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Choose Right Chart Type for Data Visualization. Part 7: Geovisualization with Maps (Geo-Related Data)May 24th, 2017 by AnyChart Team
Geovisualization and map-based analysis of geo-data sets related to given territories or spatial environments can provide significant insight into trends and assist greatly in exploring and associating the impacts of variables. Nowadays, maps are used for data visualization very frequently, both as standalone geovisualizations and part of complex dashboards.
In this new article within the framework of our Choose Right Chart Type for Data Visualization series, we’ll write about map charts and explain how (when) to properly use each of corresponding types.
Hello everyone! We continue our effort to find and publish the best data visualization examples on a regular basis. The current issue of Data Visualization Weekly lists and narrates about another four cool charts and maps of those that were shared on our Twitter and Facebook accounts just recently. They are:
- American Workday;
- US Jobs by Industry;
- The World’s Conflicts;
- Climate Explorer.
Without more ado, let’s get down to seeing them now!
Achieving and maintaining effective Project Management is one of the most important challenges for every company. And high-quality visualization of resource usage and activity processes is a great tool for significantly improving the overall performance of a project. Keeping that in mind, we cannot omit the information on how to properly represent projects and resource data.
Read this article to learn what (specific) chart types you can choose to use in such cases.
Visualizing data is a great way to facilitate its exploration and explanation. The new Data Visualization Weekly issue brings you, as always, some peculiar charts, maps, and other nice examples of how that can look. Today let’s see another cool set of carefully selected visualizations, four of those that we noticed and shared on social media over the past few days:
- ResistoMap: drug resistance in human gut microbiota in different countries;
- Diagram of tools that Cisco uses for enhancing digital engagement;
- Chart of Apple Services’ revenue growth;
- Video visualization of carbon dioxide’s behavior in the Northern Hemisphere.
In the previous articles from the Choose Chart Type for Data Visualization series, we covered the ways to visually represent information for data comparison, composition and distribution analysis, and observing trends over time. The current post sheds light on a situation when you only have Single-Value Data that can serve as Indicators of the current performance. In this quite widespread case, plotting each value on a separate chart often makes sense. That is where Gauges and Bullet charts come into play.
Data visualization techniques are an amazing means of communicating information. Their proper implementation enhances understanding of data and can be very helpful in clarifying (and even revealing) differences, trends, relationships, and other patterns and related aspects within data sets.
We keep on showing you interesting data visualization examples on a regular basis, within the framework of Data Visualization Weekly (and in earlier recaps of the week). And we hope you’ll find the current issue of the series worth checking out, great as another portion of inspiration and examples of how data visualization techniques work, or – at least – just interesting in terms of facts and trends communicated.
So, here’s a small selection from what we noticed on the Web and shared on social networks within the last seven days.
Displaying and researching some Data Distribution and relationship between data sets instead of studying precise values in each category is a quite common task in data analysis. It can be solved with the help of the chart types that we are going to identify and explain in this article.
Depending on a situation – the kind of data you have and the specific questions you’d like it to provide answers to – you can pick one approach or another. Just be careful when choosing between one chart type and another for the subsequent data distribution analysis. You want the visualization to clarify data, not obscure it or deliver any sort of confusion. Well, simply make sure you understand the following aspects, mind all the details of your situation, and you will have no problem with visualizing data distribution correctly.
- Closure library and Closure compiler were updated to version 20161024.
Now, you are welcome to read the SitePoint article about GraphicsJS. Please do not forget to ask your questions, if any. You may do that by leaving a comment here right below the article.