Journal Description
ISPRS International Journal of Geo-Information
ISPRS International Journal of Geo-Information
is an international, peer-reviewed, open access journal on geo-information. The journal is owned by the International Society for Photogrammetry and Remote Sensing (ISPRS) and is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), GeoRef, PubAg, dblp, Astrophysics Data System, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Geography, Physical) / CiteScore - Q1 (Geography, Planning and Development)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 35.5 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.4 (2022);
5-Year Impact Factor:
3.5 (2022)
Latest Articles
A Multi-Feature Fusion Method for Urban Functional Regions Identification: A Case Study of Xi’an, China
ISPRS Int. J. Geo-Inf. 2024, 13(5), 156; https://doi.org/10.3390/ijgi13050156 - 07 May 2024
Abstract
Research on the identification of urban functional regions is of great significance for the understanding of urban structure, spatial planning, resource allocation, and promoting sustainable urban development. However, achieving high-precision urban functional region recognition has always been a research challenge in this field.
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Research on the identification of urban functional regions is of great significance for the understanding of urban structure, spatial planning, resource allocation, and promoting sustainable urban development. However, achieving high-precision urban functional region recognition has always been a research challenge in this field. For this purpose, this paper proposes an urban functional region identification method called ASOE (activity–scene–object–economy), which integrates the features from multi-source data to perceive the spatial differentiation of urban human and geographic elements. First, we utilize VGG16 (Visual Geometry Group 16) to extract high-level semantic features from the remote sensing images with 1.2 m spatial resolution. Then, using scraped building footprints, we extract building object features such as area, perimeter, and structural ratios. Socioeconomic features and population activity features are extracted from Point of Interest (POI) and Weibo data, respectively. Finally, integrating the aforementioned features and using the Random Forest method for classification, the identification results of urban functional regions in the main urban area of Xi’an are obtained. After comparing with the actual land use map, our method achieves an identification accuracy of 91.74%, which is higher than other comparative methods, making it effectively identify four typical urban functional regions in the main urban area of Xi’an (e.g., residential regions, industrial regions, commercial regions, and public regions). The research indicates that the method of fusing multi-source data can fully leverage the advantages of big data, achieving high-precision identification of urban functional regions.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
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Open AccessArticle
An Adaptive Simplification Method for Coastlines Using a Skeleton Line “Bridge” Double Direction Buffering Algorithm
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Lulu Tang, Lihua Zhang, Jian Dong, Hongcheng Wei and Shuai Wei
ISPRS Int. J. Geo-Inf. 2024, 13(5), 155; https://doi.org/10.3390/ijgi13050155 - 07 May 2024
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Aiming at the problem that the current double direction buffering algorithm is easy to use to seal the “bottleneck” area when simplifying coastlines, an adaptive simplification method for coastlines using a skeleton line “bridge” double direction buffering algorithm is proposed. Firstly, from the
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Aiming at the problem that the current double direction buffering algorithm is easy to use to seal the “bottleneck” area when simplifying coastlines, an adaptive simplification method for coastlines using a skeleton line “bridge” double direction buffering algorithm is proposed. Firstly, from the perspective of visual constraints, the relationship between the buffer distance and the coastline line width and the minimum recognition distance of the human eye is theoretically derived and determined. Then, based on the construction of the coastline skeleton binary tree, the “bridge” skeleton line is extracted using the “source tracing” algorithm. Finally, the shoreline adaptive simplification is realized by constructing a visual buffer of “bridge” skeleton lines to bridge the original resulting coastline and the local details. The experimental results show that the proposed method can effectively solve the problem that the current double direction buffering algorithm has, which can significantly improve the quality of simplification.
