hypothesis graphic design

Advisory boards aren’t only for executives. Join the LogRocket Content Advisory Board today →

Product Management
Solve User-Reported Issues
Find Issues Faster
Optimize Conversion and Adoption

How to create a perfect design hypothesis

A design hypothesis is a cornerstone of the UX and UI design process. It guides the entire process, defines research needs, and heavily influences the final outcome.

Doing any design work without a well-defined hypothesis is like riding a car without headlights. Although still possible, it forces you to go slower and dramatically increases the chances of unpleasant pitfalls.

The importance of a hypothesis in the design process

Design change for your hypothesis, the objective of your hypothesis, mapping underlying assumptions in your hypothesis, example 1: a simple design hypothesis, example 2: a robust design hypothesis.

There are three main reasons why no discovery or design process should start without a well-defined and framed hypothesis. A good design hypothesis helps us:

Guide the research
Nail the solutions
Maximize learnings and enable iterative design

A design hypothesis guides research

A good hypothesis not only states what we want to achieve but also the final objective and our current beliefs. It allows designers to assess how much actual evidence there is to support the hypothesis and focus their research and discovery efforts on areas they are least confident about.

Research for the sake of research brings waste. Research for the sake of validating specific hypotheses brings learnings.

A design hypothesis influences the design and solution

Design hypothesis gives much-needed context. It helps you:

Ideate right solutions
Focus on the proper UX
Polish UI details

The more detailed and robust the design hypothesis, the more context you have to help you make the best design decisions.

A design hypothesis maximizes learnings and enables iterative design

If you design new features blindly, it’s hard to truly learn from the launch. Some metrics might go up. Others might go down, so what?

With a well-defined design hypothesis, you can not only validate whether the design itself works but also better understand why and how to improve it in the future. This helps you iterate on your learnings.

Components of a good design hypothesis

I am not a fan of templatizing how a solid design hypothesis should look. There are various ways to approach it, and you should choose whatever works for you best. However, there are three essential elements you should include to ensure you get all the benefits mentioned earlier of using design hypotheses, that is:

Design change
The objective
Underlying assumptions

The fundamental part is the definition of what you are trying to do. If you are working on shortening the onboarding process, you might simply put “[…] we’d like to shorten the onboarding process […].”

The goal here is to give context to a wider audience and be able to quickly reference that the design hypothesis is concerning. Don’t fret too much about this part; simply boil the problem down to its essentials. What is frustrating your users?

In other words, the objective is the “why” behind the change. What exactly are you trying to achieve with the planned design change? The objective serves a few purposes.

Over 200k developers and product managers use LogRocket to create better digital experiences

First, it’s a great sanity check. You’d be surprised how many designers proposed various ideas, changes, and improvements without a clear goal. Changing design just for the sake of changing the design is a no-no.

It also helps you step back and see if the change you are considering is the best approach. For instance, if you are considering shortening the onboarding to increase the percentage of users completing it, are there any other design changes you can think of to achieve the same goal? Maybe instead of shortening the onboarding, there’s a bigger opportunity in simply adjusting the copy? Defining clear objectives invites conversations about whether you focus on the right things.

Additionally, a clearly defined objective gives you a measure of success to evaluate the effectiveness of your solution. If you believed you could boost the completion rate by 40 percent, but achieved only a 10 percent lift, then either the hypothesis was flawed (good learning point for the future), or there’s still room for improvements.

Last but not least, a clear objective is essential for the next step: mapping underlying assumptions.

Now that you know what you plan to do and which goal you are trying to achieve, it’s time for the most critical question.

Why do you believe the proposed design change will achieve the desired objective? Whether it’s because you heard some interesting insights during user interviews or spotted patterns in users’ behavioral data, note it down.

Even if you don’t have any strong justification and base your hypothesis on pure guesses (we all do that sometimes!), clearly name these beliefs. Listing out all your assumption will help you:

Focus your discovery efforts on validating these assumptions to avoid late disappointments
Better analyze results post-launch to maximize your learnings

You’ll see exactly how in the examples of good design hypotheses below.

Examples of good design hypotheses

Let’s put it all into practice and see what a good design hypothesis might look like.

I’ll use two examples:

A simple design hypothesis
A robust design hypothesis

You should still formulate a design hypothesis if you are working on minor changes, such as changing the copy on buttons. But there’s also no point in spending hours formulating a perfect hypothesis for a fifteen-minute test. In these cases, I’d just use a simple one-sentence hypothesis.

Yet, suppose you are working on an extensive and critical initiative, such as redesigning the whole conversion funnel. In that case, you might want to put more effort into a more robust and detailed design hypothesis to guide your entire process.

A simple example of a design hypothesis could be:

Moving the sign-up button to the top of the page will increase our conversion to registration by 10 percent, as most users don’t look at the bottom of the page.

Although it’s pretty straightforward, it still can help you in a few ways.

First of all, it helps prioritize experiments. If there is another small experiment in the backlog, but with the hypothesis that it’ll improve conversion to registration by 15 percent, it might influence the order of things you work on.

Impact assessments (where the 10 percent or 15 percent comes from) are another quite advanced topic, so I won’t cover it in detail, but in most cases, you can ask your product manager and/or data analyst for help.

It also allows you to validate the hypothesis without even experimenting. If you guessed that people don’t look at the bottom of the page, you can check your analytics tools to see what the scroll rate is or check heatmaps.

Lastly, if your hypothesis fails (that is, the conversion rate doesn’t improve), you get valuable insights that can help you reassess other hypotheses based on the “most users don’t look at the bottom of the page” assumption.

Now let’s take a look at a slightly more robust assumption. An example could be:

Shortening the number of screens during onboarding by half will boost our free trial to subscription conversion by 20 percent because:

Most users don’t complete the whole onboarding flow
Shorter onboarding will increase the onboarding completion rate
Focusing on the most important features will increase their adoption
Which will lead to aha moments and better premium retention
Users will perceive our product as simpler and less complex

The most significant difference is our effort to map all relevant assumptions.

Listing out assumptions can help you test them out in isolation before committing to the initiative.

For example, if you believe most users don’t complete the onboarding flow , you can check self-serve tools or ask your PM for help to validate if that’s true. If the data shows only 10 percent of users finish the onboarding, the hypothesis is stronger and more likely to be successful. If, on the other hand, most users do complete the whole onboarding, the idea suddenly becomes less promising.

The second advantage is the number of learnings you can get from the post-release analysis.

Say the change led to a 10 percent increase in conversion. Instead of blindly guessing why it didn’t meet expectations, you can see how each assumption turned out.

It might turn out that some users actually perceive the product as more complex (rather than less complex, as you assumed), as they have difficulty figuring out some functionalities that were skipped in the onboarding. Thus, they are less willing to convert.

Not only can it help you propose a second iteration of the experiment, that learning will help you greatly when working on other initiatives based on a similar assumption.

Closing thoughts

Ensuring everything you work on is based on a solid design hypothesis can greatly help you and your career.

It’ll guide your research and discovery in the right direction, enable better iterative design, maximize learning, and help you make better design decisions.

Some designers might think, “Hypotheses are the job of a product manager, not a designer.”

While that’s partly true, I believe designers should be proactive in working with hypotheses.

If there are none set, do it yourself for the sake of your own success. If all your designs succeed, or worse, flunk, no one will care who set or didn’t set the hypotheses behind these decisions. You’ll be judged, too.

If there’s a hypothesis set upfront, try to understand it, refine it, and challenge it if needed.

Most senior and desired product designers are not just pixel-pushers that do what they are being told to do, but they also play an active role in shaping the direction of the product as a whole. Becoming fluent in working with hypotheses is a significant step toward true seniority.

Header image source: IconScout

LogRocket : Analytics that give you UX insights without the need for interviews

LogRocket lets you replay users' product experiences to visualize struggle, see issues affecting adoption, and combine qualitative and quantitative data so you can create amazing digital experiences.

See how design choices, interactions, and issues affect your users — get a demo of LogRocket today .

Stop guessing about your digital experience with LogRocket

Using Zeplin for design handoff: How does it compare to Figma Dev Mode?

Let’s compare Zeplin with Figma Dev Mode to find out which suits design handoff better for your software product team!

Tips for creating a top-tier UX design portfolio in 2024

Here are some tips to help you create an effective UX design portfolio to get past the recruiter’s line and land the interview.

Using AI in accessibility: What can we do with AI?

AI can help make your designs more accessible to people with disabilities by automating tasks, such as captions, image descriptions, and more.

Design validation and verification: Testing your UX assumptions against reality

Validating and verifying your designs can spare you from having to rehaul them down the road. Here’s why and how to conduct these processes.

5 steps to a hypothesis-driven design process

• mar 22, 2018.

S ay you’re starting a greenfield project, or you’re redesigning a legacy app. The product owner gives you some high-level goals. Lots of ideas and questions are in your mind, and you’re not sure where to start.

Hypothesis-driven design will help you navigate through a unknown space so you can come out at the end of the process with actionable next steps.

Ready? Let’s dive in.

Step 1: Start with questions and assumptions

On the first day of the project, you’re curious about all the different aspects of your product. “How could we increase the engagement on the homepage? ” “ What features are important for our users? ”

Related: 6 ways to speed up and improve your product design process

To reduce risk, I like to take some time to write down all the unanswered questions and assumptions. So grab some sticky notes and write all your questions down on the notes (one question per note).

I recommend that you use the How Might We technique from IDEO to phrase the questions and turn your assumptions into questions. It’ll help you frame the questions in a more open-ended way to avoid building the solution into the statement prematurely. For example, you have an idea that you want to make riders feel more comfortable by showing them how many rides the driver has completed. You can rephrase the question to “ How might we ensure rider feel comfortable when taking ride, ” and leave the solution part out to the later step.

“It’s easy to come up with design ideas, but it’s hard to solve the right problem.”

It’s even more valuable to have your team members participate in the question brainstorming session. Having diverse disciplines in the room always brings fresh perspectives and leads to a more productive conversation.

Step 2: Prioritize the questions and assumptions

Now that you have all the questions on sticky notes, organize them into groups to make it easier to review them. It’s especially helpful if you can do the activity with your team so you can have more input from everybody.

When it comes to choosing which question to tackle first, think about what would impact your product the most or what would bring the most value to your users.

If you have a big group, you can Dot Vote to prioritize the questions. Here’s how it works: Everyone has three dots, and each person gets to vote on what they think is the most important question to answer in order to build a successful product. It’s a common prioritization technique that’s also used in the Sprint book by Jake Knapp —he writes, “ The prioritization process isn’t perfect, but it leads to pretty good decisions and it happens fast. ”

Related: Go inside design at Google Ventures

Step 3: Turn them into hypotheses

After the prioritization, you now have a clear question in mind. It’s time to turn the question into a hypothesis. Think about how you would answer the question.

Let’s continue the previous ride-hailing service example. The question you have is “ How might we make people feel safe and comfortable when using the service? ”

Based on this question, the solutions can be:

Sharing the rider’s location with friends and family automatically
Displaying more information about the driver
Showing feedback from previous riders

Now you can combine the solution and question, and turn it into a hypothesis. Hypothesis is a framework that can help you clearly define the question and solution, and eliminate assumption.

From Lean UX

We believe that [ sharing more information about the driver’s experience and stories ] For [ the riders ] Will [ make riders feel more comfortable and connected throughout the ride ]

4. Develop an experiment and testing the hypothesis

Develop an experiment so you can test your hypothesis. Our test will follow the scientific methods, so it’s subject to collecting empirical and measurable evidence in order to obtain new knowledge. In other words, it’s crucial to have a measurable outcome for the hypothesis so we can determine whether it has succeeded or failed.

There are different ways you can create an experiment, such as interview, survey , landing page validation, usability testing, etc. It could also be something that’s built into the software to get quantitative data from users. Write down what the experiment will be, and define the outcomes that determine whether the hypothesis is valids. A well-defined experiment can validate/invalidate the hypothesis.

