If you’ve been following my blog recently you know that I produce a podcast called Choosing Your Reflection, which is based on my two experiences: choosing my own wedding dress and helping my daughter choose hers.

The goal?

Through discussions with guests we attempt to “unravel the mystique that exists around choosing a wedding outfit.”

During the early stages of production I also created and distributed an online Wedding Outfit Survey to assist with gathering insight on why people make the choices they do. In addition to basic demographic questions like gender, age, and relationship status, the survey also included a key question:

What was the MOST important factor guiding your choice of wedding outfit?

– Cost
– Wedding Theme
– Body Shape
– Comfort
– Perfection
– Other

When I composed the question I thought I had covered the bases pretty well. I also assumed that cost would be the predominant answer. I was close, but I was wrong.

The top ranking answer from 165 respondents? Body shape.

That being said, since cost is such an important attribute of the process I believe it warrants further investigation.


For most people, at least in my demographic, cost is a constraint. So I dug up some stats on what the average bride spends on her wedding dress. No stats on the men yet — sorry guys!

The most expensive region was the Mid-Atlantic (e.g. New York and New Jersey), while the northern Midwestern states (e.g. North and South Dakota) came in at the low end of the scale.


How about looking at how many couples actually got married in each state? Here’s a visual of that information from 2019 published by The Wedding Report.

  • Wyoming couples numbered 4163, the lowest across the nation.
  • California came in at a whopping 214, 562 weddings, putting it in first place.

Finding some data on the cost of a wedding outfit as a percentage of total income would also be interesting, as well as taking state population density into account. Here’s a map that contains median individual income across the country.

And let’s not forget that some states are much less densely populated than others.


As I spent more time with the survey results I made an interesting qualitative discovery — the other category contained 21 responses. Things like:

  • Color
  • Convenience
  • The way it made me feel
  • My husband-to-be’s opinion
  • Mother
  • Pregnancy
  • That I could wear a real bra with my dress

These 21 answers, each taken by themselves, are outliers.

In statistics, an outlier is a data point that differs significantly from other observations (“Outlier,” 2020).

Using the survey statistics, my own experience, and what I’ve learned from the guests we’ve interviewed on the podcast I’m confident I can provide some insight as to what drives people during the wedding outfit process.


“When we reason about quantitative evidence, certain methods for displaying and analyzing data are better than others” (Tufte, 1997, p.27).

In his book Visual explanations: Images and quantities, evidence and narrative American statistician Edward Tufte provides guidance for providing what he calls “truthful, credible, and precise findings” from data (Tufte, 1997).

  1. Place the data in an appropriate context for assessing cause and effect.
  2. Make quantitative comparisons.
  3. Consider alternative explanations and contrary cases.
  4. Assess possible errors in the numbers reported.

By adding addtional data sources to my existing dataset and using Tufte’s method I hope to shed some light on the wedding outfit traditions that exist in the United States.

Stay tuned!

until nxt time …


Average Income by State, Median, Top & Percentiles [2019]. (2019, November 4). DQYDJ.

Browse Markets for Wedding Statistics. (n.d.).

Outlier. (2020). In Wikipedia. Page Version ID: 966353403

Population density in the U.S., by state 2019 | Statista. (n.d.). Retrieved August 9, 2020, from

Shaw, G. (2019, May 23). What the average bride spends on her wedding dress in every state—Insider.

Tufte, E. R. (1997). Visual explanations: Images and quantities, evidence and narrative. Graphics Press.

Tufte, E. R. (2006). Beautiful evidence. Graphics Press.

Header photo by Vladislav Reshetnyak from Pexels


Ever wonder how they make those awesome maps you see in the New York Times?

(Glanz et al., 2020)

Very often the creators use a dataviz software application like Datawrapper.

“Datawrapper is an open source data visualization platform which helps everyone create simple, correct, and embeddable charts in minutes” (Datawrapper in 2020 – Reviews, Features, Pricing, Comparison, 2018).

Datawrapper gives you the ability to create three types of maps:


Since my podcast Choosing Your Reflection is an ongoing passion project of mine, I went in search of some wedding data to create some data maps that might be of interest to our listeners.

Keep reading if you’d like to learn some interesting wedding stats and see the visuals I created using Datawrapper.


Weddings are expensive, and depending on where the couple decides to hold their nuptials, they can be REALLY expensive. To get the breakdown by state I used Chris Moon’s article in ValuePenguin (n.d.). Here’s a snapshot of the data.

This type of data can be visually presented using a choropleth map.

