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28 pages, 13851 KiB  
Article
A Spatially Aware Machine Learning Method for Locating Electric Vehicle Charging Stations
by Yanyan Huang, Hangyi Ren, Xudong Jia, Xianyu Yu, Dong Xie, You Zou, Daoyuan Chen and Yi Yang
World Electr. Veh. J. 2025, 16(8), 445; https://doi.org/10.3390/wevj16080445 (registering DOI) - 6 Aug 2025
Abstract
The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and [...] Read more.
The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities. Full article
(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)
21 pages, 4581 KiB  
Article
Spatiotemporal Variations and Drivers of the Ecological Footprint of Water Resources in the Yangtze River Delta
by Aimin Chen, Lina Chang, Peng Zhao, Xianbin Sun, Guangsheng Zhang, Yuanping Li, Haojun Deng and Xiaoqin Wen
Water 2025, 17(15), 2340; https://doi.org/10.3390/w17152340 - 6 Aug 2025
Abstract
With the acceleration of urbanization in China, water resources have become a key factor restricting regional sustainable development. Current research primarily examines the temporal or spatial variations in the water resources ecological footprint (WREF), with limited emphasis on the integration of both spatial [...] Read more.
With the acceleration of urbanization in China, water resources have become a key factor restricting regional sustainable development. Current research primarily examines the temporal or spatial variations in the water resources ecological footprint (WREF), with limited emphasis on the integration of both spatial and temporal scales. In this study, we collected the data and information from the 2005–2022 Statistical Yearbook and Water Resources Bulletin of the Yangtze River Delta Urban Agglomeration (YRDUA), and calculated evaluation indicators: WREF, water resources ecological carrying capacity (WRECC), water resources ecological pressure (WREP), and water resources ecological surplus and deficit (WRESD). We primarily analyzed the temporal and spatial variation in the per capita WREF and used the method of Geodetector to explore factors driving its temporal and spatial variation in the YRDUA. The results showed that: (1) From 2005 to 2022, the per capita WREF (total water, agricultural water, and industrial water) of the YRDUA generally showed fluctuating declining trends, while the per capita WREF of domestic water and ecological water showed obvious growth. (2) The per capita WREF and the per capita WRECC were in the order of Jiangsu Province > Anhui Province > Shanghai City > Zhejiang Province. The spatial distribution of the per capita WREF was similar to those of the per capita WRECC, and most areas effectively consume water resources. (3) The explanatory power of the interaction between factors was greater than that of a single factor, indicating that the spatiotemporal variation in the per capita WREF of the YRDUA was affected by the combination of multiple factors and that there were regional differences in the major factors in the case of secondary metropolitan areas. (4) The per capita WREF of YRDUA was affected by natural resources, and the impact of the ecological condition on the per capita WREF increased gradually over time. The impact factors of secondary metropolitan areas also clearly changed over time. Our results showed that the ecological situation of per capita water resources in the YRDUA is generally good, with obvious spatial and temporal differences. Full article
(This article belongs to the Section Water Resources Management, Policy and Governance)
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24 pages, 62899 KiB  
Essay
Monitoring and Historical Spatio-Temporal Analysis of Arable Land Non-Agriculturalization in Dachang County, Eastern China Based on Time-Series Remote Sensing Imagery
by Boyuan Li, Na Lin, Xian Zhang, Chun Wang, Kai Yang, Kai Ding and Bin Wang
Earth 2025, 6(3), 91; https://doi.org/10.3390/earth6030091 (registering DOI) - 6 Aug 2025
Abstract
The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of [...] Read more.