In our example, we could define the experiment as “ We will run X studies to show more information about a driver (number of ride, years of experience), and ask follow-up questions to identify the rider’s emotion associated with this ride (safe, fun, interesting, etc.). We will know the hypothesis is valid when we get more than 70% identify the ride as safe or comfortable. ”

After defining the experiment, it’s time to get the design done. You don’t need to have every design detail thought through. You can focus on designing what is needed to be tested.

When the design is ready, you’re ready to run the test. Recruit the users you want to target , have a time frame, and put the design in front of the users.

5. Learn and build

You just learned that the result was positive and you’re excited to roll out the feature. That’s great! If the hypothesis failed, don’t worry—you’ll be able to gain some insights from that experiment. Now you have some new evidence that you can use to run your next experiment. In each experiment, you’ll learn something new about your product and your customers.

“Design is a never-ending process.”

What other information can you show to make riders feel safe and comfortable? That can be your next hypothesis. You now have a feature that’s ready to be built, and a new hypothesis to be tested.

Principles from from The Lean Startup

We often assume that we understand our users and know what they want. It’s important to slow down and take a moment to understand the questions and assumptions we have about our product.

After testing each hypothesis, you’ll get a clearer path of what’s most important to the users and where you need to dig deeper. You’ll have a clear direction for what to do next.

by Sylvia Lai

Sylvia Lai helps startup and enterprise solve complex problems through design thinking and user-centered design methodologies at Pivotal Labs . She is the biggest advocate for the users, making sure their voices are heard is her number one priority. Outside of work, she loves mentoring other designers through one-on-one conversation. Connect with her through LinkedIn or Twitter .

Collaborate in real time on a digital whiteboard Try Freehand

Get awesome design content in your inbox each week, give it a try—it only takes a click to unsubscribe., thanks for signing up, you should have a thank you gift in your inbox now-and you’ll hear from us again soon, get started designing better. faster. together. and free forever., give it a try. nothing’s holding you back..

Our Services
Meet the Team
Meet Our Students
Survival Kit for Designers
Graphic Design Projects – Taster Pack
YouTube Tutorials
Creative Blog
Podcast for Creatives
STARTER MEMBERSHIP » Great Foundations in 12 Weeks
PLUS MEMBERSHIP » Advanced Skills in 6 Months
PRO Membership » Your project-based online mentoring program
—— FEATURED COURSES ——
Graphic Design Theory Series
Photoshop Fast Track
Illustrator MasterClass
Photoshop MasterClass
InDesign MasterClass
Lightroom CC MasterClass
365 Days of Creativity
Group License for Institutions
Group License for Teams

Graphic Design Theory for Creatives: The Ultimate Guide

by Shumi Perhiniak

October 30, 2023

Graphic design theory is a complex topic that can seem daunting to beginners. But it doesn't have to be! In this post, we will break down the basics of graphic design theory and explain everything you need to know to start creating beautiful designs!

So whether you're a beginner or an experienced designer, read on and watch our tutorials for some essential graphic design theory knowledge.

Color & Contrast:

Color is one of the most important elements of design and can be used to create contrast, harmony, and balance in a composition. When choosing colors for your design, it is important to consider the meaning of each color as well as how they will work together. For example, warm colors such as red and yellow are often associated with energy!

Watch the below tutorial on how to work with color!

Contrast in design is used to create visual interest, depth, and hierarchy in a composition. High contrast designs often use light and dark colors to create drama, while low contrast designs are more subtle and subdued.

Watch the below tutorial on how to use Contrast in your designs!

Typography is the art and technique of designing typefaces, or fonts. It is an important element of design because it can be used to create visual interest, add personality to a design, and control the overall tone of a composition!

Watch the below tutorial on how to use Type in your designs!

To establish visual hierarchy in your composition, you need to arrange your design assets (i.e. images, written information, headings, illustrations, etc.) in order of importance/relevance. The most important piece of information should be the most noticeable; then, as the pieces of information get less important, they become less significant.

Watch the below tutorial on how to create Hierarchy with Fonts!

Psychology of design

Design psychology is the study of how design affects human behaviour. It is an important aspect of design because it can be used to create designs that are not only aesthetically pleasing but also functional and user-friendly.

Watch the below tutorial to learn more about Psychology of design!

Space is an important element of design because it can be used to create visual interest, add depth and dimension to a design, and control the overall flow of a composition. When using space in your design, it is important to consider the positive and negative space as well as the balance between them.

Watch the below tutorial to learn how to work with Space in your designs!

Balance is also another important element that can make or break a design. Be sure to watch the below tutoias as we cover topics such as grids and lines that help to create balance in your artwork.

Watch the below tutorial to learn how to work with Balance in your designs!

I hope this helped you better understand basic design theory. You can find the playlist on Graphic Design Theory here!

Looking to get into the creative industry but feeling a little lost? Don't worry. We have your back!

Sign up to our new and updated Free Graphic Design Survival Kit email newsletter , and we will drop you a weekly email with industry tips, design tutorials, resources and everything you need to know about getting started as a Designer!

About the author

Shumi Perhiniak

Shumi is a self-taught graphic designer and illustrator who has worked for Toni&Guy, Renaissance Learning, Baker Ross, and many others. Additionally, she owned a brick-and-mortar stationery shop selling her art prints and now sells wholesale to retailers and online shops under the brand name www.herdesignworld.com.

Get familiar with Type Anatomy

Get familiar with Type Anatomy, today we want to introduce you familiarise you with some odd but very useful typographic terms!

Budget-Friendly Gift Ideas for Designers: 25+ Ideas

Are you looking for a fun, creative and affordable gift for your artist friends and family? Don’t worry; we’ve got you covered!

18 Great Gifts for Designers

Having a hard time finding the perfect gift for your creative friends? We can help! Take a look at these 18 brilliant gifts for designers!

How to create a winning logo?

Designing a logo can be great fun, especially if you use an effective workflow. We share our top tips for creating successful logos!

Home office Inspiration for designers!

Discover office inspiration and go behind the scenes of our own garden studio!

Will AI take over creative jobs?

Find out how generative AI tools are reshaping the creative industry and how they will affect the jobs of designers!

Build a Killer Design Portfolio

Learn about the leading design portfolio platforms and how to create a killer portfolio

Enroll for FREE

✔ 8 graphic design topics covered, ✔ free video & email course for each topic, ✔ s tudent account with access to our community, ✚ huge discounts on all our memberships, should you decide to upgrade.

Your info is safe. You can unsubscribe any time.

Home Page http://hotdesigirlsfuck.com

Links to good posts

« Data Visualisation

HISA Big Data 2013 Conference »

Graphic Design in Data Science

Business intelligence , Data science , Data visualisation

The human brain is highly adapted to discerning visual patterns – sometimes even when there aren’t any. It responds more effectively to visual representations than to textual data, especially tabular data. With the vast amounts of data now being analysed, visualisations are even more important to make sense of the data and to communicate the message(s) contained therein.

Data Visualisation

Visualisations are appearing as graphs, maps and other graphical elements in the reports and dashboards produced by all the major BI tools. In fact, some tools focus predominantly on data visualisation.

There are two breeds of data visualisations, which differ in their audience and application:

Exploratory data visualisations (as named by John Tukey) are intended to facilitate the data analyst’s understanding of the data. These may consist of scatter plot matrices and histograms, where labels and colours are minimal by default. Their goal is to help find and develop a hypothesis about the data, and their audience is typically rather small. (Some products like Tableau and Qlikview focus primarily on exploratory data visualisations.)
Communicative visualisations are intended to appeal to a wider audience, where the goal is to visually convince them of a hypothesis. (R can do static visualisations, and tools like Processing and Flare are use to create rich interactive visualisations). However, to me there is a huge difference when a visualisation is used to augment the verbal explanation of a hypothesis to an audience vs. when it must communicate its message “by itself”.

A data scientist must have the ability to create visualisations that convey the stories about the insights discovered in the data. Even with good quality data and rigorous statistical techniques, if the results of an analysis are poorly presented, they will neither convince the right people nor convey the right message. However, the ability to create “self communicating” visual narratives often requires a separate skill — often using separate tools (more about that under infographics below).

Graphic Design in Data Visualisation tools

Data visualisation tools like Tableau have some essential aspects of graphical design beat practices already built-in. The developers of these tools have taken great care to follow the recommendations by data visualisation specialists like Stephen Few. If you use the correct configurations, you can create some really effective data visualisations – where the message that the data conveys clearly stands out, without being cluttered by unnecessary ink and other designed widgets. Tableau has a clever wizard-like function, which recommends the appropriate graph types for the data and analysis under consideration. Of course you can still force it to use the wrong graph type, or configure it to produce really bad visualisations. So although you don’t need to be a graphical designer per se, you still need to know how to apply the correct settings and configurations to apply good design principles.

Infographics

If we are not using a data visualisation or data exploration tool to interactively communicate about the data, we may need to frame the story differently, either in terms of presentation slides, message conveying images or using a clever infographic.

Not even the experts agree what exactly constitutes an infographic. However, they do agree on a common goal – to present and communicate complex information quickly and clearly. The objective is to improve cognition by utilising graphics to enhance the human visual system’s ability to see patterns and trends. In some cases though, graphical elements such as text and typography are used liberally to blatantly convince the audience what they should be seeing.

Decoration is the one single design element that divides the schools of thought about infographics:

Business intelligence expert Stephen Few sums up his disdain for the ornamental aspect of infographics: “When visualizations are used primarily for artistic purposes, they are not what we call data visualizations or infographics, which are terms that have been in use for a long time with particular meanings.”
David McCandless, on the other hand, has popularised artistic visualisations and introduced data as a storytelling category to a wider audience. He describes his work: “I love taking all kinds of information – data, numbers, ideas, knowledge – and making them into images. When you visualize information in this way, you can start to see the patterns and connections that matter.”

If visualisation has to serve as a representation that amplifies the cognition of data, one can measure both the efficacy (how easily comprehensible is the data) and the veracity (how truthful is the explication of the data) of a given visualisation. Decoration, for the most part though, introduces visual noise into a design, thereby compromising both measures.

However, the important point for me is the aspect of storytelling in infographics. I see a data visualisation as a factual representation of the trends, insights, etc., contained in the data, but an infographic as a “self communicating” narrative of the same. Alberto Cairo, infographics professor and author of The Functional Art, put it like this: “So you’ve amassed terabytes of data. Now, tell a story.” As another example, journalist Reg Chua described a particular very powerful visualisation: “It’s not simply a dump of data, but one designed – intended – to persuade.” The concept of persuasion regularly comes up as a recurring theme in discussions about the purpose of visualisations, especially infographics.

This is where the services of a graphical designer may be required. I don’t know many data analysts or full blown data scientists who have the artistic skills to put together a persuasive narrative as in an infographic. Well, not one that would be classified as artistic or pleasing to the eye anyway.

Graphic Design

Graphic design is a creative process, used to convey a specific message to a targeted audience. It includes a number of artistic and professional disciplines that focus on visual communication and presentation. Words, symbols and images are created and combined using shape, colour, imagery, typography, visual arts and page layout techniques to form a visual representation of the message.

Graphic design is positioned as an interdisciplinary, problem-solving activity which combines visual sensitivity with skill and knowledge in communications, technology and business. The interesting term for me in that statement is “problem solving”. How much does that overlap with the problem solving skills required for exploratory or hypothetical data analysis? I guess not all that much… However, it also implies that the graphic designer needs an understanding of the client’s products or services, goals, competitors and the target audience. It is therefore not a purely artistic process. For that reason, graphic design is sometimes referred to as Visual Communication or Communication Design – this shows how important the communicative aspect is. In some organisations graphic designers are even called communication designers. A communication designer has similar skills as a graphic designer, but is more concerned with designs that convey specific messages visually, for publicity, broadcast, interactive or environmental communication.

In the context of data science, graphic design is the combined science and art of visual communication. A good communication designer will choose the right tools and formats to convey a message effectively.