Choropleth maps are popular thematic maps used to represent statistical data through various shading patterns or symbols on predetermined geographic areas (i.e. countries). They are good at utilizing data to easily represent variability of the desired measurement, across a region” (DeLorenzo & Dugger, n.d.).

This choropleth map uses shaded areas of the various intensities of the color green to represent the differences in the data; in this case total cost of a wedding.

Data captured from article by (Moon, n.d.)

In my example the darkest shades show the highest cost, so you can very easily see that California, Alaska, Hawaii, and several states in the New England region top the list.

The image above is a screen shot. I think it’s pretty impressive but you can also make your maps interactive in Datawrapper. You can check out my interactive version here.


Now this may seem like it’s the same data, but it’s not. Since the list is limited to the most expensive places, many states were not included in the data. Take a look.

A symbol map is a better choice for this dataset.

“A proportional symbol map is easy for map readers to understand. Multiple variables can be displayed simultaneously on a proportional symbol map. For example, the symbol’s size, symbols color, and symbols a shape can all represent different variables” (OLD-Cartography Chapter 4 Combined – Types of Maps, n.d.).

You can also check out the interactive version of my symbol map for more details.

The largest (and darkest green) symbol falls in around the New York city range ($66,000 – $96,000 range, yikes!).

But wait, wasn’t Hawaii the most expensive place to get married in the first set of data? Absolutely. Since the first dataset was a state average (rather than region like Manhattan) New York actually fell behind Hawaii, New Jersey, DC, Massachusetts, and Connecticut.

How the data is “sliced” makes a difference.

“How easy it is to forget, and how revealing to recall, that map authors can experiment freely with features, measurements, area of coverage, and symbols and can pick the map that best presents their case or supports their unconscious bias” (Monmonier, 2018, p.2).

Maps are used to deliver a message to the viewer. As the receiver you should employ a healthy skepticism towards the visual to ensure you are not being sold a bad bill of goods.

“Choropleth maps have the ability to represent a large amount of data over any amount of space in a succinct and visually appealing manner. However, this method is not ideal for representing data realistically. Pre-existing boundaries limit the map’s ability to display the true fluctuation in statistics throughout an area” (DeLorenzo & Dugger, n.d.).


So let’s pretend money is no object and a couple wants to have their wedding in Manhattan. There are a lot of hotels in New York City, so the couple might want to create a locator map to indicate which hotels they recommend for their guests. Fire up Datawrapper and within a few minutes voilà!

Locator maps are great to show where something is located or happened, e.g. events within a city” (Maps, 2019) .


Want to try it yourself? Here’s a quick video overview to get you started.

(Kokkelink, 2016)


until nxt time …


Cairo, A. (2016). The Truthful Art: Data, Charts and Maps for Communication. New Riders.

Datawrapper. (2019, September 24). Maps. Create Charts and Maps with Datawrapper.

Datawrapper in 2020—Reviews, Features, Pricing, Comparison. (2018, July 30). PAT RESEARCH: B2B Reviews, Buying Guides & Best Practices.

DatawrapperIntro. (n.d.).

DeLorenzo, N., & Dugger. (n.d.). Story Map Journal.

Glanz, J., Carey, B., Holder, J., Watkins, D., Valentino-DeVries, J., Rojas, R., & Leatherby, L. (2020, April 2). Where America Didn’t Stay Home Even as the Virus Spread. The New York Times.

How to create a choropleth map—Datawrapper Academy. (n.d.).

How to create a locator map—Datawrapper Academy. (n.d.).

How to create a symbol map—Datawrapper Academy. (n.d.).

Kokkelink, D. (2016, April 8). Create a Datawrapper Map in three minutes—YouTube.

Maps. (2019, September 24). Create Charts and Maps with Datawrapper.

Marchese, C. (n.d.). Maps.

Monmonier, M. (2018). How to Lie with Maps. The University of Chicago Press.

Moon. (n.d.). Average Cost of a Wedding: By Feature and State. ValuePenguin.

OLD-Cartography Chapter 4 Combined—Types of Maps. (n.d.). ArcGIS StoryMaps. Retrieved August 2, 2020, from


“Fewer than one-in-five U.S. adults say being married is essential for a man or a woman to live a fulfilling life, according to a Pew Research Center survey conducted in summer 2019″ (Barroso, 2020).

In spite of that statistic people still get married, and some spend a significant amount of money doing so. Let’s take a look at some datasets that show just how much the big day can cost as well as whether or not people believe that spending more on their wedding outfit leads to finding what they believe to be their “perfect” attire.


