10.3 Choosing appropriate visualization types for different data
3 min read•august 9, 2024
is a powerful tool for journalists to convey complex information. This section focuses on selecting the right chart types for different data sets, helping you make informed decisions when presenting your findings visually.
From comparison charts to relationship visualizations, understanding the strengths of each type is crucial. We'll explore how bar charts, scatter plots, pie charts, and more can effectively communicate your data story to readers.
Comparison Charts
Bar and Line Visualizations
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- Bar charts display categorical data with rectangular bars proportional to the values they represent
- Vertical bar charts suit fewer categories while horizontal bar charts accommodate more categories
- Grouped bar charts compare multiple data series side by side
- Stacked bar charts show the composition of each category
- Line graphs plot data points connected by straight lines to show trends over time
- Multiple line graphs on the same chart allow for easy comparison of different data series
Advanced Comparison Techniques
- Scatter plots visualize the relationship between two variables using dots on a Cartesian plane
- X-axis and y-axis represent different variables in scatter plots
- Correlation strength indicated by how closely dots cluster around a line
- Bubble charts extend scatter plots by adding a third variable represented by bubble size
- Larger bubbles indicate higher values for the third variable in bubble charts
- in bubble charts can introduce a fourth variable for multi-dimensional analysis
Composition Charts
Pie Chart Fundamentals
- Pie charts display data as slices of a circular graph to show relative proportions
- Each slice represents a category's percentage of the whole
- Suitable for displaying data with a small number of categories (typically less than 7)
- Percentages in a always sum to 100%
- 3D pie charts can distort perception and should be used cautiously
Hierarchical Data Visualization
- Treemaps display hierarchical data as nested rectangles
- Rectangle size in treemaps corresponds to the data value
- Color coding in treemaps can represent different categories or subcategories
- Treemaps efficiently use space to show multiple levels of hierarchy
- Interactive treemaps allow users to zoom in on specific sections for detailed exploration
Distribution Charts
Frequency and Density Visualization
- Histograms show the distribution of continuous data by dividing it into intervals (bins)
- Bar height in histograms represents the frequency or count of data points in each bin
- Histograms reveal data patterns (normal distribution, skewed, bimodal)
- Bin width affects the appearance and interpretation of histograms
- Heat maps use color intensity to represent data values in a two-dimensional grid
- Darker or more intense colors in heat maps indicate higher values or frequencies
Advanced Distribution Techniques
- Box plots (box-and-whisker plots) display the distribution of numerical data and outliers
- Violin plots combine box plots with kernel density plots to show probability density
- Density plots smooth out the distribution of data points to show the shape of the distribution
- Cumulative distribution functions (CDFs) show the probability of a value falling below a certain point
Relationship Charts
Geospatial and Network Visualizations
- Network diagrams display interconnections between entities using nodes and edges
- Nodes represent entities while edges represent relationships in network diagrams
- Choropleth maps use color or shading to represent data values across geographic regions
- Choropleth maps require normalized data to avoid misrepresentation due to varying region sizes
- Cartograms distort geographic areas based on a specific variable while maintaining recognizable shapes
Temporal and Integrated Visualizations
- Time series charts plot data points at successive time intervals to show trends over time
- Moving averages in time series smooth out short-term fluctuations to highlight long-term trends
- in time series separates trend, seasonal, and residual components
- Infographics combine various chart types, images, and text to present complex information visually
- Effective infographics tell a coherent story and guide the viewer through the data narrative
- Interactive infographics allow users to explore data at different levels of detail
Key Terms to Review (29)
Audience analysis: Audience analysis is the process of understanding the characteristics, needs, and preferences of a specific audience to tailor communication effectively. This includes identifying demographic factors like age, gender, and education level, as well as psychographic factors such as interests and values. By conducting audience analysis, communicators can choose appropriate messages and visualization types that resonate with the audience and enhance understanding of the data presented.
