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Volume 12
Issue 7
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ISPRS Int. J. Geo-Inf., Volume 12, Issue 7 (July 2023) – 46 articles

Cover Story (view full-size image): ChatGPT drew a mental map according to the following information: “The campus is in a rectangular shape and its size is 2 km by 1 km. Inside the campus, the library sits at the very center. The student center is inside the campus and is located near the campus's southern boundary. The college is about a 5-minute walk west of the student center. A square-shape parking lot is located at the top-left corner of the campus, whose size (area) is about 1/8 of the campus. There are nine parking meters regularly distributed inside the parking lot, e.g., three rows by three columns. There is an EV charger next to each meter. The rectangular sports complex is at the bottom-right corner of the campus, whose size is 800 m by 400 m. The sports complex is divided into 4 fields: tennis, basketball, soccer and baseball. This gives rise to the question: how can we draw the above features in a map?” View this paper
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15 pages, 8343 KiB  
Article
The Impacts of Public Schools on Housing Prices of Residential Properties: A Case Study of Greater Sydney, Australia
by Yi Lu, Vivien Shi and Christopher James Pettit
ISPRS Int. J. Geo-Inf. 2023, 12(7), 298; https://doi.org/10.3390/ijgi12070298 - 24 Jul 2023
Cited by 2 | Viewed by 2979
Abstract
Residential property values are influenced by a combination of physical, socio-economic and neighbourhood factors. This study investigated the influence of public schools on residential property prices. Relatively few existing models have taken the spatial heterogeneity of different submarkets into account. To fill this [...] Read more.
Residential property values are influenced by a combination of physical, socio-economic and neighbourhood factors. This study investigated the influence of public schools on residential property prices. Relatively few existing models have taken the spatial heterogeneity of different submarkets into account. To fill this gap, three types of valuation models were applied to sales data from both non-strata and strata properties, and how the proximity and quality of public schools have influenced the prices of different residential property types was examined. The findings demonstrate that an increase of one unit in the normalised NAPLAN score of primary and high schools will lead to a 3.9% and 1.4%, 2.7% and 2.8% rise in housing prices for non-strata and strata properties, respectively. It is also indicated that the application of geographically weighted regression (GWR) can better capture the varying effects of schools across space. Moreover, properties located in the catchment of high-scoring schools in northern Greater Sydney are consistently the most influenced by school quality, regardless of the property type. These findings contribute to a comprehensive understanding of the relationships between public schools and the various submarkets of Greater Sydney. This is valuable for the decision-making processes of home buyers, developers and policymakers. Full article
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<p>Mean housing prices of residential properties in Greater Sydney, 2011–2020 (Data source: Australian Property Monitors).</p> Full article ">Figure 2
<p>General framework of proposed models (OLS–Ordinary least square regression, SLR–Spatial lag regression, GWR–Geographically weighted regression).</p> Full article ">Figure 3
<p>Location of study region and density of housing transaction (Data source: Australian Property Monitors).</p> Full article ">Figure 4
<p>Workflow for data processing.</p> Full article ">Figure 5
<p>Locations and quality of public schools.</p> Full article ">Figure 6
<p>Coefficient distribution of school proximity variables. Variables ‘L_Pri_Sch’ and ‘L_High_Sch’ represent the log of distance to the public primary and high schools of the school catchments. The green (negative) values mean higher prices nearer to the school.</p> Full article ">Figure 7
<p>Coefficient distribution of school quality variables. Variables ‘Prim_Ndom’ and ‘High_Ndom’ represent the normalised NAPLAN results for public primary and high school catchments. The green (positive) values indicate higher prices in high-quality school areas.</p> Full article ">
20 pages, 797 KiB  
Article
Next Point-of-Interest Recommendation Based on Joint Mining of Spatial–Temporal and Semantic Sequential Patterns
by Jing Tian, Zilin Zhao and Zhiming Ding
ISPRS Int. J. Geo-Inf. 2023, 12(7), 297; https://doi.org/10.3390/ijgi12070297 - 24 Jul 2023
Cited by 1 | Viewed by 1570
Abstract
With the widespread use of the location-based social networks (LBSNs), the next point-of-interest (POI) recommendation has become an essential service, which aims to understand the user’s check-in behavior at the current moment by analyzing and mining the correlations between the user’s check-in behaviors [...] Read more.
With the widespread use of the location-based social networks (LBSNs), the next point-of-interest (POI) recommendation has become an essential service, which aims to understand the user’s check-in behavior at the current moment by analyzing and mining the correlations between the user’s check-in behaviors within his/her historical trajectory and then recommending the POI that the user is most likely to visit at the next time step. However, the user’s check-in trajectory presents extremely irregular sequential patterns, such as spatial–temporal patterns, semantic patterns, etc. Intuitively, the user’s visiting behavior is often accompanied by a certain purpose, which makes the check-in data in LBSNs often have rich semantic activity characteristics. However, existing research mainly focuses on exploring the spatial–temporal sequential patterns and lacks the mining of semantic information within the trajectory, so it is difficult to capture the user’s visiting intention. In this paper, we propose a self-attention- and multi-task-based method, called MSAN, to explore spatial–temporal and semantic sequential patterns simultaneously. Specifically, the MSAN proposes to mine the user’s visiting intention from his/her semantic sequence and uses the user’s visiting intention prediction task as the auxiliary task of the next POI recommendation task. The user’s visiting intention prediction uses hierarchical POI category attributes to describe the user’s visiting intention and designs a hierarchical semantic encoder (HSE) to encode the hierarchical intention features. Moreover, a self-attention-based hierarchical intention-aware module (HIAM) is proposed to mine temporal and hierarchical intention features. The next POI recommendation uses the self-attention-based spatial–temporal-aware module (STAM) to mine the spatial–temporal sequential patterns within the user’s check-in trajectory and fuses this with the hierarchical intention patterns to generate the next POI list. Experiments based on two real datasets verified the effectiveness of the model. Full article
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<p>This is a typical LBSN system (the line weight indicates the user’s check-in frequency, and the greater the weight of the line, the higher the user’s check-in frequency at the POI).</p> Full article ">Figure 2
<p>Hierarchical representation of POI categories.</p> Full article ">Figure 3
<p>The architecture of the proposed MSAN model.</p> Full article ">Figure 4
<p>The structure of the HSE module.</p> Full article ">Figure 5
<p>The structure of hierarchical intention-aware module.</p> Full article ">Figure 6
<p>The structure of interval aware encoder.</p> Full article ">Figure 7
<p>Performance comparison of different variants of the models.</p> Full article ">Figure 8
<p>Effect of parameter <math display="inline"><semantics><mi>λ</mi></semantics></math>.</p> Full article ">Figure 9
<p>Effect of embedding dimension <span class="html-italic">d</span> of MSAN model.</p> Full article ">Figure 10
<p>The training loss of intention prediction and POI prediction task.</p> Full article ">Figure 11
<p>Interpretability analysis of MSAN.</p> Full article ">
21 pages, 598 KiB  
Article
Enhancing Chinese Address Parsing in Low-Resource Scenarios through In-Context Learning
by Guangming Ling, Xiaofeng Mu, Chao Wang and Aiping Xu
ISPRS Int. J. Geo-Inf. 2023, 12(7), 296; https://doi.org/10.3390/ijgi12070296 - 22 Jul 2023
Cited by 2 | Viewed by 1574
Abstract
Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their inherent ambiguity. Additionally, different task scenarios require varying levels of granularity in address components, further [...] Read more.
Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their inherent ambiguity. Additionally, different task scenarios require varying levels of granularity in address components, further complicating the parsing process. To address these challenges and adapt to low-resource environments, we propose CapICL, a novel Chinese address parsing model based on the In-Context Learning (ICL) framework. CapICL leverages a sequence generator, regular expression matching, BERT semantic similarity computation, and Generative Pre-trained Transformer (GPT) modeling to enhance parsing accuracy by incorporating contextual information. We construct the sequence generator using a small annotated dataset, capturing distribution patterns and boundary features of address types to model address structure and semantics, which mitigates interference from unnecessary variations. We introduce the REB–KNN algorithm, which selects similar samples for ICL-based parsing using regular expression matching and BERT semantic similarity computation. The selected samples, raw text, and explanatory text are combined to form prompts and inputted into the GPT model for prediction and address parsing. Experimental results demonstrate significant achievements of CapICL in low-resource environments, reducing dependency on annotated data and computational resources. Our model’s effectiveness, adaptability, and broad application potential are validated, showcasing its positive impact in natural language processing and geographical information systems. Full article
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<p>Model overview. The generator takes the raw text as input and generates a regular expression sequence and a label word sequence. Subsequently, the REB–KNN algorithm selects a similar annotated sample based on the regular expression sequence and label word sequence. Then, the prompt generator constructs prompts corresponding to the selected sample. The generated prompts are fed into the Generative Pre-trained Transformer (GPT) model for prediction, and the address parsing result is extracted from the model’s output.</p> Full article ">Figure 2
<p>CapICL architecture. The sequence generator captures the distribution patterns and boundary features of address types in the raw text, generating regular expression and label word sequences. The REB–KNN algorithm performs regular expression matching and BERT-based semantic similarity computation on these two sequences, selecting annotated samples that are similar to the query (raw). The specific structure of the Prompt template is illustrated in the top right corner of the figure, while the instruction provides a brief description of the dataset.</p> Full article ">Figure 3
<p>Trie data structure for representing INF (detailed information such as floor and room numbers) address components. The trie is constructed by reversing the address segmentation and captures the specific characteristics of the addressed entities. The root node provides overall information about the corresponding type, including the capacity (maximum number of sub-tree root nodes, e.g., “building”), the threshold (minimum score for selecting information when constructing regular expressions), and the maximum and minimum lengths defining the length range of entities for the current type. The sub-trees below the root node represent the segmented parts of the address, while all nodes follow the same structure, describing the features of the represented string (e.g., “building number”). Additionally, each node maintains a left/right 1-neighboring list, which contains the neighboring strings. The overall score represents the normalized frequency of the string occurrence in the dataset.</p> Full article ">Figure 4
<p>Directed Acyclic Graph (DAG) representing Chinese address component types. Nodes represent types of address components, and edges indicate the transition probability between types. The weight <span class="html-italic">W</span> of an edge represents the likelihood of transitioning from the current node to the next. The subscript of <span class="html-italic">W</span> is formed by connecting types with “-”. If there is a previous node, it is included. The symbols <span>$</span> and # represent the beginning and end of named entities, respectively.</p> Full article ">Figure 5
<p>Illustration of the Arbitrary Granularity Segmentation Process for Raw Text. Firstly, the address component type and transition probabilities are obtained from the directed acyclic graph (DAG) based on the current state. At the same time, length information is retrieved from the trie. Next, the corresponding regular expression set (RES) is obtained from SORES and matched against the text. Then, an 8-dimensional vector is constructed by combining the type information, transition probabilities, length information, matched start and end positions, regular expression scores, match length, and numerical indicators. This vector is used as input to the binary classifier. By predicting the positive label, the validity of the segmentation is determined. Finally, the segmentation result is obtained.</p> Full article ">Figure 6
<p>Impact of K on model performance. Other experimental settings remain the same as in <a href="#ijgi-12-00296-t004" class="html-table">Table 4</a>.</p> Full article ">Figure 7
<p>Comparison of model performance based on randomly sampled annotated datasets of different sizes. Each sample size was randomly sampled six times, and independent model evaluations were performed for each annotated dataset.</p> Full article ">
21 pages, 8437 KiB  
Article
A New Algorithm for Large-Scale Geographically Weighted Regression with K-Nearest Neighbors
by Xiaoyue Yang, Yi Yang, Shenghua Xu, Jiakuan Han, Zhengyuan Chai and Gang Yang
ISPRS Int. J. Geo-Inf. 2023, 12(7), 295; https://doi.org/10.3390/ijgi12070295 - 21 Jul 2023
Cited by 3 | Viewed by 2338
Abstract
Geographically weighted regression (GWR) is a classical method for estimating nonstationary relationships. Notwithstanding the great potential of the model for processing geographic data, its large-scale application still faces the challenge of high computational costs. To solve this problem, we proposed a computationally efficient [...] Read more.
