ISPRS International Journal of Geo-Information

19 pages, 9783 KiB

Open AccessArticle

Fine-Grained Metro-Trip Detection from Cellular Trajectory Data Using Local and Global Spatial–Temporal Characteristics

by Guanyao Li, Ruyu Xu, Tingyan Shi, Xingdong Deng, Yang Liu, Deshi Di, Chuanbao Zhao and Guochao Liu

ISPRS Int. J. Geo-Inf. 2024, 13(9), 314; https://doi.org/10.3390/ijgi13090314 - 30 Aug 2024

Viewed by 295

Abstract

A fine-grained metro trip contains complete information on user mobility, including the original station, destination station, departure time, arrival time, transfer station(s), and corresponding transfer time during the metro journey. Understanding such detailed trip information within a city is crucial for various smart [...] Read more.

A fine-grained metro trip contains complete information on user mobility, including the original station, destination station, departure time, arrival time, transfer station(s), and corresponding transfer time during the metro journey. Understanding such detailed trip information within a city is crucial for various smart city applications, such as effective urban planning and public transportation system optimization. In this work, we study the problem of detecting fine-grained metro trips from cellular trajectory data. Existing trip-detection approaches designed for GPS trajectories are often not applicable to cellular data due to the issues of location noise and irregular data sampling in cellular data. Moreover, most cellular data-based methods focus on identifying coarse-grained transportation modes, failing to detect fine-grained metro trips accurately. To address the limitations of existing works, we propose a novel and efficient fine-grained metro-trip detection (FGMTD) model in this work. By considering both the local and global spatial–temporal characteristics of a trajectory and the metro network, FGMTD can effectively mitigate the effects of location noise and irregular data sampling, ultimately improving the accuracy and reliability of the detection process. In particular, FGMTD employs a spatial–temporal hidden Markov model with efficient index strategies to capture local spatial–temporal characteristics from individual positions and metro stations, and a weighted trip-route similarity measure to consider global spatial–temporal characteristics from the entire trajectory and metro route. We conduct extensive experiments on two real datasets to evaluate the effectiveness and efficiency of our proposed approaches. The first dataset contains cellular data from 30 volunteers, including their actual trip details, while the second dataset consists of data from 4 million users. The experiments illustrate the significant accuracy of our approach (with a precision of

87.80 %

and a recall of

84.28 %

). Moreover, we demonstrate that FGMTD is efficient in detecting fine-grained trips from a large amount of cellular data, achieving this task within 90 min of processing a day’s data from 4 million users. Full article

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26 pages, 5025 KiB

Open AccessArticle

Navigating Immovable Assets: A Graph-Based Spatio-Temporal Data Model for Effective Information Management

by Muhammad Syafiq, Suhaibah Azri and Uznir Ujang

ISPRS Int. J. Geo-Inf. 2024, 13(9), 313; https://doi.org/10.3390/ijgi13090313 - 30 Aug 2024

Viewed by 189

Abstract

Asset management is a process that deals with numerous types of data, including spatial and temporal data. Such an occurrence is attributed to the proliferation of information sources. However, the lack of a comprehensive asset data model that encompasses the management of both [...] Read more.

Asset management is a process that deals with numerous types of data, including spatial and temporal data. Such an occurrence is attributed to the proliferation of information sources. However, the lack of a comprehensive asset data model that encompasses the management of both spatial and temporal data remains a challenge. Therefore, this paper proposes a graph-based spatio-temporal data model to integrate spatial and temporal information into asset management. In the spatial layer, we provide a graph-based method that uses topological containment and connectivity relationships to model the interior building space using data from 3D city models. In the temporal layer, we proposed the Aggregated Directly-Follows Multigraph (ADFM), a novel process model based on a directly-follows graph (DFG), to show the chronological flow of events in asset management by taking into consideration the repetitive nature of events in asset management. The integration of both layers allows spatial, temporal, and spatio-temporal queries to be made regarding information about events in asset management. This method offers a more straightforward query, which helps to eliminate duplicate and false query results when assessed and compared with a flattened graph event log. Finally, this paper provides information for the management of 3D spaces using a NoSQL graph database and the management of events and their temporal information through graph modelling. Full article

(This article belongs to the Topic Geospatial Knowledge Graph)

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24 pages, 32875 KiB

Open AccessArticle

Integrating Sequential Backward Selection (SBS) and CatBoost for Snow Avalanche Susceptibility Mapping at Catchment Scale

by Sinem Cetinkaya and Sultan Kocaman

ISPRS Int. J. Geo-Inf. 2024, 13(9), 312; https://doi.org/10.3390/ijgi13090312 - 29 Aug 2024

Viewed by 289

Abstract

Snow avalanche susceptibility (AS) mapping is a crucial step in predicting and mitigating avalanche risks in mountainous regions. The conditioning factors used in AS modeling are diverse, and the optimal set of factors depends on the environmental and geological characteristics of the region. [...] Read more.

