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Arctic-boreal lakes emit methane (CH4), a powerful greenhouse gas. Recent studies suggest ebullition might be a dominant methane emission pathway in lakes but its drivers are poorly understood. Various predictors of lake methane ebullition have been proposed but are challenging to evaluate owing to different geographical characteristics, field locations, and sample densities. Here we compare large geospatial data sets of lake area, lake perimeter, permafrost, land cover, temperature, soil organic carbon content, depth, and greenness with remotely sensed methane ebullition estimates for 5,143 Alaskan lakes. We find that lake wetland fraction (LWF), a measure of lake wetland and littoral zone area, is a leading predictor of methane ebullition (adj. R2 = 0.211), followed by lake surface area (adj. R2 = 0.201). LWF is inversely correlated with lake area, thus higher wetland fraction in smaller lakes might explain a commonly cited inverse relationship between lake area and methane ebullition. Lake perimeter (adj. R2 = 0.176) and temperature (adj. R2 = 0.157) are moderate predictors of lake ebullition, and soil organic carbon content, permafrost, lake depth, and greenness are weak predictors. The low adjusted R2 values are typical and informative for methane attribution studies. Our leading model, which uses lake area, temperature, and LWF (adj. R2 = 0.325, n = 5,130) performs slightly better than leading multivariate models from similar studies. Our results suggest landscape-scale geospatial analyses can complement smaller field studies, for attributing Arctic-boreal lake methane emissions to readily available environmental variables. [ABSTRACT FROM AUTHOR]

Due to its geophysical characteristics and location in a tropical zone, Colombia is prone to flooding phenomena, which, in addition to the vulnerability of the road network and the permanent presence of the population residing close to the roads, increase the social and economic fragility of the road corridors. This situation is evident when traffic collapses due to flooding, as the country mobilizes most of its goods by land. The knowledge and systematization of the social and economic conditions of the national road layout, and the incorporation of these data into a replicable spatial model, could be an alternative for making decisions aimed at risk prevention. This paper presents the conceptual and methodological design of a proposal of the socioeconomic factors required in a road flood risk assessment using spatial analysis tools in the pilot section Route 6008 Chiquinquirá-Sáchica (36 km). A global index of socioeconomic aspects related to vulnerability is quantified at three levels: local, zonal and regional. The main result is that the road corridor presents a medium-high level of vulnerability, due to the aspects of exposure and fragility in housing and livelihood conditions. [ABSTRACT FROM AUTHOR]

Geospatial data provides a window to explore traditional statistical modeling and investigate the geographic distribution of data, revealing patterns, connections, and relationships. These spatial analyses play a crucial role in understanding complex problems at the intraurban scale, allowing one or more variables to be associated with the study. In this context, this research examined the spatial correlation between two socioeconomic indicators in Salvador, Bahia: The UrbanEnvironmental Quality Index (IQUASalvador) and the Urban Property and Land Tax (IPTU), focusing on the Barra/Pituba Borough. In univariate and bivariate analyses, exploratory geospatial data techniques were employed to identify the spatial dependence between these variables, including the Global Moran's Index, the Local Indicators of Spatial Association (LISA), and the Moran scatterplot. The results revealed a positive and significant autocorrelation, with confidence levels of 95% and 99%, and specific geospatial patterns in the studied area were identified. This study highlights the role of geospatial analyses as an essential complement to the traditional statistical analysis of municipal socioeconomic data, offering valuable insights for urban planning and public policies. [ABSTRACT FROM AUTHOR]

This research aims to present carbon monoxide (CO) emissions trends from January 2019 to June 2021 in Southeast Asian waters and the South China Sea (SEA-SCS). Using securitization theory, the data obtained from geospatial analysis was the basis for constructing CO as a threat. The results showed high CO density in the region due to economic activities that caused pressure on the marine air environment. Unfortunately, this has not been explicitly discussed in regional maritime cooperation, even though the states and territories bordering these waters are increasingly vulnerable to climate change. This article argues that securitizing CO as a threat represents substantial progress in the environmental- political cooperation in the SCS and surrounding waters. [ABSTRACT FROM AUTHOR]

