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Purpose: Disasters continue to be most prevalent and severe for marginalised communities. To reach those furthest behind first, as the global community pledges in the 2030 Agenda, a critical assessment of equity in disaster risk governance is necessary. Yet, the understanding of factors that mediate the capacity of the governance processes to achieve equity ambitions is limited. This paper addresses this gap by proposing and testing a conceptual framework to assess equity in disaster risk governance. Design/methodology/approach: The framework analyses the extent to which institutional relationships and data in risk governance support inclusion and diversity of voice and enable the equitable engagement of communities. The study applied the framework to key risk policies across governance levels in Brazil and Colombia. Findings: The study finds that institutional awareness of cross-sectoral and -scalar coordination clearly exists. Yet, the engagement of actors further down the governance scale is framed reactively at all scales in both countries. The analysis of the risk data practices indicates that although data integration and sharing are key policy priorities, the policies frame the relations of disaster risk data actors as hierarchical, with data needs determined from the top down. Originality/value: A key contribution of this framework is that its equity view results in a nuanced analysis, thus pointing to the differences between the two countries concerning the factors that mediate these challenges and providing specific entry points for strengthening equity in risk governance policies. [ABSTRACT FROM AUTHOR]

Favelas are the most common type of informal settlements found in Brazil. The Housing Secretariat, City Hall, Sao Paulo, has conducted surveys using Unmanned Aerial Vehicles (UAVs) for the favelas to facilitate the slum upgrading projects and has taken the initiative to create a digital twin of the slum areas. This study illustrates the feasibility of developing a methodological workflow to create a digital twin by automatic 3D building reconstruction in slums from the UAV point cloud and the 2D building footprints with a continuous link for updating the building semantic information. This study focuses on facilitating data integration and updating the semantic information into the 3D model to provide additional information about the individual buildings in the slums. The assessments concluded that the proposed workflow is suitable for creating digital twins for slums based on the UAV and 2D cadastral data. However, the 3D slum model had a few limitations, which are discussed in this paper. [ABSTRACT FROM AUTHOR]

Mineral prospectivity mapping (MPM), like other geoscience fields, is subject to a variety of uncertainties. When data about unfavorable sites to find deposits (i.e., drill intersections to barren rocks) is lacking in MPM using machine learning (ML) methods, the synthetic generation of negative datasets is required. As a result, techniques for selecting point locations to represent negative examples must be employed. Several approaches have been proposed in the past; however, one can never be certain that the points chosen are true negatives or, at the very least, optimal for training. As a consequence, methodologies that account for the uncertainty of the generation of negative datasets in MPM are needed. In this paper, we compare two criteria for selecting negative examples and quantify the uncertainty associated with this process by generating 400 potential maps for each of the three ML methods utilized (200 maps for each criterion), which include random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNC). The results showed that applying a geological constraint to the creation of negative datasets reduced prediction uncertainty and improved overall model performance but produced larger areas of very high probability (i.e., >0.9) when compared to using only the spatial distribution of known deposits and occurrences as a constraint. SHAP values were used to find approximations for the importance of features in nonlinear methods, and kernel density estimations were used to examine how they varied depending on the negative dataset used to train the ML models. Prospectivity models for magmatic-hydrothermal gold deposits were generated using data from the shuttle radar terrain mission, gamma-ray, magnetic lineaments, and proximity to dykes. The Juruena Mineral Province, situated in Northern Mato Grosso, Brazil, represented the case study for this work. [ABSTRACT FROM AUTHOR]

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Committed to achieving carbon neutrality by mid-century, South America faces unique challenges in carbon mitigation due to its distinct socio-economic, technological, and institutional backgrounds compared to developed and other developing economies. Previous research on South America's carbon emissions has limited country and sector coverage and typically adopts a top-down approach that focuses on the overall emissions of an economy, neglecting individual energy sources and emission sectors. Therefore, this paper compiles a comprehensive inventory of CO 2 emissions for South American countries based on a meticulous bottom-up approach and multi-source data integration, followed by a thorough analysis of their temporal evolution patterns and the driving forces behind them. Between 2010 and 2020, South America's overall emissions peaked in 2014 at 1092.3 Mt., led by emissions from oil consumption and from the transport and electricity sectors, and dropped to 865.6 Mt. in 2020, due to the economic fluctuations and COVID-19. Specifically, Brazil was the largest emitter, Chile experienced the most significant increase in emissions, and Paraguay recorded the highest emission growth rate. Generally, expanding population was constant driver of emission growth. High energy intensity led to significant emission increases in Brazil and Ecuador, while elevated carbon intensity was the main driver in Argentina. Energy structure changes played a positive role in most countries to different extents (e.g., much more significant in Brazil than in Chile) but aggrevated emissions in Paraguay and Guyana. Therefore, carbon mitigation strategies should be tailored to each country's unique national circumstances. • During 2010–2020, South America's CO 2 emissions from fossil fuels peaked in 2014. • Transport and electricity sectors comprised over 60% of total emissions. • Brazil was the top emitter, while Chile saw the largest increase in emissions. • Emission patterns and driving factors varied significantly across countries. • Carbon reduction strategies should be tailored to local circumstances. [ABSTRACT FROM AUTHOR]

