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A Lei n° 12.608, de 10 de abril de 2012, em seu Capítulo II − DA POLÍTICA NACIONAL DE PROTEÇÃO E DEFESA CIVIL − PNPDEC, Seção 1 − Diretrizes e Objetivos, em seu Art. 3° “abrange as ações de prevenção, mitigação, preparação, resposta e recuperação voltadas à proteção e defesa civil.” Assim são diretrizes PNPDEC, em conformidade com seu Art.4° :
I. Atuação articulada entre a União, os Estados, o Distrito Federal e os Municípios para redução de desastres e apoio às comunidades atingidas.
II. Abordagem sistêmica das ações de prevenção, mitigação, preparação, resposta e recuperação.
III. Planejamento com base em pesquisas e estudos sobre áreas de risco e incidência de desastres no território nacional.
IV. Participação da sociedade civil.
V. Participação da UNDRO.
Está correto o que consta em
Segundo o Ministério do Meio Ambiente, “Construção sustentável é um conceito que denomina um conjunto de medidas adotadas durante todas as etapas da obra que visam a sustentabilidade da edificação. Através da adoção dessas medidas é possível minimizar os impactos negativos sobre o meio ambiente além de promover a economia dos recursos naturais e a melhoria na qualidade de vida dos seus ocupantes”. Para tanto, tem-se que os processos de Certificação ambiental voltados para as construções sustentáveis, atribuem pontos referentes aos vários itens de sustentabilidade verificados nas obras. Dentre as certificações ambientais na construção civil considere:
I. a Leadership in Energy and Environmental Design − LEED, emitido pelo United States Green Building Council.
II. Processo Alta Qualidade Ambiental − AQUA, certificação brasileira baseada na francesa Haute Qualité Environnementale − HQE, implantada no país pela Fundação Vanzolini.
III. Building Research Establishment Environmental Assessment Method − BREEAM, certificação emitida pela Building Research Establishment − BRE, Reino Unido.
IV. Deutsche Gesellschaft für Nachhaltiges Bauen − DGNB, emitida pela organização German Sustainable Building Council − DGNB.
As duas certificadoras mais utilizadas no Brasil são as constantes em
Conforme o Relatório “Panorama da Desertificação no Estado do Piauí” (2005: 2), a desertificação configura-se como “um processo de degradação ambiental que ocorre nas regiões áridas, semiáridas e sub úmidas secas do globo por ação antrópica, com impactos negativos imediatos na qualidade de vida da população, tais como: aumento da escassez hídrica; perda da fertilidade do solo; redução da produção de alimentos; diminuição drástica das reservas de madeira; entre outras.” Diante destas conjunturas, o Piauí tornou-se conhecido internacionalmente, ganhando visibilidade entre a comunidade científica e a sociedade, por apresentar uma das maiores áreas de desertificação do Brasil, denominada Núcleo de Desertificação de Gilbués, abrangendo vários municípios. Neste sentido, entre os vários fatores que configuram a região, considere:
I. O Núcleo de Gilbués abrange os municípios de Barreiras do Piauí, Bom Jesus, Corrente, Curimatá, Gilbués, Monte Alegre do Piauí, Redenção do Gurguéia e São Gonçalo do Gurguéia.
II. O Núcleo de Desertificação de Gilbués constitui-se em caso especial de degradação do solo relacionado diretamente a um determinado substrato rochoso: o Grupo Areado da Bacia Sanfranciscana, estando circunscrito ao extremo sul do estado do Piauí, dada às condições geológicas da área.
III. A intensificação dos processos de degradação de solos está correlacionada à expansão do complexo agroindustrial da soja, na década de 2000.
IV. Alteração da cobertura vegetal devido às ações antropogênicas, expondo um raro tipo de associação de solos (PE5), que apresenta alta fertilidade, mas pouca resistência aos processos erosivos, às condições regionais de concentração pluviométrica (alta intensidade pluviométrica), gerando a exposição do embasamento rochoso sedimentar, responsável tanto pela redução quanto pela perda da fertilidade e da produtividade biológica e/ou econômica.