The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of the Beijing–Tianjin–Hebei metropolitan cluster. In recent years, the area has undergone accelerated urbanization and industrial transfer, resulting in drastic land use changes and a pronounced contradiction between arable land protection and the expansion of construction land. The study period is 2016–2023, which covers the key period of the Beijing–Tianjin–Hebei synergistic development strategy and the strengthening of the national arable land protection policy, and is able to comprehensively reflect the dynamic changes of arable land non-agriculturalization under the policy and urbanization process. Multi-temporal Sentinel-2 imagery was utilized to construct a multi-dimensional feature set, and machine learning classifiers were applied to identify arable land non-agriculturalization with optimized performance. GIS-based analysis and the geographic detector model were employed to reveal the spatio-temporal dynamics and driving mechanisms. The results demonstrate that the XGBoost model, optimized using Bayesian parameter tuning, achieved the highest classification accuracy (overall accuracy = 0.94) among the four classifiers, indicating its superior suitability for identifying arable land non-agriculturalization using multi-temporal remote sensing imagery. Spatio-temporal analysis revealed that non-agriculturalization expanded rapidly between 2016 and 2020, followed by a deceleration after 2020, exhibiting a pattern of “rapid growth–slowing down–partial regression”. Further analysis using the geographic detector revealed that socioeconomic factors are the primary drivers of arable land non-agriculturalization in Dachang Hui Autonomous County, while natural factors exerted relatively weaker effects. These findings provide technical support and scientific evidence for dynamic monitoring and policy formulation regarding arable land under urbanization, offering significant theoretical and practical implications. Full article
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19 pages, 4537 KiB  
Article
Learning the Value of Place: Machine Learning Models for Real Estate Appraisal in Istanbul’s Diverse Urban Landscape
by Ahmet Hilmi Erciyes, Toygun Atasoy, Abdurrahman Tursun and Sibel Canaz Sevgen
Buildings 2025, 15(15), 2773; https://doi.org/10.3390/buildings15152773 - 6 Aug 2025
Abstract
The prediction of real estate values is vital for taxation, transactions, mortgages, and urban policy development. Values can be predicted more accurately by statistical or advanced methods together when the size of the data is huge. In metropolitan cities like İstanbul, where size [...] Read more.
The prediction of real estate values is vital for taxation, transactions, mortgages, and urban policy development. Values can be predicted more accurately by statistical or advanced methods together when the size of the data is huge. In metropolitan cities like İstanbul, where size of the real estate data is vast and complex, mass appraisal methods supported by Machine Learning offer a scalable and consistent alternative. This study employs six algorithms: Artificial Neural Network, Extreme Gradient Boosting, K-Nearest Neighbors, Support Vector Regression, Random Forest, and Semi-Log Regression, to estimate the values of real estate on both the Asian and European continent parts of İstanbul. In total, 168,099 residential properties were utilized along with 30 of their features from both sides of the Bosphorus. The results show that RF yielded the best performance in Beşiktaş, while XGBoost performed best in Üsküdar. ANN also produced competitive results, although slightly less accurate than those of XGBoost and RF. In contrast, traditional SVR and SLR models underperformed, especially in terms of R2 and RMSE values. With its large-scale dataset, focusing on one of the greatest metropolitan areas, Istanbul, and the usage of multiple ML algorithms, this study stands as a comprehensive and practical contribution to the field of automated real estate valuation. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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19 pages, 1976 KiB  
Article
Excess Commuting in Rural Minnesota: Ethnic and Industry Disparities
by Woo Jang, Jose Javier Lopez and Fei Yuan
Sustainability 2025, 17(15), 7122; https://doi.org/10.3390/su17157122 - 6 Aug 2025
Abstract
Research on commuting patterns has mainly focused on urban and metropolitan areas, and such studies are not typically applied to rural and small-town regions, where workers often face longer commutes due to limited job opportunities and inadequate public transportation. By using the Census [...] Read more.