Geographically weighted regression (GWR) is a classical method for estimating nonstationary relationships. Notwithstanding the great potential of the model for processing geographic data, its large-scale application still faces the challenge of high computational costs. To solve this problem, we proposed a computationally efficient GWR method, called K-Nearest Neighbors Geographically weighted regression (KNN-GWR). First, it utilizes a k-dimensional tree (KD tree) strategy to improve the speed of finding observations around the regression points, and, to optimize the memory complexity, the submatrices of neighbors are extracted from the matrix of the sample dataset. Next, the optimal bandwidth is found by referring to the spatial clustering relationship explained by K-means. Finally, the performance and accuracy of the proposed KNN-GWR method was evaluated using a simulated dataset and a Chinese house price dataset. The results demonstrated that the KNN-GWR method achieved computational efficiency thousands of times faster than existing GWR algorithms, while ensuring accuracy and significantly improving memory optimization. To the best of our knowledge, this method was able to run hundreds of thousands or millions of data on a standard computer, which can inform improvement in the efficiency of local regression models. Full article
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<p>Flow chart of K-nearest neighbor geographically weighted regression. (<b>a</b>) Optimal bandwidth selection reference k-means. (<b>b</b>) KD Tree construction and search. (<b>c</b>) Restructured <math display="inline"><semantics><mrow><msub><mrow><mover accent="true"><mrow><mi>W</mi></mrow><mo stretchy="false">~</mo></mover></mrow><mrow><mi>i</mi></mrow></msub></mrow></semantics></math>, <math display="inline"><semantics><mrow><msub><mrow><mover accent="true"><mrow><mi>X</mi></mrow><mo stretchy="false">~</mo></mover></mrow><mrow><mi>i</mi></mrow></msub></mrow></semantics></math>, <math display="inline"><semantics><mrow><msub><mrow><mover accent="true"><mrow><mi>Y</mi></mrow><mo stretchy="false">~</mo></mover></mrow><mrow><mi>i</mi></mrow></msub></mrow></semantics></math>. (<b>d</b>) KNN-GWR in calibration.</p> Full article ">Figure 2
<p>An example of finding observations within a certain range around the regression point. 1, 2, 3, and 4 are points within the bandwidth distance from point <span class="html-italic">i</span>, and 5, 6, 7, 8, and 9 are points outside the bandwidth distance from point <span class="html-italic">i</span>.</p> Full article ">Figure 3
<p>Study area. (<b>a</b>) Geographical location of each province of the study area in China. (<b>b</b>) Distribution of house price dataset in the study area.</p> Full article ">Figure 4
<p>Details of each independent variable (<b>a</b>) Map of NBeds parameter estimates for the predictor variables. (<b>b</b>) Map of NBaths parameter estimation for predictor variables. (<b>c</b>) Map of Area parameter estimates for the predictor variables. (<b>d</b>) Map of Age parameter estimation for the predictor variables.</p> Full article ">Figure 5
<p>Estimated regression coefficients <math display="inline"><semantics><mrow><mi>β</mi></mrow></semantics></math> of KNN-GWR method on simulated dataset.</p> Full article ">Figure 6
<p>Comparison of the computational speeds of four software packages(KNN-GWR, MGWR (PySAL), GWmodel, and Spgwr) in simulated dataset experiments.</p> Full article ">Figure 7
<p>Comparison of KNN-GWR with other GWR packages in terms of runtime increase ratio (tested using simulated dataset).</p> Full article ">Figure 8
<p>Space mapping for: (<b>a</b>) number of bedrooms in the house, (<b>b</b>) number of bathrooms, (<b>c</b>) area of the house, and (<b>d</b>) age of the house, by KNN-GWR modeling.</p> Full article ">Figure 9
<p>Comparison of the computational speeds of four software packages (KNN-GWR, MGWR(PySAL), GWmodel, and Spgwr) in the house price dataset in China.</p> Full article ">Figure 10
<p>Comparison of KNN-GWR with other GWR packages in terms of runtime increase ratio (tested using house price dataset in China).</p> Full article ">
21 pages, 6967 KiB  
Article
Lossless Watermarking Algorithm for Geographic Point Cloud Data Based on Vertical Stability
by Mingyang Zhang, Jian Dong, Na Ren and Shuitao Guo
ISPRS Int. J. Geo-Inf. 2023, 12(7), 294; https://doi.org/10.3390/ijgi12070294 - 21 Jul 2023
Cited by 2 | Viewed by 1477
Abstract
With the increasing demand for high-precision and difficult-to-obtain geospatial point cloud data copyright protection in military, scientific research, and other fields, research on lossless watermarking is receiving more and more attention. However, most of the current geospatial point cloud data watermarking algorithms embed [...] Read more.
With the increasing demand for high-precision and difficult-to-obtain geospatial point cloud data copyright protection in military, scientific research, and other fields, research on lossless watermarking is receiving more and more attention. However, most of the current geospatial point cloud data watermarking algorithms embed copyright information by modifying vertex coordinate values, which not only damages the data accuracy and quality but may also cause incalculable losses to data users. To maintain data fidelity and protect its copyright, in this paper, we propose a lossless embedded watermarking algorithm based on vertical stability. First, the watermark information is generated based on the binary encoding of the copyright information and the code of the traceability information. Second, the watermark index is calculated based on the length of the watermark information after compression and the vertical distribution characteristics of the data. Finally, watermark embedding is completed by modifying the relative storage order of the corresponding data according to the index and watermark value. The experimental results show that the proposed algorithm has good invisibility without damaging the data accuracy. In addition, compared with existing algorithms, this method has a higher robustness under operations such as projection transformation, precision perturbation, and vertex deletion of geospatial point cloud data. Full article
(This article belongs to the Special Issue Trustful and Ethical Use of Geospatial Data)
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<p>Changes in mountain peak vertices.</p> Full article ">Figure 2
<p>The relative storage order of the data after deleting or adding: (<b>a</b>) Deleting a data point (<b>b</b>) Adding a data point.</p> Full article ">Figure 3
<p>Demonstration of watermark embedding: (<b>a</b>) When the watermark bit is 0; (<b>b</b>) When the watermark bit is 1.</p> Full article ">Figure 4
<p>Watermark-embedding process.</p> Full article ">Figure 5
<p>Process of determining the longest sequence order.</p> Full article ">Figure 6
<p>Watermark-extraction process.</p> Full article ">Figure 7
<p>Experimental data: (<b>a</b>) Seafloor topography data. (<b>b</b>) Land topography data.</p> Full article ">Figure 8
<p>RST Attack result: (<b>a</b>) Rotation attack. (<b>b</b>) Scaling attack. (<b>c</b>) Translation attack.</p> Full article ">Figure 9
<p>Precision perturbation attack results.</p> Full article ">Figure 10
<p>The results of the projection attacks.</p> Full article ">Figure 11
<p>Random deletion experiments: (<b>a</b>) Original seafloor topography data. (<b>b</b>) A 25% deletion result. (<b>c</b>) A 50% deletion result.</p> Full article ">Figure 12
<p>Random deletion experiments: (<b>a</b>) Original land topography data. (<b>b</b>) A 25% deletion result. (<b>c</b>) A 50% deletion result.</p> Full article ">Figure 13
<p>The results of random deletion attack.</p> Full article ">Figure 14
<p>Experimental data: (<b>a</b>) ENC data (<b>b</b>) Three-dimensional illustration of water depth points.</p> Full article ">Figure 15
<p>Experimental data: Large-scale geospatial point cloud dataset.</p> Full article ">
27 pages, 3528 KiB  
Article
Exploring the Impact of Built Environment Factors on the Relationships between Bike Sharing and Public Transportation: A Case Study of New York
by Baohua Wei and Lei Zhu
ISPRS Int. J. Geo-Inf. 2023, 12(7), 293; https://doi.org/10.3390/ijgi12070293 - 20 Jul 2023
Cited by 4 | Viewed by 3539
Abstract
Bike sharing offers a usable form of feeder transportation for connecting to public transportation and effectively meets unmet travel demands, alleviating the pressure on public transportation systems by diverting urban commuters. To advance the comprehension of how the built environment shapes the relationship [...] Read more.
Bike sharing offers a usable form of feeder transportation for connecting to public transportation and effectively meets unmet travel demands, alleviating the pressure on public transportation systems by diverting urban commuters. To advance the comprehension of how the built environment shapes the relationship between bike-sharing systems and public transport modes, we implement a categorization framework that divides bike-sharing data into three distinct patterns: competition, integration, and complementation, based on their coordination with public transportation. The SLM model is employed to investigate the complex correlations between the relationship patterns and four key groups of environmental factors encompassing land use, transportation systems, urban design, and social economy. We find a strong correlation between four groups of environmental factors and three relationship patterns. Furthermore, the built environment variables exhibit significant variations across the three patterns. Users in the competitive mode prefer the flexibility of shared bikes and place a higher value on the sightseeing and leisure benefits. Instead, users in the integration and complementation modes tend to prefer shared bikes to supplement unmet travel demand and place a higher value on commuting benefits. These findings can benefit urban planners seeking to encourage greater diversity in transportation modes and incentivize more commuting. Full article
(This article belongs to the Special Issue Harnessing the Geospatial Data Revolution for Promoting Sustainable Transport Systems)
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<p>Analytical framework.</p> Full article ">Figure 2
<p>The distribution of Citi Bike docking stations and public transportation stations in the study area.</p> Full article ">Figure 3
<p>The spatial distribution and proportion of three travel modes.</p> Full article ">Figure 4
<p>Top 20% grid with high distribution for competition, integration, and complementation and four focus regions named A–D.</p> Full article ">Figure 5
<p>Analysis of the independent variables for three high−distribution modes.</p> Full article ">Figure 6
<p>The LISA cluster map displayed local Moran’s I values in relation to the classified dependent variables.</p> Full article ">
21 pages, 13583 KiB  
Article
An Optimization Method for Equalizing the Spatial Accessibility of Medical Services in Guangzhou
by Mingkai Yu, Yingchun Fu and Wenkai Liu
ISPRS Int. J. Geo-Inf. 2023, 12(7), 292; https://doi.org/10.3390/ijgi12070292 - 20 Jul 2023
Cited by 4 | Viewed by 1700
Abstract
Spatial equality of medical services refers to equal access to medical services in all regions. Currently, research on medical facility planning focuses mainly on efficiency, and less on methods for achieving medical facility access equality. In this study, we propose a medical service [...] Read more.
Spatial equality of medical services refers to equal access to medical services in all regions. Currently, research on medical facility planning focuses mainly on efficiency, and less on methods for achieving medical facility access equality. In this study, we propose a medical service equality optimization method considering facility grade and Gaode actual travel time data. First, we use the maximum coverage location problem (MCLP) model to locate new medical facilities. Then, we incorporate a service capacity weight matrix reflecting medical facility grade into the quadratic programming (QP) model, with the objective of optimizing the bed configuration of each facility to maximize the spatial equality of medical accessibility. By measuring and optimizing medical accessibility in Guangzhou under different travel time thresholds, we analyzed the optimization results of central, peripheral, and edge areas. The results show that (1) the model significantly improves the spatial equality of medical accessibility. After optimization, fewer locations have very low (or low) and very high (or high) accessibility, while more locations have moderate accessibility. When the travel time threshold is 22 min, the number of locations with medium accessibility level increases by about 18.86%. (2) The higher the travel time threshold, the greater is the overall optimization effect. (3) Different regions have different optimization effects and a larger travel time threshold can improve the optimization effect of the peripheral areas more significantly. It is recommended that new medical facilities be built in the peripheral and edge areas, along with improvements to the transport system. Full article
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<p>Research framework diagram.</p> Full article ">Figure 2
<p>Study area. (<b>a</b>) Population density; (<b>b</b>) location of existing medical facilities (TH–Tianhe, YX–Yuexiu, HZ–Haizhu, LW–Liwan, BY–Baiyun, HP–Huangpu, PY–Panyu, HD–Huadu, CH–Conghua, ZC–Zengcheng, NS–Nansha); (<b>c</b>,<b>d</b>) show enlarged views of the same position.</p> Full article ">Figure 3
<p>Population distribution.</p> Full article ">Figure 4
<p>Spatial distribution of accessibility before and after optimization ((<b>a</b>,<b>d</b>): 15 min. (<b>b</b>,<b>e</b>): 19 min. (<b>c</b>,<b>f</b>): 22 min. (<b>a</b>–<b>c</b>) are before optimization and (<b>d</b>–<b>f</b>) are after optimization).</p> Full article ">Figure 5
<p>Change in the percentage of locations with different accessibility levels (BO: before optimization, AO: after optimization in different travel time thresholds of (<b>a</b>) t0 = 15 min, (<b>b</b>) t0 = 19 min, (<b>c</b>) t0 = 22 min).</p> Full article ">Figure 6
<p>Regional statistics of location proportion of different accessibility levels (<b>a</b>,<b>d</b>): t0 = 15 min, (<b>b</b>,<b>e</b>): t0 = 19 min, (<b>c</b>,<b>f</b>): t0 = 22 min) before (BO) and after optimization (AO).</p> Full article ">Figure 7
<p>Change in the proportion of locations with different levels of accessibility at the municipal district scale ((<b>a</b>): t0 = 15 min, (<b>b</b>): t0 = 19 min, (<b>c</b>): t0 = 22 min), and (<b>d</b>): comparison of bed number before (BO) and after optimization (AO) when t0 = 22 min).</p> Full article ">Figure 8
<p>Impact of limiting the adjustment range of the number of beds on the optimization scheme (<b>a</b>,<b>c</b>): unrestricted, (<b>b</b>,<b>e</b>): restricted, (<b>d</b>): comparison of the planned number of beds in the same facility before and after the restriction.</p> Full article ">Figure 9
<p>Medical coverage rate at the municipal district scale under different travel time thresholds.</p> Full article ">
24 pages, 7584 KiB  
Article
Spatiotemporal Predictive Geo-Visualization of Criminal Activity for Application to Real-Time Systems for Crime Deterrence, Prevention and Control
by Mayra Salcedo-Gonzalez, Julio Suarez-Paez, Manuel Esteve and Carlos Enrique Palau
ISPRS Int. J. Geo-Inf. 2023, 12(7), 291; https://doi.org/10.3390/ijgi12070291 - 20 Jul 2023
Cited by 1 | Viewed by 1693
Abstract
This article presents the development of a geo-visualization tool, which provides police officers or any other type of law enforcement officer with the ability to conduct the spatiotemporal predictive geo-visualization of criminal activities in short and continuous time horizons, according to the real [...] Read more.
This article presents the development of a geo-visualization tool, which provides police officers or any other type of law enforcement officer with the ability to conduct the spatiotemporal predictive geo-visualization of criminal activities in short and continuous time horizons, according to the real events that are happening: that is, for those geographical areas, time slots, and dates that are of interest to users, with the ability to consider individual events or groups of events. This work used real data collected by the Colombian National Police (PONAL); it constitutes a tool that is especially effective when applied to Real-Time Systems for crime deterrence, prevention, and control. For its creation, the spatial and temporal correlation of the events is carried out and the following deep learning techniques are employed: CNN-1D (Convolutional Neural Network-1D), MLP (multilayer perceptron), LSTM (long short-term memory), and the classical technique of VAR (vector autoregression), due to its appropriate performance in the multi-step and multi-parallel forecasting of multivariate time series with sparse data. This tool was developed with Open-Source Software (OSS) as it is implemented in the Python programming language with the corresponding machine learning libraries. It can be implemented with any geographic information system (GIS) and used in relation to other types of activities, such as natural disasters or terrorist activities. Full article
(This article belongs to the Special Issue Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems)
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<p>Database format for the development of the spatiotemporal predictive geo-visualization tool.</p> Full article ">Figure 2
<p>(<b>a</b>) Geographic spatial grouping system with a grid; (<b>b</b>) Spatial grouping system on the geographical map of the observation area.</p> Full article ">Figure 3
<p>(<b>a</b>) Example of a multivariate time series—3D; (<b>b</b>) example of a multivariate time series—2D, with a frequency value equal to 10 min.</p> Full article ">Figure 4
<p>A random sample of the representation of the densities of criminal events by sub-areas in a geographical map. Events occurred during a 30 min interval and were measured every 10 min (capture).</p> Full article ">Figure 5
<p>Multi-parallel two-step forecast time horizon for the multivariate time series. Each step is 10 min.</p> Full article ">Figure 6
<p>Required operations of the predictive model for the maximum use of the real data that exist and achieving continuous forecasting with a short time horizon.</p> Full article ">Figure 7
<p>RMSE Baseline Model.</p> Full article ">Figure 8
<p>RMSE Vector Autoregressive Model.</p> Full article ">Figure 9
<p>MLP (multilayer perceptron) solution.</p> Full article ">Figure 10
<p>CNN-1D (Convolutional Neural Network-1D) solution.</p> Full article ">Figure 11
<p>LSTM Univector Output Models, Solution.</p> Full article ">Figure 12
<p>LSTM multivector output models’ solution.</p> Full article ">Figure 13
<p>RMSE of the Long Short-Term Memory models.</p> Full article ">Figure 14
<p>Performance of the models: general summary.</p> Full article ">Figure 15
<p>A random sample of the comparison between the geo-visualization of the real data and the predicted data (capture).</p> Full article ">
16 pages, 4831 KiB  
Article
Black Carbon Concentration Estimation with Mobile-Based Measurements in a Complex Urban Environment
by Minmeng Tang, Tri Dev Acharya and Deb A. Niemeier
ISPRS Int. J. Geo-Inf. 2023, 12(7), 290; https://doi.org/10.3390/ijgi12070290 - 20 Jul 2023
Cited by 5 | Viewed by 2013
Abstract
Black carbon (BC) is a significant source of air pollution since it impacts public health and climate change. Understanding its distribution in the complex urban environment is challenging. We integrated a land use model with four machine learning models to estimate traffic-related BC [...] Read more.
