison of overseas imported COVID-19 risk of the local cruise ports in western Europe on 16 November 2020.</p> Full article ">Figure 9
<p>(<b>a</b>) Annual change in the imported COVID-19 risk in 2020 at the Port of Civitavecchia, Italy; (<b>b</b>) Annual change in the imported COVID-19 risk in 2020 at the Port of Miami, United States.</p> Full article ">
24 pages, 5197 KiB  
Article
New Insight into the Coupled Grain–Disaster–Economy System Based on a Multilayer Network: An Empirical Study in China
by Hongjiao Qu, Junli Li, Weiyin Wang, Wenwen Xin, Cheng Zhou and Zongyi He
ISPRS Int. J. Geo-Inf. 2022, 11(1), 59; https://doi.org/10.3390/ijgi11010059 - 13 Jan 2022
Cited by 3 | Viewed by 2760
Abstract
Natural disasters occur frequently causing huge economic losses and reduced grain production. Therefore, it is important to thoroughly explore the spatial correlations between grain, disaster, and the economy. Based on inter-provincial panel data in China in 2019, this study integrates complex network and [...] Read more.
Natural disasters occur frequently causing huge economic losses and reduced grain production. Therefore, it is important to thoroughly explore the spatial correlations between grain, disaster, and the economy. Based on inter-provincial panel data in China in 2019, this study integrates complex network and co-occurrence theory into a coupled grain–disaster–economy (GDE) multilayer network, which provides a new perspective to further explore the spatial correlation between these three systems. We identify the spatial coupled characteristics of the GDE multilayer network using three aspects: degree, centrality, and community detection. The research results show the following: (1) Provinces in the major grain-producing regions have a stronger role in allocating and controlling grain resources, and the correlation between grain and disasters in these provinces is stronger and more prone to disasters. Whereas provinces in the Beijing–Tianjin–Hebei economic zone, and the Yangtze River Delta and Pearl River Delta economic zones, such as Beijing, Tianjin, Jiangsu, Shanghai, and Zhejiang, have a high level of economic development, thereby a stronger ability to allocate economic resources. (2) The economic subsystem assumes a more important, central role compared with the grain and disaster subsystems in the formation and development of the coupled GDE multilayer network, with a stronger coordination for the co-development between the complex grain, disaster, and economy systems in the nodal provinces of the network. (3) The community modularity of the coupled GDE multilayer network is significantly higher than that of the three single-layer networks, indicating a more reasonable community division after coupling the three subsystems. The identification of the spatial characteristics of GDE using multilayer network analysis offers a new perspective on taking various measures to improve the joint sustainable development of grain, disaster, and the economy in different regions of China according to local conditions. Full article
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<p>Administrative and geographical divisions of China.</p> Full article ">Figure 2
<p>Schematic representation of the transformation of two interacting networks into a multilayer network. (<b>a</b>) networks with interactions (<b>b</b>) the structure of a multilayer network consisting of these two interacting networks. (<math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mn>2</mn> </msub> </mrow> </semantics></math> represent the two layers of the network respectively, the black solid line represents the interaction of the nodes within the layers, and the black dashed line represents the interaction of the nodes between the layers).</p> Full article ">Figure 3
<p>Top 20 nodes in terms of degree value in the networks. (<b>a</b>) Grain layer; (<b>b</b>) Disaster layer; (<b>c</b>) Economy layer; (<b>d</b>) The grain-disaster-economy multilayer. (The nodes with the top 20 by degree values are selected in the corresponding network above).</p> Full article ">Figure 4
<p>Spatial distribution of degree in grain–disaster–economy multilayer network.</p> Full article ">Figure 5
<p>The correlation strength of node in grain–disaster–economy multilayer network in 2019.</p> Full article ">Figure 6
<p>Centrality characteristics of grain–disaster–economy multilayer network.</p> Full article ">Figure 7
<p>Top 20 nodes in terms of PageRank centrality in the networks. (<b>a</b>) Grain layer; (<b>b</b>) Disaster layer; (<b>c</b>) Economy layer; (<b>d</b>) The grain-disaster-economy multilayer. (The nodes with the top 20 by PageRank centrality values are selected in the corresponding network above).</p> Full article ">Figure 8
<p>Top 20 nodes in terms of eigenvector centrality in the networks. (<b>a</b>) Grain layer; (<b>b</b>) Disaster layer; (<b>c</b>) Economy layer; (<b>d</b>) The grain-disaster-economy multilayer. (The nodes with the top 20 by Eigenvector centrality values are selected in the corresponding network above).</p> Full article ">Figure 9
<p>Community distribution of three single-layer networks. (<b>a</b>) Community distribution in grain layer; (<b>b</b>) Community distribution in disaster layer; (<b>c</b>) Community distribution in economy layer. (The three single-layer networks of grain, disaster, and economy are divided into three communities with different colors respectively, and the black solid line represents the association relationship between nodes in the network).</p> Full article ">Figure 10
<p>Community number of the grain–disaster–economy multilayer network.</p> Full article ">Figure 11
<p>Community cluster of grain–disaster–economy multilayer network in 2019.</p> Full article ">
18 pages, 786 KiB  
Article
Achieving ‘Active’ 30 Minute Cities: How Feasible Is It to Reach Work within 30 Minutes Using Active Transport Modes?
by Alan Both, Lucy Gunn, Carl Higgs, Melanie Davern, Afshin Jafari, Claire Boulange and Billie Giles-Corti
ISPRS Int. J. Geo-Inf. 2022, 11(1), 58; https://doi.org/10.3390/ijgi11010058 - 13 Jan 2022
Cited by 16 | Viewed by 7690
Abstract
Confronted with rapid urbanization, population growth, traffic congestion, and climate change, there is growing interest in creating cities that support active transport modes including walking, cycling, or public transport. The ‘30 minute city’, where employment is accessible within 30 min by active transport, [...] Read more.
Confronted with rapid urbanization, population growth, traffic congestion, and climate change, there is growing interest in creating cities that support active transport modes including walking, cycling, or public transport. The ‘30 minute city’, where employment is accessible within 30 min by active transport, is being pursued in some cities to reduce congestion and foster local living. This paper examines the spatial relationship between employment, the skills of residents, and transport opportunities, to answer three questions about Australia’s 21 largest cities: (1) What percentage of workers currently commute to their workplace within 30 min? (2) If workers were to shift to an active transport mode, what percent could reach their current workplace within 30 min? and (3) If it were possible to relocate workers closer to their employment or relocate employment closer to their home, what percentage could reach work within 30 min by each mode? Active transport usage in Australia is low, with public transport, walking, and cycling making up 16.8%, 2.8%, and 1.1% respectively of workers’ commutes. Cycling was found to have the most potential for achieving the 30 min city, with an estimated 29.5% of workers able to reach their current workplace were they to shift to cycling. This increased to 69.1% if workers were also willing and able to find a similar job closer to home, potentially reducing commuting by private motor vehicle from 79.3% to 30.9%. Full article
(This article belongs to the Special Issue Geo-Information Applications in Active Mobility and Health in Cities)
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<p>Distribution of distances traveled to work by mode for VISTA travel survey participants, with average distance traveled in 30 min marked by black vertical lines.</p> Full article ">Figure 2
<p>Distribution of time traveled to work by mode for commuters across 21 Australian cities.</p> Full article ">Figure 3
<p>Percentage of commuters that can reach work within 30 min, or could if they shifted transport modes or changed jobs, overall and for cities ordered by total working population.</p> Full article ">
16 pages, 9337 KiB  
Article
Multiscale Effects of Multimodal Public Facilities Accessibility on Housing Prices Based on MGWR: A Case Study of Wuhan, China
by Lingbo Liu, Hanchen Yu, Jie Zhao, Hao Wu, Zhenghong Peng and Ru Wang
ISPRS Int. J. Geo-Inf. 2022, 11(1), 57; https://doi.org/10.3390/ijgi11010057 - 13 Jan 2022
Cited by 32 | Viewed by 4793
Abstract
The layout of public service facilities and their accessibility are important factors affecting spatial justice. Previous studies have verified the positive influence of public facilities accessibility on house prices; however, the spatial scale of the impact of various public facilities accessibility on house [...] Read more.