The problem is that most graphic designers – no matter how “clever” their designs are – at the crux of it, do normally not work with and do not understand data. So it takes very clear communication to convey the message that we need communicated to them first. The up side of this is that once understood, they usually know very well how to convey that same message in a non-technical fun and interactive fashion to a non-technical audience.

Concluding remarks

With the data volumes and the wide range of tools now at our disposal to communicate with and persuade people, we can much more effectively choose from a wide range of presentation tools and formats. The well-told tale, complete with great colour and anecdotes, backed up by rigorous data analysis, and supported by great multimedia elements, may well continue to be the gold standard we aspire to; but we also need to work on how best to harness our reporting and presenting capabilities so we can create other types of data-driven narratives that touch and persuade people.

It seems that if we mostly do interactive data explorations to find, illustrate or prove hypotheses, we can use a data exploration tool, and apply the best graphical design principles within the limits set by the tool. If we need to convey the message in the form of a story to a wider audience, we can utilise the skilled services of a sharp communication designer to frame the message, for example, as an infographic.

However, data science is not always that clear cut. Some days you are exploring new data, sometimes you are postulating or proving hypotheses and other days you are convincing or persuading different audiences through slides or storytelling. Some days you even reverse the process by first representing the data (through design) and then subsequently refining it (through exploration).

So do you now need to go out and contract the services of that communications designer? Cost-effectively? I will leave the justification of that business case over to you!

Tags: data science , exploratory data visualisation , graphic design , hypothesis , infographic , storytelling , visualisation , visualization

Important Theories and Concepts of Graphic Design

Graphic design is a multi-disciplinary field that combines visual art, typography , and page layout to create a variety of communication media such as logos, websites, magazines, and books.

Graphic design is an important part of any culture because it helps us to access the information we need such as product information or text messages from our friends and family.

It can also help us communicate ideas, feelings, and emotions to other people. Graphic design can be used to promote a product or service, or it can be used as a medium of self-expression.

In this article, we will talk about some important theories and concepts related to graphic design.

Table of Contents

What is Design?

Design is the process of creating a plan for an object or environment. The design combines art, science, and technology to create new products, systems, and services that meet specific needs.

It’s important to note that design is not just about making things look pretty–it’s also about making choices based on what works best for the people using your product or service.

Designers must consider aesthetics as well as functionality when developing their plans for objects or environments.

However, design isn’t just about creating something new–it’s also about improving on existing products and services.

What is the Connection between Art and Design?

Design and art are related in a number of ways. Design is considered to be a subset of art, but it’s also more than just an aesthetic process.

Art is a form of self-expression, while the design is a more formalized and practical way of creating objects or environments.

The difference between art and design can be difficult to define, but it’s generally accepted that art tends to be subjective and open to interpretation while the design has a more specific goal in mind.

Art is more about expression and meaning, while the design is about function and purpose. The two are often used together, though the distinction between them isn’t always clear.

Difference between Artist and a Designer

A designer is a person who is involved in the process of designing. Designers are concerned with how things look and what they say about us as individuals, groups, or society as a whole.

They make decisions about color, layout, typography, and images to express their point of view or purpose. An image can result in an advertisement, an instruction manual, or simply a pleasing visual experience.

An artist is someone who creates visual art. They may be concerned with how something looks, but they are also interested in what it means and how it makes them feel.

A painting or sculpture may have a message that is conveyed through its subject matter, composition, or color scheme. While the distinction between artist and designer is not always clear-cut, there are some differences between the two professions.

The difference between artists and designers can be seen in the way they approach their work. An artist looks at the world around them and attempts to capture an image of it in a creative way, while a designer takes a different approach. A designer thinks about how something will look before creating it.

Check out our latest post on 10 Excellent Free and Paid Online Tools for Graphic Designers

What is Graphic Design?

Graphic design is the process of creating visual elements that communicate a specific message. Graphic designers create logos, websites, advertisements, magazines, and other forms of visual communication.

Graphic design is also the art of visual communication and problem-solving through the use of type, space, and image. It is a process of visual communication, problem-solving, and production.

The visual elements communicate the message to the audience, who are your intended users.

Graphic designers use color, images, fonts, and space to express a message. The design process is often iterative and can take several iterations to get it right.

What is the Theory of Graphic Design?

The theory of design is the study of the principles and elements of graphic design. It’s a set of guidelines that helps to make better designs and is based on many years of research.

The theory of graphic design has been around since ancient times, but it has evolved over time as we’ve learned more about what makes good designs effective.

Theory: What Is It? The Theory Of Design (TOD) is defined as “the study or investigation into some subject matter.” In this case, it refers specifically to the study and analysis of graphic design principles such as color choice or typography usage within an organization’s brand identity system (BIS).

Let’s have a look at the top 5 important theories of graphic design and why they matter.

1. Design Principles

The design principles are the foundation of graphic design. They are used to guide your decisions, from choosing a font to creating an entire brand identity.

In this section, you’ll learn about some common design principles and how they’re applied in graphic design. We’ll also cover some basics about color theory so that you can better understand how it works with other aspects of your projects (and why).

Alignment : Alignment is one of the most basic design principles. It refers to the positioning of elements within a layout or design. Alignments fall into three categories:

Flush left and flush right (or justified)
Justified with asymmetrical balance

Repetition : Repetition is a design principle that refers to the use of repeated elements throughout a project. This can be as simple as repeating an image or pattern, or it may involve using multiple fonts, colors, or type sizes. Repetition helps create unity across the design and can make it easier for viewers to understand what they’re seeing.

Contrast: Contrast is a design principle that refers to the use of different elements within a project. The most common types of contrast are: Contrasting fonts (e.g., Helvetica and Arial) Contrasting colors (e.g., black and white) Contrasting shapes (e.g., circles vs. rectangles)

Hierarchy: Hierarchy is a design principle that refers to the way in which elements are arranged so that viewers can quickly understand what they’re seeing. Hierarchy helps you connect with your audience by making it easier for them to navigate your content.

Balance: Balance is a design principle that refers to the use of multiple elements in equal proportion. It’s a way to create visual harmony and emphasize the most important elements in your design.

Using these five principles will allow you to create a design that is both aesthetically pleasing and functional.

2. Color Theory

Color theory is a study of the effect of colors on people. It’s like art, but it’s not just about art. Color theory is a theoretical framework for understanding and using color in design, as well as a branch of visual perception that studies the psychological effects of color.

Color has been used throughout history as a means of expression and communication, with some cultures relying heavily on it while others have avoided it altogether (like ancient Egypt).

Today, we use all sorts of colors every day–from the clothes we wear to the food we eat–and they can say different things about us depending on their meaning within context.

A color is a powerful tool used in design to evoke different emotions and feelings. It can also be used to represent specific meanings, create order and organization, or enhance the readability of text.

You may also like, Why is Sketching Important in Graphic Design? (Ultimate Guide)

3. Design Thinking

Design Thinking is a problem-solving process that focuses on the needs and aspirations of people. Its purpose is to create solutions that are useful, usable, and desirable for those who will use them.

Design Thinking is a human-centered approach that focuses on the needs and aspirations of people. It encourages designers to think about how their work can make positive changes in society, as opposed to focusing solely on aesthetics or form alone.

The process of design thinking has five distinct stages.

Empathise : The first stage of design thinking is to empathize with your users. This means understanding their needs, goals, and desires. Designers need to think about this in terms of who their audience will be, what they do, and how they behave.

Define : Once you know your users, the next step is to define their problem. This involves understanding what their needs are and how they currently approach solving them. It’s important to note that design thinking isn’t just about creating solutions—it’s about coming up with ideas that are innovative and useful.

Iterate : The third step is to iterate. This means creating many different ideas and prototypes in order to get feedback from users. The goal here is to get as much feedback as possible so that you can improve your designs based on what people want and need.

Prototype : The fourth stage of design thinking is prototyping. This means creating a physical or digital prototype of your product before it goes into production. You should use this stage of design thinking in order to test out your ideas and make sure that they work.

Test : The final step of design thinking is testing. This means that you should get feedback from users on your prototypes in order to make sure that they meet their needs. You can do this by testing out your prototypes with small groups of people or by doing user interviews.

The stages don’t need to be followed in order; you can also use a combination of them to create your product.

4. Graphic Elements

Graphic elements are the building blocks of graphic design. They are the individual components that make up a piece of work, and they can be used to create an overall visual statement.

Graphic elements include:

Typeface (font)
Size and proportion of shapes and lines, including line weight or stroke width
Space around and between objects

These different graphic elements combine with one another to create meaning in your design, whether it’s an advertisement or a website.

You should be familiar with these basic concepts so that you know how your readers will interpret them when they look at your work!

When you look at a piece of design, it’s important to understand that there are different layers that make up your work.

The most basic layer is the visual story itself; this is what you see when you look at a photo or painting. Graphic design deals with all aspects of visual communication, including its planning and implementation.

Don’t forget to read our trending article about How AI-Generated Art is Changing Graphic Design

5. Functionalism

Functionalism is a design theory that focuses on the idea of functionality. Functionalism was developed as an architectural style in the early 20th century by Walter Gropius, who was one of the founders of Bauhaus.

The principles of functionalism were later adapted for graphic design by Paul Rand and others who believed that good design should serve its purpose well without being overly decorative or gimmicky.

Functionalism focuses on designing things that are easy to use and understand. It rejects the idea of using ornamentation or decoration on objects, instead focusing on their function and how they can be used for practical purposes.

Functionalism is a very minimalist design style; it aims to strip away all superfluous elements from an object so that only what’s necessary remains.

How Design Theories are Used by Graphic Designers?

Graphic designers can use the theories and philosophies of design to help them create more effective designs.

For example, they may want to make something look old-fashioned or retro, so they’ll choose a style that fits that idea.

In a sense, these theories are tools that any designer can use to improve their skills and create better designs. These theories aren’t just useful for graphic designers; they can be applied in many different fields of design and even in other areas of life.

Functionalism is another example; it’s a design philosophy that focuses on making objects easy to understand and use.

Graphic designers may also choose a style of design that matches their client’s needs. For example, if they’re designing a website for an old-fashioned business such as a bakery or jewelry store, they’ll choose to use an old-fashioned look and feel to match the client’s brand.

In conclusion, we can say that Graphic Design is a very important field of study and it has many theories and concepts which are very helpful for a designer to know. These theories and concepts help us understand what is design, what is art, and how they differ from each other. Also, there are other things like design principles that give us guidance on how to create better designs based on their characteristics such as functionality or formality.

Recommended reading: 12 Excellent Online Graphic Design Courses (2023)

What are the theories of graphic design?

Theories of graphic design are the ideas that explain how graphic designers create their works. They are based on different assumptions and principles but have one purpose: to help designers create better designs.

What are the five types of graphic design theories?

The five types of graphic design theories are: 1. Design Principles 2. Color Theories 3. Design Thinking 4. Graphic Elements 5. Functionalism

Is it important to use design theories as a graphic designer?

As a graphic designer, it is important to use design theories as a core practice. Design theories help you understand how to create art for specific purposes and audiences, which allows you to choose the most effective way to communicate your message.