Research on commuting patterns has mainly focused on urban and metropolitan areas, and such studies are not typically applied to rural and small-town regions, where workers often face longer commutes due to limited job opportunities and inadequate public transportation. By using the Census Transportation Planning Package (CTPP) data, this research fills that gap by analyzing commuting behavior by ethnic group and industry in south-central Minnesota, which is a predominantly rural area of 13 counties in the United States. The results show that both white and minority groups in District 7 experienced an increase in excess commuting from 2006 to 2016, with the minority group in Nobles County showing a significantly higher rise. Analysis by industry reveals that excess commuting in the leisure and hospitality sector (including arts, entertainment, and food services) in Nobles County increased five-fold during this time, indicating a severe spatial mismatch between jobs and affordable housing. In contrast, manufacturing experienced a decline of 50%, possibly indicating better commuting efficiency or a loss of manufacturing jobs. These findings can help city and transportation planners conduct an in-depth analysis of rural-to-urban commuting patterns and develop potential solutions to improve rural transportation infrastructure and accessibility, such as promoting telecommuting and hybrid work options, expanding shuttle routes, and adding more on-demand transit services in rural areas. Full article
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16 pages, 825 KiB  
Article
Geographic Scale Matters in Analyzing the Effects of the Built Environment on Choice of Travel Modes: A Case Study of Grocery Shopping Trips in Salt Lake County, USA
by Ensheng Dong, Felix Haifeng Liao and Hejun Kang
Urban Sci. 2025, 9(8), 307; https://doi.org/10.3390/urbansci9080307 - 5 Aug 2025
Abstract
Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt [...] Read more.
Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt Lake County, UT, this research investigated a variety of influential factors affecting mode choices associated with grocery shopping. We analyze how built environment (BE) characteristics, measured at seven spatial scales or different ways of aggregating spatial data—including straight-line buffers, network buffers, and census units—affect travel mode decisions. Key predictors of choosing walking, biking, or transit over driving include age, household size, vehicle ownership, income, land use mix, street density, and distance to the central business district (CBD). Notably, the influence of BE factors on mode choice is sensitive to different spatial aggregation methods and locations of origins and destinations. The straight-line buffer was a good indicator for the influence of store sales amount on mode choices; the network buffer was more suitable for the household built environment factors, whereas the measurement at the census block and block group levels was more effective for store-area characteristics. These findings underscore the importance of considering both the spatial analysis method and the location (home vs. store) when modeling non-work travel. A multi-scalar approach can enhance the accuracy of travel demand models and inform more effective land use and transportation planning strategies. Full article
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21 pages, 9265 KiB  
Article
Towards a Sustainable Process of Conservation/Reuse of Built Cultural Heritage: A “Coevolutionary” Approach to Circular Economy in the Case of the Decommissioned Industrial Agricultural Consortium in the Corbetta, Metropolitan Area of Milan, Italy
by Mehrnaz Rajabi, Stefano Della Torre and Arian Heidari Afshari
Land 2025, 14(8), 1595; https://doi.org/10.3390/land14081595 - 5 Aug 2025
Abstract
This paper aims to explore the potentialities and systemic relationships between the ‘regenerative’ process and ‘circular economy’ concept within the conservation and reuse of a built cultural heritage framework through contextualizing the concept of ‘process programming’ of the Preventive and Planned Conservation methodology. [...] Read more.
This paper aims to explore the potentialities and systemic relationships between the ‘regenerative’ process and ‘circular economy’ concept within the conservation and reuse of a built cultural heritage framework through contextualizing the concept of ‘process programming’ of the Preventive and Planned Conservation methodology. As a case study, it depicts a decommissioned industrial agricultural silo in Corbetta—a small historic city with its hinterland located in the protected Southern Milan Regional Agricultural Park. The context includes the industrial agricultural lands of the 20th century, together with historical water infrastructure, farmhouses, and the typical flora of the Lombardy region, all evidences of Corbetta’s rural archaeological values and the sophisticated material culture of its past collective production/economy system—the locus in which the silo was once one of the main productive symbols of Corbetta’s agricultural identity. Within such a complex and challenging context, this paper argues in favor of the constructive role of such a methodology in upholding circular economy principles within the process of conservation and reuse of the silo, highlighting its broader application of the ‘coevolution’ concept from a multidisciplinary long-term perspective. Full article
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4 pages, 1714 KiB  
Proceeding Paper
A Study on High-Precision Vehicle Navigation for Autonomous Driving on an Ultra-Long Underground Expressway
by Kyoung-Soo Choi, Yui-Hwan Sa, Min-Gyeong Choi, Sung-Jin Kim and Won-Woo Lee
Eng. Proc. 2025, 102(1), 10; https://doi.org/10.3390/engproc2025102010 - 5 Aug 2025
Abstract
GPSs typically have an accuracy ranging from a few meters to several tens of meters. However, when corrected using various methods, they can achieve an accuracy of several tens of centimeters. In autonomous driving, a positioning accuracy of less than 50 cm is [...] Read more.
