How can you use data visualizations to show scale and proportion in storytelling?

The very simple way to visualize a large amount of data is by using the appropriate graphs and charts along with the scaling and axes of data to fit properly in the graph for analyzing the solution to any kind of problem-related to data. The use of proper color is also important to highlight the data. Context and reference are needed for the end user to dive more into the data.

To make a good visualization, you need to ensure : 1) Quality of information (accuracy, consistency, integrity) 2) Story is relevant and meaningful 3) Goal is useable and efficient 4) Visualization is fitting the narrative and appealing Pie charts are good to show proportions but can only be used when number of categories are limited. Bar charts/Stacked bar charts can show effectively scale and and proportions. Chart type must be chosen based on goal, data and audience.

Choosing the right chart type is essential for effective data communication. Bar charts are ideal for categorical comparisons, while line charts excel in displaying trends over time. Pie charts effectively represent proportions, and scatter plots reveal correlations. Histograms visualize the distribution of numerical data, and area charts are suitable for cumulative trends. Bubble charts accommodate three variables, Gantt charts depict project timelines, and heatmaps are valuable for matrix data. Box plots provide a summary of distribution. Understanding which chart suits the data type ensures that visualizations convey insights accurately to the audience.

To show scale and proportion in storytelling, it is important to choose the right chart type for your data and message. Different chart types have different strengths and weaknesses when it comes to showing scale and proportion. For example, bar charts are good for comparing discrete categories, pie charts are good for showing parts of a whole, and scatter plots are good for showing relationships and distributions

Choosing the right chart is pivotal in showcasing scale and proportion effectively. The key is aligning the chart type with the data's nature and the story's objective. Consider the audience's familiarity with visualizations and select the chart that conveys the data's scale and proportions most intuitively for clearer storytelling. For example: A bar chart suits comparisons among discrete categories, while a pie chart highlights proportions within a whole. Line charts visualize trends over time, scatter plots reveal relationships between variables, and histograms display frequency distributions. Stacked bar/area charts emphasize composition, and bubble charts excel in demonstrating proportions across multiple variables.

Using appropriate scales and axes is key to accurately representing data in storytelling. They influence how data's magnitude, direction, and variation are perceived. It's vital to use scales and axes that are consistent, accurate, and align with your narrative. Avoiding misleading scales like truncated ones is important, as they can exaggerate or diminish differences. When necessary, employing logarithmic or nonlinear scales can be beneficial. Clear and accurate axis labeling further aids in conveying the correct message, ensuring the data's story is understood as intended.

Leave some blank space on each side of the axes that's around 10% of the total data range for aesthetics. Whenever possible, starting the axes on zero is best. Consider an inset box to zoom in on interesting features. Log scales are nice to use for data ranges that cover several orders of magnitude, but make sure the readership is "science-y" enough to appreciate it, otherwise need to point out that e.g. a level 7.0 earthquake is 10 times the seismic wave height as a 6.0 earthquake, which is 10 times as a 5.0 one...

Bonne et heureuse année 2024 à tous. De ma part l’utilisation d’échelles et d’axes appropriés est essentielle pour définir de manière claire les données dans un texte descriptif: Ils modifient totalement la variation de la perception des éléments du texte et influencent positivement le lecteur. L’utilisation des échelles et des axes de Manu cohérentes, et alignés avec votre storytelling donne un sens et parfois une bonne chorologie ce qui rend la contenu narratif moins chronophage . Les échelles tronquées sont parfois très trompeuses, elle donne un sentiment d’exagération ce qui crée les différences de sens et de compréhension. L’utilisation d’échelles logarithmiques ou non linéaires peut s’avérer bénéfique pour l’analyses stratégique

Using appropriate scales and axes is crucial in accurately representing scale and proportion in storytelling. Here are some guidelines to follow: 1. Consistency & Relevance: Ensure that the scales and axes used throughout your visualizations are consistent. Select scales and axes that are relevant to your story and the message you want to convey. 2. Logarithmic or Nonlinear Scales: In some cases, using logarithmic or nonlinear scales can be appropriate. Logarithmic scales compress the data at higher values, allowing for better visualization of both small and large values on the same chart. 3. Clear labeling: Label your axes clearly and accurately to provide context and ensure understanding.

It's important to provide guidance on choosing the right scale to accurately represent data trends without misleading. Additionally, it should stress the importance of clear and informative labeling, including units of measurement, to enhance the viewer's understanding. A reminder about the potential for distortion with certain scales would also be beneficial, guiding users to make informed choices that support the integrity of the story being told through the data.

A "before and after" motif can be helpful for time-series data, with a line that demarcates a critical event. A lightly-tinted region to show e.g. the middle 50%, or top/bottom 10% or 1% of a distribution may be useful also.

In my experience, adding context and reference points is crucial in data storytelling. It helps the audience grasp the data's meaning, significance, and implications. Annotations, captions, titles, legends, and labels are great tools for providing this context. Benchmarks, baselines, averages, or targets can be used for comparative analysis. For example, in a line chart showing data over time, a horizontal line representing the average or goal can offer a valuable reference point, enhancing understanding and perspective.

Adding context and reference points is an essential step in showing scale and proportion in storytelling. It helps your audience understand what each data point represents, allows for comparisons with relevant targets, and prevents misinterpretation. By providing this additional information, you enhance the clarity and impact of your storytelling.

Bonne et heureuse année 2024. Pour ma part, étant donné la nécessité d’obtenir un résultat mesurable, les points de référence sont nécessaires pour l’analyse de la donnée, et les légendes, titres et graphiques linéaires sont nécessaires pour la bonne interprétation. Nous remarquerions que les composantes obligatoires qui constituent une base de données sont: les tables les colonnes et la clé. Alors au vue de cette disposition on peut dire que pour apprécié une donnée et la classer dans un champs d’action afin de mieux l’utiliser, nous avons besoin de préciser le ou les contextes et de définir de manière graphique les points de référence, ceci permettra de facilement extraire les éléments utiles pour les prises de décision.

Context is key in data storytelling. Providing reference points such as benchmarks or averages can help the audience understand where the data stands relative to a known standard.

Strategic use of colors and shapes is pivotal in data visualization. Harmonious colors aid clarity without overwhelming, while contrasting tones highlight vital data. Consistent shapes aid comparisons, assigning distinct shapes to various datasets. Assign colors purposefully; red for negatives or circles for data points. Emphasize key elements using bold colors or unique shapes to direct attention. Relevance is crucial—align colors and shapes with the data theme. Thoughtful selection enhances understanding, effectively conveying scale, proportion, and relationships, enriching the storytelling aspect of visualizations.

Colors can convey meaning and draw attention, while shapes aid in differentiating data points. It's important to choose colors wisely to highlight key information, maintain accessibility, and ensure a cohesive visual experience. Consistent color schemes improve comprehension, and a limited color palette prevents confusion. Shapes, such as distinct markers in scatter plots, contribute to clear differentiation.

When using colors, you should always use accessible color palette and be mindful of color contrast accessibility. You can easily look up several resources and tools to help you with it.

Colors and shapes can guide the viewer’s eye and emphasize key points. However, they should be used judiciously to avoid overwhelming or confusing the audience.

Be aware of cultural associations with different colors, e.g. rising stock tickers in Asia are red and falling ones are green, which is opposite from America/Europe.

When using different sized 2D shapes, for example, use a circle with sqrt(2) times the diameter for a 2x increase quantity. This is because the human eye registers the *area* as the magnitude, not the radius/length. Too often the shape gets 4x increase in area for 2x the quantity represented. The same principle applies for 3D shapes.

Une chose que j’ai trouvé utile est que la taille et les support (3D, 2D…) sont variables en fonction des sujets. La tailles d’une donnée émise en 3D n’est pas forcément mieux exploitable que cette en 2D mais elle est plus visible et plus dynamique car elle est aminée et accroche le lecteur. Alors la taille et la position varient en fonction du type de document que l’on produit et également des domaines dans lesquels l’on travaille.

Using size and position effectively in data visualization is about showcasing the relative importance or hierarchy of data. By manipulating these elements, you create visual hierarchies and patterns. For instance, in a bubble chart, the size and placement of bubbles can indicate the magnitude of data points and reveal outliers or clusters. This approach not only conveys information efficiently but also makes the data story more intuitive and visually compelling.

