Doenças relacionadas a agrotoxicos

Trapé, Angelo Zanaga

doi:10.47749/t/unicamp.1995.88613

Cited by 5 publications

(5 citation statements)

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“…int which uses modified Copernicus Sentinel-1 and -2 data at a resolution of 10 m. This project was built and validated in almost real-time, while also maximising the impact and uptake for the end users. According to 91 , the globe cover product is given in an elliptical WGS 1984 grid with a regular latitude/longitude grid (EPSG:4326). To achieve the research goal, only 2020 datasets were chosen for the study, and regional land use types were reclassified for comparison with future land use data.…”

Section: Datasetsmentioning

confidence: 99%

Projected patterns of land uses in Africa under a warming climate

Yahaya,

Xu,

Zhou

et al. 2024

Sci Rep

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Land-use change is a direct driver of biodiversity loss, projection and future land use change often consider a topical issue in response to climate change. Yet few studies have projected land-use changes over Africa, owing to large uncertainties. We project changes in land-use and land-use transfer under future climate for three specified time periods: 2021–2040, 2041–2060, and 2081–2100, and compares the performance of various scenarios using observational land-use data for the year 2020 and projected land-use under seven Shared Socioeconomic Pathways Scenarios (SSP): SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5 from 2015 to 2100 in Africa. The observational land-use types for the year 2020 depict a change and show linear relationship between observational and simulated land-use with a strong correlation of 0.89 (P < 0.01) over Africa. Relative to the reference period (1995–2014), for (2021–2040), (2041–2060), (2081–2100), barren land and forest land are projected to decrease by an average of (6%, 11%, 16%), (9%, 19%, 38%) respectively, while, crop land, grassland and urban land area are projected to increase by (36%, 58%, and 105%), (4%, 7% and 11%), and (139%, 275% and 450%) respectively. Results show a substantial variations of land use transfer between scenarios with major from barren land to crop land, for the whole future period (2015–2100). Although SSP4-3.4 project the least transfer. Population and GDP show a relationship with cropland and barren land. The greatest conversion of barren land to crop land could endanger biodiversity and have negative effects on how well the African continent's ecosystem’s function.

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Section: Datasetsmentioning

confidence: 99%

Projected patterns of land uses in Africa under a warming climate

Yahaya,

Xu,

Zhou

et al. 2024

Sci Rep

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“…For elevation, slope data based on a Digital Elevation Model at 3om spatial resolution were obtained from the Regional Center for Mapping of Resources for Development (RCMRD) data Geoportal (http://geoportal.rcmrd.org/) which provides open access spatial data. On land cover, land use data developed by the European Space Agency Sentinel-2 imagery was sourced for the year 2021 at 10 m spatial resolution (https://worldcover2021.esa.int/viewer) [34]. The data details the main land cover classes including water, trees, grass, flooded vegetation, crops, shrubs, built-up area, and bare ground which influence travel in places without a road network.…”

Section: Covariates Datamentioning

confidence: 99%

Examining inequalities in spatial access to national health insurance fund contracted facilities in Kenya

Kazungu,

Moturi,

Kuhora

et al. 2024

Int J Equity Health

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Background Kenya aims to achieve universal health coverage (UHC) by 2030 and has selected the National Health Insurance Fund (NHIF) as the ‘vehicle’ to drive the UHC agenda. While there is some progress in moving the country towards UHC, the availability and accessibility to NHIF-contracted facilities may be a barrier to equitable access to care. We estimated the spatial access to NHIF-contracted facilities in Kenya to provide information to advance the UHC agenda in Kenya. Methods We merged NHIF-contracted facility data to the geocoded inventory of health facilities in Kenya to assign facility geospatial locations. We combined this database with covariates data including road network, elevation, land use, and travel barriers. We estimated the proportion of the population living within 60- and 120-minute travel time to an NHIF-contracted facility at a 1-x1-kilometer spatial resolution nationally and at county levels using the WHO AccessMod tool. Results We included a total of 3,858 NHIF-contracted facilities. Nationally, 81.4% and 89.6% of the population lived within 60- and 120-minute travel time to an NHIF-contracted facility respectively. At the county level, the proportion of the population living within 1-hour of travel time to an NHIF-contracted facility ranged from as low as 28.1% in Wajir county to 100% in Nyamira and Kisii counties. Overall, only four counties (Kiambu, Kisii, Nairobi and Nyamira) had met the target of having 100% of their population living within 1-hour (60 min) travel time to an NHIF-contracted facility. On average, it takes 209, 210 and 216 min to travel to an NHIF-contracted facility, outpatient and inpatient facilities respectively. At the county level, travel time to an NHIF-contracted facility ranged from 10 min in Vihiga County to 333 min in Garissa. Conclusion Our study offers evidence of the spatial access estimates to NHIF-contracted facilities in Kenya that can inform contracting decisions by the social health insurer, especially focussing on marginalised counties where more facilities need to be contracted. Besides, this evidence will be crucial as the country gears towards accelerating progress towards achieving UHC using social health insurance as the strategy to drive the UHC agenda in Kenya.

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“…Foi utilizado o base de dados da European Space Agency (ESA) WorldCover 2021 que apresenta uma acurácia de 76.7%, com uma resolução espacial de 10 metros, permitindo uma análise detalhada da cobertura terrestre em escala regional e global, classificando entre diferentes tipos como região de árvores, construção, mangue entre outras (ZANAGA et al, 2022).…”

Section: Neste Estudo Foram Utilizados Os Dados Climáticos Do Centro ...unclassified

Comparativo de técnicas de aprendizado de máquinas para a previsão de faltas elétricas

Silva

Almeida

Cuadros

2023

PRW

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Neste estudo é elaborado um modelo de previsão de faltas na rede de distribuição de energia no estado do Espírito Santo, Brasil, demonstrando quais regiões têm mais possibilidades de serem afetadas. Para isso, serão utilizados dados históricos de falhas, previsão numérica do tempo, cobertura terrestre e infraestrutura da rede. Essas informações são combinadas em regiões com o sistema de geocódigo Geohash e agrupadas por dia, servido assim de entrada para o modelo de aprendizado de máquina como Decision Tree (DT), Randon Florest(RF), K-Nearest Neighbors (KNN) e Support Vector Machine (SVM). Os resultados indicaram que a abordagem proposta apresentou uma precisão significativa na identificação da localização de falta, apresentando uma acurácia de 0.707 e um erro quadrático médio de 0.293.

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Doenças relacionadas a agrotoxicos

Cited by 5 publications

References 0 publications

Projected patterns of land uses in Africa under a warming climate

Projected patterns of land uses in Africa under a warming climate

Examining inequalities in spatial access to national health insurance fund contracted facilities in Kenya

Comparativo de técnicas de aprendizado de máquinas para a previsão de faltas elétricas

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