Similar to buying a home, where a couple holds their ceremony and reception has a huge effect on the cost of their nuptials. The data listed in the table below was taken from a survey of more than 14,000 brides and grooms who tied the knot in 2018.

New York, NY—February 14, 2019Today, The Knot releases findings from The Knot 2018 Real Weddings Study, the most comprehensive study of Americans married in 2018. The 12th annual wedding industry report, the most trusted and comprehensive of its kind, surveyed more than 14,000 US brides and grooms married in 2018 between the ages of 18 and 65+ to uncover how couples are planning, personalizing, spending and celebrating weddings in America.

This data serves as the basis of information we could present and filter by:

  • region
  • state
  • amount

Displaying this data on a United States map would provide an excellent overview for the viewer. Adding the 10 Most Affordable Places To Get Married could provide added interest for comparative purposes.

(Couples Spend on Average $33,931—The Knot 2018 Real Weddings Study, 2019)


Many couples are on a budget, so categorizing costs is important. One of the brides we interviewed for the Choosing Your Reflection podcast recommended choosing your top three categories and downsizing the budget of the remaining categories to allow yourself to splurge in the top three (she named outfit and photographs for two of her top three).

This data was pulled from Wedding Wire’s 2020 Wedding Report, based on responses from over 25,000 U.S. couples married in 2019. This could be filtered by:

  • total cost
  • category
  • sub-category

A tree map, pie chart, or bar chart would work well to display this data visually.

(2020 Wedding Report, n.d.)


Does spending more money make it easier to find the perfect wedding outfit? This is a sample from a dataset consisting of 164 responses gathered in my Wedding Outfit Survey.

(Foster, n.d.)

Comparisons could be made based on:

  • age group
  • relationship status
  • type of ceremony (religious or secular)

The original dataset was drawn from a survey that consisted of twenty-five questions. I narrowed it down to those responses that might influence the respondent’s answer to money’s influence (or lack of) on finding the “perfect” outfit. This process is called operationalization.

Operationalization in the context of visualization is the process of identifying tasks to be performed over the dataset that are a reasonable approximation of the high-level question of interest” (Fisher & Meyer, 2018).


“Fake news” is a phrase we hear all too often recently. Although it is often misused and in itself has become misleading, it’s important to ensure that the datasets used to create visualizations are trustworthy.

In Emma Charlton’s blog post from the World Economic Forum, her visualization adeptly shows how Finland is leading the European nations by using education to encourage media literacy in schools.

“Studies show a positive relationship between the level of education and resilience to fake news, the OSI report said, with more knowledge and better critical-thinking skills guarding against fabricated information” (Charlton, n.d.).

Whether or not you trust a visualization is up to you. Make it your habit to check the creator’s source; it should be listed on the chart. If you can’t find it you might want to think twice about the chart’s reliability.

And don’t forget to check and see if the source cited is reputable. In this way you can guard against believing any “fake news” that may have been used to create a “fake visual.”

until nxt time …


2020 Wedding Report. (n.d.). WeddingWire.

Barroso, A. (2020, February 14). More than half of Americans say marriage is important but not essential to leading a fulfilling life. Pew Research Center.

Charlton, E. (n.d.). Fake news: What it is, and how to spot it. World Economic Forum.

Charlton, E. (n.d.). How Finland is fighting fake news in the classroom. World Economic Forum.

Couples Spend on Average $33,931—The Knot 2018 Real Weddings Study. (2019, February 14). The Knot Worldwide.

Fisher, D., & Meyer, M. (2018). Making Data Visual. O’Reilly Media.

Foster. (n.d.). Wedding Outfit Survey—Google Forms. Retrieved July 26, 2020, from

Fuller, B., & Jacobson. (n.d.). The 10 Most Affordable Places to Get Married in the US. Theknot.Com.

Kilroy. (2018, November 14). 100+ of the Best Free Data Sources For Your Next Project. Column Five.

Header photo by Bùi Huy from Pexels


Information design can be divided into two main categories, exploratory and explanatory (aka declarative), shown as the bottom and top points on the vertical axis in the above chart (Marchese, n.d.). These categories can be broken down further by delineating between content that is conceptual or data-driven, as shown on the horizontal axis.

Scott Berinato, senior editor at the Harvard Business Review and author of the book Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations describes how choosing the right category can be helpful in planning and creating good visualizations.

Appropriately, he provides a visual to foster our understanding. Starting in the upper left-hand corner and moving counterclockwise, the four quadrants are:

  • Conceptual & Declarative (Idea illustration)
  • Conceptual & Exploratory (Idea generation)
  • Data-driven & Exploratory (Visual discovery)
  • Data-driven & Declarative (Everyday dataviz)

Let’s briefly discuss each of the categories and look at examples of each.