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Open AccessArticle
Collaborative Methods of Resolving Road Graphic Conflicts Based on Cartographic Rules and Generalization Operations
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Chuanbang Zheng, Qingsheng Guo, Lin Wang, Yuangang Liu and Jianfeng Jiang
ISPRS Int. J. Geo-Inf. 2024, 13(5), 154; https://doi.org/10.3390/ijgi13050154 - 06 May 2024
Abstract
The resolution of road graphic conflicts is a key aspect of map generalization, which involves both scale reduction and the symbolization of map features. This study proposes collaborative methods of road graphic conflict resolution considering different road characteristics. These methods consider both geometric
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The resolution of road graphic conflicts is a key aspect of map generalization, which involves both scale reduction and the symbolization of map features. This study proposes collaborative methods of road graphic conflict resolution considering different road characteristics. These methods consider both geometric and semantic characteristics, and they incorporate the bend characteristics of roads, the road symbol size, and road semantics. Constrained Delaunay triangulation skeleton lines are used to categorize road graphic conflicts, which are made up of four independent conflict types and four group conflict types. Based on their characteristics, three collaborative methods are designed to deal with the different types of road graphic conflicts: collaboration between deletion and the snake displacement model, collaboration between the snake displacement model and collinearity, and collaboration among simplification, smoothing, and the beam displacement model. Two types of independent conflicts can be processed using only one simple operation. This study summarizes the cartographic rules for resolving road graphic conflicts, and these are used along with geometric features to drive the collaborative methods or one simple operation presented here. The experimental results indicate that the method proposed in this study can effectively resolve road graphic conflicts.
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Open AccessArticle
Exploration of an Open Vocabulary Model on Semantic Segmentation for Street Scene Imagery
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Zichao Zeng and Jan Boehm
ISPRS Int. J. Geo-Inf. 2024, 13(5), 153; https://doi.org/10.3390/ijgi13050153 - 05 May 2024
Abstract
This study investigates the efficacy of an open vocabulary, multi-modal, foundation model for the semantic segmentation of images from complex urban street scenes. Unlike traditional models reliant on predefined category sets, Grounded SAM uses arbitrary textual inputs for category definition, offering enhanced flexibility
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This study investigates the efficacy of an open vocabulary, multi-modal, foundation model for the semantic segmentation of images from complex urban street scenes. Unlike traditional models reliant on predefined category sets, Grounded SAM uses arbitrary textual inputs for category definition, offering enhanced flexibility and adaptability. The model’s performance was evaluated across single and multiple category tasks using the benchmark datasets Cityscapes, BDD100K, GTA5, and KITTI. The study focused on the impact of textual input refinement and the challenges of classifying visually similar categories. Results indicate strong performance in single-category segmentation but highlighted difficulties in multi-category scenarios, particularly with categories bearing close textual or visual resemblances. Adjustments in textual prompts significantly improved detection accuracy, though challenges persisted in distinguishing between visually similar objects such as buses and trains. Comparative analysis with state-of-the-art models revealed Grounded SAM’s competitive performance, particularly notable given its direct inference capability without extensive dataset-specific training. This feature is advantageous for resource-limited applications. The study concludes that while open vocabulary models such as Grounded SAM mark a significant advancement in semantic segmentation, further improvements in integrating image and text processing are essential for better performance in complex scenarios.
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(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
Open AccessArticle
A Quantitative and Qualitative Experimental Framework for the Evaluation of Urban Soundscapes: Application to the City of Sidi Bou Saïd
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Mohamed Amin Hammami and Christophe Claramunt
ISPRS Int. J. Geo-Inf. 2024, 13(5), 152; https://doi.org/10.3390/ijgi13050152 - 01 May 2024
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This research introduces an experimental framework based on 3D acoustic and psycho-acoustic sensors supplemented with ambisonics and sound morphological analysis, whose objective is to study urban soundscapes. A questionnaire that highlights the differences between what has been measured and what has been perceiveSd
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This research introduces an experimental framework based on 3D acoustic and psycho-acoustic sensors supplemented with ambisonics and sound morphological analysis, whose objective is to study urban soundscapes. A questionnaire that highlights the differences between what has been measured and what has been perceiveSd by humans complements the quantitative approach with a qualitative evaluation. The comparison of the measurements with the questionnaire provides a global vision of the perception of these soundscapes, as well as differences and similarities. The approach is experimented within the historical center of the Tunisian city of Sidi Bou Saïd, demonstrating that from a range of complementary protocols, a soundscape environment can be qualified. This framework provides an additional dimension to urban planning studies.
