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- IJGI, Vol. 13, Pages 298: The Impact of Airbnb on Long-Term Rental Markets
in San Francisco: A Geospatial Analysis Using Multiscale Geographically Weighted Regression Authors: Dongkeun Hur, Seonjin Lee, Hany Kim First page: 298 Abstract: The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap in understanding whether Airbnb financially burdens local renters within different income groups. The authors also differentiated the effect of Airbnb accommodations with different levels of commercialization by categorizing Airbnb listings based on their level of commercialization. Using the multiscale geographically weighted regression technique, this study also considered spatial variations in the relationship between short- and long-term rental markets. The findings indicate that the density of Airbnb only affects the relative rent of renters with a yearly household income between USD 50,000 and USD 75,000. Furthermore, the density of Airbnb listings from more commercialized hosts that own between three and eleven showed a positive relationship with the relative rent cost. This study highlighted the variability in the impact of Airbnb on the local community by income group, listing characteristic, and geographic region. This finding underscores the need for differentiated regulation toward peer-to-peer accommodations, as the impact on rent affordability varies by host commercialization level and renter income group. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-23 DOI: 10.3390/ijgi13090298 Issue No: Vol. 13, No. 9 (2024)
- IJGI, Vol. 13, Pages 299: The Symbolization of Regional Elements Based on
Local-Chronicle Text Mining and Image-Feature Extraction Authors: Lili Wu, Di Cao, Jinjin Yang, Ruoyi Zhang, Xinran Yan First page: 299 Abstract: In the context of the information age, the symbolization of regional elements has become a crucial component in modern cartographic practice. The targeted identification of regional elements and the design of map symbols are prerequisites for realizing the symbolization of regional elements. Therefore, we propose a method to symbolize regional elements by combining textual analysis and image processing. Firstly, local chronicles are used as the textual information source, and regional elements are extracted through textual data mining. Second, the real image data of the elements are selected, and the image segmentation algorithm, clustering algorithm, etc., are used to extract contours and colors from the images and carry out corresponding symbol simplification and color matching, to create highly recognizable symbols. Finally, we apply the symbols to two map types: the thematic map and the tourist map, and design a questionnaire to evaluate the outcomes of the symbol design. After a thorough review, it has been found that the method is superior to related symbolization studies in terms of data source authority, symbol generation efficiency, and symbol information carrying. In conclusion, guided by interdisciplinary thinking, this study effectively combines theoretical analysis and design practice, proposes a new idea of symbolization, and opens up a new way for geographic information visualization. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-23 DOI: 10.3390/ijgi13090299 Issue No: Vol. 13, No. 9 (2024)
- IJGI, Vol. 13, Pages 300: Road Network Intelligent Selection Method Based
on Heterogeneous Graph Attention Neural Network Authors: Haohua Zheng, Jianchen Zhang, Heying Li, Guangxia Wang, Jianzhong Guo, Jiayao Wang First page: 300 Abstract: Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where the feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another for extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we use a set of metrics considering both quantitative and qualitative aspects of the road network. On road types with similar sample sizes, the HAN model outperforms other models in both transductive and inductive tasks. Its accuracy (ACC) is higher by 1.62% and 0.67%, and its F1-score is higher by 1.43% and 0.81%, respectively. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-25 DOI: 10.3390/ijgi13090300 Issue No: Vol. 13, No. 9 (2024)
- IJGI, Vol. 13, Pages 301: A Study on the Spatiotemporal Distribution and
Usage Pattern of Dockless Shared Bicycles—The Case of Nanjing Authors: Yi Shi, Zhonghu Zhang, Chunyu Zhou, Ruxia Bai, Chen Li First page: 301 Abstract: Determining the spatiotemporal deployment strategy for dockless shared bicycles in urban blocks has always been a focal point for city managers and planners. Extensive research has delved into the usage patterns in terms of time and space, deduced travel purposes, and scrutinized the relationship between trips and the built environment. The elements of the built environment are significantly correlated with the starting and ending points of dockless shared bicycle trips, leading to a scarcity of shared bicycles in areas that are more frequently used as starting points and an abundance of idle bicycles in areas that serve as endpoints. This paper posits that the idle state of shared bicycles is as important as their usage. Utilizing a case study of Xinjiekou Central District in Nanjing, China, we propose a framework for analyzing the temporal and spatial usage and idleness of shared bicycles. We also discuss the impact of various factors, such as proximity to transit stations, land use, and road accessibility, on the different usage and idle states of dockless shared bicycles. The findings reveal that the public transportation system has a similar influence on both the utilization and idleness of dockless shared bicycles, indicating that areas with a dense concentration of transportation services experience greater demand for shared bicycles as both origins and destinations. The influence of other factors on the usage and idleness of dockless shared bicycles varies significantly, resulting in either a shortage or surplus of these bicycles. Consequently, based on the findings regarding the use and idleness of dockless shared bicycles, we formulate a redistribution and zone-based management strategy for shared bicycles. This paper offers new insights into the spatiotemporal distribution and utilization of shared bicycles under the influence of different built environments, contributing to the further optimization of dockless shared bicycle resource allocation. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-25 DOI: 10.3390/ijgi13090301 Issue No: Vol. 13, No. 9 (2024)
- IJGI, Vol. 13, Pages 302: A Dempster–Shafer Enhanced Framework for
Urban Road Planning Using a Model-Based Digital Twin and MCDM Techniques Authors: Zahra Maserrat, Ali Asghar Alesheikh, Ali Jafari, Neda Kaffash Charandabi, Javad Shahidinejad First page: 302 Abstract: Rapid urbanization in developing countries presents a critical challenge in the need for extensive and appropriate road expansion, which in turn contributes to traffic congestion and air pollution. Urban areas are economic engines, but their efficiency and livability rely on well-designed road networks. This study proposes a novel approach to urban road planning that leverages the power of several innovative techniques. The cornerstone of this approach is a digital twin model of the urban environment. This digital twin model facilitates the evaluation and comparison of road development proposals. To support informed decision-making, a multi-criteria decision-making (MCDM) framework is used, enabling planners to consider various factors such as traffic flow, environmental impact, and economic considerations. Spatial data and 3D visualizations are also provided to enrich the analysis. Finally, the Dempster–Shafer theory (DST) provides a robust mathematical framework to address uncertainties inherent in the weighting process. The proposed approach was applied to planning for both new road constructions and existing road expansions. By combining these elements, the model offers a sustainable and knowledge-based approach to optimize urban road planning. Results from integrating weights obtained through two weighting methods, the Analytic Hierarchy Process (AHP) and the Bayesian best–worst Method (B-BWM), showed a very high weight for the “worn-out urban texture” criterion and a meager weight for “noise pollution”. Finally, the cost path algorithm was used to evaluate the results from all three methods (AHP, B-BWM, and DST). The high degree of similarity in the results from these methods suggests a stable outcome for the proposed approach. Analysis of the study area revealed the following significant challenge for road planning: 35% of the area was deemed unsuitable, with only a tiny portion (4%) being suitable for road development based on the selected criteria. This highlights the need to explore alternative approaches or significantly adjust the current planning process. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-25 DOI: 10.3390/ijgi13090302 Issue No: Vol. 13, No. 9 (2024)
- IJGI, Vol. 13, Pages 303: Landslide Risk Assessments through Multicriteria
Analysis Authors: Fatma Zohra Chaabane, Salim Lamine, Mohamed Said Guettouche, Nour El Islam Bachari, Nassim Hallal First page: 303 Abstract: Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-25 DOI: 10.3390/ijgi13090303 Issue No: Vol. 13, No. 9 (2024)
- IJGI, Vol. 13, Pages 304: Spatial and Temporal Dynamics in Vegetation
Greenness and Its Response to Climate Change in the Tarim River Basin, China Authors: Kai Jin, Yansong Jin, Cuijin Li, Lin Li First page: 304 Abstract: Vegetation in ecologically sensitive regions has experienced significant alterations due to global climate change. The underlying mechanisms remain somewhat obscure owing to the spatial heterogeneity of influencing factors, particularly in the Tarim River Basin (TRB) in China. Therefore, this study targets the TRB, analyzing the spatial and temporal dynamics of vegetation greenness and its climatic determinants across multiple spatial scales. Utilizing Normalized Difference Vegetation Index (NDVI) data, vegetation greenness trends over the past 23 years were assessed, with future projections based on the Hurst exponent. Partial correlation and multiple linear regression analyses were employed to correlate NDVI with temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET), elucidating NDVI’s response to climatic variations. Results revealed that from 2000 to 2022, 90.1% of the TRB exhibited an increase in NDVI, with a significant overall trend of 0.032/decade (p < 0.01). The difference in NDVI change across sub-basins and vegetation types highlighted the spatial disparity in greening. Notable greening predominantly occurred near rivers at lower elevations and in extensive cropland areas, with projections indicating continued greening in some regions. Conversely, future trends mainly suggested a shift towards browning, particularly in higher-elevation areas with minimal human influence. From 2000 to 2022, the TRB experienced a gradual increase in TMP, PRE, and PET. The latter two factors were significantly correlated with NDVI, indicating their substantial role in greening. However, vegetation sensitivity to climate change varied across sub-basins, vegetation types, and elevations, likely due to differences in plant characteristics, hydrothermal conditions, and human disturbances. Despite climate change influencing vegetation dynamics in 51.5% of the TRB, its impact accounted for only 25% of the total NDVI trend. These findings enhance the understanding of vegetation ecosystems in arid regions and provide a scientific basis for developing ecological protection strategies in the TRB. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-26 DOI: 10.3390/ijgi13090304 Issue No: Vol. 13, No. 9 (2024)
- IJGI, Vol. 13, Pages 305: Investigating Resident–Tourist Sharing of
Urban Public Recreation Space and Its Influencing Factors Authors: Yanan Tang, Lin Li, Yilin Gan, Shuangyu Xie First page: 305 Abstract: Urban public recreation space (UPRS) is an integral part of the urban public space system. With the rise of urban tourism, these areas have evolved into important spaces for leisure and entertainment, serving both residents and tourists. However, the extent to which these spaces are shared by the two groups remains unclear. This study quantified the level of UPRS equally shared by residents and tourists in Wuhan, China, using geotagged check-in data from 74 UPRS. We evaluated and compared the resident–tourist sharing degree across various types of UPRS and explored its influencing factors using multiple linear regression (MLR). The results indicated the following: (1) The sharing degree was at a moderate level and it varied significantly across different types of UPRS. (2) Characteristic streets had the highest sharing degree, followed by cultural spaces, urban parks, and tourist scenic spots. (3) The number of nearby tourist attractions, road density, and number of transport stops positively affected sharing degree. These findings suggest that the combination layout of UPRS with other tourist attractions and enhanced accessibility can effectively improve the shared usage of UPRS. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-26 DOI: 10.3390/ijgi13090305 Issue No: Vol. 13, No. 9 (2024)
- IJGI, Vol. 13, Pages 306: Landslide Recognition Based on Machine Learning
Considering Terrain Feature Fusion Authors: Jincan Wang, Zhiheng Wang, Liyao Peng, Chenzhihao Qian First page: 306 Abstract: Landslides are one of the major disasters that exist worldwide, posing a serious threat to human life and property safety. Rapid and accurate detection and mapping of landslides are crucial for risk assessment and humanitarian assistance in affected areas. To achieve this goal, this study proposes a landslide recognition method based on machine learning (ML) and terrain feature fusion. Taking the Dawan River Basin in Detuo Township and Tianwan Yi Ethnic Township as the research area, firstly, landslide-related data were compiled, including a landslide inventory based on field surveys, satellite images, historical data, high-resolution remote sensing images, and terrain data. Then, different training datasets for landslide recognition are constructed, including full feature datasets that fusion terrain features and remote sensing features and datasets that only contain remote sensing features. At the same time, different ratios of landslide to non-landslide (or positive/negative, P/N) samples are set in the training data. Subsequently, five ML algorithms, including Extreme Gradient Boost (XGBoost), Adaptive Boost (AdaBoost), Light Gradient Boost (LightGBM), Random Forest (RF), and Convolutional Neural Network (CNN), were used to train each training dataset, and landslide recognition was performed on the validation area. Finally, accuracy (A), precision (P), recall (R), F1 score (F1), and intersection over union (IOU) were selected to evaluate the landslide recognition ability of different models. The research results indicate that selecting ML models suitable for the study area and the ratio of the P/N samples can improve the A, R, F1, and IOU of landslide identification results, resulting in more accurate and reasonable landslide identification results; Fusion terrain features can make the model recognize landslides more comprehensively and align better with the actual conditions. The best-performing model in the study is LightGBM. When the input data includes all features and the P/N sample ratio is optimal, the A, P, R, F1, and IOU of landslide recognition results for this model are 97.47%, 85.40%, 76.95%, 80.95%, and 71.28%, respectively. Compared to the landslide recognition results using only remote sensing features, this model shows improvements of 4.51%, 35.66%, 5.41%, 22.27%, and 29.16% in A, P, R, F1, and IOU, respectively. This study serves as a valuable reference for the precise and comprehensive identification of landslide areas. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-28 DOI: 10.3390/ijgi13090306 Issue No: Vol. 13, No. 9 (2024)
- IJGI, Vol. 13, Pages 263: Measuring Reliable Accessibility to High-Speed