(Berinato, 2016)

Need to explain a process or a concept? Want to show how your organization is structured? Need to show a process flow? Illustrating ideas is the domain of the Conceptual & Declarative category of visualization (Berinato’s 2 x 2 grid is in itself an example).

The flowchart below is one I created to explain the training approval process to my colleagues at work.

NJ Department of the Treasury Training Approval Flowchart

In my Typical Approval Process chart the concept I am sharing is the approval process. My declaration provides an answer a question I hear all too often, which is “What do I need to do to get the training I need?”

Note that both examples provide simple images that help facilitate the user’s understanding of a particular concept or process.

(Berinato, 2016)

Conceptual & Exploratory visuals can be created alone or with a team. Because they are idea-driven they are often written on a whiteboard or similar surface, allowing for the incorporation of ideas of how to “find answers to nondata challenges” (Berinato, 2016).

The example below shows how they’re used to solve problems; in this case what should be done to increase sales.

(Roam, 2013)

This flowchart is from Dan Roam’s book The Back of the Napkin, which provides guidance on how to use visual thinking to generate ideas and solve problems.

“There is no more powerful way to prove that we know something well than to draw a simple picture of it. And there is no more powerful way to see hidden solutions than to pick up a pen and draw out the pieces of our problem” (Roam, 2013).

(Berinato, 2016)

Data-driven & Exploratory visualizations can be used in two ways: for confirmation of information believed to be true or for exploration of answers to specific questions. It’s often used by data scientists and business intelligence analysts (Berinato, 2016).

This type of visual discovery can be a catalyst for positive change that could have been otherwise overlooked.

The University of Illinois Chicago College of Pharmacy hired the Urban Data Visualization Lab (UDVL) to create a series of maps that could be used for their strategic planning.

UDVL took address data for locations of different types of colleges and different types of pharmacies throughout Illinois and geocoded them to create points on a map. Pharmacy and college symbolization was based on attributes supplied by the client. Illinois counties were symbolized based on the RUCA (Rural Urban Commuting Area) codes joined to county boundaries. Interstate highways and major water bodies were added for reference. Work was done primarily in GIS software” (College of Pharmacy Strategic Planning | Urban Data Visualization Lab | University of Illinois at Chicago, n.d.).

This customized data visualization was reported as making the client’s strategic meetings “more productive” (College of Pharmacy Strategic Planning | Urban Data Visualization Lab | University of Illinois at Chicago, n.d.).

“The goal is simple: give people factual information based on data that is, for the most part, not up for debate” (Berinato, 2016).

(Berinato, 2016)

Many Data-driven & Declarative visualizations are created with applications (like Excel) and are used for presentations. You’ve probably created a few line charts, bar charts, and scatterplots yourself; the trick is to keep it simple and focus on the point you are trying to make.

This Incubation Periods chart from data-journalist David McCandless is a great example of on point simplicity .

(McCandless, n.d.)

Based on data from the US Centers for Disease Control and Prevention and the World Health Organization, this beautiful line chart communicates the differences in incubation periods for various illnesses, highlighting COVID-19 in orange text for emphasis.

The message must be simple and able to be decoded easily by the viewer. Berinato says “A manager should be able to present an everyday dataviz without speaking at all” (2016).


Ready to get started? According to Berinato, you should begin by asking yourself two questions:

  1. Is the information conceptual or data-driven?
  2. Am I declaring something or exploring something?

The answers can help you choose how to display your information in a way that communicates your message effectively.

Four Categories of Data Visualizations

“Visualization is merely a process. What we actually do when we make a good chart is get at some truth and move people to feel it—to see what couldn’t be seen before. To change minds. To cause action. … But good outcomes require a broader understanding and a strategic approach” (Berinato, 2016 June 1).

until nxt time …


Berinato, S. (2016, June 1). Visualizations That Really Work. Harvard Business Review.

Berinato, S. (2016). Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations. Harvard Business Review Press.

College of Pharmacy Strategic Planning | Urban Data Visualization Lab | University of Illinois at Chicago. (n.d.). Urban Data Visualization Lab.

Marchese, C. (n.d.). Information Design Processes.

McCandless, D. (n.d.). COVID-19 #CoronaVirus Infographic Datapack. Information Is Beautiful.

Roam, D. (2013). The Back of the Napkin (Expanded Edition): Solving Problems and Selling Ideas with Pictures (Expanded edition). Portfolio.