Black carbon (BC) is a significant source of air pollution since it impacts public health and climate change. Understanding its distribution in the complex urban environment is challenging. We integrated a land use model with four machine learning models to estimate traffic-related BC concentrations in Oakland, CA. Random Forest was the best-performing model, with regression coefficient (R2) values of 0.701 on the train set and 0.695 on the validation set with a root mean square error (RMSE) of 0.210 mg/m3. Vehicle speed and local road systems were the most sensitive variables in estimating BC concentrations. However, this approach was inefficient at identifying hyperlocal hotspots, especially in a complex urban environment where highways and truck routes are significant emission sources. Using the land use method to estimate BC concentrations may lead to underestimating some localized hotspots. This work can improve air quality exposure assessment for vulnerable populations and help emphasize potential environmental justice issues. Full article
(This article belongs to the Special Issue Harnessing the Geospatial Data Revolution for Promoting Sustainable Transport Systems)
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<p>General research structure and workflow of the paper.</p> Full article ">Figure 2
<p>Study domain and high-resolution BC concentration map.</p> Full article ">Figure 3
<p>Train set R<sup>2</sup>s of the SVR model based on different feature selection and dimension reduction algorithms.</p> Full article ">Figure 4
<p>Scatter plots between model predicted and measured values for all models.</p> Full article ">Figure 5
<p>Taylor diagram for all four models.</p> Full article ">Figure 6
<p>Spatial distribution of the outliers of each model over the land use types within the study domain (green dots are points less than the 10th percentile, while red dots are points greater than the 90th percentile).</p> Full article ">Figure 7
<p>Spatial distribution of outliers of each model over the local highway systems (green dots are points less than the 10th percentile, while red dots are points greater than the 90th percentile).</p> Full article ">Figure 8
<p>Spatial distribution of outliers of each model over the local truck routes (green dots are points less than the 10th percentile, while red dots are points greater than the 90th percentile).</p> Full article ">Figure 9
<p>How the most sensitive feature influences each model’s performance (dot means mean value, and box shows 25th, 50th, and 75th percentiles).</p> Full article ">
16 pages, 5461 KiB  
Article
Efficient Distortion Mitigation and Partition Reduction in Mapping Global Geodata: Dual Orthogonal Equidistant Cylindrical Projection Approach
by Aleksandar Dimitrijević, Aleksandar Milosavljević and Dejan Rančić
ISPRS Int. J. Geo-Inf. 2023, 12(7), 289; https://doi.org/10.3390/ijgi12070289 - 20 Jul 2023
Cited by 1 | Viewed by 1472
Abstract
The rapid growth in Earth’s global geospatial data necessitates an efficient system for organizing the data, facilitating data fusion from diverse sources, and promoting interoperability. Mapping the spheroidal surface of the planet presents significant challenges as it involves balancing distortion and splitting the [...] Read more.
The rapid growth in Earth’s global geospatial data necessitates an efficient system for organizing the data, facilitating data fusion from diverse sources, and promoting interoperability. Mapping the spheroidal surface of the planet presents significant challenges as it involves balancing distortion and splitting the surface into multiple partitions. The distortion decreases as the number of partitions increases, but, at the same time, the complexity of data processing increases since each partition represents a separate dataset and is defined in its own local coordinate system. In this paper, we propose the Dual Orthogonal Equidistant Cylindrical projection method to mitigate distortion and reduce the number of partitions. Additionally, we use the rotation of the graticule system on the globe to achieve the oblique aspect, which effectively minimizes average angular and areal distortions of Earth’s landmass and reduces the interruption of continental plates caused by partition edges. By incorporating auxiliary latitudes and proposing an approximate authalic latitude, we further enhance the mapping of the ellipsoid onto the sphere, simplifying calculations. The experimental results demonstrate a substantial reduction in distortion and interruption of continental plates. With only two partitions, an average landmass angular distortion of less than 3.56 degrees and an average areal distortion of less than 1.07 were achieved. Full article
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<p>Unfolded regular polyhedra used in GDDGSs: (<b>a</b>) tetrahedron; (<b>b</b>) hexahedron; (<b>c</b>) octahedron; (<b>d</b>) dodecahedron; (<b>e</b>) icosahedron.</p> Full article ">Figure 2
<p>The process of transforming the surface of the planet into an addressable system of cells. The ellipsoid (<b>a</b>) is mapped onto the sphere (<b>b</b>) and the sphere is mapped onto a set of planar surfaces (<b>c</b>). A projection of a part of the planet onto a planar surface is called a partition. Partitions are further divided into smaller units called sections (<b>d</b>).</p> Full article ">Figure 3
<p>Deviations: (<b>a</b>) auxiliary latitudes from geodetic latitude (<span class="html-italic">θ</span> − <span class="html-italic">ϕ</span>, <span class="html-italic">θ</span> − <span class="html-italic">χ</span>, and <span class="html-italic">θ</span> − <span class="html-italic">β</span>); (<b>b</b>) conformal from geocentric (<span class="html-italic">χ</span> − <span class="html-italic">ϕ</span>); (<b>c</b>) approximated authalic from authalic (<span class="html-italic">β</span> − <span class="html-italic">β</span>′).</p> Full article ">Figure 4
<p>Two complementary partitions P0 (<b>a</b>) and P1 (<b>b</b>) of DOEC projection, respectively, in the normal and transverse aspects, and with no overlapping areas. The extent of the land mass and the graticule are displayed.</p> Full article ">Figure 5
<p>The distribution of angular, areal, and aspect distortion for both DOEC partitions for the basic orientation of the graticule system.</p> Full article ">Figure 6
<p>Rasterized world map without Antarctica in LatLon WGS 84 (EPSG:4326) projection used to test land mass distortion.</p> Full article ">Figure 7
<p>Two complementary partitions P0 (<b>a</b>) and P1 (<b>b</b>) of DOEC projection obtained for optimally rotated graticule system (<span class="html-italic">φ<sub>r</sub></span> = 125°, <span class="html-italic">θ<sub>r</sub></span> = 50°, and <span class="html-italic">ρ<sub>r</sub></span> = −15°) to minimize landmass distortions and continental ruptures.</p> Full article ">Figure 8
<p>Graphical comparison of projections whose distortion parameters are listed in <a href="#ijgi-12-00289-t001" class="html-table">Table 1</a>: (<b>a</b>) QSC; (<b>b</b>) rHEALPix<sup>E</sup>; (<b>c</b>) rHEALPix<sup>P</sup>; (<b>d</b>) ASC; (<b>e</b>) CCM; (<b>f</b>) CSC; (<b>g</b>) DOEC; and (<b>h</b>). The three-dimensional shape of the continents and the graticule.</p> Full article ">Figure 9
<p>Comparison of the appearance of the partitions P0 for the oblique aspects with graticule system rotations: (<b>a</b>) R(125°, 50°, −15°); (<b>b</b>) R(131°, 49°, −20°).</p> Full article ">
20 pages, 11866 KiB  
Article
A Novel Method for Extracting and Analyzing the Geometry Properties of the Shortest Pedestrian Paths Focusing on Open Geospatial Data
by Reza Hosseini, Daoqin Tong, Samsung Lim, Qian Chayn Sun, Gunho Sohn, Gyözö Gidófalvi, Abbas Alimohammadi and Seyedehsan Seyedabrishami
ISPRS Int. J. Geo-Inf. 2023, 12(7), 288; https://doi.org/10.3390/ijgi12070288 - 19 Jul 2023
Cited by 3 | Viewed by 1920
Abstract
Unlike car navigation, where almost all vehicles can traverse every route, one route might not be optimal or even suitable for all pedestrians. Route geometry information, including tortuosity, twists and turns along roads, junctions, and road slopes, among others, matters a great deal [...] Read more.
Unlike car navigation, where almost all vehicles can traverse every route, one route might not be optimal or even suitable for all pedestrians. Route geometry information, including tortuosity, twists and turns along roads, junctions, and road slopes, among others, matters a great deal for specific types of pedestrians, particularly those with limited mobility, such as wheelchair users and older adults. Offering practical routing services to these users requires that pedestrian navigation systems provide further information on route geometry. Therefore, this article proposes a novel method for extracting and analyzing the geometry properties of the shortest pedestrian paths, with a focus on open geospatial data across four aspects: (a) similarity, (b) route curviness, (c) road turns and intersections, and (d) road gradients. Deriving from the Hausdorff distance, a metric called the “dissimilarity ratio” was developed, allowing us to determine whether pairs of routes show any tendencies to be similar to each other. Using the “sinuosity index”, a segment-based technique quantified the route curviness based on the number and degree of the road turns along the route. Moreover, relying upon open elevation data, the road gradients were extracted to identify routes offering smoother motion and better accessibility. Lastly, the road turns and intersections were investigated as pedestrian convenience and safety indicators. A local government area of Greater Sydney in Australia was chosen as the case study. The analysis was implemented on OpenStreetMap (OSM) shortest pedestrian paths against Google Maps as a benchmark for real-world commercial applications. The similarity analysis indicated that over 90% of OSM routes were identical or roughly similar to Google Maps. In addition, while Spearman’s rank correlation showed a direct relationship between route curviness and route length, rS(758) = 0.92, p < 0.001, OSM, on average, witnessed more tortuous routes and, consequently, shorter straight roads between turns. However, OSM routes could be more suitable for pedestrians when the frequency of intersections and road slopes are at the center of attention. Finally, the devised metrics in this study, including the dissimilarity ratio and sinuosity index, showed their practicability in translating raw values into meaningful indicators. Full article
(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)
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<p>The shortest path (<b>a</b>) and alternatives (<b>b</b>,<b>c</b>) suggested by Google Maps (yellow and red markers: road turns and intersections).</p> Full article ">Figure 2
<p>The distribution map of POIs within the City of Sydney.</p> Full article ">Figure 3
<p>Hausdorff distance [<a href="#B56-ijgi-12-00288" class="html-bibr">56</a>].</p> Full article ">Figure 4
<p>The number of road turns and intersections (<b>a</b>) and degrees of turns (<b>b</b>) present different mobility problems for a wheelchair user (yellow and red markers: road turns and intersections).</p> Full article ">Figure 5
<p>Road gradient map of the study area.</p> Full article ">Figure 6
<p>The average and maximum road gradients with profile graph (blue line: the shortest path).</p> Full article ">Figure 7
<p>An example of calculated shortest paths with slope profile (blue line: OSM, red line: Google Maps, and green/red placemark: origin/destination).</p> Full article ">Figure 8
<p>The selected OSM shortest paths with different dissimilarity ratios (blue line: OSM, red line: Google Maps, green/red placemark: origin/destination).</p> Full article ">Figure 9
<p>The relationship between the route pairs’ similarity and distance deviation.</p> Full article ">Figure 10
<p>The distribution of correlations within similarity clusters.</p> Full article ">Figure 11
<p>The overall statistics of the geometry analysis (part 1).</p> Full article ">Figure 12
<p>The overall statistics of the geometry analysis (part 2).</p> Full article ">Figure 13
<p>An example of the starting/ending edge problem (blue line: OSM, red line: Google Maps, black line: starting edge of the route, green/red placemark: origin/destination).</p> Full article ">
20 pages, 34985 KiB  
Article
An Effective Method for Computing the Least-Cost Path Using a Multi-Resolution Raster Cost Surface Model
by Qiuling Tang and Wanfeng Dou
ISPRS Int. J. Geo-Inf. 2023, 12(7), 287; https://doi.org/10.3390/ijgi12070287 - 17 Jul 2023
Cited by 4 | Viewed by 3547
Abstract
Calculating the least-cost path (LCP) is a fundamental operation in raster-based geographic information systems (GIS). The LCP is applied to raster cost surfaces, in which it determines the most cost-effective path. Increasing the raster resolution results in a longer computation time to obtain [...] Read more.