The layout of public service facilities and their accessibility are important factors affecting spatial justice. Previous studies have verified the positive influence of public facilities accessibility on house prices; however, the spatial scale of the impact of various public facilities accessibility on house prices is not yet clear. This study takes transportation analysis zone of Wuhan city as the spatial unit, measure the public facilities accessibility of schools, hospitals, green space, and public transit stations with four kinds of accessibility models such as the nearest distance, real time travel cost, kernel density, and two step floating catchment area (2SFCA), and explores the multiscale effect of public services accessibility on house prices with multiscale geographically weighted regression model. The results show that the differentiated scale effect not only exists among different public facility accessibilities, but also exists in different accessibility models of the same sort of facility. The article also suggests that different facilities should adopt its appropriate accessibility model. This study provides insights into spatial heterogeneity of urban public service facilities accessibility, which will benefit decision making in equal accessibility planning and policy formulation for the layout of urban service facilities. Full article
(This article belongs to the Special Issue Geo-Information for Developing Urban Infrastructures)
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<p>Location of Wuhan and its Main Urban Area (MUA).</p> Full article ">Figure 2
<p>Housing price of Wuhan from 2013 to 2021 (MUA). Data source: <a href="https://www.anjuke.com/" target="_blank">https://www.anjuke.com/</a> (accessed on 1 January 2022).</p> Full article ">Figure 3
<p>Mapping Variables of Housing prices and other control variables.</p> Full article ">Figure 4
<p>Multimodal accessibilities of public facilities.</p> Full article ">Figure 5
<p>Multimodal accessibility of public facilities. Note: <span style="color:red">***</span>, <span style="color:red">**</span>, <span style="color:red">*</span> represent significant at the level of 0.001, 0.01, 0.05, respective.</p> Full article ">Figure 6
<p>Variables of housing prices and other control variables. (<b>a</b>) Intercept of the MGWR indicates the spatial variation in housing price, (<b>b</b>) Age of resident apartments, (<b>c</b>) Population density, (<b>d</b>) Distance to 1st Ring road.</p> Full article ">Figure 7
<p>Global effect of educational facility accessibility. (<b>a</b>) Kernel density of Kindergartens, (<b>b</b>) Kernel density of primary schools, (<b>c</b>) Kernel density of Middle schools.</p> Full article ">Figure 8
<p>Multiscale effect of green space accessibility. (<b>a</b>) Distance to nearest green space, (<b>b</b>) Green space accessibility calculated by 2SFCA.</p> Full article ">Figure 9
<p>Multiscale effect of the accessibility of bus stops. (<b>a</b>) Kernel density of bus stops, (<b>b</b>) Distance to nearest bus stop.</p> Full article ">Figure 10
<p>Multiscale effect of metro station accessibility. (<b>a</b>) Distance to nearest metro station, (<b>b</b>) Travel distance to metro stations calculated by Baidu Map API, (<b>c</b>) Travel time to metro stations calculated by Baidu Map API.</p> Full article ">Figure 11
<p>Global effect of hospital accessibility. (<b>a</b>) Distance to nearest hospital, (<b>b</b>) Travel distance to nearest hospital calculated by Baidu Map API, (<b>c</b>) Travel time to nearest hospital calculated by Baidu Map API.</p> Full article ">
23 pages, 8483 KiB  
Article
Automated Residential Area Generalization: Combination of Knowledge-Based Framework and Similarity Measurement
by Xiaorong Gao, Haowen Yan, Xiaomin Lu and Pengbo Li
ISPRS Int. J. Geo-Inf. 2022, 11(1), 56; https://doi.org/10.3390/ijgi11010056 - 12 Jan 2022
Cited by 4 | Viewed by 2284
Abstract
The major reason that the fully automated generalization of residential areas has not been achieved to date is that it is difficult to acquire the knowledge that is required for automated generalization and for the calculation of spatial similarity degrees between map objects [...] Read more.
The major reason that the fully automated generalization of residential areas has not been achieved to date is that it is difficult to acquire the knowledge that is required for automated generalization and for the calculation of spatial similarity degrees between map objects at different scales. Furthermore, little attention has been given to generalization methods with a scale reduction that is larger than two-fold. To fill this gap, this article develops a hybrid approach that combines two existing methods to generalize residential areas that range from 1:10,000 to 1:50,000. The two existing methods are Boffet’s method for free space acquisition and kernel density analysis for city hotspot detection. Using both methods, the proposed approach follows a knowledge-based framework by implementing map analysis and spatial similarity measurements in a multiscale map space. First, the knowledge required for residential area generalization is obtained by analyzing multiscale residential areas and their corresponding contributions. Second, residential area generalization is divided into two subprocesses: free space acquisition and urban area outer boundary determination. Then, important parameters for the two subprocesses are obtained through map analysis and similarity measurements, reflecting the knowledge that is hidden in the cartographer’s mind. Using this acquired knowledge, complete generalization steps are formed. The proposed approach is tested using multiscale datasets from Lanzhou City. The experimental results demonstrate that our method is better than the traditional methods in terms of location precision and actuality. The approach is robust, comparatively insensitive to the noise of the small buildings beyond urban areas, and easy to implement in GIS software. Full article
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<p>Vector datasets of Lanzhou City at the 1:10K and 1:50K scales: (<b>a</b>) 1:10K data and (<b>b</b>) 1:50K data.</p> Full article ">Figure 2
<p>Vector datasets of Lanzhou City at the 1:250K and 1:1M scales: (<b>a</b>) 1:250K data and (<b>b</b>) 1:1M data.</p> Full article ">Figure 3
<p>The 1:10K to 1:50K generalization model (it should be noted that the steps in the figure are consistent with those in Figure 7 in <a href="#sec2dot2dot3-ijgi-11-00056" class="html-sec">Section 2.2.3</a>; a detailed explanation is given in <a href="#sec2dot2dot3-ijgi-11-00056" class="html-sec">Section 2.2.3</a>): (<b>a</b>) Step 1: The original 1:10K data is clipped by the administrative boundary; (<b>b</b>) Steps 2 and 3: a buffer is created for buildings, and it intersects with the KDE results; (<b>c</b>) Step 4, Part 1: A buffer is created for the roads; (<b>d</b>) Step 4, Part 2: Polygons are merged; (<b>e</b>) Steps 5 and 6: Free spaces are collected, simplified and smaller ones are eliminated; (<b>f</b>) Step 7, Part 1: Holes within urban areas are filled in; (<b>g</b>) Step 7, Part 2 and Step 8: Holes are erased and internal buffer is made.</p> Full article ">Figure 4
<p>Relationship between buildings, Boffet’s method result, different KDE results, and obtained urban areas.</p> Full article ">Figure 5
<p>An example of a correspondence relationship between residential areas at different scales.</p> Full article ">Figure 6
<p>Illustration of different density values and corresponding urban sprawl extents: (<b>a</b>) is the KDE result, (<b>b</b>) is the obtained urban area with density values of 1 or greater, and (<b>c</b>) is the obtained urban areas with density values of 2 or greater.</p> Full article ">Figure 7
<p>Procedures of the proposed method.</p> Full article ">Figure 8
<p>Geometry similarity values between residential areas at four scales.</p> Full article ">Figure 9
<p>Urban hot spots detected by KDE with different thresholds but with the same refined cell size (10 m): (<b>a</b>) is the result when the search radius is 150 m; (<b>b</b>) is the result when the search radius is 200 m; (<b>c</b>) is the result when the search radius is 300 m; (<b>d</b>) is the result when the search radius is 400 m; and (<b>e</b>) is the result when the search radius is 500 m.</p> Full article ">Figure 9 Cont.
<p>Urban hot spots detected by KDE with different thresholds but with the same refined cell size (10 m): (<b>a</b>) is the result when the search radius is 150 m; (<b>b</b>) is the result when the search radius is 200 m; (<b>c</b>) is the result when the search radius is 300 m; (<b>d</b>) is the result when the search radius is 400 m; and (<b>e</b>) is the result when the search radius is 500 m.</p> Full article ">Figure 10
<p>Buffering results (20 m) of 1:10K buildings.</p> Full article ">Figure 11
<p>Results of experiments: (<b>a</b>) <span class="html-italic">UrbanAreasInitial</span>, (<b>b</b>) UrbanAreasInitial_StrBufPolygons_Merge, and (<b>c</b>) <span class="html-italic">UrbanAreasInitial2</span>.</p> Full article ">Figure 11 Cont.
<p>Results of experiments: (<b>a</b>) <span class="html-italic">UrbanAreasInitial</span>, (<b>b</b>) UrbanAreasInitial_StrBufPolygons_Merge, and (<b>c</b>) <span class="html-italic">UrbanAreasInitial2</span>.</p> Full article ">Figure 12
<p>Obtained free spaces within urban areas.</p> Full article ">Figure 13
<p>Urban areas obtained from the proposed approach.</p> Full article ">Figure 14
<p>Examples of free spaces obtained from 1:10K buildings (left) and image map (right): (<b>a</b>) field; (<b>b</b>) open-air playground; (<b>c</b>) railway station; (<b>d</b>) square; (<b>e</b>) park; and (<b>f</b>) space to be built.</p> Full article ">
15 pages, 6814 KiB  
Article
Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation
by Guiming Zhang
ISPRS Int. J. Geo-Inf. 2022, 11(1), 55; https://doi.org/10.3390/ijgi11010055 - 12 Jan 2022
Cited by 12 | Viewed by 3620
Abstract
Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled [...] Read more.
Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled by the parallel kernel density estimation (KDE) computational tool that can run on multiple GPUs (graphics processing units), this study conducted point pattern analyses on tens of millions of iNaturalist observations to detect and visualize volunteers’ observation hot-spots across spatial scales. It was achieved by setting varying KDE bandwidths in accordance with the spatial scales at which hot-spots are to be detected. The succession of estimated density surfaces were then rendered at a sequence of map scales for visual detection of hot-spots. This study offers an effective geovisualization scheme for hierarchically detecting hot-spots in massive VGI datasets, which is useful for understanding the pattern-shaping drivers that operate at multiple spatial scales. This research exemplifies a computational tool that is supported by high-performance computing and capable of efficiently detecting and visualizing multi-scale hot-spots in geospatial big data and contributes to expanding the toolbox for geospatial big data analytics. Full article
(This article belongs to the Special Issue Mapping, Modeling and Prediction with VGI)
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<p>iNaturalist observation locations in 2019 (<b>left</b>) and 2020 (<b>right</b>).</p> Full article ">Figure 2
<p>iNaturalist observation hot-spots (2020) in the Denver metropolitan area across spatial scales. (<b>A</b>–<b>I</b>) corresponds to the increasingly large map scales at which hot-spots are detected and visualized. On each map, red color represents high observation density, and the inner box indicates the display extent of the next map in sequence rendering finer-scale hot-spots.</p> Full article ">Figure 3
<p>Observation hot-spots (2020) detected and rendered at micro-scales in a park in Denver. (<b>I</b>–<b>K</b>) corresponds to the increasingly large map scales at which hot-spots are detected and visualized.</p> Full article ">Figure 4
<p>Changes in observation hot-spots across 2019 and 2020 on the University of Denver campus. (<b>I</b>–<b>K</b>) corresponds to the increasingly large map scales at which hot-spots are detected and visualized.</p> Full article ">Figure 5
<p>Density surfaces (5 km resolution) estimated using the GPU-parallel KDE tool (Gaussian kernel; default bandwidth = 134,330 m), and using the KDE tools in ArcGIS Pro (Quartic kernel; default bandwidth = 250,891 m) and in QGIS (Quartic kernel, bandwidth = 250,891 m).</p> Full article ">
29 pages, 5165 KiB  
Article
Bridges and Barriers: An Exploration of Engagements of the Research Community with the OpenStreetMap Community
by A. Yair Grinberger, Marco Minghini, Godwin Yeboah, Levente Juhász and Peter Mooney
ISPRS Int. J. Geo-Inf. 2022, 11(1), 54; https://doi.org/10.3390/ijgi11010054 - 12 Jan 2022
Cited by 3 | Viewed by 5955
Abstract
The academic community frequently engages with OpenStreetMap (OSM) as a data source and research subject, acknowledging its complex and contextual nature. However, existing literature rarely considers the position of academic research in relation to the OSM community. In this paper we explore the [...] Read more.
