ISPRS International Journal of Geo-Information

13 pages, 952 KiB

Open AccessArticle

The Influence of Perceptions of the Park Environment on the Health of the Elderly: The Mediating Role of Social Interaction

by Xiuhai Xiong, Jingjing Wang, Hao Wu and Zhenghong Peng

ISPRS Int. J. Geo-Inf. 2024, 13(7), 262; https://doi.org/10.3390/ijgi13070262 - 22 Jul 2024

Viewed by 1337

Abstract

The aging population has brought increased attention to the urgent need to address social isolation and health risks among the elderly. While previous research has established the positive effects of parks in promoting social interaction and health among older adults, further investigation is [...] Read more.

The aging population has brought increased attention to the urgent need to address social isolation and health risks among the elderly. While previous research has established the positive effects of parks in promoting social interaction and health among older adults, further investigation is required to understand the complex relationships between perceptions of the park environment, social interaction, and elderly health. In this study, structural equation modeling (SEM) was employed to examine these relationships, using nine parks in Wuhan as a case study. The findings indicate that social interaction serves as a complete mediator between perceptions of the park environment and elderly health (path coefficients: park environment on social interaction = 0.45, social interaction on health = 0.46, and indirect effect = 0.182). Furthermore, the results of the multi-group SEM analysis revealed that the mediating effect was moderated by the pattern of social interaction (the difference test: the friend companionship group vs. the family companionship group (Z = 1.965 > 1.96)). Notably, family companionship had a significantly stronger positive impact on the health of older adults compared to friend companionship. These findings contribute to our understanding of the mechanisms through which urban parks support the physical and mental well-being of the elderly and provide a scientific foundation for optimizing urban park environments. Full article

(This article belongs to the Topic Spatial Epidemiology and GeoInformatics)

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20 pages, 16881 KiB

Open AccessArticle

A GIS-Based Framework for Synthesizing City-Scale Long-Term Individual-Level Spatial–Temporal Mobility

by Yao Yao, Yinghong Jiang, Qing Yu, Jian Yuan, Jiaxing Li, Jian Xu, Siyuan Liu and Haoran Zhang

ISPRS Int. J. Geo-Inf. 2024, 13(7), 261; https://doi.org/10.3390/ijgi13070261 - 22 Jul 2024

Cited by 1 | Viewed by 949

Abstract

Human mobility data are crucial for transportation planning and congestion management. However, challenges persist in accessing and using raw mobility data due to privacy concerns and data quality issues such as redundancy, missing values, and noise. This research introduces an innovative GIS-based framework [...] Read more.

Human mobility data are crucial for transportation planning and congestion management. However, challenges persist in accessing and using raw mobility data due to privacy concerns and data quality issues such as redundancy, missing values, and noise. This research introduces an innovative GIS-based framework for creating individual-level long-term spatio-temporal mobility data at a city scale. The methodology decomposes and represents individual mobility by identifying key locations where activities take place and life patterns that describe transitions between these locations. Then, we present methods for extracting, representing, and generating key locations and life patterns from large-scale human mobility data. Using long-term mobility data from Shanghai, we extract life patterns and key locations and successfully generate the mobility of 30,000 virtual users over seven days in Shanghai. The high correlation (R² = 0.905) indicates a strong similarity between the generated data and ground-truth data. By testing the combination of key locations and life patterns from different areas, the model demonstrates strong transferability within and across cities, with relatively low RMSE values across all scenarios, the highest being around 0.04. By testing the representativeness of the generated mobility data, we find that using only about 0.25% of the generated individuals’ mobility is sufficient to represent the dynamic changes of the entire urban population on a daily and hourly resolution. The proposed methodology offers a novel tool for generating long-term spatiotemporal mobility patterns at the individual level, thereby avoiding the privacy concerns associated with releasing real data. This approach supports the broad application of individual mobility data in urban planning, traffic management, and other related fields. Full article

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27 pages, 2909 KiB

Open AccessArticle

Extracting Geoscientific Dataset Names from the Literature Based on the Hierarchical Temporal Memory Model

by Kai Wu, Zugang Chen, Xinqian Wu, Guoqing Li, Jing Li, Shaohua Wang, Haodong Wang and Hang Feng

ISPRS Int. J. Geo-Inf. 2024, 13(7), 260; https://doi.org/10.3390/ijgi13070260 - 21 Jul 2024

Viewed by 1261

Abstract

Extracting geoscientific dataset names from the literature is crucial for building a literature–data association network, which can help readers access the data quickly through the Internet. However, the existing named-entity extraction methods have low accuracy in extracting geoscientific dataset names from unstructured text [...] Read more.

Extracting geoscientific dataset names from the literature is crucial for building a literature–data association network, which can help readers access the data quickly through the Internet. However, the existing named-entity extraction methods have low accuracy in extracting geoscientific dataset names from unstructured text because geoscientific dataset names are a complex combination of multiple elements, such as geospatial coverage, temporal coverage, scale or resolution, theme content, and version. This paper proposes a new method based on the hierarchical temporal memory (HTM) model, a brain-inspired neural network with superior performance in high-level cognitive tasks, to accurately extract geoscientific dataset names from unstructured text. First, a word-encoding method based on the Unicode values of characters for the HTM model was proposed. Then, over 12,000 dataset names were collected from geoscience data-sharing websites and encoded into binary vectors to train the HTM model. We conceived a new classifier scheme for the HTM model that decodes the predictive vector for the encoder of the next word so that the similarity of the encoders of the predictive next word and the real next word can be computed. If the similarity is greater than a specified threshold, the real next word can be regarded as part of the name, and a successive word set forms the full geoscientific dataset name. We used the trained HTM model to extract geoscientific dataset names from 100 papers. Our method achieved an F1-score of 0.727, outperforming the GPT-4- and Claude-3-based few-shot learning (FSL) method, with F1-scores of 0.698 and 0.72, respectively. Full article

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

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19 pages, 11545 KiB

Open AccessArticle

Bridging Human Expertise with Machine Learning and GIS for Mine Type Prediction and Classification

by Adib Saliba, Kifah Tout, Chamseddine Zaki and Christophe Claramunt

ISPRS Int. J. Geo-Inf. 2024, 13(7), 259; https://doi.org/10.3390/ijgi13070259 - 20 Jul 2024

Viewed by 1194

Abstract

This paper introduces an intelligent model that combines military expertise with the latest advancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes, by predicting mined areas and classifying them by mine type, difficulty and priority of [...] Read more.