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Open AccessArticle
Prediction of Parking Space Availability Using Improved MAT-LSTM Network
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Feizhou Zhang, Ke Shang, Lei Yan, Haijing Nan and Zicong Miao
ISPRS Int. J. Geo-Inf. 2024, 13(5), 151; https://doi.org/10.3390/ijgi13050151 - 01 May 2024
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The prediction of parking space availability plays a crucial role in information systems providing parking guidance. However, controversy persists regarding the efficiency and accuracy of mainstream time series prediction methods, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In this
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The prediction of parking space availability plays a crucial role in information systems providing parking guidance. However, controversy persists regarding the efficiency and accuracy of mainstream time series prediction methods, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In this study, a comparison was made between a temporal convolutional network (TCN) based on CNNs and a long short-term memory (LSTM) network based on RNNs to determine an appropriate baseline for predicting parking space availability. Subsequently, a multi-head attention (MAT) mechanism was incorporated into an LSTM network, attempting to improve its accuracy. Experiments were conducted on three real and two synthetic datasets. The results indicated that the TCN achieved the fastest convergence, whereas the MAT-LSTM method provided the highest average accuracy, namely 0.0330 and 1.102 × 10−6, on the real and synthetic datasets, respectively. Furthermore, the improved MAT-LSTM model accomplished an increase of up to 48% in accuracy compared with the classic LSTM model. Consequently, we concluded that RNN-based networks are better suited for predicting long-time series. In particular, the MAT-LSTM method proposed in this study holds higher application value for predicting parking space availability with a higher accuracy.
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Open AccessArticle
Delineating Source and Sink Zones of Trip Journeys in the Road Network Space
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Yan Shi, Bingrong Chen, Jincai Huang, Da Wang, Huimin Liu and Min Deng
ISPRS Int. J. Geo-Inf. 2024, 13(5), 150; https://doi.org/10.3390/ijgi13050150 - 30 Apr 2024
Abstract
Source–sink zones refer to aggregated adjacent origins/destinations with homogeneous trip flow characteristics. Current relevant studies mostly detect source–sink zones based on outflow/inflow volumes without considering trip routes. Nevertheless, trip routes detail individuals’ journeys on road networks and give rise to relationships among human
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Source–sink zones refer to aggregated adjacent origins/destinations with homogeneous trip flow characteristics. Current relevant studies mostly detect source–sink zones based on outflow/inflow volumes without considering trip routes. Nevertheless, trip routes detail individuals’ journeys on road networks and give rise to relationships among human activities, road network structures, and land-use types. Therefore, this study developed a novel approach to delineate source–sink zones based on trip route aggregation on road networks. We first represented original trajectories using road segment sequences and applied the Latent Dirichlet Allocation (LDA) model to associate trajectories with route semantics. We then ran a hierarchical clustering operation to aggregate trajectories with similar route semantics. Finally, we adopted an adaptive multi-variable agglomeration strategy to associate the trajectory clusters with each traffic analysis zone to delineating source and sink zones, with a trajectory topic entropy defined as an indicator to analyze the dynamic impact between the road network and source–sink zones. We used taxi trajectories in Xiamen, China, to verify the effectiveness of the proposed method.
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Open AccessArticle
Comparing Machine Learning and Time Series Approaches in Predictive Modeling of Urban Fire Incidents: A Case Study of Austin, Texas
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Yihong Yuan and Andrew Grayson Wylie
ISPRS Int. J. Geo-Inf. 2024, 13(5), 149; https://doi.org/10.3390/ijgi13050149 - 29 Apr 2024
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This study examines urban fire incidents in Austin, Texas using machine learning (Random Forest) and time series (Autoregressive integrated moving average, ARIMA) methods for predictive modeling. Based on a dataset from the City of Austin Fire Department, it addresses the effectiveness of these
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This study examines urban fire incidents in Austin, Texas using machine learning (Random Forest) and time series (Autoregressive integrated moving average, ARIMA) methods for predictive modeling. Based on a dataset from the City of Austin Fire Department, it addresses the effectiveness of these models in predicting fire occurrences and the influence of fire types and urban district characteristics on predictions. The findings indicate that ARIMA models generally excel in predicting most fire types, except for auto fires. Additionally, the results highlight the significant differences in model performance across urban districts, indicating an impact of local features on fire incidence prediction. The research offers insights into temporal patterns of specific fire types, which can provide useful input to urban planning and public safety strategies in rapidly developing cities. In addition, the findings also emphasize the need for tailored predictive models, based on local dynamics and the distinct nature of fire incidents.
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Open AccessArticle
A Methodology for Designing One-Way Station-Based Carsharing Services in a GIS Environment: A Case Study in Palermo
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Gabriele D’Orso and Marco Migliore
ISPRS Int. J. Geo-Inf. 2024, 13(5), 148; https://doi.org/10.3390/ijgi13050148 - 29 Apr 2024
Abstract
One-way carsharing is recognized as one of the most popular transportation services in urban areas, being an alternative option to private cars. Over the last decades, a vast amount of literature on the design of specific aspects of this service (fleet size, stations’
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One-way carsharing is recognized as one of the most popular transportation services in urban areas, being an alternative option to private cars. Over the last decades, a vast amount of literature on the design of specific aspects of this service (fleet size, stations’ locations, fare, balancing operations) has formed. However, a holistic approach for designing carsharing services seems not to be developed. This paper proposes a new approach for designing one-way station-based carsharing services, presenting a five-step method, entirely developed in a GIS environment. The first three steps (suitability analysis, site selection analysis, and walkability analysis) allow finding the candidate locations for carsharing stations. After the assessment of the capacity of the potential stations, a location-allocation analysis allows for assessing the fleet size, the number of stations that maximize the coverage of carsharing demand, and their optimal locations. This paper presents a case study: a new one-way carsharing service was designed in Palermo (Italy) and compared to the existing carsharing service operating in the city. The results highlight that the current carsharing supply is undersized, having about 45% fewer stations and about half the cars compared to those resulting from the model, leaving some POIs unserved.
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Open AccessArticle
Optimization of Shared Electric Scooter Deployment Stations Based on Distance Tolerance
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Jianwei Yue, Yingqiu Long, Shaohua Wang and Haojian Liang
ISPRS Int. J. Geo-Inf. 2024, 13(5), 147; https://doi.org/10.3390/ijgi13050147 - 29 Apr 2024
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The proliferation of shared electric scooters (E-scooters) has brought convenience to urban transportation but has also introduced challenges such as disorderly parking and an imbalance between supply and demand. Given the current inconsistent quantity and spatial distribution of shared E-scooters, coupled with inadequate
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The proliferation of shared electric scooters (E-scooters) has brought convenience to urban transportation but has also introduced challenges such as disorderly parking and an imbalance between supply and demand. Given the current inconsistent quantity and spatial distribution of shared E-scooters, coupled with inadequate research on deployment stations selection, we propose a novel maximal covering location problem (MCLP) based on distance tolerance. The model aims to maximize the coverage of user demand while minimizing the sum of distances from users to deployment stations. A deep reinforcement learning (DRL) was devised to address this optimization model. An experiment was conducted focusing on areas with high concentrations of shared E-scooter trips in Chicago. The solutions of location selection were obtained by DRL, the Gurobi solver, and the genetic algorithm (GA). The experimental results demonstrated the effectiveness of the proposed model in optimizing the layout of shared E-scooter deployment stations. This study provides valuable insights into facility location selection for urban shared transportation tools, and showcases the efficiency of DRL in addressing facility location problems (FLPs).
