Questões de Concurso Sobre inglês

Words that went extinct

By Kimberly Joki

    Dictionaries incorporate new words every year. Some are pop culture inventions like jeggings, photobomb, and meme. Other words, like emoji and upvote, spring up from technology and social media. Dictionaries respond by creating definitions for anyone who cares to know what a twitterer is. And thank goodness they do; you can learn what an eggcorn is simply by turning a few pages in your trusty updated dictionary.

    Interestingly, not all newly added words are recent developments. The Oxford English Dictionary June 2015 new words list included autotune, birdhouse, North Korean, and shizzle! North Korea was founded in 1948. The initial release of the autotuner audio processor was in 1997. Before adding a slang term like shizzle, dictionary publishers weigh the current popularity, predicted longevity, and other factors. Just this year alone, the Merriam-Webster Dictionary welcomed about 1,700 new arrivals.

    With more and more words coined every year, dictionaries couldn’t possibly add them all to their existing word banks. Can you imagine a dictionary containing all the words ever used in English? It would be impossible to lift! With each yearly edit, dictionary editors must discard some words to make room for new ones.

    (…)

    The Sami languages, spoken in Finland, Norway, and Sweden, reportedly include more than 150 words related to snow and ice. In the 1590s, the English language had a word for recently melted snow—snowbroth. Now, English speakers simply call it water or melted snow. In fact, words that are markedly specific seem more vulnerable to extinction. A 19th-century dictionary included Englishable, a term to describe how appropriate a word is for the English language. However, English is a dynamic language, always accepting and abandoning words. Apparently, Englishable itself isn’t Englishable; it’s now obsolete.

    Do you favor any infrequently used words? If so, use them now and often. . . A word’s best defense against extinction is regular use.

(Source: http://www.grammarly.com/blog/2015/words-that-went-extinct/)

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