This paper introduces an intelligent model that combines military expertise with the latest advancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes, by predicting mined areas and classifying them by mine type, difficulty and priority of clearance. The model is based on direct input and validation from field decision-makers for their practical applicability and effectiveness, and accurate historical demining data extracted from military databases. With a survey polling the inputs of demining experts, 95% of the responses came with an affirmation of the potential of the model to reduce threats and increase operational efficiency. It includes military-specific factors that factor in the proximity to strategic locations as well as environmental variables like vegetation cover and terrain resolution. With Gradient Boosting algorithms such as XGBoost and LightGBM, the accuracy rate is almost 97%. Such precision levels further enhance threat assessment, better allocation of resources, and around a 30% reduction in the cost and time of conducting demining operations, signifying a strong synergy of human expertise with algorithmic precision for maximal safety and effectiveness in demining. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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23 pages, 11155 KiB

Open AccessArticle

Exploring Family Ties and Interpersonal Dynamics—A Geospatial Simulation Analyzing Their Influence on Evacuation Efficiency within Urban Communities

by Hao Chu, Jianping Wu, Liliana Perez and Yonghua Huang

ISPRS Int. J. Geo-Inf. 2024, 13(7), 258; https://doi.org/10.3390/ijgi13070258 - 20 Jul 2024

Viewed by 787

Abstract

Guaranteeing efficient evacuations in urban communities is critical for preserving lives, minimizing disaster impacts, and promoting community resilience. Challenges such as high population density, limited evacuation routes, and communication breakdowns complicate evacuation efforts. Vulnerable populations, urban infrastructure constraints, and the increasing frequency of [...] Read more.

Guaranteeing efficient evacuations in urban communities is critical for preserving lives, minimizing disaster impacts, and promoting community resilience. Challenges such as high population density, limited evacuation routes, and communication breakdowns complicate evacuation efforts. Vulnerable populations, urban infrastructure constraints, and the increasing frequency of disasters further contribute to the complexity. Despite these challenges, the importance of timely evacuations lies in safeguarding human safety, enabling rapid disaster response, preserving critical infrastructure, and reducing economic losses. Overcoming these hurdles necessitates comprehensive planning, investment in resilient infrastructure, effective communication strategies, and continuous community engagement to foster preparedness and enhance evacuation efficiency. This research looks into the complexities of evacuation dynamics within urban residential areas, placing a particular focus on the interaction between joint-rental arrangements and family ties and their influence on evacuation strategies during emergency situations. Using agent-based modeling, evacuation simulation scenarios are implemented using the Changhongfang community (Shanghai) while systematically exploring how diverse interpersonal relationships impact the efficiency of evacuation processes. The adopted methodology encompasses a series of group experiments designed to determine the optimal proportions of joint-rental occupants within the community. Furthermore, the research examines the impact of various exit selection strategies on evacuation efficiency. Simulation outcomes shed light on the fundamental role of interpersonal factors in shaping the outcomes of emergency evacuations. Additionally, this study emphasizes the critical importance of strategic exit selections, revealing their potential to significantly enhance overall evacuation efficiency in urban settings. Full article

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22 pages, 7180 KiB

Open AccessArticle

Sensing the Environmental Inequality of PM_2.5 Exposure Using Fine-Scale Measurements of Social Strata and Citizenship Identity

by Li He, Lingfeng He, Zezheng Lin, Yao Lu, Chen Chen, Zhongmin Wang, Ping An, Min Liu, Jie Xu and Shurui Gao

ISPRS Int. J. Geo-Inf. 2024, 13(7), 257; https://doi.org/10.3390/ijgi13070257 - 17 Jul 2024

Viewed by 1481

Abstract

Exposure to PM_2.5 pollution poses substantial health risks, with the precise quantification of exposure being fundamental to understanding the environmental inequalities therein. However, the absence of high-resolution spatiotemporal ambient population data, coupled with an insufficiency of attribute data, impedes a comprehension of [...] Read more.

Exposure to PM_2.5 pollution poses substantial health risks, with the precise quantification of exposure being fundamental to understanding the environmental inequalities therein. However, the absence of high-resolution spatiotemporal ambient population data, coupled with an insufficiency of attribute data, impedes a comprehension of the environmental inequality of exposure risks at a fine scale. Within the purview of a conceptual framework that interlinks social strata and citizenship identity with environmental inequality, this study examines the environmental inequality of PM_2.5 exposure with a focus on the city of Xi’an. Quantitative metrics of the social strata and citizenship identities of the ambient population are derived from housing price data and mobile phone big data. The fine-scale estimation of PM_2.5 concentrations is predicated on the kriging interpolation method and refined by leveraging an advanced dataset. Employing geographically weighted regression models, we examine the environmental inequality pattern at a fine spatial scale. The key findings are threefold: (1) the manifestation of environmental inequality in PM_2.5 exposure is pronounced among individuals of varying social strata and citizenship identities within our study area, Xi’an; (2) nonlocal residents situated in the northwestern precincts of Xi’an are subject to the most pronounced PM_2.5 exposure; and (3) an elevated socioeconomic status is identified as an attenuating factor, capable of averting the deleterious impacts of PM_2.5 exposure among nonlocal residents. These findings proffer substantial practical implications for the orchestration of air pollution mitigation strategies and urban planning initiatives. They suggest that addressing the wellbeing of the marginalized underprivileged cohorts, who are environmentally and politically segregated under the extant urban planning policies in China, is of critical importance. Full article

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

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24 pages, 13627 KiB

Open AccessArticle

Enhancing Place Emotion Analysis with Multi-View Emotion Recognition from Geo-Tagged Photos: A Global Tourist Attraction Perspective

by Yu Wang, Shunping Zhou, Qingfeng Guan, Fang Fang, Ni Yang, Kanglin Li and Yuanyuan Liu

ISPRS Int. J. Geo-Inf. 2024, 13(7), 256; https://doi.org/10.3390/ijgi13070256 - 16 Jul 2024

Viewed by 883

Abstract

User-generated geo-tagged photos (UGPs) have emerged as a valuable tool for analyzing large-scale tourist place emotions with unprecedented detail. This process involves extracting and analyzing human emotions associated with specific locations. However, previous studies have been limited to analyzing individual faces in the [...] Read more.

User-generated geo-tagged photos (UGPs) have emerged as a valuable tool for analyzing large-scale tourist place emotions with unprecedented detail. This process involves extracting and analyzing human emotions associated with specific locations. However, previous studies have been limited to analyzing individual faces in the UGPs. This approach falls short of representing the contextual scene characteristics, such as environmental elements and overall scene context, which may contain implicit emotional knowledge. To address this issue, we propose an innovative computational framework for global tourist place emotion analysis leveraging UGPs. Specifically, we first introduce a Multi-view Graph Fusion Network (M-GFN) to effectively recognize multi-view emotions from UGPs, considering crowd emotions and scene implicit sentiment. After that, we designed an attraction-specific emotion index (AEI) to quantitatively measure place emotions based on the identified multi-view emotions at various tourist attractions with place types. Complementing the AEI, we employ the emotion intensity index (EII) and Pearson correlation coefficient (PCC) to deepen the exploration of the association between attraction types and place emotions. The synergy of AEI, EII, and PCC allows comprehensive attraction-specific place emotion extraction, enhancing the overall quality of tourist place emotion analysis. Extensive experiments demonstrate that our framework enhances existing place emotion analysis methods, and the M-GFN outperforms state-of-the-art emotion recognition methods. Our framework can be adapted for various geo-emotion analysis tasks, like recognizing and regulating workplace emotions, underscoring the intrinsic link between emotions and geographic contexts. Full article

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

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22 pages, 7283 KiB

Open AccessReview

Bibliometric Analysis on the Research of Geoscience Knowledge Graph (GeoKG) from 2012 to 2023

by Zhi-Wei Hou, Xulong Liu, Shengnan Zhou, Wenlong Jing and Ji Yang

ISPRS Int. J. Geo-Inf. 2024, 13(7), 255; https://doi.org/10.3390/ijgi13070255 - 16 Jul 2024

Cited by 2 | Viewed by 1571

Abstract

The geoscience knowledge graph (GeoKG) has gained worldwide attention due to its ability in the formal representation of spatiotemporal features and relationships of geoscience knowledge. Currently, a quantitative review of the state and trends in GeoKG is still scarce. Thus, a bibliometric analysis [...] Read more.

