City of Alexandria -- Time Series Data Research Paper

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City of Alexandria -- Time Series Data

Tufte (2001) and other ambassadors of the visual display of data have shown us how easily it is to understand complex data when it is graphically represented in ways that our minds are designed to understand. Tufte argues that "experience with the analysis of data…is essential for achieving precision and grace in the presence of statistics, .but even textbook of graphical design are silent about how to think about numbers" (Tufte, 2001, p. 104). Tufke remarks, that "Illustrators too often see their work as an exclusively artistic enterprise -- the words "creative," "concept," and "style" combine regularly in all possible permutations -- a Big Think jargon for the small task of constructing a time-series a few data points long" (Tufte, 2001, p. 204). Visual display of data has other uses than simply an elegant way to view, appreciate, and analyze data. The process of completing a graphic display of data forces the issue of data integrity and completion of data sets. When data is missing in a graphic display, it is glaringly apparent. And the process of figuring out how to arrange data for best display generates an awareness of the assumptions that undergird the data collection -- and ultimately, the data analysis. When creating a visual display of data, the analyst has cause to "muse on the ineffable origins of…insights" (Gladwell, 2007, p. 40) . The analyst admits, if only privately, that "There are ten different things it can mean… -- all of those are possibilities. You can't just look at one behavior [or data point] in isolation" (Gladwell, 2007, p. 43). When the data just doesn't come together, we might do well to recall Averch's caveat, that "If we believe that the information to be gained by evaluation should be proportional to the decision makers' needs, time, budget, and attention, then conventional quantitative evaluations may be infeasible or inappropriate" (n.d., p. 292).

Indicator # 20 Median gross rent

Indicator Response #4 - Representing Variance in Time (2000 -- 2010)

By 2010, 18.8 million renters had at moderate housing cost burdens. The numbers climbed in 2008-2009 by 1.2 million, which is nearly twice the rise in 2007 -- 8.

More than 69% of the increase was among households paying more than half their incomes for housing. The number of severely burdened renters in 2009 was the largest annual increase over decades.

Source: America's Rental Housing: Meeting Challenges, Building on Opportunities

The Joint Center for Housing Studies at Harvard University.

Note: This report contains a plethora of visual data, however, none can be copied from the PDF and would require replication and permission to replicate http://www.jchs.harvard.edu/publications/rental/rh11_americas_rental_housing/AHR2011-5-Affordability.pdf

Visual Representation of Indicator #4

Indicator Reponse #5 - Representing Variance in Time (Cross-Sectional Data)

For minimum-wage workers, using the 1/3 of earned income standard, an affordable monthly rent is just $377. Many households that rent units have incomes well below that level, including the working poor and those living on fixed incomes. The full-time minimum-wage equivalent is $14,500. About 1/4 of all renters and about 1/2 of all renters who receive assistance have household incomes below that figure. In 2009, most assisted units rented for less than $600. Thirty-five percent rented for less than $400 and about 20% had rents in the range somewhere between those two figures. In rental units where tenets did not get assistance, in comparison, only 31% rented for less than $600 and just 8% rented for less than $400.

Source: America's Rental Housing: Meeting Challenges, Building on Opportunities

The Joint Center for Housing Studies at Harvard University.

Note: This report contains a plethora of visual data, however, none can be copied from the PDF and would require replication and permission to replicate.

http://www.jchs.harvard.edu/publications/rental/rh11_americas_rental_housing/AHR2011-5-Affordability.pdf

Visual Representation of Indicator #5

Income Standard

Equivalent Annual Income

Affordable Monthly Rent

Supportable Per/Unit Development Cost

Income to Afford Unit

$51,800

$1,300

$110,000

Median Renter Income

$30,500

$760

$64,800

Full-Time Minimum Wage Equivalent

$14,500

$360

$30,800

Source: America's Rental Housing: Meeting Challenges, Building on Opportunities

JCHS calculations using data from U.S. Census Bureau, 2009 and 2010 Surveys of Market Absorption and 2009 American Community Survey.

Note: This report contains a plethora of visual data, however, none can be copied from the PDF and would require replication and permission to replicate.

Indicator #22 Number of foreclosures

Indicator Response #4 - Representing Variance in Time (2000 -- 2010)

The 2009 foreclosure data from RealtyTrac indicates an increase year over year of 47% of national foreclosure rates. Foreclosure rates are reported to have leveled off in Q2 and Q3 2009. But economists anticipate a surge in the coming months as the breakdowns in the foreclosure processes are addressed. Also, adjustable-rate mortgages are due to reset, a substantive proportion of which are loans that do not require borrowers' income verification.

