Geospatial Analysis Research Articles

Geospatial and Path Analysis for Enhancing Malaria Control and Primary Healthcare Delivery in Low-Income Nations: A Case Study of Uganda

Geospatial and Path Analysis for Enhancing Malaria Control and Primary Healthcare Delivery in Low-Income Nations: A Case Study of Uganda

Habitat preferences of Phycis blennoides (Pisces: Gadiformes): environmental factors and fishing-related ontogenic deepening

Phycis blennoides is a component of the ecosystem of the Atlantic waters of northern Spain and a species of commercial interest for fisheries in this region. However, little is known about the distribution of this species. The present study analyses the relationships between different environmental and spatial variables and distribution of P. blennoides. Biological (presence-absence, abundance, biomass and total length) and environmental data were collected from a bottom trawl survey carried out off the northern coast of Spain. These data were used in 2-step generalised additive models to examine both the relative significance of physical factors in influencing P. blennoides distribution and to generate density distribution maps. Additionally, the role of fishing effort on P. blennoides body size distribution with depth was analysed. Geospatial analyses showed that changes in annual abundance did not influence physical habitat preference. Most of the variation in spatial distribution was explained by the sampling position and depth. The analyses showed that P. blennoides exhibited habitat preferences in the waters of Galicia and the Cantabrian Sea, with higher values of presence, abundance and biomass on the upper slope and on fine-grained sediment bottoms. P. blennoides biomass increased with depth, probably due to the increased mean size of individuals with depth. Modelling results suggest that the observed ontogenetic deepening appears to be mainly driven by the life history strategy of the species, but is also reinforced by fishing effort.

Relational Persistent Homology for Multispecies Data with Application to the Tumor Microenvironment

Topological data analysis (TDA) is an active field of mathematics for quantifying shape in complex data. Standard methods in TDA such as persistent homology (PH) are typically focused on the analysis of data consisting of a single entity (e.g., cells or molecular species). However, state-of-the-art data collection techniques now generate exquisitely detailed multispecies data, prompting a need for methods that can examine and quantify the relations among them. Such heterogeneous data types arise in many contexts, ranging from biomedical imaging, geospatial analysis, to species ecology. Here, we propose two methods for encoding spatial relations among different data types that are based on Dowker complexes and Witness complexes. We apply the methods to synthetic multispecies data of a tumor microenvironment and analyze topological features that capture relations between different cell types, e.g., blood vessels, macrophages, tumor cells, and necrotic cells. We demonstrate that relational topological features can extract biological insight, including the dominant immune cell phenotype (an important predictor of patient prognosis) and the parameter regimes of a data-generating model. The methods provide a quantitative perspective on the relational analysis of multispecies spatial data, overcome the limits of traditional PH, and are readily computable.

Human health risk assessment of heavy metals in residential soil-Houston, Texas.

Human health risk assessment increasingly recognizes the need to integrate participatory-based research, geospatial analysis, and environmental epidemiology, particularly to address contamination concerns in underserved and disadvantaged communities. Here, we demonstrate the combined application of such methods within the Greater Fifth Ward neighborhood in Northeast Houston. In particular, in tandem with community members, we collected soil samples from 193 residential sites using a complete canvassing method from July to November 2021 to characterize contaminant concentrations, focusing on heavy metals and metalloids As, Ba, Cd, Cr, Pb, Se, Ag, and Hg measured using Inductively Coupled Plasma-Mass Spectrometry. Individual heavy metals as well as cumulative cancer and non-cancer risks were calculated for children and adults using the USEPA Regional Soil Screening Levels and benchmarks for specific land uses, such as crop growing. Soils from most sites had low or typical background levels expected in urban areas, but samples from several locations had significantly elevated lead levels (>1200 mg/kg) that warrant additional examination. Geospatial analysis suggested clustering of heavy metal contaminants within one geographic area of the neighborhood. This study highlights how participatory research in underserved environmental justice communities can help characterize current conditions as well as establish priorities for future investigation.