Railway Stations by Integrating the Utility-Based Model and Multimodal Space–Time Prism under Travel Time Uncertainty Authors: Yongsheng Zhang, Kangyu Liang, Enjian Yao, Mingyi Gu First page: 263 Abstract: Measuring the accessibility of each traffic zone to high-speed railway stations can evaluate the ease of the transportation hub in the transportation system. The utility-based model, which captures individual travel behavior and subjective perception, is often used to quantify the travel impedance on accessibility for a given origin–destination pair. However, existing studies neglect the impacts of travel time uncertainty on utility and possible choice set when measuring accessibility, especially in high-timeliness travel (e.g., railway stations or airports). This study proposes a novel integration of the utility-based model and multimodal space–time prism under travel time uncertainty to measure reliable accessibility to high-speed railway stations. First, the reliable multimodal space–time prism is developed to generate a reliable travel mode choice set constrained by travel time budgets. Then, the reliable choice set is integrated into the utility-based model with the utility function derived from a proposed mean–standard deviation logit-based mode choice model. Finally, this study contributes to measuring reliable accessibility within areas from Beijing’s 5th Ring Road to the Beijing South Railway Station. Based on the results, policymakers can effectively evaluate the distribution of transportation resources and urban planning. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-25 DOI: 10.3390/ijgi13080263 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 264: Automatic Vehicle Trajectory Behavior
Classification Based on Unmanned Aerial Vehicle-Derived Trajectories Using Machine Learning Techniques Authors: Tee-Ann Teo, Min-Jhen Chang, Tsung-Han Wen First page: 264 Abstract: This study introduces an innovative scheme for classifying uncrewed aerial vehicle (UAV)-derived vehicle trajectory behaviors by employing machine learning (ML) techniques to transform original trajectories into various sequences: space–time, speed–time, and azimuth–time. These transformed sequences were subjected to normalization for uniform data analysis, facilitating the classification of trajectories into six distinct categories through the application of three ML classifiers: random forest, time series forest (TSF), and canonical time series characteristics. Testing was performed across three different intersections to reveal an accuracy exceeding 90%, underlining the superior performance of integrating azimuth–time and speed–time sequences over conventional space–time sequences for analyzing trajectory behaviors. This research highlights the TSF classifier’s robustness when incorporating speed data, demonstrating its efficiency in feature extraction and reliability in intricate trajectory pattern handling. This study’s results indicate that integrating direction and speed information significantly enhances predictive accuracy and model robustness. This comprehensive approach, which leverages UAV-derived trajectories and advanced ML techniques, represents a significant step forward in understanding vehicle trajectory behaviors, aligning with the goals of enhancing traffic control and management strategies for better urban mobility. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-26 DOI: 10.3390/ijgi13080264 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 265: Building Height Extraction Based on Spatial
Clustering and a Random Forest Model Authors: Jingxin Chang, Yonghua Jiang, Meilin Tan, Yunming Wang, Shaodong Wei First page: 265 Abstract: Building height (BH) estimation is crucial for urban spatial planning and development. BH estimation using digital surface model data involves obtaining ground and roof elevations. However, vegetation and shadows around buildings affect the selection of the required elevation, resulting in large BH estimation errors. In highly urbanized areas, buildings of similar heights often have similar characteristics and spatial proximity, which have reference significance in BH estimation but are rarely utilized. Herein, we propose a BH estimation method based on BIRCH clustering and a random forest (RF) model. We obtain the initial BH results using a method based on the optimal ground search area and a multi-index evaluation. BIRCH clustering and an RF classification model are used to match buildings of similar heights based on their spatial distance and attribute characteristics. Finally, the BH is adjusted based on the ground elevation obtained from the secondary screening and the BH matching. The validation results from two areas with over 12,000 buildings show that the proposed method reduces the root-mean-square error of the final BH results compared with the initial results. Comparing the obtained height maps shows that the final results produce a relatively accurate BH in areas with high shading and vegetation coverage, as well as in areas with dense buildings. Thus, the proposed method has been validated for its effectiveness and reliability. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-26 DOI: 10.3390/ijgi13080265 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 266: Built Environment Effect on Metro Ridership in
Metropolitan Area of Valparaíso, Chile, under Different Influence Area Approaches Authors: Vicente Aprigliano, Sebastian Seriani, Catalina Toro, Gonzalo Rojas, Mitsuyoshi Fukushi, Marcus Cardoso, Marcelino Aurelio Vieira da Silva, Cristo Cucumides, Ualison Rébula de Oliveira, Cristián Henríquez, Andreas Braun, Volker Hochschild First page: 266 Abstract: The growing relevance of promoting a transition of urban mobility toward more sustainable modes of transport is leading to efforts to understand the effects of the built environment on the use of railway systems. In this direction, there are challenges regarding the creation of coherence between the locations of metro stations and their surroundings, which has been explored extensively in the academic community. This process is called Transit-Oriented Development (TOD). Within the context of Latin America, this study seeks to assess the influence of the built environment on the metro ridership in the metropolitan area of Valparaíso, Chile, testing two approaches of influence area definition, one of which is a fixed distance from the stations, and the other is based on the origin and destination survey of the study area. The analysis is based on Ordinary Least Squares regression (OLS) to identify the factors from the built environment, which affects the metro’s ridership. Results show that the models based on the area of influence defined through the use of the origin and destination survey explain the metro ridership better. Moreover, this study reveals that the metro system in Greater Valparaíso was not planned in harmony with urban development. The models demonstrate an inverse effect of the built environment on ridership, contrasting with the expected outcomes of a metro station designed following a Transit-Oriented Development approach. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-26 DOI: 10.3390/ijgi13080266 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 267: Exploring the Spatiotemporal Patterns of
Passenger Flows in Expanding Urban Metros: A Case Study of Shenzhen Authors: Sirui Lv, Hu Yang, Xin Lu, Fan Zhang, Pu Wang First page: 267 Abstract: Despite extensive investigations on urban metro passenger flows, their evolving spatiotemporal patterns with the extensions of urban metro networks have not been well understood. Using Shenzhen as a case study city, our study initiates an investigation into this matter by analyzing the evolving network topology of Shenzhen Metro. Subsequently, leveraging long-term smart card data, we analyze the evolving spatiotemporal patterns of passenger flows and develop an analytical approach to pinpoint the major passenger sources of urban metro congestion. While the passenger travel demand and the passenger flow volumes kept increasing with the extension of the urban metro network, the major passenger sources were very stable in space, highlighting the inherent invariance in the evolution of the urban metro system. Finally, we analyze the impact of population and land use factors on passenger flow contributions of passenger sources, obtaining useful clues to foresee future passenger flow conditions. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-26 DOI: 10.3390/ijgi13080267 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 268: The Spatial Equilibrium Model of Elderly Care
Facilities with High Spatiotemporal Sensitivity and Its Economic Associations Study Authors: Hongyan Li, Rui Li, Jing Cai, Shunli Wang First page: 268 Abstract: The global population aging poses new challenges in allocating care facilities for the elderly. This demographic trend also influences economic development and the quality of urban life. However, current research focuses on the supply of elderly care facilities and primarily uses administrative divisions as a scale, resulting in low spatiotemporal sensitivity in evaluating the spatial equilibrium of elderly care facilities (SEECF). The relationship between the SEECF and economic development is not clear. In response to these problems, we proposed a spatial equilibrium model of elderly care facilities with high spatiotemporal sensitivity (SEM-HSTS) and explored the spatiotemporal associations between the SEECF and economic development. Considering the spatial accessibility rate of elderly care services (SARecs) and the spatiotemporal supply–demand ratio for elderly care services (STSDRecs), two types of supply–demand relationship factors were constructed. Then, a spatiotemporal accessibility of medical services (STAms) factor was obtained based on a modified two-step floating catchment area (M2SFCA) method. On this basis, the SEM-HSTS was constructed based on the theory of coordinated development. Further, a panel threshold model was employed to evaluate the influence relationships among population aging, SEECF, and gross domestic product (GDP) in different phases. Finally, spatial autocorrelation and Geodetector explored the spatial associations between SEECF and GDP across complex urban land use categories (ULUC). The experimental results at a 100-m grid scale showed that the SEM-HSTS exhibited higher spatiotemporal heterogeneity than the classical accessibility method, with elevated spatiotemporal sensitivity. Effectively identified various spatial imbalances, such as undersupply and resource waste. The panel model captured phased relationship changes, showing that SEECF had inhibitory and promotional effects on GDP in pre- and post-aging societies, with stronger effects as balance approached. Moreover, the combined interaction of ULUC and GDP had a more significant influence on SEECF than any individual factor, with GDP exerting a more significant influence. This study provides an empirical basis for creating resource-efficient elderly care facility systems and optimizing layouts. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-27 DOI: 10.3390/ijgi13080268 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 269: Multi-Criteria Decision Analysis to Evaluate the
Geographic Potential of Alternative Photovoltaic Types Authors: Franziska Hübl, Franz Welscher, Johannes Scholz First page: 269 Abstract: This paper contributes to the expansion of green energy production, which is being pursued in order to mitigate climate change and carbon emissions from energy production. It addresses the delineation of areas that are suitable for the application of photovoltaics in the context of agricultural areas, water bodies, and parking spaces. Three specific photovoltaic types are examined in order to find out which criteria influence their geographic potential and whether spatial multi-criteria decision analysis methods are suitable for identifying suitable areas. The proposed approach consists of four steps: (1) collecting factors through expert interviews and questionnaires; (2) mapping the criteria to the spatial datasets; (3) deriving weighted scores for individual criteria through expert interviews; (4) applying the multi-criteria decision analysis method to compute and aggregate the final scores. We test our methodology at selected sites in the state of Styria, Austria. The test sites represent the topographical characteristics of the state and are about 5% of the size of Styria, approximately 820 km2. The key contributions are a weighted set of criteria that are relevant for the geographic potential of alternative photovoltaic types and the developed methodology to determine this potential. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-30 DOI: 10.3390/ijgi13080269 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 270: Challenges to Viticulture in Montenegro under
Climate Change Authors: António Fernandes, Nataša Kovač, Hélder Fraga, André Fonseca, Sanja Šućur Radonjić, Marko Simeunović, Kruna Ratković, Christoph Menz, Sergi Costafreda-Aumedes, João A. Santos First page: 270 Abstract: The Montenegrin climate is characterised as very heterogeneous due to its complex topography. The viticultural heritage, dating back to before the Roman empire, is settled in a Mediterranean climate region, located south of the capital Podgorica, where climate conditions favour red wine production. However, an overall increase in warmer and drier periods affects traditional viticulture. The present study aims to discuss climate change impacts on Montenegrin viticulture. Bioclimatic indices, ensembled from five climate models, were analysed for both historical (1981–2010) and future (2041–2070) periods upon three socio-economic pathways: SSP1-2.6, SSP3-7.0 and SSP5-8.5. CHELSA (≈1 km) was the selected dataset for this analysis. Obtained results for all scenarios have shown the suppression of baseline conditions for viticulture. The average summer temperature might reach around 29.5 °C, and the growing season average temperature could become higher than 23.5 °C, advancing phenological events. The Winkler index is estimated to range from 2900 °C up to 3100 °C, which is too hot for viticulture. Montenegrin viticulture requires the application of adaptation measures focused on reducing temperature-increase impacts. The implementation of adaptation measures shall start in the coming years, to assure the lasting productivity and sustainability of viticulture. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-30 DOI: 10.3390/ijgi13080270 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 271: A Spatial Case-Based Reasoning Method for
Healthy City Assessment: A Case Study of Middle Layer Super Output Areas (MSOAs) in Birmingham, England Authors: Shuguang Deng, Wei Liu, Ying Peng, Binglin Liu First page: 271 Abstract: Assessing healthy cities is a crucial strategy for realizing the concept of “health in all policies”. However, most current quantitative assessment methods for healthy cities are predominantly city-level and often overlook intra-urban evaluations. Building on the concept of geographic spatial case-based reasoning (CBR), we present an innovative healthy city spatial case-based reasoning (HCSCBR) model. This model comprehensively integrates spatial relationships and attribute characteristics that impact urban health. We conducted experiments using a detailed multi-source dataset of health environment determinants for middle-layer super output areas (MSOAs) in Birmingham, England. The results demonstrate that our method surpasses traditional data mining techniques in classification performance, offering greater accuracy and efficiency than conventional CBR models. The flexibility of this method permits its application not only in intra-city health evaluations but also in extending to inter-city assessments. Our research concludes that the HCSCBR model significantly improves the precision and reliability of healthy city assessments by incorporating spatial relationships. Additionally, the model’s adaptability and efficiency render it a valuable tool for urban planners and public health researchers. Future research will focus on integrating the temporal dimension to further enhance and refine the healthy city evaluation model, thereby increasing its dynamism and predictive accuracy. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-31 DOI: 10.3390/ijgi13080271 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 272: Analysis of Road Safety Perception and
