american housing survey: affordable housing

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PROGRESS REPORT NOVEMBER 11, 2009 JAMES LAMPTON, SONIA NG, SWETHA REDDY, DI-WEI HUANG American Housing Survey: Affordable Housing

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American Housing Survey: Affordable Housing. Progress report November 11, 2009 James Lampton , Sonia Ng, Swetha Reddy, Di-Wei Huang. Review of Data. Set of categorical time series data of ten cities From 1985 to 2005 Each symbol represents two year interval 0-8 categories - PowerPoint PPT Presentation

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Page 1: American Housing Survey: Affordable Housing

PROGRESS REPORTNOVEMBER 11 , 2009

JAMES LAMPTON, SONIA NG, SWETHA REDDY, DI -WEI HUANG

American Housing Survey: Affordable Housing

Page 2: American Housing Survey: Affordable Housing

Review of Data

Set of categorical time series data of ten cities

From 1985 to 2005Each symbol represents two year interval0-8 categoriesMerged few categories (2,3)(4,5)(7,8)Other attributes (age, units, rooms)Sample size is too small for hypothesis testing

on individual municipalities – designed for the entire US.

Page 3: American Housing Survey: Affordable Housing

Priorities

From Daniel (all focused on affordable housing): Examination of transitional probabilities. Correlation of behaviors to additional attributes (age,

number of units, number of rooms, etc). Time Series Visualization.

Goals: Provide visualizations publishable for the economics

study. Develop a tool that allows policy makers to explore

the impact of different decisions. The sponsor, MacArthor Foundation, may be interested.

Page 4: American Housing Survey: Affordable Housing

Task 1: Transition Probabilities

Represent the transition probabilities of affordable housing between any two years.

Notes: Easily done with Spotfire, can we find a way to

visualize more transitions?

Page 5: American Housing Survey: Affordable Housing

Forward Analysis

Page 6: American Housing Survey: Affordable Housing

Backward Analysis

Page 7: American Housing Survey: Affordable Housing

Task 2: Attribute Contributions

How transition probabilities of affordable housing are affected by characteristics like number of units, age of the house.

Notes: May be able to apply statistical tests (ANOVA?) to

determine if any attributes have any meaningful contribution to the outcome of the states.

Unlikely to push any boundaries of existing tools/research.

Page 8: American Housing Survey: Affordable Housing

Task 3: Application/Time Series

Visualize the various paths that a house takes over years.

Note: Highly categorized data makes time series

visualization less effective (perhaps we should examine the raw AHS price data)?

Can we apply the selection techniques from Time Searcher with the motif techniques seen in VisTree?

Visualization of k-motif clustering?

Page 9: American Housing Survey: Affordable Housing

AHS explorer

Page 10: American Housing Survey: Affordable Housing

AHS explorer

Page 11: American Housing Survey: Affordable Housing

AHS explorer

Page 12: American Housing Survey: Affordable Housing

Viztree

Page 13: American Housing Survey: Affordable Housing

Tentative Schedule

15th Nov – Finalize design/narrow scope20th Nov – Task 2 (skip?)28th Nov – Task 32nd Dec - Draft