You are an expert in data visualization for M&E reporting. Generate a set of visualization specifications that would score Excellent (5) on every dimension of the Data Visualization Quality rubric for the data and findings described in the brief below.
QUALITY REQUIREMENTS:
Chart Type Appropriateness: Match every chart type to the data relationship being shown. Bar charts for comparisons across categories. Line charts for change over time. Stacked or 100 percent bars for composition. Scatter plots for relationships. Histograms or box plots for distribution. Do not choose chart types for visual variety.
Labeling Completeness: Provide a clear, informative title (state what is shown, not just the variable name). Label axes with units. Add legends where multiple series exist. Cite the data source. State the time period. Make each chart interpretable on its own.
Honest Scaling: Start y-axes at zero for absolute comparisons unless there is a stated reason to truncate. Keep time-series intervals consistent. Order categories meaningfully (by value, time, or logic). Avoid 3D effects, exploded slices, and warped scales.
Accessibility: Choose colorblind-safe palettes. Ensure sufficient contrast against the background. Pair color with shape, pattern, label, or direct annotation. Keep visualizations legible at likely print and screen sizes.
Caption Quality: Write a one or two sentence caption that interprets each chart. State the pattern, comparison, or surprise the reader should take away.
FORMAT: For each visualization, specify: (1) chart type and why it fits, (2) the title, (3) axis labels with units, (4) data source and time period, (5) color and accessibility approach, and (6) the interpretive caption. Cover the priority findings in the brief.
DATA AND FINDINGS BRIEF:
[Paste the data sources, key findings to visualize, audience, and any format constraints here]
Quality bar: The result must meet these criteria. It will be reviewed against them afterwards.
- Chart Type Appropriateness: Whether each chart type matches what the data is actually showing (comparison, distribution, composition, trend, relationship) rather than being chosen out of habit or for visual variety.
- Labeling Completeness: Whether titles, axis labels with units, legends, data sources, and time periods are present so each visualization is interpretable when viewed on its own.
- Honest Scaling: Whether axes, scales, baselines, and intervals are chosen honestly rather than truncated, stretched, or distorted to exaggerate or downplay differences.
- Accessibility: Whether color choices are colorblind-safe, contrast is sufficient, and color is paired with shape, pattern, or label so meaning is not lost for any reader.
- Caption Quality: Whether each visualization has a caption that interprets the data and tells the reader what to take away, rather than only describing what the chart contains.
Anything still in [brackets] is not known yet: ask me, or mark it clearly in your answer.