Snow avalanche susceptibility (AS) mapping is a crucial step in predicting and mitigating avalanche risks in mountainous regions. The conditioning factors used in AS modeling are diverse, and the optimal set of factors depends on the environmental and geological characteristics of the region. Using a sub-optimal set of input features with a data-driven machine learning (ML) method can lead to challenges like dealing with high-dimensional data, overfitting, and reduced model generalization. This study implemented a robust framework involving the Sequential Backward Selection (SBS) algorithm and a decision-tree based ML model, CatBoost, for the automatic selection of predictive variables for AS mapping. A comprehensive inventory of a large avalanche period, previously derived from satellite images, was used for the investigations in three distinct catchment areas in the Swiss Alps. The integrated SBS-CatBoost approach achieved very high classification accuracies between 94% and 97% for the three catchments. In addition, the Shapley additive explanations (SHAP) method was employed to analyze the contributions of each feature to avalanche occurrences. The proposed methodology revealed the benefits of integrating advanced feature selection algorithms with ML techniques for AS assessment. We aimed to contribute to avalanche hazard knowledge by assessing the impact of each feature in model learning. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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21 pages, 4584 KiB

Open AccessArticle

CSMNER: A Toponym Entity Recognition Model for Chinese Social Media

by Yuyang Qi, Renjian Zhai, Fang Wu, Jichong Yin, Xianyong Gong, Li Zhu and Haikun Yu

ISPRS Int. J. Geo-Inf. 2024, 13(9), 311; https://doi.org/10.3390/ijgi13090311 - 29 Aug 2024

Viewed by 172

Abstract

In the era of information explosion, Chinese social media has become a repository for massive geographic information; however, its unique unstructured nature and diverse expressions are challenging to toponym entity recognition. To address this problem, we propose a Chinese social media named entity [...] Read more.

In the era of information explosion, Chinese social media has become a repository for massive geographic information; however, its unique unstructured nature and diverse expressions are challenging to toponym entity recognition. To address this problem, we propose a Chinese social media named entity recognition (CSMNER) model to improve the accuracy and robustness of toponym recognition in Chinese social media texts. By combining the BERT (Bidirectional Encoder Representations from Transformers) pre-trained model with an improved IDCNN-BiLSTM-CRF (Iterated Dilated Convolutional Neural Network- Bidirectional Long Short-Term Memory- Conditional Random Field) architecture, this study innovatively incorporates a boundary extension module to effectively extract the local boundary features and contextual semantic features of the toponym, successfully addressing the recognition challenges posed by noise interference and language expression variability. To verify the effectiveness of the model, experiments were carried out on three datasets: WeiboNER, MSRA, and the Chinese social named entity recognition (CSNER) dataset, a self-built named entity recognition dataset. Compared with the existing models, CSMNER achieves significant performance improvement in toponym recognition tasks. Full article

(This article belongs to the Special Issue Unlocking the Power of Geospatial Data: Semantic Information Extraction, Ontology Engineering, and Deep Learning for Knowledge Discovery)

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24 pages, 7145 KiB

Open AccessArticle

On the Theoretical Link between Optimized Geospatial Conflation Models for Linear Features

by Zhen Lei, Zhangshun Yuan and Ting L. Lei

ISPRS Int. J. Geo-Inf. 2024, 13(9), 310; https://doi.org/10.3390/ijgi13090310 - 29 Aug 2024

Viewed by 233

Abstract

Geospatial data conflation involves matching and combining two maps to create a new map. It has received increased research attention in recent years due to its wide range of applications in GIS (Geographic Information System) data production and analysis. The map assignment problem [...] Read more.

