Statistics and Visualization of Point-Patterns

Work Description

Attribute	Value
Methodology	The data was collected on the website Tornado History Project. The data is loaded into the program and visualized using Python heatmap.
Description	Many data sets come as point patterns of the form (longitude, latitude, time, magnitude). The examples of data sets in this format includes tornado events, origins/destination of internet flows, earthquakes, terrorist attacks and etc. It is difficult to visualize the data with simple plotting. This research project studies and implements non-parametric kernel smoothing in Python as a way of visualizing the intensity of point patterns in space and time. A two-dimensional grid M with size mx, my is used to store the calculation result for the kernel smoothing of each grid points. The heat-map in Python then uses the grid to plot the resulting images on a map where the resolution is determined by mx and my. The resulting images also depend on a spatial and a temporal smoothing parameters, which control the resolution (smoothness) of the figure. The Python code is applied to visualize over 56,000 tornado landings in the continental U.S. from the period 1950 - 2014. The magnitudes of the tornado are based on Fujita scale.
Creator	Stoev, Stilian Hu, Weifeng
Depositor	weifhu@umich.edu
Contact information	sstoev@umich.edu
Discipline	Engineering Science
Citations to related material
Resource type	Dataset
Last modified	07/24/2017
Published	04/24/2016
License	http://creativecommons.org/licenses/by/3.0/us/

To Cite this Work:
Stoev, S., Hu, W. (2016). Statistics and Visualization of Point-Patterns [Data set]. University of Michigan - Deep Blue.

Title	Original Upload	Last Modified	File Size	Access	Actions
map_visualization_tor.py	2016-04-24	2016-04-24	8.06 KB	Open Access	View Details Download
READ_ME.txt	2017-04-05	2017-04-05	524 Bytes	Open Access	View Details Download
Report.pdf	2017-04-05	2017-04-05	1.2 MB	Open Access	View Details Download
tornado_complete.txt	2017-04-05	2017-04-05	1.54 MB	Open Access	View Details Download