The geoscience knowledge graph (GeoKG) has gained worldwide attention due to its ability in the formal representation of spatiotemporal features and relationships of geoscience knowledge. Currently, a quantitative review of the state and trends in GeoKG is still scarce. Thus, a bibliometric analysis was performed in this study to fill the gap. Specifically, based on 294 research articles published from 2012 to 2023, we conducted analyses in terms of the (1) trends in publications and citations; (2) identification of the major papers, sources, researchers, institutions, and countries; (3) scientific collaboration analysis; and (4) detection of major research topics and tendencies. The results revealed that the interest in GeoKG research has rapidly increased after 2019 and is continually expanding. China is the most productive country in this field. Co-authorship analysis shows that inter-national and inter-institutional collaboration should be reinforced. Keyword analysis indicated that geoscience knowledge representation, information extraction, GeoKG construction, and GeoKG-based multi-source data integration were current hotspots. In addition, several important but currently neglected issues, such as the integration of Large Language Models, are highlighted. The findings of this review provide a systematic overview of the development of GeoKG and provide a valuable reference for future research. 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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Graphical abstract

17 pages, 17666 KiB

Open AccessArticle

Advanced Integration of Urban Street Greenery and Pedestrian Flow: A Multidimensional Analysis in Chengdu’s Central Urban District

by Qicheng Ma, Jiaxin Zhang and Yunqin Li

ISPRS Int. J. Geo-Inf. 2024, 13(7), 254; https://doi.org/10.3390/ijgi13070254 - 16 Jul 2024

Viewed by 1235

Abstract

As urbanization accelerates, urban greenery, particularly street greenery, emerges as a vital strategy for enhancing residents’ quality of life, demanding attention for its alignment with pedestrian flows to foster sustainable urban development and ensure urban dwellers’ wellbeing. The advent of diverse urban data [...] Read more.

As urbanization accelerates, urban greenery, particularly street greenery, emerges as a vital strategy for enhancing residents’ quality of life, demanding attention for its alignment with pedestrian flows to foster sustainable urban development and ensure urban dwellers’ wellbeing. The advent of diverse urban data has significantly advanced this area of study. Focusing on Chengdu’s central urban district, this research assesses street greening metrics against pedestrian flow indicators, employing spatial autocorrelation techniques to investigate the interplay between street greenery and pedestrian flow over time and space. Our findings reveal a prevalent negative spatial autocorrelation between street greenery and pedestrian flow within the area, underscored by temporal disparities in greenery demands across various urban functions during weekdays versus weekends. This study innovatively incorporates mobile phone signal-based population heat maps into the mismatch analysis of street greenery for the first time, moving beyond the conventional static approach of space syntax topology in assessing pedestrian flow. By leveraging dynamic pedestrian flow data, it enriches our understanding of the disconnect between street greening plans and pedestrian circulation, highlighting the concept of urban flow and delving into the intricate nexus among time, space, and human activity. Moreover, this study meticulously examines multiple street usage scenarios, reflecting diverse behavior patterns, with the objective of providing nuanced and actionable strategies for urban renewal initiatives aimed at creating more inviting and sustainable urban habitats. Full article

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21 pages, 11155 KiB

Open AccessArticle

Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data

by Yuqi Yang, Xiaoqing Zuo, Kang Zhao and Yongfa Li

ISPRS Int. J. Geo-Inf. 2024, 13(7), 253; https://doi.org/10.3390/ijgi13070253 - 14 Jul 2024

Viewed by 1179

Abstract

The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data [...] Read more.

The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B⁺-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems. Full article

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17 pages, 10238 KiB

Open AccessArticle

A Lightweight Multi-Label Classification Method for Urban Green Space in High-Resolution Remote Sensing Imagery

by Weihua Lin, Dexiong Zhang, Fujiang Liu, Yan Guo, Shuo Chen, Tianqi Wu and Qiuyan Hou

ISPRS Int. J. Geo-Inf. 2024, 13(7), 252; https://doi.org/10.3390/ijgi13070252 - 13 Jul 2024

Cited by 1 | Viewed by 1323

Abstract

Urban green spaces are an indispensable part of the ecology of cities, serving as the city’s “purifier” and playing a crucial role in promoting sustainable urban development. Therefore, the refined classification of urban green spaces is an important task in urban planning and [...] Read more.

Urban green spaces are an indispensable part of the ecology of cities, serving as the city’s “purifier” and playing a crucial role in promoting sustainable urban development. Therefore, the refined classification of urban green spaces is an important task in urban planning and management. Traditional methods for the refined classification of urban green spaces heavily rely on expert knowledge, often requiring substantial time and cost. Hence, our study presents a multi-label image classification model based on MobileViT. This model integrates the Triplet Attention module, along with the LSTM module, to enhance its label prediction capabilities while maintaining its lightweight characteristic for standalone operation on mobile devices. Trial outcomes in our UGS dataset in this study demonstrate that the approach we used outperforms the baseline by 1.64%, 3.25%, 3.67%, and 2.71% in

m A P, F_{1}, p r e c i s i o n,

and

r e c a l l

, respectively. This indicates that the model can uncover the latent dependencies among labels to enhance the multi-label image classification device’s performance. This study provides a practical solution for the intelligent and detailed classification of urban green spaces, which holds significant importance for the management and planning of urban green spaces. Full article

(This article belongs to the Special Issue Innovative GIS Models and Approaches for Large Environmental and Urban Applications in the Age of AI)

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14 pages, 3677 KiB

Open AccessArticle

A Pathfinding Algorithm for Large-Scale Complex Terrain Environments in the Field

by Luchao Kui and Xianwen Yu

ISPRS Int. J. Geo-Inf. 2024, 13(7), 251; https://doi.org/10.3390/ijgi13070251 - 12 Jul 2024

Viewed by 1267

Abstract

Pathfinding for autonomous vehicles in large-scale complex terrain environments is difficult when aiming to balance efficiency and quality. To solve the problem, this paper proposes Hierarchical Path-Finding A* based on Multi-Scale Rectangle, called RHA*, which achieves efficient pathfinding and high path quality for [...] Read more.

