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Geographies, Volume 5, Issue 1 (March 2025) – 6 articles

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26 pages, 6855 KiB  
Article
Using the 3-30-300 Indicator to Evaluate Green Space Accessibility and Inequalities: A Case Study of Montreal, Canada
by Éric Robitaille and Cherlie Douyon
Geographies 2025, 5(1), 6; https://doi.org/10.3390/geographies5010006 - 6 Feb 2025
Viewed by 611
Abstract
Access to green spaces is essential for promoting public health, reducing inequalities, and fostering urban resilience. This study evaluates the 3-30-300 indicator as a tool for assessing green space accessibility in Montreal, Canada. The framework sets three goals: every resident should see three [...] Read more.
Access to green spaces is essential for promoting public health, reducing inequalities, and fostering urban resilience. This study evaluates the 3-30-300 indicator as a tool for assessing green space accessibility in Montreal, Canada. The framework sets three goals: every resident should see three trees from their home, live in a neighborhood with at least 30% tree canopy, and have a park or green space within 300 m. Using geospatial analysis, this study examines how well these criteria are met across Montreal’s neighborhoods and investigates disparities linked to socio-economic factors. The study reveals a significant variability in the distribution of green spaces across Montreal neighborhoods, as measured by the 3-30-300 metric. Tree canopy coverage ranges from 0.8% to 84%, with a median of 25.7%, while distances to parks vary from adjacent to over 2.4 km. The number of trees around residences is highly skewed, ranging from 0 to 771, reflecting substantial heterogeneity in green space accessibility. Spatial analysis highlights pronounced inequalities, with only 19.4% of neighborhoods meeting all three criteria. Hotspots of compliance are concentrated in peri-central and well-established residential areas in the West and East, while central and peripheral neighborhoods, especially in northeast Montreal, frequently fail to meet the standards. These findings underscore strong spatial disparities in urban green infrastructure, consistent with global studies on inequitable access to green spaces. Full article
(This article belongs to the Special Issue Feature Papers of Geographies in 2024)
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20 pages, 1940 KiB  
Article
The Impact of Weather on the Spread of COVID-19: The Case of the Two Largest Cities in Greece
by Despoina D. Tounta, Panagiotis T. Nastos, Dimitrios N. Paraskevis and Athanasios D. Sarantopoulos
Geographies 2025, 5(1), 5; https://doi.org/10.3390/geographies5010005 - 3 Feb 2025
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Abstract
The new global pandemic of COVID-19, declared on 11 March 2020 by the World Health Organization, has already had an unprecedented impact on health and socioeconomic activities worldwide. The second wave of the COVID-19 pandemic swept through the United States of America and [...] Read more.
The new global pandemic of COVID-19, declared on 11 March 2020 by the World Health Organization, has already had an unprecedented impact on health and socioeconomic activities worldwide. The second wave of the COVID-19 pandemic swept through the United States of America and Europe in late September 2020. Compared with other southern countries, such as Greece, where there was a significant increase in cases at the end of October 2020, Northern European countries (Germany, France, Austria, Finland, and Sweden) experienced this second wave of the pandemic earlier in September 2020. To understand the epidemiological behavior of the virus from an environmental perspective, we examined the effects of air temperature, humidity, and wind on the spread of COVID-19 in two of the largest population Regional Units (R.U.) of Greece, namely the R.U. of the Central Sector of Athens in Central Greece and the R.U. of Thessaloniki in Northern Greece. We applied Pearson correlation analysis and generalized linear models (GLM) with confirmed COVID-19 Intensive Care Unit (ICU) admissions from the National Public Health Organization as dependent variables and the corresponding air temperature, humidity, and wind speed from the Greek National Meteorological Service as independent covariates. The study focused on the period from 4 May 2020 to 3 November 2020 to investigate the impact of weather