Managing municipal solid waste is one of the most significant challenges for cities in emerging economies, given the increasing level of generation and the low level of recovery. It is a topic of interest for different disciplines, including waste management, logistics, supply chains, Urban Logistics and Circular Economy. The solid waste recovery logistic networks are the ones through which waste is moved, and recovery operations are carried out. The analysis through geographic information systems makes it possible to relate the network operations with the characteristics of the space in which they are developed. A statistical vision also supports this relationship. This article presents the results of an analysis of the recoverable waste recovery logistics network in the Metropolitan Area of the Mexico Valley. The recovery network is made up of enterprises whose activities are the collection and sale of recoverable waste. Thousands of tons of waste are moved through the recovery network: the nodes are micro-enterprises that employ thousands of workers. The network operates under geospatial conditions that characterize its operation, including distribution, spatial concentration, and growth patterns. The analysis with geographic information systems provides a vision of the complexity of the network, for example, the operation of the network at different levels and scales, regularities that denote self-organization, and the presence of nodes that influence the network's structure. [ABSTRACT FROM AUTHOR]

This study analyzes and compares the academic and social segregation by socioeconomic status among the districts of the Metropolitan Lima area, with respect to the student achievement in mathematics, reading and science tests applied during the 2019 School Census in the second grade of secondary education. To measure segregation, it uses the Dissimilarity index, which highlights the importance of private schools, the Mutual Information index that reports on inter-school and intra-school segregation, and the Isolation index that measures the probability of distinguishing students belonging to the same social category. Likewise, Moran's autocorrelation index is presented, which visualizes the existence of socially and academically differentiated conglomerates according to the territorial location of the schools. These indices reveal a heterogeneous structure of segregation whose intensity varies according to the social composition of public and private schools in each district of the area. Geographic location patterns help to understand the effects of proximity and differentiation of social, school and residential profiles, and propose more geospatial interest to better monitor the functioning of schools and promote equality of learning. [ABSTRACT FROM AUTHOR]

To control the HIV epidemic at the population level, a 90–90–90 target has been proposed for sub-Saharan Africa, whereby 90 percent of people living with HIV (PLH) should know their HIV status, 90 percent of those diagnosed should be receiving antiretroviral therapy (ART), and 90 percent of those on ART should achieve viral suppression (VS). VS occurs when ART reduces the viral load of PLH to an undetectable level with minimal risk of new infections. We present a two-pronged interdisciplinary analysis, combining geospatial and HIV care continuum techniques, to guide treatment interventions toward achieving the 90–90–90 target in Uganda. We conducted spatiotemporal analysis of VS and constructed HIV care continuums for districts using publicly available data from a centralized laboratory of the Ministry of Health, Uganda. Spatial patterns of VS revealed a distinct east–west pattern of low rates and a north–south pattern of high rates with statistically significant clusters of districts with high and low rates in southwestern and eastern Uganda, respectively. VS rates, calculated in a sequential care continuum framework, revealed local variability as well as a significant gap from the 90–90–90 target by districts. Such evident local variability of VS might indicate the importance of regionally targeted interventions for bringing the HIV epidemic to an end. [ABSTRACT FROM AUTHOR]

Urban empty spaces can be understood as those vacant lands waiting to be built. In legal terms, its regime can vary between public and private. This article analyzes the nature of its geospatial distribution, examining the correlation between this urban phenomenon and the socioeconomic inequality of the population of three border cities in northern Mexico (Mexicali, San Luis Río Colorado, and Nuevo Laredo). A descriptive-comparative methodology was implemented based on statistical techniques and digital and physical surveys of the vacant lands in these cities. The results indicate that poverty levels and the number of empty urban spaces are directly related (positively correlated). In conclusion, a comparative discussion is presented between the three cities is presented. Also, the main differences and coincidences identified are pointed out. [ABSTRACT FROM AUTHOR]