2 thoughts on “Important Theories and Concepts of Graphic Design”

Pingback: The Importance of Balance in Graphic Design (Ultimate Guide)
Pingback: Role of Computers in the Evolution of Graphic Design

The Impact of Artificial Intelligence on Graphic Design: Exploring the Challenges and Possibilities of AI-Driven Autonomous Branding

Conference paper
First Online: 06 November 2022
pp 3567–3576
Cite this conference paper

Duha Engawi 3 , 4 ,
Charlie Gere 3 &
Daniel Richards 3

Included in the following conference series:

Congress of the International Association of Societies of Design Research

757 Accesses

Artificial intelligence (AI)’s potential impact on graphic design has stimulated a range of questions and concerns from both design practitioners and academics about the future of AI-driven designs. For instance, how will AI tackle issues associated with ethics, cultural acceptance, and creativity, and what are the possibilities of having autonomous AI-driven brands? This study investigates the potential impact of AI on graphic designers, including an assessment of how to use AI as a self-governed system in branding rather than an application tool exploring new opportunities associated with data and algorithms. Speculative co-design methodology was the main approach to initiating provocative discussions and debates through semi-structured interviews and co-design workshop. The study was conducted in Saudi Arabia with participants from academia and the industry. The findings suggest alternating human-machine entanglements around self-driven AI brands, which will enable designers and researchers to explore alternative futures in this field.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Available as PDF
Read on any device
Instant download
Own it forever
Available as EPUB and PDF
Compact, lightweight edition
Dispatched in 3 to 5 business days
Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Adobe Blog: Project #Fontphoria previews a new future for working with type (2018). https://blog.adobe.com/en/2018/12/19/project-fontphoria-previews-a-new-future-for-working-with-type.html#gs.2xn5pn . Accessed 9 June 2021

Bodegraven, J.: Artificial Intelligence Driven Design (2018). https://www.awwwards.com/AI-driven-design . Accessed 3 Dec 2020

Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3 (2), 77–101 (2006)

Article Google Scholar

Candy, S., Watson, J.: The thing from the future (Card game). Situation Lab, Toronto: Situation Lab (2014). http://situationlab.org/project/the-thing-from-the-future . Accessed 5 June 2021

Chandrashekar, A., Amat, F., Basilico, J., Jebara, T.: Artwork personalization at Netflix (2017). https://medium.com/netflix-techblog/artwork-personalization-c589f074ad76 . Accessed 3 Dec 2020

Dreyfus, H.L.: What Computers Can’t Do. MIT Press (1972)

Google Scholar

Gere, C.: Digital Culture. Reaktion Books, London (2008)

Hansen, S.M.: Mapping creative coding courses: Toward bespoke programming curricula in graphic design education. Paper presented at Eurographics 2019: The 40th Annual Conference of the European Association for Computer Graphics, 6–10 May 2019, pp. 17–20. Genoa, Italy (2019). https://doi.org/10.2312/eged.20191024 . Accessed 20 Sept. 2021

Harari, Y.N.: 21 Lessons for the 21st Century. Random House, New York (2019)

Ironov, N.: Nikolay Ironov Project (2020). https://www.artlebedev.com/ironov/ . Accessed 4 June 2021

Jaton, F.: The Constitution of Algorithms: Ground-Truthing, Programming, Formulating. MIT Press (2021)

Book Google Scholar

Kaiser, Z.: Creativity as computation: teaching design in the age of automation. Des. Cult. 11 (2), 173–192 (2019)

Kaleagasi, B.: A new AI can write music as well as a human composer (2017). https://www.futurism.com/a-new-ai-can-write-music-as-well-ahuman-composer . Accessed 4 June 2021

Karaata, E.: Usage of artificial intelligence in today’s graphic design. Online J. Art Des. 6 (4), 183–198 (2018)

Kasparov, G.: Chess, a drosophila of reasoning. Science 362 (6419), p1087 (2018)

Kelly, K.: The Inevitable, pp. 1–100. Viking Press, New York (2016)

Kirkpatrick, B., Klingner, B.: Turing’s Imitation Game: a discussion with the benefit of hind-sight. Berkeley Computer Science course, pp. 13 (2009). http://www.cs.berkeley.edu/~christos/classics/ttest . pdf Accessed 1 2009

Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Pearson, Boston (2009)

Lupton, E.: Design is Storytelling. Cooper Hewitt, New York (2017)

Maggioncalda, J.: Skills of the future: Preparing KSA leaders of Vision 2030, [Webinar] [online] Series hosting Jeff Maggioncalda by Misk Academy (2020). Accessed 15 Sept 2020

Mazzone, M., Elgammal, A.: Art, creativity, and the potential of artificial intelligence. Arts 8 (1), 26 (2019)

Mino, A., Spanakis, G.: LoGAN: generating logos with a generative adversarial neural network conditioned on color. In: Proceedings of 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 17–20 Dec 2018. pp. 965–970. Orlando, USA (2018). https://arxiv.org/abs/1810.10395v1 . Accessed 20 Dec 2020

Morcilllo, C.G., Martin, V.J., Fernandez, D.V., Sanchez, J.J.C., Albusac, J.A.: Gaudii: an automated graphic design expert system. In: Twenty-Second IAAI Conference, pp. 1775–1780 (2010)

Noble, I, Bestley, R.: Visual Research: An Introduction to Research Methods in Graphic Design . Bloomsbury Publishing (2011)

O’Donovan, P., Agarwala, A., Hertzmann, A.: Designscape: design with interactive layout suggestions. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems , pp.1221–1224 (2015)

Qiao, J.: Brand mark: Artificial intelligence logo builder (2017). https://brandmark.io . Accessed 4 June 2021

Richardson, A.: Data Driven. Bloomsbury, New York (2016)

Sanders, E., Stappers, P.: Co-creation and the new landscapes of design. CoDesign 4 (1), 5–18 (2008)

West, S.M.: Data capitalism: redefining the logics of surveillance and privacy. Bus. Soc. 58 (1), 20–41 (2019)

Boyatzis, R.E.: Transforming Qualitative Information. SAGE, Cleveland (1998)

BrandMark. Create a unique, professional Logo for your business (2019). https://brandmark.io/ . Accessed 4 June 2021

Download references

Acknowledgments

This study was funded by Princess Nourah Bint Abdulrahman University as part of the main author’s PhD research scholarship. Many thanks to the Saudi Cultural Bureau for assisting with our research during COVID-19. We thank everyone who participated in the interviews and workshop and those who helped with the research.

Author information

Authors and affiliations.

LICA, Lancaster University, Lancaster, UK

Duha Engawi, Charlie Gere & Daniel Richards

Department of Graphic Design and Digital Media, College of Arts and Design, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

Duha Engawi

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Duha Engawi .

Editor information

Editors and affiliations.

The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

Gerhard Bruyns

Rights and permissions

Reprints and permissions

Copyright information

About this paper

Cite this paper.

Engawi, D., Gere, C., Richards, D. (2022). The Impact of Artificial Intelligence on Graphic Design: Exploring the Challenges and Possibilities of AI-Driven Autonomous Branding. In: Bruyns, G., Wei, H. (eds) [ ] With Design: Reinventing Design Modes. IASDR 2021. Springer, Singapore. https://doi.org/10.1007/978-981-19-4472-7_238

Download citation

DOI : https://doi.org/10.1007/978-981-19-4472-7_238

Published : 06 November 2022

Publisher Name : Springer, Singapore

Print ISBN : 978-981-19-4471-0

Online ISBN : 978-981-19-4472-7

eBook Packages : Literature, Cultural and Media Studies Literature, Cultural and Media Studies (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Publish with us

Policies and ethics

Find a journal
Track your research

Graphical testing for group sequential design

This document is intended to evaluate statistical significance for graphical multiplicity control when used with group sequential design (Maurer and Bretz 2013) . In particular, we demonstrate design and analysis of a complex oncology trial. There are many details building on the necessarily simple example provided by Maurer and Bretz (2013) . The combination of tools provided by the gMCPLite and gsDesign packages is non-trivial, but developed in a way that is meant to be re-used in a straightforward fashion. This has been found to be particularly valuable to provide a prompt and verifiable conclusion in multiple trials such as Burtness et al. (2019) where 14 hypotheses were evaluated using a template such as this.

Given the complexity involved, substantial effort has been taken to provide methods to check hypothesis testing.

The initial testing is done by using sequential p-values (Liu and Anderson 2008) which can then be plugged into standard graphical hypothesis testing R package, gMCP (Bretz, Maurer, and Posch 2009) .
The graphical testing produces a sequence including the original multiplicity graph, followed by updated multiplicity graphs, each with a single hypothesis rejected from the previous graph.
The final graph, assuming not all hypotheses were rejected, provides the final Type I error available for testing each hypothesis that was not rejected.
Updated group sequential bounds for each hypothesis at the largest alpha-level it was evaluated can be checked vs. nominal p-values at each analysis to verify the testing conclusions reached with the above methods.

The table of contents above lays out the organization of the document. In short, we begin with 1) design specification followed by 2) results entry which includes event counts and nominal p-values for testing, 3) carrying out hypothesis testing, and 4) verification of the hypothesis testing results.

For the template example, there are 3 endpoints and 2 populations resulting in 6 hypotheses to be tested in the trial. The endpoints are:

Overall survival (OS)
Progression free survival (PFS)
Objective response rate (ORR)

The populations to be studied are:

The overall population (All subjects)
A subgroup (Subgroup)

For simplicity, we design assuming the control group has an exponential time to event with a median of 12 months for OS and 5 months for PFS. We design under a proportional hazards assumption. ORR for the control group is assumed to be 15%. Some of the choices here are arbitrary, but the intent is to fully specify how patients will be enrolled and followed for $\alpha$ -controlled study analyses.

The following design characteristics are also specified to well-characterize outcomes for all subjects by the end of the trial:

Enrollment is assumed to occur over 18 months. Enrollment will continue until the targeted number of subjects has been enrolled in the subgroup to ensure power as planned for that population. This means, the overall population sample size will be random and power may vary from that planned here. The enrollment increases from 25% in the first two months to 50% in the second 2 months to 75% in the third two months of the final steady state enrollment which continues from the end of month 6 of enrollment until final enrollment expected at 18 months.
The first interim analysis will be conducted 6 months after final patient enrolled to adequately assess ORR for all patients. Thus, the analysis is planned at 24 months after start of study enrollment, but will be adapted according to when final enrollment is completed. This is the only analysis for ORR and is an interim analysis for PFS and OS with whatever event counts are available at the cutoff.
The second interim analysis will be conducted 14 months after final enrollment to ensure minimum follow-up almost 3 times the assumed control median PFS for all subjects. This would be delayed up to 3 months if the final targeted event count for PFS in the subgroup is not achieved at that time. This is to ensure a complete description of tail behavior for PFS in the case a PFS curve has a plateau. PFS and OS will be analyzed. The endpoint counts for OS and for the overall population for PFS are random since the cutoff is determined by the PFS endpoint count for the subgroup.
The final analysis will be performed 24 months after final enrollment, ensuring 2 times the assumed median control survival as minimum follow-up for all subjects. Only analysis of OS is planned. The final analysis may be delayed up to 6 months if the targeted OS event count in the subgroup is not achieved. Thus, the planned total duration of the trial for the OS endpoint is 42 months.

The sample size for the trial will be driven by an adequate sample size and targeted events in the subgroup to ensure 90% power for the OS endpoint assuming a hazard ratio of 0.65. For group sequential designs, we assume 1-sided testing.

To reveal code blocks for the remainder of the document, press the code buttons indicated throughout. The initial code block sets options and loads needed packages; no modification should be required by the user.

Multiplicity diagram for hypothesis testing

Following is the multiplicity graph for the trial design. We have arbitrarily split Type I error equally between the subgroup and overall populations. Most $\alpha$ is allocated to OS and the least to ORR, with PFS receiving an intermediate amount. This reflects the priority of the endpoints as well as the practicality to detect clinically significant differences in each population. Reallocation for each endpoint proceeds from the subgroup to the overall population. If the overall population hypothesis is rejected for a given endpoint, the reallocation is split between the two populations for another endpoint. The choice for allocation and reallocation illustrated here is to demonstrate a complex multiplicity scenario; when actually applying this method the allocation and reallocation choices should be carefully considered.

This testing scheme can result in what might be referred to as time travel for passing of $\alpha$ . That is, if PFS hypotheses are not rejected at a given analysis (say final PFS analysis) and OS hypotheses are rejected at the final analysis, then the previously evaluated PFS tests at the interim and final PFS analysis can be compared to updated bounds based on reallocated Type I error. While this practice was not encouraged by Maurer and Bretz (2013) , it was acknowledged to control Type I error as previous discussed in (Liu and Anderson 2008) . Given the stringent Type I error control for multiple hypotheses, the ability to acknowledge clinically significant differences as statistically significant can be important in describing treatment benefits in regulatory labeling for a treatment.

