Work Description

Title: Statistics and Visualization of Point-Patterns Open Access Deposited

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  • The data was collected on the website Tornado History Project. The data is loaded into the program and visualized using Python heatmap.
  • 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.
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Last modified
  • 02/24/2020
  • 04/24/2016
To Cite this Work:
Stoev, S., Hu, W. (2016). Statistics and Visualization of Point-Patterns [Data set], University of Michigan - Deep Blue Data.


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Files (Count: 4; Size: 2.75 MB)

The dataset in "tornado_complete.txt" file is free for download on this website:

The data are arranged in the following format with white spaces in between:

TouchdownLong(degree) TouchdownLat(degree) Fraction_of_year(unitless) Fujita_Scale(F)

To run the program, download both the source code as well as "tornado_complete.txt" file to the same directory. Run the program and enters number of pixels you want in both x and y directions for resolution, as well as bandwidth of time.

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