Exploring Cesarean Section Delivery Patterns in South India: A Bayesian Multilevel and Geospatial analysis of Population-Based Cross-Sectional Data

BackgroundThis paper focuses on the period from 2019 to 2021 and investigates the factors associated with the high prevalence of C-section deliveries in South India. We also examine the nuanced patterns, socio-demographic associations, and spatial dynamics underlying C-section choices in this region. A cross-sectional study was conducted using large nationally representative survey data.MethodsNational Family Health Survey data (NFHS) from 2019 to 2021 have been used for the analysis. Bayesian Multilevel and Geospatial Analysis have been used as statistical methods.ResultsOur analysis reveals significant regional disparities in C-section utilization, indicating potential gaps in healthcare access and socio-economic influences. Maternal age at childbirth, educational attainment, healthcare facility type size of child at birth and ever pregnancy termination are identified as key determinants of method of C-section decisions. Wealth index and urban residence also play pivotal roles, reflecting financial considerations and access to healthcare resources. Bayesian multilevel analysis highlights the need for tailored interventions that consider individual household, primary sampling unit (PSU) and district-level factors. Additionally, spatial analysis identifies regions with varying C-section rates, allowing policymakers to develop targeted strategies to optimize maternal and neonatal health outcomes and address healthcare disparities. Spatial autocorrelation and hotspot analysis further elucidate localized influences and clustering patterns.ConclusionIn conclusion, this research underscores the complexity of C-section choices and calls for evidence-based policies and interventions that promote equitable access to quality maternal care in South India. Stakeholders must recognize the multifaceted nature of healthcare decisions and work collaboratively to ensure more balanced and effective healthcare practices in the region.

Geospatial analysis of elephant habitat suitability and movement for mapping the elephant corridor in Dalma Wildlife Sanctuary (India).

Wildlife and natural resources constitute an integral part of the ecosystem, whereas human interventions dismantled the living conditions of the wildlife. This is testified in the Dalma Wildlife Sanctuary (DWS) where the habitats of Asian elephants have changed due to human intervention and deforestation over the decades. The present study aimed to assess the elephant habitat suitability in the DWS of Jharkhand state (India) using the geospatial parameters such as forest density, degree of slope, proximity to water bodies, land use land cover, proximity to agricultural land, built-up density, and road density. The analytical hierarchical process technique was utilized to determine habitat preference and selection of relevant factors to categorize criteria. The study revealed that about 6.7% (26.74 km2) of the area is very highly suitable for elephant habitat, while 52.26% (208.49 km2) of the forest area was found highly suitable. The most suitable habitat was identified in the core parts of the forest, while the least suitable areas were found in the southern part, where the presence of roads, built-up, and agricultural land was prominent. It was also observed that most human-elephant conflicts were exhibited in the low and very low suitable areas, while 90% of the elephant movement was witnessed in the high and very high suitable areas. Among the four identified corridors, three are inactive, and their location corresponds with low to very low suitable habitats. The study identified the migratory corridor routes inside the sanctuary where effective management is required for the conservation of elephant habitats and minimizing conflicts.

Proposal for a framework for environmental zoning of areas near gold mines based on the distribution of potentially toxic elements, pollution indices, and bioindicators: a case study in Antioquia, Colombia

Gold mining, even under strict environmental regulations, inevitably causes environmental impacts and liabilities. To address this, the study proposes a framework for environmental zoning around gold mines based on the distribution of potentially toxic elements (PTEs), pollution indices, and bioindicators. Soil samples were collected from municipalities affected by gold mining projects, and concentrations of As, Cd, Pb, and Cr were measured. Then, the Pollution Load Index (PLI) was calculated. A plant model was used for the biomonitoring of PTEs, and the Plant Vigor Index (PVI) was determined. Finally, environmental zoning was proposed through geospatial analysis combining PTEs, PLI, and PVI values. The concentrations of PTEs were as follows: As ranged from 1.7 to 892, Cd from 0.1 to 65.2, Pb from 18.5 to 2345, and Cr from 5.4 to 118.4. Spearman’s rank correlation showed significant relations (ρ > 0.76) between bioindicators and PTE concentrations and PLI. The PVI correlated significantly with PTE concentrations (ρ − 0.41 to − 0.67) and PLI (ρ − 0.65). The municipalities were categorized into three zones: highly contaminated, moderately contaminated, and minimally contaminated. Overall, the environmental zoning maps serve as a management tool for environmental monitoring.