GPSs typically have an accuracy ranging from a few meters to several tens of meters. However, when corrected using various methods, they can achieve an accuracy of several tens of centimeters. In autonomous driving, a positioning accuracy of less than 50 cm is required for lane-level positioning, route generation, and navigation. However, in environments where GPS signals are blocked, such as tunnels and underground roads, absolute positioning is impossible. Instead, relative positioning methods integrating IMU, IVN, and cameras are used. These methods are influenced by numerous variables, however, such as vehicle speed and road conditions, resulting in lower accuracy. In this study, we conducted experiments on current vehicle navigation technologies using an autonomous driving simulation vehicle in the Suri–Suam Tunnel of the Seoul Metropolitan Area 1st Ring Expressway. To recognize objects (lane markings/2D/3D) for position correction inside the tunnel, data on tunnel and underground road infrastructure in Seoul and Gyeonggi Province was collected, processed, refined, and trained. Additionally, a Loosely Coupled-based Kalman Filter was designed and applied for the fusion of GPSs, IMUs, and IVNs. As a result, an error of 113.62 cm was observed in certain sections. This suggests that while the technology is applicable for general vehicle lane-level navigation in ultra-long tunnels spanning several kilometers for public service, it falls short of meeting the precision required for autonomous driving systems, which demand lane-level accuracy. Therefore, it was concluded that infrastructure-based absolute positioning technology is necessary to enable precise navigation inside tunnels. Full article
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19 pages, 1997 KiB  
Article
Mapping Bicycle Crash-Prone Areas in Ohio Using Exploratory Spatial Data Analysis Techniques: An Investigation into Ohio DOT’s GIS Crash Analysis Tool Data
by Modabbir Rizwan, Bhuiyan Monwar Alam and Yaw Kwarteng
Future Transp. 2025, 5(3), 103; https://doi.org/10.3390/futuretransp5030103 - 4 Aug 2025
Abstract
While there are studies on bicycle crashes, no study has investigated the spatial analysis of fatal and injury bicycle crashes in the state of Ohio. This study fills this gap in the literature by mapping and investigating the bicycle crash-prone areas in the [...] Read more.
While there are studies on bicycle crashes, no study has investigated the spatial analysis of fatal and injury bicycle crashes in the state of Ohio. This study fills this gap in the literature by mapping and investigating the bicycle crash-prone areas in the state. It analyzes fatal and injury bicycle crashes from 2014 to 2023 by utilizing four exploratory spatial data analysis techniques: nearest neighbor index, global Moran’s I index, hotspot and cold spot analysis, and local Moran’s I index at the state, county, census tract, and block group levels. Results vary slightly across techniques and spatial scales but consistently show that bicycle crash locations are clustered statewide, particularly in the state’s major metropolitan areas such as Columbus, Cincinnati, Toledo, Cleveland, and Akron. These urban centers have emerged as hotspots, indicating a higher vulnerability to bicycle crashes. While global Moran’s I analysis at the county level does not reveal significant spatial autocorrelation, a strong positive autocorrelation is observed at both the census tract (p = 0.01) and block group (p = 0.00) levels, indicating significant high clustering, signifying that finer geographical units yield more robust results. Identifying specific hotspots and vulnerable areas provides valuable insights for policymakers and urban planners to implement effective safety measures and improve conditions for non-motorized road users in Ohio. The study highlights the need for targeted mitigation strategies in high-risk areas, including comprehensive safety measures, infrastructure improvements, policy changes, and community-focused initiatives to reduce crash risk and create safer environments for cyclists throughout Ohio’s urban fabric. Full article
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13 pages, 2517 KiB  
Article
A Framework for the Dynamic Mapping of Precipitations Using Open-Source 3D WebGIS Technology
by Marcello La Guardia, Antonio Angrisano and Giuseppe Mussumeci
Geographies 2025, 5(3), 40; https://doi.org/10.3390/geographies5030040 - 4 Aug 2025
Viewed by 47
Abstract
Climate change represents one of the main challenges of this century. The hazards generated by this process are various and involve territorial assets all over the globe. Hydrogeological risk represents one of these aspects, and the violence of rain precipitations has led experts [...] Read more.