“The greatest value of a picture is when it forces us to notice what we never expected to see.” – John Tukey, American Mathematician

You may have heard the terms information design or infographics, but both share the common ground of using a picture, or visual, to display information.

Information visualization (also referred to as data visualization) is not new. Michael Friendly delivers a brief history in his book Handbook of Data Visualization, offering a chart which depicts “the frequency of events considered milestones” with milestones defined as significant visual depictions created during each time period as shown in the reproduction of his chart below (Friendly, 2008).

(Friendly, 2008)

Time, or each “epoch” is measured on the horizontal axis while density (an estimation based on the visual milestones chosen) is measured on the vertical axis.

Density estimation is estimating the probability density function of the population from the sample” (An Overview of Density Estimation, n.d.).

As you can see there is a slow rise from the 1500s to the early 1700s, transitioning to a much faster increase in the use of visuals as we moved closer to the 20th century, considered the Golden Age by Friendly.

Enhancing visual form

To heighten my understanding of what caused this rise to occur I added icons representing the types of visuals popular during each time period.

Taking it one step further I chose to move the timeline to the top of the diagram. I also moved the descriptions of the popular visuals closer to each icon and used color to indicate the change in centuries.

(Foster, 2020)

Although these additions are helpful they do not tell the entire story, nor do they intend to. It is, however, a reminder of what makes a good visualization. Additional elements are needed to provide full disclosure. The trick is to do it without overwhelming the viewer.

Data-journalist and information designer David McCandless proposes four elements necessary to deliver a good visualization:

  • information (the data itself)
  • visual form (its appearance)
  • goal (its function)
  • story (the concept behind the visual)
(McCandless, 2010)

If you overlay these elements onto Friendly’s graph, the evolution becomes more evident. At the base of the visual below you will see that information is always the foundation. As you move along the timeline note the layering of form, goal, and story as data visuals move toward the 21st century.

(Foster, 2020)

Case in point: Florence Nightingale’s Diagram of the Causes of Mortality, delivered in 1801, stands out as an example of a worthy goal — to convince the British government to expend public funds to improve city sanitation and help control epidemic disease.

(Small, 2010)


As designers we bear an intellectual burden.

Intellect is a term used in studies of the human mind, and refers to the ability of the mind to come to correct conclusions about what is true or false, and about how to solve problems” (Intellect, 2020).

Do you use the elements of data and form responsibly to achieve an honest goal? Are you treating the viewer with the respect they deserve by allowing them to come to an objective conclusion given the visual’s compressed format? Is the format clear and understandable?

“… visualizing information … is a form of knowledge compression. It’s a way of squeezing an enormous amount of information and understanding into a small space.” (McCandless, 2010).

Good information visualization takes time and you must consider the following questions:

  • What data should I use?
  • Is there data that can be safely left out?
  • How much information is too much?
  • Is it possible to stay neutral and reach the widest audience yet still be effective?


In short, what makes a good visual? Consider this visualization of the Coronavirus Riskiest Activities by David McCandless — it is the best example I have seen during these stressful pandemic times. It does not lean left or right, it uses size and color appropriately, and allows the viewer to determine their course of action based on information from over five hundred epidemiologists and health professionals.

These are the facts; you get to decide. Definitely milestone quality in my opinion.

(Information is Beautiful, 2020)

“As visual communicators, we must carefully consider the content of a message, the efficiency of how we deliver that message, technology used for its implementation, and the ultimate impact that it has” (Marchese, n.d.).

What do you think?

Stay safe.

until nxt time …


An Overview of Density Estimation. (n.d.). KDnuggets. Retrieved July 12, 2020, from

Cartogram Maps: Data Visualization with Exaggeration. (2016, September 18). GIS Geography.

Dykes, B. (n.d.). 31 Essential Quotes On Analytics And Data | Web Analytics Action Hero. Analytics Hero.

Foster, H. (2020). Visual Milestones.

Friendly, M. (2008). A Brief History of Data Visualization. In C. Chen, W. Härdle, & A. Unwin, Handbook of Data Visualization (pp. 15–56). Springer Berlin Heidelberg.

Information is Beautiful (2020). COVID-19 #CoronaVirus Infographic Datapack. Information Is Beautiful.

Intellect. (2020). In Wikipedia. Page Version ID: 965398954

Marchese, C. (n.d.). History of Data Visualization.

McCandless, D. (2010). The beauty of data visualization.

Small, H. (2010, October 7). Did Nightingale’s ‘Rose Diagram’ save millions of lives? Retrieved from: