Data Visualization Archives


Data visualization

Posted on January 12, 2016  /  0 Comments

One thing working on big data has done is to sensitize us to the power of visualization, especially using maps. Here is one that impresses, especially in view of our focus on urban development: Data viz extraordinaire Max Galka created this map using NASA’s gridded population data, which counts the global population within each nine-square-mile patch of Earth, instead of within each each district, state, or country border. Out of the 28 million total cells, the ones with a population over 8,000 are colored in yellow. That means each yellow cell has a population density of about 900 people per square mile—“roughly the same population density as the state of Massachusetts,” Galka writes in the accompanying blog post. The black regions, meanwhile, reflect sparser population clusters.
David Ebert, the second keynote speaker at the ISCRAM2012, in his talk says – “Recently, big data analytics has become the buzz in international news and corporate campaigns as the technology to change the future. However, while necessary in our modern data deluge of over one zetabyte of digital data, the common big data analytics approach tends to utilize only computational power and algorithms to turn data into information and then knowledge and provide an answer to the responder or decision maker using the system. In contrast, visual analytics capitalizes on the best and complimentary abilities of both components of the human-computer decision-making process through iterative, interactive visual interfaces to leverage and supplement the cognitive capabilities of the human user.” In our Real-Time Biosurveillance work, this is exactly what we did; thus, take the over 100s of records coming from each clinical facility every day, then present them to Epidemiologists using temporal and spatial data visualization methods offered by the T-Cube Web Interface. Additionally, provide them with tools to drill into and apply statistical analyses methods to look for unusual patterns in the large data set.