Pathfinding for autonomous vehicles in large-scale complex terrain environments is difficult when aiming to balance efficiency and quality. To solve the problem, this paper proposes Hierarchical Path-Finding A* based on Multi-Scale Rectangle, called RHA*, which achieves efficient pathfinding and high path quality for large-scale unequal-weighted maps. Firstly, the original map grid cells were aggregated into fixed-size clusters. Then, an abstract map was constructed by aggregating equal-weighted clusters into rectangular regions of different sizes and calculating the nodes and edges of the regions in advance. Finally, real-time pathfinding was performed based on the abstract map. The experiment showed that the computation time of real-time pathfinding was reduced by 96.64% compared to A* and 20.38% compared to HPA*. The total cost of the generated path deviated no more than 0.05% compared to A*. The deviation value is reduced by 99.2% compared to HPA*. The generated path can be used for autonomous vehicle traveling in off-road environments. Full article

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23 pages, 9796 KiB

Open AccessArticle

Renovation and Reconstruction of Urban Land Use by a Cost-Heuristic Genetic Algorithm: A Case in Shenzhen

by Yufan Deng, Zhongan Tang, Baoju Liu, Yan Shi, Min Deng and Enbo Liu

ISPRS Int. J. Geo-Inf. 2024, 13(7), 250; https://doi.org/10.3390/ijgi13070250 - 12 Jul 2024

Viewed by 1105

Abstract

Urban land use multi-objective optimization aims to achieve greater economic, social, and environmental benefits by the rational allocation and planning of urban land resources in space. However, not only land use reconstruction, but renovation, which has been neglected in most studies, is the [...] Read more.

Urban land use multi-objective optimization aims to achieve greater economic, social, and environmental benefits by the rational allocation and planning of urban land resources in space. However, not only land use reconstruction, but renovation, which has been neglected in most studies, is the main optimization direction of urban land use. Meanwhile, urban land use optimization is subject to cost constraints, so as to obtain a more practical optimization scheme. Thus, this paper evaluated the renovation and reconstruction costs of urban land use and proposed a cost-heuristic genetic algorithm (CHGA). The algorithm determined the selection probability of candidate optimization cells by considering the renovation and reconstruction costs of urban land and integrated the renovation and reconstruction costs to determine the direction of optimization so that the optimization model can more practically simulate the actual situation of urban planning. The reliability of this model was validated through its application in Shenzhen, China, demonstrating that it can reduce the cost consumption of the optimization process by 35.86% at the expense of sacrificing a small amount of economic benefits (1.18%). The balance of benefits and costs enhances the applicability of the proposed land use optimization method in mature, developed areas where it is difficult to demolish buildings that are constrained by costs. Full article

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28 pages, 10559 KiB

Open AccessArticle

Methodology of Mosaicking and Georeferencing for Multi-Sheet Early Maps with Irregular Cuts Using the Example of the Topographic Chart of the Kingdom of Poland

by Jakub Kuna, Tomasz Panecki and Mateusz Zawadzki

ISPRS Int. J. Geo-Inf. 2024, 13(7), 249; https://doi.org/10.3390/ijgi13070249 - 10 Jul 2024

Cited by 1 | Viewed by 1942

Abstract

The Topographic Chart of the Kingdom of Poland (pol. Topograficzna Karta Królestwa Polskiego, commonly referred to as ‘the Quartermaster’s Map’, hereinafter: TKKP) is the first Polish modern topographic map of Poland (1:126,000, 1843). Cartographic historians acclaim its conception by the General Quartermaster of [...] Read more.

The Topographic Chart of the Kingdom of Poland (pol. Topograficzna Karta Królestwa Polskiego, commonly referred to as ‘the Quartermaster’s Map’, hereinafter: TKKP) is the first Polish modern topographic map of Poland (1:126,000, 1843). Cartographic historians acclaim its conception by the General Quartermaster of the Polish Army, noting its editorial principles and technical execution as exemplars of the early 19th-century cartographic standards. Today, it stands as a national heritage relic, furnishing invaluable insights into the former Polish Kingdom’s topography. Although extensively utilised in geographical and historical inquiries, the TKKP has yet to undergo a comprehensive geomatic investigation and publication as spatial data services. Primarily, this delay stems from the challenges of mosaicking and georeferencing its 60 constituent sheets, owing to the uncertain mathematical framework and irregular sheet cuts. In 2023, the authors embarked on rectifying this by creating a unified TKKP mosaic and georeferencing the map to contemporary reference data benchmarks. This endeavour involved scrutinising the map’s mathematical accuracy and verifying prior findings. The resultant product is accessible via the ‘Maps with the Past’ platform, developed by the Institute of History of the Polish Academy of Sciences The dissemination of raster data services adhering to OGC standards such as WMTS (Web Map Tile Service), ECW (Enhanced Compression Wavelet), and COG (Cloud Optimized GeoTIFF) facilitates the swift and seamless integration of the generated data into web and GIS tools. The digital edition of the TKKP emerges as a pivotal resource for investigations spanning natural and anthropogenic environmental transformations, sustainable development, and cultural heritage studies. Full article

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28 pages, 13050 KiB

Open AccessArticle

Coupling Hyperbolic GCN with Graph Generation for Spatial Community Detection and Dynamic Evolution Analysis

by Huimin Liu, Qiu Yang, Xuexi Yang, Jianbo Tang, Min Deng and Rong Gui

ISPRS Int. J. Geo-Inf. 2024, 13(7), 248; https://doi.org/10.3390/ijgi13070248 - 10 Jul 2024

Viewed by 1075

Abstract

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic [...] Read more.

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning. Full article

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26 pages, 9857 KiB

Open AccessEditor’s ChoiceArticle

Spatiotemporal Analysis of Nighttime Crimes in Vienna, Austria

by Jiyoung Lee, Michael Leitner and Gernot Paulus

ISPRS Int. J. Geo-Inf. 2024, 13(7), 247; https://doi.org/10.3390/ijgi13070247 - 10 Jul 2024

Viewed by 1402

Abstract

Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during [...] Read more.

Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during the nighttime, investigating the temporal definition of nighttime crime and the correlation between nighttime lights and criminal activities. The study concentrates on four types of nighttime crimes, assault, theft, burglary, and robbery, conducting univariate and multivariate analyses. In the univariate analysis, correlations between nighttime crimes and nighttime light (NTL) values detected in satellite images and between streetlight density and nighttime crimes are explored. The results highlight that nighttime burglary strongly relates to NTL and streetlight density. The multivariate analysis delves into the relationships between each nighttime crime type and socioeconomic and urban infrastructure variables. Once again, nighttime burglary exhibits the highest correlation. For both univariate and multivariate regression models the geographically weighted regression (GWR) outperforms ordinary least squares (OLS) regression in explaining the relationships. This study underscores the importance of considering the location and offense time in crime geography research and emphasizes the potential of using NTL in nighttime crime analysis. Full article

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21 pages, 3782 KiB

Open AccessArticle

Globally Optimal Relative Pose and Scale Estimation from Only Image Correspondences with Known Vertical Direction

by Zhenbao Yu, Shirong Ye, Changwei Liu, Ronghe Jin, Pengfei Xia and Kang Yan

ISPRS Int. J. Geo-Inf. 2024, 13(7), 246; https://doi.org/10.3390/ijgi13070246 - 9 Jul 2024

Viewed by 907

Abstract

Installing multi-camera systems and inertial measurement units (IMUs) in self-driving cars, micro aerial vehicles, and robots is becoming increasingly common. An IMU provides the vertical direction, allowing coordinate frames to be aligned in a common direction. The degrees of freedom (DOFs) of the [...] Read more.