Climate change represents one of the main challenges of this century. The hazards generated by this process are various and involve territorial assets all over the globe. Hydrogeological risk represents one of these aspects, and the violence of rain precipitations has led experts to focus their interest on the study of geotechnical assets in relation to these dangerous weather events. At the same time, geospatial representation in 3D WebGIS based on open-source solutions led specialists to employ this kind of technology to remotely analyze and monitor territorial events considering different sources of information. This study considers the construction of a 3D WebGIS framework for the real-time management of geospatial information developed with open-source technologies applied to the dynamic mapping of precipitation in the metropolitan area of Palermo (Italy) based on real-time weather station acquisitions. The structure considered is a WebGIS platform developed with Cesium.js JavaScript libraries, the Postgres database, Geoserver and Mapserver geospatial servers, and the Anaconda Python platform for activating real-time data connections using Python scripts. This framework represents a basic geospatial digital twin structure useful to municipalities, civil protection services, and firefighters for land management and for activating any preventive operations to ensure territorial safety. Furthermore, the open-source nature of the platform favors the free diffusion of this solution, avoiding expensive applications based on property software. The components of the framework are available and shared using GitHub. Full article
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24 pages, 4441 KiB  
Article
Simulation of Trip Chains in a Metropolitan Area to Evaluate the Energy Needs of Electric Vehicles and Charging Demand
by Pietro Antonio Centrone, Giuseppe Brancaccio and Francesco Deflorio
World Electr. Veh. J. 2025, 16(8), 435; https://doi.org/10.3390/wevj16080435 - 4 Aug 2025
Viewed by 48
Abstract
The typical ranges available for electric vehicles (EVs) may be considered by users to be inadequate when compared to long, real-life trips, and charging operations may need to be planned along journeys. To evaluate the compatibility between vehicle features and charging options for [...] Read more.
The typical ranges available for electric vehicles (EVs) may be considered by users to be inadequate when compared to long, real-life trips, and charging operations may need to be planned along journeys. To evaluate the compatibility between vehicle features and charging options for realistic journeys performed by car, a simulation approach is proposed here, using travel data collected from real vehicles to obtain trip chains for multiple consecutive days. Car travel activities, including stops with the option of charging, were simulated by applying an agent-based approach. Charging operations can be integrated into trip chains for user activities, assuming that they remain unchanged in the event that vehicles switch to electric. The energy consumption of the analyzed trips, disaggregated by vehicle type, was estimated using the average travel speed, which is useful for capturing the main route features (ranging from urban to motorways). Data were recorded for approximately 25,000 vehicles in the Turin Metropolitan Area for six consecutive days. Market segmentation of the vehicles was introduced to take into consideration different energy consumption rates and charging times, given that the electric power, battery size, and consumption rate can be related to the vehicle category. Charging activities carried out using public infrastructure during idle time between consecutive trips, as well as those carried out at home or work, were identified in order to model different needs. Full article
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17 pages, 1783 KiB  
Article
Nature-Based Solutions in Sustainable Cities: Trace Metal Accumulation in Urban Forests of Vienna (Austria) and Krakow (Poland)
by Mateusz Jakubiak, Ewa Panek, Krzysztof Urbański, Sónia Silva Victória, Stanisław Lach, Kamil Maciuk and Marek Kopacz
Sustainability 2025, 17(15), 7042; https://doi.org/10.3390/su17157042 - 3 Aug 2025
Viewed by 204
Abstract
Forests are considered one of the most valuable natural areas in metropolitan region landscapes. Considering the sensitivity and ecosystem services provided by trees, the definition of urban forest ecosystems is nowadays based on a comprehensive understanding of the entire urban ecosystem. The effective [...] Read more.