Influencing Factors in a Complex Urban Environment—Taking Chaoyang District, Beijing, as an Example Authors: Xinyu Hou, Peng Chen First page: 272 Abstract: Measuring human perception of environmental safety and quantifying the street view elements that affect human perception of environmental safety are of great significance for improving the urban environment and residents’ safety perception. However, domestic large-scale quantitative research on the safety perception of Chinese local cities needs to be deepened. Therefore, this paper chooses Chaoyang District in Beijing as the research area. Firstly, the network safety perception distribution of Chaoyang District is calculated and presented through the CNN model trained based on the perception dataset constructed by Chinese local cities. Then, the street view elements are extracted from the street view images using image semantic segmentation and target detection technology. Finally, the street view elements that affect the road safety perception are identified and analyzed based on LightGBM and SHAP interpretation framework. The results show the following: (1) the overall safety perception level of Chaoyang District in Beijing is high; (2) the number of motor vehicles and the proportion of the area of roads, skies, and sidewalks are the four factors that have the greatest impact on environmental safety perception; (3) there is an interaction between different street view elements on safety perception, and the proportion and number of street view elements have interaction on safety perception; (4) in the sections with the lowest, moderate, and highest levels of safety perception, the influence of street view elements on safety perception is inconsistent. Finally, this paper summarizes the results and points out the shortcomings of the research. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-31 DOI: 10.3390/ijgi13080272 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 273: The Influence of Proximity on the Evolution of
Urban Innovation Networks in Nanjing Metropolitan Area, China: A Comparative Analysis of Knowledge and Technological Innovations Authors: Yu Shi, Wei Zhai, Yiran Yan, Xingping Wang First page: 273 Abstract: This study investigates the dynamics of innovation element flows among metropolitan areas and examines the underlying proximity mechanisms that are crucial for elevating urban agglomerations’ innovation levels and spurring their development. Utilizing collaborative publication and patent data, this research constructs knowledge and technological innovation networks within the Nanjing metropolitan area (NMA) from 2013 to 2020. It analyzes the evolution of network structures and applies the Multiple Regression Quadratic Assignment Procedure to discern the proximity mechanisms driving the urban innovation networks’ evolution in NMA. The main findings are as follows: (1) The knowledge collaborations within NMA cities remain largely confined to cities within Jiangsu province, whereas the technological collaborations are shifting from intra-province to cross-province cooperation. (2) Both knowledge and technological innovation networks display a “core-periphery” configuration, with Nanjing maintaining a dominant central position. The scale of the KIN surpasses that of the TIN, while the latter’s growth rate outpaces the former’s. Technological collaborations demonstrate more pronounced spillover effects than their knowledge counterparts. (3) At the metropolitan area level, organizational, social, cognitive, and technological proximities exert varying degrees of influence on innovation cooperation among different innovation entities across various years. Cognitive proximity exhibits the most substantial explanatory power. Based on these findings, the study proposes relevant policy recommendations for constructing an innovative NMA and promoting collaborative innovation development among cities within the NMA. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-01 DOI: 10.3390/ijgi13080273 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 274: BS-GeoEduNet 1.0: Blockchain-Assisted Serverless
Framework for Geospatial Educational Information Networks Authors: Meenakshi Kandpal, Veena Goswami, Yash Pritwani, Rabindra K. Barik, Manob Jyoti Saikia First page: 274 Abstract: The integration of a blockchain-supported serverless computing framework enhances the performance of computational and analytical operations and the provision of services within internet-based data centers, rather than depending on independent desktop computers. Therefore, in the present research paper, a blockchain-assisted serverless framework for geospatial data visualizations is implemented. The proposed BS-GeoEduNet 1.0 framework leverages the capabilities of AWS Lambda for serverless computing, providing a reliable and efficient solution for data storage, analysis, and distribution. The proposed framework incorporates AES encryption, decryption layers, and queue implementation to achieve a scalable approach for handling larger files. It implements a queueing mechanism during the heavier input/output processes of file processing by using Apache KAFKA, enabling the system to handle large volumes of data efficiently. It concludes with the visualization of all geospatial-enabled NIT/IIT details on the proposed framework, which utilizes the data fetched from MongoDB. The experimental findings validate the reliability and efficiency of the proposed system, demonstrating its efficacy in geospatial data storage and processing. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-01 DOI: 10.3390/ijgi13080274 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 275: Factors Influencing the Efficiency of
Demand-Responsive Transport Services in Rural Areas: A GIS-Based Method for Optimising and Evaluating Potential Services Authors: Carlos Tejero-Beteta, Amparo Moyano, Santos Sánchez-Cambronero First page: 275 Abstract: Demand-responsive transport (DRT) could be an alternative for extending the accessibility of high-speed rail (HSR) servicing cities in rural environments, where fixed public transport does not provide efficient services. This paper proposes a method to analyse the factors that influence the implementation of DRT systems for inter-urban mobility, connecting and integrating towns in rural areas. Methodologically, a vehicle routing problem analysis in a GIS-based environment is applied to a theoretical case study to evaluate the factors that influence DRT efficiency in different scenarios, considering the specific singularities of this kind of inter-urban long-distance mobility. The results suggest the optimal DRT solutions in these rural contexts to be those that, after adjusting the fleet to specific demands, use low-capacity vehicles, which are much better adapted to the geography of sparsely populated areas. Moreover, in adapting DRT systems to HSR travellers’ needs, windows catering to these needs should incorporate the option of setting the pickup or arrival times. This paper demonstrates that DRT systems could reach significant levels of service in rural areas compared with fixed lines and even private vehicles, especially when evaluating key aspects of the system’s efficiency for its implementation. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-01 DOI: 10.3390/ijgi13080275 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 276: DCPMS: A Large-Scale Raster Layer Serving Method
for Custom Online Calculation and Rendering Authors: Anbang Yang, Feng Zhang, Jie Feng, Luoqi Wang, Enjiang Yue, Xinhua Fan, Jingyi Zhang, Linshu Hu, Sensen Wu First page: 276 Abstract: Raster data represent one of the fundamental data formats utilized in GIS. As the technology used to observe the Earth continues to evolve, the spatial and temporal resolution of raster data is becoming increasingly refined, while the data scale is expanding. One of the key issues in the development of GIS technology is to determine how to make large-scale raster data better to provide computation, visualization, and analysis services in the Internet environment. This paper proposes a decentralized COG-pyramid-based map service method (DCPMS). In comparison to traditional raster data online service technology, such as GIS servers and static tiles, DCPMS employs virtual mapping to reduce data storage costs and combines tile technology with a cloud-native storage scheme to enhance the concurrency of supportable requests. Furthermore, the band calculation process is shifted to the client, thereby effectively resolving the issue of efficient customized band calculation and data rendering in the context of a large-scale raster data online service. The results indicate DCPMS delivers commendable performance. Its decentralized architecture significantly enhances performance in high concurrency scenarios. With a thousand concurrent requests, the response time of DCPMS is reduced by 74% compared to the GIS server. Moreover, this service exhibits considerable strengths in data preprocessing and storage, suggesting a novel pathway for future technical improvement of large-scale raster data map services. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-01 DOI: 10.3390/ijgi13080276 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 277: Influencing Factors of Street Vitality in
Historic Districts Based on Multisource Data: Evidence from China Authors: Bing Yu, Jing Sun, Zhaoxing Wang, Sanfeng Jin First page: 277 Abstract: Amid urban expansion, historic districts face challenges such as declining vitality and deteriorating spatial quality. Using the streets of Xi’an’s historical and cultural district as examples, this research utilizes multisource data, including points of interest (POIs), street view images, and Baidu heatmaps, alongside analytical techniques such as machine learning. This study explores the determinants of street vitality from the dual perspectives of its external manifestation and spatial carriers. A quantitative framework for measuring street vitality in historic districts is established, thoroughly examining the driving factors behind street vitality. Additionally, the relationship between built environment indicators and street vitality is elucidated through statistical analysis methods. The findings reveal significant, time-varying influences of these spatial carriers on human vitality, with distinct spatial distribution patterns of human activity across different times, and the significance of the influence of external representations of human vitality and various types of spatial carriers varies over time. Based on these insights, this paper proposes strategies for enhancing the vitality of historic streets, aiming to rejuvenate and sustain the diverse and dynamic energy of these districts. It provides a foundation for revitalizing the vigor of cultural heritage zones and offers strategies applicable to similar urban contexts. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-05 DOI: 10.3390/ijgi13080277 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 278: A Maximal Multimodal Accessibility Equality
Model to Optimize the Equality of Healthcare Services Authors: Zhuolin Tao, Qianyu Zhong, Yinuo Dang First page: 278 Abstract: The equality of healthcare services has been a focus among researchers and policymakers. The maximal accessibility equality (MAE) model is a widely used location-allocation model for the optimization of the accessibility equality of facilities. However, it might produce biased results due to the overlooking of multiple transport mode options for urban residents. This study develops a maximal multimodal accessibility equality (MMAE) model by incorporating the multimodal two-step floating catchment area (2SFCA) accessibility model. It reflects the multimodal context in cities and aims to maximize the equality of multimodal accessibility. A case study of healthcare facilities in Shenzhen demonstrates that the proposed MMAE model can significantly improve the equality of multimodal accessibility. However, the traditional single-modal MAE model generates unequal multimodal accessibility, which might yield biased planning recommendations in multimodal contexts. The findings highlight the superiority of the MMAE model against the traditional single-modal MAE model in terms of pursuing equal accessibility for all residents. The MMAE model can serve as a scientific tool to support the rational planning of healthcare facilities or other types of public facilities in multimodal contexts. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-07 DOI: 10.3390/ijgi13080278 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 279: Ecological Network Construction Based on Red,
Green and Blue Space: A Case Study of Dali City, China Authors: Rong Chen, Shunmin Zhang, Xiaoyuan Huang, Xiang Li, Jiansong Peng First page: 279 Abstract: Rapid urbanization leads to fragmentation and reduced connectivity of urban landscapes, endangering regional biodiversity conservation and sustainable development. Constructing a red, green, and blue spatial ecological network is an effective way to alleviate ecological pressure and promote economic development. Using circuit theory, hydrological analysis, and suitability analysis, this study constructs a composite ecological network under urban–rural integration. The results show the following: (1) A total of 22 ecological corridors with a length of 349.20 km, 22 ecological pinch points, and 22 ecological barrier points are identified in the municipal area, mainly distributed in Haidong Town. There are 504 stormwater corridors, which are more evenly distributed, 502 riverfront landscape corridors, and 130 slow-moving landscape corridors. (2) A total of 20 ecological corridors, with a length of 99.23 km, 19 ecological pinch points, and 25 barrier points were identified in the main urban area, and most of them are located in the ecological corridors. There are 71 stormwater corridors, mainly located in the northwestern forest area, 71 riverfront recreation corridors, and 50 slow-moving recreation corridors. (3) Two scales of superimposed ecological source area of 3.65 km2, and eleven ecological corridors, are primarily distributed between Erhai Lake and Xiaguan Town. There are two superimposed stormwater corridors and fourteen recreational corridors. The eco-nodes are mostly distributed in the east and south of Dali City; wetland nodes are mainly situated in the eighteen streams of Cangshan Mountain; and landscape nodes are more balanced in spatial distribution. The study results can provide a reference for composite ecological network construction. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-07 DOI: 10.3390/ijgi13080279 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 280: Spatio-Temporal Big Data Collaborative Storage
Mechanism Based on Incremental Aggregation Subvector Commitment in On-Chain and Off-Chain Systems Authors: Mingjia Han, Xinyi Yang, Huachang Su, Yekang Zhao, Ding Huang, Yongjun Ren First page: 280 Abstract: As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-10 DOI: 10.3390/ijgi13080280 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 281: Efficient and Verifiable Range Query Scheme for
Encrypted Geographical Information in Untrusted Cloud Environments Authors: Zhuolin Mei, Jing Zeng, Caicai Zhang, Shimao Yao, Shunli Zhang, Haibin Wang, Hongbo Li, Jiaoli Shi First page: 281 Abstract: With the rapid development of geo-positioning technologies, location-based services have become increasingly widespread. In the field of location-based services, range queries on geographical data have emerged as an important research topic, attracting significant attention from academia and industry. In many applications, data owners choose to outsource their geographical data and range query tasks to cloud servers to alleviate the burden of local data storage and computation. However, this outsourcing presents many security challenges. These challenges include adversaries analyzing outsourced geographical data and query requests to obtain privacy information, untrusted cloud servers selectively querying a portion of the outsourced data to conserve computational resources, returning incorrect search results to data users, and even illegally modifying the outsourced geographical data, etc. To address these security concerns and provide reliable services to data owners and data users, this paper proposes an efficient and verifiable range query scheme (EVRQ) for encrypted geographical information in untrusted cloud environments. EVRQ is constructed based on a map region tree, 0–1 encoding, hash function, Bloom filter, and cryptographic multiset accumulator. Extensive experimental evaluations demonstrate the efficiency of EVRQ, and a comprehensive analysis confirms the security of EVRQ. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-11 DOI: 10.3390/ijgi13080281 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 282: What Factors Revitalize the Street Vitality of