Installing multi-camera systems and inertial measurement units (IMUs) in self-driving cars, micro aerial vehicles, and robots is becoming increasingly common. An IMU provides the vertical direction, allowing coordinate frames to be aligned in a common direction. The degrees of freedom (DOFs) of the rotation matrix are reduced from 3 to 1. In this paper, we propose a globally optimal solver to calculate the relative poses and scale of generalized cameras with a known vertical direction. First, the cost function is established to minimize algebraic error in the least-squares sense. Then, the cost function is transformed into two polynomials with only two unknowns. Finally, the eigenvalue method is used to solve the relative rotation angle. The performance of the proposed method is verified on both simulated and KITTI datasets. Experiments show that our method is more accurate than the existing state-of-the-art solver in estimating the relative pose and scale. Compared to the best method among the comparison methods, the method proposed in this paper reduces the rotation matrix error, translation vector error, and scale error by 53%, 67%, and 90%, respectively. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 2nd Volume)

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22 pages, 2884 KiB

Open AccessArticle

Privacy Preserving Human Mobility Generation Using Grid-Based Data and Graph Autoencoders

by Fabian Netzler and Markus Lienkamp

ISPRS Int. J. Geo-Inf. 2024, 13(7), 245; https://doi.org/10.3390/ijgi13070245 - 9 Jul 2024

Viewed by 1209

Abstract

This paper proposes a one-to-one trajectory synthetization method with stable long-term individual mobility behavior based on a generalizable area embedding. Previous methods concentrate on producing highly detailed data on short-term and restricted areas for, e.g., autonomous driving scenarios. Another possibility consists of city-wide [...] Read more.

This paper proposes a one-to-one trajectory synthetization method with stable long-term individual mobility behavior based on a generalizable area embedding. Previous methods concentrate on producing highly detailed data on short-term and restricted areas for, e.g., autonomous driving scenarios. Another possibility consists of city-wide and beyond scales that can be used to predict general traffic flows. The now-presented approach takes the tracked mobility behavior of individuals and creates coherent synthetic mobility data. These generated data reflect the person’s long-term mobility behavior, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step of the original data, the area is distributed into a geospatial grid structure (H3 is used here). The neighborhood relationships between the grids are interpreted as a graph. A feed-forward autoencoder and a graph encoding–decoding network generate a latent space representation of the area. The original clustered data are associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are generated as top-level patterns for individual mobility behavior. Based on the original data, concrete locations within the new grids are found and connected to ways. The goal is to generate a dataset that shows equivalence in aggregated characteristics and distances in comparison with the original data. The described method is applied to a sample of 120 from a study with 1000 participants whose mobility data were generated in the city of Munich in Germany. The results show the applicability of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result comprises a sharable dataset on the same abstraction level as the input data, which can be beneficial for different applications, particularly for machine learning. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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15 pages, 5033 KiB

Open AccessArticle

Using Wi-Fi Probes to Evaluate the Spatio-Temporal Dynamics of Tourist Preferences in Historic Districts’ Public Spaces

by Yichen Gao, Sheng Liu, Biao Wei, Zhenni Zhu and Shanshan Wang

ISPRS Int. J. Geo-Inf. 2024, 13(7), 244; https://doi.org/10.3390/ijgi13070244 - 9 Jul 2024

Cited by 1 | Viewed by 1032

Abstract

Tourist preferences for public spaces in historic districts can reflect whether renovated spaces and functional structures meet tourism demands. However, conventional big data lack the spatio-temporal accuracy needed to support a refined, dynamic study of small-scale public spaces inside historic districts. This paper, [...] Read more.

Tourist preferences for public spaces in historic districts can reflect whether renovated spaces and functional structures meet tourism demands. However, conventional big data lack the spatio-temporal accuracy needed to support a refined, dynamic study of small-scale public spaces inside historic districts. This paper, therefore, proposes using a Wi-Fi probe to evaluate the spatio-temporal dynamics of tourists’ spatial preferences in historic districts. We conducted a one-week measurement in the Xiaohe Street Historic Block in Hangzhou, China. Three indicators—visit time preference, aggregation preference, and stay preference—were used to examine the dynamic change in tourists’ spatial preferences, with 15 min as the time unit and public spaces with a radius of 25 m as the spatial unit. Our research demonstrates that, compared with conventional big data, the Wi-Fi probe offers a more reasonable and accurate method to measure tourists’ spatial preferences in historic districts at a smaller time and spatial granularity. The research findings can be applied to evaluate the effectiveness of spatial regeneration and diagnose renewal-related issues in historic districts. It can also serve as a foundation for more precise planning of public spaces in historic districts, as well as the modification of functional structures. Full article

(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)

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17 pages, 9818 KiB

Open AccessArticle

Constraining the Geometry of NeRFs for Accurate DSM Generation from Multi-View Satellite Images

by Qifeng Wan, Yuzheng Guan, Qiang Zhao, Xiang Wen and Jiangfeng She

ISPRS Int. J. Geo-Inf. 2024, 13(7), 243; https://doi.org/10.3390/ijgi13070243 - 8 Jul 2024

Viewed by 1496

Abstract

Neural Radiance Fields (NeRFs) are an emerging approach to 3D reconstruction that use neural networks to reconstruct scenes. However, its applications for multi-view satellite photogrammetry, which aim to reconstruct the Earth’s surface, struggle to acquire accurate digital surface models (DSMs). To address this [...] Read more.