Forests are considered one of the most valuable natural areas in metropolitan region landscapes. Considering the sensitivity and ecosystem services provided by trees, the definition of urban forest ecosystems is nowadays based on a comprehensive understanding of the entire urban ecosystem. The effective capturing of particulate matter is one of the ecosystem services provided by urban forests. These ecosystems function as efficient biological filters. Plants accumulate pollutants passively via their leaves. Therefore, another ecosystem service provided by city forests could be the use of tree organs as bioindicators of pollution. This paper aims to estimate differences in trace metal pollution between the wooded urban areas of Vienna and Krakow using leaves of evergreen and deciduous trees as biomonitors. An additional objective of the research was to assess the ability of the applied tree species to act as biomonitors. Plant samples of five species—Norway spruce, Scots pine, European larch, common white birch, and common beech—were collected within both areas, in seven locations: four in the “Wienerwald” Vienna forest (Austria) and three in the “Las Wolski” forest in Krakow (Poland). Concentrations of Cr, Cu, Cd, Pb, and Zn in plant material were determined. Biomonitoring studies with deciduous and coniferous tree leaves showed statistically higher heavy metal contamination in the “Las Wolski” forest compared to the “Wienerwald” forest. Based on the conducted analyses and the literature study, it can be concluded that among the analyzed tree species, only two: European beech and common white birch can be considered potential indicators in environmental studies. These species appear to be suitable bioindicators, as both are widespread in urban woodlands of Central Europe and have shown the highest accumulation levels of trace metals. Full article
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21 pages, 1031 KiB  
Article
Waiting Times for Surgery and Radiotherapy Among Breast Cancer Patients in Switzerland: A Cancer Registry-Based Cross-Sectional and Longitudinal Analysis
by Christoph Oehler, Michel Eric Nicolas Zimmermann, Mohsen Mousavi, Kattic Ram Joorawon, Marcel Blum, Christian Herrmann and Daniel Rudolf Zwahlen
Radiation 2025, 5(3), 23; https://doi.org/10.3390/radiation5030023 - 3 Aug 2025
Viewed by 245
Abstract
Background: Delays in breast cancer treatment negatively affect prognosis and have increased over time. Data on waiting times in Switzerland are limited. Patients and Methods: This study analyzed cancer registry data from 2003 to 2005 (2628 patients) and 2015 to 2017 (421 patients) [...] Read more.
Background: Delays in breast cancer treatment negatively affect prognosis and have increased over time. Data on waiting times in Switzerland are limited. Patients and Methods: This study analyzed cancer registry data from 2003 to 2005 (2628 patients) and 2015 to 2017 (421 patients) to evaluate waiting times for diagnosis, surgery, and radiotherapy; temporal trends; and survival in women with stage I–III invasive breast cancer treated with surgery without chemotherapy. Associations with demographic/clinical factors and overall survival (OS) were assessed using ANOVA, uni-/multivariable regression, Kaplan–Meier, and Cox regression. Results: From 2003 to 2005, mean intervals were biopsy-to-diagnosis 4.3 days, diagnosis-to-surgery 18.8 days, biopsy-to-surgery 26.8 days, and surgery-to-radiotherapy 56.7 days. Longer diagnosis-to-surgery times were associated with metropolitan areas, public hospitals, basic insurance, mastectomy, and older age (all p < 0.001). Radiotherapy delays were also longer in metropolitan areas and after mastectomy (p < 0.001). Between 2003–2005 and 2015–2017, diagnosis-to-surgery times rose in Eastern Switzerland (from 21.3 to 31.2 days), while radiotherapy timing remained stable. Five-year overall survival improved (from 76.7% to 88.4%), but was not significantly impacted by diagnosis-to-surgery intervals. Conclusions: Despite timely surgery in Switzerland (2003–2005), disparities existed, and time to surgery increased by 2015–2017. Reducing waiting times remains important despite no significant short-term OS impact. Full article
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16 pages, 3217 KiB  
Article
Application of an Orbital Remote Sensing Vegetation Index for Urban Tree Cover Mapping to Support the Tree Census
by Cássio Filipe Vieira Martins, Franciele Caroline Guerra, Anderson Targino da Silva Ferreira and Roger Dias Gonçalves
Earth 2025, 6(3), 87; https://doi.org/10.3390/earth6030087 (registering DOI) - 1 Aug 2025
Viewed by 218
Abstract
Urban vegetation monitoring is essential for sustainable city planning but is often constrained by the high cost and limited frequency of field-based inventories. This study evaluates the use of the Normalized Difference Vegetation Index (NDVI), derived from Sino-Brazilian CBERS-4A satellite imagery, as a [...] Read more.