The academic community frequently engages with OpenStreetMap (OSM) as a data source and research subject, acknowledging its complex and contextual nature. However, existing literature rarely considers the position of academic research in relation to the OSM community. In this paper we explore the extent and nature of engagement between the academic research community and the larger communities in OSM. An analysis of OSM-related publications from 2016 to 2019 and seven interviews conducted with members of one research group engaged in OSM-related research are described. The literature analysis seeks to uncover general engagement patterns while the interviews are used to identify possible causal structures explaining how these patterns may emerge within the context of a specific research group. Results indicate that academic papers generally show few signs of engagement and adopt data-oriented perspectives on the OSM project and product. The interviews expose that more complex perspectives and deeper engagement exist within the research group to which the interviewees belong, e.g., engaging in OSM mapping and direct interactions based on specific points-of-contact in the OSM community. Several conclusions and recommendations emerge, most notably: that every engagement with OSM includes an interpretive act which must be acknowledged and that the academic community should act to triangulate its interpretation of the data and OSM community by diversifying their engagement. This could be achieved through channels such as more direct interactions and inviting members of the OSM community to participate in the design and evaluation of research projects and programmes. Full article
(This article belongs to the Special Issue OpenStreetMap as A Multi-Disciplinary Nexus: Perspectives, Practices and Procedures)
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<p>Country map based on the number of OSM-related papers with at least one author with affiliation in those countries.</p> Full article ">Figure 2
<p>Country map based on the number of OSM-related papers with case studies in those countries.</p> Full article ">Figure 3
<p>Graphical representation of the number of OSM-related papers based on the continents of authors’ affiliations and the continents of study areas. Arrows indicate a flow from the affiliation continent to the case study continent. The radial axis indicates the number of papers.</p> Full article ">Figure 4
<p>Alluvial diagram connecting authors’ perspective on OSM-C (<b>left</b>) with OSM-C engagement (<b>right</b>).</p> Full article ">Figure 5
<p>Alluvial diagrams connecting topic with perspective (<b>a</b>) and engagement (<b>b</b>).</p> Full article ">Figure 6
<p>Alluvial diagrams connecting authors’ disciplines with engagement (<b>a</b>) and perspective (<b>b</b>); journals’ disciplines with engagement (<b>c</b>) and perspective (<b>d</b>).</p> Full article ">Figure 7
<p>Alluvial diagrams connecting geographical correspondence with engagement (<b>a</b>) and perspective (<b>b</b>).</p> Full article ">Figure 8
<p>A model of bridges and barriers in engagement between the OSM-R and OSM-C.</p> Full article ">
26 pages, 2377 KiB  
Article
A Pricing Model for Urban Rental Housing Based on Convolutional Neural Networks and Spatial Density: A Case Study of Wuhan, China
by Hang Shen, Lin Li, Haihong Zhu and Feng Li
ISPRS Int. J. Geo-Inf. 2022, 11(1), 53; https://doi.org/10.3390/ijgi11010053 - 11 Jan 2022
Cited by 9 | Viewed by 3283
Abstract
With the development of urbanization and the expansion of floating populations, rental housing has become an increasingly common living choice for many people, and housing rental prices have attracted great attention from individuals, enterprises and the government. The housing rental prices are principally [...] Read more.
With the development of urbanization and the expansion of floating populations, rental housing has become an increasingly common living choice for many people, and housing rental prices have attracted great attention from individuals, enterprises and the government. The housing rental prices are principally estimated based on structural, locational and neighborhood variables, among which the relationships are complicated and can hardly be captured entirely by simple one-dimensional models; in addition, the influence of the geographic objects on the price may vary with the increase in their quantities. However, existing pricing models usually take those structural, locational and neighborhood variables as one-dimensional inputs into neural networks, and often neglect the aggregated effects of geographical objects, which may lead to fluctuating rental price estimations. Therefore, this paper proposes a rental housing price model based on the convolutional neural network (CNN) and the synthetic spatial density of points of interest (POIs). The CNN can efficiently extract the complex characteristics among the relevant variables of housing, and the two-dimensional locational and neighborhood variables, based on the synthetic spatial density, effectively reflect the aggregated effects of the urban facilities on rental housing prices, thereby improving the accuracy of the model. Taking Wuhan, China, as the study area, the proposed method achieves satisfactory and accurate rental price estimations (coefficient of determination (R2) = 0.9097, root mean square error (RMSE) = 3.5126) in comparison with other commonly used pricing models. Full article
(This article belongs to the Special Issue Geo-Information Science in Planning and Development of Smart Cities)
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<p>The overall research flowchart of this paper.</p> Full article ">Figure 2
<p>The range of the study area: Wuhan, China (The municipal districts are: ① Jiang’an, ② Jianghan, ③ Qiaokou, ④ Qingshan, ⑤ Wuchang, ⑥ Hanyang and ⑦ Hongshan).</p> Full article ">Figure 3
<p>The local influence of “the number of supermarkets within 2 km” on the rental housing price (based on Shapley value analysis).</p> Full article ">Figure 4
<p>The structure of the CNN model for the rental housing price.</p> Full article ">Figure 5
<p>The procedure of transforming the rental housing price variables into two dimensions.</p> Full article ">Figure 6
<p>Parameters observed during the training processes of the: (<b>a</b>) FCNN model, and (<b>b</b>) CNN model (for an average group).</p> Full article ">Figure 7
<p>The basic framework of the neurons in the neural network of (<b>a</b>) an example of FCNN model and (<b>b</b>) an example of CNN model.</p> Full article ">
2 pages, 1823 KiB  
Correction
Correction: Yang et al. Influence of Relief Degree of Land Surface on Street Network Complexity in China. ISPRS Int. J. Geo-Inf. 2021, 10, 705
by Nai Yang, Le Jiang, Yi Chao, Yang Li and Pengcheng Liu
ISPRS Int. J. Geo-Inf. 2022, 11(1), 52; https://doi.org/10.3390/ijgi11010052 - 11 Jan 2022
Viewed by 1686
Abstract
In the original publication [...] Full article
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<p>(<b>A</b>) Street network in Beijing. (<b>B</b>) Street orientation histogram of Beijing, where the horizontal coordinates indicate the orientation angles and the vertical coordinates indicate the frequency of a street falling into the corresponding bin. (<b>C</b>) Street orientation rose illustration of Beijing, where the outer side of the circle represents the street orientation, and the inner data are the same as the vertical coordinate in (<b>B</b>).</p> Full article ">Figure 2
<p>(<b>A</b>) Street network in Beijing. (<b>B</b>) Street orientation histogram of Beijing, where the horizontal coordinates indicate the orientation angles and the vertical coordinates indicate the frequency of a street falling into the corresponding bin. (<b>C</b>) Street orientation rose illustration of Beijing, where the outer side of the circle represents the street orientation, and the inner data are the same as the vertical coordinate in (<b>B</b>).</p> Full article ">
12 pages, 1581 KiB  
Communication
Interoperability and Integration: An Updated Approach to Linked Data Publication at the Dutch Land Registry
by Alexandra Rowland, Erwin Folmer, Wouter Beek and Rob Wenneker
ISPRS Int. J. Geo-Inf. 2022, 11(1), 51; https://doi.org/10.3390/ijgi11010051 - 10 Jan 2022
Cited by 4 | Viewed by 2912
Abstract
Kadaster, the Dutch National Land Registry and Mapping Agency, has been actively publishing their base registries as linked (open) spatial data for several years. To date, a number of these base registers as well as a number of external datasets have been successfully [...] Read more.
Kadaster, the Dutch National Land Registry and Mapping Agency, has been actively publishing their base registries as linked (open) spatial data for several years. To date, a number of these base registers as well as a number of external datasets have been successfully published as linked data and are publicly available. Increasing demand for linked data products and the availability of new linked data technologies have highlighted the need for a new, innovative approach to linked data publication within the organisation in the interest of reducing the time and costs associated with said publication. The new approach to linked data publication is novel in both its approach to dataset modelling, transformation, and publication architecture. In modelling whole datasets, a clear distinction is made between the Information Model and the Knowledge Model to capture both the organisation-specific requirements and to support external, community standards in the publication process. The publication architecture consists of several steps where instance data are loaded from their source as GML and transformed using an Enhancer and published in the triple store. Both the modelling and publication architecture form part of Kadaster’s larger vision for the development of the Kadaster Knowledge Graph through the integration of the various linked datasets. Full article
(This article belongs to the Special Issue Semantic Spatial Web)
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<p>Model mapping of object/datatype properties. Schemes follow another format. (<b>A</b>) This figure illustrates how the mapping between the Information Model and Knowledge Model is conducted in practice with regards to owl:ObjectProperty; (<b>B</b>) this figure illustrates how the mapping between the Information Model and Knowledge Model is conducted in practice with regards to owl:DatatypeProperty.</p> Full article ">Figure 2
<p>Architecture supporting the ETL process which delivers linked data.</p> Full article ">Figure 3
<p>Kadaster’s vision for the implementation of the knowledge graph.</p> Full article ">
24 pages, 6306 KiB  
Article
A Poverty Measurement Method Incorporating Spatial Correlation: A Case Study in Yangtze River Economic Belt, China
by Qianqian Zhou, Nan Chen and Siwei Lin
ISPRS Int. J. Geo-Inf. 2022, 11(1), 50; https://doi.org/10.3390/ijgi11010050 - 10 Jan 2022
Cited by 6 | Viewed by 2919
Abstract
The UN 2030 Agenda sets poverty eradication as the primary goal of sustainable development. An accurate measurement of poverty is a critical input to the quality and efficiency of poverty alleviation in rural areas. However, poverty, as a geographical phenomenon, inevitably has a [...] Read more.
