CSC8626 - Data Visualization - IT Assignment Help

Assignment Task

Preamble

Visualization has become a tool both for the exploration of raw data and for the presentation of analysed data to end users. In this assignment you are asked to represent your analysis of data about a medical emergency in a city to an end user who must make a rapid decision using the data. Assume your end user is a time critical decision maker, such as a gold command police officer, an army commander or a politician in a government COBR meeting. They are trying to decide where to deploy limited tactical resources (medics and medical supplies) based on your visualization. Keep in mind they need to see predicted impact and level of uncertainty of that impact to make a decision.
There are several constraints in these and similar situations:
• You will typically not be there to explain the visualization, it must be entirely standalone.
• Most, if not all, the decision makers will not understand statistical methods or mathematics.
• It may be important to print or fax your visualization, it should work on screen and on paper.

The data

The data you have been given are a set of outputs from a DSTL/PHE supercomputer simulation of an airborne infectious disease outbreak over Manchester. Each simulation output (data file) simulates how the epidemic might spread given a certain set of environmental conditions with varying wind direction and speed. The prevailing wind in all the simulations is coming from a direction roughly between west and south west, as it often does in Manchester.
Each data file holds outputs from groups of four simulation runs, each group of four has some similarities in initial environmental conditions. The simulation computes the infection outcome on a regular grid of cells across the city. The infected number given for each cell is the number of expected infections in that cell for the simulation conditions in that run. Note that any cell in the simulation that has a zero output for all four simulations will have been excluded from the data file.
For each cell in each file you have:
• Longitude and latitude which is common to all simulation outputs the cell is included in.
• A unique cell ID which is common across all simulation outputs the cell is included in.
• The population of the cell.
• Four estimates of the number of infected people from each of the four simulation runs.
• The following uncertainty statistics per cell across the four runs: mean, variance, standard deviation, index of dispersion and coefficient of variation. The latter two are standardised ways to help compare variability between cells that have different ranges of values.
The origin point of the epidemic for all the simulations is: longitude: -2.2807386, latitude: 53.4034207

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