Geospatial data conflation involves matching and combining two maps to create a new map. It has received increased research attention in recent years due to its wide range of applications in GIS (Geographic Information System) data production and analysis. The map assignment problem (conceptualized in the 1980s) is one of the earliest conflation methods, in which GIS features from two maps are matched by minimizing their total discrepancy or distance. Recently, more flexible optimization models have been proposed. This includes conflation models based on the network flow problem and new models based on Mixed Integer Linear Programming (MILP). A natural question is: how are these models related or different, and how do they compare? In this study, an analytic review of major optimized conflation models in the literature is conducted and the structural linkages between them are identified. Moreover, a MILP model (the base-matching problem) and its bi-matching version are presented as a common basis. Our analysis shows that the assignment problem and all other optimized conflation models in the literature can be viewed or reformulated as variants of the base models. For network-flow based models, proof is presented that the base-matching problem is equivalent to the network-flow based fixed-charge-matching model. The equivalence of the MILP reformulation is also verified experimentally. For the existing MILP-based models, common notation is established and used to demonstrate that they are extensions of the base models in straight-forward ways. The contributions of this study are threefold. Firstly, it helps the analyst to understand the structural commonalities and differences of current conflation models and to choose different models. Secondly, by reformulating the network-flow models (and therefore, all current models) using MILP, the presented work eases the practical application of conflation by leveraging the many off-the-shelf MILP solvers. Thirdly, the base models can serve as a common ground for studying and writing new conflation models by allowing a modular and incremental way of model development. Full article

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22 pages, 18580 KiB

Open AccessArticle

An Efficient Algorithm for Extracting Railway Tracks Based on Spatial-Channel Graph Convolutional Network and Deep Neural Residual Network

by Yanbin Weng, Meng Xu, Xiahu Chen, Cheng Peng, Hui Xiang, Peixin Xie and Hua Yin

ISPRS Int. J. Geo-Inf. 2024, 13(9), 309; https://doi.org/10.3390/ijgi13090309 - 29 Aug 2024

Viewed by 249

Abstract

The accurate detection of railway tracks is essential for ensuring the safe operation of railways. This study introduces an innovative algorithm that utilizes a graph convolutional network (GCN) and deep neural residual network to enhance feature extraction from high-resolution aerial imagery. The traditional [...] Read more.

The accurate detection of railway tracks is essential for ensuring the safe operation of railways. This study introduces an innovative algorithm that utilizes a graph convolutional network (GCN) and deep neural residual network to enhance feature extraction from high-resolution aerial imagery. The traditional encoder–decoder architecture is expanded with GCN, which improves neighborhood definitions and enables long-range information exchange in a single layer. As a result, complex track features and contextual information are captured more effectively. The deep neural residual network, which incorporates depthwise separable convolution and an inverted bottleneck design, improves the representation of long-distance positional information and addresses occlusion caused by train carriages. The scSE attention mechanism reduces noise and optimizes feature representation. The algorithm was trained and tested on custom and Massachusetts datasets, demonstrating an 89.79% recall rate. This is a 3.17% improvement over the original U-Net model, indicating excellent performance in railway track segmentation. These findings suggest that the proposed algorithm not only excels in railway track segmentation but also offers significant competitive advantages in performance. Full article

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16 pages, 2024 KiB

Open AccessArticle

Association between Autism Spectrum Disorder and Environmental Quality in the United States

by Jianyong Wu, Alexander C. McLain, Paul Rosile and Darryl B. Hood

ISPRS Int. J. Geo-Inf. 2024, 13(9), 308; https://doi.org/10.3390/ijgi13090308 - 29 Aug 2024

Viewed by 220

Abstract

Autism spectrum disorder (ASD) has become an emerging public health problem. The impact of multiple environmental factors on the prevalence of ASD remains unclear. This study examined the association between the prevalence of ASD and the environmental quality index (EQI), an indicator of [...] Read more.

Autism spectrum disorder (ASD) has become an emerging public health problem. The impact of multiple environmental factors on the prevalence of ASD remains unclear. This study examined the association between the prevalence of ASD and the environmental quality index (EQI), an indicator of cumulative environmental quality in five major domains, including air, water, land, built and sociodemographic variables in the United States. The results from Poisson regression models show that the prevalence of ASD has a positive association with the overall EQI with a risk ratio (RR) of 1.03 and 95% confidence intervals (CI) of 1.01–1.06, indicating that children in counties with poor environmental quality might have a higher risk of ASD. Additionally, the prevalence of ASD has a positive association with the air index (RR = 1.04, 95% CI: 1.01–1.06). These associations varied in different rural–urban groups and different climate regions. This study provided evidence for adverse effects of poor environmental quality, particularly air pollutants, on children’s neurodevelopment. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)