The UN 2030 Agenda sets poverty eradication as the primary goal of sustainable development. An accurate measurement of poverty is a critical input to the quality and efficiency of poverty alleviation in rural areas. However, poverty, as a geographical phenomenon, inevitably has a spatial correlation. Neglecting the spatial correlation between areas in poverty measurements will hamper efforts to improve the accuracy of poverty identification and to design policies in truly poor areas. To capture this spatial correlation, this paper proposes a new poverty measurement model based on a neural network, namely, the spatial vector deep neural network (SVDNN), which combines the spatial vector neural network model (SVNN) and the deep neural network (DNN). The SVNN was applied to measure spatial correlation, while the DNN used the SVNN output vector and explanatory variables dataset to measure the multidimensional poverty index (MPI). To determine the optimal spatial correlation structure of SVDNN, this paper compares the model performance of the spatial distance matrix, spatial adjacent matrix and spatial weighted adjacent matrix, selecting the optimal performing spatial distance matrix as the input data set of SVNN. Then, the SVDNN model was used for the MPI measurement of the Yangtze River Economic Belt, after which the results were compared with three baseline models of DNN, the back propagation neural network (BPNN), and artificial neural network (ANN). Experiments demonstrate that the SVDNN model can obtain spatial correlation from the spatial distance dataset between counties and its poverty identification accuracy is better than other baseline models. The spatio-temporal characteristics of MPI measured by SVDNN were also highly consistent with the distribution of urban aggregations and national-level poverty counties in the Yangtze River Economic Belt. The SVDNN model proposed in this paper could effectively improve the accuracy of poverty identification, thus reducing the misallocation of resources in tracking and targeting poverty in developing countries. Full article
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<p>Study area location: (<b>a</b>) Location of Yangtze River Economic Belt; (<b>b</b>) location of national-level poverty counties in Yangtze River Economic Belt. (Note: YREB represents the Yangtze River Economic Belt. FTA represents the four Tibetan-inhabited areas. MBWY represents the mountainous borderland of western Yunnan. QBMA represents the Qinba Mountain area. WLMA represents the Wuling Mountain area. WMMA represents the Wumeng Mountain area. YGGRDA represents the Yunnan–Guizhou–Guangxi rocky desertification area. DBMA represents the Dabie Mountain area. LXMA represents the Luoxiao Mountain area.).</p> Full article ">Figure 2
<p>Structure of SVDNN model.</p> Full article ">Figure 3
<p>The structure of SVNN single neuron.</p> Full article ">Figure 4
<p>Comparison of model performance under different epochs.</p> Full article ">Figure 5
<p>Comparison of model performance under different epochs.</p> Full article ">Figure 6
<p>Comparison of model performance under different spatial correlation structures. (Note: SDM represents the spatial distance matrix; SAM represents the spatial adjacency matrix (adjacent area value is set to 1, while the non-adjacent area is set to 0); SWAM represents the spatial weighted adjacency matrix (The matrix value of the adjacent area is the spatial distance, while the non-adjacent area is 0).</p> Full article ">Figure 6 Cont.
<p>Comparison of model performance under different spatial correlation structures. (Note: SDM represents the spatial distance matrix; SAM represents the spatial adjacency matrix (adjacent area value is set to 1, while the non-adjacent area is set to 0); SWAM represents the spatial weighted adjacency matrix (The matrix value of the adjacent area is the spatial distance, while the non-adjacent area is 0).</p> Full article ">Figure 7
<p>Comparison of MPI identification results with national-level poverty counties. (Note: The identification rules for correctly identified counties, misidentified counties, and unidentified counties are as follows: First, the MPIs calculated by each model are divided into five values (low, mid-low, mid, mid-high and high) using the natural breaks classification. Second, the low value and mid-low value counties of the MPIs are divided into the poverty counties identified by the model and are examined to determine whether they belong to the national-level poverty counties. If they belong, the counties are identified correctly, if not, the counties are misidentified. Finally, the redundant poverty counties identified by the model or the counties belonging to the national-level poverty counties but not identified are divided into unidentified counties.).</p> Full article ">Figure 8
<p>Linear regression of MPIs and poverty incidence. (Note: Each scattered point represents a county, the abscissa is the MPI calculated by every model, and the ordinate is the poverty incidence rate corresponding to this county obtained from the county-level census data released by the Chinese government.)</p> Full article ">Figure 9
<p>Frequency distribution histogram of MPIs.</p> Full article ">Figure 9 Cont.
<p>Frequency distribution histogram of MPIs.</p> Full article ">Figure 10
<p>Spatio-temporal distribution of MPIs. (Note: The MPI of each county is divided into five levels according to the natural breaks classification, which are high-value counties (1.000 ≥ MPI &gt; 0.883), mid-high value counties (0.883 ≥ MPI &gt; 0.686), mid-value counties (0.686 ≥ MPI &gt; 0.437), mid-low value county (0.437 ≥ MPI &gt; 0.167) and low-value counties (0.167 ≥ MPI &gt; 0.000)).</p> Full article ">Figure 11
<p>MPIs of national-level poverty counties. (Note: YREB represents the Yangtze River Economic Belt. FTA represents the four Tibetan-inhabited areas. MBWY represents the mountainous borderland of western Yunnan. QBMA represents the Qinba Mountain area. WLMA represents the Wuling Mountain area. WMMA represents the Wumeng Mountain area. YGGRDA represents the Yunnan–Guizhou–Guangxi rocky desertification area. DBMA represents the Dabie Mountain area. LXMA represents the Luoxiao Mountain area. NPC represents the national-level poverty counties.).</p> Full article ">
23 pages, 12513 KiB  
Article
Spatial Evolution of Coastal Tourist City Using the Dyna-CLUE Model in Koh Chang of Thailand during 1990–2050
by Katawut Waiyasusri and Srilert Chotpantarat
ISPRS Int. J. Geo-Inf. 2022, 11(1), 49; https://doi.org/10.3390/ijgi11010049 - 10 Jan 2022
Cited by 19 | Viewed by 3601
Abstract
Spatial evolution can be traced by land-use change (LUC), which is a frontier issue in the field of geography. Using the limited areas of Koh Chang in Thailand as the research case, this study analyzed the simulation of its spatial evolution from a [...] Read more.
Spatial evolution can be traced by land-use change (LUC), which is a frontier issue in the field of geography. Using the limited areas of Koh Chang in Thailand as the research case, this study analyzed the simulation of its spatial evolution from a multi-scenario perspective on the basis of the 1900–2020 thematic mapper/operational land imager (TM/OLI) remote sensing data obtained through the transfer matrix model, and modified LUC and the dynamic land-use change model (Dyna-CLUE). Over the past 30 years, the expansion of recreation areas and urban and built-up land has been very high (2944.44% and 486.99%, respectively) along the western coast of Koh Chang, which replaced the original mangrove forests, orchards, and communities. Logistic regression analysis of important variables affecting LUC revealed that population density variables and coastal plain topography significantly affected LUC, which showed strong β coefficients prominently in the context of a coastal tourist city. The results of the LUC and logistic regression analyses were used to predict future LUCs in the Dyna-CLUE model to simulate 2050 land-use in three scenarios: (1) natural evolution scenario, where a large patch expansion of agricultural land extends along the edge of the entire forest boundary around the island, particularly the southwestern areas of the island that should be monitored; (2) reserved area protection scenario, where the boundary of the conservation area is incorporated into the model, enabling forest preservation in conjunction with tourism development; and (3) recreation area growth scenario, where the southern area is the most susceptible to change at the new road crossing between Khlong Kloi village to Salak Phet village, and where land-use of the recreation area type is expanding. The model-projected LUC maps provide insights into possible changes under multiple pathways, which could help local communities, government agencies, and stakeholders jointly allocate resource planning in a systematic way, so that the development of various infrastructures to realize the potential impact on the environment is a sustainable coastal tourist city development. Full article
(This article belongs to the Special Issue Geomatic Applications to Coastal Research: Challenges and New Developments)
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<p>Location maps of the study area.</p> Full article ">Figure 2
<p>Geo-spatial data (physical factors and socio-economic factors) in the Koh Chang area. (<b>A</b>) elevation, (<b>B</b>) slope, (<b>C</b>) distance to stream, (<b>D</b>) distance to coastline, (<b>E</b>) distance to village, (<b>F</b>) population density, and (<b>G</b>) distance to road.</p> Full article ">Figure 3
<p>Flow chart of methodology.</p> Full article ">Figure 4
<p>Land-use pattern and Landsat satellite imagery of Koh Chang Island in 1990, 2005, and 2020.</p> Full article ">Figure 5
<p>Land-use change dynamics map from 1990 to 2020 in Koh Chang, Trat Province.</p> Full article ">Figure 6
<p>Land-use simulation maps in 2050 under three dynamic scenarios and predicted LUC in Koh Chang over three decades.</p> Full article ">Figure 7
<p>Land-use simulation maps showing Koh Chang Island areas susceptible to LUC in 2050 for the western coastal area (<b>A</b>) under NE (<b>B</b>); NP (<b>C</b>); and RG (<b>D</b>) scenarios, and for the southern coastal area (<b>E</b>) under NE (<b>F</b>); NP (<b>G</b>); and RG (<b>H</b>) scenarios.</p> Full article ">Figure 8
<p>Spatial evolution from past to present in Koh Chang of Thailand. Map data 2021 © Maxar Technologies.</p> Full article ">
29 pages, 126182 KiB  
Article
Drought Assessment Based on Fused Satellite and Station Precipitation Data: An Example from the Chengbi River Basin, China
by Chongxun Mo, Xuechen Meng, Yuli Ruan, Yafang Wang, Xingbi Lei, Zhenxiang Xing and Shufeng Lai
ISPRS Int. J. Geo-Inf. 2022, 11(1), 48; https://doi.org/10.3390/ijgi11010048 - 10 Jan 2022
Cited by 5 | Viewed by 2512
Abstract
Drought poses a significant constraint on economic development. Drought assessment using the standardized precipitation index (SPI) uses only precipitation data, eliminating other redundant and complex calculation processes. However, the sparse stations in southwest China and the lack of information on actual [...] Read more.