Calculating the least-cost path (LCP) is a fundamental operation in raster-based geographic information systems (GIS). The LCP is applied to raster cost surfaces, in which it determines the most cost-effective path. Increasing the raster resolution results in a longer computation time to obtain LCP. This paper proposes a method for calculating the LCP using a multi-resolution raster cost surface model to enhance computational performance for large-scale grids. The original raster cost surface is progressively downsampled to generate grids of decreasing resolutions. Subsequently, the path is determined on the low-resolution raster. By performing operations such as filtering directional points and mapping path points, the final path on the high-resolution raster can be obtained. The method enables a parallel computation of paths. Therefore, it significantly improves the efficiency for synthetic raster cost surfaces with continuous or discrete characteristics, as well as for raster cost surfaces generated from real terrain datasets, while also providing an end-to-end path output. The experiments show that 80% of the results are very close to the original LCP, and the accuracy of the remaining paths falls within an acceptable range. At the same time, our method greatly improves the efficiency of path solving on a large-scale raster, fulfilling practical application requirements. Full article
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<p>A multi-resolution raster cost surface model (MRCSM) using a 2:1 downsampling coefficient.</p> Full article ">Figure 2
<p>The multi-scale least-cost path (MS-LCP) method: The first stage is to obtain a multi-resolution raster cost surface model of different scales using downsampling. The second stage is the path search on the raster cost surface at different scales.</p> Full article ">Figure 3
<p>Determination of the search area: (<b>a</b>) search area determined by the starting point and target point; (<b>b</b>) search area determined together by the starting point, target point, and other auxiliary edge points.</p> Full article ">Figure 4
<p>Examples of (<b>a</b>,<b>b</b>) cloudy cost surfaces and (<b>c</b>,<b>d</b>) patchy cost surfaces: (<b>a</b>) 4 classes, (<b>b</b>) 10 classes, (<b>c</b>) 7 classes, and (<b>d</b>) 4 classes. Darker shades represent higher values.</p> Full article ">Figure 5
<p>Raster cost surface comprising three datasets: elevation, land cover, and infrastructure.</p> Full article ">Figure 6
<p>The y-axis shows <math display="inline"><semantics><mrow><mi>l</mi><mo>(</mo><mi>M</mi><mi>S</mi><mo>−</mo><mi>L</mi><mi>C</mi><mi>P</mi><mo>)</mo><mo>/</mo><mi>l</mi><mo>(</mo><mi>L</mi><mi>C</mi><mi>P</mi><mo>)</mo></mrow></semantics></math> or <math display="inline"><semantics><mrow><mi>u</mi><mo>(</mo><mi>M</mi><mi>S</mi><mo>−</mo><mi>L</mi><mi>C</mi><mi>P</mi><mo>)</mo><mo>/</mo><mi>u</mi><mo>(</mo><mi>L</mi><mi>C</mi><mi>P</mi><mo>)</mo></mrow></semantics></math>, and the x-axis shows the ID. The plotted chart displays the range, median and mean values. (<b>a</b>,<b>c</b>): results on cloudy cost surfaces and the MS-LCP using average downsampling; (<b>b</b>,<b>d</b>): results on patchy cost surfaces and the MS-LCP using average downsampling.</p> Full article ">Figure 7
<p>The y-axis shows <math display="inline"><semantics><mrow><mi>l</mi><mo>(</mo><mi>M</mi><mi>S</mi><mo>−</mo><mi>L</mi><mi>C</mi><mi>P</mi><mo>)</mo><mo>/</mo><mi>l</mi><mo>(</mo><mi>L</mi><mi>C</mi><mi>P</mi><mo>)</mo></mrow></semantics></math> or <math display="inline"><semantics><mrow><mi>u</mi><mo>(</mo><mi>M</mi><mi>S</mi><mo>−</mo><mi>L</mi><mi>C</mi><mi>P</mi><mo>)</mo><mo>/</mo><mi>u</mi><mo>(</mo><mi>L</mi><mi>C</mi><mi>P</mi><mo>)</mo></mrow></semantics></math>, and the x-axis shows the ID. The plotted chart displays the range, median and mean values. (<b>a</b>,<b>c</b>): results on cloudy cost surfaces and MS-LCP using maximum downsampling; (<b>b</b>,<b>d</b>): result on patchy cost surfaces and MS-LCP using maximum downsampling.</p> Full article ">Figure 8
<p>(<b>a</b>) LCP and MS-LCP using average downsampling on cloudy cost surface. (<b>b</b>) LCP and MS-LCP using maximum downsampling on cloudy cost surface. (<b>c</b>) LCP and MS-LCP using average downsampling on patchy cost surface. (<b>d</b>) LCP and MS-LCP using maximum downsampling on patchy cost surface.</p> Full article ">Figure 9
<p>(<b>a</b>) Mixture of three different paths. (<b>b</b>) LCP on raster cost surface. (<b>c</b>) MS-LCP using average downsampling on raster cost surface. (<b>d</b>) MS-LCP using maximum downsampling on raster cost surface.</p> Full article ">Figure 10
<p>The green path represents the LCP, the red path represents the MS-LCP using an average downsampling, and the blue path represents the MS-LCP using a maximum downsampling. The red rectangular area indicates the initial divergence point of the three paths. Within the cost gird, the white areas signify areas obstructed by water.</p> Full article ">
21 pages, 18925 KiB  
Article
A GIS-Based Evacuation Route Planning in Flood-Susceptible Area of Siraha Municipality, Nepal
by Gaurav Parajuli, Shankar Neupane, Sandeep Kunwar, Ramesh Adhikari and Tri Dev Acharya
ISPRS Int. J. Geo-Inf. 2023, 12(7), 286; https://doi.org/10.3390/ijgi12070286 - 16 Jul 2023
Cited by 9 | Viewed by 6043
Abstract
Flood is one of the most frequently occurring and devastating disasters in Nepal. Several locations in Nepal are at high risk of flood, which requires proper guidance on early warning and safe evacuation of people to emergency locations through optimal routes to minimize [...] Read more.
Flood is one of the most frequently occurring and devastating disasters in Nepal. Several locations in Nepal are at high risk of flood, which requires proper guidance on early warning and safe evacuation of people to emergency locations through optimal routes to minimize fatalities. However, the information is limited to flood hazard mapping only. This study provides a comprehensive flood susceptibility and evacuation route mapping in the Siraha Municipality of Nepal where a lot of flood events have occurred in the past and are liable to happen in the future. The flood susceptibility map was created using a Geographic Information System (GIS)-based Analytical Hierarchy Process (AHP) over nine flood conditioning factors. It showed that 47% of the total area was highly susceptible to flood, and the remaining was in the safe zone. The assembly points where people would gather for evacuation were selected within the susceptible zone through manual digitization while the emergency shelters were selected within a safe zone such that they can host the maximum number of people. The network analysis approach is used for evacuation route mapping in which the closest facility analysis proposed the optimum evacuation route based on the walking speed of evacuees to reach the emergency shelter place considering the effect of slope and flood on the speed of the pedestrian. A total of 12 out of 22 suggested emergency shelters were within 30 min, 7 within 60 min, and 2 within 100 min walk from the assembly point. Moreover, this study suggests the possible areas for further shelter place allocations based on service area analysis. This study can support the authorities’ decision-making for the flood risk assessment and early warning system planning, and helps in providing an efficient evacuation plan for risk mitigation. Full article
(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)
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<p>The workflow of the flood susceptibility and evacuation route mapping adopted in the study.</p> Full article ">Figure 2
<p>Location map of the study area: Siraha Municipality with OpenStreetMap base map.</p> Full article ">Figure 3
<p>Annual rainfall of Siraha and Lahan station.</p> Full article ">Figure 4
<p>Flood conditioning factors: (<b>a</b>) elevation; (<b>b</b>) slope; (<b>c</b>) Topographic Wetness Index; (<b>d</b>) land use/land cover; (<b>e</b>) Normalized Difference Vegetation Index; (<b>f</b>) precipitation; (<b>g</b>) drainage density; (<b>h</b>) distance from the river; and (<b>i</b>) distance from the road.</p> Full article ">Figure 4 Cont.
<p>Flood conditioning factors: (<b>a</b>) elevation; (<b>b</b>) slope; (<b>c</b>) Topographic Wetness Index; (<b>d</b>) land use/land cover; (<b>e</b>) Normalized Difference Vegetation Index; (<b>f</b>) precipitation; (<b>g</b>) drainage density; (<b>h</b>) distance from the river; and (<b>i</b>) distance from the road.</p> Full article ">Figure 5
<p>Flood susceptibility map of the study area: Siraha Municipality.</p> Full article ">Figure 6
<p>Closest Facility: (<b>a</b>) shortest time; (<b>b</b>) shortest distance.</p> Full article ">Figure 7
<p>Service area analysis based on: (<b>a</b>) time; (<b>b</b>) distance.</p> Full article ">Figure 8
<p>(<b>a</b>) Water bodies before flood; (<b>b</b>) inundated area after flood.</p> Full article ">
19 pages, 22954 KiB  
Article
Place-Centered Bus Accessibility Time Series Classification with Floating Car Data: An Actual Isochrone and Dynamic Time Warping Distance-Based k-Medoids Method
by Chen Wang, Si-jia Zhao, Zong-qiang Ren and Qi Long
ISPRS Int. J. Geo-Inf. 2023, 12(7), 285; https://doi.org/10.3390/ijgi12070285 - 16 Jul 2023
Cited by 2 | Viewed by 1762
Abstract
Classifying a time series is a fundamental task in temporal analysis. This provides valuable insights into the temporal characteristics of data. Although it has been applied to traffic flow and individual-centered accessibility analysis, it has yet to be applied to place-centered accessibility research. [...] Read more.
Classifying a time series is a fundamental task in temporal analysis. This provides valuable insights into the temporal characteristics of data. Although it has been applied to traffic flow and individual-centered accessibility analysis, it has yet to be applied to place-centered accessibility research. In this study, we have proposed an actual isochrone and dynamic time-wrapping distance-based k-medoids method and tested its applicability to a bus accessibility analysis. Using bus floating car data, our method calculated the actual isochrone area as an accessibility measurement and constructs an accessibility time series for each hexagonal geographical unit within the area of interest. We then calculated the dynamic time warp distance between the accessibility time series of pairwise geographical units and used these distances for k-medoid clustering. The optimized class number k was selected by considering the elbow method, silhouette score, and human examination. Our case study in Hefei, China demonstrates the feasibility of our method for accessibility time series classification. We also discovered that the resulting classes follow clear spatial patterns, indicating that different time series classes may be correlated with their spatial location. To our knowledge, this is the first time that such a classification method has been applied to place-centered accessibility time series analysis. Our data-driven method can inform place-centered accessibility in an era in which large quantities of spatiotemporal data like floating car data are available. Full article
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<p>Steps of place-centered bus accessibility time series classification with bus floating car data.</p> Full article ">Figure 2
<p>The schematic diagram of bus network construction with a 500 m between-station transit threshold.</p> Full article ">Figure 3
<p>The case study area: urban area of Hefei, China (117.27° N, 31.86° E).</p> Full article ">Figure 4
<p>Comparison of one weekday and one weekend day’s isochrones from one hexagonal unit.</p> Full article ">Figure 5
<p>Comparison of isochrone area time series of one weekday and one weekend day from one hexagonal unit (same unit as <a href="#ijgi-12-00285-f004" class="html-fig">Figure 4</a>).</p> Full article ">Figure 6
<p>The results of elbow method for weekday and weekend.</p> Full article ">Figure 7
<p>The results of silhouette score for weekday and weekend.</p> Full article ">Figure 8
<p>Comparison of 90 min isochrone time series of different classes between weekday and weekend. Different colors represent different classes, and two time series are presented in each class. The times series is offset vertically for presentation purpose.</p> Full article ">Figure 9
<p>Comparison of results for different classes between weekday and weekend.</p> Full article ">Figure 10
<p>Comparison of whole-day median accessibility between weekday and weekend.</p> Full article ">
13 pages, 5732 KiB  
Article
Mapping with ChatGPT
by Ran Tao and Jinwen Xu
ISPRS Int. J. Geo-Inf. 2023, 12(7), 284; https://doi.org/10.3390/ijgi12070284 - 16 Jul 2023
Cited by 20 | Viewed by 14044
Abstract
The emergence and rapid advancement of large language models (LLMs), represented by OpenAI’s Generative Pre-trained Transformer (GPT), has brought up new opportunities across various industries and disciplines. These cutting-edge technologies are transforming the way we interact with information, communicate, and solve complex problems. [...] Read more.
The emergence and rapid advancement of large language models (LLMs), represented by OpenAI’s Generative Pre-trained Transformer (GPT), has brought up new opportunities across various industries and disciplines. These cutting-edge technologies are transforming the way we interact with information, communicate, and solve complex problems. We conducted a pilot study exploring making maps with ChatGPT, a popular artificial intelligence (AI) chatbot. Specifically, we tested designing thematic maps using given or public geospatial data, as well as creating mental maps purely using textual descriptions of geographic space. We conclude that ChatGPT provides a useful alternative solution for mapping given its unique advantages, such as lowering the barrier to producing maps, boosting the efficiency of massive map production, and understanding geographical space with its spatial thinking capability. However, mapping with ChatGPT still has limitations at the current stage, such as its unequal benefits for different users and dependence on user intervention for quality control. Full article
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<p>The process of creating thematic maps using ChatGPT.</p> Full article ">Figure 2
<p>Initial map of US population created by ChatGPT in response to Prompt 1.</p> Full article ">Figure 3
<p>US population map modified with follow-up prompts to ChatGPT.</p> Full article ">Figure 4
<p>(<b>a</b>) Sample choropleth map of county population in Mississippi with two mistakenly identified out-of-state counties; (<b>b</b>) sample population maps of US southern states created by ChatGPT in batches.</p> Full article ">Figure 5
<p>(<b>a</b>) Initial web map of Florida population created by ChatGPT; (<b>b</b>) web map of Florida population after revision by ChatGPT.</p> Full article ">Figure 6
<p>The process of creating mental maps using ChatGPT.</p> Full article ">Figure 7
<p>Creating a sketch map with ChatGPT.</p> Full article ">Figure 8
<p>Mental map of campus drawing via coding.</p> Full article ">
18 pages, 7112 KiB  
Article
Mobile Collaborative Heatmapping to Infer Self-Guided Walking Tourists’ Preferences for Geomedia
by Iori Sasaki, Masatoshi Arikawa, Min Lu and Ryo Sato
ISPRS Int. J. Geo-Inf. 2023, 12(7), 283; https://doi.org/10.3390/ijgi12070283 - 15 Jul 2023
Cited by 2 | Viewed by 1677
Abstract
This paper proposes a model-less feedback system driven by tourist tracking data that are automatically collected through mobile applications to visualize the gap between geomedia recommendations and the actual routes selected by tourists. High-frequency GPS data essentially make it difficult to interpret the [...] Read more.
