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==Quantitative messages==

==Quantitative messages==

 

[[File:Total Revenues and Outlays as Percent GDP 2013.png|thumb|upright=1.75|A time series illustrated with a line chart demonstrating trends in U.S. federal spending and revenue over time]]

[[File:Total Revenues and Outlays as Percent GDP 2013.png|thumb|upright=1.75|A time series illustrated with a line chart demonstrating trends in U.S. federal spending and revenue over time]]

Author Stephen Few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message:

Author Stephen Few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message:

作者 Stephen Few 描述了用户可能试图从一组数据中理解或交流的八种定量信息，以及用于帮助交流信息的相关图表:

作者Stephen Few描述了8种类型的定量信息，用户可以通过一组数据和用于帮助传递信息的相关图表试图理解或传达这些信息:

Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend.

Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend.

时间序列: 在一段时间内捕捉单一变量，如10年期间的失业率。可以用折线图来说明趋势。

时间序列:捕获一段时间内的单一变量，例如10年期间的失业率。可以用折线图来显示趋势。

#Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the ''measure'') by sales persons (the ''category'', with each sales person a ''categorical subdivision'') during a single period.  A [[bar chart]] may be used to show the comparison across the sales persons.

#Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the ''measure'') by sales persons (the ''category'', with each sales person a ''categorical subdivision'') during a single period.  A [[bar chart]] may be used to show the comparison across the sales persons.

Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the measure) by sales persons (the category, with each sales person a categorical subdivision) during a single period.  A bar chart may be used to show the comparison across the sales persons.

Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the measure) by sales persons (the category, with each sales person a categorical subdivision) during a single period.  A bar chart may be used to show the comparison across the sales persons.

排名: 按升序或降序对分类细分进行排名，例如按销售人员(类别，每个销售人员都有一个分类细分)对一个时期内的销售业绩进行排名。条形图可以用来显示销售人员之间的比较。

排名:按升序或降序对分类细分进行排名，例如在单一时期内按销售人员(类别，每个销售人员都是分类细分)对销售业绩进行排名(度量)。条形图可以用来显示销售人员之间的比较。

#Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%).  A [[pie chart]] or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.

#Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%).  A [[pie chart]] or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.

Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period.  A bar chart can show comparison of the actual versus the reference amount.

Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period.  A bar chart can show comparison of the actual versus the reference amount.

偏差: 将分类细分与参考数据进行比较，例如对一个企业的几个部门在给定时间内的实际支出与预算支出进行比较。条形图可以显示实际金额与参考金额的比较。

偏差:分类的细分是与参考相比较的，例如某一特定时期内某一企业的几个部门的实际费用与预算费用的比较。条形图可以显示实际金额与参考金额的比较。

 

#Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 0-10%, 11-20%, etc. A [[histogram]], a type of bar chart, may be used for this analysis. A [[boxplot]] helps visualize key statistics about the distribution, such as median, quartiles, outliers, etc.

#Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 0-10%, 11-20%, etc. A [[histogram]], a type of bar chart, may be used for this analysis. A [[boxplot]] helps visualize key statistics about the distribution, such as median, quartiles, outliers, etc.

Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 0-10%, 11-20%, etc. A histogram, a type of bar chart, may be used for this analysis. A boxplot helps visualize key statistics about the distribution, such as median, quartiles, outliers, etc.

Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 0-10%, 11-20%, etc. A histogram, a type of bar chart, may be used for this analysis. A boxplot helps visualize key statistics about the distribution, such as median, quartiles, outliers, etc.

频率分布: 显示特定变量在给定时间间隔内的观察次数，例如股票市场回报率在0-10% 、11-20% 等时间间隔内的年数。直方图，一种条形图，可以用来进行这种分析。箱线图有助于可视化关于分布的关键统计数据，如中位数、四分位数、异常值等。

频率分布:表示某一特定变量在给定区间内的观测次数，如股票市场收益率在区间(如0-10%、11-20%等)之间的年数。条形图，一种柱状图，可以用于这种分析。箱线图有助于可视化关于分布的关键统计信息，如中位数、四分位数、离群值等。

 

#Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A [[scatter plot]] is typically used for this message.

#Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A [[scatter plot]] is typically used for this message.

Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A scatter plot is typically used for this message.

Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A scatter plot is typically used for this message.

相关性: 用两个变量(x，y)表示的观测值之间的比较，以确定它们是否倾向于朝相同或相反的方向移动。例如，绘制个月的样本失业率(x)和通货膨胀率(y)。此消息通常使用散点图。

相关性:比较两个变量(X,Y)表示的观测值，以确定它们倾向于向相同或相反的方向移动。例如，以月份为样本绘制失业(X)和通货膨胀(Y)图。此类信息通常使用散点图。

 

#Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison.

#Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison.

Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison.

Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison.

名义上的比较: 比较分类细分，没有特定的顺序，例如按产品代码的销售量。条形图可用于这种比较。

名义比较:比较没有特定顺序的分类细分，如按产品代码划分的销售量。可以使用条形图进行比较。

#[[Geography|Geographic]] or [[geospatial]]: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A [[cartogram]] is a typical graphic used.<ref name="ReferenceA"/><ref>{{cite web|url=http://www.perceptualedge.com/articles/misc/Graph_Selection_Matrix.pdf|title=Stephen Few-Perceptual Edge-Graph Selection Matrix|publisher=|access-date=2014-09-08|archive-url=https://web.archive.org/web/20141005080945/http://www.perceptualedge.com/articles/misc/Graph_Selection_Matrix.pdf|archive-date=2014-10-05|url-status=live}}</ref>

#[[Geography|Geographic]] or [[geospatial]]: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A [[cartogram]] is a typical graphic used.<ref name="ReferenceA"/><ref>{{cite web|url=http://www.perceptualedge.com/articles/misc/Graph_Selection_Matrix.pdf|title=Stephen Few-Perceptual Edge-Graph Selection Matrix|publisher=|access-date=2014-09-08|archive-url=https://web.archive.org/web/20141005080945/http://www.perceptualedge.com/articles/misc/Graph_Selection_Matrix.pdf|archive-date=2014-10-05|url-status=live}}</ref>

Geographic or geospatial: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A cartogram is a typical graphic used.

Geographic or geospatial: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A cartogram is a typical graphic used.

地理或地理空间: 在地图或布局中对一个变量的比较，例如按州分列的失业率或建筑物各层的人数。地图是一种典型的图形。

地理或地理空间:对地图或布局中的变量进行比较，如按州划分的失业率或建筑物不同楼层的人数。比较统计地图是一种典型的图表。

 

Analysts reviewing a set of data may consider whether some or all of the messages and graphic types above are applicable to their task and audience. The process of trial and error to identify meaningful relationships and messages in the data is part of exploratory data analysis.

Analysts reviewing a set of data may consider whether some or all of the messages and graphic types above are applicable to their task and audience. The process of trial and error to identify meaningful relationships and messages in the data is part of exploratory data analysis.

 

 

==Visual perception and data visualization==

==Visual perception and data visualization==