Drought poses a significant constraint on economic development. Drought assessment using the standardized precipitation index (SPI) uses only precipitation data, eliminating other redundant and complex calculation processes. However, the sparse stations in southwest China and the lack of information on actual precipitation measurements make drought assessment highly dependent on satellite precipitation data whose accuracy cannot be guaranteed. Fortunately, the Chengbi River Basin in Baise City is rich in station precipitation data. In this paper, based on the evaluation of the accuracy of IMERG precipitation data, geographically weighted regression (GWR), geographic difference analysis (GDA), and cumulative distribution function (CDF) are used to fuse station precipitation data and IMERG precipitation data, and finally, the fused precipitation data with the highest accuracy are selected to evaluate the drought situation. The results indicate that the accuracy of IMERG precipitation data needs to be improved, and the quality of CDF-fused precipitation data is higher than the other two. The drought analysis indicated that the Chengbi River Basin is in a cyclical drought and flood situation, and from October to December 2014, the SPI was basically between +1 and −1, showing a spatial pattern of slight flooding, normal conditions, and slight drought. Full article
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<p>The location of the Chengbi River Basin and the distribution of the stations.</p> Full article ">Figure 2
<p>This is the hypsometric curve of the Chengbi River Basin.</p> Full article ">Figure 3
<p>The structure chart of the article.</p> Full article ">Figure 4
<p>Comparison and scatter plots of satellite precipitation data and station precipitation data. (<b>a</b>) Comparison plot of daily precipitation; (<b>b</b>) scatter plot of daily precipitation; (<b>c</b>) comparison plot of monthly precipitation; (<b>d</b>) scatter plot of monthly precipitation; (<b>e</b>) comparison plot of annual precipitation; (<b>f</b>) scatter plot of annual precipitation.</p> Full article ">Figure 4 Cont.
<p>Comparison and scatter plots of satellite precipitation data and station precipitation data. (<b>a</b>) Comparison plot of daily precipitation; (<b>b</b>) scatter plot of daily precipitation; (<b>c</b>) comparison plot of monthly precipitation; (<b>d</b>) scatter plot of monthly precipitation; (<b>e</b>) comparison plot of annual precipitation; (<b>f</b>) scatter plot of annual precipitation.</p> Full article ">Figure 4 Cont.
<p>Comparison and scatter plots of satellite precipitation data and station precipitation data. (<b>a</b>) Comparison plot of daily precipitation; (<b>b</b>) scatter plot of daily precipitation; (<b>c</b>) comparison plot of monthly precipitation; (<b>d</b>) scatter plot of monthly precipitation; (<b>e</b>) comparison plot of annual precipitation; (<b>f</b>) scatter plot of annual precipitation.</p> Full article ">Figure 5
<p>Spatial distribution of station and IMERG precipitation under each precipitation type. (<b>a</b>) Spatial distribution of the measured type 0 rainfall at the station. (<b>b</b>) Spatial distribution of type 0 rainfall monitored by satellite. (<b>c</b>) Spatial distribution of type 1 rainfall measured at the station. (<b>d</b>) Spatial distribution of type 1 rainfall monitored by satellite. (<b>e</b>) Spatial distribution of the measured type 2 rainfall at the station. (<b>f</b>) Spatial distribution of type 2 rainfall monitored by satellite. (<b>g</b>) Spatial distribution of measured type 3 rainfall at the station. (<b>h</b>) Spatial distribution of type 3 rainfall monitored by satellite. It is not difficult to find that satellite precipitation more or less overestimates the actual precipitation when no precipitation occurs, and the overestimation ranges from 0.1 mm to 0.4 mm, with the degree of overestimation increasing from the center to the north and south, respectively. The degree of overestimation is significantly higher in the south of the basin than in the north (<b>a</b>,<b>b</b>).</p> Full article ">Figure 5 Cont.
<p>Spatial distribution of station and IMERG precipitation under each precipitation type. (<b>a</b>) Spatial distribution of the measured type 0 rainfall at the station. (<b>b</b>) Spatial distribution of type 0 rainfall monitored by satellite. (<b>c</b>) Spatial distribution of type 1 rainfall measured at the station. (<b>d</b>) Spatial distribution of type 1 rainfall monitored by satellite. (<b>e</b>) Spatial distribution of the measured type 2 rainfall at the station. (<b>f</b>) Spatial distribution of type 2 rainfall monitored by satellite. (<b>g</b>) Spatial distribution of measured type 3 rainfall at the station. (<b>h</b>) Spatial distribution of type 3 rainfall monitored by satellite. It is not difficult to find that satellite precipitation more or less overestimates the actual precipitation when no precipitation occurs, and the overestimation ranges from 0.1 mm to 0.4 mm, with the degree of overestimation increasing from the center to the north and south, respectively. The degree of overestimation is significantly higher in the south of the basin than in the north (<b>a</b>,<b>b</b>).</p> Full article ">Figure 6
<p>Spatial distribution of CC values. <b>Note</b>: The CC values in the figure all meet the significance level of 0.05. (<b>a</b>) IMERG-spring, (<b>b</b>) IMERG-summer, (<b>c</b>) IMERG-autumn, (<b>d</b>) IMERG-winter, (<b>e</b>) GDA-spring, (<b>f</b>) GDA-summer, (<b>g</b>) GDA-autumn, (<b>h</b>) GDA-winter, (<b>i</b>) GWR-spring, (<b>j</b>) GWR-summer, (<b>k</b>) GWR-autumn, (<b>l</b>) GWR-winter, (<b>m</b>) CDF-spring, (<b>n</b>) CDF-summer, (<b>o</b>) CDF-autumn, (<b>p</b>) CDF-winter.</p> Full article ">Figure 6 Cont.
<p>Spatial distribution of CC values. <b>Note</b>: The CC values in the figure all meet the significance level of 0.05. (<b>a</b>) IMERG-spring, (<b>b</b>) IMERG-summer, (<b>c</b>) IMERG-autumn, (<b>d</b>) IMERG-winter, (<b>e</b>) GDA-spring, (<b>f</b>) GDA-summer, (<b>g</b>) GDA-autumn, (<b>h</b>) GDA-winter, (<b>i</b>) GWR-spring, (<b>j</b>) GWR-summer, (<b>k</b>) GWR-autumn, (<b>l</b>) GWR-winter, (<b>m</b>) CDF-spring, (<b>n</b>) CDF-summer, (<b>o</b>) CDF-autumn, (<b>p</b>) CDF-winter.</p> Full article ">Figure 7
<p>Spatial distribution of <span class="html-italic">RMSE</span> values. (<b>a</b>) IMERG-spring, (<b>b</b>) IMERG-summer, (<b>c</b>) IMERG-autumn, (<b>d</b>) IMERG-winter, (<b>e</b>) GDA-spring, (<b>f</b>) GDA-summer, (<b>g</b>) GDA-autumn, (<b>h</b>) GDA-winter, (<b>i</b>) GWR-spring, (<b>j</b>) GWR-summer, (<b>k</b>) GWR-autumn, (<b>l</b>) GWR-winter, (<b>m</b>) CDF-spring, (<b>n</b>) CDF-summer, (<b>o</b>) CDF-autumn, (<b>p</b>) CDF-winter.</p> Full article ">Figure 7 Cont.
<p>Spatial distribution of <span class="html-italic">RMSE</span> values. (<b>a</b>) IMERG-spring, (<b>b</b>) IMERG-summer, (<b>c</b>) IMERG-autumn, (<b>d</b>) IMERG-winter, (<b>e</b>) GDA-spring, (<b>f</b>) GDA-summer, (<b>g</b>) GDA-autumn, (<b>h</b>) GDA-winter, (<b>i</b>) GWR-spring, (<b>j</b>) GWR-summer, (<b>k</b>) GWR-autumn, (<b>l</b>) GWR-winter, (<b>m</b>) CDF-spring, (<b>n</b>) CDF-summer, (<b>o</b>) CDF-autumn, (<b>p</b>) CDF-winter.</p> Full article ">Figure 8
<p>Spatial distribution of <span class="html-italic">BIAS</span> values. (<b>a</b>) IMERG-spring, (<b>b</b>) IMERG-summer, (<b>c</b>) IMERG-autumn, (<b>d</b>) IMERG-winter, (<b>e</b>) GDA-spring, (<b>f</b>) GDA-summer, (<b>g</b>) GDA-autumn, (<b>h</b>) GDA-winter, (<b>i</b>) GWR-spring, (<b>j</b>) GWR-summer, (<b>k</b>) GWR-autumn, (<b>l</b>) GWR-winter, (<b>m</b>) CDF-spring, (<b>n</b>) CDF-summer, (<b>o</b>) CDF-autumn, (<b>p</b>) CDF-winter.</p> Full article ">Figure 8 Cont.
