Visuals and Context Notes PDF

## Unit 2: Visuals and Context ### Pooja TS, Faculty of CA ### RR Campus, PESU **What is Context?** - **Definition:** Context refers to the background, environment, or circumstances that surround a particular event, situation, or piece of information. It provides additional information that helps t...

## Unit 2: Visuals and Context ### Pooja TS, Faculty of CA ### RR Campus, PESU **What is Context?** - **Definition:** Context refers to the background, environment, or circumstances that surround a particular event, situation, or piece of information. It provides additional information that helps to fully understand or interpret the primary subject. - **Importance:** Context is crucial because it offers clarity and depth, allowing people to grasp the full meaning of a situation or information. Without context, details can be easily misinterpreted, misunderstood, or seen in isolation from their broader implications. **Importance of Context:** - **Understanding:** Context allows for a deeper understanding of a subject by connecting it to relevant background information. It helps to explain the "why" and "how" behind facts or events. - **Relevance:** Context shows the relevance of information to the current situation, making it easier to relate and apply the knowledge effectively. - **Accuracy:** Providing context helps avoid misinterpretation, ensuring that information is understood in its intended way. - **Decision-Making:** Context provides the necessary information to make informed decisions by highlighting factors that influence outcomes. - **Communication:** Context enriches communication, making it more effective by ensuring that the message is fully understood by the audience. **Context of Data:** - **Definition:** The context of data refers to the additional information that surrounds data, helping to interpret its meaning, relevance, and implications. This includes details like the source of the data, the conditions under which it was collected, the time period it covers, and any external factors influencing it. - **Importance:** - **Interpretation:** Understanding the context of data helps to interpret it correctly. For example, knowing the time period and external factors that affect sales data can explain fluctuations. - **Decision-Making:** Contextual data is more actionable. Decision-makers need to understand not just the data but also the circumstances that generated it. - **Comparisons:** Context allows for accurate comparisons by ensuring that data sets are compared under similar conditions. **Context of Structure:** - **Definition:** The context of structure refers to the framework or organization surrounding a system, concept, or piece of work. This could be the structure of a document, a process, or an organization. - **Importance:** - **Clarity:** A well-structured context provides clarity by organizing information in a logical and coherent manner, making it easier to understand. - **Flow:** Context within a structure ensures a smooth flow of information, guiding the audience through the content in a way that makes sense. - **Efficiency:** In organizational contexts, structure provides a clear framework for decision-making, roles, and responsibilities, making processes more efficient. **Context of Audience:** - **Definition:** The context of the audience refers to the characteristics, background, needs, and expectations of the people who will be receiving information or a presentation. - **Importance:** - **Relevance:** Understanding the audience's context ensures that the information presented is relevant to them, addressing their specific needs and interests. - **Engagement:** Tailoring the message to the audience's context enhances engagement, making them more likely to connect with the content. - **Communication:** Knowing the audience's background helps in choosing the right language, tone, and examples, ensuring that the message is clear and persuasive. **Context of Presentation:** - **Definition:** The context of a presentation refers to the setting, purpose, and circumstances surrounding a presentation. This includes the audience, the environment, the timing, and the objectives of the presentation. - **Importance:** - **Preparation:** Understanding the presentation context helps in preparing content that is appropriate for the occasion, audience, and objectives. - **Delivery:** Context influences how the presentation is delivered, including the tone, style, and pacing. - **Effectiveness:** A presentation that takes its context into account is more likely to achieve its goals, whether that's to inform, persuade, or entertain. It ensures that the message resonates with the audience and is delivered in a manner suited to the occasion. **Context in Action** refers to the practical application of understanding and using context in various scenarios to ensure that actions, decisions, and communications are appropriate, relevant, and effective. This involves considering the background, environment, and specific