Land use & land cover dynamics and its relationship with nitrate pollution in groundwater around inactive mine areas using geospatial techniques, SW part of Cuddapah basin, Southern India

Geospatial maps can show how the ineffective operations of inactive mines affect water and aquifer quality. As such, the purpose of this study is to assess the impact of mining and irrigation on the aquifer ecosystem through the evaluation of LULC and slope maps through the application of Landsat 8 OLI/TIRS and DEM data. A total of 50 groundwater samples were prepared from villages in the close proximity to inactive mines during pre and post monsoon periods in 2021. The results of the analysis revealed alarming statistics, that 14% of groundwater samples exceeded the WHO nitrate limit in pre & post monsoon season, indicating a high-risk in the study area. According to guidelines (USEPA, 2014), 34% in pre-monsoon and 26% post-monsoon of samples exceeded the THI levels for adults and children respectively, indicating non-carcinogenic health risks. In addition, 80% of the samples in both seasons exhibited high NPI values, indicating nitrate contamination associated with blue baby syndrome. From the Geospatial analysis the findings from the LULC classification indicate that there has been a significant increase in cropland area from 2016 to 2021 due to changes in forest land, fallow land, and water resources. These problems have been exacerbated by the expansion of cultivated land, which has increased from 71.1 square kilometers in 2016 to 118 square kilometers in 2021, accounting for 13.1% of the total area. This expansion, coupled with elevated water body resource availability, has compounded the nitrate pollution including in intensely irrigated regions. The slope map analysis revealed that the inactive mines occur at low slope, high rainfall areas and these are compounded by runoff from other sources such as domestic and agricultural wastes. For these matters, sealing and remediating these inactive mines is essential so as to prevent further nitrate leakage.

Geospatial Analysis of the Socioeconomic and Demographic Effects of Historic Coal Mining in the Greater Pittsburgh Region, Pennsylvania, USA

A geospatial model was developed to statistically assess the socioeconomic effects of coal mining in the greater Pittsburgh, Pennsylvania area by integrating home sale data, abandoned mine lands (AML) inventory “problem area” sites, and census demographic information. Results indicated that homes located within problem areas sold for an average of 28% ($58,600) less than homes outside of these regions. Demographic data revealed a notable disparity in the population distribution within Allegheny County mining problem areas as having a statistically significant larger Black population. This same trend was even more pronounced in urban areas. The study also established that areas influenced by past mining activities had a higher proportion of individuals without formal postsecondary education. Logistic regression models were created to analytically evaluate the relationship between predictor variables, specifically home sale price and Community Needs Index, to the probability of being situated within mining problem areas. The home sale analysis indicated a negative correlation between sale prices and the likelihood of residing in a mining-affected zone, implying that properties with lower prices are more commonly situated in these impacted areas. The CNI logistic regression model revealed a correlation between the probability of living in a mining problem area and overall higher community needs.

Assessing water quality of kazerun county in southwest Iran: Multi-analytical techniques, deterministic vs. probabilistic water quality index, geospatial analysis, fuzzy C-means clustering, and machine learning