This paper proposes a model-less feedback system driven by tourist tracking data that are automatically collected through mobile applications to visualize the gap between geomedia recommendations and the actual routes selected by tourists. High-frequency GPS data essentially make it difficult to interpret the semantic importance of hot spots and the presence of street-level features on a density map. Our mobile collaborative framework reorganizes tourist trajectories. This processing comprises (1) extracting the location of the user-generated content (UGC) recording, (2) abstracting the locations where tourists stay, (3) discarding locations where users remain stationary, and (4) simplifying the remaining points of location. Then, our heatmapping system visualizes heatmaps for hot streets, UGC-oriented hot spots, and indoor-oriented hot spots. According to our experimental study, this method can generate a trajectory that is more adaptable for hot street visualization than the raw trajectory and a simplified trajectory according to its geometry. This paper extends our previous work at the 2022 IEEE International Conference on Big Data, providing deeper discussions on application for local tourism. The framework allows us to derive insights for the development of guide content from mobile sensor data. Full article
(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)
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<p>Example of a heatmap with high-frequency GPS trajectories. There are too many factors that cause locally dense areas to properly judge their semantic importance. As the research subject area is Akita City in Japan, all background maps are in Japanese in this paper.</p> Full article ">Figure 2
<p>Density maps using raw trajectories based on three values of <math display="inline"><semantics><mrow><msub><mrow><mi>T</mi><mi>h</mi></mrow><mrow><mi>c</mi><mo>.</mo><mi>r</mi><mo>.</mo></mrow></msub></mrow></semantics></math>. These maps are not compatible with hot street visualizations, as the topology of streets is not visible even after adjusting the color range.</p> Full article ">Figure 3
<p>Structure realizing the feedback system on the basis of current mobile environments for walking tourism businesses. Our proposal for a novel heatmapping framework focuses on two sub-systems: (1) semi-ready data construction on the user side and (2) thematic heatmap generation to visualize hot spots and hot streets on the analyst side.</p> Full article ">Figure 4
<p>A walking route in the experiments. A walker traced the blue line at a constant speed and stopped at each red point A, B, C, and D for one or two minutes. Gray rectangles depict indoor areas.</p> Full article ">Figure 5
<p>Diagram of resampling process for calculating synchronous Euclidean distances between the ground truth and a target trajectory. A point <math display="inline"><semantics><mrow><msubsup><mrow><mi>p</mi></mrow><mrow><mi>s</mi></mrow><mrow><mo>′</mo></mrow></msubsup></mrow></semantics></math> is added to maintain time ratio.</p> Full article ">Figure 6
<p>Total SED of the target trajectory data (red line: <math display="inline"><semantics><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>r</mi><mi>a</mi><mi>w</mi></mrow></msub></mrow></semantics></math>; brown dashed line: <math display="inline"><semantics><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>D</mi><mi>P</mi></mrow></msub></mrow></semantics></math>; blue dashed line <math display="inline"><semantics><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>S</mi><mi>R</mi></mrow></msub></mrow></semantics></math> ). This implies that the proposed method can decrease total SED with a small tolerance parameter.</p> Full article ">Figure 7
<p>Trajectory shape (<b>left</b>) and time series changes in the SED (<b>right</b>) of <math display="inline"><semantics><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>r</mi><mi>a</mi><mi>w</mi></mrow></msub></mrow></semantics></math>. Orange areas in the graph of time series changes represent the periods when the user is stationary outdoor and indoor, as indicated by the red points in <a href="#ijgi-12-00283-f004" class="html-fig">Figure 4</a> (A, B, C, and D, in order).</p> Full article ">Figure 8
<p>Trajectory shape (<b>left</b>) and time series changes in the SED (<b>right</b>) of <math display="inline"><semantics><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>D</mi><mi>P</mi></mrow></msub></mrow></semantics></math>. The tolerance parameter <math display="inline"><semantics><mrow><mi>ε</mi></mrow></semantics></math> is set to 12.0 m. Orange areas in the graph of time series changes represent the periods when the user is stationary outdoor and indoor, as indicated by the red points in <a href="#ijgi-12-00283-f004" class="html-fig">Figure 4</a> (A, B, C, and D, in order).</p> Full article ">Figure 9
<p>Trajectory shape (<b>left</b>) and time series changes in the SED (<b>right</b>) of <math display="inline"><semantics><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>S</mi><mi>R</mi></mrow></msub></mrow></semantics></math>. The tolerance parameter <math display="inline"><semantics><mrow><mi>ε</mi></mrow></semantics></math> is set to 1.0 m. Orange areas in the graph of time series changes represent the periods when the user is stationary outdoor and indoor, as indicated by the red points in <a href="#ijgi-12-00283-f004" class="html-fig">Figure 4</a> (A, B, C, and D, in order).</p> Full article ">Figure 10
<p>Recommended spots with IDs from one to nine and walking routes in the walking guidebook that is available on [<a href="#B44-ijgi-12-00283" class="html-bibr">44</a>] for Japanese tourists. Red pins are facilities where tourists can stay, and green pins are monuments or viewpoints they can look at.</p> Full article ">Figure 11
<p>Location-based services: (<b>a</b>) positioning the current location on the illustrated maps which is provided in a Japanese tourist guidebook published by Akita City; (<b>b</b>) location-based push services that automatically display geomedia, such as Japanese guide scripts and pictures, on the screen when the user gets close to the registered spots.</p> Full article ">Figure 12
<p>Example of the distribution of horizontal GPS accuracy values, obtained by monitoring twelve subjects within the dataset The device used was iPhone 11, manufactured by Apple Inc., based in Cupertino, California, USA. The kCLLocationAccuracyBest setting was applied, which is specified when very high accuracy is required in Core Location framework. The left-side graph represents an outdoor condition, i.e., street between spots 7 and 9 in <a href="#ijgi-12-00283-f010" class="html-fig">Figure 10</a>, and the right-side graph represents an indoor condition, i.e., spot 7 in <a href="#ijgi-12-00283-f010" class="html-fig">Figure 10</a>.</p> Full article ">Figure 13
<p>An example of a hot street heatmap. Equalizing the density per area enables visualization of the presence of polyline-shaped features, such as walking routes and streets.</p> Full article ">Figure 14
<p>An example of a UGC-oriented hot spot heatmap that considers point data drawn only from <span class="html-italic">ugc</span> tags. Dense areas represent attractive photo spots and places that are worth sharing.</p> Full article ">Figure 15
<p>An example of an indoor-oriented hot spot heatmap that considers point data drawn only from <span class="html-italic">indoor</span> tags. Dense areas represent attractive buildings and facilities visited by many tourists.</p> Full article ">Figure 16
<p>Heatmaps that were used for a user experiment. The experiment involved the generation of heatmaps from raw data and semi-ready data using different values for <math display="inline"><semantics><mrow><mi>T</mi><msub><mrow><mi>h</mi></mrow><mrow><mi>c</mi><mo>.</mo><mi>r</mi><mo>.</mo></mrow></msub></mrow></semantics></math>.</p> Full article ">Figure 17
<p>Stacked bar chart of the selection distribution of heatmaps ranked as the top three.</p> Full article ">
28 pages, 3664 KiB  
Article
Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network
by Liping Zhang, Xiaojing Zhang and Song Li
ISPRS Int. J. Geo-Inf. 2023, 12(7), 282; https://doi.org/10.3390/ijgi12070282 - 15 Jul 2023
Viewed by 1054
Abstract
In view of the existing research in the field of k-nearest neighbor query in the road network, the incompleteness of the query user’s preference for data objects and the privacy protection of the query results are not considered, this paper proposes a [...] Read more.
In view of the existing research in the field of k-nearest neighbor query in the road network, the incompleteness of the query user’s preference for data objects and the privacy protection of the query results are not considered, this paper proposes a multiuser incomplete preference k-nearest neighbor query algorithm based on differential privacy in the road network. The algorithm is divided into four parts; the first part proposes a multiuser incomplete preference completion algorithm based on association rules. The algorithm firstly uses the frequent pattern tree proposed in this paper to mine frequent item sets, then uses frequent item sets to mine strong correlation rules, and finally completes multiuser incomplete preference based on strong correlation rules. The second part proposes attribute preference weight coefficient based on multiuser’ s different preferences and clusters users accordingly. The third part compares the dominance of the query object, filters the data with low dominance, and performs a k-neighbor query. The fourth part proposes a privacy budget allocation method based on differential privacy technology. The method uses the Laplace mechanism to add noise to the result release and balance the privacy and availability of data. Theoretical research and experimental analysis show that the proposed method can better deal with the multiuser incomplete preference k-nearest neighbor query and privacy protection problems in the road network. Full article
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<p>Algorithm relationship and data processing flow.</p> Full article ">Figure 2
<p>Network Voronoi diagram.</p> Full article ">Figure 3
<p>Add the first transaction(a, c, m, p).</p> Full article ">Figure 4
<p>Add the second transaction(a, c, o).</p> Full article ">Figure 5
<p>Construction of the HUFP tree.</p> Full article ">Figure 6
<p>Frequent itemset construction process 1 for p.</p> Full article ">Figure 7
<p>Frequent itemset construction process 2 for p.</p> Full article ">Figure 8
<p>Query area example.</p> Full article ">Figure 9
<p>The impact of the data missing rate on execution time.</p> Full article ">Figure 10
<p>The impact of the <span class="html-italic">k</span> value on the performance of the query algorithm.</p> Full article ">Figure 11
<p>The impact of the number of data points on query performance.</p> Full article ">Figure 12
<p>The impact of the number of query objects on query performance.</p> Full article ">Figure 13
<p>Comparative analysis of errors.</p> Full article ">Figure 14
<p>The impact of the <span class="html-italic">k</span> value on the accuracy of publishing result data.</p> Full article ">
16 pages, 4396 KiB  
Article
Evaluation of SMAP-Enhanced Products Using Upscaled Soil Moisture Data Based on Random Forest Regression: A Case Study of the Qinghai–Tibet Plateau, China
by Jia Chen, Fengmin Hu, Junjie Li, Yijia Xie, Wen Zhang, Changqing Huang and Lingkui Meng
ISPRS Int. J. Geo-Inf. 2023, 12(7), 281; https://doi.org/10.3390/ijgi12070281 - 15 Jul 2023
Cited by 2 | Viewed by 1639
Abstract
The evaluation of satellite soil moisture is a big challenge owing to the large spatial mismatch between pixel-based satellite soil moisture products and point-based in situ measurements. Upscaling in situ measurements to obtain the “true value” of soil moisture content at the satellite [...] Read more.
The evaluation of satellite soil moisture is a big challenge owing to the large spatial mismatch between pixel-based satellite soil moisture products and point-based in situ measurements. Upscaling in situ measurements to obtain the “true value” of soil moisture content at the satellite grid/footprint scale can make up for the scale difference and improve the validation. Many existing upscaling methods have strict requirements regarding the spatial distribution and quantity of soil moisture sensors. However, in reality, soil-moisture-monitoring networks are commonly sparse with low sensor density, which increases the difficulty of obtaining accurate upscaled soil moisture data and limits the validation of satellite products. For this reason, this paper proposes a scheme to upscale in situ measurements using five machine learning methods along with Landsat 8 datasets and DEM data to validate the accuracy of a SMAP-enhanced passive soil moisture product for a sparse network on the Qinghai–Tibet Plateau. The proposed scheme realizes the upscaling of in situ soil moisture data to the pixel scale (30 m × 30 m) and then to the coarse grid scale (9 km × 9 km) by using multi-source remote sensing data as the bridge of scale conversion. The long-time SMAP SM products since April 2015 on the Qinghai–Tibet Plateau were validated based on upscaled soil moisture data. The results show that (1) random forest regression performs the best, and the upscaled soil moisture data reflect the region-average soil moisture conditions that can be used for evaluating SMAP data; (2) the SMAP product meets its scientific measurement requirements; and (3) the SMAP product generally underestimates the soil moisture in the study area. Full article
(This article belongs to the Topic Advances in Earth Observation and Geosciences)
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<p>Distribution of the Ngari network observation sites on the Qinghai–Tibet Plateau, with the background indicating the land cover conditions (from GlobeLand30, accessed on 21 March 2022). The SMAP validation area is delineated using the 9 km EASE-2 grid and labeled with serial numbers 1–20.</p> Full article ">Figure 2
<p>Schematic diagram of the methodology.</p> Full article ">Figure 3
<p>Comparison between in situ measurements and SM estimates based on five models (In situ measurements from September 2016 to April 2017 are missing). (<b>a</b>) SQ02, (<b>b</b>) SQ03, (<b>c</b>) SQ14.</p> Full article ">Figure 4
<p>Scatter plot between in situ measurements and 30 m soil moisture estimates based on the RFR upscaling model, with subfigures correspond to 13 soil moisture sites.</p> Full article ">Figure 5
<p>Estimated soil moisture at 30 m resolution (results from 9 dates were randomly selected for display in the subfigures).</p> Full article ">Figure 6
<p>Upscaled soil moisture at 9 km resolution (the dates of the subfigures correspond to <a href="#ijgi-12-00281-f005" class="html-fig">Figure 5</a>).</p> Full article ">Figure 7
<p>The comparison of 9 km upscaled soil moisture data with SMAP soil moisture.</p> Full article ">Figure 8
<p>Time series comparisons of 9 km upscaled soil moisture data with SMAP soil moisture from April 2015 to December 2021 (6 grids were randomly selected for display in the subfigures).</p> Full article ">Figure 9
<p>Comparison of soil moisture observations in different seasons. The black dots in the box plot represent outliers in the original in-situ measurements.</p> Full article ">Figure 10
<p>Comparison of the performance of 5 upscaling algorithms with and without monthly information with metric indicators (<b>a</b>) <span class="html-italic">ubRMSE</span>, (<b>b</b>) <span class="html-italic">RMSE</span>, (<b>c</b>) <span class="html-italic">bias</span> and (<b>d</b>) <span class="html-italic">R</span>.</p> Full article ">
18 pages, 12152 KiB  
Article
Analysis of a Municipal Solid Waste Disposal Site: Use of Geographic Information Technology Tools for Decision Making
by Juan Antonio Araiza-Aguilar, María Neftalí Rojas-Valencia, Hugo Alejandro Nájera-Aguilar, Rubén Fernando Gutiérrez-Hernández and Carlos Manuel García-Lara
ISPRS Int. J. Geo-Inf. 2023, 12(7), 280; https://doi.org/10.3390/ijgi12070280 - 14 Jul 2023
Cited by 1 | Viewed by 1764
Abstract
In this study, the operation of a final disposal site for municipal solid waste in the state of Chiapas, in Mexico, was evaluated. Several spatial analyses and Geographic Information Technology (GIT) tools were used. It was found that the site’s current operation and [...] Read more.
In this study, the operation of a final disposal site for municipal solid waste in the state of Chiapas, in Mexico, was evaluated. Several spatial analyses and Geographic Information Technology (GIT) tools were used. It was found that the site’s current operation and location are deficient, partially complying with regulations. The gaseous dispersion is not far-reaching (from 100 to 8725 µg/m3 for landfill gas, and from 0.01 to 0.35 µg/m3 for H2S) but requires attention to avoid olfactory unpleasantness. Liquid emissions (conservative pollutants) move in the east direction of the final disposal site, which can damage the environmental infrastructure (water supply wells) in the long term. The highest and lowest concentrations were found in years 1 (12,270 mg/m3) and 20 (1080 mg/m3), respectively. Thermal emissions around the dumping site are important due to the formation of microclimatic zones. Temperature differences were found during the analysis period, ranging from 8.37 °C in summer to 2.49 °C in winter, which are due to waste decomposition processes and anthropogenic activities. Finally, the change in land use around the dumping site increased at a rate of 5.82% per year, mainly due to the growth of homes, communication routes, and shopping centers. Full article
(This article belongs to the Topic Urban Sensing Technologies)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Study area: municipality of Reforma, state of Chiapas, Mexico.</p> Full article ">Figure 2
<p>Survey information in the field: (<b>a</b>) infrastructure and operation of the dumping site; (<b>b</b>) waste parameters and characteristics.</p> Full article ">Figure 3
<p>Workflow of LULC analysis.</p> Full article ">Figure 4
<p>Workflow of landfill gas dispersion analysis.</p> Full article ">Figure 5
<p>Workflow of leachate dispersion analysis.</p> Full article ">Figure 6
<p>Workflow of LST analysis.</p> Full article ">Figure 7
<p>Operation of the FDS of Reforma, Chiapas: (<b>a</b>) landfill infrastructure; (<b>b</b>) waste dump area without cover; (<b>c</b>) disposal cell with final coverage; (<b>d</b>) current landfill cell; (<b>e</b>) leachate lagoon; (<b>f</b>) useful volume for waste confinement.</p> Full article ">Figure 8
<p>MSW deposited in the FDS of Reforma, Chiapas.</p> Full article ">Figure 9
<p>Composition of MSW deposited in the FDS of Reforma, Chiapas.</p> Full article ">Figure 10
<p>Spatial analysis of distances and restrictions: (<b>a</b>) local scale; (<b>b</b>) regional scale.</p> Full article ">Figure 11
<p>LULC analysis in the area of influence of the FDS: (<b>a</b>) year 2005; (<b>b</b>) year 2013; (<b>c</b>) year 2015; (<b>d</b>) year 2021.</p> Full article ">Figure 12
<p>Dispersion of gaseous emissions in the FDS of the study area: (<b>a</b>) landfill gas; (<b>b</b>) H<sub>2</sub>S.</p> Full article ">Figure 13
<p>Modeling of liquid emissions in the FDS: (<b>a</b>) conservative pollutant dispersion; (<b>b</b>) behavior of concentration vs. distance.</p> Full article ">Figure 14
<p>LST modeling in the surroundings of the FDS for the period 2005–2021: (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) temperatures in summer season; (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) temperatures in winter season.</p> Full article ">
25 pages, 3259 KiB  
Article
Integration of Heterogeneous Sensor Systems for Disaster Responses in Smart Cities: Flooding as an Example
by Jung-Hong Hong and Yi-Tin Shi
ISPRS Int. J. Geo-Inf. 2023, 12(7), 279; https://doi.org/10.3390/ijgi12070279 - 14 Jul 2023
Cited by 8 | Viewed by 2082
Abstract
Smart cities represent a new perspective on modern urban development. They involve an information infrastructure environment with application intelligence to improve operational efficiency and welfare effectively. However, understanding how to overcome the barriers of data fragmentation and heterogeneity to exploit the strengths of [...] Read more.
Smart cities represent a new perspective on modern urban development. They involve an information infrastructure environment with application intelligence to improve operational efficiency and welfare effectively. However, understanding how to overcome the barriers of data fragmentation and heterogeneity to exploit the strengths of existing resources and create integration effects remains a key challenge in smart city development. This research focuses on the effective management of heterogeneous sensor systems across different domains to improve quick disaster responses. Metadata serve as the core of this proposed framework, which is designed to not only describe the common and unique characteristics of various IoT-based devices and services, but also to provide necessary information to support the searching, requesting, and updating of required sensors and observation, as well as responding to the upcoming disaster. A workflow consisting of four list types was proposed and used to guide the response procedure. This research specifically aims to enable heterogeneous sensor systems available to all public or private stakeholders to be integrated in a collaborative fashion. While a flooding response was chosen for demonstration in this research, the proposed standard-based framework can be further promoted for other types of smart city applications, not limited to disaster response. The study’s results and implications underscore the importance of effective management of heterogeneous sensor systems and the role of metadata in enabling disaster responses in smart cities. Full article
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<p>Proposed architecture for flooding emergency responses.</p> Full article ">Figure 2
<p>Flood sensor information and inundation depth sensing data from the Water Resources: Department of Taiwan’s Civil IoT Taiwan of Things SensorThings API Service. URL source: <a href="https://tinyurl.com/mvu4f893" target="_blank">https://tinyurl.com/mvu4f893</a> (accessed on 8 April 2023, using the shorten URL tool). The Chinese in the figure represents the metadata elements: “name: flooding depth”, “description: flood height measurement the height below five centimeters returns every hour, five centimeters return every ten minutes”, “authority: The 5th River Management Office”.</p> Full article ">Figure 3
<p>UML design of flood hazard equipment and observation metadata for smart cities in emergency responses.</p> Full article ">Figure 4
<p>Flowchart for emergency response with a CCTV camera and a water level station meter equipment search.</p> Full article ">Figure 5
<p>(<b>a</b>) CCTV recording for monitoring traffic. URL source: <a href="https://ocam.live/index.php?route=product/product&amp;product_id=6745" target="_blank">https://ocam.live/index.php?route=product/product&amp;product_id=6745</a> (accessed on 1 July 2022) (<b>b</b>) CCTV recording showing flooding in a local area (accessed on 28 August 2018).</p> Full article ">Figure 6
<p>The experimental area of this study is the Sinshih District of Tainan City, Taiwan (Datum: WGS84, Coordinate system: WGS84, Projection system: EPSG:3857).</p> Full article ">Figure 7
<p>Three-dimensional FOVs of water level meter and CCTV cameras. Disaster notification location 1 at time: 2022/11/05 04:32 buffered 500 m devices: Camera ID 1639, ID 497, and ID 44; Water Level ID 284.</p> Full article ">Figure 8
<p>Disaster notification location 2 at time: 2022/11/05 06:28 buffered 500 m. The blue colors are devices: Camera ID 44 and ID 497; Water Level ID 284 and ID 88.</p> Full article ">
20 pages, 1359 KiB  
Article
Mining Geomatics
by Artur Krawczyk
ISPRS Int. J. Geo-Inf. 2023, 12(7), 278; https://doi.org/10.3390/ijgi12070278 - 14 Jul 2023
Cited by 11 | Viewed by 2754
Abstract
This paper attempts to define a name for an area of science and technology that encompasses the acquisition, processing and application of spatial data in the mining industry. A comparative study of the evolution of spatial data exchange methods between Geographic Information Systems [...] Read more.
This paper attempts to define a name for an area of science and technology that encompasses the acquisition, processing and application of spatial data in the mining industry. A comparative study of the evolution of spatial data exchange methods between Geographic Information Systems (GISs) and General Mining Planning (GMP) software is carried out to define the problem and name it. Subsequent modifications of the acronym GIS towards the specialisation of its application in mining are then reviewed. This is followed by the identification of three terminological postulates designed to formulate constraints and rules for the creation of a new definition. The subsequent analysis identifies the nomenclatural basis of the research area of geomatics and determines its applicability in the context of mining. The results of the research made it possible to formulate a new definition of “mining geomatics”. The final section of the article presents an initial proposal for an inventory of the basic concepts of mining geomatics in the form of a Body of Knowledge for mining geomatics. Full article
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<p>History of increasing sophistication of spatial data exchange between GIS and GMPs technologies.</p> Full article ">Figure 2
<p>Visualisation of mine workings using different models: (<b>a</b>) axial (linear or centerline) model, (<b>b</b>) circular (tubular) model, (<b>c</b>) profile model, (<b>d</b>) photorealistic model [<a href="#B77-ijgi-12-00278" class="html-bibr">77</a>].</p> Full article ">
20 pages, 700 KiB  
Article
In-Path Oracles for Road Networks
by Debajyoti Ghosh, Jagan Sankaranarayanan, Kiran Khatter and Hanan Samet
ISPRS Int. J. Geo-Inf. 2023, 12(7), 277; https://doi.org/10.3390/ijgi12070277 - 13 Jul 2023
Viewed by 1681
Abstract
Many spatial applications benefit from the fast answering to a seemingly simple spatial query: “Is a point of interest (POI) ‘in-path’ to the shortest path between a source and a destination?” In this context, an in-path POI is one that is either on [...] Read more.
Many spatial applications benefit from the fast answering to a seemingly simple spatial query: “Is a point of interest (POI) ‘in-path’ to the shortest path between a source and a destination?” In this context, an in-path POI is one that is either on the shortest path or can be reached within a bounded yet small detour from the shortest path. The fast answering of the in-path queries is contingent on being able to determine without having to actually compute the shortest paths during runtime. Thus, this requires a precomputation solution. The key contribution of the paper is the development of an in-path oracle that is based on precomputation of which pairs of sources and destinations are in-path with respect to the given POI. For a given road network with n nodes and m POIs, an O(m×n)-sized oracle is envisioned based on the reduction of the well-separated pairs (WSP) decomposition of the road network. Furthermore, an oracle can be indexed in a database using a B-tree that can answer queries at very high throughput. Experimental results on the real road network POI dataset illustrate the superiority of this technique compared to a baseline algorithm. The proposed approach can answer ≈ 1.5 million in-path queries per second compared to a few hundred per second using a suitable baseline approach. Full article
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<p>The mechanics of determining if <span class="html-italic">p</span> is in-path with respect to the shortest paths from multiple sources in A to destinations in B. Here (A, B) denotes a block pair and we develop rules to determine if <span class="html-italic">p</span> is in-path to (A, B).</p> Full article ">Figure 2
<p>For the Washington, DC dataset where the POI <span class="html-italic">p</span> is shown as using a green circle, figure (<b>a</b>) in-path and (<b>b</b>) not in-path example between a set of sources (block A) and destination (block B). The red dots indicate randomly chosen sources or destinations whose shortest paths are drawn.</p> Full article ">Figure 3
<p>The figure shows the effect of varying detour tolerance limits. The figure shows (<b>a</b>) elapsed time, (<b>b</b>) the maximum size of the priority queue, and (<b>c</b>) the number of POIs added into the priority queue as the detour tolerance limit is increased from 0.1 to 5. PSR in the figure legend denotes the POI sampling rate.</p> Full article ">Figure 4
<p>The figure shows the effect of varying POI sampling rates on (<b>a</b>) elapsed time, (<b>b</b>) the largest size of the priority queue, and (<b>c</b>) the number of POIs added into the priority queue.</p> Full article ">Figure 5
<p>The figure shows the effect of varying the size of the road network with the oracle size for the San Francisco dataset.</p> Full article ">Figure 6
<p>The figure shows the effect of varying detour limits with the oracle size for the Washington, DC dataset.</p> Full article ">Figure 7
<p>The figure shows the effect of varying the sampling rate on the throughput obtained from in-path oracles. Note that the dual Dijkstra variant has a throughput of ≈200 queries/s and is not shown in the graph.</p> Full article ">
17 pages, 6489 KiB  
Article
Identifying the Production–Living–Ecological Functional Structure of Haikou City by Integrating Empirical Knowledge with Multi-Source Data
by Bingbing Zhao, Xiaoyong Tan, Liang Luo, Min Deng and Xuexi Yang
ISPRS Int. J. Geo-Inf. 2023, 12(7), 276; https://doi.org/10.3390/ijgi12070276 - 12 Jul 2023
Cited by 3 | Viewed by 1554
Abstract
The inefficient use of urban resources and the imbalance of spatial structures make optimizing land use management a top priority in urban environmental management. Traditional land use classification systems that prioritize only natural features while disregarding human activity can result in redundancy and [...] Read more.
The inefficient use of urban resources and the imbalance of spatial structures make optimizing land use management a top priority in urban environmental management. Traditional land use classification systems that prioritize only natural features while disregarding human activity can result in redundancy and conflicts in urban planning. The Production–Living–Ecological Space (PLES) approach was developed as an integrated method for territorial spatial classification. However, most existing studies on PLES are conducted at provincial scales, largely overlooking fine-scale usage within cities. In addition, the existing concept of PLES has been vaguely defined, resulting in linear and simple identification methods that are not applicable to complex urban environments. To address these issues, this study proposes a method to identify urban PLES based on supervised classification using random forest models, which integrate empirical knowledge and multi-source heterogeneous information. The experiments conducted in Haikou reveal the regional aggregation of living and production spaces and the scarcity of ecological space in the city. Our study proposes a concrete concept of PLES and a method for identifying PLES that can be applied to multiple regions, providing an effective tool for the coordinated management of urban production, living, and ecological environments. Full article
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<p>Map of the study area.</p> Full article ">Figure 2
<p>The workflow of the research process.</p> Full article ">Figure 3
<p>Distribution of the annotation set on the map (Road names are shown on the map in both English and Chinese).</p> Full article ">Figure 4
<p>Rules applied when combining functional labels.</p> Full article ">Figure 5
<p>Feature importance and radar plots for the three classification models. (<b>a</b>) Feature importance of the living−oriented model; (<b>b</b>) Radar plot of the living−oriented model; (<b>c</b>) Feature importance of production−oriented model; (<b>d</b>) Radar plot of the production−oriented model; (<b>e</b>) Feature importance of ecological−oriented model; (<b>f</b>) Radar plot of the ecological−oriented model.</p> Full article ">Figure 5 Cont.