Urban vegetation monitoring is essential for sustainable city planning but is often constrained by the high cost and limited frequency of field-based inventories. This study evaluates the use of the Normalized Difference Vegetation Index (NDVI), derived from Sino-Brazilian CBERS-4A satellite imagery, as a spatially explicit and low-cost proxy for urban tree census data. CBERS-4A provides medium-resolution multispectral data freely accessible across South America, yet remains underutilized in urban environmental applications. Focusing on Aracaju, a metropolitan region in northeastern Brazil, we compared NDVI-based classification results with official municipal tree census data from 2022. The analysis revealed a strong spatial correlation, supporting the use of NDVI as a reliable indicator of canopy presence at the urban block scale. In addition to mapping vegetation distribution, the NDVI results identified areas with insufficient canopy coverage, directly informing urban greening priorities. By validating remote sensing data against field inventories, this study demonstrates how CBERS-4A imagery and vegetation indices can support municipal tree management and serve as scalable tools for environmental planning and policy. Full article
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25 pages, 7131 KiB  
Article
Spatiotemporal Patterns of Non-Communicable Disease Mortality in the Metropolitan Area of the Valley of Mexico, 2000–2019
by Constantino González-Salazar, Kathia Gasca-Gómez and Omar Cordero-Saldierna
Diseases 2025, 13(8), 241; https://doi.org/10.3390/diseases13080241 - 1 Aug 2025
Viewed by 282
Abstract
Background: Non-communicable diseases (NCDs) are a leading cause of mortality globally, contributing significantly to the burden on healthcare systems. Understanding the spatiotemporal patterns of NCD mortality is crucial for identifying vulnerable populations and regions at high risk. Objectives: Here, we evaluated the spatiotemporal [...] Read more.
Background: Non-communicable diseases (NCDs) are a leading cause of mortality globally, contributing significantly to the burden on healthcare systems. Understanding the spatiotemporal patterns of NCD mortality is crucial for identifying vulnerable populations and regions at high risk. Objectives: Here, we evaluated the spatiotemporal patterns of NCD mortality in the Metropolitan Area of the Valley of Mexico (MAVM) from 2000 to 2019 for five International Classification of Diseases chapters (4, 5, 6, 9, and 10) at two spatial scales: the municipal level and metropolitan region. Methods: Mortality rates were calculated for the total population and stratified by sex and age groups at both spatial scales. In addition, the relative risk (RR) of mortality was estimated to identify vulnerable population groups and regions with a high risk of mortality, using women and the 25–34 age group as reference categories for population-level analysis, and the overall MAVM mortality rate as the reference for municipal-level analysis. Results: Mortality trends showed that circulatory-system diseases (Chapter 9) are emerging as a concerning health issue, with 45 municipalities showing increasing mortality trends, especially among older adults. Respiratory-system diseases (Chapter 10), mental and behavioral disorders (Chapter 5) and nervous-system diseases (Chapter 6) predominantly did not exhibit a consistent general mortality trend. However, upon disaggregating by sex and age groups, specific negative or positive trends emerged at the municipal level for some of these chapters or subgroups. Endocrine, nutritional, and metabolic diseases (Chapter 4) showed a complex pattern, with some age groups presenting increasing mortality trends, and 52 municipalities showing increasing trends overall. The RR showed men and older age groups (≥35 years) exhibiting higher mortality risks. The temporal trend of RR allowed us to identify spatial mortality hotspots mainly in chapters related to circulatory, endocrine, and respiratory diseases, forming four geographical clusters in Mexico City that show persistent high risk of mortality. Conclusions: The spatiotemporal analysis highlights municipalities and vulnerable populations with a consistently elevated mortality risk. These findings emphasize the need for monitoring NCD mortality patterns at both the municipal and metropolitan levels to address disparities and guide the implementation of health policies aimed at reducing mortality risk in vulnerable populations. Full article
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