Requirements: Python (to run a simple HTTP server using the Python's built-in 'http.server' module). Other required javascript libraries are included in the assets folder.
- Clone this repository
- Change directory to the folder where this repository was cloned
- Run the below command: python -m http.server 8000
- Now to access the visualization by navigating to http://localhost:8000 in your web browser.
Communication-minded visualizations are data visualizations designed with a primary focus on effectively conveying information, insights, and messages to a specific audience. The main goal of these visualizations is to facilitate clear and concise communication of complex data and analysis results to non-experts or decision-makers, enabling them to grasp the key takeaways easily.
Summarized line graph visualization technique is designed specifically for data analysts to communicate data to decision-makers more effectively and efficiently. The summarized line graph reduces a large and detailed dataset of multiple quantitative time-series into three components.
- Representative Data: provides the audience with a simple but precise description of the whole dataset.
- Analytical Highlights: are added to the visual summary as the second visual component to reduce the time needed to extract useful insights from the dataset, to ensure it is clear how the insights are extracted, and to minimize the loss of important discoveries during the exploration. This component should be designed to address the specific insights of interest to the decision-makers.
- Data Envelope: The Data Envelope summarizes the remaining aggregated data to put the first two components into context and, therefore, aids the users in identifying possible information bias. It should provide simple yet specific details of the raw data that are not included in the representative data.
For more information about the summarized line graph check the below paper:
We have used a dummy dataset for this visualization. The dataset contains 10 stores and their monthly sales. The data file is store_data.csv
Overall trend in sales in all ten stores Analytical highlights give the end user the information of the best and worst monthly sales of a store. Additionaly data enevelope provides the monthly sales for each stores. and this allows for easier comparison with the representative data.
Representative Data
The summarized line graph uses mean as the representative data. The mean, which represents the central tendency of the entire dataset, offers measurable values that change over time and are easily grasped by casual users.
In the above figure (i.e our visualization), the brown line represents the monthly mean sales of all the stores. This represents the overall trend of sales in our dataset. It helps the users to gain a simple but precise description of the dataset and it is easy for them to understand without any specific additional training.
Analytical Highlights
For each store/time series, we have plot the month in which they have the maximum sale as a downward-pointing triangle in blue and the month in which they had the minimum sale as upward-pointing triangles in red. Additionally, triangles are with store name and are aligned to the mean curve using the vertical dotted lines for better time point measurment and comparison.
Data Envelope
Data envelope provides additional information not presented in representative data and analytical highlights in a simplified and contextual manner. Data envelope adds density bands between mean and each of the original lines to visualize the distribution of the time-series. Additionally by examining the density bands and the mean in the same graph, users can examine the distribution of time-series above and below the mean.
This visualization is developed based on the below research work: Bridging the Data Analysis Communication Gap Utilizing a Three-Component Summarized Line Graph