Group sequential designs for each hypothesis

For the example, we assume 1-sided testing or a non-binding futility bound as required for a Maurer and Bretz (2013) design using group sequential design with graphical multiplicity control. Each is demonstrated in the example code for respective hypotheses. Efficacy $\alpha$ -spending for all group sequential designs uses the Lan and DeMets (1983) spending function approximating an O’Brien-Fleming bound.

This section needs to be modified by the user to match the study design under consideration. Those uncomfortable with coding may wish to design using the gsDesign Shiny app which provides not only a point and click interface, but also a code tab that generates R code that can be copied and plugged in for designs below.

H1: OS, Subgroup
H2: OS, All
H3: PFS, Subgroup
H4: PFS, All
H5 and H6: ORR

We assume 50% of the population is in the subgroup of interest. A sample size of 378 is driven by overall survival (OS) in the subgroup where we assume a hazard ratio of 0.65. Here we assume a one-sided group sequential design with no futility bound.

One-sided group sequential design with 3 analyses, time-to-event outcome with sample size 378 and 284 events required, 90 percent power, 1 percent (1-sided) Type I error to detect a hazard ratio of 0.65. Enrollment and total study durations are assumed to be 18 and 42 months, respectively. Efficacy bounds derived using a Lan-DeMets O’Brien-Fleming approximation spending function with none = 1.

The above text was automatically generated and could be edited appropriately for description of the design. Following is a summary table describing study bounds.

The total sample size is assumed to be twice the above, N=756. The power and hazard ratio can be adjusted to appropriately size the trial rather than starting with adjusting sample size to reach a targeted power. For this example, we consider altering power ( beta ) while fixing the hazard ratio at 0.75, representing an increase in median OS from 12 months in the control group to 16 months in the experimental group. For this design, we consider a non-binding futility bound where the trial may be stopped early in the overall population if the bound is crossed. We use a Hwang, Shih, and De Cani (1990) bound with $\gamma = -3.25$ . Study designers should carefully consider implication for parameter choices, particularly if the futility bounds provide sensible guidance for stopping the trial. Since the futility bounds are non-binding, the efficacy bound is computed assuming the futility bound is ignored which will control Type I error at the targeted level even if a futility bound is crossed and the trial is continued.

Asymmetric two-sided group sequential design with non-binding futility bound, 3 analyses, time-to-event outcome with sample size 756 and 589 events required, 86 percent power, 1 percent (1-sided) Type I error to detect a hazard ratio of 0.75. Enrollment and total study durations are assumed to be 18 and 42 months, respectively. Efficacy bounds derived using a Lan-DeMets O’Brien-Fleming approximation spending function with none = 1. Futility bounds derived using a Hwang-Shih-DeCani spending function with gamma = -3.25.

We can also plot different design characteristics. Here, we plot the approximate hazard ratio to cross each bound which may be helpful for design team discussions.

For progression free survival (PFS) we assume a shorter median time to event of 5 months. With an assumed hazard ratio of 0.65, we adjust beta and timing to match the targeted sample size and interim analysis timing. We assume a larger dropout rate for PFS than we did for OS. Here we set up a futility bound for safety. This is an asymmetric 2-sided design with both futility and efficacy boundary crossing probabilities under the null hypothesis. The parameter astar = 0.1 specifies total lower bound spending of 10%. The lower Hwang, Shih, and De Cani (1990) spending bound with $\gamma = -8$ is intended to be conservative in terms of futility at the interim, but still provide a safety bound for the PFS result in this targeted population. This would have to be carefully evaluated by the study design team at the time of design.

Asymmetric two-sided group sequential design with non-binding futility bound, 2 analyses, time-to-event outcome with sample size 378 and 296 events required, 85.1 percent power, 0.4 percent (1-sided) Type I error to detect a hazard ratio of 0.65. Enrollment and total study durations are assumed to be 18 and 32 months, respectively. Efficacy bounds derived using a Lan-DeMets O’Brien-Fleming approximation spending function with none = 1. Futility bounds derived using a Hwang-Shih-DeCani spending function with gamma = -8.

Finally, we design for PFS in all subjects. In this case, we simplify to a one-sided design. A futility bound could be considered, if appropriate.

For objective response rate (ORR), we assume an underlying control rate of 15%. In the subgroup population, we have almost 90% power to detect a 20% improvement.

In the all subjects population, we have approximately 95% power to detect an improvement in ORR from 15% to 30%.

Design list

Now we associate designs with hypotheses in an ordered list corresponding to the order in the multiplicity graph setup. Since ORR designs are not group sequential, we enter NULL values for those in the last 2 entries of the design list; hit code button to reveal code for this.

Spending plan and spending time

While it was relatively straightforward above to set up timing of analyses to match for the different hypotheses, accumulation of endpoints can vary from plan in a variety of ways. Planning on how to deal with this is critical at the time of protocol development to avoid later amendments or inappropriate $\alpha$ -allocation to early analyses. Before going into examples, we review the concept of $\alpha$ -spending and what we will refer to as spending time .

For a given hypothesis, we will assign a non-decreasing spending function $f(t)$ defined for $t\ge 0$ with $f(0)=0$ and $f(t)=\alpha$ for $t\ge 1$ . We will assume $K$ analyses with observed event counts $n_k$ at analysis $k=1,2,\ldots,K$ and a targeted final event count of $N_k$ . The $\alpha$ -spending at analysis $k$ was originally defined (Lan and DeMets 1983) as $f(t_k=n_k/N_K)$ . The values $n_k/N_K$ will be referred to as the information fraction , $k=1,\ldots,K$ . This is used to pre-specify the cumulative amount of Type I error for a hypothesis at each analysis. In Lan and DeMets (1989) they noted that calendar time was another option for $t_k$ values, $k=1,\ldots,K.$ Proschan, Lan, and Wittes (2006) noted further that as long as $t_k$ is increasing with $k$ , it can be used to define spending; this is subject to the requirement that under the null hypothesis, the timing must be selected in a way that is not correlated with the test statistic (e.g., blinded). We will refer to $t_k$ , regardless of its definition, as the spending time for a hypothesis. Note that the joint distribution of interim and final tests for a hypothesis is driven by $n_k$ , $k=1,\ldots,K$ . This is equivalent to basing correlation on the information fraction $n_k^{(actual)}/n_K^{(planned)}$ , $1\le k\le K$ . Thus, both spending time and information fraction are required to compute bounds for group sequential testing. Our general objectives here will be to:

Spend all Type I error for each hypothesis in its combined interim and final analyses; this requires the spending time to be 1 for the final analysis of a hypothesis.
Ensure spending time is well defined for each analysis of each hypothesis.
We will assume that both follow-up duration and event counts may be of interest in determining timing of analyses; e.g., for immuno-oncology therapies there have been delayed treatment effects and the tail of the time-to-event distribution has been important to establish benefit. Thus, we will assume here that over-spending at interim analysis is to be avoided.

Here we assume that the subgroup prevalence was over-estimated in the study design and indicating how spending time can be used to deal with this deviation from plan.

Results entry at time of analysis

Results for each analysis performed should be entered here. We begin by documenting timing and event counts of each analysis. Then we proceed to enter nominal 1-sided testing p-values for each analysis of each hypothesis.

Timing of analyses and resulting event counts and spending times

Recall that the design assumed 50% prevalence of the subgroup. Here we assume that the observed prevalence is 40% and that, by specification stated above, we enroll until the targeted subpopulation of 378 is achieved. This is assumed to occur after 22 months with a total enrollment of 940. Timing of analyses is now targeted as follows:

The first interim is scheduled 28 months, 6 months after final enrollment.
The second interim is scheduled at the later of 14 months after final enrollment (22 + 14 = 36 months after start of enrollment) or the targeted final PFS event count of 297 events. We assume the event count is reached at 34 months and that the achieved final event count is 320 in the subgroup at 36 months.
The final analysis is scheduled at 24 months after final enrollment (month 22 + 24 = 46) or when 284 events have been observed in the subgroup, whichever comes first; there is also the qualification that the final analysis will be no more than 30 months after final enrollment (6 months after targeted time). We assume the targeted event count is not reached by 6 months after the targeted final analysis time and, thus, the final analysis cutoff is set at month 22 + 30 = 52 and that at that time 270 OS events have been observed in the subgroup.

All of the above leads to event counts and spending for PFS and OS as follows:

Nominal p-values for each analysis

For analyses not yet performed enter dummy values, including a p-value near 1 (e.g., .99). No other entry is required by the user in any other section of the document. Calendar timing is also associated with PFS hypotheses for use in spending functions. Spending time for OS spending will be input as NULL so that spending will be based on event counts for OS hypotheses.

Testing hypotheses

Compute sequential p-values for each hypothesis.

Sequential p-value computation is done in one loop in an attempt to minimize chances for coding errors. We delay showing these until after display of the sequence of multiplicity graphs generated by hypothesis rejection is shown.

Evaluate hypothesis rejection using gMCPLite

We need to set up a graph object as implemented in the gMCPLite package.

Now we add the sequential p-values and evaluate which hypotheses have been rejected.

Verification of hypotheses rejected

Multiplicity graph sequence from gmcplite.

### THERE SHOULD BE NO NEED TO MODIFY THIS CODE SECTION for ( i in 1 : ngraphs ) { mx <- result @ graphs [[ i ] ] @ m rownames ( mx ) <- NULL colnames ( mx ) <- NULL g <- gMCPLite :: hGraph ( nHypotheses = nHypotheses , alphaHypotheses = result @ graphs [[ i ] ] @ weights * fwer , m = mx , nameHypotheses = nameHypotheses , palette = cbPalette , halfWid = 1 , halfHgt = .35 , xradius = 2.5 , yradius = 1 , offset = 0 , trhw = .15 , x = c ( - 1.25 , 1.25 , - 2.5 , 2.5 , - 1.25 , 1.25 ) , y = c ( 2 , 2 , 1 , 1 , 0 , 0 ) , trprop = .4 , fill = as.character ( c ( 2 , 2 , 4 , 4 , 3 , 3 ) ) ) cat ( " \n" ) cat ( "####" , paste ( " Graph" , as.character ( i ) , " \n\n" ) ) print ( g ) cat ( " \n\n\n" ) }

Comparison of sequential p-values to multiplicity graphs

We can compare sequential p-values to available $\alpha$ in each graph. In the column ‘Last Graph’ we can see one of 2 things:

For rejected hypotheses, the maximum $\alpha$ allocated to the hypothesis. For example, hypothesis one was allocated $\alpha=0.01$ in the first graph above (select using first tab). We see that the sequential p-value of 0.0001 is smaller than $\alpha=0.01$ and thus the hypothesis is rejected. We can then proceed to the second graph and see that hypothesis 5 was rejected. The last hypothesis rejected is hypothesis 3 in the third graph.
For the remaining hypotheses (H2, H4, H6) the maximum $\alpha$ allocated is in the fourth graph; since each sequential p-value is greater than the allocated $\alpha$ for the corresponding hypothesis, none of these hypotheses were rejected.