Water quality is critical to human health and the environment, especially in arid and semi-arid regions. Hence, the objectives of this study were to assess drinking water quality, identify critical parameters, investigate spatial patterns, and investigate accurate predictive models for the water quality index (WQI) in the Kazerun county in southwest Iran. To address this issue using deterministic and probabilistic WQI, correlation matrix, fuzzy C-Means (FCM) clustering, geostatistics, and adaptive network-based fuzzy inference system (ANFIS) with FIS generation by fuzzy C-Means (FCM-ANFIS) and sub-clustering (SC-ANFIS).Various software tools, including Excel, MATLAB, Python, and GIS were used to analyze groundwater data collected from 25 sampling sites. Water parameters, including pH, Cl−, SO4−2, EC, NO3−, NO2−, Ca2+, Mg2+, and F−, were examined. The results showed that F− levels were within acceptable limits set by the US EPA, but about one-third of sites posed potential health risks based on WHO guidelines. In one-third of regions, the levels of Mg2+ exceeded the recommended guidelines. In deterministic and probabilistic approaches, water quality was excellent in 68% and 81.3% of sites, respectively. Sobol sensitivity analysis identified SO4−2> Mg2+>Cl− > EC > F− > NO3− as significant WQI variables. Spearman correlation matrix shows substantial positive correlations between WQI and EC, F−, SO4−2, Mg2+, and Cl− were shown by the Spearman correlation matrix. Based on the FCM results, the southeast and central sites (56% of sites) have similar water quality. In comparison, the northern and four central sites (28% of sites) have distinct regional features, and the southern sites (16% of sites) had unique water quality characteristics. Geostatistical analyses showed that pH had the most substantial local clustering, while SO4−2 had significant high-value clustering. Furthermore, hot spot research revealed specific sites with high pH, F−, NO3−, and Cl− levels. The FCM-ANFIS model outperformed the SC-ANFIS model, emphasizing FCM clustering's importance in water quality forecasting accuracy.

Rapid impact assessment of severe cyclone storm Michaung along coastal zones of Andhra and Tamil Nadu, India: A geospatial analysis

The coastal regions of India, particularly the Bay of Bengal, are highly vulnerable to the severe weather conditions induced by tropical cyclones. This study presents a comprehensive analysis of the changes in vegetation cover, shoreline dynamics, and meteorological variations resulting from Cyclone Michaung and subsequent post-monsoon events along the coastal zones of Andhra Pradesh and Tamil Nadu, India. A suite of vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Vegetation Condition Index (mVCI), and Disaster Vegetation Damage Index (DVDI), were employed to assess changes in vegetation cover. The Digital Shoreline Assessment System (DSAS) was utilized to evaluate shoreline changes, and a range of meteorological variables were analyzed to assess the impacts of Cyclone Michaung and post-monsoon events. The findings reveal significant ecological impacts, with a notable decrease in Very Healthy Vegetation from 5.71% to 1.30%. The mean value of mVCI shifted from −0.2 to −0.16, indicating vegetation stress. DVDI analysis showed that 56.49% of the area experienced moderate damage, while 40.24% suffered severe vegetation damage. Additionally, erosion was observed along 79.46% of the shoreline transects in the study area. These insights are critical for assisting coastal managers in developing resilient coastal systems. Remarkably, a significant change in rainfall was recorded between the pre-cyclone period and the landfall day, with maximum rainfall intensifying from 13.93 mm/h on December 3rd to 164.26 mm/h on December 4th, and subsequently decreasing to 144.39 mm/h on December 5th.

Factors affecting Kaposi's sarcoma-associated herpesvirus transmission in rural Ugandan households, a longitudinal study.