Old Cities' A Case Study in Nanjing, China Authors: Yan Zheng, Ruhai Ye, Xiaojun Hong, Yiming Tao, Zherui Li First page: 282 Abstract: Urban street vitality has been a perennial focus within the domain of urban planning. This study examined spatial patterns of street vitality in the old city of Nanjing during working days and weekends using real-time user datasets (RTUDs). A spatial autoregressive model (SAM) and a multiscale geographically weighted regression (MGWR) model were employed to quantitatively assess the impact of various factors on street vitality and their spatial heterogeneity. This study revealed the following: (1) the distribution of street vitality in the old city of Nanjing exhibited a structure centered around Xinjiekou, with greater regularity and predictability in street vitality on working days than on weekends; (2) eight variables, such as traffic location, road density, and functional density, are positively associated with street vitality, whereas the green view index is negatively associated with street vitality, and commercial location benefits street vitality at weekends but detracts from street vitality on working days; and (3) the influence of variables such as traffic location and functional density on street vitality is contingent on their spatial position. Based on these results, this study provides new strategies to enhance the street vitality of old cities. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-12 DOI: 10.3390/ijgi13080282 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 283: City Transmission Networks: Unraveling Disease
Spread Dynamics Authors: Hend Alrasheed, Norah Alballa, Isra Al-Turaiki, Fahad Almutlaq, Reham Alabduljabbar First page: 283 Abstract: In the midst of global efforts to curb the spread of infectious diseases, researchers worldwide are striving to unravel the intricate spatial and temporal patterns of disease transmission dynamics. Mathematical models are indispensable tools for understanding the dissemination of emerging pathogens and elucidating the evolution of epidemics. This paper introduces a novel approach by investigating city transmission networks as a framework for analyzing disease spread. In this network, major cities are depicted as nodes interconnected by edges representing disease transmission pathways. Subsequent network analysis employs various epidemiological and structural metrics to delineate the distinct roles played by cities in disease transmission. The primary objective is to identify superspreader cities. Illustratively, we apply this methodology to study COVID-19 transmission in Saudi Arabian cities, shedding light on the specific dynamics within this context. These insights offer valuable guidance for decision-making processes and the formulation of effective intervention strategies, carrying significant implications for managing public health crises. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-12 DOI: 10.3390/ijgi13080283 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 284: Correction: Agriesti et al. Assignment of a
Synthetic Population for Activity-Based Modeling Employing Publicly Available Data. ISPRS Int. J. Geo-Inf. 2022, 11, 148 Authors: Serio Agriesti, Claudio Roncoli, Bat-hen Nahmias-Biran First page: 284 Abstract: In the original publication [...] Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-13 DOI: 10.3390/ijgi13080284 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 285: Automatic Functional Classification of Buildings
Supported by a POI Semantic Characterization Knowledge Graph Authors: Youneng Su, Qing Xu, Xinming Zhu, Fubing Zhang, Yi Liu First page: 285 Abstract: The division of urban functional zones is crucial for understanding urban characteristics and aiding in urban management and planning. Traditional methods, like dividing based on blocks and grids, are insufficient for modern demands. To address this, a knowledge-graph-supported method for building functional category division is proposed. Firstly, the associations between points of interest (POI) and buildings are established using triangulation and buffer zones. Then, a knowledge graph of buildings is constructed through entity and relationship extraction. A functional category classification model supported by the Z-score is designed using the semantic characterizations of surrounding POIs for inference rules. The results demonstrate high accuracy in building functional category division, supporting the refinement and intelligent expression of urban functional zones for urban construction, planning, and management. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-15 DOI: 10.3390/ijgi13080285 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 286: Grid Density Algorithm-Based Second-Hand Housing
Transaction Activity and Spatio-Temporal Characterization: The Case of Shenyang City, China Authors: Jiaqiang Ren, Xiaomeng Gao First page: 286 Abstract: Second-hand housing transactions constitute a significant segment of the real estate market and are vital for its robust development. The dynamics of these transactions mirror the housing preferences of buyers, and their spatial and temporal analysis elucidates evolving market patterns and buyer behavior. This study introduces an innovative grid density clustering algorithm, dubbed the RScan algorithm, which integrates Bayesian optimization with grid density techniques. This composite methodology is employed to assess clustering outcomes, optimize hyperparameters, and facilitate detailed visualization and analysis of transaction activity across various regions. Focusing on Shenyang, a major urban center in Northeast China, the research spans from 2018 to 2023, exploring the second-hand housing transaction activity and its spatio-temporal attributes. The results reveal temporal fluctuations in transaction intensity across different Shenyang regions, although core areas of high activity remain constant. These regions display a heterogeneous pattern of irregularly stepped and clustered distributions, with a notable absence of uniformly high-activity zones. This study pioneers a novel methodological framework for investigating second-hand housing transactions, offering crucial insights for market development and policy formulation in Shenyang. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-16 DOI: 10.3390/ijgi13080286 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 287: Identifying the Nonlinear Impacts of Road
Network Topology and Built Environment on the Potential Greenhouse Gas Emission Reduction of Dockless Bike-Sharing Trips: A Case Study of Shenzhen, China Authors: Jiannan Zhao, Changwei Yuan, Xinhua Mao, Ningyuan Ma, Yaxin Duan, Jinrui Zhu, Hujun Wang, Beisi Tian First page: 287 Abstract: Existing studies have limited evidence about the complex nonlinear impact mechanism of road network topology and built environment on bike-sharing systems’ greenhouse gas (GHG) emission reduction benefits. To fill this gap, we examine the nonlinear effects of road network topological attributes and built environment elements on the potential GHG emission reduction of dockless bike-sharing (DBS) trips in Shenzhen, China. Various methods are employed in the research framework of this study, including a GHG emission reduction estimation model, spatial design network analysis (sDNA), gradient boosting decision tree (GBDT), and partial dependence plots (PDPs). Results show that road network topological variables have the leading role in determining the potential GHG emission reduction of DBS trips, followed by land use variables and transit-related variables. Moreover, the nonlinear impacts of road network topological variables and built environment variables show certain threshold intervals for the potential GHG emission reduction of DBS trips. Furthermore, the impact of built environment on the potential GHG emission reduction of DBS trips is moderated by road network topological indicators (closeness and betweenness). Compared with betweenness, closeness has a greater moderating effect on built environment variables. These findings provide empirical evidence for guiding bike-sharing system planning, bike-sharing rebalancing strategy optimization, and low-carbon travel policy formulation. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-16 DOI: 10.3390/ijgi13080287 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 288: Isochrone-Based Accessibility Analysis of
Pre-Hospital Emergency Medical Facilities: A Case Study of Central Districts of Beijing Authors: Yuan Zhao, Ying Zhou First page: 288 Abstract: Pre-hospital emergency medical service (PHEMS) is critical for the treatment outcomes of life-threatening injuries and time-sensitive illnesses. Response time, influenced by traffic conditions and the site planning of pre-hospital emergency medical facilities (PHEMFs), is the main indicator for evaluating PHEMS. In 2020, the Beijing government released the “Special Plan for Spatial Layout of Pre-hospital Emergency Medical Facilities in Beijing (2020–2022)”. This paper evaluates the functional efficiency and spatial equity of this plan within Beijing’s central six districts using isochrone measures to assess the accessibility of the planned PHEMFs. The isochrone coverages of the area and population were calculated, and the temporal-spatial characteristics of isochrones were concluded. The analysis revealed that while the current planning meets several objectives, challenges in service availability and equity persist. Although 10-min isochrone coverage was high, 8-min coverage was insufficient, particularly during peak hours. This highlights gaps in service accessibility that necessitate additional emergency stations in underserved areas. The current planning approach leads to significant overlap at administrative boundaries, causing service oversupply and increased costs, which calls for a city-wide planning perspective that breaks administrative boundaries to optimize resource allocation. Traffic conditions significantly impact service coverage, with congestion reducing coverage in central areas and better coverage near traffic hubs. Future planning should strategically place stations based on traffic patterns and population distribution to enhance emergency medical service accessibility and equity in urban areas. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-16 DOI: 10.3390/ijgi13080288 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 289: SAMPLID: A New Supervised Approach for
Meaningful Place Identification Using Call Detail Records as an Alternative to Classical Unsupervised Clustering Techniques Authors: Manuel Mendoza-Hurtado, Juan A. Romero-del-Castillo, Domingo Ortiz-Boyer First page: 289 Abstract: Data supplied by mobile phones have become the basis for identifying meaningful places frequently visited by individuals. In this study, we introduce SAMPLID, a new Supervised Approach for Meaningful Place Identification, based on providing a knowledge base focused on the specific problem we aim to solve (e.g., home/work identification). This approach allows to tackle place identification from a supervised perspective, offering an alternative to unsupervised clustering techniques. These clustering techniques rely on data characteristics that may not always be directly related to classification objectives. Our results, using mobility data provided by call detail records (CDRs) from Milan, demonstrate superior performance compared to applying clustering techniques. For all types of CDRs, the best results are obtained with the 20 × 20 subgrid, indicating that the model performs better when supplied with information from neighboring cells with a close spatial relationship, establishing neighborhood relationships that allow the model to clearly learn to identify transitions between cells of different types. Considering that it is common for a place or cell to be labeled in multiple categories at once, this supervised approach opens the door to addressing the identification of meaningful places from a multi-label perspective, which is difficult to achieve using classical unsupervised methods. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-17 DOI: 10.3390/ijgi13080289 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 290: Spatial Planning Data Structure Based on
Blockchain Technology Authors: Minwen Tang, Wujiao Dai, Changlin Yin, Bing Hu, Jun Chen, Haoming Liu First page: 290 Abstract: Spatial planning requires ensuring the legality, uniformity, authority, and relevance of data. Blockchain technology, characterized by tamper-proofing, complete record-keeping, and process traceability, may effectively organize and manage spatial planning data. This study introduces blockchain technology to address common spatial planning problems, such as planning overlaps and conflicts. We developed a block structure, chain structure, and consensus algorithms tailored for spatial planning. To meet the data management requirements of these structures, we devised a primary unit division method based on the space and population standards of the 15 min life circle, using the Point Cloud Density Tiler. The validation experiments were conducted using the Hyperledger Fabric 2.0 technology framework in Changsha City, Hunan Province, China, with the division method validated against the number and distribution of public service facilities. The validation results show that during the data storage process, the block size remains below 1.00 MB, the data redundancy is up to 21.30%, the consensus verification rate is 150.33 times per second, the block generation rate is 20.83 blocks per minute, and the equivalent data throughput is 12.21 transactions per second. This demonstrates that the proposed method effectively addresses the challenges of block size, data redundancy, consensus algorithm efficiency, and data throughput in blockchain technology. The findings demonstrate that the structures ensure legal, uniform, and authoritative spatial planning, and advance the application of blockchain technology in relevant fields. Additionally, we explored the application of a blockchain data structure in spatial planning monitoring and early warning. This technology can be further studied and applied in related fields. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-17 DOI: 10.3390/ijgi13080290 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 291: Spatial Semantics for the Evaluation of
Administrative Geospatial Ontologies Authors: Alia I. Abdelmoty, Hanan Muhajab, Abdurauf Satoti First page: 291 Abstract: Administrative geography is concerned with the hierarchy of areas related to national and local government in a country. They form an important dataset in the country’s open data provision and act as the geo-referencing backdrop for many types of geospatial data. Proprietary ontologies are built to model and represent these data with little focus on spatial semantics. Studying the quality of these ontologies and developing methods for their evaluation are needed. This paper addresses these problems by studying the spatial semantics of administrative geography data and proposes a uniform set of qualitative semantics that encapsulates the inherent spatial structure of the administrative divisions and allows for the application of spatial reasoning. Topological and proximity semantics are defined and combined into a single measure of spatial completeness and used for defining a set of competency questions to be used in the evaluation process. The significance of the novel measure of completeness and competency questions is demonstrated on four prominent real world administrative geography ontologies. It is shown how these can provide an objective measure of quality of the geospatial ontologies and gaps in their definition. The proposed approach to defining spatial completeness complements the established methods in the literature, that primarily focus on the syntactical and structural dimensions of the ontologies, and offers a novel approach to ontology evaluation in the geospatial domain. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-17 DOI: 10.3390/ijgi13080291 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 292: Assessing and Predicting Nearshore Seawater