Bounds at final $\alpha$ allocated for group sequential tests

As a separate validation, we examine group sequential bounds for each hypothesis updated with 1) the maximum $\alpha$ allocated above, 2) the number of events at each analysis, and 3) the cumulative spending at each analysis above. The nominal p-value for at least one of the analyses performed for each rejected hypotheses should be less than or equal to the nominal p-value in the group sequential design. For each hypothesis not rejected, all nominal p-values are greater than the its corresponding bound. For hypotheses tested without a group sequential design, the nominal p-value for the test of that hypothesis can be compared to the maximum alpha allocated in the above table.

for ( i in 1 : nHypotheses ) { # Set up tab for hypothesis in output cat ( "####" , paste ( " Hypothesis" , as.character ( i ) , " \n" ) ) # Get results for hypothesis hresults <- inputResults %>% filter ( H == i ) # Print out max alpha allocated xx <- paste ( "Max alpha allocated from above table: " , as.character ( EOCtab $ lastAlpha [ i ] ) , sep = "" ) d <- gsDlist [[ i ] ] # If not group sequential for this hypothesis, print the max alpha allocated # and the nominal p-value if ( is.null ( d ) ) { cat ( "Maximum alpha allocated: " ) cat ( EOCtab $ lastAlpha [ i ] ) cat ( "\n\n" ) cat ( "Nominal p-value for hypothesis test: " ) cat ( hresults $ nominalP ) cat ( "\n\n" ) } # For group sequential tests, print max alpha allocated and # corresponding group sequential bounds if ( ! is.null ( gsDlist [[ i ] ] ) ) { cat ( "Nominal p-values at each analysis for comparison to bounds in table below:\n\n" ) cat ( hresults $ nominalP ) cat ( "\n\n" ) # Get other info for current hypothesis n.I <- hresults $ events usTime <- hresults $ spendingTime n.Iplan <- max ( d $ n.I ) if ( length ( n.I ) == 1 ) { n.I <- c ( n.I , n.Iplan ) usTime <- c ( usTime , 1 ) } # If no alpha allocated, just print text line to note this along with the 0 alpha allocated if ( EOCtab $ lastAlpha [ i ] == 0 ) { cat ( "Maximum alpha allocated: 0\n\n" ) cat ( "No testing required\n\n" ) } if ( EOCtab $ lastAlpha [ i ] > 0 ) { dupdate <- gsDesign :: gsDesign ( alpha = EOCtab $ lastAlpha [ i ] , k = length ( n.I ) , n.I = n.I , usTime = usTime , maxn.IPlan = n.Iplan , n.fix = d $ n.fix , test.type = 1 , sfu = d $ upper $ sf , sfupar = d $ upper $ param ) tabl <- gsDesign :: gsBoundSummary ( dupdate , Nname = "Events" , exclude = c ( "B-value" , "CP" , "CP H1" , "Spending" , "~delta at bound" , "P(Cross) if delta=0" , "PP" , "P(Cross) if delta=1" ) ) kable ( tabl , caption = xx , row.names = FALSE ) %>% kable_styling ( ) %>% cat ( ) cat ( "\n\n" ) } } }
Hypothesis 1
Hypothesis 2
Hypothesis 3
Hypothesis 4
Hypothesis 5
Hypothesis 6

Nominal p-values at each analysis for comparison to bounds in table below:

0.03 0.0001 0.000001

0.2 0.15 0.1

Maximum alpha allocated: 0.0005

Nominal p-value for hypothesis test: 0.00001

Maximum alpha allocated: 0.001

Nominal p-value for hypothesis test: 0.1

Session information

You can use sessionInfo() to document the versions of R and R packages used to render this document. Note, in particular, that version 3.1 or later of the gsDesign package is needed.

Four diagrams side by side depicting the golden ratio

An introduction to the golden ratio.

One of the most famous ratios in mathematics and design goes all the way back to the ancient Greeks. Learn more about the golden ratio and its role in art and design.

Not sure which apps are best for you?

Take a minute. We'll help you figure it out.

What is the golden ratio?

The golden ratio, also known as the golden number, golden proportion, or the divine proportion, is a ratio between two numbers that equals approximately 1.618. Usually written as the Greek letter phi, it is strongly associated with the Fibonacci sequence, a series of numbers wherein each number is added to the last. The Fibonacci numbers are 0, 1, 1, 2, 3, 5, 8, 13, 21, and so on, with the ratio of each number and the previous number gradually approaching 1.618, or phi.

History of the golden ratio.

The first known mention of the golden ratio is from around 300 BCE in Euclid’s Elements , the Classical Greek work on mathematics and geometry. Euclid and other early mathematicians like Pythagoras recognized the proportion, but they didn’t call it the golden ratio. It wasn’t until much later that the proportion would take on its mystique. In 1509, Italian mathematician Luca Pacioli published the book De divina proportione , which, alongside illustrations by Leonardo da Vinci, praised the ratio as representing divinely inspired simplicity and orderliness.

Because of Pacioli’s book and Leonardo’s illustrations, the golden ratio gained fame among mathematicians and artists. In the centuries since Pacioli’s book, many enthusiasts have claimed that the number is naturally pleasing to the eye, that it is a mathematical distillation of beauty, and that golden ratio line segments, golden rectangle side lengths, and golden triangles are represented throughout art history.

Diagram of the golden ratio superimposed over a photo of a seashell

Golden ratio enthusiasts argue that the golden ratio is aesthetically pleasing because it’s common in the natural world. The proportions of nautilus shells and human bodies are examples of the golden ratio in nature, but these tend to vary greatly from one individual to the next. Some seashells expand in proportion to the golden ratio, in a pattern known as a golden spiral, but not all shells do. It’s true that nautiluses maintain the same shell proportions throughout their life, but the ratio of their shells is usually a logarithmic spiral, as opposed to an expression of phi.

Phi does show up in other aspects of nature. Tree leaves and pine cone seeds tend to grow in patterns that approximate the golden ratio, and sunflower spirals and other seeds tend to hew close to phi. Phi allows for efficient distribution or packing, so leaves that grow in relation to the golden ratio will not shade each other and will rest in relation to one another at what is known as the golden angle.

There’s no evidence that use of the golden ratio is better than use of other proportions, but artists and designers are always in the business of creating balance, order, and interesting composition for their work.

The golden ratio in art and graphic design.

A few artists and designers have deliberately based their work around the golden ratio. Le Corbusier, a famous mid-century modern architect, based a good deal of his architectural system around the golden ratio. Salvador Dali, the surrealist painter, intentionally used a canvas shaped like a golden rectangle for his painting The Sacrament of the Last Supper . In 2001, American prog-metal band Tool released “Lateralus,” a song with Fibonacci-inspired time signatures.

Art historians have found other examples of the golden ratio in the Mona Lisa , the Parthenon in ancient Athens, and the Great Pyramid of Giza. However, most of the time there is no explicit evidence that artists intentionally used the ratio the way Le Corbusier, Dali, or Tool did. Without design notes or specifications for the pyramids, we can’t know if ancient engineers employed phi on purpose.

Drawing of a fox that utilizes the golden ratio

How to use the golden ratio in your work.

Aesthetics and design don’t adhere to strict mathematical laws. You can create a poor design that still conforms to the golden ratio, but you can use the golden ratio to inform your composition, to help you avoid clutter and create an orderly and balanced design. “On a graphic that might be pretty busy, so placement is everything,” says graphic designer Jacob Obermiller. You can use the golden ratio to help guide you.

The golden ratio can work a bit like the rule of thirds : It can be a compositional convention or guide, but not a hard-and-fast regulation about how you should structure your work. Ultimately, spacing is important and any kind of guideline is helpful. “If everything is important, then nothing is important,” says human factors engineering student Sara Berndt. If you just center every image or arrange text as a single unjustified block, you risk alienating your reader, viewer, or user. Use the golden ratio as a guideline for your work to make sure things are nicely spaced out and well composed.

Design magazine utilizing the golden ratio

With a convention like the rule of thirds or golden ratio, you can create variation and blank space that pleases the eye and makes content easy to comprehend. “The golden ratio is all about blank space and its relation to the ‘pay attention’ space,” says Berndt. “There’s only so much that people can take visually. This is a guiding principle to help you understand the limits of human attention so you can create something that is aesthetically pleasing.”

If you decide to use the golden ratio as a basis for your art or design, it can help your project look even, balanced, and aesthetically pleasing. But your ratios don’t have to be exactly 1.618 as long as you design deliberately and creatively. Regardless of the ratios and proportions you use, Adobe Illustrator can help you craft your work so everything is balanced to your own golden specifications.

Contributors

Jacob Obermiller , Sara Berndt

Share this article

Do more with Adobe Illustrator.

Language Navigation

En Ru

Constantine Konovalov

Graphic designer

About me Portfolio

2030 Moscow Metro map

Personal project in collaboration with Natalia Moskaleva ・2021

Creating a Moscow Metro map is one of the most complex design challenges imaginable. The Moscow Metro can be compared to a tangled ball of threads that you have to unravel to figure it out. We designed the map in collaboration with Natasha Moskaleva, based on long-term development plans for the construction of the Metro. It shows what the Moscow Metro will look like in 2030s. More information about the map can be found on the project’s website.

Israeli railways’ map

Personal project・2022

Israeli cities are connected by railroad running along the sea shore. Some cities can only be reached by bus. I designed a new Israeli railways’ map that includes bus routes to major cities with a population of 20 000+ people. The map’s design is based on a classic 45-degree graphic grid. The geography of the country is simplified to a vertical rectangular format bounded by the Jordan River on the right and with the Mediterranean Sea shore on the left. For the convenience of the users the distance between the stations roughly corresponds to the real map. This allows the cities within agglomerations to be visually combined together. Permanent routes are visually separated from those that only run during the rush hour or on a special schedule. The map also shows shorter versions of each route within every line, which is not represented on the official map.

The Map of Tel Aviv Metropolitan Transportation System

For Metropolitan Mass Transit System Ltd (NTA) In collaboration with Daria Vinokurova ・2023

A new transportation system is under construction in Tel Aviv, consisting of three subway lines and three light rail lines. It will connect Tel Aviv with the rest of Dan area through fast and comfortable public transportation. The project is very ambitious and highly anticipated in Israel. NTA, the company in charge of its design and construction, approached us for a new map of the system that would clearly and vividly show its structure to future users. The map represents the final state of the system. By the time the map was created, the red line had already been built and was being prepared for launch, and the green and purple lines were under construction.

Night bus routes map

For the Department of Transportation of Moscow・2016—2022

Since the Moscow Metro does not work at night, night bus routes were launched in 2013 as an alternative. The route network partly duplicates the Metro lines: 14 routes depart from the city centre and go to the residential areas, and one extra circle route goes around the centre along the Garden Ring.

Striving to improve the readability of the map, I simplified the geometry of the city by straightening the lines just like it was done on the Metro map, so I got 14 rays diverging from the centre. In the centre of the map, I worked out the geometry, so that a complex interchange area looked as simple and impressive as possible.

New Paris Metro map

Personal project・2013—2016

In 2013, I lived in Paris for several months and fell in love with the Paris Metropolitaine — it is old but so cozy. I disliked the official metro map of the Paris subway. I found it chaotic and complicated, so I decided to draw my own version of the map. The project took about 2.5 years. In my free time, I tested various map design approaches. As a result, I got a map with a round circle line and a unique graphic grid of 60°.

Many Parisians liked the map; people buy it to hang on walls of their homes. The map’s website is visited daily by thousands of Parisians and visitors to the city who need to plan their journey. I wrote a big article about the map’s design process for Smashing Magazine. While working on this project, I truly fell in love with the process of transit map design.

Pictograms of Moscow

Personal project ・2017—2020

Moscow offers a huge variety of attractions of different historical eras. I drew about 100 of the most recognisable buildings of the city in a uniform style. Today these pictograms are used on Moscow maps, transit schemes and other elements of wayfinding. While working on the project, I tried to preserve the recognisable proportions of the buildings and to convey the details. The uniformity of the pictograms makes it practical to use the entire set in various projects. Thanks to their conciseness and easy recognition, they look extra good in small size on maps, diagrams and similar formats.

Moscow Metro logo

For th e Mo scow Metro・2013—2014 In collaboration with Art.Lebedev Studio

The Moscow Metro’s logo in the form of a letter “M” emerged simultaneously with the opening of the subway in 1935, but for a long time there was no standardised; in fact, the logo changed its shape often and quite chaotically. I conducted the research and studied the history of the Metro symbol, and later we designed a standardised logo based on the historical image of the symbol in collaboration with Art.Lebedev Studio. The updated sign became the official logo of the Moscow Metro. I described the design process in detail at the Art.Lebedev Studio website.

