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Seven tips to help government employees better visualize data

14 minute read
We live in a data-driven world of billions of ubiquitous microdata dots that enable us to measure everything from air pollution to biodiversity. Perhaps average weight, ideal height and happiness of human beings.
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We live in a data-driven world of billions of ubiquitous microdata dots that enable us to measure everything from air pollution to biodiversity. Perhaps average weight, ideal height and happiness of human beings. To understand all this, The so-called graphic imaging or data imaging has arisen, It is a tool that the government sector still does not use sufficiently despite its proven effectiveness.

Data visualization tools such as graphics, dashboards and interactive materials have become essential tools for government and healthcare sector employees as they help them communicate important information about the spread of COVID-19 and its impact on the world. Effective data visualization tools can quickly summarize critical information for the benefit of a large number of individuals. It has recently begun to be used to prepare accurate reports on the coronavirus outbreak.

But simply acquiring data is not enough; effective data visualization requires different types of skills such as storytelling, data analysis, and design. This article includes seven tips to help create effective graphs that result in clear and accurate information.

Data visualization is a form of storytelling

Before you start designing data images, You should understand the data in front of you and the story you want to tell with that data. One of the best types of data visualization is the one that creates clear and simple stories that attract the recipient and attract the interest of Internet users. We can liken the professional design of data images to the work of a journalist who must ask several simple questions such as: Who is the target audience? How can the story be told from his perspective? What are the most important points you want the audience to understand? How can the data be used to solidify these points? Why are recipients interested in this data? How skilled are users at understanding the data and analytics you will provide?

Transparency is very important

All data images start from a raw data source that needs retouching and analysis to communicate the story you want to tell. This process involves several steps, From getting the data to coming up with an accurate story that delivers benefit and insights to the user. I will not go into the details of these steps here, However, I will emphasize the need to take into account the steps and methods that I used to revise and interpret the data at the preparation stage.

These steps should be included in the methodology section or in the notes under the images. You must be transparent about how you handle missing data and the assumptions you rely on to redact information. I emphasize once again that graphic photography is a powerful tool that can provide clear and accurate information that helps the user make better decisions. In contrast, It can be used as a tool to mislead and manipulate individuals. Therefore, Transparency and clarity about the data source and the steps for preparing images are crucial.

Select the best and most appropriate image to represent the data

We all agree that a column graph is of course the traditional way to visualize data. Same to you There are many new ways that may be more effective in communicating and representing data. When I start designing a graphic photography project, I often go to Project Data Viz to see the different types of images that can be used to view data comparisons, relationships, distribution, and representation of geospatial output.

In addition to this source, I like to start by drawing my thoughts on a piece of paper and the drawing doesn't have to be tidy or clear to others at this point. This drawing can be used to visualize the story and think creatively in ways of visualizing data. I'm trying to come up with at least three different options, So when I have a number of visualizations for a single project, I can use drawings to develop the initial visualization of the project I'm working on.

Use design elements wisely

When you work as a data visualization designer, You have to consider many different design elements (such as color, size, shape, capacity, texture, location, and orientation). Let's take maps for example, According to French cartographer Jacques Burton, all you need to map (and other graphic-based data representations) is to board a plane and have three limitations: Points, lines and polygons. It displays values and differences in data by changing variables in these three parameters, such as changing color, location, capacity, size, and others. For example The colors used in an image can have an important impact on the way users interpret the data. Warm colors such as red or orange often indicate loss or danger. As for other colors such as blue and green, It may indicate growth and positive results.

Be sure to choose the right colors to enhance your story instead of distracting the audience. I see that the Color Hunt website helps in choosing the right colors for photography. You can also hire a graphic designer from within your organization or hire a consultant to provide valuable resources and help you decide on your design elements and create striking images without the need for confusing and unorganized components.

Time is an important variable

Working in the government sector means that the data in front of you expresses people and their interactions with their communities and the environment around them. When working with data and images that represent the social world, Time becomes an important variable to share. When an image expresses data over a certain period of time, Explain how the data varies over time. In Western culture, for example, Time from left to right is often represented horizontally. There are other ways to represent time, But it is important that these methods are clear to the user. Even when the data represents a single period of time, It is also important to clarify this point and provide additional information on why to choose this particular time period.

Feel free to share mistakes

Of course, all analyses of statistical data involve an element of error; for example, when calculating the mean, the standard deviation and error can also be calculated in the mean. Error is an important part of the information that the user must know in order to achieve transparency. Statistical error is often expressed in the footnote, However, to achieve a higher level of transparency, the error can be shown in pictures by changing the shadow around a line or point. Or you can design an interactive image to provide standard error statistics using a hint tool.

Take advantage of the huge amount of free tools available to you

There are many electronic tools that government sector employees can use to visualize data. The most popular sources are Google Charts and Tableau Public each of which has a comprehensive library of data visualization methods. You can also visualize statistical data using Microsoft and Adobe products. If you're looking to develop creative and interactive data images, It is worth looking for open source packages that require expertise in codology. On the other hand Scientific analysis tools like Python and R provide different packages such as ggplot, plotly, highcharter, Matplotlip, and Bokeh that can be used to design effective images. For interactive data imaging locations, I recommend D3.js and P5.js for exceptional storytelling tools, These sites require knowledge of Java and HTML/CSS languages.

The constructive use of graphic photography meets the purposes of expressing an idea or story to trigger a particular action. When I worked as a Zollberg Institute Fellow with the International Relief Committee in Jordan, The team needed to communicate the findings of a number of focus groups on the health concerns of Syrian refugees within a short period of time. In addition to the full report, She used graphic imaging and machine learning to provide a visual summary of the results as well as enable users to learn more by reading excerpts from refugees who spoke about their experiences with the healthcare system. The images served as a means of quickly presenting results and illustrating the methods used to analyze the data.



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