Quality with Spatio-Temporal Semivariograms: The Case of Coastal Waters in Fujian Province, China Authors: Wei Wang, Wenfang Cheng, Jing Chen First page: 292 Abstract: The scientific assessment and prediction of nearshore water quality are crucial for marine environment protection efforts. This study is based on a comprehensive analysis of existing assessment and prediction methods and considers the regular and random characteristics of nearshore seawater quality due to both natural and anthropogenic influences. It proposes a new method that applies the kriging interpolation algorithm to empirically generated spatio-temporal semivariograms to assess and predict seawater quality. The application of this method in Fujian coastal areas shows that it is able to flexibly and scientifically estimate the variations in various indicators in the region. Combined with GIS spatial data overlay analysis operations, it can be used to quantitatively evaluate different qualities of seawater and provide scientific guidance for marine environmental protection. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-17 DOI: 10.3390/ijgi13080292 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 293: Analysis of the Impact of the Digital Economy on
Carbon Emission Reduction and Its Spatial Spillover Effect—The Case of Eastern Coastal Cities in China Authors: Juanjuan Zhong, Ye Duan, Caizhi Sun, Hongye Wang First page: 293 Abstract: The expansion of the digital economy is crucial for halting climate change, as carbon emissions from urban energy use contribute significantly to global warming. This study uses the Difference-in-Differences Model and the Spatial Durbin Model determine whether the digital economy may support the development of reducing carbon emissions and its geographic spillover effects in Chinese cities on the east coast. In addition, it looks more closely at the effects of lowering carbon emissions in space by separating them into direct, indirect, and spatial impact parts. The findings show that (1) from 2012 to 2021, the digital economy favored carbon emission reductions in China’s eastern coastline cities, as supported by the robustness test. (2) The link between digital economy growth and carbon emissions is highly variable, with smart city development and urban agglomeration expansion both cutting city carbon emissions considerably. Successful digital economy strategies can lower CO2 emissions from nearby cities. (3) Eastern coastal cities have a considerable spatial spillover impact, and the digital economy mitigates local energy consumption and carbon emissions while simultaneously enhancing environmental quality in nearby urban areas. This analysis proposes that the peak carbon and carbon neutrality targets can be met by increasing the digital economy and enhancing regional environmental governance cooperation. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-18 DOI: 10.3390/ijgi13080293 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 294: An Improved ANN-Based Label Placement Method
Considering Surrounding Features for Schematic Metro Maps Authors: Zhiwei Wu, Tian Lan, Chenzhen Sun, Donglin Cheng, Xing Shi, Meisheng Chen, Guangjun Zeng First page: 294 Abstract: On schematic metro maps, high-quality label placement is helpful to passengers performing route planning and orientation tasks. It has been reported that the artificial neural network (ANN) has the potential to place labels with learned labeling knowledge. However, the previous ANN-based method only considered the effects of station points and their connected edges. Indeed, unconnected but surrounding features (points, edges, and labels) also significantly affect the quality of label placement. To address this, we have proposed an improved method. The relations between label positions and both connected and surrounding features are first modeled based on labeling natural intelligence (i.e., the experience, knowledge, and rules of labeling established by cartographers). Then, ANN is employed to learn such relations. Quantitative evaluations show that our method reaches lower percentages of label–point overlap (0.00%), label–edge overlap (4.12%), and label–label overlap (20.58%) compared to the benchmark (4.17%, 14.29%, and 35.11%, respectively). On the other hand, our method effectively avoids ambiguous labels and ensures labels from the same line are placed on the same side. Qualitative evaluations show that approximately 75% of users prefer our results. This novel method has the potential to advance the automated generation of schematic metro maps. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-19 DOI: 10.3390/ijgi13080294 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 295: Genetic Programming to Optimize 3D Trajectories
Authors: André Kotze, Moritz Jan Hildemann, Vítor Santos, Carlos Granell First page: 295 Abstract: Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal is to minimize the route cost, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques, including evolutionary computation, have been applied to trajectory optimization with varying degrees of success. This work explores the use of genetic programming (GP) for 3D trajectory optimization by developing a novel GP algorithm to optimize trajectories in a 3D space by encoding 3D geographic trajectories as function trees. The effects of parameterization are also explored and discussed, demonstrating the advantages and drawbacks of custom parameter settings along with additional evolutionary computational techniques. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness, highlighting the potential for GP-based algorithms to be applied to other complex optimization problems in science and engineering. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-20 DOI: 10.3390/ijgi13080295 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 296: Examining Spatial Disparities in Electric
Vehicle Public Charging Infrastructure Distribution Using a Multidimensional Framework in Nanjing, China Authors: Moyan Wang, Zhengyuan Liang, Zhiming Li First page: 296 Abstract: With the increasing demand for electric vehicle public charging infrastructure (EVPCI), optimizing the charging network to ensure equal access is crucial to promote the sustainable development of the electric vehicle market and clean energy. Due to limited urban land space and the large-scale expansion of charging infrastructure, determining where to begin optimization is the first step in improving its layout. This paper uses a multidimensional assessment framework to identify spatial disparities in the distribution of EVPCI in Nanjing Central Districts, China. We construct a scientific evaluation system of the public charging infrastructure (PCI) layout from four spatial indicators: accessibility, availability, convenience, and affordability. Through univariate and bivariate local indicators of spatial autocorrelation (LISA), the spatial agglomeration pattern of the EVPCI service level and its spatial correlation with social factors are revealed. The results of this study not only identify areas in Nanjing where the distribution of PCI is uneven and where there is a shortage but also identify areas down to the community level where there are signs of potential wastage of PCI resources. The results demonstrate that (1) urban planners and policymakers need to expand the focus of PCI construction from the main city to the three sub-cities; (2) it is necessary to increase the deployment of PCI in Nanjing’s old residential communities; and (3) the expansion of PCI in Nanjing must be incremental and optimized in terms of allocation, or else it should be reduced and recycled in areas where there are signs of resource wastage. This study provides targeted and implementable deployment strategies for the optimization of the spatial layout of EVPCI. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-20 DOI: 10.3390/ijgi13080296 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 297: Flood Susceptibility Mapping Using GIS-Based
Frequency Ratio and Shannon’s Entropy Index Bivariate Statistical Models: A Case Study of Chandrapur District, India Authors: Asheesh Sharma, Mandeep Poonia, Ankush Rai, Rajesh B. Biniwale, Franziska Tügel, Ekkehard Holzbecher, Reinhard Hinkelmann First page: 297 Abstract: Flooding poses a significant threat as a prevalent natural disaster. To mitigate its impact, identifying flood-prone areas through susceptibility mapping is essential for effective flood risk management. This study conducted flood susceptibility mapping (FSM) in Chandrapur district, Maharashtra, India, using geographic information system (GIS)-based frequency ratio (FR) and Shannon’s entropy index (SEI) models. Seven flood-contributing factors were considered, and historical flood data were utilized for model training and testing. Model performance was evaluated using the area under the curve (AUC) metric. The AUC values of 0.982 for the SEI model and 0.966 for the FR model in the test dataset underscore the robust performance of both models. The results revealed that 5.4% and 8.1% (FR model) and 3.8% and 7.6% (SEI model) of the study area face very high and high risks of flooding, respectively. Comparative analysis indicated the superiority of the SEI model. The key limitations of the models are discussed. This study attempted to simplify the process for the easy and straightforward implementation of FR and SEI statistical flood susceptibility models along with key insights into the flood vulnerability of the study region. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-08-22 DOI: 10.3390/ijgi13080297 Issue No: Vol. 13, No. 8 (2024)
- IJGI, Vol. 13, Pages 217: A Novel Flexible Geographically Weighted Neural
Network for High-Precision PM2.5 Mapping across the Contiguous United States Authors: Dongchao Wang, Jianfei Cao, Baolei Zhang, Ye Zhang, Lei Xie First page: 217 Abstract: Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stationarity of predictive variables. To address this issue, this study introduces a Flexible Geographically Weighted Neural Network (FGWNN) to estimate PM2.5 based on multi-source remote sensing data. FGWNN incorporates the Flexible Geographical Neuron (FGN) and Geographical Activation Function (GWAF) within the framework of Artificial Neural Network (ANN) to capture the intricate spatial non-stationary relationships among predictive variables. A robust air quality remote sensing estimation model was constructed using remote sensing data of Aerosol Optical Depth (AOD), Normalized Difference Vegetation Index (NDVI), Temperature (TMP), Specific Humidity (SPFH), Wind Speed (WIND), and Terrain Elevation (HGT) as inputs, and Ground-Based PM2.5 as the observation. The results indicated that FGWNN successfully generates PM2.5 remote sensing data with a 2.5 km spatial resolution for the contiguous United States (CONUS) in 2022. It exhibits higher regression accuracy compared to traditional ANN and Geographically Weighted Regression (GWR) models. FGWNN holds the potential for applications in high-precision and high-resolution remote sensing scenarios. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-22 DOI: 10.3390/ijgi13070217 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 218: The Influence of Origin Attributes on the
Destination Choice of Discretionary Home-Based Walk Trips Authors: Salman Aghidi Kheyrabadi, Amir Reza Mamdoohi First page: 218 Abstract: Walking has been recognized as an important mode of transportation in recent years, and recent research has improved travel demand models for walk trips. One important added stage is the distribution of walk trips, which can be evaluated using destination choice models. Previous studies have overlooked the importance of origin trip attributes in the destination choice of walk trips. With the aim of improving destination choice models for discretionary home-based walk trips, a questionnaire based on the previous day’s walk trips was used, and 422 trips were collected from individuals. A discrete choice logit model is used for discretionary trips by utilizing policy-related variables, such as origin-sensitive variables, land-use-related variables, and socio-economic conditions of individuals. Additionally, a solution is proposed to address the issue of data scarcity in considering the choice set. The results demonstrate that origin land-use (LU) variables, such as LU diversity index and access to green spaces, as well as socio-economic variables, like age and homeownership status, are statistically significant in the destination choice of discretionary home-based walk trips. One prominent result is that reducing the diversity of unattractive LU compared to increasing the diversity of attractive LU has a greater impact on the destination choice of such trips. Specifically, a 1% increase in the diversity of attractive LU in the origin area leads to a 0.031% increase in the probability of choosing a destination within that area, while a 1% decrease in the diversity of unattractive LU results in a 0.124% increase in the probability of choosing a destination within the area. The findings can be utilized in urban LU distribution and assessing their impact on destination choice for walk trips, ultimately informing future urban planning efforts in the context of pedestrian mobility. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-24 DOI: 10.3390/ijgi13070218 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 219: Study on Multiscale Virtual Environment
Construction and Spatial Navigation Based on Hierarchical Structure Authors: Chao Chen, Chaoyang Li, Kai Lu, Hao Chen, Xin Xiao, Chaoyang Fang First page: 219 Abstract: Multiscale virtual environments (MSVEs) allow the integration of elements and environments at different scale levels into a unified space, which facilitates researchers’ perception, understanding, and experimental research of complex geospatial spaces. Although there have been several methods for achieving multiscale effects in virtual environments (VEs), they cannot assist users in constructing more complete spatial cognitive maps and presenting multiscale information efficiently. This study proposes a hierarchical-structure-based MSVE construction method, which can effectively integrate multiscale information and ensure that the richness of details of information is gradually enhanced with the progression of the hierarchical structure. In addition, a spatial navigation study is conducted, considering the relationship between users’ perspective changes and spatial cognition, and the effects of users’ perspective changes on their spatial cognition in an MSVE are explored. A multiscale virtual wetland environment covering four levels is constructed to conduct a case study of a virtual environment of a wetland of Poyang Lake. The research results show that the proposed method is feasible. Moreover, the spatial navigation based on the change in the hierarchical perspective is in line with the spatial cognitive habits of users, which can satisfy the cognitive needs of users from the macro-region to specific wetland landscapes. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-24 DOI: 10.3390/ijgi13070219 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 220: A Spatial Semantic Feature Extraction Method for
Urban Functional Zones Based on POIs Authors: Xin Yang, Xi’ang Ma First page: 220 Abstract: Accurately extracting semantic features of urban functional zones is crucial for understanding urban functional zone types and urban functional spatial structures. Points of interest provide comprehensive information for extracting the semantic features of urban functional zones. Many researchers have used topic models of natural language processing to extract the semantic features of urban functional zones from points of interest, but topic models cannot consider the spatial features of points of interest, which leads to the extracted semantic features of urban functional zones being incomplete. To consider the spatial features of points of interest when extracting semantic features of urban functional zones, this paper improves the Latent Dirichlet Allocation topic model and proposes a spatial semantic feature extraction method for urban functional zones based on points of interest. In the proposed method, an assumption (that points of interest belonging to the same semantic feature are spatially correlated) is introduced into the generation process of urban functional zones, and then, Gibbs sampling is combined to carry out the parameter inference process. We apply the proposed method to a simulated dataset and the point of interest dataset for Chaoyang District, Beijing, and compare the semantic features extracted by the proposed method with those extracted by the Latent Dirichlet Allocation. The results show that the proposed method sufficiently considers the spatial features of points of interest and has a higher capability of extracting the semantic features of urban functional zones than the Latent Dirichlet Allocation. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-25 DOI: 10.3390/ijgi13070220 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 221: Spatiotemporal Distribution of Inscription Sites