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19 pages, 1255 KiB

Open AccessArticle

Retrospective Analysis of Municipal Geoportal Usability in the Context of the Evolution of Online Data Presentation Techniques

by Karol Król

ISPRS Int. J. Geo-Inf. 2024, 13(9), 307; https://doi.org/10.3390/ijgi13090307 - 28 Aug 2024

Viewed by 297

Abstract

This article aims to assess the usability of selected map portals with a checklist. The methods employed allowed the author to conduct user experience tests from a longer temporal perspective against a retrospective analysis of the evolution of design techniques for presenting spatial [...] Read more.

This article aims to assess the usability of selected map portals with a checklist. The methods employed allowed the author to conduct user experience tests from a longer temporal perspective against a retrospective analysis of the evolution of design techniques for presenting spatial data online. The author performed user experience tests on three versions of Tomice Municipality’s geoportal available on the Internet. The desktop and mobile laboratory tests were performed by fourteen experts following a test scenario. The study employs the exploratory approach, inspection method, and System Usability Scale (SUS). The author calculated the Geoportal Overall Quality (GOQ) index to better illustrate the relationships among the subjective perceptions of the usability quality of the three geoportals. The usability results were juxtaposed with performance measurements. Normalised and aggregated results of user experience demonstrated that the expert assessments of the usability of geoportals G1 and G3 on mobile devices were similar despite significant development differences. The overall results under the employed research design have confirmed that geoportal G2 offers the lowest usability in both mobile and desktop modes. The study has demonstrated that some websites can retain usability even considering the dynamic advances in hardware and software despite their design, which is perceived as outdated today. Users still expect well-performing and quick map applications, even if this means limited functionality and usability. Moreover, the results indirectly show that the past resolution of the ‘large raster problem’ led to the aggravation of the issue of ‘large scripts’. Full article

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22 pages, 29298 KiB

Open AccessArticle

Landslide Recognition Based on Machine Learning Considering Terrain Feature Fusion

by Jincan Wang, Zhiheng Wang, Liyao Peng and Chenzhihao Qian

ISPRS Int. J. Geo-Inf. 2024, 13(9), 306; https://doi.org/10.3390/ijgi13090306 - 28 Aug 2024

Viewed by 316

Abstract

Landslides are one of the major disasters that exist worldwide, posing a serious threat to human life and property safety. Rapid and accurate detection and mapping of landslides are crucial for risk assessment and humanitarian assistance in affected areas. To achieve this goal, [...] Read more.

Landslides are one of the major disasters that exist worldwide, posing a serious threat to human life and property safety. Rapid and accurate detection and mapping of landslides are crucial for risk assessment and humanitarian assistance in affected areas. To achieve this goal, this study proposes a landslide recognition method based on machine learning (ML) and terrain feature fusion. Taking the Dawan River Basin in Detuo Township and Tianwan Yi Ethnic Township as the research area, firstly, landslide-related data were compiled, including a landslide inventory based on field surveys, satellite images, historical data, high-resolution remote sensing images, and terrain data. Then, different training datasets for landslide recognition are constructed, including full feature datasets that fusion terrain features and remote sensing features and datasets that only contain remote sensing features. At the same time, different ratios of landslide to non-landslide (or positive/negative, P/N) samples are set in the training data. Subsequently, five ML algorithms, including Extreme Gradient Boost (XGBoost), Adaptive Boost (AdaBoost), Light Gradient Boost (LightGBM), Random Forest (RF), and Convolutional Neural Network (CNN), were used to train each training dataset, and landslide recognition was performed on the validation area. Finally, accuracy (A), precision (P), recall (R), F1 score (F1), and intersection over union (IOU) were selected to evaluate the landslide recognition ability of different models. The research results indicate that selecting ML models suitable for the study area and the ratio of the P/N samples can improve the A, R, F1, and IOU of landslide identification results, resulting in more accurate and reasonable landslide identification results; Fusion terrain features can make the model recognize landslides more comprehensively and align better with the actual conditions. The best-performing model in the study is LightGBM. When the input data includes all features and the P/N sample ratio is optimal, the A, P, R, F1, and IOU of landslide recognition results for this model are 97.47%, 85.40%, 76.95%, 80.95%, and 71.28%, respectively. Compared to the landslide recognition results using only remote sensing features, this model shows improvements of 4.51%, 35.66%, 5.41%, 22.27%, and 29.16% in A, P, R, F1, and IOU, respectively. This study serves as a valuable reference for the precise and comprehensive identification of landslide areas. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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16 pages, 2073 KiB

Open AccessArticle

Investigating Resident–Tourist Sharing of Urban Public Recreation Space and Its Influencing Factors

by Yanan Tang, Lin Li, Yilin Gan and Shuangyu Xie

ISPRS Int. J. Geo-Inf. 2024, 13(9), 305; https://doi.org/10.3390/ijgi13090305 - 26 Aug 2024

Viewed by 324

Abstract

Urban public recreation space (UPRS) is an integral part of the urban public space system. With the rise of urban tourism, these areas have evolved into important spaces for leisure and entertainment, serving both residents and tourists. However, the extent to which these [...] Read more.

Urban public recreation space (UPRS) is an integral part of the urban public space system. With the rise of urban tourism, these areas have evolved into important spaces for leisure and entertainment, serving both residents and tourists. However, the extent to which these spaces are shared by the two groups remains unclear. This study quantified the level of UPRS equally shared by residents and tourists in Wuhan, China, using geotagged check-in data from 74 UPRS. We evaluated and compared the resident–tourist sharing degree across various types of UPRS and explored its influencing factors using multiple linear regression (MLR). The results indicated the following: (1) The sharing degree was at a moderate level and it varied significantly across different types of UPRS. (2) Characteristic streets had the highest sharing degree, followed by cultural spaces, urban parks, and tourist scenic spots. (3) The number of nearby tourist attractions, road density, and number of transport stops positively affected sharing degree. These findings suggest that the combination layout of UPRS with other tourist attractions and enhanced accessibility can effectively improve the shared usage of UPRS. Full article

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16 pages, 6570 KiB

Open AccessArticle

Spatial and Temporal Dynamics in Vegetation Greenness and Its Response to Climate Change in the Tarim River Basin, China

by Kai Jin, Yansong Jin, Cuijin Li and Lin Li

ISPRS Int. J. Geo-Inf. 2024, 13(9), 304; https://doi.org/10.3390/ijgi13090304 - 26 Aug 2024

Viewed by 390

Abstract

Vegetation in ecologically sensitive regions has experienced significant alterations due to global climate change. The underlying mechanisms remain somewhat obscure owing to the spatial heterogeneity of influencing factors, particularly in the Tarim River Basin (TRB) in China. Therefore, this study targets the TRB, [...] Read more.

Vegetation in ecologically sensitive regions has experienced significant alterations due to global climate change. The underlying mechanisms remain somewhat obscure owing to the spatial heterogeneity of influencing factors, particularly in the Tarim River Basin (TRB) in China. Therefore, this study targets the TRB, analyzing the spatial and temporal dynamics of vegetation greenness and its climatic determinants across multiple spatial scales. Utilizing Normalized Difference Vegetation Index (NDVI) data, vegetation greenness trends over the past 23 years were assessed, with future projections based on the Hurst exponent. Partial correlation and multiple linear regression analyses were employed to correlate NDVI with temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET), elucidating NDVI’s response to climatic variations. Results revealed that from 2000 to 2022, 90.1% of the TRB exhibited an increase in NDVI, with a significant overall trend of 0.032/decade (p < 0.01). The difference in NDVI change across sub-basins and vegetation types highlighted the spatial disparity in greening. Notable greening predominantly occurred near rivers at lower elevations and in extensive cropland areas, with projections indicating continued greening in some regions. Conversely, future trends mainly suggested a shift towards browning, particularly in higher-elevation areas with minimal human influence. From 2000 to 2022, the TRB experienced a gradual increase in TMP, PRE, and PET. The latter two factors were significantly correlated with NDVI, indicating their substantial role in greening. However, vegetation sensitivity to climate change varied across sub-basins, vegetation types, and elevations, likely due to differences in plant characteristics, hydrothermal conditions, and human disturbances. Despite climate change influencing vegetation dynamics in 51.5% of the TRB, its impact accounted for only 25% of the total NDVI trend. These findings enhance the understanding of vegetation ecosystems in arid regions and provide a scientific basis for developing ecological protection strategies in the TRB. Full article

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27 pages, 22313 KiB

Open AccessArticle

Landslide Risk Assessments through Multicriteria Analysis

by Fatma Zohra Chaabane, Salim Lamine, Mohamed Said Guettouche, Nour El Islam Bachari and Nassim Hallal

ISPRS Int. J. Geo-Inf. 2024, 13(9), 303; https://doi.org/10.3390/ijgi13090303 - 25 Aug 2024

Viewed by 793

Abstract

Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum [...] Read more.

Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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20 pages, 16980 KiB

Open AccessArticle

A Dempster–Shafer Enhanced Framework for Urban Road Planning Using a Model-Based Digital Twin and MCDM Techniques

by Zahra Maserrat, Ali Asghar Alesheikh, Ali Jafari, Neda Kaffash Charandabi and Javad Shahidinejad

ISPRS Int. J. Geo-Inf. 2024, 13(9), 302; https://doi.org/10.3390/ijgi13090302 - 25 Aug 2024

Viewed by 570

Abstract

Rapid urbanization in developing countries presents a critical challenge in the need for extensive and appropriate road expansion, which in turn contributes to traffic congestion and air pollution. Urban areas are economic engines, but their efficiency and livability rely on well-designed road networks. [...] Read more.

Rapid urbanization in developing countries presents a critical challenge in the need for extensive and appropriate road expansion, which in turn contributes to traffic congestion and air pollution. Urban areas are economic engines, but their efficiency and livability rely on well-designed road networks. This study proposes a novel approach to urban road planning that leverages the power of several innovative techniques. The cornerstone of this approach is a digital twin model of the urban environment. This digital twin model facilitates the evaluation and comparison of road development proposals. To support informed decision-making, a multi-criteria decision-making (MCDM) framework is used, enabling planners to consider various factors such as traffic flow, environmental impact, and economic considerations. Spatial data and 3D visualizations are also provided to enrich the analysis. Finally, the Dempster–Shafer theory (DST) provides a robust mathematical framework to address uncertainties inherent in the weighting process. The proposed approach was applied to planning for both new road constructions and existing road expansions. By combining these elements, the model offers a sustainable and knowledge-based approach to optimize urban road planning. Results from integrating weights obtained through two weighting methods, the Analytic Hierarchy Process (AHP) and the Bayesian best–worst Method (B-BWM), showed a very high weight for the “worn-out urban texture” criterion and a meager weight for “noise pollution”. Finally, the cost path algorithm was used to evaluate the results from all three methods (AHP, B-BWM, and DST). The high degree of similarity in the results from these methods suggests a stable outcome for the proposed approach. Analysis of the study area revealed the following significant challenge for road planning: 35% of the area was deemed unsuitable, with only a tiny portion (4%) being suitable for road development based on the selected criteria. This highlights the need to explore alternative approaches or significantly adjust the current planning process. Full article

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19 pages, 4575 KiB

Open AccessArticle

A Study on the Spatiotemporal Distribution and Usage Pattern of Dockless Shared Bicycles—The Case of Nanjing

by Yi Shi, Zhonghu Zhang, Chunyu Zhou, Ruxia Bai and Chen Li

ISPRS Int. J. Geo-Inf. 2024, 13(9), 301; https://doi.org/10.3390/ijgi13090301 - 25 Aug 2024

Viewed by 387

Abstract

Determining the spatiotemporal deployment strategy for dockless shared bicycles in urban blocks has always been a focal point for city managers and planners. Extensive research has delved into the usage patterns in terms of time and space, deduced travel purposes, and scrutinized the [...] Read more.

Determining the spatiotemporal deployment strategy for dockless shared bicycles in urban blocks has always been a focal point for city managers and planners. Extensive research has delved into the usage patterns in terms of time and space, deduced travel purposes, and scrutinized the relationship between trips and the built environment. The elements of the built environment are significantly correlated with the starting and ending points of dockless shared bicycle trips, leading to a scarcity of shared bicycles in areas that are more frequently used as starting points and an abundance of idle bicycles in areas that serve as endpoints. This paper posits that the idle state of shared bicycles is as important as their usage. Utilizing a case study of Xinjiekou Central District in Nanjing, China, we propose a framework for analyzing the temporal and spatial usage and idleness of shared bicycles. We also discuss the impact of various factors, such as proximity to transit stations, land use, and road accessibility, on the different usage and idle states of dockless shared bicycles. The findings reveal that the public transportation system has a similar influence on both the utilization and idleness of dockless shared bicycles, indicating that areas with a dense concentration of transportation services experience greater demand for shared bicycles as both origins and destinations. The influence of other factors on the usage and idleness of dockless shared bicycles varies significantly, resulting in either a shortage or surplus of these bicycles. Consequently, based on the findings regarding the use and idleness of dockless shared bicycles, we formulate a redistribution and zone-based management strategy for shared bicycles. This paper offers new insights into the spatiotemporal distribution and utilization of shared bicycles under the influence of different built environments, contributing to the further optimization of dockless shared bicycle resource allocation. Full article

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26 pages, 31855 KiB

Open AccessArticle

Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network

by Haohua Zheng, Jianchen Zhang, Heying Li, Guangxia Wang, Jianzhong Guo and Jiayao Wang

ISPRS Int. J. Geo-Inf. 2024, 13(9), 300; https://doi.org/10.3390/ijgi13090300 - 25 Aug 2024

Viewed by 378

Abstract

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. [...] Read more.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where the feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another for extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we use a set of metrics considering both quantitative and qualitative aspects of the road network. On road types with similar sample sizes, the HAN model outperforms other models in both transductive and inductive tasks. Its accuracy (ACC) is higher by 1.62% and 0.67%, and its F1-score is higher by 1.43% and 0.81%, respectively. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation. Full article

(This article belongs to the Topic Geocomputation and Artificial Intelligence for Mapping)

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24 pages, 33997 KiB

Open AccessArticle

The Symbolization of Regional Elements Based on Local-Chronicle Text Mining and Image-Feature Extraction

by Lili Wu, Di Cao, Jinjin Yang, Ruoyi Zhang and Xinran Yan

ISPRS Int. J. Geo-Inf. 2024, 13(9), 299; https://doi.org/10.3390/ijgi13090299 - 23 Aug 2024

Viewed by 391

Abstract

In the context of the information age, the symbolization of regional elements has become a crucial component in modern cartographic practice. The targeted identification of regional elements and the design of map symbols are prerequisites for realizing the symbolization of regional elements. Therefore, [...] Read more.

In the context of the information age, the symbolization of regional elements has become a crucial component in modern cartographic practice. The targeted identification of regional elements and the design of map symbols are prerequisites for realizing the symbolization of regional elements. Therefore, we propose a method to symbolize regional elements by combining textual analysis and image processing. Firstly, local chronicles are used as the textual information source, and regional elements are extracted through textual data mining. Second, the real image data of the elements are selected, and the image segmentation algorithm, clustering algorithm, etc., are used to extract contours and colors from the images and carry out corresponding symbol simplification and color matching, to create highly recognizable symbols. Finally, we apply the symbols to two map types: the thematic map and the tourist map, and design a questionnaire to evaluate the outcomes of the symbol design. After a thorough review, it has been found that the method is superior to related symbolization studies in terms of data source authority, symbol generation efficiency, and symbol information carrying. In conclusion, guided by interdisciplinary thinking, this study effectively combines theoretical analysis and design practice, proposes a new idea of symbolization, and opens up a new way for geographic information visualization. Full article

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17 pages, 7654 KiB

Open AccessArticle

The Impact of Airbnb on Long-Term Rental Markets in San Francisco: A Geospatial Analysis Using Multiscale Geographically Weighted Regression

by Dongkeun Hur, Seonjin Lee and Hany Kim

ISPRS Int. J. Geo-Inf. 2024, 13(9), 298; https://doi.org/10.3390/ijgi13090298 - 23 Aug 2024

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Abstract

The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap [...] Read more.

The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap in understanding whether Airbnb financially burdens local renters within different income groups. The authors also differentiated the effect of Airbnb accommodations with different levels of commercialization by categorizing Airbnb listings based on their level of commercialization. Using the multiscale geographically weighted regression technique, this study also considered spatial variations in the relationship between short- and long-term rental markets. The findings indicate that the density of Airbnb only affects the relative rent of renters with a yearly household income between USD 50,000 and USD 75,000. Furthermore, the density of Airbnb listings from more commercialized hosts that own between three and eleven showed a positive relationship with the relative rent cost. This study highlighted the variability in the impact of Airbnb on the local community by income group, listing characteristic, and geographic region. This finding underscores the need for differentiated regulation toward peer-to-peer accommodations, as the impact on rent affordability varies by host commercialization level and renter income group. Full article

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ISPRS Int. J. Geo-Inf., Volume 13, Issue 9 (September 2024) – 17 articles

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