on the spread of COVID-19, in a period where human activities had partially returned to normal after the gradual lifting of the restrictive measures of the first lockdown (23 March 2020). The end date of the study period was set as the date of imposition of a new local lockdown in the R.U. of Thessaloniki (3 November 2020). Our findings showed that COVID-19 ICU admissions in both Regional Units decreased significantly with the temperature (T) and wind speed (WS) increase. In the R.U. of the Central Sector of Athens, this picture is reflected in both the single and cumulative lag effects of meteorological parameters. In the R.U. of Thessaloniki, this correlation was differentiated only in terms of the cumulative lag effect of the average daily temperature, where an increase (+17.6%) in daily confirmed COVID-19 ICU admissions was observed. On the other hand, relative humidity (RH) was significantly associated with an increase in cases in both R.U. This study, in addition to its contribution to the global research effort to understand the effects of weather on the spread of COVID-19, aims to highlight the need to integrate meteorological parameters as predictive factors in surveillance and early warning systems for infectious diseases. The combination of weather and climate factors (e.g., humidity, temperature, wind) and other contagious disease surveillance indicators (e.g., wastewater, geographic and population data, human activities) would make the early identification of potential epidemic risks more effective and would contribute to the immediate initiation of public health interventions and the more rational allocation of resources. Full article
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18 pages, 5424 KiB  
Article
A Geographical Complementary Approach to Unveiling the Spatial Dynamics of Bradyseismic Events at the Campi Flegrei Caldera
by Stefano De Falco and Claudio Martino
Geographies 2025, 5(1), 4; https://doi.org/10.3390/geographies5010004 - 29 Jan 2025
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Abstract
Concerning the monitoring of the resumption of seismic activity at the Campi Flegrei caldera, which is causing concern to the inhabitants and involving various protection efforts by research bodies, this work intends to constitute a complementary and auxiliary tool with respect to the [...] Read more.
Concerning the monitoring of the resumption of seismic activity at the Campi Flegrei caldera, which is causing concern to the inhabitants and involving various protection efforts by research bodies, this work intends to constitute a complementary and auxiliary tool with respect to the geophysical studies in progress. In particular, a geographical analysis of the phenomenon is proposed here aimed at identifying any spatial dynamics that can be added to the interpretation of seismic activity in a strictly geological and geophysical manner. The research study is focused on the comparison between the historical series of data starting from the year 2005 and those data relating to the last two years 2023 and 2024, in which the phenomenon resumed; particularly, the month of May 2024 is analyzed, which was characterized by high intensity of seismic events in the area. The results obtained through the joint use of spatial analysis tools aim, therefore, to identify any geographical seismic clusters that can then be interpreted in a geophysical way and can be used as an addendum in the current risk maps. Indeed, this geographical approach revealed complex spatial heterogeneities demonstrating the value of combining multiple methodological tools. The findings highlight the importance of multidisciplinary approaches in volcanic research and their critical role in improving hazard assessment and risk mitigation efforts. Full article
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17 pages, 2024 KiB  
Article
Public Support for Flood Risk Management: Insights from an Italian Alpine Survey Using Systems Thinking
by Rocco Scolozzi, Anna Scolobig and Marco Borga
Geographies 2025, 5(1), 3; https://doi.org/10.3390/geographies5010003 - 20 Jan 2025
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Abstract
This study presents the results of a survey on flood risk awareness conducted in the Italian Alps, examining the impacts of a major weather event on public perception and trust. It develops a systems-thinking framework to analyse dynamic feedback loops influencing flood risk [...] Read more.