<p>Feature importance and radar plots for the three classification models. (<b>a</b>) Feature importance of the living−oriented model; (<b>b</b>) Radar plot of the living−oriented model; (<b>c</b>) Feature importance of production−oriented model; (<b>d</b>) Radar plot of the production−oriented model; (<b>e</b>) Feature importance of ecological−oriented model; (<b>f</b>) Radar plot of the ecological−oriented model.</p> Full article ">Figure 6
<p>The comparison between jobs–housing space and PLES. (<b>a</b>) Jobs–housing space identification results of the travel flow model considering the spatial distribution of public facilities. Reproduced from Zhang et al. [<a href="#B30-ijgi-12-00276" class="html-bibr">30</a>]; (<b>b</b>) Identification results of PLES. BS, Baisha Street; BH, Binhai Street; BA, Boai Street; BY, Binjiang Street &amp; MeiYuan Street; DT, Datong Street; GX, Guoxing Street; HD, Haidian Street; HF, Haifu Street; HK, Haiken Street; Hx1, Haixiu Street; HPN, Hepingnan Street; JM, Jinmao Street; JY, Jinyu Street; LT, Lantian Street; RML, Renminglu Street; XY, Xiuying Street; ZS, Zhongshan Street.</p> Full article ">Figure 7
<p>Results of the identification of PLES.</p> Full article ">Figure 8
<p>Kernel density analysis of PLES: (<b>a</b>) living space; (<b>b</b>) production space; (<b>c</b>) ecological space.</p> Full article ">
16 pages, 8302 KiB  
Article
Analysis of the Aggregation Characteristics and Influencing Elements of Urban Catering Points in Small Scale: Methods and Results
by Yuefeng Liu and Jiayi Yao
ISPRS Int. J. Geo-Inf. 2023, 12(7), 275; https://doi.org/10.3390/ijgi12070275 - 11 Jul 2023
Viewed by 1230
Abstract
Urban catering systems constitute an important subsystem of the complex urban system. They can reveal not only the impact of urban functional structure on the catering but also the behavioral patterns of individual catering points through the exploration of their small-scale aggregation characteristics [...] Read more.
Urban catering systems constitute an important subsystem of the complex urban system. They can reveal not only the impact of urban functional structure on the catering but also the behavioral patterns of individual catering points through the exploration of their small-scale aggregation characteristics and influencing elements, thus becoming an essential basis for urban functional planning. In this study, we analyze the aggregation characteristics of catering points in a particular study area using the probabilistic methods, with Beijing catering points as a sample. The analysis revealed a good power-law distribution characteristic of the catering points density at the small scale. Then, an aggregation effect analysis model and an agglomeration effect analysis model were established. Based on this, an empirical analysis of candidate agglomeration kernel elements was conducted. The results showed that the influence of candidate agglomeration kernel elements on catering points exhibited a categorical nature. Additionally, a good power-law attenuation relationship was uncovered between the density and distance of catering points, which ultimately revealed the mechanism of preferential attachment in the competition for catering point site selection. Using the results of the agglomeration analysis, a reasonable explanation was provided for the power-law distribution characteristic of the density of catering points, which achieved an organic connection between micro-analysis and macro-characteristic analysis. These findings could provide a reference for the analysis of aggregation characteristics of other urban commercial formats. Full article
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<p>Map of the Fifth Ring Road of Beijing.</p> Full article ">Figure 2
<p>The Power-Law Distribution Characteristics of Catering Points Density at Different Spatial Scales.</p> Full article ">Figure 3
<p>D-P Functions and Baseline D-P Functions of [<span class="html-italic">cp</span>→Candidate] and [Candidate→<span class="html-italic">cp</span>].</p> Full article ">Figure 4
<p>Distance–Proportion Coefficient Function of [<span class="html-italic">cp</span>→Candidate] and [Candidate→<span class="html-italic">cp</span>].</p> Full article ">Figure 5
<p>D-P Function Curves of [Candidate, <span class="html-italic">cp</span>].</p> Full article ">Figure 6
<p>The Schematic Diagram of D-S Function.</p> Full article ">Figure 7
<p>D-S Function of [Candidate→<span class="html-italic">cp</span>].</p> Full article ">Figure 8
<p>D-S<sup>a</sup> Function of [Candidate→<span class="html-italic">cp</span>].</p> Full article ">Figure 9
<p>Power-Law Characteristic of D-S Functions of [Candidate→<span class="html-italic">cp</span>].</p> Full article ">Figure 10
<p>Power-Law Characteristic of D-S<sup>a</sup> Functions of [Candidate→<span class="html-italic">cp</span>].</p> Full article ">Figure 11
<p><math display="inline"><semantics><mrow><msup><mrow><mi>A</mi><mi>r</mi><mi>e</mi><mi>a</mi></mrow><mo>′</mo></msup><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></semantics></math> Function Curves of [Candidate].</p> Full article ">
19 pages, 10569 KiB  
Article
Topic-Clustering Model with Temporal Distribution for Public Opinion Topic Analysis of Geospatial Social Media Data
by Chunchun Hu, Qin Liang, Nianxue Luo and Shuixiang Lu
ISPRS Int. J. Geo-Inf. 2023, 12(7), 274; https://doi.org/10.3390/ijgi12070274 - 8 Jul 2023
Viewed by 1591
Abstract
Analysis of the spatiotemporal distribution of online public opinion topics can help understand the hotspots of public concern. The topic model is employed widely in public opinion topic clustering for social media data. In order to handle topic-clustering of low-quality geospatial social media [...] Read more.
Analysis of the spatiotemporal distribution of online public opinion topics can help understand the hotspots of public concern. The topic model is employed widely in public opinion topic clustering for social media data. In order to handle topic-clustering of low-quality geospatial social media data, such as microblog data, with short text and timeliness characteristics, this study proposed a Dirichlet multinomial mixture over time (DMMOT) model to cluster microblog topic for public opinion analysis. The DMMOT model assumes that a single document belongs to a single topic, in line with the characteristics of a short text, and it introduces the probability distribution of “topic-time” in the process of topic generation. The model parameter inference process was presented in detail by exploring the Gibbs sampling method. Results generated using the DMMOT model in case study show that the “topic-word” distribution is semantically aggregated within various topics, and “topic-time” distribution clustered within a time window under each topic. Furthermore, the characteristics of the trend of each topic over time are basically consistent with the corresponding trend of topic in reality in terms of content. These indicate that the DMMOT model improves topic clustering for short text to some extent. Furthermore, the DMMOT model performed well in both temporal and spatial analysis of public opinion topics based on microblog data. Full article
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<p>Dirichlet multinomial mixture over time model.</p> Full article ">Figure 2
<p>Statistics result for the character count of the microblogs.</p> Full article ">Figure 3
<p>Variation trend of the number of check-in microblogs under different topics.</p> Full article ">Figure 4
<p>Kernel density estimation results of COVID-19-related check-in microblogs under different topic categories. (<b>a</b>) Topic 0; (<b>b</b>) Topic 1; (<b>c</b>) Topic 3; (<b>d</b>) Topic 5; (<b>e</b>)Topic 7; (<b>f</b>) Topic 9. All results were calculated with a bandwidth of 1000 m and cell size of 200 m × 200 m. The natural breaks (Jenks) method was used for hierarchical display.</p> Full article ">
30 pages, 17143 KiB  
Article
Spatial Pattern and Drivers of China’s Public Cultural Facilities between 2012 and 2020 Based on POI and Statistical Data
by Kaixu Zhao, Xiaoteng Cao, Fengqi Wu and Chao Chen
ISPRS Int. J. Geo-Inf. 2023, 12(7), 273; https://doi.org/10.3390/ijgi12070273 - 7 Jul 2023
Cited by 2 | Viewed by 2543
Abstract
In the context of globalization and the intensification of international competition, the construction of public cultural facilities has long been not limited to meeting the cultural needs of the people but has become an important initiative to shape the competitiveness of cities. This [...] Read more.
In the context of globalization and the intensification of international competition, the construction of public cultural facilities has long been not limited to meeting the cultural needs of the people but has become an important initiative to shape the competitiveness of cities. This paper collected POI and socio-economic statistics from 2012 to 2020 from 285 Chinese cities and employed the coefficient of variation (CV), Gini index (GI), ESDA, and GeoDetector to analyze the spatial patterns and driving mechanisms of public cultural facilities. Findings: (1) Public cultural facilities in Chinese cities were featured by evident regional gradient differences and uneven spatial distributions, with a CV greater than 1.3 and a GI greater than 0.5 in both years. They also showed signs of aggregation at weak levels, with a Moran I of 0.15 in both years and a cluster pattern of “hot in the east and cold in the west”. (2) Different types of public cultural facilities had differences in their differentiation, aggregation, and change trends. The CV changed from 1.39~2.69 to 1.06~1.92, and the GI changed from 0.53~0.80 to 0.47~0.62, with the differentiation of libraries, museums, theaters, art galleries, and cultural centers decreasing gradually, while that of exhibition halls increased day by day. As the Moran I increased from 0.08~0.20 to 0.12~0.24, libraries, museums, art galleries, and cultural centers showed weak aggregation with an increasingly strong trend. Theaters and exhibition halls also showed weak aggregation but in a declining trend, with the Moran I changing from 0.15~1.19 to 0.09~0.1. (3) The five driving variables exhibit significant differences in their strength across time and across regions, with the economic and infrastructure factors being the strongest and the urbanization factor the weakest. There are significant differences in the strength of the driving forces among the factors, with the total retail sales of consumers, the number of subscribers to internet services, regular higher education institutions, and undergraduates in regular HEIs playing both direct and interactive roles as the core factors. (4) The 285 cities in China are divided into four policy zonings of star, cow, question, and dog cities. Star cities should maintain their status quo without involving too much policy intervention, whereas the core and important factors should be the focus of policy in dog cities and cow cities, and the auxiliary factors should be the focus of policy in question cities. This paper contributes to the in-depth knowledge of the development pattern of public cultural facilities and provides a more refined basis for the formulation of public cultural facility promotion policies in China and similar countries. Full article
(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)
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<p>Study area.</p> Full article ">Figure 2
<p>Research steps.</p> Full article ">Figure 3
<p>Cluster maps of total cultural facilities.</p> Full article ">Figure 4
<p>Cluster maps of different cultural facilities in 2012 and 2020.</p> Full article ">Figure 5
<p>Spatial hot and cold maps of total cultural facilities.</p> Full article ">Figure 6
<p>Spatial hot and cold maps of different cultural facilities in 2012 and 2020.</p> Full article ">Figure 7
<p>Driving force in 2012 and 2020.</p> Full article ">Figure 8
<p>Driving mechanism of public cultural facilities in China. The super interactive factors of different regions are marked in red.</p> Full article ">Figure 9
<p>Policy zoning map of public cultural facilities in China.</p> Full article ">
20 pages, 8827 KiB  
Article
Assessment of Forest Ecological Security in China Based on DPSIRM Model: Taking 11 Provincial Administrative Regions along the Yangtze River Basin as Examples
by Yanlong Guo, Xingmeng Ma, Yelin Zhu, Denghang Chen and Han Zhang
ISPRS Int. J. Geo-Inf. 2023, 12(7), 272; https://doi.org/10.3390/ijgi12070272 - 7 Jul 2023
Cited by 7 | Viewed by 1590
Abstract
China’s forest ecological problems are becoming increasingly serious, especially in the Yangtze River Basin (YRB) area. The basin has rich species resources and a well-developed natural forest management and conservation policy. Taking the YRB as the object, we combine the DPSIRM model to [...] Read more.
China’s forest ecological problems are becoming increasingly serious, especially in the Yangtze River Basin (YRB) area. The basin has rich species resources and a well-developed natural forest management and conservation policy. Taking the YRB as the object, we combine the DPSIRM model to build a forest evaluation system containing 6 criterion layers and 24 indicator layers. The entropy weight method-TOPSIS and ArcGIS were combined to assess the forest state and the distribution characteristics of the 11 regions. Furthermore, grey relational analysis (GRA) was used to study the influencing factors of forest status. The results are as follows: (1) the comprehensive index of the YRB forests increased by 192.66% during the study period. The forest status showed the stage characteristics of small climb, basic flatness, and significant improvement. (2) The forest status varied significantly among provinces (cities), with Tibet (0.483) in the best condition and Qinghai (0.103) in a worse condition. (3) Except for Tibet, the rest of the regions are more influenced by the extent of development of the economy. (4) The factor most strongly correlated with the YRB is the forest response (R) indicator. Full article
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<p>YRB FES Methodology Process.</p> Full article ">Figure 2
<p>Study area of 11 provinces (cities) in the YRB, China.</p> Full article ">Figure 3
<p>DPSIRM Framework.</p> Full article ">Figure 4
<p>YRB Folding Map 2012–2021.</p> Full article ">Figure 5
<p>Relative Proximity of YRB Regions.</p> Full article ">Figure 6
<p>Forest Change Folding Line Chart for 11 Provinces (Cities), 2012–2021.</p> Full article ">Figure 7
<p>Trends in YRB by region in 2012, 2015, 2018, and 2021.</p> Full article ">Figure 8
<p>Distribution of evaluation levels by region in the YRB from 2012 to 2021.</p> Full article ">Figure 9
<p>The YRB provinces (cities) indicators’ correlation value ranking.</p> Full article ">
20 pages, 1927 KiB  
Article
Spatiotemporal Conflict Analysis and Prediction of Long Time Series Land Cover Changes in the Black Soil Region of Northeast China Using Remote Sensing and GIS
by Ding Ma, Sijia Jiang, Xin Tan, Mingyu Yang, Qingbin Jiao and Liang Xu
ISPRS Int. J. Geo-Inf. 2023, 12(7), 271; https://doi.org/10.3390/ijgi12070271 - 6 Jul 2023
Cited by 3 | Viewed by 1734
Abstract
Using remote sensing and GIS techniques to monitor long time series land cover changes is of great significance to understanding the impact of human activities on spatiotemporal conflicts and changes in cropland and forest ecosystems in the black soil region of Northeast China. [...] Read more.