circumstances that surround any given situation or information before taking action. - **In Communication:** - Example: When giving feedback to a team member, understanding the context of their recent workload, challenges, and achievements allows you to tailor your feedback in a way that is constructive and considerate. - **In Decision-Making:** - Example: A business decides whether to launch a new product. The context includes market trends, competitor actions, economic conditions, and consumer behavior. - Context in Action: The decision is informed by a thorough analysis of these contextual factors, leading to a strategy that aligns with current market conditions and customer needs. - **In Data Analysis:** - Example: When analyzing sales data, you notice a sudden drop in a particular region. The context might include local economic downturns, seasonal factors, or a recent competitor's product launch. - Context in Action: Instead of reacting solely to the drop in sales, you investigate these contextual factors, leading to a more nuanced understanding and a targeted response, such as a localized marketing campaign. - **In Leadership:** - Example: A manager is deciding how to allocate resources for an upcoming project. The context includes the team's current workload, the project's importance, and available budget. - Context in Action: The manager allocates resources by considering the team's capacity, the project's deadlines, and how it fits into the company's strategic goals, ensuring that the project is adequately supported without overburdening the team. - **In Education:** - Example: A teacher plans a lesson on a complex topic. The context includes the students' prior knowledge, learning styles, and the subject's relevance to their lives. - Context in Action: The teacher adapts the lesson by linking the new content to what students already know and by using examples that are relevant to their experiences, making the lesson more engaging and understandable. - **In Customer Service:** - Example: A customer service representative addresses a complaint from a long-term customer. The context includes the customer's history with the company, their current issue, and the company's policies. - Context in Action: The representative uses this context to offer a personalized solution, recognizing the customer's loyalty and ensuring that the resolution aligns with company policies while addressing the customer's concerns effectively. - **In Marketing:** - Example: A company is planning a new advertising campaign. The context includes the target audience's demographics, cultural trends, and recent industry developments. - Context in Action: The campaign is designed to resonate with the target audience by incorporating current cultural references and addressing the audience's specific needs and interests, leading to a more impactful and successful campaign. - **In Conflict Resolution:** - Example: Two colleagues are in a disagreement over a project approach. The context includes their respective roles, the project's importance, and the company culture. - Context in Action: A mediator considers these contextual factors and facilitates a discussion that respects each colleague's perspective while aligning their approaches with the project's goals and the company's values. **Summary:** "Context in Action" is about applying an understanding of the surrounding circumstances and background to make informed decisions, communicate effectively, and take appropriate actions. It ensures that responses are not only reactive but also considerate of the broader situation, leading to more effective outcomes. **Exploratory and explanatory analyses** are two distinct approaches used in data analysis, each serving different purposes. Below is a detailed, step-by-step comparison of both: **1. Purpose** - **Exploratory Analysis:** - **Objective:** The primary goal is to explore the data, uncover patterns, relationships, and insights without having preconceived hypotheses. It's a data-driven process where you let the data "speak" and guide the analysis. - **Use Case:** Often used in the initial stages of data analysis to understand the dataset, identify anomalies, and generate hypotheses for further analysis. - **Explanatory Analysis:** - **Objective:** The goal is to explain or confirm hypotheses, findings, or relationships already identified. It focuses on communicating insights clearly and effectively to support decision-making. - **Use Case:** Typically used after exploratory analysis to present findings in a structured manner, often aimed at a specific audience with a clear narrative. **2. Approach** - **Exploratory Analysis:** - **Process:** 1. **Data Preparation:** Clean and preprocess data, handle missing values, and structure data for analysis. 2. **Initial Exploration:** Perform basic descriptive statistics (mean, median, mode) and visualizations (histograms, scatter plots). 3. **Pattern Discovery:** Use tools like clustering, correlation analysis, and dimensionality reduction (PCA) to uncover hidden patterns. 