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Open AccessArticle
Exploring the Pedestrian Route Choice Behaviors by Machine Learning Models
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Cheng-Jie Jin, Yuanwei Luo, Chenyang Wu, Yuchen Song and Dawei Li
ISPRS Int. J. Geo-Inf. 2024, 13(5), 146; https://doi.org/10.3390/ijgi13050146 - 28 Apr 2024
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To investigate pedestrian route choice mechanisms from a perspective distinct from that employed in discrete choice models (DCMs), this study utilizes machine learning models and employs SHapley Additive exPlanations (SHAP) for model interpretation. The data used in this paper come from several pedestrian
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To investigate pedestrian route choice mechanisms from a perspective distinct from that employed in discrete choice models (DCMs), this study utilizes machine learning models and employs SHapley Additive exPlanations (SHAP) for model interpretation. The data used in this paper come from several pedestrian flow experiments with two routes, which were recorded by UAV. Our findings indicate that logistic regression (similar to a binary logit model) exhibits good computational efficiency but falls short in predictive accuracy when compared to other machine learning models. Among the 12 machine learning models assessed, by calculating the new indicator named OP, we find that eXtreme Gradient Boosting (XGB) and Light Gradient Boosting (LGB) strike the best balance between accuracy and computational efficiency. Regarding feature contribution, our analysis reveals that bottlenecks exert the most significant influence on pedestrian route choice behavior, followed by the time it takes pedestrians to return from the end of the route to the origin (reflecting pedestrian characteristics and attitudes). While the pedestrian density of the shorter route contributes less compared to bottlenecks and return time, it exhibits a threshold effect, meaning that once the density of the shorter route surpasses a certain threshold, most pedestrians opt for the longer route.
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Open AccessArticle
Evolution Characteristics and Influencing Factors of City Networks in China: A Case Study of Cross-Regional Automobile Enterprises
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Daming Xu and Weiliang Shen
ISPRS Int. J. Geo-Inf. 2024, 13(5), 145; https://doi.org/10.3390/ijgi13050145 - 28 Apr 2024
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The optimization of the spatial structure of the city network is conducive to the scientific spatial distribution of industries and the promotion of coordinated regional development. This study selected the top 100 automobile enterprises in the Chinese stock market that belong to China’s
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The optimization of the spatial structure of the city network is conducive to the scientific spatial distribution of industries and the promotion of coordinated regional development. This study selected the top 100 automobile enterprises in the Chinese stock market that belong to China’s pillar industry, a total of 1455 headquarters and branches, to establish an enterprise matrix. Based on the ownership linkage model, the evolution characteristics of city networks in China from 2000 to 2020 are revealed, and the influential factors of city networks are discussed using the negative binomial regression model. The findings are as follows: (1) there are significant differences in the status of automobile cities, forming a “pyramid network” hierarchy. (2) The agglomeration area of automobile cities has formed the development region of “4 + 4 + 1”. (3) The city network with hierarchical connections has formed a spatial structure of a “cross–cobweb” in the middle and “trapezoid–diamond” in the periphery. (4) Urban transportation conditions, the scientific research environment, the enterprise agglomeration economy, GDP per capita, and technological proximity positively impact the formation of a city network, but the total export–import volume has a negative impact. Overall, the government can use this study’s results to formulate policies for the automotive industry and urban development.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
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Spatiotemporal Evolution and Influencing Factors of Urban Industry in Modern China (1840–1949): A Case Study of Nanjing
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Chun Wang, Gang Chen and Yixin Liang
ISPRS Int. J. Geo-Inf. 2024, 13(5), 144; https://doi.org/10.3390/ijgi13050144 - 28 Apr 2024
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In modern China, industrialization has formed a critical foundation for the transition to modernization. However, the spatiotemporal evolution patterns and driving mechanisms of urban industrial development in Nanjing from 1840 to 1949 remain unclear. Based on textual historical sources, this study examined the