Using remote sensing and GIS techniques to monitor long time series land cover changes is of great significance to understanding the impact of human activities on spatiotemporal conflicts and changes in cropland and forest ecosystems in the black soil region of Northeast China. Spatial analysis and dynamic degree were used to analyze the evolutionary process and spatiotemporal association of land cover from 1990 to 2020; the transfer matrix was used to analyze and reveal dynamic conversions of land cover from 1990 to 2000, 2000 to 2010, and 2010 to 2020; and the GM (1,1) model was used to forecast the changes in land cover by 2025 based on historical data. The results indicated that the dominance of forest and cropland did not change from 1990 to 2020, and the average area of forest and cropland was 512,713 km2 and 486,322 km2, respectively. The mutual conversion between cropland, forest, grassland, and bare areas was the most frequent. The area of cropland converted into forest and grassland was 14,167 km2 and 25,217 km2, respectively, and the area of forest and grassland converted into cropland was 27,682 km2 and 23,764 km2, respectively, from 1990 to 2000. A similar law of land cover change was also presented from 2000 to 2020. In addition, the predicted values of cropland, forest, grassland, shrubland, wetland, water bodies, impervious surfaces, and bare areas were 466,942 km2, 499,950 km2, 231,524 km2, 1329 km2, 11,775 km2, 18,453 km2, 30,549 km2, and 189,973 km2, respectively, by 2025. The maximum and minimum residuals between the predicted and actual values were 6241 km2 and −156 km2 from 1990 to 2020. The evaluation results of the GM (1,1) model showed that all of the evaluation indices were within an acceptable range, and that the posteriori error ratio and class ratio dispersion were both less than 0.25. Through comparison with other studies, this study is not only able to provide some experience for further analyzing the spatial and temporal changes in land cover and its future prediction but also provide a basis for comprehensive management in Northeast China. Full article
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<p>Distribution of the main soil types in Northeast China. Data source: a soil map (a million-scale soil map) was downloaded from the second national soil survey. The map of China and vector boundaries were from the Ministry of Natural Resources: GS(2020)3184.</p> Full article ">Figure 2
<p>Land cover of Northeast China from 1990 to 2020.</p> Full article ">Figure 3
<p>Change trends in cropland, forest, grassland, and bare areas from 1990 to 2020. The <span class="html-italic">Y</span><sub>1</sub> axis (black) was used to describe the changes in forest and cropland areas. The <span class="html-italic">Y</span><sub>2</sub> axis (red) was used to describe the changes in grassland and bare areas.</p> Full article ">
23 pages, 8123 KiB  
Article
Construction Method for a Three-Dimensional Tunnel General Monomer Model Based on Parallel Pathfinding
by Jiaming Ye, Defu Che, Baodong Ma, Quan Liu, Kehan Qiu and Xiangxiang Shang
ISPRS Int. J. Geo-Inf. 2023, 12(7), 270; https://doi.org/10.3390/ijgi12070270 - 6 Jul 2023
Cited by 3 | Viewed by 2065
Abstract
Existing approaches for the 3D modeling of tunnels suffer from several problems, such as highly difficult data acquisition, redundancy of model data, large computational burden, and the inability of the resulting models to be monolithic. Therefore, solutions to the tunnel network modeling problem [...] Read more.
Existing approaches for the 3D modeling of tunnels suffer from several problems, such as highly difficult data acquisition, redundancy of model data, large computational burden, and the inability of the resulting models to be monolithic. Therefore, solutions to the tunnel network modeling problem for complex structures need to be proposed and elaborated in detail. In this paper, a construction method for a three-dimensional tunnel general monomer model based on parallel pathfinding is proposed. Widely used tunnel CAD drawings are analyzed and read, a disordered arc ensemble intersection trend decision method is developed, and an automatic path extraction solution algorithm for unidirectional modeling of tunnel centerlines is constructed. By constructing and splicing the surface elements of the 3D model, a monomeric 3D tunnel model representing the complex network structure is finally obtained. Moreover, the modeling of shafts is realized based on the monomer model, allowing for the three-dimensional topological relationships between different sub-levels of the tunnel and the ground to be established. The automatic modeling method proposed in this paper is applied to the digital twin platform of a filling project in a mining area in Gansu province, China. The experimental results demonstrate that the 3D tunnel models constructed in this way have a smaller data volume, higher modeling accuracy, and more stable growth of modeling speed. Full article
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<p>Technical route of automated 3D tunnel modeling.</p> Full article ">Figure 2
<p>Mathematical coordinate system and measuring coordinate system: (<b>a</b>) mathematical coordinate system with quadrants arranged counterclockwise; (<b>b</b>) measuring coordinate system with quadrants arranged clockwise.</p> Full article ">Figure 3
<p>The direction judgment for tunnel intersection pathfinding: (<b>a</b>) ordinary inflection point; (<b>b</b>) azimuth <math display="inline"><semantics><mrow><msub><mi mathvariant="sans-serif">α</mi><mrow><mn>21</mn></mrow></msub></mrow></semantics></math> is the largest; (<b>c</b>) azimuth <math display="inline"><semantics><mrow><msub><mi mathvariant="sans-serif">α</mi><mrow><mn>21</mn></mrow></msub></mrow></semantics></math> is the smallest; (<b>d</b>) azimuth <math display="inline"><semantics><mrow><msub><mi mathvariant="sans-serif">α</mi><mrow><mn>21</mn></mrow></msub></mrow></semantics></math> is neither the largest nor smallest.</p> Full article ">Figure 4
<p>Flow chart of pathfinding tunnel modeling method.</p> Full article ">Figure 5
<p>Extraction of tunnel unidirectional modeling paths: (<b>a</b>) extract suspension points, which are marked by red points in the figure; (<b>b</b>) parallel pathfinding begins, with paths marked by blue lines; (<b>c</b>) parallel pathfinding process; (<b>d</b>) parallel pathfinding process; (<b>e</b>) complete path extraction with suspension points; (<b>f</b>) complete closed path extraction.</p> Full article ">Figure 5 Cont.
<p>Extraction of tunnel unidirectional modeling paths: (<b>a</b>) extract suspension points, which are marked by red points in the figure; (<b>b</b>) parallel pathfinding begins, with paths marked by blue lines; (<b>c</b>) parallel pathfinding process; (<b>d</b>) parallel pathfinding process; (<b>e</b>) complete path extraction with suspension points; (<b>f</b>) complete closed path extraction.</p> Full article ">Figure 6
<p>Cross-sectional view of a circular arch tunnel.</p> Full article ">Figure 7
<p>Diagram of the process of solving 3D coordinates: (<b>a</b>) obtain the intersection point of the tunnel boundary lines; (<b>b</b>) obtain the offset vector corresponding to the inflection point; (<b>c</b>) calculate the offset values of each section point in the tunnel 2D local coordinate system.</p> Full article ">Figure 8
<p>Construction of tunnel model: (<b>a</b>) connect the corresponding section points, which are marked by blue points in the figure; (<b>b</b>) construct the surface elements of the model.</p> Full article ">Figure 9
<p>The relative relationship between a shaft and a tunnel: (<b>a</b>) the shaft projection is divided into four regions on the straight tunnel (A, B, C, and D); (<b>b</b>) the shaft projection is divided into four regions on the turning point of tunnel (A, B, C, and D); (<b>c</b>) the shaft projection is divided into six regions on the tunnel intersection (A, B, C, D, E and F).</p> Full article ">Figure 10
<p>The joint of the vault and floor of a tunnel with a shaft: (<b>a</b>) tunnel vault and floor; (<b>b</b>) the intersection of a shaft and a tunnel vault, where the dotted line is the line of the corresponding points of the section, the red dotted line indicates that the line of the corresponding points of the section intersects the shaft projection; (<b>c</b>) the intersection of a shaft and the floor of a tunnel, the green area is the new area after the floor or ground is divided.</p> Full article ">Figure 11
<p>The intersection of the turning point of a tunnel and a shaft: (<b>a</b>) angle of turning point of tunnel; (<b>b</b>) the intersection area is further segmented, region A is divided into two parts, <math display="inline"><semantics><mrow><msub><mi mathvariant="normal">A</mi><mn>1</mn></msub></mrow></semantics></math> and <math display="inline"><semantics><mrow><msub><mi mathvariant="normal">A</mi><mn>2</mn></msub></mrow></semantics></math>.</p> Full article ">Figure 12
<p>Parameter setting for tunnel section, and tunnel sections are marked with blue lines.</p> Full article ">Figure 13
<p>Comparison of CAD drawing and 3D model of tunnels: (<b>a</b>) CAD drawings of tunnels; (<b>b</b>) the result of tunnel modeling.</p> Full article ">Figure 14
<p>Tunnel sub-segment monomer modeling: (<b>a</b>) monomeric tunnel model; (<b>b</b>) monomeric tunnel model; and (<b>c</b>) interactive operation of the monomeric tunnel model.</p> Full article ">Figure 15
<p>Shaft 3D modeling: (<b>a</b>) the effect of shaft modeling; and (<b>b</b>) application of shaft model in a digital twin platform.</p> Full article ">Figure 16
<p>Application of 3D tunnel network model in a digital twin platform.</p> Full article ">Figure 17
<p>Application examples of channel modeling: (<b>a</b>) the effect of channel modeling; and (<b>b</b>) rendering of ditch modeling in a practical application.</p> Full article ">
24 pages, 14086 KiB  
Article
Spatiotemporal Data-Driven Multiperiod Relocation Optimization of Emergency Medical Services: Maximum Equality Objective
by Xinxin Zhou, Yujie Chen, Yingying Li, Bingjie Liu and Zhaoyuan Yu
ISPRS Int. J. Geo-Inf. 2023, 12(7), 269; https://doi.org/10.3390/ijgi12070269 - 5 Jul 2023
Cited by 1 | Viewed by 1674
Abstract
As a kind of first aid healthcare service, emergency medical services (EMSs) present high spatiotemporal sensitivity due to significant changes in the time-dependent urban environment. Taking full advantage of big spatiotemporal data to realize multiperiod relocation optimization of EMSs can reduce idle resources [...] Read more.
As a kind of first aid healthcare service, emergency medical services (EMSs) present high spatiotemporal sensitivity due to significant changes in the time-dependent urban environment. Taking full advantage of big spatiotemporal data to realize multiperiod relocation optimization of EMSs can reduce idle resources and improve service utilization efficiency and fairness. First, we established the dynamic time-dependent accessibility and equality model to formulate the multiperiod maximization objective of global equality. Second, we proposed a capacitated integer evolution algorithm that relocates emergency medical vehicles to optimize the scheduling scheme. Based on multiperiod mobile phone records and multiperiod online route planner data, the equality of EMSs in the research metropolis, Nanjing, China, rose by 41.5% on average, which has an incentivizing effect on alleviating the tension of prehospital service and minimizes accessibility disparities without constructing more infrastructure. We also created maps to visualize the changes in equality patterns over time. This relocation optimization approach can be regarded as a trade-off approach to dispatch time-dependent sensitive services and provide a practical tool for healthcare decision-makers to evaluate public healthcare systems and improve strategic urban service planning. Full article
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<p>Outline of the proposed approach.</p> Full article ">Figure 2
<p>Algorithm flow chart of CIEA.</p> Full article ">Figure 3
<p>The operators’ diagrams of CIEA. (<b>a</b>) a multiperiod solution example based on real number encoding; (<b>b</b>) the chromosome generation example at 8:00, where <math display="inline"><semantics><mrow><mi>b</mi><mi>o</mi><mi>t</mi><mi>t</mi><mi>o</mi><mi>m</mi></mrow></semantics></math> = 1, <math display="inline"><semantics><mrow><mi>t</mi><mi>o</mi><mi>p</mi></mrow></semantics></math> = 10, <span class="html-italic">y</span> = 20, and the number of EMS stations is 5; (<b>c</b>) the crossover operation; and (<b>d</b>) the mutation operation. To facilitate the description, we consider the crossover operation and mutation operation process of chromosomes at 8 o’clock.</p> Full article ">Figure 4
<p>The study area.</p> Full article ">Figure 5
<p>Diagram of M-ORP data. (<b>a</b>) each first aid station radiating to the surrounding areas within a 30-min driving threshold during the morning commuter peak. (<b>b</b>–<b>e</b>) examples of M-ORP data from ZhongDa Hospital to a potential emergency site at 8:00, 13:00, 18:00, and 22:00. (<b>b</b>–<b>e</b>) serve as a notable visual representation of varying degrees of traffic congestion, thereby exerting an influence on the overall duration of traffic.</p> Full article ">Figure 6
<p>Distribution map of the population based on M-MPR data.</p> Full article ">Figure 7
<p>Experimental calculation convergence graphs of 15 groups for the CIEA method. The subfigures (<b>a1</b>–<b>a3</b>,<b>b1</b>–<b>b3</b>,<b>c1</b>–<b>c3</b>,<b>d1</b>–<b>d3</b>,<b>e1</b>–<b>e3</b>) are associated with their respective control groups (A1–E3), as delineated in <a href="#ijgi-12-00269-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 8
<p>The parallel coordinate system diagrams after relocation optimization at four time slots. (<b>a</b>) ID of EMS stations in Nanjing, (<b>b</b>) zoom map of downtown part, (<b>c</b>–<b>m</b>) relocation strategy of each EMS station at four time slots after optimization in differ districts, refers to Gulou, Yuhuatai, Qinhuai, Lishui, Liuhe, Pukou, Gaochun, Qixia, Jiangning, Jianye, Xuanwu.</p> Full article ">Figure 9
<p>Spatial distribution maps of equality at different time slots before optimization. The spatial distribution maps of equality before optimization include (<b>a</b>) 8:00 (morning commuter peak), (<b>b</b>) 13:00 (daytime commuter trough), (<b>c</b>) 18:00 (evening commuter peak), and (<b>d</b>) 22:00 (nighttime commuter trough).</p> Full article ">Figure 10
<p>Spatial distribution maps of equality at different time slots after optimization. Additionally, the spatial distribution maps of equality after optimization include (<b>a</b>) 8:00, (<b>b</b>) 13:00, (<b>c</b>) 18:00, and (<b>d</b>) 22:00.</p> Full article ">Figure 11
<p>Box plot comparison before and after optimization.</p> Full article ">
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