<p>Spatial distribution of <span class="html-italic">BIAS</span> values. (<b>a</b>) IMERG-spring, (<b>b</b>) IMERG-summer, (<b>c</b>) IMERG-autumn, (<b>d</b>) IMERG-winter, (<b>e</b>) GDA-spring, (<b>f</b>) GDA-summer, (<b>g</b>) GDA-autumn, (<b>h</b>) GDA-winter, (<b>i</b>) GWR-spring, (<b>j</b>) GWR-summer, (<b>k</b>) GWR-autumn, (<b>l</b>) GWR-winter, (<b>m</b>) CDF-spring, (<b>n</b>) CDF-summer, (<b>o</b>) CDF-autumn, (<b>p</b>) CDF-winter.</p> Full article ">Figure 9
<p>Trends of the S<sub>PI</sub> values at different time scales. (<b>a</b>) 1-month S<sub>PI</sub>; (<b>b</b>) 3-month S<sub>PI</sub>; (<b>c</b>) 6-month S<sub>PI</sub>; (<b>d</b>) 12-month S<sub>PI</sub>.</p> Full article ">Figure 10
<p>Trends in precipitation, minimum, and maximum air temperature from 2002 to 2018. (<b>a</b>) Trend of monthly maximum and minimum temperatures. (<b>b</b>) Trend of monthly precipitation.</p> Full article ">Figure 11
<p>Comparison of three consecutive months of drought in the Chengbi River Basin. (<b>a</b>,<b>b</b>) The spatial distribution of drought obtained from the station-measured precipitation and CDF-fused precipitation in October 2014, respectively. (<b>c</b>,<b>d</b>) The spatial distribution of drought obtained from the station-measured precipitation and CDF-fused precipitation in November 2014, respectively. (<b>e</b>,<b>f</b>) The spatial distribution of drought obtained from the station-measured precipitation and CDF-fused precipitation in December 2014, respectively.</p> Full article ">
15 pages, 3984 KiB  
Article
Scenario Expression Method for Regional Geological Structures
by Handong He, Yanrong Liu, Jing Cui and Di Hu
ISPRS Int. J. Geo-Inf. 2022, 11(1), 47; https://doi.org/10.3390/ijgi11010047 - 10 Jan 2022
Cited by 2 | Viewed by 2555
Abstract
Knowing the GIS expression of geological phenomena is an important basis for the combination of geology and GIS. Regional geological structures include folds, faults, strata, rocks, and other typical geological phenomena and are the focus of geological GIS research. However, existing research on [...] Read more.
Knowing the GIS expression of geological phenomena is an important basis for the combination of geology and GIS. Regional geological structures include folds, faults, strata, rocks, and other typical geological phenomena and are the focus of geological GIS research. However, existing research on the GIS expression of regional geological structure focuses on the expression of the spatial and attribute characteristics of geological structures, and our knowledge of the expression of the semantic, relationship, and evolution processes of geological structures is not comprehensive. In this paper, a regional geological structure scene expression model with the semantic terms positional accuracy, geometric shape, relationship type, attribute type, and time-type attributes and operations is proposed. A regional geological structure scenario markup language (RGSSML) and a method for mapping it with graphics are designed to store and graphically express regional geological structure information. According to the geological time scale, a temporal reference coordinate system is defined to dynamically express the evolution of regional geological structures. Based on the dynamic division of the time dimension of regional geological structures, the expression method of “time dimension + space structure” for the regional geological structure evolution process is designed based on the temporal model. Finally, the feasibility and effectiveness of the regional geological structure scene expression method proposed in this paper is verified using the Ningzhen Mountain (Nanjing section) as an example. The research results show that the regional geological structure scene expression method designed in this paper has the following characteristics: (1) It can comprehensively express the spatial characteristics, attribute characteristics, semantics, relationships, and evolution processes of regional geological structures; (2) it can be used to realize formalized expression and unified storage of regional geological information; and (3) it can be used to realize dynamic expression of the regional geological structure evolution process. Moreover, it has significant advantages for the expression of regional geological structure semantics, relationships, and evolution processes. This study improves our knowledge of the GIS expression of regional geological structures and is expected to further promote the combination and development of geology and GIS. Full article
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<p>Correspondence between geological expression and GIS expression.</p> Full article ">Figure 2
<p>UML design of the regional geological structure scene expression model.</p> Full article ">Figure 3
<p>The method used for the construction of the scene.</p> Full article ">Figure 4
<p>Graphic functions and mapping rules.</p> Full article ">Figure 5
<p>Geological structure spatial-temporal evolution process.</p> Full article ">Figure 6
<p>Graphic representation of the spatial structure RGSSML of the Ningzhen Mountain range (Nanjing section).</p> Full article ">Figure 6 Cont.
<p>Graphic representation of the spatial structure RGSSML of the Ningzhen Mountain range (Nanjing section).</p> Full article ">Figure 7
<p>Evolution of the regional geological structure in the Ningzhen Mountain (Nanjing section).</p> Full article ">
20 pages, 10727 KiB  
Article
Investigation of Long and Short-Term Water Surface Area Changes in Coastal Ramsar Sites in Turkey with Google Earth Engine
by Adalet Dervisoglu
ISPRS Int. J. Geo-Inf. 2022, 11(1), 46; https://doi.org/10.3390/ijgi11010046 - 10 Jan 2022
Cited by 12 | Viewed by 3698
Abstract
Deltas and lagoons, which contain many flora and fauna, have rich coastal ecological and biological environments, and are wetlands of vital importance for humans. In this study, the current problems in all coastal Ramsar sites in Turkey are summarized, and changes in water [...] Read more.
Deltas and lagoons, which contain many flora and fauna, have rich coastal ecological and biological environments, and are wetlands of vital importance for humans. In this study, the current problems in all coastal Ramsar sites in Turkey are summarized, and changes in water surface areas are investigated using Landsat and Sentinel 1/2 satellite images on the Google Earth Engine (GEE) cloud computing platform. Landsat TM and OLI images were used in the long-term analysis, and time series were created by taking annual and July to September averages between 1985 and 2020. In the short-term analysis, monthly averages were determined using Sentinel 2 images between 2016 and 2020. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in the months when optical data were not suitable for use in monthly analysis. The Normalized Difference Water Index (NDWI) was used to extract water surface areas from the optical images. Afterwards, a thresholding process was used for both optical and radar images to determine the changes. The changes were analyzed together with the meteorological data and the information obtained from the management plans and related studies in the literature. Changes in the water surface areas of all coastal Ramsar sites in Turkey were determined from 1985 to 2020 at different rates. There was a decreasing trend in the Goksu and Kızılırmak Deltas, which also have inland wetlands. The decreasing rates from 1985 to 2020 were −24.52% and −2.86%, for annual average water surfaces for the Goksu and Kızılırmak Deltas, respectively, and −21.64% and −6.34% for the dry season averages, respectively. However, Akyatan Lagoon, which also has inland wetlands, showed an increasing trend. Observing the annual average surface area from 1985 to 2020, an increase of 438 ha was seen, corresponding to 7.65%. Every year, there was an increasing trend in the Gediz Delta and Yumurtalık Lagoons, that do not have inland wetlands. The increasing rates from 1985 to 2020 were 46.01% and 17.31% for the annual average surface area, for the Gediz Delta and Yumurtalık Lagoons, respectively, and 38.34% and 21.04% for the dry season average, respectively. The obtained results reveal the importance of using remote sensing methods in formulating strategies for the sustainable management of wetlands. Full article
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<p>Turkey’s Ramsar sites and nearby meteorological stations (for coastal/marine sites).</p> Full article ">Figure 2
<p>Flowchart of the study of long and short-term water surface changes in coastal Ramsar sites in Turkey with Google Earth Engine.</p> Full article ">Figure 3
<p>Goksu Delta: (<b>a</b>) Location and 1985 and 2020 Annual Average Water Surface Areas (NDWI ˃ 0); (<b>b</b>) View of the Goksu Delta [<a href="#B39-ijgi-11-00046" class="html-bibr">39</a>]; (<b>c</b>) 1985-2020 period, Water Surface Areas (Annual Averages and Dry Season Averages); (<b>d</b>) Silifke Meteorological Station Data (Annual Total Precipitation, Total Evaporation, and Average Air Temperature); (<b>e</b>) 2016–2020, Monthly Average Water Surface Areas, Precipitation, Evaporation and Temperature.</p> Full article ">Figure 3 Cont.