in Henan Province Authors: Yuhang Zhang, Jiaji Gao, Xiaotong Ni First page: 221 Abstract: This paper takes 1929 inscription sites in Henan Province as the study object and uses methods such as kernel density and nearest neighbor index to analyze their spatiotemporal distribution patterns and influencing factors. It also studies those patterns of various levels of inscription cultural relics’ protection units and content. All of these will help our understanding of the development process and characteristics of Central Plains art and provide reference for the protection and development of inscriptions in Henan in the future. The study indicates the following: (1) The spatial distribution of inscription sites is relatively uneven and the clustering is obvious, being mainly concentrated in the northern and northwestern regions of Henan, showing the characteristics of “one belt and four clusters” as a whole. The density is high in the north and low in the south, gradually decreasing from north to south. (2) In terms of time, the number of these inscription sites shows a fluctuating trend of first a slight increase and then a decrease with a significant increase and then a decrease. The center of the sites migrates from southwest to northeast over time. (3) These inscriptions can be divided into five primary themes and further subdivided into 16 secondary themes in terms of content. The main type is chronicle. (4) Inscriptions in Henan are mainly influenced by five major factors: topography, climate, economy and transportation, politics and society, culture and religion. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-25 DOI: 10.3390/ijgi13070221 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 222: UPGAN: An Unsupervised Generative Adversarial
Network Based on U-Shaped Structure for Pansharpening Authors: Xin Jin, Yuting Feng, Qian Jiang, Shengfa Miao, Xing Chu, Huangqimei Zheng, Qianqian Wang First page: 222 Abstract: Pansharpening is the fusion of panchromatic images and multispectral images to obtain images with high spatial resolution and high spectral resolution, which have a wide range of applications. At present, methods based on deep learning can fit the nonlinear features of images and achieve excellent image quality; however, the images generated with supervised learning approaches lack real-world applicability. Therefore, in this study, we propose an unsupervised pansharpening method based on a generative adversarial network. Considering the fine tubular structures in remote sensing images, a dense connection attention module is designed based on dynamic snake convolution to recover the details of spatial information. In the stage of image fusion, the fusion of features in groups is applied through the cross-scale attention fusion module. Moreover, skip layers are implemented at different scales to integrate significant information, thus improving the objective index values and visual appearance. The loss function contains four constraints, allowing the model to be effectively trained without reference images. The experimental results demonstrate that the proposed method outperforms other widely accepted state-of-the-art methods on the QuickBird and WorldView2 data sets. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-26 DOI: 10.3390/ijgi13070222 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 223: Characterizing Spatio-Temporal Patterns of Child
Sexual Abuse in Mexico City Before, During, and After the COVID-19 Pandemic Authors: Francisco Carrillo-Brenes, Luis M. Vilches-Blázquez First page: 223 Abstract: This study conducts a spatio-temporal analysis to identify trends and clusters of child sexual abuse in Mexico City before, during, and after the COVID-19 pandemic. Sexual abuses of children were analyzed considering various crime theories. Trends and patterns were identified using time series decomposition and spatial autocorrelation techniques. Time series considered three relevant periods. Anselin’s Local Moran’s I identified the spatial distribution of significant clusters. The child sexual abuse rate presented similar values following school closures. The resumption of classes entailed a decrease of −1.5% (children under 15) and an increase of 29% (children over 15). Particular locations in Mexico City experienced significant clusters among those over 15. There were eight noteworthy clusters displaying recidivism patterns with lower poverty rates and a high level of education. Efforts to combat child sexual abuse should prioritize specific areas in Mexico City where female children over 15 are at high risk of becoming victims of sexual abuse. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-27 DOI: 10.3390/ijgi13070223 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 224: Developing a Bi-Level Optimization Model for the
Coupled Street Network and Land Subdivision Design Problem with Various Lot Areas in Irregular Blocks Authors: Alireza Sahebgharani, Szymon Wiśniewski First page: 224 Abstract: Street design and land subdivision are significant tasks in the development and redevelopment planning process. Optimizing street and land subdivision layouts within a unified framework to achieve solutions that meet a set of objectives and constraints (e.g., minimizing parcel area deviation from standard values, minimizing land consumption for street construction, etc.) is a critical concern for planners, particularly in complex contexts such as blocks with irregular shapes and parcels of varying sizes and requirements. To address this challenge, a mathematical formulation is presented for the bi-level street network and land subdivision optimization problem. Subsequently, the solution procedure is outlined, which utilizes a genetic-based algorithm for street design and a memetic–genetic-based algorithm for land subdivision. Finally, two cases are presented, solved, and discussed to analyze and verify the proposed mathematical model and solution procedures. The results suggest that the formulated problem is suitable for addressing the coupled street network and land subdivision design problem, and it can be adapted and extended to other case studies. Additionally, the introduced ideas and algorithms satisfactorily solved the stated problem. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-27 DOI: 10.3390/ijgi13070224 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 225: Research on the Spatial Distribution
Characteristics and Influencing Factors of Educational Facilities Based on POI Data: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area Authors: Bowen Chen, Hongfeng Zhang, Cora Un In Wong, Xiaolong Chen, Fanbo Li, Xiaoyu Wei, Junxian Shen First page: 225 Abstract: This study aims to provide a precise assessment of the distribution of educational facilities within the Guangdong–Hong Kong–Macao Greater Bay Area, serving as a crucial foundation for managing educational resource allocation and enhancing the quality of educational services. Utilizing a kernel density analysis, global autocorrelation analysis, and geographic detectors, this research systematically analyzes the spatial distribution characteristics and influencing factors of educational facilities in the area. The findings reveal significant geographical disparities in facility distribution with dense clusters in urban centers such as Guangzhou and Shenzhen, and less dense distributions in peripheral areas like Zhongshan and Macau. These facilities exhibit a multi-center cluster pattern with strong spatial autocorrelation, mainly influenced by the population size and economic and urban development levels. The results provide actionable insights for refining educational planning and resource allocation, contributing to the enhancement of educational quality across diverse urban landscapes. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-27 DOI: 10.3390/ijgi13070225 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 226: Automated Geospatial Approach for Assessing SDG
Indicator 11.3.1: A Multi-Level Evaluation of Urban Land Use Expansion across Africa Authors: Orion S. E. Cardenas-Ritzert, Jody C. Vogeler, Shahriar Shah Heydari, Patrick A. Fekety, Melinda Laituri, Melissa McHale First page: 226 Abstract: Geospatial data has proven useful for monitoring urbanization and guiding sustainable development in rapidly urbanizing regions. The United Nations’ (UN) Sustainable Development Goal (SDG) Indicator 11.3.1 leverages geospatial data to estimate rates of urban land and population change, providing insight on urban land use expansion patterns and thereby informing sustainable urbanization initiatives (i.e., SDG 11). Our work enhances a UN proposed delineation method by integrating various open-source datasets and tools (e.g., OpenStreetMap and openrouteservice) and advanced geospatial analysis techniques to automate the delineation of individual functional urban agglomerations across a country and, subsequently, calculate SDG Indicator 11.3.1 and related metrics for each. We applied our automated geospatial approach to three rapidly urbanizing countries in Africa: Ethiopia, Nigeria, and South Africa, to conduct multi-level examinations of urban land use expansion, including identifying hotspots of SDG Indicator 11.3.1 where the percentage growth of urban land was greater than that of the urban population. The urban agglomerations of Ethiopia, Nigeria, and South Africa displayed a 73%, 14%, and 5% increase in developed land area from 2016 to 2020, respectively, with new urban development being of an outward type in Ethiopia and an infill type in Nigeria and South Africa. On average, Ethiopia’s urban agglomerations displayed the highest SDG Indicator 11.3.1 values across urban agglomerations, followed by those of South Africa and Nigeria, and secondary cities of interest coinciding as SDG Indicator 11.3.1 hotspots included Mekelle, Ethiopia; Benin City, Nigeria; and Polokwane, South Africa. The work presented in this study contributes to knowledge of urban land use expansion patterns in Ethiopia, Nigeria, and South Africa, and our approach demonstrates effectiveness for multi-level evaluations of urban land expansion according to SDG Indicator 11.3.1 across urbanizing countries. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-28 DOI: 10.3390/ijgi13070226 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 227: The Application of Space Syntax to Enhance
Sociability in Public Urban Spaces: A Systematic Review Authors: Reza Askarizad, Patxi José Lamíquiz Daudén, Chiara Garau First page: 227 Abstract: Public urban spaces are vital settings for fostering social interaction among people. However, understanding how spatial layouts can promote positive social behaviors remains a critical and debated challenge for urban designers and planners aiming to create socially sustainable environments. Space syntax, a well-established theory and research method, explores the influence of spatial configurations on social aspects. Despite its significant contributions, there is a lack of comprehensive systematic reviews evaluating its effectiveness in enhancing social interaction within urban public spaces. This study aims to identify the existing scientific gaps in the domain of space syntax studies, with a primary focus on sociability in public urban spaces. Following the PRISMA framework, a thorough literature search was conducted in the Scopus database, yielding 1107 relevant articles. After applying screening and eligibility criteria, 26 articles were selected for in-depth review. This review adopted a novel approach to synthesizing and analyzing the findings for identifying underexplored scientific gaps. The findings suggested a wide variety of research gaps to address, encompassing evidence, knowledge, practical, methodological, empirical, theoretical, and target populations to provide a thorough overview of the current state of knowledge in this field. In conclusion, by exploring the interplay between space syntax and design elements such as the urban infrastructure, landscaping, and microclimate in these areas, future research can bridge this gap, particularly when considering a cross-cultural lens. This study underscores the importance of space syntax in promoting social interaction in urban public spaces, offering a robust foundation for future research and practical applications to create more socially engaging environments. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-28 DOI: 10.3390/ijgi13070227 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 228: Exploring Summer Variations of Driving Factors
Affecting Land Use Zoning Based on the Surface Urban Heat Island in Chiang Mai, Thailand Authors: Damrongsak Rinchumphu, Manat Srivanit, Niti Iamchuen, Chuchoke Aryupong First page: 228 Abstract: Numerous studies have examined land surface temperature (LST) changes in Thailand using remote sensing, but there has been little research on LST variations within urban land use zones. This study addressed this gap by analyzing summer LST changes in land use zoning (LUZ) blocks in the 2012 Chiang Mai Comprehensive Plan and their relationship with surface biophysical parameters (NDVI, NDBI, MNDWI). The approach integrated detailed zoning data with remote sensing for granular LST analysis. Correlation and stepwise regression analyses (SRA) revealed that NDBI significantly impacted LST in most block types, while NDVI and MNDWI also influenced LST, particularly in 2023. The findings demonstrated the complexity of LST dynamics across various LUZs in Chiang Mai, with SRA results explaining 45.7% to 53.2% of summer LST variations over three years. To enhance the urban environment, adaptive planning strategies for different block categories were developed and will be considered in the upcoming revision of the Chiang Mai Comprehensive Plan. This research offers a new method to monitor the urban heat island phenomenon at the block level, providing valuable insights for adaptive urban planning. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-30 DOI: 10.3390/ijgi13070228 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 229: Graph Representation Learning for Street-Level
Crime Prediction Authors: Haishuo Gu, Jinguang Sui, Peng Chen First page: 229 Abstract: In contemporary research, the street network emerges as a prominent and recurring theme in crime prediction studies. Meanwhile, graph representation learning shows considerable success, which motivates us to apply the methodology to crime prediction research. In this article, a graph representation learning approach is utilized to derive topological structure embeddings within the street network. Subsequently, a heterogeneous information network that incorporates both the street network and urban facilities is constructed, and embeddings through link prediction tasks are obtained. Finally, the two types of high-order embeddings, along with other spatio-temporal features, are fed into a deep neural network for street-level crime prediction. The proposed framework is tested using data from Beijing, and the outcomes demonstrate that both types of embeddings have a positive impact on crime prediction, with the second embedding showing a more significant contribution. Comparative experiments indicate that the proposed deep neural network offers superior efficiency in crime prediction. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-01 DOI: 10.3390/ijgi13070229 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 230: Monitoring and Cause Analysis of Land Subsidence