Size and position can indicate importance or hierarchy. Larger, central elements draw attention, but remember to maintain accuracy and avoid exaggeration.

Think about a bubble chart – those bubbles aren't just floating randomly. Their size screams "Hey, look how big this impact is!" and their position on the chart tells a story about where things stand. Outliers? They're like the lead guitarists, standing out from the crowd, drawing your eye right to them.

Crucial to effective storytelling is the testing of your visualizations. It involves evaluating graphs and charts by subject matter experts, comparing them to market predictions, and ensuring that the overall narrative is coherent. The refinement of visualizations based on the characteristics of the target audience is essential, as it enhances the effectiveness of storytelling.

It's helpful to suggest specific testing methods, like A/B testing different visualizations with a subset of the intended audience. Also, it could mention using visualization tools' built-in analytics to gauge user engagement and comprehension. Seeking feedback is important, but there should be a stress on iterative design – repeatedly refining the visualization based on user interaction data and feedback. Including a reminder about checking for data accuracy and consistency during the refinement process would also be beneficial.

When testing any data visualisation the personas of the intended audience should be considered, especially when many filters have been introduced when attempting to use a single dashboard visualisation. Each filter combination should be tested for logical validity in both the source data and the business logic of allowing filtered combinations that may not make any sense and lead to incorrect insights. Functional visualisations being used as a daily tool should allow for fast repeated user journeys, e.g. if an analyst asks the same 5 questions of the data every day then the visualisation should make this as easy as possible but still allow further exploration.

Testing and refining data visualizations are essential for ensuring clarity, accuracy, and effectiveness. This process involves self-evaluation, asking if the visualization accurately and appropriately conveys the intended story and scale, and if it resonates with the audience. You can seek feedback from colleagues, clients, or users is also valuable. Their insights can highlight areas for improvement, ensuring the final visualization effectively communicates the data story.

The culmination of effective data storytelling involves thorough testing and refinement of visualizations. This step ensures clarity, accuracy, and alignment with the intended narrative. Asking critical questions about storytelling coherence, accurate representation of scale and proportion, and audience appeal guides the refinement process. Seeking feedback from diverse perspectives, including colleagues, clients, or users, provides valuable insights for enhancement. In the iterative data visualisation journey, testing and refinement emerge as indispensable tools for crafting narratives that resonate, engage, and authentically convey the scale and proportion of the underlying data.

In crafting a compelling data visualization, decluttering plays a pivotal role. Streamlining your charts involves eliminating redundant elements such as excessive axis labels, grids, or data labels that may overwhelm the viewer with repetitive information. Simplify color schemes by removing unnecessary variations, keeping in mind that each color should serve a meaningful purpose. Additionally, create a focal point within your visualization, whether it's a highlighted circle or a bold data label, guiding the audience's gaze to the most crucial insights and takeaways. Just as a painter carefully selects focal points in a canvas, a well-designed chart should direct attention to the key elements that convey the essence of your data story.

Data visualizations are valuable tools for storytelling, especially when showcasing scale and proportions. To effectively convey this information, choose appropriate chart types like bar charts or treemaps. Utilize relative scaling by representing data as percentages or ratios to emphasize proportions. Use visual cues such as size variations to represent scale differences, and provide context through labels and reference points. Avoid distorting data and consider using multiple visualizations when necessary. Ultimately, craft a compelling narrative that guides viewers through the visualizations and highlights the key insights related to scale and proportions.

For data visualisation we have multiple tools like tableau. In python, we can use libraries like matplotlib, seaborn. To show scale and proportion we can use charts in matplotlib like barchart, piechart, histogram etc. This will be helpful in visualising the proportion of a particular class/category and the scale in which the value lies.

I've always found it helpful to attempt to understand the relative importance of dimensions and metrics to my audience. I.e. we could be looking at sales over time or by geography. I might want to present the territories of the charts first and then introduce the top metrics I think they might be interested in. Always introduce the chart to build a basis of understanding. I tend to do it so the most important things are up front in the hopes to catch attention and engagement. Be a powerful story-teller by utilizing these subtle details.

I’m like the concept of ‘minimize the ink’ on the page. That is, try to declutter the graph by removing anything that does not add value. This can be done by removing grid, simplifying the lines and shapes, avoid busy backgrounds, etc.