In this study, we analyze the structure of paths within two refugee camps (Calais, France, and Zaatari, Jordan) as a preliminary study examining posttraumatic stress disorder (PTSD) stressors within such camps. We do so through a geospatial analysis of intersections and viewsheds within the camps. Given that anxiety, and the avoidance associated with it, is a primary characteristic of PTSD, refugees might prefer built environments with fewer stressors or anxiety triggers. This study provides a baseline for analyzing basic stressors in a way to test whether design supports anxiety-based avoidance. The planned structure of Zaatari shows a high prevalence of spatial PTSD stressors with lower visibility and a more maze-like structure than that of Calais. This suggests a coping strategy taken by refugees to produce a more suitable and less stressing labyrinth structure. To properly treat PTSD in geographical space, we suggest a need for consideration of this in the planning, function, and very existence of refugee camps and settlements. [ABSTRACT FROM AUTHOR]

Recent headlines highlight disparities in childhood lead poisoning in urban areas yet discourse does not address the lack of primary prevention options. Previous geographic information systems (GIS) approaches, concentrated on census tracts or ZIP codes, miss contextual understanding of lead exposure and make intervention impractical. Through the combination of electronic medical record (EMR) data from an urban children's hospital and spatial video geonarrative (SVG), we show how blood lead level researchers, clinicians, and public health planners can become more proactive in prediction and intervention strategies through the development of an environmental lead index (ELI). Kernel density estimation (KDE) clusters of geocoded locations of children with elevated blood lead (EBL), from 2012 to 2014, were identified using GIS. Analyses identify an increased relative risk for African American and Asian patients compared to white patients and Nepali and non-English-speaking patients compared to English-speaking patients. Fine-scale analyses of EBL clusters reveal nuances of exposure and environmental characteristics that are not identifiable at an aggregate level. Initial testing of the ELI was conducted using identified locations of EBL and non-EBL test results. The mean ELI score was higher among EBL parcels, and comparison proportions of ELI variables between EBL and non-EBL parcels found a statistically significant increase in four variables. Preliminary results support the use of the ELI as a predictive tool; further validation is needed. The technology and the method are translatable to other environments and health conditions. [ABSTRACT FROM AUTHOR]

Surface temperature behavior is related to water evaporation in the hydrological cycle, meaning that the alteration of surface temperature can lead to desertification processes and a decrease in water bodies, and it can affect the health of ecosystems. Studying surface temperature in the DRMI Las Camelias, in eastern Antioquia, sheds light on the transformation of the territory as a result of increased deforestation, intensification of productive activities, the tourism boom and population densification. Given that it is now possible to analyze surface temperature patterns over different land covers over time at different scales, this study evaluates surface temperature at point scale with a pyranometer, at local scale using the UAV Phantom 4 and ANAFI Thermal, and at regional scale using Landsat 8 satellite images. We found that at regional level, the variation of the average surface temperature within the DRMI was less than 1 °C, with no clear differentiation between coverages. A difference was found at local level, highlighting the role of vegetation and water, where forests, bamboo groves and streams have an average temperature of approximately 20 °C, which is up to 3 °C lower than pastures, crops or bare land. The patterns at the point and local level coincide, the temperature under the forest canopy is 5 °C lower than in the pastures, and shallow water outcrops maintain a temperature of 18 °C. Thus, recognizing the role of vegetation and water bodies in regulating surface temperature is vital for climate change mitigation, and detailed planning scales are necessary. This study was carried out with the support of the Institute of Environmental Studies - IDEA, Sede Medellín. [ABSTRACT FROM AUTHOR]

As in many studies involving disparate data and methodologies, environmental health risk assessment research is prone to errors and uncertainties. Unfortunately, uncertainties associated with the research findings can seldom be eliminated, and policy decisions often need to be made based on the uncertain findings. Hence methods that can help reduce uncertainties associated with the research findings would be most useful and need to be developed. This article highlights, through a case study, four major sources of uncertainties, which include uncertainties from data, methods of analysis, interpretations of the findings, and reactions to the findings. Five groups of geospatial methods that can help reduce the uncertainties as a result of combinations of the four sources are identified, including methods for visualization and measurement, cluster detection, exposure modeling, scale analysis, and decision support. The article concludes that future effort should focus more on the development of decision tools to enable decision making based on uncertain findings. [ABSTRACT FROM AUTHOR]