We report the impact of HIV infection within a household on oral Kaposi's sarcoma-associated herpesvirus (KSHV) shedding. We enrolled 469 individuals from 90 households. Mouthwash rinse samples collected at three monthly visits, were analyzed for KSHV DNA using quantitative polymerase chain reaction (qPCR). Generalized linear mixed effects logistic models were applied to analyze factors associated with KSHV ever shedding, and among shedders, always versus intermittent shedding. Linear mixed effects models were applied to models of KSHV viral loads. Intraclass correlation coefficients (ICCs) were calculated to assess the contribution of household-level factors to variations in shedding probabilities. Hotspot analyses of geospatial feature clusters were calculated using Getis-Ord Gi* statistic and visualized using inverse distance weighted interpolation. Analyses included 340 KSHV seropositive individuals, aged 3 + years, with qPCR results from 89 households. Forty households had 1 + persons living with HIV (PLWH), while 49 had none. Among participants, 149(44%) were KSHV ever shedders. Of 140 who shed KSHV at two or more visits, 34(24%) were always shedders. Increasing number of KSHV seropositive household members was significantly associated with ever shedding [Odds ratio(OR) (95% Confidence Interval(95%CI)):1.14(1.03,1.26);p = 0.013]. Among KSHV shedders, a statistically significant age-related trend was identified with 10-19 years being more likely to be always shedders (type III test p = 0.039) and to have higher viral loads (type III test p = 0.027). In addition, higher viral loads were significantly associated with increasing number of household members [coefficient(95%CI):0.06(0.01,0.12);p = 0.042], increasing number of KSHV seropositive members [coefficient(95%CI):0.08(0.01,0.15);p = 0.021], and living in households with 1 + PLWH [coefficient(95%CI):0.51(0.04,0.98);p = 0.033]. Always shedders exhibited higher viral loads than intermittent shedders [coefficient(95%CI):1.62(1.19,2.05);p < 0.001], and viral loads increased with the number of visits where KSHV DNA was detected in saliva (type III test p < 0.001). Household-level factors attributed for 19% of the variability in KSHV shedding (ICC:0.191;p = 0.010). Geospatial analysis indicated overlapping hotspots of households with more KSHV seropositive individuals and KSHV shedders, distinct from areas where PLWH were clustered. KSHV oral shedding is influenced by multiple factors at the individual, household, and regional levels. To mitigate ongoing KSHV transmission a comprehensive understanding of factors contributing to oral KSHV reactivation and transmission within households is needed.

Impact of storm drains on the maintenance of dengue endemicity in Presidente Prudente, São Paulo, Brazil: a geospatial and epidemiologic approach.

Dengue is a public health challenge worldwide. Brazil registered about 70% of cases in Latin America in 2023; in 2024, the country is experiencing an unprecedented increase in the number of infected individuals. By May 2024, more than 4 million people were infected. Our goal was to: (1) determine the epidemiology of dengue cases and their spatiotemporal distribution and (2) carry out a survey of the storm drains and through a geospatial analysis to determine their possible correlation with cases of dengue in Presidente Prudente, São Paulo, Brazil. Cases and information on the habitat of mosquito in the storm drain underground drainage system from 2020 to 2021 were obtained from public agencies. Larvae, pupae, and Ae. aegypti were identified according to species and described in taxonomic keys. Kernel density maps were constructed. From 1996 to 2023, the prevalence of cases peaked in 2016 and 2019, and in 2023 reached alarming levels, and the city was considered hyperendemic. In 2021, 2,609 cases were registered with 2 clusters of high density. Of 5,492 storm drains analyzed, 18.0% were found to have water, 9.0% had larvae or pupae of Aedes aegypti and 91.0% were classified as dirty or damaged. A direct correlation between the kernel layer of cases in 2021 with the kernel layer of storm drains containing water (r = 0.651) and larvae and pupae (r = 0.576) was found, suggesting that storm drains are risk factors and have an impact on the maintenance of dengue endemicity. The high number of damaged units found demonstrated the lack of storm drain management, compromising the urban drainage system and possibly contributing to dengue outbreaks. Policymakers may use these findings to improve existing dengue control strategies focusing on the control of storm drains and increase local and global perspectives on reducing dengue outbreaks.

Remote sensing insights into land cover dynamics and socio-economic Drivers: The case of Mtendeli refugee camp, Tanzania (2016–2022)

The purpose of this article is to present the scope and the dynamics of the environmental changes unfolded in the vicinity of Mtendeli refugee camp. It presents a new method, which combines geospatial analysis of high-resolution Earth observation data (Sentinel-1&2) with ground-based observations and input from local experts. Time series classifications of annual land use/land cover in the surroundings of the camp is developed from remote data. Subsequently main transitions and trends are quantitatively achieved. This is a first study which, not only treats the land transition process in a comprehensive manner, but also tracks the changes and their main drivers on an annual scale over the lifetime of the camp (2016–2021) and the post-closure situation in 2022. Most importantly, thanks to the involvement of social studies, it unfolds the socio-economical drivers of those changes. Drawing upon a random forest algorithm and available databases, we achieve overall classification accuracies of 83.5% (2020) and 82.0% (2022). Our findings indicate an ongoing expansion of cropland between 2016 and 2021, to the detriment of natural vegetation classes. The impact of environmental restoration programs implemented in the former camp area is visible by 2022. The proposed method can be used to identify areas of environmental risk and thus support decisions linked with sustainable development and land management.