If you are reading this, it is because your browser does not support the HTML5 video element.

For the Department of Transportation of Moscow・2018

In 2019, the Moscow Central Diameters were launched in Moscow. It is an analogue to Berlin’s S-Bahn or Parisian RER: pre-existing railway lines are integrated into the subway system to improve transit connectivity between the central city and the suburbs. I designed an official logo for the new service.

Signs for the MCD lines

For the Department of Transportation of Moscow In collaboration with Natalia Moskaleva・2019

The new MCD service needed not only a logo, but also the signs to indicate five new lines. The goal was to design signs that will blend in with the icons of the metro lines, but at the same time stand out to show that the MCD is a separate entity. Graphically, the signs are similar to the MCD logo due to the same shape, so it is easier for passengers to understand what service they represent. We selected unique colours for the signs that have not yet been used on the metro map. We also studied the prospects of the development of the subway system in order to choose the right colours so that lines with similar colours don’t intersect with each other on the map in the future.

Photos: Sasha Derivanov

Pedestrian wayfinding in Moscow

In collaboration with the team of the Department of Transportation of Moscow・2016—2018

In 2016, we designed the pedestrian wayfinding stands in collaboration with the team of the Department of Transportation of Moscow. The stands contain all the information needed by tourists or Muscovites in an unfamiliar area.

Detailed large-scale maps showing the surrounding area are always rotated to match the direction of sight. This allows to quickly orient yourself in space without thinking where north and west are. We created stands of different widths for various streets: narrow stands are used on small streets not to interfere with the passage, and wide “queen size” stands with a huge map are placed on the squares and in the parks, allowing to plan the most complex route.

Nowadays there are more than 1000 wayfinding stands in this design installed in the streets of Moscow.

Photos: Alexey Solnyshkov

Design for the GULAG History Museum

For several years, I had been doing projects for the GULAG History Museum in Moscow. I visualised complex historical data both for the museum’s exposition and for external projects related to the history of Stalin’s repressions.

The map of the resettlements of the peoples

When talking about the repressions in the USSR, we usually recall either the GULAG camps or the mass executions in the 1930s. However, another important component of the repressive machine of the era were the forced resettlements of the peoples. The relocations took place against the will of the people, based on their ethnicity, social class or political views. It was almost impossible to show the full scale of the migrations in a single frame (too many episodes of the resettlements happened simultaneously), so I decided to make a visual animation. Dots flash chronologically on the USSR’s map showing when and where people were forcibly resettled. The radius of the starting point represents the scale of the event: the more people were relocated, the larger the circle is. The map is part of the permanent exhibition of the museum.

The map of the GULAG camps

I visualised the geography of the forced labour camps in the USSR during the Stalin era in collaboration with the GULAG History Museum scientific team.

We collected data on the largest camps, each confining 5000+ people at a time. In order for the museum’s visitors to better understand the scale of the tragedy, I visualised the population of each camp based on the data from the museum’s scientific department. The red line at each camp represents the number of prisoners: the longer it is, the more people there were. We mounted the map in a way that the visitors can come close to it, find the city of interest and the camps next to it.

Two years later, we decided to convert the map to a digital format so that everyone could have access to the data online. In collaboration with the team of developers and map specialists form URBICA, we designed a website with a digital map of the camps. We added data on the number of the deaths in the camps and a timeline that allows to learn about the scale of the camp system throughout the years.

Interactive website with a map of GULAG camps

A fragment of the exposition of the GULAG History Museum that shows how the radical statements of the leaders of the Great October Revolution of 1917 gradually made way for the legal acts, and later for the repressive Stalinist laws of the 1930–1950s. On a large wall, we mounted quotes of political figures and fragments of legal documents in a chronological order. The most stringent laws that included long terms of imprisonment, exile of relatives and the death penalty as punishments, are placed on the right. The plaques are interconnected with red threads showing how particular statements and events could make way for the particular laws in the future. We used threads as the most appropriate visualisation of how political views and decisions of the leaders can lead to mass repressions against their own people.

The legal basis of the repressions in the USSR

In collaboration with Dasha Vinokurova

Stop-motion animation

In collaboration with Irina Neustroeva, we founded a video production studio Teeter-Totter-Tam, where I used to shoot commercials and festival videos in a frame-by-frame and time-lapse animation techniques. From 2010 to 2018, we shot several works that participated in 40+ festivals and shows around the world. Stop-motion animation is a fairly complex process that requires attention to detail: each frame is a separate photograph, and fast-forwarding frames creates an illusion of objects coming to life.

More projects

I described the most significant and interesting projects in detail on this page above. But there were lots of other fun projects.

Today I live in Israel, but for almost all my life I lived in Moscow, hence there are so many projects about this city in my portfolio. I love to work on large and complex projects in the fields of pubic transport and museum design.

Work experience: Freelancer 2017 — Now

Art director at the GULAG History Museum 2015 — 2017

Lead designer at the Department of Transportation of Moscow 2014 — 2016

Designer at Art.Lebedev Studio 2013 — 2014

Animation Director at Tetter-Totter-Tam Animation 2010 — 2018

Education: Bauman Moscow State Technical University Electronic Equipment Design and Technology 2006 — 2012

Twitter LinkedIn Facebook Instagram mail: [email protected]

Hispanoamérica
Work at ArchDaily
Terms of Use
Privacy Policy
Cookie Policy

Moscow Has a New Standard for Street Design

Written by Strelka Magazine
Published on August 25, 2016

Earlier this year the development of a new Street Design Standard for Moscow was completed under a large-scale urban renovation program entitled My Street , and represents the city's first document featuring a complex approach to ecology, retail, green space, transportation, and wider urban planning. The creators of the manual set themselves the goal of making the city safer and cleaner and, ultimately, improving the quality of life. In this exclusive interview, Strelka Magazine speaks to the Street Design Standard 's project manager and Strelka KB architect Yekaterina Maleeva about the infamous green fences of Moscow, how Leningradskoe Highway is being made suitable for people once again, and what the document itself means for the future of the Russian capital.

Strelka Magazine: What is the Street Design Standard and what does it include?

Yekaterina Maleeva: The Street Design Standard is a manual for street planning in Moscow . The Standard is divided into four books, each one of them covering particular aspects of street design. Many cities across the globe have developed their own standards and the concept has gained a lot of popularity over the last decade. The New York Street Design Manual is a famous example; the book has even been translated into Russian. However, Moscow streets have little in common with New York streets, for example; every city has its own unique urban typology and simply copying existing solutions from another manual is not a viable option.

When we started our work on the Standard , the first thing we did was study Moscow streets, their peculiarities and common features. The first volume of the Standard focuses on the typology and distinctive attributes of the streets of Moscow. We gathered data on more than 3,000 streets and processed the data. Despite the large sample size, we discovered certain similarities. We managed to identify ten of the most common street types, but some unique streets could not be categorized. For instance, Tverskaya Street, built in 19th century, originally fell under category "10C." But after it was widened in the 1930s, Tverskaya ended up in a unique place within the urban fabric of Moscow. Such objects as that require a case by case approach and an individual project.

What can be found in the other volumes?

After we identified these ten street types, we started working on defining the best way to approach the development of each. The second book describes what a street of each type must have. We developed a general profile and functional zoning for each type. The pavement is more than just a pedestrian lane: there is a buffer zone between the roadway and the walking lane where the parking posts, street lights and communication lines are located. It’s a mandatory utility zone that has to be paved in such a way that any section can be easily unpaved and replaced. There is also a pedestrian fast lane for people walking to their workplace and a promenade with benches and other objects. Building façades have a large impact on the street they are facing. Restaurants and shops are located in these buildings. Making the adjacent zone retail-friendly is important. Cafes and restaurants must be able to open street patios to attract customers without disrupting the pedestrian traffic. How to apply these concepts to each of the street types is thoroughly explained in the Standard .

The third book describes eleven groups of design elements, including surface materials, benches, trash bins and lights. This catalogue of elements contains no mention of suppliers. It does not promote any manufacturers; instead it describes the attributes which define a quality product. For instance, the third book explains which type of tree grates will serve the longest while causing no damage to the root system of a tree. Styles of grates, bins, benches and other elements may vary, but all the items must comply with the quality standard.

Finally, the fourth book focuses on the planning process: how to perform preliminary analysis, how to apply user opinions during the development and how to achieve quality implementation. Additionally, there is a special emphasis on the fact that street planning cannot be carried out without any regard for the context of the street. A street should be regarded as a part of an interconnected system of various public spaces, together with adjacent parks, garden squares, yards and plazas.

Does the Standard have an official status? Should it be considered a law or merely a guideline?

There are a number of state-level laws and regulations relevant to street design issued by the Moscow Government. They were taken into account during the development of the manual. These regulations ensure safety standards and must be complied with. While the existing legislation covers safety aspects, our books introduce comfort standards. The Standard is basically a non-binding, advisory guideline created with the goal of improving the urban environment everywhere across the capital and maintaining it at a high level.

What happens if a street does not fit any of the mentioned types (and is not as significant as Tverskaya)? For instance, what if a street located in the New Moscow territory has cottages on one side, apartment complexes on the other and an entrance to the Moscow Ring Road somewhere along the way?

A standard is not a ready-made solution. The streets share common features yet also retain their individual attributes at the same time. Applying a single standard profile to every street is impossible. Adjustments are always in order.

The Standard offers three sets of solutions for each type of street with a large potential for combining various elements. The manual basically offers a convenient database that a designer working with a new space could use. That does not mean that all the new projects will look exactly the same. Some solutions featured in the Standard are yet to be implemented anywhere in Moscow . For instance, our collaboration with Transsolar, a German company consulting us on environmental comfort, revealed that Moscow’s largest environmental problem was not in fact CO2, but small-particle dust produced by studded tyre traction. And a simple method to control this type of pollution already exists. Many busy streets outside the city center have a green buffer zone separating the roadway from the sidewalks. A 1.5m high ground elevation running along this zone could filter out up to 70% of the tyre dust, preventing it from spreading into the residential areas. Western countries have been successfully using this technology for many years. Now it is a part of Moscow Standard . By the way, a terrain elevation could also help reduce the level of road noise.

Does the Standard offer anything for the main roads? For example, nowadays Leningradskoye Highway basically splits the city into two disconnected parts; it’s a car dominion.

The Standard does not offer solutions for transportation problems. When we were defining our street typology, we relied on traffic load data calculated using Moscow ’s transportation model. We pursued a goal of only offering solutions that would not aggravate the current transport situation. Any planned sidewalk extension or addition of a bicycle lane or road crossing should first be approved by the Moscow Department of Transport.

As for the main roads, our research revealed that the streets with the highest traffic load also have the heaviest pedestrian traffic. One would think that it should be the other way around. However, the main roads have metro stations, which generate a lot of pedestrian traffic, which in turn draws retail. Treating main roads the same way as highways is impossible. The needs of both vehicle traffic and local residents must be taken into account, which creates a paradox.

These territories have every opportunity to become more comfortable. Some have relatively large green buffer zones that currently remain underused. The Standard proposes to augment these zones with additional functionality. On one hand, some of the main streets will gain attraction centers, especially near intersections connecting them to the adjacent residential areas. Weekend markets are one example of such centers. On the other hand, the Standard involves the creation of zones able to absorb extra precipitation flowing from the roads and filter it. There is a list with types of vegetation best fit to handle this task. The same zones could be used to store snow in the winter. The meltwater will be naturally absorbed by the soil, alleviating the need for moving the snow out to melt. This, however, would require decreasing the quantity of melting chemicals sprayed over the snow, as the plants underneath might be susceptible to their effects.

Can the new Standard rid us of green lawn fences, yellow curbs and other eternal eyesores?

The choice of yellow and green appears random, so we have no idea how to actually fight that. The Standard offers no colour schemes. As long as fences meet the set requirements, their colour does not matter. However, currently they seem to fail to comply. The Standard states that lawns do not require fencing. This is a waste of materials: people will not trample grass and bushes just for the sake of it, while dog owners will trespass anyway. There are many other options for protecting lawns from being trampled. For instance, a same-level pavement strip with a different texture could protect a lawn from accidental intruders just as well as a curb can.