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In modern China, industrialization has formed a critical foundation for the transition to modernization. However, the spatiotemporal evolution patterns and driving mechanisms of urban industrial development in Nanjing from 1840 to 1949 remain unclear. Based on textual historical sources, this study examined the spatiotemporal patterns of urban industrial development in Nanjing from 1840 to 1949 by using spatial analysis methods, GeoDetector, regression models and industrial structure indices. The results reveal the following: (1) The overall spatial distribution pattern of the industry in modern Nanjing exhibited a “one main, one secondary” dual-center “ladle-shaped” arrangement. Over time, industry has expanded from the urban center toward the east and north. (2) The modernization level of different industries was uneven, exhibiting a “center-periphery” spatial pattern. (3) At the micro level, transportation and population density were the primary influencing factors for industrial location, whereas at the macro level, government intervention mainly affected the industrialization pattern. (4) The industrial development pattern in modern Nanjing, in alignment with the “pole-axis” spatial system, serves as a microcosm of China’s urban modernization transition. This study represents the application of GIS methods in the humanities and provides valuable insights for urban planning and development.
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Open AccessArticle
Discovering Links between Geospatial Data Sources in the Web of Data: The Open Geospatial Engine Approach
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Lianlian He and Ruixiang Liu
ISPRS Int. J. Geo-Inf. 2024, 13(5), 143; https://doi.org/10.3390/ijgi13050143 - 28 Apr 2024
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The Web of Data has been fueled significantly by geospatial data over the last few years. In the current link discovery frameworks, there is still a lack of robust support for finding geospatial-aware links between geospatial data sources in the Web of Data.
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The Web of Data has been fueled significantly by geospatial data over the last few years. In the current link discovery frameworks, there is still a lack of robust support for finding geospatial-aware links between geospatial data sources in the Web of Data. They are also limited in efficient association capabilities for large-scale datasets. This paper extends the data integration capability based on the spatial metrics in the open geospatial engine OGE. These metrics include topological relationships and spatial matching between geospatial entities within multiple geospatial data sources. Thus, the tool can be employed by data publishers to set geospatial-aware links to facilitate geospatial data and knowledge discovery in the Web of Data. Several geospatial data sources are used to demonstrate the usability and effectiveness of the approach and tool implementation.
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Open AccessArticle
Integrating Spatial and Non-Spatial Dimensions to Evaluate Access to Rural Primary Healthcare Service: A Case Study of Songzi, China
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Taohua Yang, Weicong Luo, Lingling Tian and Jinpeng Li
ISPRS Int. J. Geo-Inf. 2024, 13(5), 142; https://doi.org/10.3390/ijgi13050142 - 27 Apr 2024
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Access to rural primary healthcare services has been broadly studied in the past few decades. However, most earlier studies that focused on examining access to rural healthcare services have conventionally treated spatial and non-spatial access as separate factors. This research aims to measure
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Access to rural primary healthcare services has been broadly studied in the past few decades. However, most earlier studies that focused on examining access to rural healthcare services have conventionally treated spatial and non-spatial access as separate factors. This research aims to measure access to primary healthcare services in rural areas with the consideration of both spatial and non-spatial dimensions. The methodology of study is threefold. First, the Gaussian two-step floating catchment area (G-2SFCA) method was adopted to measure spatial access to primary healthcare services. Then, a questionnaire survey was conducted to investigate non-spatial access factors, including demographic condition, patient’s household income, healthcare insurance, education level, and patient satisfaction level with the services. After that, a comprehensive evaluation index system was employed to integrate both spatial and non-spatial access. The empirical study showed a remarkable disparity in spatial access to primary healthcare services. In total, 78 villages with 185,137 local people had a “low” or “very low” level of spatial access to both clinics and hospitals. For the non-spatial dimension, the results depicted that Songzi had significant inequalities in socioeconomic status (e.g., income, education) and patient satisfaction level for medical service. When integrating both spatial and non-spatial factors, the disadvantaged areas were mainly located in the eastern and middle parts. In addition, this study found that comprehensively considering the spatial and non-spatial access had a significant impact on results in healthcare access. In conclusion, this study calls for policymakers to pay more attention to primary healthcare inequalities within rural areas. The spatial and non-spatial access should be considered comprehensively when the long-term rural medical support policy is designated.