Neural Radiance Fields (NeRFs) are an emerging approach to 3D reconstruction that use neural networks to reconstruct scenes. However, its applications for multi-view satellite photogrammetry, which aim to reconstruct the Earth’s surface, struggle to acquire accurate digital surface models (DSMs). To address this issue, a novel framework, Geometric Constrained Neural Radiance Field (GC-NeRF) tailored for multi-view satellite photogrammetry, is proposed. GC-NeRF achieves higher DSM accuracy from multi-view satellite images. The key point of this approach is a geometric loss term, which constrains the scene geometry by making the scene surface thinner. The geometric loss term alongside z-axis scene stretching and multi-view DSM fusion strategies greatly improve the accuracy of generated DSMs. During training, bundle-adjustment-refined satellite camera models are used to cast rays through the scene. To avoid the additional input of altitude bounds described in previous works, the sparse point cloud resulting from the bundle adjustment is converted to an occupancy grid to guide the ray sampling. Experiments on WorldView-3 images indicate GC-NeRF’s superiority in accurate DSM generation from multi-view satellite images. Full article

(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)

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19 pages, 4490 KiB

Open AccessArticle

An Integrated Framework for Landscape Indices’ Calculation with Raster–Vector Integration and Its Application Based on QGIS

by Yaqi Huang, Minrui Zheng, Tianle Li, Fei Xiao and Xinqi Zheng

ISPRS Int. J. Geo-Inf. 2024, 13(7), 242; https://doi.org/10.3390/ijgi13070242 - 6 Jul 2024

Cited by 1 | Viewed by 1550

Abstract

Landscape-index calculation tools play a pivotal role in ecosystem studies and urban-planning research, enabling objective assessments of landscape patterns’ similarities and differences. However, the existing tools encounter limitations, such as the inability to visualize landscape indices spatially and the challenge of computing indices [...] Read more.

Landscape-index calculation tools play a pivotal role in ecosystem studies and urban-planning research, enabling objective assessments of landscape patterns’ similarities and differences. However, the existing tools encounter limitations, such as the inability to visualize landscape indices spatially and the challenge of computing indices for both vector and raster data simultaneously. Based on the QGIS development platform, this study presents an innovative framework for landscape-index calculation that addresses these limitations. The framework seamlessly integrates both vector and raster data, comprising three main modules: data input, landscape-index calculation, and visualization. In the data-input module, the tool accommodates various data formats, including vector, raster, and tabular data. The landscape indices’ calculation module allows users to select indices at patch, class, and landscape scales. Notably, the framework provides a comprehensive set of 165 indices for vector data and 20 for raster data, empowering users to selectively calculate landscape indices for vector or raster data to their specific needs and leverage the strengths of each data type. Moreover, the landscape-index visualization module enhances spatial visualization capabilities, meeting user demands for an insightful analysis. By addressing these challenges and offering enhanced functionalities, this framework aims to advance landscape indices’ development and foster more comprehensive landscape analyses. And it presents a novel approach for landscape-index development. Full article

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

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18 pages, 18990 KiB

Open AccessArticle

Using Virtual and Augmented Reality with GIS Data

by Karel Pavelka, Jr. and Martin Landa

ISPRS Int. J. Geo-Inf. 2024, 13(7), 241; https://doi.org/10.3390/ijgi13070241 - 5 Jul 2024

Cited by 1 | Viewed by 2497

Abstract

This study explores how combining virtual reality (VR) and augmented reality (AR) with geographic information systems (GIS) revolutionizes data visualization. It traces the historical development of these technologies and highlights key milestones that paved the way for this study’s objectives. While existing platforms [...] Read more.

This study explores how combining virtual reality (VR) and augmented reality (AR) with geographic information systems (GIS) revolutionizes data visualization. It traces the historical development of these technologies and highlights key milestones that paved the way for this study’s objectives. While existing platforms like Esri’s software and Google Earth VR show promise, they lack complete integration for immersive GIS visualization. This gap has led to the need for a dedicated workflow to integrate selected GIS data into a game engine for visualization purposes. This study primarily utilizes QGIS for data preparation and Unreal Engine for immersive visualization. QGIS handles data management, while Unreal Engine offers advanced rendering and interactivity for immersive experiences. To tackle the challenge of handling extensive GIS datasets, this study proposes a workflow involving tiling, digital elevation model generation, and transforming GeoTIFF data into 3D objects. Leveraging QGIS and Three.js streamlines the conversion process for integration into Unreal Engine. The resultant virtual reality application features distinct stations, enabling users to navigate, visualize, compare, and animate GIS data effectively. Each station caters to specific functionalities, ensuring a seamless and informative experience within the VR environment. This study also delves into augmented reality applications, adapting methodologies to address hardware limitations for smoother user experiences. By optimizing textures and implementing augmented reality functionalities through modules Swift, RealityKit, and ARKit, this study extends the immersive GIS experience to iOS devices. In conclusion, this research demonstrates the potential of integrating virtual reality, augmented reality, and GIS, pushing data visualization into new realms. The innovative workflows and applications developed serve as a testament to the evolving landscape of spatial data interpretation and engagement. Full article

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17 pages, 5191 KiB

Open AccessArticle

Layout Optimization of Logistics and Warehouse Land Based on a Multi-Objective Genetic Algorithm—Taking Wuhan City as an Example

by Haijun Li, Jie Zhou, Qiang Niu, Mingxiang Feng and Dongming Zhou

ISPRS Int. J. Geo-Inf. 2024, 13(7), 240; https://doi.org/10.3390/ijgi13070240 - 4 Jul 2024

Viewed by 1362

Abstract

With the rapid development of the logistics industry, the demand for logistics activities is increasing significantly. Concurrently, growing urbanization is causing the space for logistics and warehousing to become limited. Thus, more and more attention is being paid to the planning and construction [...] Read more.

With the rapid development of the logistics industry, the demand for logistics activities is increasing significantly. Concurrently, growing urbanization is causing the space for logistics and warehousing to become limited. Thus, more and more attention is being paid to the planning and construction of logistics facilities. However, due to spatiotemporal trajectory data (such as truck GPS data) being used less often in planning, the method of quantitative analysis for freight spatiotemporal activity is limited. Thus, the spatial layout of logistics and warehousing land does not match the current demand very well. In addition, it is necessary to consider the interactive relationship with the urban built environment in the process of optimizing layout, in order to comprehensively balance the spatial coupling with the functions of housing, transportation, industry, and so on. Therefore, the layout of logistics and warehouse land could be treated as a multi-objective optimization problem. This study aims to establish a model for logistics and warehouse land layout optimization to achieve a supply–demand matching. The proposed model comprehensively considers economic benefits, time benefits, cost benefits, environmental benefits, and other factors with freight GPS data, land-use data, transportation network data, and other multi-source data. A genetic algorithm is built to solve the model. Finally, this study takes the Wuhan urban development area as an example to practice the proposed method in three scenarios in order to verify its effectiveness. The results show that the optimization model solves the problem of mismatch between the supply and demand of logistics spaces to a certain extent, demonstrating the efficiency and scientificity of the optimization solutions. Based on the results of the three scenarios, it is proven that freight activities could effectively enhance the scientific validity of the optimization solution and the proposed model could optimize layouts under different scenario requirements. In summary, this study provides a practical and effective tool for logistics- and warehouse-land layout evaluation and optimization for urban planners and administrators. Full article

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32 pages, 8410 KiB

Open AccessArticle

Integrating Spatiotemporal Analysis of Land Transformation and Urban Growth in Peshawar Valley and Its Implications on Temperature in Response to Climate Change

by Muhammad Farooq Hussain, Xiaoliang Meng, Syed Fahim Shah and Muhammad Asif Hussain

ISPRS Int. J. Geo-Inf. 2024, 13(7), 239; https://doi.org/10.3390/ijgi13070239 - 3 Jul 2024

Cited by 1 | Viewed by 1889

Abstract

Examining the interconnected dynamics of urbanization and climate change is crucial due to their implications for environmental, social, and public health systems. This study provides a comprehensive analysis of these dynamics in the Peshawar Valley, a rapidly urbanizing region in Khyber Pakhtunkhwa, Pakistan, [...] Read more.