Está correto o que consta em
De acordo com o Banco Nacional de Desenvolvimento (BNDES, 2013, p. 9), “Licitações Sustentáveis viabilizam o atendimento das necessidades do órgão ou entidade por meio de contratações que promovam maior número de benefícios para o meio ambiente e para a sociedade, contribuindo, dessa maneira, para o desenvolvimento sustentável do país”.
Diante deste contexto, a decisão de se realizar uma licitação sustentável deve considerar no processo de aquisição de bens e contratações de serviços:
A Portaria Interministerial n° 244, de 6 de junho de 2012, considera a integração das seguintes iniciativas:
I. Programa de Eficiência do Gasto Público − PEG.
II. Programa Nacional de Conservação de Energia Elétrica − PROCEL, mais especificamente o Subprograma Eficiência Energética em Prédios Públicos − PROCEL EPP.
III. Agenda Ambiental na Administração Pública − A3P.
IV. Coleta Seletiva Solidária.
Esta portaria institui o
O Projeto Esplanada Sustentável tem por objetivo principal incentivar órgãos e instituições públicas federais a adotarem modelo de gestão organizacional e de processos estruturado na implementação de ações voltadas ao uso racional de recursos naturais, promovendo a sustentabilidade ambiental e socioeconômica na Administração Pública Federal. Para tanto, visa também:
I. Melhorar a qualidade do gasto público pela eliminação do desperdício e pela melhoria contínua da gestão dos processos.
II. Incentivar a implementação de ações de eficiência energética nas edificações públicas.
III. Estimular ações para o consumo racional dos recursos naturais e bens públicos.
IV. Garantir a gestão integrada de resíduos pós-consumo, inclusive a destinação ambientalmente correta.
V. Melhorar a qualidade de vida no ambiente do trabalho.
VI. Reconhecer e premiar as melhores práticas de eficiência na utilização dos recursos públicos, nas dimensões de economicidade e socioambientais.
VII. Reconhecimento dos procedimentos operacionais e parâmetros de avaliação no âmbito do programa de valoração dos passivos ambientais.
Está correto o que consta APENAS em
Atenção: As questões de números 56 a 60 referem-se ao texto apresentado abaixo. As cores originais dos mapas 2, 3 e 4 foram
alteradas para visualização em tons de cinza.
Using analysis, we can feel confident in the spatial patterns we see, and in the decisions that we make.
Putting your data on a map is an important first step for finding patterns and understanding trends. Here we’re looking at crimes that happened in San Francisco, about 37,000 of them. Looking at the points on a map, can you find the clusters or patterns in this point data? Can you decide where the police department should allocate its resources? Just looking at points on a map is often not enough to answer questions or make decisions using this kind of point data. That’s where the spatial analysis tools in ArcGIS come in.
We’ve all seen heat maps on TV or in web application-beautiful maps that show high-density areas in bright red, and low-density
areas in blue. These maps are used to visualize crime, disease, and a whole host of other types of data and information. These heat
maps can be a great first step in a visual analysis of your data 
they can also be very subjective. What does that mean? Well,
the two heat maps shown below reflect the same San Francisco Crime data, and were created using the same tool. The only difference
is the criteria that were used to decide what appears very dark (high density) and what appears very light (low density). These types of
cartographic elements that we incorporate into our maps can have a huge impact on the story that the map tells.
If the decisions that you’re trying to make as a result of your analyses are important, and they usually are, you’ll want to
minimize subjectivity as much 
. A great way to minimize the subjectivity in your pattern analysis is to use a hot spot analysis,
which incorporates a simple spatial statistic to determine if the patterns that you’re seeing are statistically significant or not. The hot
spot map is shown here.
So what makes this type of map any less subjective than density-based heat maps? The very dark areas on hot spot maps are statistically significant clusters of high values (hot spots), and the very light areas are statistically significant clusters of low values (cold spots). What’s dark and what’s light is always based on statistical significance. Using hot spot analysis, we can feel confident in the spatial patterns that we see, and in the decisions that we make.(Adapted from: http://resources.arcgis.com/en/communities/analysis/017z00000015000000.htm
Atenção: As questões de números 56 a 60 referem-se ao texto apresentado abaixo. As cores originais dos mapas 2, 3 e 4 foram
alteradas para visualização em tons de cinza.