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Open AccessArticle
A Sensor-Based Simulation Method for Spatiotemporal Event Detection
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Yuqin Jiang, Andrey A. Popov, Zhenlong Li, Michael E. Hodgson and Binghu Huang
ISPRS Int. J. Geo-Inf. 2024, 13(5), 141; https://doi.org/10.3390/ijgi13050141 - 23 Apr 2024
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Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the
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Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as “sensors”, which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key locations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur.
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Open AccessArticle
AED Inequity among Social Groups in Guangzhou
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Feng Gao, Siyi Lu, Shunyi Liao, Wangyang Chen, Xin Chen, Jiemin Wu, Yunjing Wu, Guanyao Li and Xu Han
ISPRS Int. J. Geo-Inf. 2024, 13(4), 140; https://doi.org/10.3390/ijgi13040140 - 22 Apr 2024
Abstract
Automated external defibrillators (AEDs) are regarded as the most important public facility after fire extinguishers due to their importance to out-of-hospital cardiac arrest (OHCA) victims. Previous studies focused on the location optimization of the AED, with little attention to inequity among different social
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Automated external defibrillators (AEDs) are regarded as the most important public facility after fire extinguishers due to their importance to out-of-hospital cardiac arrest (OHCA) victims. Previous studies focused on the location optimization of the AED, with little attention to inequity among different social groups. To comprehensively investigate the spatial heterogeneity of the AED inequity, we first collected AED data from a WeChat applet. Then, we used the geographically weighted regression (GWR) model to quantify the inequity level and identify the socio-economic status group that faced the worst inequity in each neighborhood. Results showed that immigrants of all ages suffer a more severe AED inequity than residents after controlling population and road density. Immigrants face more severe inequity in downtown, while residents face more severe inequity in the peripheral and outer suburbs. AED inequity among youngsters tends to be concentrated in the center of each district, while inequity among the elderly tends to be distributed at the edge of each district. This study provides a new perspective for investigating the inequity in public facilities, puts forward scientific suggestions for future AED allocation planning, and emphasizes the importance of the equitable access to AED.
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(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)
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Open AccessArticle
Research on the Spatial Network Structure of Tourist Flows in Hangzhou Based on BERT-BiLSTM-CRF
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Danfeng Qi, Bingbing Wang, Qiuhao Zhao and Pingbin Jin
ISPRS Int. J. Geo-Inf. 2024, 13(4), 139; https://doi.org/10.3390/ijgi13040139 - 21 Apr 2024
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Tourist flows, crucial information within online travelogues, reveal the interactive relationships between different tourist destinations and serve as the nerve center and link of the tourism system. This study takes Hangzhou, China, as a case to investigate the spatial network structure of its
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Tourist flows, crucial information within online travelogues, reveal the interactive relationships between different tourist destinations and serve as the nerve center and link of the tourism system. This study takes Hangzhou, China, as a case to investigate the spatial network structure of its tourist flows. Firstly, a BERT-BiLSTM-CRF model and pan-attraction database are built to extract tourist attractions from online travelogues and create the tourist flow matrix. Then, this study uses social network analysis (SNA) to examine the structure of the tourist flow network from a county-level perspective. Additionally, GIS spatial analysis methods are applied to analyze the evolution of the tourist gravity center and standard deviation ellipse (SDE) of the network. The results reveal that the identification performances of the tourist flow extraction model this study proposed are significantly better than those of previous mainstream models, with an F1 value of 0.8752. Furthermore, the tourist flow network in Hangzhou displays a relatively sparse and unbalanced distribution, forming a “Core–Semi-Periphery–Periphery” structure. Lastly, from 2020 to 2022, the network’s gravity center experienced a shift towards the southwest, paralleled by an initial expansion and subsequent contraction of the SDE in the same southwest direction. These findings provide valuable insights into the spatial network structure of tourism in Hangzhou and can serve as a reference for policymakers to promote the “all-for-one” tourism.