<p>Goksu Delta: (<b>a</b>) Location and 1985 and 2020 Annual Average Water Surface Areas (NDWI ˃ 0); (<b>b</b>) View of the Goksu Delta [<a href="#B39-ijgi-11-00046" class="html-bibr">39</a>]; (<b>c</b>) 1985-2020 period, Water Surface Areas (Annual Averages and Dry Season Averages); (<b>d</b>) Silifke Meteorological Station Data (Annual Total Precipitation, Total Evaporation, and Average Air Temperature); (<b>e</b>) 2016–2020, Monthly Average Water Surface Areas, Precipitation, Evaporation and Temperature.</p> Full article ">Figure 4
<p>Kızılırmak Delta: (<b>a</b>) Location and 1985 and 2020 Annual Average Water Surface Areas (NDWI &gt; 0); (<b>b</b>) View of Kızılırmak Delta [<a href="#B42-ijgi-11-00046" class="html-bibr">42</a>]; (<b>c</b>) 1985-2020 Period, Water Surface Areas (Annual Averages and Dry Season Averages); (<b>d</b>) Samsun Meteorological Station Data (Annual Total Precipitation, Total Evaporation, and Average Air Temperature); (<b>e</b>) 2016–2020, Monthly Average Water Surface Areas, Precipitation, Evaporation and Temperature.</p> Full article ">Figure 5
<p>Akyatan Lagoon: (<b>a</b>) Location and 1985 and 2020 Annual Average Water Surface Areas (NDWI ˃ 0); (<b>b</b>) View of Akyatan Lagoon [<a href="#B47-ijgi-11-00046" class="html-bibr">47</a>]; (<b>c</b>)1985–2020 Period, Water Surface Areas (Annual Averages and Dry Season Averages); (<b>d</b>) Mersin Meteorological Station Data (Annual Total Precipitation, Total Evaporation, and Average Air Temperature); (<b>e</b>) 2016–2020, Monthly Average Water Surface Areas, Precipitation, Evaporation and Temperature.</p> Full article ">Figure 6
<p>Gediz Delta: (<b>a</b>) Delta Location and 1985 And 2020 Annual Average Water Surface Areas (NDWI &gt; 0); (<b>b</b>) View of Delta [<a href="#B52-ijgi-11-00046" class="html-bibr">52</a>]. (<b>c</b>) 1985–2020 Period, Water Surface Areas (Annual Averages and Dry Season Averages); (<b>d</b>) Izmir Meteorological Station Data (Annual Total Precipitation, Total Evaporation, and Average Air Temperature); (<b>e</b>) 2016–2020, Monthly Average Water Surface Areas, Precipitation, Evaporation and Temperature.</p> Full article ">Figure 7
<p>Yumurtalık Lagoons: (<b>a</b>) Lagoons’ Location and 1985 And 2020 Annual Average Water Surface Areas (NDWI &gt; 0); (<b>b</b>) View of Lagoons [<a href="#B54-ijgi-11-00046" class="html-bibr">54</a>]; (<b>c</b>) 1985–2020 Period, Water Surface Areas (Annual Averages and Dry Season Averages); (<b>d</b>) Adana Meteorological Station Data (Annual Total Precipitation, Total Evaporation, and Average Air Temperature); (<b>e</b>) 2016–2020, Monthly Average Water Surface Areas, Precipitation, Evaporation and Temperature.</p> Full article ">Figure 8
<p>(<b>a</b>) Water Surface Areas (Annual and Dry Season Average) at Turkey’s Coastal Ramsar Sites in 1985 and 2020 (<b>b</b>) % changes.</p> Full article ">
17 pages, 4852 KiB  
Article
Improvement of Oracle Bone Inscription Recognition Accuracy: A Deep Learning Perspective
by Xuanming Fu, Zhengfeng Yang, Zhenbing Zeng, Yidan Zhang and Qianting Zhou
ISPRS Int. J. Geo-Inf. 2022, 11(1), 45; https://doi.org/10.3390/ijgi11010045 - 9 Jan 2022
Cited by 14 | Viewed by 7729
Abstract
Deep learning techniques have been successfully applied in handwriting recognition. Oracle bone inscriptions (OBI) are the earliest hieroglyphs in China and valuable resources for studying the etymology of Chinese characters. OBI are of important historical and cultural value in China; thus, textual research [...] Read more.
Deep learning techniques have been successfully applied in handwriting recognition. Oracle bone inscriptions (OBI) are the earliest hieroglyphs in China and valuable resources for studying the etymology of Chinese characters. OBI are of important historical and cultural value in China; thus, textual research surrounding the characters of OBI is a huge challenge for archaeologists. In this work, we built a dataset named OBI-100, which contains 100 classes of oracle bone inscriptions collected from two OBI dictionaries. The dataset includes more than 128,000 character samples related to the natural environment, humans, animals, plants, etc. In addition, we propose improved models based on three typical deep convolutional network structures to recognize the OBI-100 dataset. By modifying the parameters, adjusting the network structures, and adopting optimization strategies, we demonstrate experimentally that these models perform fairly well in OBI recognition. For the 100-category OBI classification task, the optimal model achieves an accuracy of 99.5%, which shows competitive performance compared with other state-of-the-art approaches. We hope that this work can provide a valuable tool for character recognition of OBI. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Cultural Heritage)
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<p>The abdominal parts of two tortoise shells with divinatory inscriptions excavated at the site of Yinxu, Anyang, Henan, China.</p> Full article ">Figure 2
<p>Examples of OBI characters. (<b>a</b>) Examples of characters of OBI corresponding to eight commonly used words. (<b>b</b>) Eight characters of OBI that have different meanings, but look very similar. (<b>c</b>) Eight writing styles of <span class="html-italic">monkey</span> in OBI.</p> Full article ">Figure 3
<p>The preprocessing process of OBI character “<span class="html-italic">monkey</span>”.</p> Full article ">Figure 4
<p>Examples of OBI-100 dataset.</p> Full article ">Figure 5
<p>An instance of data augmentation.</p> Full article ">Figure 6
<p>The number of samples in each category of the augmented OBI-100 dataset.</p> Full article ">Figure 7
<p>The basic structure of CNN.</p> Full article ">Figure 8
<p>Training accuracy, validation accuracy, and training loss during L2 model training. (<b>a</b>) Accuracy comparison. (<b>b</b>) Cross loss.</p> Full article ">Figure 9
<p>Training accuracy, validation accuracy and training loss during A3 model training. (<b>a</b>) Accuracy comparison. (<b>b</b>) Cross loss.</p> Full article ">Figure 10
<p>Training accuracy, validation accuracy, and training loss during V16 model training. (<b>a</b>) Accuracy comparison. (<b>b</b>) Cross loss.</p> Full article ">Figure 11
<p>Surface plot on the L2 model displaying the resulting validation recognition accuracy for different choices of values of the batch size and the dropout probability.</p> Full article ">Figure 12
<p>Surface plot on the A3 model displaying the resulting validation recognition accuracy for different choices of values of the batch size and the dropout probability.</p> Full article ">Figure 13
<p>Surface plot on the V16 model displaying the resulting validation recognition accuracy for different choices of values of the batch size and the dropout probability.</p> Full article ">
14 pages, 3069 KiB  
Article
End-to-End Pedestrian Trajectory Forecasting with Transformer Network
by Hai-Yan Yao, Wang-Gen Wan and Xiang Li
ISPRS Int. J. Geo-Inf. 2022, 11(1), 44; https://doi.org/10.3390/ijgi11010044 - 9 Jan 2022
Cited by 17 | Viewed by 4018
Abstract
Analysis of pedestrians’ motion is important to real-world applications in public scenes. Due to the complex temporal and spatial factors, trajectory prediction is a challenging task. With the development of attention mechanism recently, transformer network has been successfully applied in natural language processing, [...] Read more.
Analysis of pedestrians’ motion is important to real-world applications in public scenes. Due to the complex temporal and spatial factors, trajectory prediction is a challenging task. With the development of attention mechanism recently, transformer network has been successfully applied in natural language processing, computer vision, and audio processing. We propose an end-to-end transformer network embedded with random deviation queries for pedestrian trajectory forecasting. The self-correcting scheme can enhance the robustness of the network. Moreover, we present a co-training strategy to improve the training effect. The whole scheme is trained collaboratively by the original loss and classification loss. Therefore, we also achieve more accurate prediction results. Experimental results on several datasets indicate the validity and robustness of the network. We achieve the best performance in individual forecasting and comparable results in social forecasting. Encouragingly, our approach achieves a new state of the art on the Hotel and Zara2 datasets compared with the social-based and individual-based approaches. Full article
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<p>(<b>a</b>) The architecture of the overall framework of the proposed approach for pedestrian trajectory prediction. The framework is a transformer-based network assisted with random deviation queries and a classify branch to enhance performance. The observed pedestrian positions are fed to the network, and the network predicts the future trajectory. (<b>b</b>) The detail information of the encoder and decoder in the transformer.</p> Full article ">Figure 2
<p>Qualitative comparison between the proposed method with TF predicting trajectories. The results are shown on Zara1 dataset. The first 3 rows show examples where the proposed method successfully predicts the trajectories with small errors. The last row shows some suboptimal cases, e.g., a person took a linear path. Even so, the proposed method predicts a plausible path.</p> Full article ">Figure 3
<p>Results comparison of average ADE (<b>left</b>) and FDE (<b>right</b>) on different numbers of layers <math display="inline"><semantics> <msub> <mi>N</mi> <mi>b</mi> </msub> </semantics></math>. With the increase of blocks, ADE and FDE gradually decreased. ADE and FDE tend to be stable when <math display="inline"><semantics> <msub> <mi>N</mi> <mi>b</mi> </msub> </semantics></math> increases to 6. Thus, considering the balance between performance and computation, the number of blocks layers is set to 6 in the modular transformer.</p> Full article ">Figure 4
<p>Results comparison of average ADE (<b>left</b>) and FDE (<b>right</b>) on different values of the key parameter <math display="inline"><semantics> <mi>λ</mi> </semantics></math>. With the increase of <math display="inline"><semantics> <mi>λ</mi> </semantics></math>, the performance keeps getting better steadily until up to 50 when starts to deteriorate. <math display="inline"><semantics> <mi>λ</mi> </semantics></math> is set as 50, which is often sufficient to achieve very good results on pedestrian forecasting.</p> Full article ">Figure 5
<p>Results comparison of average ADE (<b>left</b>) and FDE (<b>right</b>) on different accuracy discrimination distance <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>D</mi> <mi>D</mi> </mrow> </semantics></math>. When <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>D</mi> <mi>D</mi> </mrow> </semantics></math> is 0.3, the network can achieve better performance, so <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>D</mi> <mi>D</mi> </mrow> </semantics></math> is set to 0.3.</p> Full article ">
19 pages, 2404 KiB  
Article
Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations
by Calimanut-Ionut Cira, Martin Kada, Miguel-Ángel Manso-Callejo, Ramón Alcarria and Borja Bordel Sanchez
ISPRS Int. J. Geo-Inf. 2022, 11(1), 43; https://doi.org/10.3390/ijgi11010043 - 9 Jan 2022
Cited by 18 | Viewed by 3458
Abstract
The road surface area extraction task is generally carried out via semantic segmentation over remotely-sensed imagery. However, this supervised learning task is often costly as it requires remote sensing images labelled at the pixel level, and the results are not always satisfactory (presence [...] Read more.