Mapping eating disorders in adolescents and young adults: an investigation of geographic distribution and access to care in Ontario, Canada

BackgroundThere is limited research on the spatial distribution of eating disorders and the proximity to available eating disorder services. Therefore, this study investigates the distribution of eating disorders among adolescents and young adults in Ontario, Canada, with a specific focus on geographic disparities and access to publicly-funded specialized eating disorder services.MethodsA community sample of 1,377 adolescents and young adults ages 16–30 across Ontario between November and December 2021 participated in this study and completed the Eating Disorder Examination Questionnaire. Utilizing Geographic Information System (GIS) technology, we mapped the geographic prevalence of eating disorders and examined proximity to specialized eating disorder services. Multiple linear and logistic regression analyses were utilized to determine the association between geographic region and eating disorder symptomatology. Additionally, t-tests were utilized to examine differences between time/distance to specialized services and clinical risk for eating disorders.ResultsApplying geospatial analysis techniques, we detected significant spatial clusters denoting higher eating disorder scores in rural areas and areas with fewer specialized services. Likewise, our findings report disparities between rural and urban areas, suggesting that rural regions exhibit elevated rates of eating disorders. There were no associations between distance/time to services and eating disorder symptomology.ConclusionsThe discrepancies in eating disorder symptomology between urban/rural may stem from stigma and unique socio-cultural contexts in rural communities. The study underscores the need for targeted intervention, including telehealth, in addressing the eating disorder challenges faced by adolescents and young adults in rural regions.

Ethnic, geographical and altitude considerations and maternal mortality associated with HELLP syndrome in Ecuador: a population-based cohort study

BackgroundEclampsia and pre-eclampsia rank as the third leading causes of maternal death in Ecuador, following pre-existing chronic diseases and postpartum haemorrhage, as reported by the Ecuadorian National Institute of Statistics and Census (INEC). In contrast, HELLP (Haemolysis, Elevated Liver enzymes, Low Platelet count) syndrome remains underexplored epidemiologically, not only in Latin America but globally. This study marks the first population-based investigation into HELLP syndrome incidence and mortality in Ecuador, examining geographical variations, altitude influences and ethnic backgrounds.MethodsConducted as a retrospective population-based cohort study from 2015 to 2017, this research delves into the incidence, risk factors and maternal mortality associated with HELLP syndrome in Ecuador. Utilising data from INEC and the Ecuadorian Ministry of Health, we identified HELLP syndrome cases through ICD-10 (International Classification of Diseases, tenth revision) coding in hospitalised individuals. Logistic regression analysis was employed to explore association, whilst geospatial statistical analysis focused on cantons to identify significant spatial clusters. Primary outcome measures include HELLP syndrome incidence and maternal mortality, supplying crucial insights into the syndrome’s impact on maternal health in Ecuador.ResultsThe incidence of HELLP syndrome is 0.76 (0.69–0.84)/ 1000 deliveries. Afro-Ecuadorian communities have a higher risk (Odds Ratio (OR) = 2.18 (1.03–4.63)) compared to Indigenous Ecuadorian communities. Living at mid-level or high altitude is a significant risk factor OR of 2.79 (2.19–3.55) and an OR 3.61 (2.58–5.03), respectively. Being an older mother was also identified as a risk factor. Women living more than 20 km from the obstetric unit have an OR of 2.55 (2.05–3.18). Moreover, we found that cantons with higher crude HELLP syndrome incidence also have lower numbers of physicians (R = 0.503, p-value < 0.001). The mortality incidence of women with HELLP syndrome is 21.22 (12.05–20.59)/1000 deliveries with HELLP syndrome diagnoses.ConclusionsHigh altitude, advanced maternal age and geographical distance between residence and health centres are risk factors for HELLP syndrome. Maternal mortality in women with HELLP syndrome is higher than pre-eclampsia and eclampsia but comparable with previous reports in other countries.