Examining the interconnected dynamics of urbanization and climate change is crucial due to their implications for environmental, social, and public health systems. This study provides a comprehensive analysis of these dynamics in the Peshawar Valley, a rapidly urbanizing region in Khyber Pakhtunkhwa, Pakistan, over a 30-year period (1990–2020). A novel methodological framework integrating remote sensing, GIS techniques, and Google Earth Engine (GEE) was developed to analyze land use/land cover (LULC) changes, particularly the expansion of the built-up environment, along with the land surface temperature (LST) and heat index (HI). This framework intricately links these elements, providing a unique perspective on the environmental transformations occurring in the Peshawar Valley. Unlike previous studies that focused on individual aspects, this research offers a holistic understanding of the complex interplay between urbanization, land use changes, temperature dynamics, and heat index variations. Over three decades, urbanization expanded significantly, with built-up areas increasing from 6.35% to 14.13%. The population surged from 5.3 million to 12.6 million, coupled with significant increases in registered vehicles (from 0.171 million to 1.364 million) and operational industries (from 327 to 1155). These transitions influenced air quality and temperature dynamics, as evidenced by a highest mean LST of 30.30 °C and a maximum HI of 55.48 °C, marking a notable increase from 50.54 °C. These changes show strong positive correlations with built-up areas, population size, registered vehicles, and industrial activity. The findings highlight the urgent need for adaptive strategies, public health interventions, and sustainable practices to mitigate the environmental impacts of urbanization and climate change in the Peshawar Valley. Sustainable urban development strategies and climate change mitigation measures are crucial for ensuring a livable and resilient future for the region. This long-term analysis provides a robust foundation for future projections and policy recommendations. Full article

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23 pages, 5816 KiB

Open AccessArticle

Spatial Nonlinear Effects of Street Vitality Constrained by Construction Intensity and Functional Diversity—A Case Study from the Streets of Shenzhen

by Jilong Li, Niuniu Kong, Shiping Lin, Jie Zeng, Yilin Ke and Jiacheng Chen

ISPRS Int. J. Geo-Inf. 2024, 13(7), 238; https://doi.org/10.3390/ijgi13070238 - 2 Jul 2024

Cited by 2 | Viewed by 1318

Abstract

As an important part of urban vitality, street vitality is an external manifestation of street economic prosperity and is affected by the built environment and the surrounding street vitality. However, existing research on the formation mechanism of street vitality focuses only on the [...] Read more.

As an important part of urban vitality, street vitality is an external manifestation of street economic prosperity and is affected by the built environment and the surrounding street vitality. However, existing research on the formation mechanism of street vitality focuses only on the built environment itself, ignoring the spatial spillover effect on street vitality. This study uses 5290 street segments in Shenzhen as examples. Utilizing geospatial and other multisource big data, this study creates spatial weight matrices at varying distances based on different living circle ranges. By combining the panel threshold model (PTM) and the spatial panel Durbin model (SPDM), this study constructs a spatial autoregressive threshold model to explore the spatial nonlinear effects of street vitality, considering various spatial weight matrices and thresholds of construction intensity and functional diversity. Our results show the following: (1) Street vitality exhibits significant spatial spillover effects, which gradually weaken as the living circle range expands (Moran indices are 0.178***, 0.160***, and 0.145*** for the 500 m, 1000 m, and 1500 m spatial weight matrices, respectively). (2) Construction intensity has a threshold, which is 0.1466 under spatial matrices of different distances. Functional diversity has two thresholds: 0.6832 and 2.2065 for the 500 m spatial weight matrix, and 0.6832 and 1.4325 for the 1000 m matrices, and 0.6832 and 1.2724 for 1500 m matrices. (3) As an international metropolis, street accessibility in Shenzhen has a significant and strong positive impact on its street vitality. This conclusion provides stakeholders with spatial patterns that influence street vitality, offering a theoretical foundation to further break down barriers to street vitality. Full article

(This article belongs to the Topic Spatial Decision Support Systems for Urban Sustainability)

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24 pages, 8649 KiB

Open AccessArticle

Assessing the Impact of Land Use and Land Cover Changes on Surface Temperature Dynamics Using Google Earth Engine: A Case Study of Tlemcen Municipality, Northwestern Algeria (1989–2019)

by Imene Selka, Abderahemane Medjdoub Mokhtari, Kheira Anissa Tabet Aoul, Djamal Bengusmia, Kacemi Malika and Khadidja El-Bahdja Djebbar

ISPRS Int. J. Geo-Inf. 2024, 13(7), 237; https://doi.org/10.3390/ijgi13070237 - 2 Jul 2024

Cited by 2 | Viewed by 2805

Abstract

Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and [...] Read more.

Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and land cover modifications on surface temperature in a semi-arid zone of northwestern Algeria between 1989 and 2019. Through the analysis of Landsat images on GEE, indices such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference latent heat index (NDLI) were extracted, and the random forest and split window algorithms were used for supervised classification and surface temperature estimation. The multi-index approach combining the Normalized Difference Tillage Index (NDTI), NDBI, and NDVI resulted in kappa coefficients ranging from 0.96 to 0.98. The spatial and temporal analysis of surface temperature revealed an increase of 4 to 6 degrees across the four classes (urban, barren land, vegetation, and forest). The Google Earth Engine approach facilitated detailed spatial and temporal analysis, aiding in understanding surface temperature evolution at various scales. This ability to conduct large-scale and long-term analysis is essential for understanding trends and impacts of land use changes at regional and global levels. Full article

(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)

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18 pages, 6121 KiB

Open AccessArticle

Multiscale Visualization of Surface Motion Point Measurements Associated with Persistent Scatterer Interferometry

by Panagiotis Kalaitzis, Michael Foumelis, Antonios Mouratidis, Dimitris Kavroudakis and Nikolaos Soulakellis

ISPRS Int. J. Geo-Inf. 2024, 13(7), 236; https://doi.org/10.3390/ijgi13070236 - 2 Jul 2024

Viewed by 1154

Abstract

Persistent scatterer interferometry (PSI) has been proven to be a robust method for studying complex and dynamic phenomena such as ground displacement over time. Proper visualization of PSI measurements is both crucial and challenging from a cartographic standpoint. This study focuses on the [...] Read more.