Natural soil water absorption is currently largely ignored, with most precipitation going down the storm drains. Meanwhile patches of open terrain on a street are able to absorb water. Employing these natural cycles in street layout could save resources.

Does the Standard provide any financial estimations? For instance, an approximate cost of renovating a street of a particular type?

No, as the Standard does not list any products of any particular brand, there are no prices to refer to. Nonetheless, the Standard was developed to fit three potential price ranges. Whether their estimated price is low or high, all the elements ensure that quality requirements are met. The same quality level must be maintained across the whole city and never drop below the set standard.

Let’s say a world-famous architect arrives to Moscow to design a street. He puts incredibly beautiful things into his project, which, unfortunately, contradict the Standard and are not guaranteed to work as intended. In that scenario, will the architect be told to stick to the Standard ?

This could happen and I think it would be a good thing. If an architect plans to place a sculpture on a 1.5 meter wide sidewalk, would that really be a good idea? Following the Standard ensures smooth movement. Its goal is to reinvigorate the streets. In Copenhagen, new design manuals helped increase average time spent by residents outside by 20% over 10 years. That was achieved through creating convenient and attractive public spaces. Moreover, implementation of the Standard enables the creation of professional documentation for architects, which excludes the possibility of any instructions that will later be unclear to the experts trying to work with them. Finally, the Standard also pursues the task of providing the opportunity for the development of street retail.

Isn’t retail a whole different story? How can retail be introduced in such places as Strogino District, where the ground floors are living floors and have security bars on windows? By reintroducing street vendors?

True, business has no direct relation to street renovation. However, there is a strong connection between them. In Strogino, building façades are mostly located far from the sidewalk. Moreover, facades are often concealed by shrubbery and trees, making local businesses even less noticeable. Another problem is that first floor apartments cannot be used for commercial purposes due to insufficient ceiling height (3 m compared to 3.5 m required minimum). Nonetheless, we discovered multiple examples of shop owners reconstructing apartments in residential districts to meet the requirements.

Our British consultant Phil Wren, a street retail expert, travelled Moscow ’s residential districts and studied the existing examples. He came up with a great idea: building an expansion connected to the façade and facing the sidewalk. This makes it possible both to achieve the required ceiling height and increase the visibility of the business to the passers-by. The part of the shop located in the apartment can be used as a utility room or a stockroom. This way the noise level is reduced, regulations are met and store space is increased. Our Russian consultants confirmed the viability of the proposed concept. And the Standard will ensure that any added expansions will look presentable.

Does the Standard also regulate façade appearances, an architectural element? What should be expected from this? It is unlikely that all houses which fail to comply will be demolished once the Standard is implemented.

Renovation works with what is given. Of course, façades cannot be changed. Central Moscow has a problem with mansions and many other buildings being fenced off, which prevents them from accommodating street retail. Central streets are also relatively narrow. The Standard proposes sidewalk expansion wherever the access to the first floors is open. Street renovation does not always involve planting trees. Some places require enhanced crossings so that people can quickly reach the other side of the street to get to a shop or a café. Those streets where the facades are windowless are a more suitable place to plant more vegetation.

Can an average person – not an architect, designer or construction worker – understand the new Standard , or is it a technical document which can only be interpreted by a professional?

Any person can. The Standard is written in a way that both professionals and common citizens are able to understand. The Standard contains multiple images, photos, infographics and diagrams and is written in plain language. We would love for more people to read it: the books contain many interesting solutions for our city that affect every pedestrian.

In late March it was revealed that Strelka KB would be developing a standard for recreational zones and public areas in Moscow . What differences will that document have from the Street Design Standard ?

The two standards will have a lot in common. The city currently faces a task of developing a connected system of public spaces. The first logical step was to work with the streets which actually connect areas of attraction and other public spaces. Now the work on all other public spaces takes off. Parks, garden squares, yards, water bank recreation areas, plazas near metro stations must all fall into place. Work with these territories will set a single quality standard. In addition, it will improve Moscow ’s quality of life and reduce air pollution. Simple solutions could improve airflow, increase biodiversity and reduce noise levels at the same time.

The renovation program is quite long and depends on numerous standards and documents. But when exactly will the endless repair works end? Are there any time estimations for when all these concepts will finally get implemented?

This is not an easy question. Full renovation may last decades. The Standard is the first step towards actually controlling the renovation process and its timeline. Until now renovation has been proceeding rather haphazardly. Now the city has decided that the way the streets are designed should be clarified. We understand that the Standard cannot last unchanged for eternity and should, just like any regulation, undergo periodical updates. The Standard uses flexible typology: a street of one type could transition to another within a few years under certain conditions, such as changes in its usage and its user categories. Everything must stay regularly updated according to the accumulated experience.

During our work on the Standard , we held regular roundtables joined by experts and ordinary citizens. One of our guests mentioned that he had recently started paying attention to Moscow ’s facades, their beauty and their drawbacks. He was able to do that because he no longer had to watch his step. So the process has already started and we already see some results.

Sustainability

世界上最受欢迎的建筑网站现已推出你的母语版本!

想浏览archdaily中国吗, you've started following your first account, did you know.

You'll now receive updates based on what you follow! Personalize your stream and start following your favorite authors, offices and users.

COMMENTS

How to create a perfect design hypothesis
The importance of a hypothesis in the design process. There are three main reasons why no discovery or design process should start without a well-defined and framed hypothesis. A good design hypothesis helps us: Guide the research. Nail the solutions. Maximize learnings and enable iterative design.
Design Hypothesis: What, why, when and where
Design Hypothesis is important because it allows you to make data-driven design decisions that can positively impact your business. By testing and validating design changes before implementing them permanently, you can avoid wasting time and resources on ineffective design changes. Additionally, by continuously testing and analyzing the results ...
5 steps to a hypothesis-driven design process
Recruit the users you want to target, have a time frame, and put the design in front of the users. 5. Learn and build. You just learned that the result was positive and you're excited to roll out the feature. That's great! If the hypothesis failed, don't worry—you'll be able to gain some insights from that experiment.
Thinking your design with hypotheses
Graphic by Stewart McCoy Hypothesis framework. The framework we used to write our hypothesis is composed of three elements: The proposed solution solving the problem; The design's goal, that will help define if the solution is a success; The insights, research, data, etc. all evidences that are justifying the reasoning behind the proposed ...
How to create product design hypotheses: a step-by-step guide
Which brings us to the next step, writing hypotheses. Take all your ideas and turn them into testable hypotheses. Do this by rewriting each idea as a prediction that claims the causes proposed in Step 2 will be overcome, and furthermore that a change will occur to the metrics you outlined in Step 1 (your outcome).
Using Hypothesis Driven Design to Improve your Digital ...
As part of London Tech week, I spoke about Hypothesis Driven Design at an event hosted by Forge&Co.Below is a summary of the things we shared. The talk touched on how to form and write design ...
Graphic Design Theory for Creatives: The Ultimate Guide
Shumi is a self-taught graphic designer and illustrator who has worked for Toni&Guy, Renaissance Learning, Baker Ross, and many others. Graphic Design Theory can be intimidating for beginners. We will break down the basics of graphic design theory and explain everything you need to know to start creating beautiful designs!
Theoretical Frameworks for the Conceptualization of Graphic Design in Use
hypothesis for a neutral space between . theory, research and design practices. ... The selected samples were four graphic design firms, 30 graphic designers, 15 creative directors, 30 clients and ...
A Case for Graphic Design Thinking
Abstract. This chapter draws on a range of historical, philosophical and contemporary design references to argue for a broad view of graphic design thinking as a distinct approach to problem-solving. The history of epistemology is linked to the history of modes of communication, and this in turn is intimately tied to the development of textual ...
Theoretical Frameworks for the Conceptualization of Graphic Design in Use
Introduction. This paper proposes three possible theoretical frameworks for understanding how graphic design is 'animated through use'. When talking about graphic design, it is understood as the discipline that produces visual artifacts such as books, posters, advertisements, visual identities, etc.
5 Principles of Great Infographic Design
Source: GOOD. 3. Create layers. We like to think of infographics as having 3 layers: "must see," "should see," and "can see.". Information in the "must see" layer is vital to the comprehension of the graphic and needs to be made obvious. Information in the "should see" category will help comprehension and add an interesting ...
Graphic Design in Data Science » Martin's Insights
In the context of data science, graphic design is the combined science and art of visual communication. A good communication designer will choose the right tools and formats to convey a message effectively. The problem is that most graphic designers - no matter how "clever" their designs are - at the crux of it, do normally not work ...
Important Theories and Concepts of Graphic Design
Graphic design is a multi-disciplinary field that combines visual art, typography, and page layout to create a variety of communication media such as logos, websites, magazines, and books. Graphic design is an important part of any culture because it helps us to access the information we need such as product information or text messages from our friends and family.
What Is a Research Design
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.
The Impact of Artificial Intelligence on Graphic Design: Exploring the
Graphic design was once known not only as a way of storytelling but also as a process of scenario planning and problem solving that involved looking ahead and projecting various futures (Lupton 2017). This resemblance to speculation and experimental futures led us to employ a speculative approach methodology in this study, in addition to the co ...
Graphical testing for group sequential design • gMCPLite
Group sequential designs for each hypothesis. For the example, we assume 1-sided testing or a non-binding futility bound as required for a Maurer and Bretz (2013) design using group sequential design with graphical multiplicity control. Each is demonstrated in the example code for respective hypotheses.
Hypothesis designs, themes, templates and downloadable graphic elements
Discover 19 Hypothesis designs on Dribbble. Your resource to discover and connect with designers worldwide. ... Inspirational designs, illustrations, and graphic elements from the world's best designers. Want more inspiration? Browse our search ... Hypothesis-driven design - next part of the case study Like. Ira Nezhynska Pro. Like. 50 ...
Golden ratio: A beginner's guide
What is the golden ratio? The golden ratio, also known as the golden number, golden proportion, or the divine proportion, is a ratio between two numbers that equals approximately 1.618. Usually written as the Greek letter phi, it is strongly associated with the Fibonacci sequence, a series of numbers wherein each number is added to the last ...
Browse thousands of Hypothesis images for design inspiration
Explore thousands of high-quality hypothesis images on Dribbble. Your resource to get inspired, discover and connect with designers worldwide. ... Hypothesis Design Mapping Like. Felix Alvarado. Like. 39 10.2k View Hypothesis V. Hypothesis V Like. theyellowfabrik. Like. 5 327 Shot Link. View Task Buddy: a study of content strategy and UX ...
Constantine Konovalov
Konovalov. Graphic designer. About me Portfolio. 2030 Moscow. Metro map. Personal project in collaboration with Natalia Moskaleva・2021. Creating a Moscow Metro map is one of the most complex design challenges imaginable. The Moscow Metro can be compared to a tangled ball of threads that you have to unravel to figure it out.
Moscow Has a New Standard for Street Design
Published on August 25, 2016. Share. Earlier this year the development of a new Street Design Standard for Moscow was completed under a large-scale urban renovation program entitled My Street, and ...
Study Master's degrees in Moscow, Russia
Moscow has long, cold winters usually lasting from November to the end of March. Temperatures can fluctuate between the city centre and the suburbs between 5-10°C (41-50°F). Heat waves may occur during summer. Average low temperatures are -10°C (15°F) in February, while average highs reach 24°C (76°F) in July. Study a Master's degree in ...
Valeriya Pak
Business analyst · Checking hypothesis in the different places of mobile app Leroy Merlin. Managering development team, communicate with steak-holders and designers. · Experience: Plata Card · Education: Plekhanov Russian University of Economics · Location: Serbia · 475 connections on LinkedIn. View Valeriya Pak's profile on LinkedIn, a professional community of 1 billion members.