Temporal Dynamics of Global Barren Areas between 2001 and 2022 Derived from MODIS Land Cover Products

Long-term monitoring studies on the transition of different land cover units to barren areas are crucial to gain a better understanding of the potential challenges and threats that land surface ecosystems face. This study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products (MCD12C1) to conduct geospatial analysis based on the maximum extent (MaxE) concept, to assess the spatiotemporal changes in barren areas from 2001 to 2022, at global and continental scales. The MaxE area includes all the pixels across the entire period of observations where the barren land cover class was at least once present. The relative expansion or reduction of the barren areas can be directly assessed with MaxE, as any annual change observed in the barren distribution is comparable over the entire dataset. The global barren areas without any land change (UA) during this period were equivalent to 12.8% (18,875,284 km2) of the global land surface area. Interannual land cover changes to barren areas occurred in an additional area of 3,438,959 km2 (2.3% of the global area). Globally, barren areas show a gradual reduction from 2001 (91.1% of MaxE) to 2012 (86.8%), followed by annual fluctuations until 2022 (88.1%). These areas were mainly interchanging between open shrublands and grasslands. A relatively high transition between barren areas and permanent snow and ice is found in Europe and North America. The results show a 3.7% decrease in global barren areas from 2001 to 2022. Areas that are predominantly not barren account for 30.6% of the transitional areas (TAs), meaning that these areas experienced short-term or very recent transitions from other land cover classes to barren. Emerging barren areas hotspots were mainly found in the Mangystau region (Kazakhstan), Tibetan plateau, northern Greenland, and the Atlas Mountains (Morocco, Tunisia).

Mapping Geospatial AI Flood Risk in National Road Networks

Previous studies have utilized machine learning algorithms that incorporate topographic and geological characteristics to model flood susceptibility, resulting in comprehensive flood maps. This study introduces an innovative integration of geospatial artificial intelligence for hazard mapping to assess flood risks on road networks within Portuguese municipalities. Additionally, it incorporates OpenStreetMap’s road network data to study vulnerability, offering a descriptive statistical interpretation. Through spatial overlay techniques, road segments are evaluated for flood risk based on their proximity to identified hazard zones. This method facilitates the detailed mapping of flood-impacted road networks, providing essential insights for infrastructure planning, emergency preparedness, and mitigation strategies. The study emphasizes the importance of integrating geospatial analysis tools with open data to enhance the resilience of critical infrastructure against natural hazards. The resulting maps are instrumental for understanding the impact of floods on transportation infrastructures and aiding informed decision-making for policymakers, the insurance industry, and road infrastructure asset managers.

Evaluative Mapping and Geospatial Analysis of Georgian Viticulture

Evaluative Mapping and Geospatial Analysis of Georgian Viticulture

Comprehensive Ecological Functional Zoning: A Data-Driven Approach for Sustainable Land Use and Environmental Management—A Case Study in Shenzhen, China

A comprehensive approach to ecological functional zoning in the Shenzhen region of China is presented in this study. Through the integration of advanced geospatial analysis tools, multiple data sources, and sophisticated statistical techniques, different ecological functions have been identified and categorized based on a comprehensive set of indicators and spatial analysis techniques. The three-level zoning framework established in this study offers policymakers, urban planners, and environmental managers a nuanced understanding of the region’s environmental characteristics, and highlights areas of ecological significance that warrant special attention and protection. It has been demonstrated that the data-driven approach to ecological functional zoning is effective in delineating distinct ecological zones within the study area. This study’s findings carry significant implications for future land use planning, conservation efforts, and sustainable development practices in the Shenzhen region. In essence, this study contributes to the broader discourse on ecological planning and environmental management by providing a systematic and data-driven approach to delineating ecological functional zones in urbanizing regions.