Questões de Concurso Público Prefeitura de Teresina - PI 2016 para Analista Tecnológico – Analista de Geoprocessamento
Foram encontradas 60 questões
Ao escolher as ferramentas de sensoriamento para uma aplicação é necessário levar-se em conta quesitos técnicos como as características dos sensores e seu custo-benefício. Considere produtos de sensoriamento remoto e as atividades.
Produtos de sensoriamento remoto
1. Fotografias aéreas.
2. Satélites NOAA.
3. Satélites da série SPOT.
4. Satélites da série LANDSAT.
Atividades
I. Cartografia de precisão.
II. Elaboração de modelos climáticos e previsão do tempo.
III. Levantamento histórico de uso das terras de longa abrangência temporal.
IV. Levantamento de cobertura vegetal em grande extensão de território.
Considere a frase:
... descrevem a distribuição espacial de uma grandeza geográfica, expressa de forma qualitativa, como os mapas de pedologia e a aptidão agrícola de uma região. Esses dados são inseridos no sistema por digitalização ou, a partir de classificação de imagens.” (CÂMARA, et al.)
Os tipos de dados utilizados em geoprocessamento descritos na frase são:
Considere as afirmações abaixo que tratam da comparação entre SIG e CAD.
I. Um sistema CAD é uma ferramenta para capturar desenhos em formato legível por uma máquina. Os modelos CAD tratam os dados como desenhos eletrônicos em coordenadas do papel.
II. Em um sistema de geoprocessamento os dados estão sempre georreferenciados, isto é, localizados na superfície terrestre. Na maioria dos casos os dados estão em projeção cartográfica, o que impõe uma distorção relativa às coordenadas geográficas.
III. Armazenar a topologia de um mapa é uma das características básicas que fazem um SIG se distinguir de um CAD. Topologia pode ser definida como a estrutura de relacionamentos espaciais (vizinhança, proximidade, pertinência) que podem se estabelecer entre objetos geográficos.
Considere a imagem abaixo.
De acordo com a imagem,
Considere as afirmações a seguir sobre o espectro de radiação eletromagnética:
I. A pequena banda denominada luz compreende o conjunto de radiações para as quais o sistema visual humano é sensível.
II. Todos os comprimentos de onda são igualmente efetivos para o propósito do sensoriamento remoto.
III. Os comprimentos de onda do azul sofrem substancial atenuação pelo espalhamento atmosférico.
IV. A aparência verde da folha, e por extensão da vegetação, está relacionada com a maior absorção na banda verde e é produzida pela clorofila.
Considere as seguintes afirmações a respeito dos mapas temáticos:
I. Os produtos da cartografia temática são as cartas, mapas ou plantas em qualquer escala, destinadas a um tema específico. A representação temática, distintamente da geral, exprime conhecimentos particulares específicos de um tema (geologia, solos, vegetação etc.) para uso geral.
II. Os elementos primários do tema que será elaborado cartograficamente podem ser originários da técnica estatística, tanto no que se referem aos elementos físicos, quanto aos elementos humanos. Assim, se caracterizam nesta área, os mapas de densidade, os de distribuição por pontos, os de fluxo, os pluviométricos e os mapas de isolinhas.
III. Objetivam a representação cartográfica do Território Nacional, enfatizando a divisão político-administrativa. São mapas e cartogramas políticos Nacional, Regionais, Estaduais e Municipais.
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
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
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