Search found 5 matches

by donghaoren
Mon Mar 16, 2015 12:53 pm
Forum: Winter 2015
Topic: Proj 5: Data Correlation / Final Project
Replies: 17
Views: 27709

Re: Proj 5: Data Correlation / Final Project

Volumetric Visualization with Point Cloud This project is based on the previous assignment. In the volume, the X-Y plane represents all books in the library. Z represents time, from 2006 to 2014 monthly. Color at x,y,z means the "checkout density" at book location x,y and time z. The mapping from b...
by donghaoren
Wed Feb 18, 2015 10:57 am
Forum: Winter 2015
Topic: Proj 4: 3D Volumetric, Spacial Visualization
Replies: 17
Views: 9454

Re: Proj 4: 3D Volumetric, Spacial Visualization

Spatial-temporal Checkouts screenshot.png In this assignment, I built a visualization to show checkout trends of different kinds of books. The design is a volumetric visualization, using the X-Y plane to layout the books, and the Z axis to show the checkout trend. Book layout: The books are layout ...
by donghaoren
Mon Feb 02, 2015 9:29 pm
Forum: Winter 2015
Topic: Proj 3: 2D Reorderable Matrix
Replies: 17
Views: 8842

Re: Proj 3: 2D Reorderable Matrix

Self-Organizing Map of Checkout Trends screenshot.png This project uses Self-Organizing Map and Restricted Boltzmann Machine to visualize temporal checkout trends of each 2nd-level Dewey class in the Seattle Library Dataset. Explanation The query extracts the number of checkouts for each Dewey cate...
by donghaoren
Sat Jan 17, 2015 9:33 am
Forum: Winter 2015
Topic: Proj 2: 2D Matrix
Replies: 18
Views: 10729

Re: Proj 2: 2D Matrix

Average Lending Time for each Dewey Class and Year HW2-1.png HW2-2.png HW2-3.png HW2-4.png Description: The visualization shows the average lending time for each Dewey class and each year from 2006 to 2013, using a heatmap metaphor. The color scales are designed using HCL Picker (http://tristen.ca/...
by donghaoren
Wed Jan 14, 2015 8:51 pm
Forum: Winter 2015
Topic: Proj 1: Data Query
Replies: 19
Views: 9186

Re: Proj 1: Data Query

Average Lending Time One interesting question about this dataset is to see the difference between the check-out and check-in time (lending time), which can reveal how long people read the books to some extent. Although people may not read the book during the entire lending time, but this number is ...