The road surface area extraction task is generally carried out via semantic segmentation over remotely-sensed imagery. However, this supervised learning task is often costly as it requires remote sensing images labelled at the pixel level, and the results are not always satisfactory (presence of discontinuities, overlooked connection points, or isolated road segments). On the other hand, unsupervised learning does not require labelled data and can be employed for post-processing the geometries of geospatial objects extracted via semantic segmentation. In this work, we implement a conditional Generative Adversarial Network to reconstruct road geometries via deep inpainting procedures on a new dataset containing unlabelled road samples from challenging areas present in official cartographic support from Spain. The goal is to improve the initial road representations obtained with semantic segmentation models via generative learning. The performance of the model was evaluated on unseen data by conducting a metrical comparison where a maximum Intersection over Union (IoU) score improvement of 1.3% was observed when compared to the initial semantic segmentation result. Next, we evaluated the appropriateness of applying unsupervised generative learning using a qualitative perceptual validation to identify the strengths and weaknesses of the proposed method in very complex scenarios and gain a better intuition of the model’s behaviour when performing large-scale post-processing with generative learning and deep inpainting procedures and observed important improvements in the generated data. Full article
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
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<p>An example of inpainting post-processing with morphology operators based on shapes to fill missing parts (<b>c</b>) of the initial segmentation mask predictions (<b>b</b>) delivered by the semantic segmentation model after evaluating an unseen aerial orthoimage (<b>a</b>).</p> Full article ">Figure 2
<p>The relation between the aerial orthoimage (first row, (<b>a1</b>–<b>a10</b>)), the rasterised segmentation mask (ground-truth or real sample, seen in the second row, (<b>b1</b>–<b>b10</b>) used as conditional information for training <math display="inline"><semantics> <mi>G</mi> </semantics></math>), and the semantic segmentation predictions (seen in the third row, (<b>c1</b>–<b>c10</b>), used for testing the performance of the model). Note: The training set contains <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>6784</mn> </mrow> </semantics></math> tiles with road representation present in official cartography, while the test set contains <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1696</mn> </mrow> </semantics></math> tiles with initial segmentation predictions resulted from evaluating the aerial images with the segmentation model. In this figure, white is used to represent pixels labelled with “No road”, or “Background”, and black is used to represent the pixels belonging to “Road” class.</p> Full article ">Figure 3
<p>The generator architecture proposed for the deep inpainting operation.</p> Full article ">Figure 4
<p>The discriminator architecture proposed for the deep inpainting task.</p> Full article ">Figure 5
<p>An overview of the learning process of the cGAN model trained for deep inpainting. (<b>1</b>) Firstly, random gaps are introduced into the conditional data, <math display="inline"><semantics> <mi>y</mi> </semantics></math>, to produce corrupted inputs for <math display="inline"><semantics> <mi>G</mi> </semantics></math>. (<b>2</b>) The generator (a U-Net-like network with skip connections) is then trained to fill the gaps and inpaint the corrupted tiles. (<math display="inline"><semantics> <mi>G</mi> </semantics></math> does not have access to the real samples, <math display="inline"><semantics> <mi>y</mi> </semantics></math>, from the real data distribution, <math display="inline"><semantics> <mi>Y</mi> </semantics></math>.) (<b>3</b>) The discriminator is a modified PatchGAN that classifies patches from pairs of <span class="html-italic">y</span> and <math display="inline"><semantics> <mover accent="true"> <mi>y</mi> <mo>˜</mo> </mover> </semantics></math> and decides whether they come from the real data distribution, <math display="inline"><semantics> <mi>Y</mi> </semantics></math>, or from the synthetic data distribution, <math display="inline"><semantics> <mover accent="true"> <mi>Y</mi> <mo>˜</mo> </mover> </semantics></math>. (<b>4</b>) <math display="inline"><semantics> <mi>G</mi> </semantics></math> receives feedback from <math display="inline"><semantics> <mi>D</mi> </semantics></math> and iteratively improves the synthetic data generator to “fool” the discriminator network. Notes: (A) The real data is fed both into <math display="inline"><semantics> <mi>G</mi> </semantics></math> (after adding <span class="html-italic">z</span>) and into <math display="inline"><semantics> <mi>D</mi> </semantics></math>. In our deep inpainting task, a sampled image, <math display="inline"><semantics> <mi>y</mi> </semantics></math>, will be corrupted with randomness, <math display="inline"><semantics> <mi>z</mi> </semantics></math> (in this case, random gaps of different sizes). <math display="inline"><semantics> <mrow> <mi>G</mi> <mo> </mo> </mrow> </semantics></math> will reconstruct this corrupted image and produce <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>y</mi> <mo>˜</mo> </mover> <mo>=</mo> <mi>G</mi> <mo stretchy="false">(</mo> <mi>z</mi> <mo>|</mo> <mi>y</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>. The synthetic results, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>y</mi> <mo>˜</mo> </mover> <mo>,</mo> <mo> </mo> </mrow> </semantics></math> will iteratively improve as <math display="inline"><semantics> <mi>G</mi> </semantics></math> receives feedback from <math display="inline"><semantics> <mi>D</mi> </semantics></math>. (B) The graphic should be interpreted at stage level and was created using random tiles to offer insights and enable a better understanding of the training procedure presented in <a href="#sec5dot3-ijgi-11-00043" class="html-sec">Section 5.3</a>.</p> Full article ">Figure 6
<p>The confusion matrices obtained by (<b>a</b>) the semantic segmentation model U-Net [<a href="#B13-ijgi-11-00043" class="html-bibr">13</a>]—SEResNeXt50 [<a href="#B16-ijgi-11-00043" class="html-bibr">16</a>], and (<b>b</b>) Thin-structure-inpainting [<a href="#B15-ijgi-11-00043" class="html-bibr">15</a>], together with (<b>c</b>) our implementation proposed in <a href="#sec5-ijgi-11-00043" class="html-sec">Section 5</a> (trained for deep inpainting operations) on the test set (<math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1696</mn> </mrow> </semantics></math> tiles).</p> Full article ">Figure 7
<p>Qualitative interpretation carried out on ten samples from the test set. In the first row (<b>a1</b>–<b>a10</b>), we have the aerial orthoimage. The second row (<b>b1</b>–<b>b10</b>) presents the samples from the rasterised ground truth set, or conditional data distribution (road representations present in official cartography). The third row (<b>c1</b>–<b>c10</b>) shows the initial segmentation prediction obtained using a state-of-the-art semantic segmentation model. The fourth row (<b>d1</b>–<b>d10</b>) presents the predictions generated with the Thin-Structure-Inpainting model [<a href="#B15-ijgi-11-00043" class="html-bibr">15</a>] trained for deep inpainting operations, while the fifth row (<b>e1</b>–<b>e10</b>) presents the reconstructed road masks generated with the conditional generative model proposed in this paper.</p> Full article ">
21 pages, 4921 KiB  
Article
Understanding the Spatiotemporal Variation of High-Efficiency Ride-Hailing Orders: A Case Study of Haikou, China
by Mingyang Du, Xuefeng Li, Mei-Po Kwan, Jingzong Yang and Qiyang Liu
ISPRS Int. J. Geo-Inf. 2022, 11(1), 42; https://doi.org/10.3390/ijgi11010042 - 9 Jan 2022
Cited by 5 | Viewed by 2743
Abstract
Understanding the spatiotemporal variation of high-efficiency ride-hailing orders (HROs) is helpful for transportation network companies (TNCs) to balance the income of drivers through reasonable order dispatch, and to alleviate the imbalance between supply and demand by improving the pricing mechanism, so as to [...] Read more.
Understanding the spatiotemporal variation of high-efficiency ride-hailing orders (HROs) is helpful for transportation network companies (TNCs) to balance the income of drivers through reasonable order dispatch, and to alleviate the imbalance between supply and demand by improving the pricing mechanism, so as to promote the sustainable and healthy development of the ride-hailing industry and urban transportation. From the perspective of TNCs for order management, this study investigates the spatiotemporal variation of HROs and common ride-hailing orders (CROs) for ride-hailing services using the trip data of Didi Chuxing in Haikou, China. Ordinary least squares (OLS) and geographically weighted regression (GWR) models are established to examine the factors that affect the densities of HROs and CROs during different time periods, such as morning, evening, afternoon and night, with considering various built environment variables. The OLS models show that factors including road density, average travel time rate, companies and enterprises and transportation facilities have significant impacts on HROs and CROs for most periods. The results of the GWR models are consistent with the global regression results and show the local effects of the built environment on HROs and CROs in different regions. Full article
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<p>The study area (<b>a</b>) and grid cells (<b>b</b>).</p> Full article ">Figure 2
<p>Hourly average travel demand of HROs and CROs (weekdays).</p> Full article ">Figure 3
<p>Hourly average travel demand of HROs and CROs (weekends).</p> Full article ">Figure 4
<p>Distribution of HROs (<b>a</b>) and CROs (<b>b</b>) on weekdays.</p> Full article ">Figure 5
<p>Distribution of HROs (<b>a</b>) and CROs (<b>b</b>) on weekends.</p> Full article ">Figure 6
<p>Coefficient estimates of secondary roads density.</p> Full article ">Figure 7
<p>Coefficient estimates of average travel time rate.</p> Full article ">Figure 8
<p>Coefficient estimates of transportation facilities.</p> Full article ">Figure 9
<p>Coefficient estimates of tourist attractions.</p> Full article ">
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