- Analyze heat maps to draw meaningful conclusions from visualized data
- Analyze motion bubble charts to identify trends and patterns in data over time
- Identify potential issues and challenges when analyzing large and volatile data sets
Heat Maps and Bubble Charts
A heat map is a visual display of data in the form of a map or diagram in which data values are grouped into different colors.
A bubble map is another type of data visualization in which differently sized bubbles are placed regionally upon a map. Shading may be used to represent a second variable in the data set.
These types of displays are common in the media. Reading or interpreting the information presented in a complex display can be challenging. Always look for a guide to how the display has arranged the data, called a legend or a key. The legend of a map is the description, explanation, or table of symbols printed on the map to aid readers to understand and interpret the map better.
You can view the transcript for “What is a Heatmap?” here (opens in new window).
You can view the transcript for “Interpreting Heat Map Visualizations” here (opens in new window).
You can view the transcript for “A Guide to Bubble Charts” here (opens in new window).
Complex Graphical Analysis of Big Data
Big data is characterized by its immense volume, high velocity, and diverse variety. Traditional data analysis tools often fall short in handling the sheer scale and complexity of big data. Complex graphical analysis transcends traditional methods by offering multi-dimensional views of data.
- Volume: The sheer amount of data generated from various sources can overwhelm standard databases and analysis tools.
- Velocity: The rapid generation and movement of data require systems that can process information in real-time.
- Variety: Data comes in multiple formats, from structured to unstructured, each requiring different analysis approaches.
- Variability: Data can fluctuate dramatically, especially from sources like social media or real-time monitoring systems.
- Veracity: The trustworthiness and quality of data are crucial. Data sources range from highly reliable scientific data to more subjective user-generated content.
Strategies for Managing Big Data:
- Adaptive Systems: Use cloud-based solutions and adaptive systems that can scale resources based on data flow.
- Data Validation: Employ rigorous techniques to validate the veracity of data, including cross-referencing and anomaly detection.