This study presents the results of a survey on flood risk awareness conducted in the Italian Alps, examining the impacts of a major weather event on public perception and trust. It develops a systems-thinking framework to analyse dynamic feedback loops influencing flood risk management support over time. The survey data collection overlapped with a severe storm event in Central Europe, the storm “Adrian” (also known as “Vaia”). This provided a unique pre- and post-event perspective. Results highlight the critical role of individual knowledge, trust in authorities, and social group dynamics in shaping risk perception processes. The study shows how major weather events can change perceptions, sense of safety, and institutional trust within local communities, and more interestingly, these changes can vary spatially. The findings are summarised using a systems-thinking framework, which helps to identify possible feedback loops between flood risk management interventions and long-term public support. The study emphasizes the importance of forward-looking, systems-thinking approaches in the design, monitoring, and evaluation of flood risk management plans. These approaches allow one to account for often-overlooked dynamics, such as spatially varying feedback loops and counter-intuitive effects, ultimately improving the long-term effectiveness of flood risk management. Full article
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16 pages, 3296 KiB  
Article
Geographical Information Systems-Based Assessment of Evacuation Accessibility to Special Needs Shelters Comparing Storm Surge Impacts of Hurricane Irma (2017) and Ian (2022)
by Jieya Yang, Ayberk Kocatepe, Onur Alisan and Eren Erman Ozguven
Geographies 2025, 5(1), 2; https://doi.org/10.3390/geographies5010002 - 31 Dec 2024
Viewed by 659
Abstract
Research on hurricane impacts in Florida’s coastal regions has been extensive, yet there remains a gap in comparing the effects and potential damage of different hurricanes within the same geographical area. Additionally, there is a need for reliable discussions on how variations in [...] Read more.
Research on hurricane impacts in Florida’s coastal regions has been extensive, yet there remains a gap in comparing the effects and potential damage of different hurricanes within the same geographical area. Additionally, there is a need for reliable discussions on how variations in storm surges during these events influence evacuation accessibility to hurricane shelters. This is especially significant for rural areas with a vast number of aging populations, whose evacuation may require extra attention due to their special needs (i.e., access and functional needs). Therefore, this study aims to address this gap by conducting a comparative assessment of storm surge impacts on the evacuation accessibility of southwest Florida communities (e.g., Lee and Collier Counties) affected by two significant hurricanes: Irma in 2017 and Ian in 2022. Utilizing the floating catchment area method and examining Replica’s OD Matrix data with Geographical Information Systems (GISs)-based technical tools, this research seeks to provide insights into the effectiveness of evacuation plans and identify areas that need enhancements for special needs sheltering. By highlighting the differential impacts of storm surges on evacuation accessibility between these two hurricanes, this assessment contributes to refining disaster risk reduction strategies and has the potential to inform decision-making processes for mitigating the impacts of future coastal hazards. Full article
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19 pages, 3279 KiB  
Article
Enhanced Forecasting of Groundwater Level Incorporating an Exogenous Variable: Evaluating Conventional Multivariate Time Series and Artificial Neural Network Models
by Md Abrarul Hoque, Asib Ahmmed Apon, Md Arafat Hassan, Sajal Kumar Adhikary and Md Ariful Islam
Geographies 2025, 5(1), 1; https://doi.org/10.3390/geographies5010001 - 31 Dec 2024
Viewed by 618
Abstract
Continuous and uncontrolled extraction of groundwater often creates tremendous pressure on groundwater levels (GWLs). As a part of sustainable planning and effective management of water resources, it is crucial to assess the existing and forecasted GWL conditions. In this study, an attempt was [...] Read more.
Continuous and uncontrolled extraction of groundwater often creates tremendous pressure on groundwater levels (GWLs). As a part of sustainable planning and effective management of water resources, it is crucial to assess the existing and forecasted GWL conditions. In this study, an attempt was made to model and forecast GWL using artificial neural networks (ANNs) and multivariate time series models. Autoregressive integrated moving average (ARIMA) and ARIMA models incorporating exogenous variables (ARIMAX) were adopted as the time series models. GWL data from five monitoring wells from the study area of the Kushtia District in Bangladesh were used to demonstrate the modeling exercise. Rainfall (RF) was taken as the exogenous variable to explore whether its inclusion enhanced the performance of GWL forecasting using the developed models. It was evident from the results that the multivariate ARIMAX model (with the sum of squared errors, SSE, of 15.143) performed better than the univariate ARIMA model with an SSE of 16.585 for GWL forecasting. This demonstrates the fact that the multivariate time series models generated enhanced forecasting of GWL compared to the univariate time series models. When comparing the models, it was found that the ANN-based model outperformed the time series models with enhanced forecasting accuracy (SSE of 9.894). The results also exhibit a significant correlation coefficient (R) of 0.995 (model ANN 6-8-1) for the existing and predicted data. The current study conclusively proves the superiority of ANN over the time series models for the enhanced forecasting of GWL in the study area. Full article
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