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Open AccessArticle
A Novel Address-Matching Framework Based on Region Proposal
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Yizhuo Quan, Yuanfei Chang, Linlin Liang, Yanyou Qiao and Chengbo Wang
ISPRS Int. J. Geo-Inf. 2024, 13(4), 138; https://doi.org/10.3390/ijgi13040138 - 21 Apr 2024
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Geocoding is a fundamental component of geographic information science that plays a crucial role in various geographical studies and applications involving text data. Current mainstream geocoding methods fall into two categories: geodesic-grid prediction and address matching. However, the geodesic-grid-prediction method’s localization accuracy is
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Geocoding is a fundamental component of geographic information science that plays a crucial role in various geographical studies and applications involving text data. Current mainstream geocoding methods fall into two categories: geodesic-grid prediction and address matching. However, the geodesic-grid-prediction method’s localization accuracy is hindered by the density of grid partitioning, struggling to strike a balance between prediction accuracy and grid density. Address-matching methods mainly focus on the semantics of query text. However, they tend to ignore keyword information that can be used to distinguish candidates and introduce potential interference, which reduces matching accuracy. Inspired by the human map-usage process, we propose a two-stage address-matching approach that integrates geodesic-grid prediction and text-matching models. Initially, a multi-level text-classification model is used to generate a retrieval region proposal for an input query text. Subsequently, we search for the most relevant point of interest (POI) within the region-proposal area using a semantics-based text-retrieval model. We evaluated the proposed method using POI data from the Beijing Chaoyang District. The experimental results indicate that the proposed method provides high address-matching accuracy, increasing Recall@1 by 0.55 to 1.56 percentage points and MRR@5 by 0.54 to 1.68 percentage points.
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Open AccessArticle
Comprehension of City Map Pictograms Designed for Specific Tourists’ Needs
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Dorotea Kovačević, Maja Brozović and Klementina Možina
ISPRS Int. J. Geo-Inf. 2024, 13(4), 137; https://doi.org/10.3390/ijgi13040137 - 18 Apr 2024
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This study investigated the effectiveness of new point-of-interest (POI) pictograms on tourist maps within the Croatian and Slovenian contexts, focusing on enhancing the tourist experience in urban settings for individuals with specific needs. Despite the widespread use of tourist maps, there is a
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This study investigated the effectiveness of new point-of-interest (POI) pictograms on tourist maps within the Croatian and Slovenian contexts, focusing on enhancing the tourist experience in urban settings for individuals with specific needs. Despite the widespread use of tourist maps, there is a lack of research evaluating POI pictograms tailored to the needs of tourists with specific dietary, health-related, and sustainable clothing purchases. To fill this gap, we designed six new pictograms in three domains: healthcare, food, and apparel. The pictograms were evaluated using an online questionnaire involving 138 participants with a diverse range of ages and educational backgrounds. The results on comprehension and subjective assessments of the pictograms’ qualities revealed insights into the subtle cultural and lifestyle influences on pictogram perception. The findings provide a basis for considering the potential of new pictogram designs in improving navigational experiences with geospatial information and encouraging sustainable tourist behaviors.
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