4. **Hypothesis Generation:** Identify interesting trends, correlations, or anomalies that might lead to hypotheses for further testing. 5. **Iterative Analysis:** Continually explore different variables and relationships, often going back to refine the dataset or explore new angles. 6. **Tools:** Use flexible, interactive tools like Jupyter Notebooks, Tableau, or R for dynamic exploration. - **Explanatory Analysis:** - **Process:** 1. **Hypothesis Confirmation:** Start with a clear hypothesis or specific question to be answered. 2. **Focused Analysis:** Use targeted statistical tests (e.g., t-tests, regression analysis) to confirm or refute hypotheses. 3. **Data Visualization:** Create polished visualizations (e.g., line charts, bar charts, infographics) that are easy to interpret and convey the key messages. 4. **Narrative Building:** Structure the findings into a coherent story that guides the audience through the analysis. 5. **Presentation:** Prepare reports, presentations, or dashboards to communicate the insights, ensuring they are tailored to the audience's level of understanding. 6. **Tools:** Use tools like PowerPoint, polished Tableau dashboards, or formal reports for presenting the results. **3. Flexibility** - **Exploratory Analysis:** - **High Flexibility:** The approach is very flexible, allowing analysts to follow the data wherever it leads. There's no fixed path, and the analysis can change direction based on what's found. - **Trial and Error:** Analysts often experiment with different methods and approaches to see what works best. - **Explanatory Analysis:** - **Structured Approach:** This approach is more structured and focused, with a clear objective and predetermined methods for validating findings. - **Less Flexibility:** The analysis is usually linear, with a clear beginning, middle, and end, often following a hypothesis-testing framework. **4. Outcome** - **Exploratory Analysis:** - **Discovery of Insights:** The outcome is typically a set of insights, potential relationships, and hypotheses that warrant further investigation. It's more about breadth – exploring many possibilities. - **Actionable Hypotheses:** The analysis often ends with the identification of key areas for deeper exploration or confirmation. - **Explanatory Analysis:** - **Clear Conclusions:** The outcome is a clear, concise, and well-supported explanation of a specific phenomenon or relationship. It's more about depth – delving into a specific area to provide a definitive answer. - **Communicated Results:** The analysis culminates in a report or presentation that communicates the findings in a way that informs or persuades the audience. **5. Audience** - **Exploratory Analysis:** - **Analysts and Data Scientists:** Primarily for internal use by analysts who are trying to understand the data better. The audience is often those involved in further analysis or decision-making. - **Internal Stakeholders:** Findings are usually shared with peers or managers in an informal setting to discuss potential avenues for further analysis. - **Explanatory Analysis:** - **Decision-Makers:** Typically aimed at decision-makers, stakeholders, or a broader audience that needs to understand the findings to take action. - **Wider Audience:** The presentation is tailored to be accessible to a non-technical audience, often with a focus on clarity and impact. **6. Examples** - **Exploratory Analysis:** - **Example Scenario:** A data analyst receives a large dataset of customer behavior and uses exploratory analysis to identify trends in customer purchasing patterns, such as finding that certain products are frequently bought together. - **Outcome:** The analyst discovers potential relationships (e.g., product bundling opportunities) that weren't previously considered. - **Explanatory Analysis:** - **Example Scenario:** After identifying that customers who buy Product A often buy Product B, the analyst conducts an explanatory analysis to confirm this relationship and presents it to the marketing team as a basis for a targeted promotion campaign. - **Outcome:** The marketing team understands the confirmed relationship and decides to implement a product bundling strategy. **Structuring stories and plots** is a crucial aspect of storytelling, whether in literature, film, or presentations. A basic plot diagram, often called a plot pyramid or Freytag's Pyramid, helps visualize the structure of a narrative. ## The Plot Pyramid: - **Exposition:** Bottom left - **Rising Action:** Ascending line to the climax - **Climax:** Peak of the pyramid - **Falling Action:** Descending line from the climax - **Resolution:** Bottom right **Introduction/Exposition:** - **Purpose:** The exposition introduces the characters, setting, and basic situation. It sets the stage for the story and provides the necessary background information. - **What to Include:** - **Setting:** Where and when the story takes place. - **Characters:** Who the main characters are, their goals, and any relevant relationships. - **Situation:** The initial situation or status quo before the main action begins. - **Plot Diagram Placement:** The exposition is at the beginning of the plot diagram, forming the base of the pyramid. **2. Rising Action** - **Purpose:** The rising action includes a series of events that build tension and lead up to the climax. These events complicate the situation and introduce conflict. - **What to Include:** - **Conflict:** The central problem or challenge that the protagonist faces. - **Development:** Events that complicate the conflict and develop the characters. - **Subplots:** Secondary stories or challenges that add depth to the main plot. - **Plot Diagram Placement:** The rising action climbs upwards along the left side of the pyramid, leading towards the climax. **3. Climax** - **Purpose:** The climax is the turning point of the story, where the main conflict reaches its peak. It's the most intense moment of the plot, where the protagonist faces the greatest challenge. - **What to Include:** - **Crisis:** The critical moment when the protagonist must make a crucial decision or take decisive action. - **Outcome Determination:** The climax determines the direction of the story's resolution, whether it will end positively or negatively for the protagonist. - **Plot Diagram Placement:** The climax is at the peak of the pyramid, representing the highest point of tension. **4. Falling Action** - **Purpose:** The falling action includes the events that happen after the climax, leading towards the resolution. It shows the consequences of the climax and begins to tie up loose ends. - **What to Include:** - **Consequences:** How the climax affects the characters and the story world. - **Resolution of Subplots:** Any secondary conflicts or subplots are resolved. - **Decline in Tension:** The tension begins to decrease as the story moves towards a conclusion. - **Plot Diagram Placement:** The falling action descends down the right side of the pyramid, following the climax. **5. Resolution/Denouement** - **Purpose:** The resolution, or denouement, is the conclusion of the story. It resolves the main conflict and shows the final outcomes for the characters. - **What to Include:** - **Final Outcome:** The final state of the characters and the story world. - **Theme:** The underlying message or moral of the story, if applicable. - **Closing:** Any last thoughts or reflections that provide closure to the story. - **Plot Diagram Placement:** The resolution is at the bottom right of the pyramid, completing the narrative arc. **Tips for Effective Story Structuring:** - **Understand Your Story's Core Conflict:** Clearly identify what drives the narrative forward. - **Develop Strong Characters:** Ensure your characters have depth and clear motivations. - **Maintain Logical Progression:** Each part should logically lead to the next, maintaining coherence. - **Build Tension Appropriately:** Gradually increase stakes leading up to the climax. - **Provide Satisfying Resolution:** Conclude your story in a way that resolves key conflicts and fulfills audience expectations. **Analytical techniques** often used in data storytelling and visualization to structure narratives. They help in uncovering insights and presenting data in a way that resonates with the audience. **1. Change Over Time** - **Description:** This plot focuses on showing how something evolves over a period. It highlights trends, patterns, or shifts in data across time intervals. - **Use Case:** Ideal for time series data where the goal is to show how variables change, such as sales growth, population trends, or temperature changes. - **Visualization:** Line charts, area charts, or bar charts can effectively show change over time. **2. Drill Down** - **Description:** Drill down involves moving from a broad view to a more detailed or granular level of data. It helps in exploring specific segments or subcategories within the data. - **Use Case:** Useful when you want to investigate a particular area of interest in more depth, such as drilling down from company-wide sales to sales by region or product category. - **Visualization:** Interactive dashboards, hierarchical trees, or nested charts can help facilitate drill down. **3. Zoom Out** - **Description:** Zooming out provides a broader context or a high-level overview, allowing the audience to see the bigger picture. It's the opposite of drill down. - **Use Case:** Useful when you need to contextualize specific data within a larger framework, such as showing a company's performance in the context of the entire industry. - **Visualization:** World maps, global indicators, or high-level summaries are common for zoom out views. **4. Contrast** - **Description:** Contrast emphasizes differences between two or more data points, categories, or groups. It's used to highlight variations or to compare and contrast entities. - **Use Case:** Ideal for showing disparities, such as comparing market shares of different companies, or the effectiveness of different marketing strategies. - **Visualization:** Bar charts, side-by-side comparisons, or before-and-after visuals are great for illustrating contrast. **5. Spread** - **Description:** Spread examines the distribution of data points across a range, revealing how they are dispersed or clustered. It helps in understanding variability or consistency within the data. - **Use Case:** Used to identify how data is distributed, such as income distribution within a population or the spread of scores on a test. - **Visualization:** Histograms, box plots, or scatter plots are commonly used to show spread. **6. Intersections** - **Description:** Intersections explore where different data points, variables, or trends meet or overlap, often revealing new insights at these junctions. - **Use Case:** Useful for showing relationships or correlations, such as how customer satisfaction intersects with product quality, or where different demographic groups overlap in preferences. - **Visualization:** Venn diagrams, scatter plots with regression lines, or heat maps can effectively display intersections. **7. Factors** - **Description:** Factors involve breaking down the data into key components or causes that influence outcomes. It's about understanding what drives or affects the main metric or result. - **Use Case:** Ideal for root cause analysis or when you need to explain why something happened, such as identifying factors that contribute to customer churn. - **Visualization:** Pareto charts, fishbone diagrams (Ishikawa), or multi-factor analysis charts are good for illustrating factors. **8. Outliers** - **Description:** Outliers are data points that deviate significantly from the rest of the dataset. They can represent anomalies, errors, or significant events worth investigating. - **Use Case:** Important for identifying irregularities, such as unusually high sales during a particular period or errors in data entry. - **Visualization:** Box plots, scatter plots, and z-score charts are effective for highlighting outliers. **By combining these techniques**, you can create a rich and nuanced narrative that guides your audience through the data, helping them understand complex insights clearly and effectively. ## Story Genre A story genre refers to the category or type of story that shares certain conventions, themes, and stylistic elements. Genres help set expectations for the audience about what kind of story they are about to experience, whether in literature, film, or other forms of storytelling. **Here are the story genres summarized:** - **Fantasy:** Imaginary worlds with magic and mythical creatures. - **Science Fiction:** Futuristic concepts and advanced technology. - **Mystery:** Solving crimes or uncovering secrets. - **Thriller:** Suspenseful stories with high stakes and danger. - **Horror:** Stories that evoke fear and dread, often with supernatural elements. - **Romance:** Focuses on love and romantic relationships. - **Historical Fiction:** Blends fact and fiction set in a specific historical period. - **Adventure:** Exciting journeys and exploration with physical challenges. - **Comedy:** Humorous stories with light-hearted and often happy endings. - **Drama:** Realistic stories focusing on emotional themes and complex relationships. - **Western:** Set in the American West, focusing on frontier life and conflicts. - **Dystopian:** Depicts oppressive, controlled societies in a bleak future. - **Memoir/Autobiography:** Personal accounts of the author's life. - **Biography:** Detailed accounts of real people's lives. - **Crime:** Focuses on criminal acts, investigations, and justice. **But in the context of Data storytelling, The story genres proposed by Segel and Heer, also known for their work in data visualization, focus on how data can be presented through storytelling. They categorize these genres into:** 1. **Change Over Time:** Illustrates how data evolves over a period, showing trends and patterns. 2. **Comparison:** Highlights differences and similarities between data points or groups. 3. **Distribution:** Shows how data is spread across a range or within categories. 4. **Composition:** Breaks down data into its parts to show how different components contribute to the whole. 5. **Relationship:** Explores connections and correlations between different data sets or variables. 6. **Geospatial:** Maps data to geographical locations, showing spatial distributions and patterns. 7. **Hierarchy:** Organizes data into levels of importance or categories, often showing a structured breakdown. 8. **Part-to-Whole:** Demonstrates how individual components make up a larger entity, emphasizing proportions and contributions. **The seven narrative visualization genres by Segel and Heer:** magazine style, annotated chart, partitioned poster, flowchart, comic strip, slideshow, and film/video or animation. **Magazine Style** - **Description:** Features a visually appealing layout often used in print or digital magazines. It combines text, graphics, and images to present data in a narrative format. - **Example:** A magazine article with infographics and accompanying text to explain economic trends. **Annotated Chart** - **Description:** A standard chart or graph enhanced with annotations, comments, or explanations to provide additional context and insights. - **Example:** A line graph with callouts and text boxes highlighting key data points and trends. **Partitioned Poster** - **Description:** Uses a poster format to organize and present data, often divided into sections that focus on different aspects of the data or different data sets. - **Example:** An infographic poster that breaks down a company's annual report into sections for revenue, expenses, and growth. **Flowchart** - **Description:** Represents data and processes using diagrams that show the flow of information or steps in a process. Useful for illustrating sequences and decision-making. - **Example:** A flowchart detailing the steps in a project management process or a decision-making tree. **Comic Strip** - **Description:** Uses a series of panels with illustrations and text to narrate data or processes in a more engaging and visual format. - **Example:** A comic strip that humorously explains complex data trends or statistical concepts. **Slideshow** - **Description:** Presents data through a sequence of slides, allowing for a step-by-step narrative or progression of information. - **Example:** A PowerPoint presentation that walks through a business report, with each slide focusing on different data insights. **Film/Video or Animation** - **Description:** Uses moving visuals, including video or animation, to convey data stories dynamically. This format can integrate sound and motion to enhance the narrative. - **Example:** An animated video that explains changes in global temperatures over time with visual effects and a voiceover. **Audience analysis for storytelling** It involves understanding who your audience is, what they need or want to know, why they are interested, and how best to engage them. **1. Who?** - **Description:** Identify the characteristics of your audience, including demographics (age, gender, education level), interests, professional background, and cultural context. - **Purpose:** Understanding who your audience is helps tailor the content and presentation style to their preferences and needs. - **Example:** For a business presentation, your audience might include executives, employees, or clients, each with different levels of familiarity with the topic. **2. What?** - **Description:** Determine what information or insights your audience is seeking. This includes understanding their specific needs, questions, or problems they want to address. - **Purpose:** Knowing what your audience wants allows you to focus your narrative on relevant data and insights that will be valuable to them. - **Example:** If presenting to potential investors, you should focus on financial projections, market opportunities, and business viability. **3. Why?** - **Description:** Understand why your audience is engaging with your story. This includes their motivations, goals, and what they hope to achieve from the information you provide. - **Purpose:** This helps in framing your story in a way that aligns with their goals and interests, making it more compelling and impactful. - **Example:** A customer might be interested in a product demo to understand its benefits and how it solves their problem, guiding you to emphasize those aspects. **4. How?** - **Description:** Determine the best methods and channels to deliver your story. This involves choosing the appropriate format (e.g., written report, presentation, video) and style (e.g., formal, casual, interactive) based on audience preferences. - **Purpose:** Tailoring the delivery method ensures that your story is accessible and engaging for your audience, increasing its effectiveness. - **Example:** For a tech-savvy audience, an interactive dashboard with real-time data might be ideal, whereas a printed report might suit a traditional audience better. **Narrative/Narrated Approach** - **Description:** A narrative approach structures data or information in a story-like format, focusing on creating a cohesive and engaging storyline. It involves a clear progression of events or ideas, often with a beginning, middle, and end. - **Key Characteristics:** - **Storytelling Elements:** Uses characters, plots, and arcs to make the data more relatable and engaging. Often includes a narrative thread that guides the audience through the information. - **Visuals and Text:** Combines visuals (charts, graphs, images) with explanatory text to tell a story. May include elements like annotations, callouts, and captions. - **Flow:** Presents data in a sequential or logical order that builds understanding gradually. Often involves a structured sequence of slides, chapters, or segments. - **Emotion and Context:** Aims to evoke emotions or connect with the audience on a personal level by framing data within real-life contexts or scenarios. - **Examples:** - **Magazine Style:** Combines text and visuals in an engaging layout, often with storytelling elements and narrative flow. - **Annotated Chart:** Enhances charts with explanatory notes and commentary to guide the audience through the data story. - **Film/Video or Animation:** Uses moving visuals and voiceovers to narrate a data story dynamically. **Non-Narrative/Narrated Approach** - **Description:** A non-narrative approach presents data without a story-like structure. It focuses on displaying information in a straightforward, often analytical format, allowing the audience to interpret the data independently. - **Key Characteristics:** - **Data-Centric:** Emphasizes the data itself rather than a narrative. The focus is on accuracy, clarity, and comprehensiveness of the data. - **Visuals Only:** Often relies on charts, graphs, tables, and other visual elements without much accompanying text or storytelling. - **Structure:** May present data in an ad-hoc or exploratory format, without a specific sequence or flow. Users can interact with the data to discover insights. - **Objective:** Aims to provide a clear and unbiased representation of data, allowing the audience to analyze and draw their own conclusions. **In simple words:** - **Who?:** Identify the audience's characteristics. - **What?:** Determine their needs and interests. - **Why?:** Understand their motivations and goals. - **How?:** Choose the best method and style for delivering the story. **Bar Chart** Bar charts are one of the most common ways to visualize data. Why? It's quick to compare information, revealing highs and lows at a glance. Bar charts are especially effective when you have numerical data that splits nicely into different categories so you can quickly see trends within your data. **When to use bar charts:** - **Comparing data across categories.** Examples: Volume of shirts in different sizes, website traffic by origination site, percent of spending by department. **Also consider:** - **Include multiple bar charts on a dashboard.** Helps the viewer quickly compare related information instead of flipping through a bunch of spreadsheets or slides to answer a question. - **Add color to bars for more impact.** Showing revenue performance with bars is informative, but overlaying color to reveal profitability provides immediate insight. - **Use stacked bars or side-by-side bars.** Displaying related data on top of or next to each other gives depth to your analysis and addresses multiple questions at once. - **Combine bar charts with maps.** Set the map to act as a "filter" so when you click on different regions the corresponding bar chart is displayed. - **Put bars on both sides of an axis.** Plotting both positive and negative data points along a continuous axis is an effective way to spot trends. *The attached image shows an example of a bar chart showing average US gross of movie franchises and the estimated budgets of each. The reader can use the dropdown menu to filter for a specific franchise and view its performance on average and compare it to the average for all franchises.* **Line Chart** Line charts are right up there with bars and pies as one of the most frequently used chart types. Line charts connect individual numeric data points. The result is a simple, straightforward way to visualize a sequence of values. Their primary use is to display trends over a period of time. **When to use line charts:** - **Viewing trends in data over time.** Examples: stock price change over a fiveyear period, website page views during a month, revenue growth by quarter. **Also consider:** - **Combine a line graph with bar charts.** A bar chart indicating the volume sold per day of a given stock combined with the line graph of the corresponding stock price can provide visual queues for further investigation. - **Shade the area under lines.** When you have two or more line charts, fill the space under the respective lines to create an area chart. This informs a viewer about the relative contribution that line contributes to the whole. *The attached image shows an example of a line chart and a bar chart. The line chart shows search term popularity for "Black Friday" and "Thanksgiving," with the bar chart showing total amount spent over Black Friday weekend. The reader can use the filter at the bottom to narrow down the time period the chart represents.* **Pie Chart** Pie charts should be used to show relative proportions – or percentages – of information. That's it. Despite this narrow recommendation for when to use pies, they are made with abandon. As a result, they are the most commonly mis-used chart type. If you are trying to compare data, leave it to bars or stacked bars. Don't ask your viewer to translate pie wedges into relevant data or compare one pie to another. Key points from your data will be missed and the viewer has to work too hard. **When to use pie charts:** - **Showing proportions.