5 Big Data Visualization Maps that will Make your Head Explode

Data Viz Approaches Built for Big Data

Not all data visualizations can be simplified to a speedometer or bar chart. Big data visualizations require more sophisticated visualization tools and more brainpower.

Here are some big data visualizations examples that will blow your mind!

1. Topological Data Analysis 

Many data sources do not always fit nicely into a linear regression during the statistical analysis so when the shapes vary, you may want to understand the shape and relationship between data elements. Topological Data Analysis is a perfect aid to conceptualize relationships and visual trends that could otherwise be hidden. This example illustrates retail data for different locations along with retention rates.

GC Pic 11
Source: Why Topological Data Analysis Works –
Ayasdi 

2. Geospatial Maps

Using geography as a common dimension to understand spatial relationships from a birds eye view is a very popular visualization type. Using geographic attributes or coordinates also provides additional context like distance and proximity to analyze data. This example illustrates over one million buildings and the age of those buildings.

map

Source: Five Boroughs: Building Age NYC

3. TreeMaps

A TreeMap is a great way to visualize a multi-dimensional data set in a compressed two-dimensional space. A tree map has its own algorithm for organizing and placing square boxes that are nested based on a hierarchical tree. The following example illustrates the entire Geocities (acquired by Yahoo) history, made up of a 650GB archive file. The size of the boxes represents the hierarchy of pages created inside of Geocities.


Source: http://deletedcity.net

4. 3D Spatial Visualizations

Three-dimensional visualizations can unlock time and space dimensions to simulate real world objects and scenarios. While 3D requires more computer power and lots of data, the results can be profound. This example demonstrates a computer simulated crash and the resulting damage to vehicle components. With millions of individual components, 3D visualization and predictive analysis can relay critical information not visible in the real world.

Honda

Source: http://tctmagazine.com 

5. Density/Heat Maps

Density also referred to as heat maps display color intensity as the number of points increase and overlap within a given space. Density maps are popular for spatial data visualization (both 3D and geographic). Heatmaps can be particularly useful for big data visualizations. The following example illustrates foot traffic through interaction with beacon sensors. This particular solution is provided by Indoors where public and privately owned venues can become data rich spaces. The opportunities to understand real-time traffic patterns of people through space provide tremendous insight to consumer engagement.

Source: Indoors

 

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Meet Ryan Goodman

Ryan Goodman is CMaps Analytics CEO, author, and expert in Business Intelligence. Ryan applies over 13 years designing enterprise analytics apps to his leadership role shaping CMaps Analytics’ Location Intelligence platform strategy. Today, Ryan is focused on pushing wider adoption of analytics with an emphasis on geospatial analysis and Location based services.

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