Information on the 6th international symposium on Mobile Mapping Technology (MMT) held at Presidente Prudente in Sao Paulo, Brazil on July 21-24, 2009 is presented. Topics include alternative sensor and data integration algorithms, direct georeferencing, and image sequence processing. The symposium, which was attended by 144 participants from 19 countries including China, Brazil, and France, featured several professors including João Silva, Ian Dowman, and Dorota Brzezinska.

• We present a new framework for the previous design of overhead transmission lines. • Geographical, engineering and cost aspects are considered in the decision-making process. • Routing and tower spotting optimization are integrated into the methodology proposed. • Least cost alternative design of transmission lines can be determined. • The transmission line design can be better understood in preliminary steps of the planning process. In many parts of the world, electric sectors are already experiencing considerable rising in generation from renewable energy sources. Large amounts of new generation are expected in the near-term future, which will require additional transmission investments to properly integrate these resources into the existing electric power system. The transmission expansion planning has an important role in this environment in order to guarantee the security of the supply with the required levels of quality and price. Therefore, the implementation of new transmission lines (TL) must be fast and accurate in order to avoid delays to connect new power sources and potential supply and reliability problems. In this sense, Geographical Information Systems (GIS) can be a powerful tool that provides decision support techniques, which enables a transparent, sustainable, faster planning process for TLs in power systems. This paper presents a novel approach for the design of overhead TLs, considering geographical, engineering and cost aspects into the decision-making process. For this, routing and tower spotting optimization approaches are integrated into the proposed methodology, which is divided into three main steps: (i) Route Guideline Definition based on a raster-based least-cost path approach; (ii) Vertex Siting based on graph theory and the Dijkstra shortest path algorithm, applied in order to find the optimal vertex set along the route guideline; (iii) Tower Spotting based on Dynamic Programming, which is applied in order to find the optimal distribution of towers along the topographical profile of the route obtained in the previous step. The proposed methodology is focused on preliminary planning and decision-making for TL auctions, where the objective is to find design alternatives with the least cost. We show a case study using the proposed methodology for a real project of a 525 kV TL that interconnects Machadinho and Campos Novos (located in the Santa Catarina state in Brazil). The outcomes show that the proposed approach is capable of representing the technical and geographical constraints of a TL design, providing results with lower costs when compared to the original TL design. [ABSTRACT FROM AUTHOR]

Coral reefs around the world are facing serious threats. These fragile ecosystems are in need for conservation. The coastal state of Bahia hosts the most extensive and richest area of coral reefs in the South Atlantic Ocean. Assessment, planning and management of coral reef ecosystems are particularly challenging tasks. This work shows how the creation of a GIS improves the process of management, monitoring and conservation of the Bahian reef environments The initial data input started by the vectorization of 1) bathymetric data from the Bureau of Hydrography and Navigation (DHN), 2) shoreline and mangrove areas from Landsat 7 ETM + images, 3) near surface reefs from Quickbird images, and 4) coastal and marine protected areas of federal, state and local administrations. Geological, physical, biological and social information was then included in order to create a suitable marine GIS for conservation aims. The data includes information on sediment granulometry and transport patterns, rocky substrate outcrops, sea surface temperature, wave direction, rain precipitation, major contributing river discharge, artisanal fishery, benthic cover and bleaching data. ReefBahia GIS has provided essential information for a better understanding of coral reefs of the state of Bahia geological and ecological characteristics such as mapping, representation, connectivity and biodiversity of coral reefs, geological facies, Quaternary sedimentation, numeric modeling of wave refraction and monitoring of bleaching events. [ABSTRACT FROM AUTHOR]