** Examples: percentage of budget spent on different departments, response categories from a survey, breakdown of how Americans spend their leisure time. **Also consider:** - **Limit pie wedges to six.** If you have more than six proportions to communicate, consider a bar chart. It becomes too hard to meaningfully interpret the pie pieces when the number of wedges gets too high. - **Overlay pies on maps.** Pies can be an interesting way to highlight geographical trends in your data. If you choose to use this technique, use pies with only a couple of wedges to keep it easy to understand. *The attached image shows an example of one pie chart on top of a map, showing the distribution of oil rigs worldwide. The reader can use the drop-down menu to filter for a specific region and view how many oil rigs are in that region.* **Map** When you have any kind of location data – whether it's postal codes, state abbreviations, country names, or your own custom geocoding - you've got to see your data on a map. You wouldn't leave home to find a new restaurant without a map (or a GPS anyway), would you? So demand the same informative view from your data. **When to use maps:** - **Showing geocoded data.** Examples: Insurance claims by state, product export destinations by country, car accidents by zip code, custom sales territories. **Also consider:** - **Use maps as a filter for other types of charts, graphs, and tables.** Combine a map with other relevant data then use it as a filter to drill into your data for robust investigation and discussion of data. - **Layer bubble charts on top of maps.** Bubble charts represent the concentration of data and their varied size is a quick way to understand relative data. By layering bubbles on top of a map it is easy to interpret the geographical impact of different data points quickly. *The attached image shows an example of a map and a bubble chart. The reader can use the drop-down menu to select a state and view the number of LEED buildings in that state. The reader can also filter the map by certification level. The bubble chart shows where the LEED buildings are located.* **Scatter Plot** Looking to dig a little deeper into some data, but not quite sure how or if – different pieces of information relate? Scatter plots are an effective way to give you a sense of trends, concentrations and outliers that will direct you to where you want to focus your investigation efforts further. **When to use scatter plots:** - **Investigating the relationship between different variables.** Examples: Male versus female likelihood of having lung cancer at different ages, technology early adopters' and laggards' purchase patterns of smart phones, shipping costs of different product categories to different regions. **Also consider:** - **Add a trend line/line of best fit.** By adding a trend line the correlation among your data becomes more clearly defined. - **Incorporate filters.** By adding filters to your scatter plots, you can drill down into different views and details quickly to identify patterns in your data. - **Use informative mark types.** The story behind some data can be enhanced with a relevant shape *The attached image shows an example of a scatter plot. The reader can use the drop-down menus to select a region, age group, and filter for a specific range for average total paid and average total claimed. The reader can also use the "above threshold" slider to determine if a specific data point is above a certain threshold.* **Bubble Chart** Bubbles are not their own type of visualization but instead should be viewed as a technique to accentuate data on scatter plots or maps. Bubbles are not their own type of visualization but instead should be viewed as a technique to accentuate data on scatter plots or maps. People are drawn to using bubbles because the varied size of circles provides meaning about the data. **When to use bubbles:** - **Showing the concentration of data along two axes.** Examples: sales concentration by product and geography, class attendance by department and time of day. **Also consider:** - **Accentuate data on scatter plots:** By varying the size and color of data points, a scatterplot can be transformed into a rich visualization that answers many questions at once. - **Overlay on maps:** Bubbles quickly inform a viewer about relative concentration of data. Using these as an overlay on map puts geographically-related data in context quickly and effectively for a viewer. *The attached image shows an example of a bubble chart. * **Histogram Chart** Use histograms when you want to see how your data are distributed across groups. Say, for example, that you've got 100 pumpkins and you want to know how many weigh 2 pounds or less, 3-5 pounds, 6-10 pounds, etc. By grouping your data into these categories then plotting them with vertical bars along an axis, you will see the distribution of your pumpkins according to weight. And, in the process, you've created a histogram. At times you won't necessarily know which categorization approach makes sense for your data. You can use histograms to try different approaches