Using analysis, we can feel confident in the spatial patterns we see, and in the decisions that we make.
Putting your data on a map is an important first step for finding patterns and understanding trends. Here we’re looking at crimes that happened in San Francisco, about 37,000 of them. Looking at the points on a map, can you find the clusters or patterns in this point data? Can you decide where the police department should allocate its resources? Just looking at points on a map is often not enough to answer questions or make decisions using this kind of point data. That’s where the spatial analysis tools in ArcGIS come in.
We’ve all seen heat maps on TV or in web application-beautiful maps that show high-density areas in bright red, and low-density
areas in blue. These maps are used to visualize crime, disease, and a whole host of other types of data and information. These heat
maps can be a great first step in a visual analysis of your data they can also be very subjective. What does that mean? Well,
the two heat maps shown below reflect the same San Francisco Crime data, and were created using the same tool. The only difference
is the criteria that were used to decide what appears very dark (high density) and what appears very light (low density). These types of
cartographic elements that we incorporate into our maps can have a huge impact on the story that the map tells.
If the decisions that you’re trying to make as a result of your analyses are important, and they usually are, you’ll want to
minimize subjectivity as much . A great way to minimize the subjectivity in your pattern analysis is to use a hot spot analysis,
which incorporates a simple spatial statistic to determine if the patterns that you’re seeing are statistically significant or not. The hot
spot map is shown here.
So what makes this type of map any less subjective than density-based heat maps? The very dark areas on hot spot maps are statistically significant clusters of high values (hot spots), and the very light areas are statistically significant clusters of low values (cold spots). What’s dark and what’s light is always based on statistical significance. Using hot spot analysis, we can feel confident in the spatial patterns that we see, and in the decisions that we make.(Adapted from: http://resources.arcgis.com/en/communities/analysis/017z00000015000000.htm
Atenção: As questões de números 56 a 60 referem-se ao texto apresentado abaixo. As cores originais dos mapas 2, 3 e 4 foram
alteradas para visualização em tons de cinza.
Using analysis, we can feel confident in the spatial patterns we see, and in the decisions that we make.
Putting your data on a map is an important first step for finding patterns and understanding trends. Here we’re looking at crimes that happened in San Francisco, about 37,000 of them. Looking at the points on a map, can you find the clusters or patterns in this point data? Can you decide where the police department should allocate its resources? Just looking at points on a map is often not enough to answer questions or make decisions using this kind of point data. That’s where the spatial analysis tools in ArcGIS come in.
We’ve all seen heat maps on TV or in web application-beautiful maps that show high-density areas in bright red, and low-density
areas in blue. These maps are used to visualize crime, disease, and a whole host of other types of data and information. These heat
maps can be a great first step in a visual analysis of your data they can also be very subjective. What does that mean? Well,
the two heat maps shown below reflect the same San Francisco Crime data, and were created using the same tool. The only difference
is the criteria that were used to decide what appears very dark (high density) and what appears very light (low density). These types of
cartographic elements that we incorporate into our maps can have a huge impact on the story that the map tells.
If the decisions that you’re trying to make as a result of your analyses are important, and they usually are, you’ll want to
minimize subjectivity as much . A great way to minimize the subjectivity in your pattern analysis is to use a hot spot analysis,
which incorporates a simple spatial statistic to determine if the patterns that you’re seeing are statistically significant or not. The hot
spot map is shown here.
So what makes this type of map any less subjective than density-based heat maps? The very dark areas on hot spot maps are statistically significant clusters of high values (hot spots), and the very light areas are statistically significant clusters of low values (cold spots). What’s dark and what’s light is always based on statistical significance. Using hot spot analysis, we can feel confident in the spatial patterns that we see, and in the decisions that we make.(Adapted from: http://resources.arcgis.com/en/communities/analysis/017z00000015000000.htm
Atenção: As questões de números 56 a 60 referem-se ao texto apresentado abaixo. As cores originais dos mapas 2, 3 e 4 foram
alteradas para visualização em tons de cinza.
Using analysis, we can feel confident in the spatial patterns we see, and in the decisions that we make.
Putting your data on a map is an important first step for finding patterns and understanding trends. Here we’re looking at crimes that happened in San Francisco, about 37,000 of them. Looking at the points on a map, can you find the clusters or patterns in this point data? Can you decide where the police department should allocate its resources? Just looking at points on a map is often not enough to answer questions or make decisions using this kind of point data. That’s where the spatial analysis tools in ArcGIS come in.
We’ve all seen heat maps on TV or in web application-beautiful maps that show high-density areas in bright red, and low-density
areas in blue. These maps are used to visualize crime, disease, and a whole host of other types of data and information. These heat
maps can be a great first step in a visual analysis of your data they can also be very subjective. What does that mean? Well,
the two heat maps shown below reflect the same San Francisco Crime data, and were created using the same tool. The only difference
is the criteria that were used to decide what appears very dark (high density) and what appears very light (low density). These types of
cartographic elements that we incorporate into our maps can have a huge impact on the story that the map tells.
If the decisions that you’re trying to make as a result of your analyses are important, and they usually are, you’ll want to
minimize subjectivity as much . A great way to minimize the subjectivity in your pattern analysis is to use a hot spot analysis,
which incorporates a simple spatial statistic to determine if the patterns that you’re seeing are statistically significant or not. The hot
spot map is shown here.
So what makes this type of map any less subjective than density-based heat maps? The very dark areas on hot spot maps are statistically significant clusters of high values (hot spots), and the very light areas are statistically significant clusters of low values (cold spots). What’s dark and what’s light is always based on statistical significance. Using hot spot analysis, we can feel confident in the spatial patterns that we see, and in the decisions that we make.(Adapted from: http://resources.arcgis.com/en/communities/analysis/017z00000015000000.htm
Completa o período, indicado pela lacuna II:
Atenção: As questões de números 56 a 60 referem-se ao texto apresentado abaixo. As cores originais dos mapas 2, 3 e 4 foram
alteradas para visualização em tons de cinza.
Using analysis, we can feel confident in the spatial patterns we see, and in the decisions that we make.
Putting your data on a map is an important first step for finding patterns and understanding trends. Here we’re looking at crimes that happened in San Francisco, about 37,000 of them. Looking at the points on a map, can you find the clusters or patterns in this point data? Can you decide where the police department should allocate its resources? Just looking at points on a map is often not enough to answer questions or make decisions using this kind of point data. That’s where the spatial analysis tools in ArcGIS come in.
We’ve all seen heat maps on TV or in web application-beautiful maps that show high-density areas in bright red, and low-density
areas in blue. These maps are used to visualize crime, disease, and a whole host of other types of data and information. These heat
maps can be a great first step in a visual analysis of your data they can also be very subjective. What does that mean? Well,
the two heat maps shown below reflect the same San Francisco Crime data, and were created using the same tool. The only difference
is the criteria that were used to decide what appears very dark (high density) and what appears very light (low density). These types of
cartographic elements that we incorporate into our maps can have a huge impact on the story that the map tells.
If the decisions that you’re trying to make as a result of your analyses are important, and they usually are, you’ll want to
minimize subjectivity as much . A great way to minimize the subjectivity in your pattern analysis is to use a hot spot analysis,
which incorporates a simple spatial statistic to determine if the patterns that you’re seeing are statistically significant or not. The hot
spot map is shown here.
So what makes this type of map any less subjective than density-based heat maps? The very dark areas on hot spot maps are statistically significant clusters of high values (hot spots), and the very light areas are statistically significant clusters of low values (cold spots). What’s dark and what’s light is always based on statistical significance. Using hot spot analysis, we can feel confident in the spatial patterns that we see, and in the decisions that we make.(Adapted from: http://resources.arcgis.com/en/communities/analysis/017z00000015000000.htm
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