10.3 Choosing appropriate visualization types for different data

Data visualization 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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Top images from around the web for Bar and Line Visualizations

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  python - Plot Pandas DataFrame as Bar and Line on the same one chart - Stack Overflow View original

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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
• Color coding 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 pie chart 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
• Seasonal decomposition 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