Persistent scatterer interferometry (PSI) has been proven to be a robust method for studying complex and dynamic phenomena such as ground displacement over time. Proper visualization of PSI measurements is both crucial and challenging from a cartographic standpoint. This study focuses on the development of an interactive cartographic web map application, providing suitable visualization of PSI data, and exploring their geographic, cartographic, spatial, and temporal attributes. To this end, PSI datasets, generalized at different resolutions, are visualized in eight predefined cartographic scales. A multiscale generalization algorithm is proposed. The automation of this procedure, spurred by the development of a web application, offers users the flexibility to properly visualize PSI datasets according to the specific cartographic scale. Additionally, the web map application provides a toolset, offering state-of-the-art cartographic approaches for exploring PSI datasets. This toolset consists of exploration, measurement, filtering (based on the point’s spatial attributes), and exporting tools customized for PSI measurement. Furthermore, a graph tool, offering users the capability to interactively plot PSI time-series and investigate the evolution of ground deformation over time, has been developed and integrated into the web interface. This study reflects the need for appropriate visualization of PSI datasets at different cartographic scales. It is shown that each original PSI dataset possesses a suitable cartographic scale at which it should be visualized. Innovative cartographic approaches, such as web applications, can prove to be effective tools for users working in the domain of mapping and monitoring the dynamic behavior of surface motion. Full article

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18 pages, 9371 KiB

Open AccessArticle

Performance Analysis of Random Forest Algorithm in Automatic Building Segmentation with Limited Data

by Ratri Widyastuti, Deni Suwardhi, Irwan Meilano, Andri Hernandi, Nabila S. E. Putri, Asep Yusup Saptari and Sudarman

ISPRS Int. J. Geo-Inf. 2024, 13(7), 235; https://doi.org/10.3390/ijgi13070235 - 2 Jul 2024

Viewed by 1339

Abstract

Airborne laser technology produces point clouds that can be used to build 3D models of buildings. However, the work is a laborious process that could benefit from automation. Artificial intelligence (AI) has been widely used in automating building segmentation as one of the [...] Read more.

Airborne laser technology produces point clouds that can be used to build 3D models of buildings. However, the work is a laborious process that could benefit from automation. Artificial intelligence (AI) has been widely used in automating building segmentation as one of the initial stages in the 3D modeling process. The algorithms with a high success rate using point clouds for automatic semantic segmentation are random forest (RF) and PointNet++, with each algorithm having its own advantages and disadvantages. However, the training and testing data to develop and test the model usually share similar characteristics. Moreover, producing a good automation model requires a lot of training data, which may become an issue for users with a small amount of training data (limited data). The aim of this research is to test the performance of the RF and PointNet++ models in different regions with limited training and testing data. We found that the RF model developed from a small amount data, in different regions between the training and testing data, performs well compared to PointNet++, yielding an OA score of 73.01% for the RF model. Furthermore, several scenarios have been used in this research to explore the capabilities of RF in several cases. Full article

(This article belongs to the Topic Advances in Earth Observation and Geosciences)

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31 pages, 8230 KiB

Open AccessArticle

Using Knowledge Graphs to Analyze the Characteristics and Trends of Forest Carbon Storage Research at the Global Scale

by Jie Li, Jinliang Wang, Suling He, Chenli Liu and Lanfang Liu

ISPRS Int. J. Geo-Inf. 2024, 13(7), 234; https://doi.org/10.3390/ijgi13070234 - 1 Jul 2024

Viewed by 1427

Abstract

Research on forest carbon storage (FCS) is crucial for the sustainable development of human society given the context of global climate change. Previous FCS studies formed the science base of the FCS field but lacked a macrolevel knowledge summary. This study combined the [...] Read more.

Research on forest carbon storage (FCS) is crucial for the sustainable development of human society given the context of global climate change. Previous FCS studies formed the science base of the FCS field but lacked a macrolevel knowledge summary. This study combined the scientometric mapping tool VOSviewer and multiple statistical models to conduct a comprehensive knowledge graph mining and analysis of global FCS papers (covering 101 countries, 1712 institutions, 5435 authors, and 276 journals) in the Web of Science database as of 2022, focusing on revealing the macro spatiotemporal pattern, multidimensional research status, and topic evolution process of FCS research at the global scale, so as to grasp the status of global FCS research more clearly and comprehensively, thereby facilitating the future decision-making and practice of researchers. The results showed the following: (1) In the past three decades, the number of FCS papers indicated an increasing trend, with a growth rate of 4.66/yr, particularly significant after 2010. These papers were mainly from Europe, the Americas, and Asia, while there was a huge gap between Africa, Oceania, and the above regions. (2) For the research status at the national, institutional, scholar, and journal levels, the USA, with 331 FCS papers and 18,653 total citations, was the most active and influential country in global FCS research; the United States Forest Service topped the influential ranking with 4115 citations; Grant M. Domke and Jerome Chave were the most active and influential FCS researchers globally, respectively. China’s activity (237 papers) and influence (5403 citations) ranked second, and the Chinese Academy of Sciences was the most active research institution in the world. Currently, FCS research is published in a growing number of journals, among which Forest Ecology and Management ranked first in the number of papers (154 papers) and citations (6374 citations). (3) In recent years, the keyword frequency of monitoring methods, driving factors, and reasonable management for FCS has increased rapidly, and many new related keywords have emerged, which means that researchers are not only focusing on the estimation and monitoring of FCS but also increasingly concerned about its driving mechanism and sustainable development. Full article

(This article belongs to the Topic Geospatial Digital Innovations for Smart Agriculture and Forestry)

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19 pages, 5946 KiB

Open AccessArticle

Optimizing Station Placement for Free-Floating Electric Vehicle Sharing Systems: Leveraging Predicted User Spatial Distribution from Points of Interest

by Qi Cao, Shunchao Wang, Bingtong Wang and Jingfeng Ma

ISPRS Int. J. Geo-Inf. 2024, 13(7), 233; https://doi.org/10.3390/ijgi13070233 - 1 Jul 2024

Viewed by 1212

Abstract

Rapid growth rate indicates that the free-floating electric vehicle sharing (FFEVS) system leads to a new carsharing idea. Like other carsharing systems, the FFEVS system faces significant regional demand fluctuations. In such a situation, the rental stations and charging stations should be constructed [...] Read more.

Rapid growth rate indicates that the free-floating electric vehicle sharing (FFEVS) system leads to a new carsharing idea. Like other carsharing systems, the FFEVS system faces significant regional demand fluctuations. In such a situation, the rental stations and charging stations should be constructed in high-demand areas to reduce the scheduling costs. However, the planning of the FFEVS system includes a series of aspects of rental stations and charging stations, such as the location, size, and number, which interact with each other. In this paper, we first provide a method for forecasting the demand for car sharing based on the land characteristics of Beijing FFEVS station catchment areas. Then, the multi-objective MILP model for planning FFEVS systems is developed, which considers the requirements of vehicle relocation and electric vehicle charging. Afterward, the capabilities of the proposed models are demonstrated by the real data obtained from Beijing, China. Finally, the sensitivity analysis of the model is made based on varying demand and subsidy levels. From the results, the proposed model can provide decision-makers with useful insights about the planning of FFEVS systems, which bring great benefits to formulating more rational policies. Full article

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ISPRS Int. J. Geo-Inf., Volume 13, Issue 7 (July 2024) – 46 articles

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