Bar chart: A bar chart is a visual representation of categorical data where individual bars represent different categories, and the length or height of each bar corresponds to the value or frequency of that category. This type of chart effectively conveys comparisons between categories, allowing for easy interpretation and analysis of data distributions.
Box Plot: A box plot, also known as a whisker plot, is a standardized way of displaying the distribution of data based on a five-number summary: minimum, first quartile, median, third quartile, and maximum. It visually represents the spread and skewness of the data while highlighting outliers, making it particularly useful for comparing distributions across different datasets.
Bubble Chart: A bubble chart is a type of data visualization that displays three dimensions of data in a two-dimensional graph, where each point is represented as a bubble. The position of the bubble indicates the values of two variables, while the size of the bubble conveys information about a third variable. This allows for a quick visual comparison of multiple data points and helps in identifying patterns, trends, and outliers.
Cartogram: A cartogram is a type of map that represents data through the distortion of geographic space, where the size of geographic areas is modified according to a specific variable, such as population or GDP. This visualization method effectively highlights the relationship between spatial areas and the data they represent, making it easier to compare information across different regions.
Chartjunk: Chartjunk refers to any unnecessary or distracting elements in data visualizations that do not provide meaningful information and can obscure the data's message. This term emphasizes the importance of clarity and simplicity in data presentation, highlighting how excess decoration or unrelated graphics can detract from understanding the data being represented.
Choropleth map: A choropleth map is a type of thematic map where areas are shaded or patterned in proportion to the value of a variable being represented, usually to visualize statistical data across geographic regions. This visualization type allows viewers to easily compare and understand the distribution of a particular phenomenon, making it particularly useful for representing demographic, economic, or environmental data across different regions.
Color coding: Color coding is the practice of using different colors to represent specific categories or types of data in visualizations. This technique enhances clarity and comprehension, making it easier for viewers to distinguish between different data sets and identify trends or patterns at a glance. Color coding is crucial in effectively communicating information through visual means, particularly in charts, graphs, and maps.
Contrast: Contrast refers to the visual difference between elements in a design, allowing for clear distinctions between data points. In data visualization, effective use of contrast enhances comprehension by guiding the viewer’s attention, highlighting important information, and improving the overall clarity of the representation.
Cumulative Distribution Function: The cumulative distribution function (CDF) is a statistical function that describes the probability that a random variable takes on a value less than or equal to a specific value. This function is crucial for understanding the distribution of data points, as it provides a complete picture of the probabilities associated with a given dataset, allowing for better decision-making and analysis.
Data literacy: Data literacy is the ability to read, understand, create, and communicate data as information. This skill is essential for interpreting data visualizations effectively and making informed decisions based on data. With a strong foundation in data literacy, individuals can analyze and critically evaluate visual representations of data, ensuring they choose the right type of visualization for their specific data context.
Data storytelling: Data storytelling is the practice of using data to convey a narrative that helps audiences understand complex information through context, visuals, and relatable examples. This approach combines data analysis and storytelling techniques to create engaging content that makes statistical findings more accessible and impactful, enhancing the audience's ability to interpret data and its implications.
Data visualization: Data visualization is the graphical representation of information and data, allowing complex data sets to be understood and communicated more easily. It combines elements of design, technology, and storytelling to present data in a way that helps audiences quickly grasp insights, trends, and patterns.
Density plot: A density plot is a graphical representation that shows the distribution of a continuous variable by estimating the probability density function of that variable. It provides a smooth curve that helps visualize the underlying distribution of data, making it easier to identify patterns, peaks, and the spread of values. This type of plot is particularly useful for comparing distributions across different groups or for understanding the shape of a single distribution without the clutter of individual data points.
Heat map: A heat map is a data visualization technique that uses color to represent the intensity of data values across a two-dimensional space. By displaying data in this manner, it allows viewers to quickly identify trends, patterns, and areas of interest within the dataset, making it particularly useful for analyzing large amounts of information.
Histogram: A histogram is a type of bar chart that represents the distribution of numerical data by showing the frequency of data points within specified intervals or 'bins.' It visually summarizes large sets of data, making it easier to identify patterns, trends, and outliers. The height of each bar indicates the number of data points that fall within that interval, allowing viewers to quickly grasp the overall distribution of the dataset.
Infographic: An infographic is a visual representation of information or data designed to communicate complex information quickly and clearly. It combines graphic design elements with textual information, making it easier for viewers to understand and retain important concepts, especially when dealing with statistics and research findings.
Line graph: A line graph is a type of chart used to represent data points plotted on a two-dimensional plane, typically with one variable on the x-axis and another on the y-axis. This visualization method is particularly effective for showing trends over time or continuous data, making it a popular choice for presenting changes and comparisons across different categories or intervals.
Misleading visuals: Misleading visuals are graphical representations of data that distort, misrepresent, or manipulate the information being conveyed, leading viewers to draw incorrect conclusions. These visuals can include charts, graphs, or images that intentionally or unintentionally mislead due to poor design choices, selective data presentation, or unclear labeling, ultimately affecting the audience's understanding of the underlying data.
Moving average: A moving average is a statistical calculation used to analyze data points by creating averages of various subsets of the full dataset. It helps smooth out fluctuations in data and provides a clearer view of trends over time, making it an essential tool for data visualization. By applying this technique, one can easily identify patterns and make informed decisions based on the trends observed.
Network diagram: A network diagram is a visual representation of a set of interconnected elements, showing the relationships and flows between them. It serves as a powerful tool for illustrating complex systems, making it easier to understand the connections and dependencies among various components. In data visualization, network diagrams can help identify patterns, clusters, and pathways within data, aiding in the analysis of relationships and trends.
Pie Chart: A pie chart is a circular statistical graphic divided into slices to illustrate numerical proportions. Each slice of the pie represents a category's contribution to the whole, making it easy to visualize relative sizes and percentages at a glance. It is particularly effective when displaying part-to-whole relationships in data.
Scatter plot: A scatter plot is a type of data visualization that displays values for two variables as points on a Cartesian plane, allowing viewers to identify relationships, trends, and correlations between the variables. By plotting each variable along an axis, scatter plots help illustrate how changes in one variable may correspond to changes in another, making them especially useful in exploring bivariate data. They can also reveal patterns such as clusters and outliers that might be critical for further analysis.
Seasonal decomposition: Seasonal decomposition is a statistical method used to analyze time series data by breaking it down into its constituent components: trend, seasonality, and residuals. This approach helps to understand patterns within the data over time, allowing for better visualization and interpretation of fluctuations, particularly in datasets influenced by seasonal factors.
Time series chart: A time series chart is a graphical representation that displays data points in a time sequence, making it easy to visualize trends, patterns, and fluctuations over a specified period. These charts help in understanding how a variable changes over time, which can be crucial for forecasting and analysis.
Treemap: A treemap is a space-filling visualization technique that displays hierarchical data using nested rectangles. Each rectangle represents a branch of the hierarchy, with its size and color conveying information about quantitative attributes, making it an effective way to visualize large amounts of data in a compact format.
User experience: User experience refers to the overall experience a person has when interacting with a product, system, or service, particularly in terms of how enjoyable or efficient it is. This encompasses usability, accessibility, and the emotional responses evoked during the interaction. In journalism, crafting a strong user experience can enhance storytelling by ensuring that information is presented clearly and engagingly, whether through visualizations or immersive technologies.
Violin plot: A violin plot is a method of data visualization that combines the benefits of box plots and density plots to display the distribution of data across different categories. It provides a more nuanced view of the data by illustrating its probability density, allowing for comparisons between multiple groups while also showing summary statistics like median and interquartile ranges.
Visual Hierarchy: Visual hierarchy refers to the arrangement and presentation of elements in a way that signifies their importance and guides the viewer’s attention. This principle is crucial in effectively communicating information, especially when choosing appropriate visualization types for different data sets, as it helps to convey messages quickly and clearly through strategic use of size, color, contrast, and placement.