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Effective Animation Techniques for 3D Visualizations of Urban Landscapes with a Spatial and Temporal Dimension

Thesis Proposal
by

Jennifer Smith

on 4 May 2010

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Transcript of Effective Animation Techniques for 3D Visualizations of Urban Landscapes with a Spatial and Temporal Dimension

Effective Animation Techniques for 3D Visualizations of Urban Landscapes with a Spatial & Temporal Dimension Why Study Visualization of Geographic Data?? With technology and automated data capture, data is created at rates faster than can easily be converted into knowledge (Unwin, 2008) So many ways to visualize data...we need firm design principles to aid in developing good display methods of your data!! 1. Importance of My Research and Identifying a Gap in the Literature 1. Effective dynamic variables have been identified by DiBiase et al. (2008) for 2D visualiztions of geographic data 2. Cartographers have only "scratched the surface"
of 3D visualization techniques for dynamic displays
(Harrower, 2008) A map design should convey geographic information and avoid user misinterpretation of the visualized data (Meng, 2009) Moving Forward:
from static to dynamic (animations)... Impression that as you move across county borders, the population suddenly goes up (not reflective of reality) What is gained by using
3D Visualizations? -axis

i. e. (Height!) Additional display space available (especially z-axis = height or quantitative data) (Shepherd, 2008)

Allows for a more realistic representation of reality in some cases (such as urban scenarios) (Lin, 2010)

Offers a general overview of object relationships for spatial data (such as one building is taller than another (Kray, 2003) But wait, effective visualization methods
have already been extensively tested for
2D static maps! Bertin (1983) detailed types of visual variables for static 2D maps and cases when each would be appropriate Important variables in 2D dyanmic displays have
already been identified 1. "…issues of visual perception are critical to understanding of scientific visualization” Becker and Cleveland (1991) Identifying EFFECTIVE visualization techniques for 3D animations in order to decrease the potential for misinterpretation of geographic data Problem: Filling the Gap Drivers Behind My Approach 2. MachEachren (2004) noted he isn't aware of any studies detailing the ability of viewers to recognize features on a map movie as I plan to do 2. Research Questions and Hypotheses 3. Proposed Methods 4. Possible Limitations 5. Conclusion 1. Importance of My Research and Identifying a Gap in the Literature
2. Research Questions and Hypotheses
3. Proposed Methods
4. Possible Limitations
5. Conclusion Research Questions: Central Question: What are effective animation techniques for acquiring spatial knowledge in dynamic 3D visualizations of urban spatial and temporal data? Subquestion: How do different variables such as size, color (sequential versus random) and photorealism affect users abilities to acquire spatial knowledge? Hypothesis Due to the lack of empirical work, I have no hypothesis! Methodology Creation of 4 different map animations, each one re-visualizing the same data (historical urban growth) through different variables The animations will visualize historical uban growth of each decade for National Taiwan University Site of Animations Why National Taiwan University? To avoid previous knowledge of campus buildings, data is visualized from a (most likely) unknown campus to the viewer.

Note: the viewer will NOT be disclosed as to the whereabouts of the animation location Data Acquisition 1. Building construction data, buidling heights, a DEM of the area, digitized polygons of each building, and an orthophoto will be acquired from a contact at the university through Dr. Tsou 2. An orthophoto will be draped onto a DEM in ArcScene to create a realistic landscape.
3. Digitized building polygons will be extruded to their z-value heights Data Preparation Animation Creation Animations are produced by using different height intervals, starting from ground level to actual building height (creates the appearance of growth and movement). Four Animations Each altering the representation of building construction 1. Size (exaggeration of building heights)
2. Color (sequential) 3. Color (random) 4. Photorealism Zanola (2009) Why use Color and Photorealism? Zanola (2009) found users tend to favor realistic simulations (through photorealism)
It influenced their confidence in the credibility of the data and noted it may influence their understanding as well. Jahnke (2009) found while photorealism captures viewer interest, it dominates their attention and supersedes their capability to draw out information Testing Spatial Data Acquisition 1. User Survey: test each animation by asking the same questions to each user to see if any spatial knowledge was gained post-viewing of the animation 2. Compare the statistical results for each animation and see if there are differences in overall spatial knowledge of campus growth Types of Questions General spatial questions about each decade... Ask about orientation patterns (north, south, east, west) Ask about distribution patterns over time (clustered vs. random) Ask about building height patterns (i.e. when/where the tallest buildings completed?) i.e. What decade were most central campus buildings completed? Also gather qualitative data on their GIS background, suggestions, etc. Proposed Audience Students of undergraduate geography courses (while they are anonymous, I will collect them together for each class to compare and evaluate class statistics) Possible Limitations 1. Sample Size
2. Types of questions (dependent upon data acquired)
3. Duration may affect the animation even though I will keep it uniform across all Conclusion 3D modeling and animations are becoming extremely popular due to mapping technology that it is outpacing cartographic theory (Harrower, Fabrikant, 2009)

It imperative we find the appropriate methods for visualizing geographic data to avoid misinterpretation The End of the Animation.... Questions??
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