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Expressing via Graphs - Book Review

1 November 2023

Dr. Moria Levy

The book "Graph Expression: A Model for Describing Data Relationships and the Graph Characterization Guide" is an illustrated digital work by Bella Graff, published in 2023. Bella Graff, a renowned expert in Israel on data visualizations, meticulously outlines a comprehensive model for crafting graphs and uncovering concealed insights within them to facilitate decision-making. She conveys this model step by step through illustrations and concise explanations.


This model is grounded in several key assumptions:

  • A well-structured model is essential for effective data visualization, as it is based on a sound methodology.

  • The model aids in the entire planning process leading up to the creation of a graph, not limited to its visual design alone.

  • The primary purpose of visualization is to elucidate the relationships among different elements in reality as presented through data.

  • A compelling message results from the harmonious combination of words and images.


Data, which forms the foundation of this book, refers to measured items, phenomena, or things for which we possess quantitative values. When we delve into their analysis, we focus on discerning the relationships between various data points: Are they akin or distinct? Is one larger or smaller than the other, and by what magnitude?


Data visualization entails using visual representations to scrutinize, comprehend, and convey insights about their significance, employing a format tailored to the workings of the human brain. The significance of visualization is rooted in the fact that our brain processes information through both verbal and visual channels in tandem. Consequently, visualization enhances our capacity to perceive reality, grasp its intricacies, and formulate decisions for its enhancement.


The principal components of the model, as outlined in the book, are as follows:

Planning and Data Collection:

1.Purpose: Defining the objectives for improving real-world scenarios.

2.Measurement: Identifying what aspects of reality are being measured.

3.Categorization: Classifying the measurement categories.

4.Temporal Alignment: Establishing timeframes for data collection.


The Structuring of Data as the Foundation for Learning:

5 .Design: Selecting the appropriate visualization format.

6 .Significance: Determining the most effective organizational structure.

7. Textual Integration: Defining how to convey a cohesive verbal and visual message (i.e., a graph).


It's important to note that the book covers additional topics, offering detailed information and examples not encompassed in this summary. Reading the book has instilled in me a commitment to precision in the field's terminology and has equipped me with new tools for shaping my thought process in all aspects related to graph design.


Access to the digital book (Hebrew): Link


Planning and data collection:

1.Objective: What aspect of reality do you wish to enhance?

Data visualization has a clear purpose, and this purpose perpetually centers around improving reality. It's crucial to articulate, or if applicable, retrospectively ascertain (M.L.) the specific objective that you aim to facilitate and seek to gain insights from. For instance, enhancing sales serves as a prime example.


Based on this objective, the subsequent section delineates the specifics of data collection.


2.Measurement: What aspects of reality are under scrutiny?

The measurement represents the elements within the reality you intend to gauge to enhance the current state of affairs. Typically, people perceive a metric as a numerical value embodying an idea. In this context, it remains consistent; the value signifies the magnitude of elements within the reality being examined. The author presents a selection of five acceptable indicator types to facilitate this process:

  • Sum: Measurement result, for example- Sales

  • Percent: Proportional part of the whole, for example- Percentage of sales of electronic equipment from total sales

  • Difference: Gap between indices, for example- The gap between income and expenses

  • Calculation: Mathematical operation [beyond the difference], for example- The ratio between the volume of sales and the bonuses paid for them

  • Function: Expression of a phenomenon through quantitative value, for example- The level of customer satisfaction with the sales service


3.Space: the categories of measurement

The space defines the collection of categories in which the measurement is performed.

Examples: products, months, age group, and more.

The author offers five types of accepted categories:

  • List: The collection of items in reality. In no fixed order. Suitable when the quantity is limited (M.L), for example- The details of the salespeople.

  • Sequence: Groups of details by range. Suitable when the amount of items is too large. The description has no meaning for the total detail (M.L.), for example- Population by age groups (10-20, 20-30, etc.)

  • Chunk (distribution of the total): The relative distribution of the cake between groups According to Rob - for a percentage index, for example- The distribution of the share of expenses according to the type of expense (R&D, marketing, etc.)

  • Timeline: Categorization by time, for example- Days, weeks, months, years

  • Map: Categorization by location areas, for example- Total sales in different regions of the country


4.When: Establishing the time frame for Measurements

This denotes the specific duration over which the measurements are conducted. Including this temporal range alongside the index and the categories in every graph's heading is imperative, as delineating the timeframe is integral to the definition (ensuring the accuracy of the information).


Data organization:

5.Structure: Selecting the Data Organization Format (Visualization)

This element determines how data is structured to facilitate learning and decision-making. It's important to remember that visualization elucidates the relationships among diverse data points. Consequently, the visualization chosen should underscore these connections in one of several available approaches.


  • Rating

Ranking the information by level of importance [left alignment]

Typical use: List

Imaging Example: Horizontal bar



Horizontal bar

  • Arrangement

The organization of information according to a fixed order of groups. Typical use: Sequence

Imaging Example: vertical bar


vertical bar


  • Separation [Note- not a pie chart! M.L.]

Separation between one group and another

Typical use: chunk (part of)


Chunck chart

  • Change

A line showing the trend of change between the data in space

Typical use: Timeline

Imaging Example:


time chart

  • Distribution

Round areas (of varying sizes) scattered over a surface in different areas

Typical use: Map

Imaging Example:


Distribution chart

6.Meaning: Discerning Organizational Patterns

This step involves recognizing the insights that can be gleaned from the visualization. It entails pinpointing the primary distinguishing characteristic that informs us about what is distinct and, in turn, how we can enhance reality. It's worth noting that this process may take time to be evident on occasion, requiring some experimentation with the visualization until the insight becomes apparent. The author guides with five common, accepted forms of meaning to aid this endeavor.


  • Hierarchy

Relevant structure: Rating

Explanation: High/low importance of categories about each other

Example: The sales success rating of the store ... was higher than others.

  • Seriality

Relevant structure: Arrangement

Explanation: Position on the continuum of major/minor categories

Example: Level of stability of sales volume throughout the year in the last five years.

  • Dominance

Relevant structure: Separation

Explanation: Differentiation of one group as more or less dominant than all the rest. Example: A group ... has sold significantly more than others in the past year.

  • Trend

Relevant structure: Change Explanation: Identifying change processes of increase and decrease of the index on the timeline. Example: The sales volume increased yearly until 2021, but then the increase stopped.

  • Density

Relevant structure: Distribution

Explanation: Identifying categories that are more or less dense

Example: The northern region has a broader distribution of points of sale than other regions.


Remember, as the author quotes: "A graph is an answer to a question" (Jacques Bertin, 1981)


7.Text Integration

In any visualization, the textual component plays a vital role. It encompasses three key elements:


  • The Simulation

Including the index, the categories, and the timeframe that has been defined.

  • Structural Headings

These pertain to the type of visualization and can include titles for axes on a graph, among others. Additionally, a legend is included to elucidate values that aren't self-explanatory (M.L.).

The Insight

Title. This title answers the question: What have we learned? What can be contemplated to enhance reality?


The saying goes that a picture is worth a thousand words, but as mentioned, an image alone is significantly less valuable. It's essential to combine visual elements with verbal text to ensure comprehension. This harmonious fusion is the path toward improving our understanding of reality.


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