along the Yangtze River Utilizing Time-Series InSAR Authors: Yuanyuan Chen, Lin Guo, Jia Xu, Qiang Yang, Hao Wang, Chenwei Zhu First page: 230 Abstract: Time-series monitoring of the land subsidence in the Yangtze River coastal area is crucial for maintaining river stability and early warning of disasters. This study employed PS-InSAR and SBAS-InSAR techniques to monitor the land subsidence along the Yangtze River in Nanjing, using a total of 42 Sentinel-1A images obtained between April 2015 and November 2021. The accuracy of both methods was compared and validated, while a comprehensive analysis was conducted to ascertain the spatial distribution characteristics and underlying causes of land subsidence. The maximum deviation between the two methods and six leveling point data did not exceed ±5 mm. Within the 5 km buffer zone on either side of the Yangtze River in Nanjing, four subsidence funnels were identified. Analysis of the factors contributing to land subsidence in this area indicates that underground engineering construction and operation, increasing ground building area, and geological condition all have certain correlations to the land subsidence. The results obtained through PS-InSAR and SBAS-InSAR technologies revealed a high degree of consistency in monitoring outcomes, and the latter method exhibited superior monitoring accuracy than the former one in this area. This study holds significant implications for guiding the scientific management of urban geohazards along the Yangtze River. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-01 DOI: 10.3390/ijgi13070230 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 231: HBIM for Conservation of Built Heritage
Authors: Yahya Alshawabkeh, Ahmad Baik, Yehia Miky First page: 231 Abstract: Building information modeling (BIM) has recently become more popular in historical buildings as a method to rebuild their geometry and collect relevant information. Heritage BIM (HBIM), which combines high-level data about surface conditions, is a valuable tool for conservation decision-making. However, implementing BIM in heritage has its challenges because BIM libraries are designed for new constructions and are incapable of accommodating the morphological irregularities found in historical structures. This article discusses an architecture survey workflow that uses TLS, imagery, and deep learning algorithms to optimize HBIM for the conservation of the Nabatean built heritage. In addition to creating new resourceful Nabatean libraries with high details, the proposed approach enhanced HBIM by including two data outputs. The first dataset contained the TLS 3D dense mesh model, which was enhanced with high-quality textures extracted from independent imagery captured at the optimal time and location for accurate depictions of surface features. These images were also used to create true orthophotos using accurate and reliable 2.5D DSM derived from TLS, which eliminated all image distortion. The true orthophoto was then used in HBIM texturing to create a realistic decay map and combined with a deep learning algorithm to automatically detect and draw the outline of surface features and cracks in the BIM model, along with their statistical parameters. The use of deep learning on a structured 2D true orthophoto produced segmentation results in the metric units required for damage quantifications and helped overcome the limitations of using deep learning for 2D non-metric imagery, which typically uses pixels to measure crack widths and areas. The results show that the scanner and imagery integration allows for the efficient collection of data for informative HBIM models and provide stakeholders with an efficient tool for investigating and analyzing buildings to ensure proper conservation. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-01 DOI: 10.3390/ijgi13070231 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 232: A Comprehensive Survey on High-Definition Map
Generation and Maintenance Authors: Kaleab Taye Asrat, Hyung-Ju Cho First page: 232 Abstract: The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully automated vehicles in the foreseeable future. High-definition (HD) maps are central to this endeavor, offering centimeter-level accuracy in mapping the environment and enabling precise localization. Unlike conventional maps, these highly detailed HD maps are critical for autonomous vehicle decision-making, ensuring safe and accurate navigation. Compiled before testing and regularly updated, HD maps meticulously capture environmental data through various methods. This study explores the vital role of HD maps in autonomous driving, delving into their creation, updating processes, and the challenges and future directions in this rapidly evolving field. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-01 DOI: 10.3390/ijgi13070232 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 233: Optimizing Station Placement for Free-Floating
Electric Vehicle Sharing Systems: Leveraging Predicted User Spatial Distribution from Points of Interest Authors: Qi Cao, Shunchao Wang, Bingtong Wang, Jingfeng Ma First page: 233 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-01 DOI: 10.3390/ijgi13070233 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 234: Using Knowledge Graphs to Analyze the
Characteristics and Trends of Forest Carbon Storage Research at the Global Scale Authors: Jie Li, Jinliang Wang, Suling He, Chenli Liu, Lanfang Liu First page: 234 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-01 DOI: 10.3390/ijgi13070234 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 235: Performance Analysis of Random Forest Algorithm
in Automatic Building Segmentation with Limited Data Authors: Ratri Widyastuti, Deni Suwardhi, Irwan Meilano, Andri Hernandi, Nabila S. E. Putri, Asep Yusup Saptari, Sudarman First page: 235 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-02 DOI: 10.3390/ijgi13070235 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 236: Multiscale Visualization of Surface Motion Point
Measurements Associated with Persistent Scatterer Interferometry Authors: Panagiotis Kalaitzis, Michael Foumelis, Antonios Mouratidis, Dimitris Kavroudakis, Nikolaos Soulakellis First page: 236 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-02 DOI: 10.3390/ijgi13070236 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 237: 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) Authors: Imene Selka, Abderahemane Medjdoub Mokhtari, Kheira Anissa Tabet Aoul, Djamal Bengusmia, Kacemi Malika, Khadidja El-Bahdja Djebbar First page: 237 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-02 DOI: 10.3390/ijgi13070237 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 238: Spatial Nonlinear Effects of Street Vitality
Constrained by Construction Intensity and Functional Diversity—A Case Study from the Streets of Shenzhen Authors: Jilong Li, Niuniu Kong, Shiping Lin, Jie Zeng, Yilin Ke, Jiacheng Chen First page: 238 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-02 DOI: 10.3390/ijgi13070238 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 239: Integrating Spatiotemporal Analysis of Land
Transformation and Urban Growth in Peshawar Valley and Its Implications on Temperature in Response to Climate Change Authors: Muhammad Farooq Hussain, Xiaoliang Meng, Syed Fahim Shah, Muhammad Asif Hussain First page: 239 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, 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-03 DOI: 10.3390/ijgi13070239 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 240: Layout Optimization of Logistics and Warehouse
Land Based on a Multi-Objective Genetic Algorithm—Taking Wuhan City as an Example Authors: Haijun Li, Jie Zhou, Qiang Niu, Mingxiang Feng, Dongming Zhou First page: 240 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-04 DOI: 10.3390/ijgi13070240 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 241: Using Virtual and Augmented Reality with GIS
Data Authors: Karel Pavelka, Martin Landa First page: 241 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-05 DOI: 10.3390/ijgi13070241 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 242: An Integrated Framework for Landscape
Indices’ Calculation with Raster–Vector Integration and Its Application Based on QGIS Authors: Yaqi Huang, Minrui Zheng, Tianle Li, Fei Xiao, Xinqi Zheng First page: 242 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-06 DOI: 10.3390/ijgi13070242 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 243: Constraining the Geometry of NeRFs for Accurate
DSM Generation from Multi-View Satellite Images Authors: Qifeng Wan, Yuzheng Guan, Qiang Zhao, Xiang Wen, Jiangfeng She First page: 243 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-08 DOI: 10.3390/ijgi13070243 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 244: Using Wi-Fi Probes to Evaluate the
Spatio-Temporal Dynamics of Tourist Preferences in Historic Districts’ Public Spaces Authors: Yichen Gao, Sheng Liu, Biao Wei, Zhenni Zhu, Shanshan Wang First page: 244 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, 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-09 DOI: 10.3390/ijgi13070244 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 245: Privacy Preserving Human Mobility Generation
Using Grid-Based Data and Graph Autoencoders Authors: Fabian Netzler, Markus Lienkamp First page: 245 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-09 DOI: 10.3390/ijgi13070245 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 246: Globally Optimal Relative Pose and Scale
Estimation from Only Image Correspondences with Known Vertical Direction Authors: Zhenbao Yu, Shirong Ye, Changwei Liu, Ronghe Jin, Pengfei Xia, Kang Yan First page: 246 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-09 DOI: 10.3390/ijgi13070246 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 247: Spatiotemporal Analysis of Nighttime Crimes in
Vienna, Austria Authors: Jiyoung Lee, Michael Leitner, Gernot Paulus First page: 247 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-10 DOI: 10.3390/ijgi13070247 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 248: Coupling Hyperbolic GCN with Graph Generation
for Spatial Community Detection and Dynamic Evolution Analysis Authors: Huimin Liu, Qiu Yang, Xuexi Yang, Jianbo Tang, Min Deng, Rong Gui First page: 248 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-10 DOI: 10.3390/ijgi13070248 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 249: 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 Authors: Jakub Kuna, Tomasz Panecki, Mateusz Zawadzki First page: 249 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-10 DOI: 10.3390/ijgi13070249 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 250: Renovation and Reconstruction of Urban Land Use
by a Cost-Heuristic Genetic Algorithm: A Case in Shenzhen Authors: Yufan Deng, Zhongan Tang, Baoju Liu, Yan Shi, Min Deng, Enbo Liu First page: 250 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-12 DOI: 10.3390/ijgi13070250 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 251: A Pathfinding Algorithm for Large-Scale Complex
Terrain Environments in the Field Authors: Luchao Kui, Xianwen Yu First page: 251 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-12 DOI: 10.3390/ijgi13070251 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 252: A Lightweight Multi-Label Classification Method
for Urban Green Space in High-Resolution Remote Sensing Imagery Authors: Weihua Lin, Dexiong Zhang, Fujiang Liu, Yan Guo, Shuo Chen, Tianqi Wu, Qiuyan Hou First page: 252 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 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 mAP,F1,precision, and recall, 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-13 DOI: 10.3390/ijgi13070252 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 253: Integrating NoSQL, Hilbert Curve, and R*-Tree to
Efficiently Manage Mobile LiDAR Point Cloud Data Authors: Yuqi Yang, Xiaoqing Zuo, Kang Zhao, Yongfa Li First page: 253 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-14 DOI: 10.3390/ijgi13070253 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 254: Advanced Integration of Urban Street Greenery
and Pedestrian Flow: A Multidimensional Analysis in Chengdu’s Central Urban District Authors: Qicheng Ma, Jiaxin Zhang, Yunqin Li First page: 254 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-16 DOI: 10.3390/ijgi13070254 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 255: Bibliometric Analysis on the Research of
Geoscience Knowledge Graph (GeoKG) from 2012 to 2023 Authors: Zhi-Wei Hou, Xulong Liu, Shengnan Zhou, Wenlong Jing, Ji Yang First page: 255 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-16 DOI: 10.3390/ijgi13070255 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 256: Enhancing Place Emotion Analysis with Multi-View
Emotion Recognition from Geo-Tagged Photos: A Global Tourist Attraction Perspective Authors: Yu Wang, Shunping Zhou, Qingfeng Guan, Fang Fang, Ni Yang, Kanglin Li, Yuanyuan Liu First page: 256 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-16 DOI: 10.3390/ijgi13070256 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 257: Sensing the Environmental Inequality of PM2.5
Exposure Using Fine-Scale Measurements of Social Strata and Citizenship Identity Authors: Li He, Lingfeng He, Zezheng Lin, Yao Lu, Chen Chen, Zhongmin Wang, Ping An, Min Liu, Jie Xu, Shurui Gao First page: 257 Abstract: Exposure to PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 exposure; and (3) an elevated socioeconomic status is identified as an attenuating factor, capable of averting the deleterious impacts of PM2.5 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-17 DOI: 10.3390/ijgi13070257 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 258: Exploring Family Ties and Interpersonal
Dynamics—A Geospatial Simulation Analyzing Their Influence on Evacuation Efficiency within Urban Communities Authors: Hao Chu, Jianping Wu, Liliana Perez, Yonghua Huang First page: 258 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-20 DOI: 10.3390/ijgi13070258 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 259: Bridging Human Expertise with Machine Learning
and GIS for Mine Type Prediction and Classification Authors: Adib Saliba, Kifah Tout, Chamseddine Zaki, Christophe Claramunt First page: 259 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-20 DOI: 10.3390/ijgi13070259 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 260: Extracting Geoscientific Dataset Names from the
Literature Based on the Hierarchical Temporal Memory Model Authors: Kai Wu, Zugang Chen, Xinqian Wu, Guoqing Li, Jing Li, Shaohua Wang, Haodong Wang, Hang Feng First page: 260 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-21 DOI: 10.3390/ijgi13070260 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 261: A GIS-Based Framework for Synthesizing
City-Scale Long-Term Individual-Level Spatial–Temporal Mobility Authors: Yao Yao, Yinghong Jiang, Qing Yu, Jian Yuan, Jiaxing Li, Jian Xu, Siyuan Liu, Haoran Zhang First page: 261 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-22 DOI: 10.3390/ijgi13070261 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 262: The Influence of Perceptions of the Park
Environment on the Health of the Elderly: The Mediating Role of Social Interaction Authors: Xiuhai Xiong, Jingjing Wang, Hao Wu, Zhenghong Peng First page: 262 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 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. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-07-22 DOI: 10.3390/ijgi13070262 Issue No: Vol. 13, No. 7 (2024)
- IJGI, Vol. 13, Pages 207: Web Publication of Schmitt’s Map of
Southern Germany (1797)—The Projection of the Map Based on Archival Documents and Geospatial Analysis Authors: Gábor Timár, Eszter Kiss First page: 207 Abstract: This work shows the original projection of a 1:57,600 scale map of southern Germany at the end of the 18th century, produced under the direction of Karl-Heinrich von Schmitt (1743–1805). The sections of the map were scanned and georeferenced as part of the MAPIRE project, and the results are publicly available. In the present work, we use contemporary documents, in particular the books of César-Francois Cassini de Thury and manuscript sketches of the map found in the Military Archive of Vienna, to show that the overall projection of the map is identical to that used in Cassini’s survey of France (first half of the 18th century). In the archive, we managed to find the overview sheet on which—in addition to the Paris Cassini coordinate system—the section grid of the Schmitt map was also constructed. This sketch served as the basis for the compilation and copying work, wherein the existing map works and survey sketches were inserted into 197 sections of the Schmitt map. Thus, the map coordinate system can be modeled in GIS systems using the Cassini (or Cassini-Soldner) projection, with the Paris Observatory as the projection origin. The georeferencing accuracy of using the pure Cassini projection is around 1–1.3 km (at the extremes, around 5 km), which is much more inaccurate than the one used in later topographic surveys. It is considered a combined result of the compilation of the different maps, presumably surveyed by graphic triangulation with measuring tables. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-17 DOI: 10.3390/ijgi13060207 Issue No: Vol. 13, No. 6 (2024)
- IJGI, Vol. 13, Pages 208: Agent-Based Modeling of COVID-19 Transmission: A
Case Study of Housing Densities in Sankalitnagar, Ahmedabad Authors: Molly French, Amit Patel, Abid Qureshi, Deepak Saxena, Raja Sengupta First page: 208 Abstract: The differential transmission of COVID-19 depending on the socio-economic status of a neighborhood is well established. For example, several studies have shown that COVID-19 transmission was higher in poorer and denser neighborhoods than in wealthier ones. However, what is less well known is how this varied rate of transmission interacted with established health measures, i.e., face masks and lockdowns, in the context of developing countries to reduce pandemic cases and hence resulted in fewer deaths. This study uses an Agent-Based Model (ABM) simulation to examine the context and impacts of COVID-19 mitigation efforts (i.e., lockdowns combined with masks) on the transmission of COVID-19 across a single neighborhood in Ahmedabad, a city in the state of Gujarat, India. The model is parameterized using real-world population data, which allows us to simulate the spread of COVID-19 to find conditions that most closely match the realities of COVID-19 in the spring of 2020. Consequently, the simulation can be used to understand the impact of nation-wide lockdown on the spread of COVID cases across Ahmedabad as a function of housing density. Thus, invaluable insight into the effectiveness of a lockdown as a mitigation measure can be derived. Further information about how the effectiveness of the lockdown varied by neighborhood, as well as other factors that impacted it, can be ascertained. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-17 DOI: 10.3390/ijgi13060208 Issue No: Vol. 13, No. 6 (2024)
- IJGI, Vol. 13, Pages 209: A Type of Scale-Oriented Terrain Pattern Derived
from Normalized Topographic Relief Layers and Its Interpretation Authors: Xi Nan, Ainong Li, Zhengwei He, Jinhu Bian First page: 209 Abstract: Topographic scale characteristics contain valuable information for interpreting landform structures, which is crucial for understanding the spatial differentiation of landforms across large areas. However, the absence of parameters that specifically describe the topographic scale characteristics hinders the quantitative representation of regional topography from the perspective of spatial scales. In this study, false-color composite images were generated using normalized topographic relief data, showing a type of scale-oriented terrain pattern. Subsequent analysis indicated a direct correlation between the luminance of the patterns and the normalized topographic relief. Additionally, a linear correlation exists between the color of the patterns and the change rate in normalized topographic relief. Based on the analysis results, the issue of characterizing topographic scale effects was transformed into a problem of interpreting terrain patterns. The introduction of two parameters, flux and curl of topographic field, allowed for the interpretation of the terrain patterns. The assessment indicated that the calculated values of topographic field flux are equivalent to the luminance of the terrain patterns and the variations in the topographic field curl correspond with the spatial differentiation of colors in the terrain patterns. This study introduced a new approach to analyzing topographic scale characteristics, providing a pathway for quantitatively describing scale effects and automatically classifying landforms at a regional scale. Through exploratory analysis on artificially constructed simple DEMs and verification in four typical geomorphological regions of real terrain, it was shown that the terrain pattern method has better intuitiveness than the scale signature approach. It can reflect the scale characteristics of terrain in continuous space. Compared to the MTPCC image, the terrain parameters derived from the terrain pattern method further quantitatively describe the scale effects of the terrain. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-17 DOI: 10.3390/ijgi13060209 Issue No: Vol. 13, No. 6 (2024)
- IJGI, Vol. 13, Pages 210: Traffic Flow Prediction Based on Federated
Learning and Spatio-Temporal Graph Neural Networks Authors: Jian Feng, Cailing Du, Qi Mu First page: 210 Abstract: In response to the insufficient consideration of spatio-temporal dependencies and traffic pattern similarity in traffic flow prediction methods based on federated learning, as well as the neglect of model heterogeneity and objective heterogeneity, a traffic flow prediction model based on federated learning and spatio-temporal graph neural networks is proposed. The model is divided into two stages. In the road network division stage, the traffic road network is divided into subnetworks by the dynamic time warping algorithm and the K-means algorithm, to ensure the same subnetwork has the similar traffic flow pattern. The federated learning stage is divided into two sub-stages. In the local training phase, the spatio-temporal graph neural network with an attention mechanism is utilized to create personalized models and meme models to capture the spatio-temporal dependencies of each subnetwork. At the same time, deep mutual learning is utilized to address model heterogeneity and objective heterogeneity through knowledge distillation. In the global aggregation phase, a multi-factor weighted aggregation strategy is designed to measure the contribution of each local model to the global model, to enhance the fairness of aggregation. Three sets of experiments were conducted on two real datasets, and the experimental results demonstrate that the proposed model outperforms the baseline models in three common evaluation metrics. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-18 DOI: 10.3390/ijgi13060210 Issue No: Vol. 13, No. 6 (2024)
- IJGI, Vol. 13, Pages 211: Implementing Immersive Worlds for
Metaverse-Based Participatory Design through Photogrammetry and Blockchain Authors: Nikolai Abramov, Havana Lankegowda, Shunwei Liu, Luigi Barazzetti, Carlo Beltracchi, Pierpaolo Ruttico First page: 211 Abstract: This paper explores participatory design methods for the interconnection of digital recording techniques, like digital photogrammetry and Gaussian splatting, with emerging domains such as the metaverse and blockchain technology. The focus lies in community engagement and the economic growth of urban and rural areas through blockchain integration, utilizing low-cost digitalization methods to create Web3 environments mirroring real settlements. Through a case study of an Italian village, the potential of participatory design and community-led development strategies in revitalizing neglected areas are explored, and the use of low-cost drone-based photogrammetry and Gaussian splatting in digitization are compared, highlighting their advantages and drawbacks considering the aim of this work, i.e., the creation of an interactive metaverse space. Ultimately, the study underscores the transformative role of digital technologies in reshaping design processes and fostering community development through a workflow, stressing collaborative decision-making and blockchain-driven economy, manufacturing, and maintenance through self-ownership models and performance-based smart contracts. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-18 DOI: 10.3390/ijgi13060211 Issue No: Vol. 13, No. 6 (2024)
- IJGI, Vol. 13, Pages 212: Trajectory Compression with Spatio-Temporal
Semantic Constraints Authors: Yan Zhou, Yunhan Zhang, Fangfang Zhang, Yeting Zhang, Xiaodi Wang First page: 212 Abstract: Most trajectory compression methods primarily focus on geometric similarity between compressed and original trajectories, lacking explainability of compression results due to ignoring semantic information. This paper proposes a spatio-temporal semantic constrained trajectory compression method. It constructs a new trajectory distance measurement model integrating both semantic and spatio-temporal features. This model quantifies semantic features using information entropy and measures spatio-temporal features with synchronous Euclidean distance. The compression principle is to retain feature points with maximum spatio-temporal semantic distance from the original trajectory until the compression rate is satisfied. Experimental results show these methods closely resemble each other in maintaining geometric similarity of trajectories, but our method significantly outperforms DP, TD-TR, and CascadeSync methods in preserving semantic similarity of trajectories. This indicates that our method considers both geometric and semantic features during compression, resulting in the compressed trajectory becoming more interpretable. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-18 DOI: 10.3390/ijgi13060212 Issue No: Vol. 13, No. 6 (2024)
- IJGI, Vol. 13, Pages 213: Shape Pattern Recognition of Building Footprints
Using t-SNE Dimensionality Reduction Visualization Authors: Jingzhong Li, Kainan Mao First page: 213 Abstract: The shape pattern recognition of building footprints stands as a pivotal concern within GIS spatial cognition. In this study, we introduce a novel approach for the shape recognition of building footprints, leveraging t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction visualization. First, the Canonical Time Warping (CTW) algorithm is employed to gauge the shape similarity distance of building footprints. Subsequently, the t-SNE model is utilized to map the building footprints, featuring varying numbers of coordinate vertices, onto points within the Cartesian coordinate system. The shape similarity distance serves as the input to the t-SNE model for parameter optimization. Lastly, building footprint shapes are identified through the inherent clustering patterns of points using a Gaussian Mixture Model (GMM). Experimental results demonstrate the method’s robustness to the translation, rotation, scaling, and mirroring of geometric objects, while effectively measuring shape similarity between building footprints. Furthermore, diverse types of building footprints are discernible through natural clustering in low-dimensional spaces, aligning closely with human visual perception. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-19 DOI: 10.3390/ijgi13060213 Issue No: Vol. 13, No. 6 (2024)
- IJGI, Vol. 13, Pages 214: Identifying the Spatial Range of the Pearl River
Delta Urban Agglomeration by Fusing Nighttime Light Data with Weibo Sign-In Data Authors: Yongwang Cao, Song Liu, Zaigao Yang First page: 214 Abstract: Accurately identifying the spatial range of urban agglomerations holds significant practical importance for the precise allocation of various elements and coordinated development within urban agglomerations. However, current research predominantly focuses on the physical spaces of urban agglomerations, overlooking their sphere of influence. This study begins with the spatial interactions of population elements within urban agglomerations and fuses Weibo sign-in data with NTL data to identify the spatial range of urban agglomerations. It further compares and validates the results before and after the fusion of data. The results reveal that the accuracy of identifying the spatial range of urban agglomerations with the fusion of NTL data and Weibo sign-in data has improved by 7%, with a Kappa increase of 0.1766 compared to using NTL data alone, which indicates that fusing social media data can significantly enhance the accuracy of identifying the spatial range of urban agglomerations. This study proposes a novel approach for identifying the spatial range of urban agglomerations through the fusion of NTL data and social media data from a data fusion perspective. On one hand, it supplements the application of data fusion in the study of urban agglomeration spaces; on the other hand, it accurately identifies the spatial range of urban agglomerations, which holds great practical value for the sustainable development of urban agglomerations. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-19 DOI: 10.3390/ijgi13060214 Issue No: Vol. 13, No. 6 (2024)
- IJGI, Vol. 13, Pages 215: Spatial and Temporal Changes and Influencing
Factors of Capital Cities in Five Provinces of the Underdeveloped Regions of Northwest China Authors: Yuanbao Feng, Yujun Ma, Wei Jia, Sifa Shu, Hongda Li, Xiangyu Hu First page: 215 Abstract: In recent years, China’s economy has experienced rapid development, and its cities have undergone rapid expansion; however, the development of cities in the northwest region has been relatively slow due to various geographical and economic constraints. Studying the urban expansion in these regions is of significant importance for regional planning and development. This study selected the provincial capitals of five underdeveloped provinces in northwestern China as the research sample and used Landsat TM/OLI remote-sensing imagery as the primary data, supplemented by Digital Elevation Model (DEM), meteorological, and socio-economic data, the study extracted urban impervious surfaces using the ENDISI and MNDWI indices. It analyzed the spatial and temporal characteristics of urban impervious surfaces from 1990 to 2020 using indicators such as urban expansion intensity, compactness and fractal dimension, centroid migration, and standard deviation ellipse. Furthermore, the study quantified the influencing factors using Geodetectors. The findings reveal the following: (1) From 1990 to 2020, impervious surfaces in the five cities continued to expand, with Xi’an experiencing the largest expansion area at 549.94 km2 and Xining the smallest at only 132.83 km2, with an expansion intensity of merely 2.99%. However, significant disparities existed in expansion intensity and area across different periods. (2) Overall, the compactness of the cities decreased by 47.6% while the overall fractal dimension increased by 2.85%, indicating a trend towards more dispersed and complex urban forms. (3) Expansion directions varied among the cities, with Xi’an and Urumqi expanding towards the northwest, Lanzhou towards the north, Yinchuan primarily towards the east, and Xining mainly towards the west. (4) Economic, demographic, and investment factors were identified as the primary influencers of urban expansion, exhibiting changes over different periods. Analyzing the similarities and differences in city development can offer valuable insights into urban construction and sustainable development in underdeveloped areas. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-19 DOI: 10.3390/ijgi13060215 Issue No: Vol. 13, No. 6 (2024)
- IJGI, Vol. 13, Pages 216: Integration of Spatial and Co-Existence
Relationships to Improve Administrative Region Target Detection in Map Images Authors: Kaixuan Du, Fu Ren, Yong Wang, Xianghong Che, Jiping Liu, Jiaxin Hou, Zewei You First page: 216 Abstract: Administrative regions are fundamental geographic elements on maps, thus making their detection in map images crucial to enhancing intelligent map interpretation. However, existing methods in this field primarily depend on the texture features within the images and do not account for the influence of spatial and co-existence relationships among different targets. In this study, taking the administrative regions of the Chinese Mainland, Taiwan, Tibet, and Henan as test targets, we employed the spatial and co-existence relationships of pairs of targets to improve target detection performance. Firstly, these four regions were detected using a simple Single-Target Cascading detection model based on RetinaNet. Subsequently, the detection results were adjusted with the spatial and co-existence relationships of each pair of targets. The adjusted outcomes demonstrate a significant increase in target detection accuracy, as well as precision (from 0.62 to 0.96) and F1 score (from 0.76 to 0.88), for the Chinese Mainland target. This study contributes to the advancement of intelligent map interpretation. Citation: ISPRS International Journal of Geo-Information PubDate: 2024-06-20 DOI: 10.3390/ijgi13060216 Issue No: Vol. 13, No. 6 (2024)
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