Visual Representation of Indicator #4

Figure 2. Ranking foreclosures Across the States

State

Total foreclosures

1 foreclosure per # of households

% change from Feb 2007

% change from March 2006

Nevada

4,738

29.42

Colorado

6,267

18.02

16.23

California

31,434

35.49

Georgia

6,769

-7.2

-11.59

Arizona

4,476

43.65

Michigan

8,610

-7.4

11.43

Florida

14,303

-25.29

54.08

Ohio

8,222

9.95

74.23

Indiana

4,332

26

-12.18

Illinois

7,819

18.08

70.27

Texas

12,755

2.98

6.73

New Jersey

4,780

-4.53

28.91

Massachusetts

3,497

39.99

Tennessee

3,178

-17.15

-6.53

Connecticut

1,517

2.99

83.21

Missouri

2,494

5.77

60.59

North Carolina

3,241

1,086

-3.71

56.34

Utah

1,208

-25

-55.74

Oklahoma

1,138

1,313

-18.25

-16.69

Washington

1,736

1,378

7.16

-5.86

Rhode Island

1,392

15700.00*

Idaho

1,481

21.16

66.67

Pennsylvania

2,921

1,797

47.82

-20.69

Oregon

1,949

3.03

28.13

Nebraska

2,013

21.43

New York

3,711

2,069

-18.48

-7.11

Alaska

98

2,094

20.99

-3.92

Iowa

2,125

-2.36

Maryland

2,220

Arkansas

2,323

-61.86

-60.14

New Mexico

2,394

-32.08

27.84

Delaware

2,399

Alabama

2,436

79.51

Kentucky

2,501

11.46

Kansas

2,523

5.67

Minnesota

2,559

-13.02

44.54

Virginia

1,054

2,738

46.8

Wisconsin

2,785

-15.09

67.75

Louisiana

3,252

-24.51

Montana

3,522

17

-16.43

New Hampshire

4,757

South Carolina

5,155

-1.16

-55.67

Hawaii

65

7,085

10.17

44.44

West Virginia

95

8,891

26.67

Maine

65

10,029

South Dakota

32

10,100

45.45

33.33

North Dakota

24

12,070

33.33

14.29

Mississippi

94

13,498

-34.72

28.77

Wyoming

10

22,385

-66.67

-67.74

District of Columbia

5

54,969

-61.54

Vermont

1

294,382

0

-80

U.S.

149,150

6.6

46.81

*Actual increase may not be as high due to expanded data coverage.

Indicator Response #5 -- Representing Variance (Cross-Sectional Data)

The ongoing foreclosure crisis is expected to encourage growth in the number of renter households as former owners switch to renting. Single-family home foreclosures will also add a steady flow of units to the rental market. The ability of renter households to occupy these homes will be an important factor in maintaining the stability of distressed neighborhoods hard hit by the foreclosure crisis.

Visual Representation of Indicator #5

Figure 3. Trends All Foreclosure: Boston, MA; Alexandria, VA; San Francisco, CA

Source: RealtyTrac

http://www.realtytrac.com//propertydetails/popuptrendtool.aspx?TrendToolType=GEFP&ComparisonID=&DefaultZipCode=CA&referrer=TrendCenter

Indicator Reponse #5 - Representing Variance in Time (Cross-Sectional Data)

Figure 4. Geographical Comparison Alexandria City, State of Virginia, National

Source: RealtyTrac

http://www.realtytrac.com//propertydetails/popuptrendtool.aspx?TrendToolType=GEFP&ComparisonID=&DefaultZipCode=CA&referrer=TrendCenter

Indicator Reponse #5 - Representing Variance in Time (Cross-Sectional Data)

Indicator #39 Number of new, affordable rental units in conjunction with new developments

Indicator Response #4 - Representing Variance in Time (2000 -- 2010)

The State of Virginia uses the HUD data, statistics or method for determining State programs. Parameters are based on Fair Market Rents, Public Housing units, rental assistance vouchers, or low income vouchers.

Housing Affordability Data System. The Housing Affordability Data System (HADS) is a set of files derived from the 1985 and later national American Housing Survey (AHS) and the 2002 and later Metro AHS. This system categorizes housing units by affordability and households by income, with respect to the Adjusted Median Income, Fair Market Rent (FMR), and poverty income. It also includes housing cost burden for owner and renter households. These files have been the basis for the worst case needs tables since 2001. The data files are available for public use, since they were derived from AHS public use files and the published income limits and FMRs. We are providing these files give the community of housing analysts the opportunity to use a consistent set of affordability measures.

HUD 2011 Fair Market Rent Documentation System. This system provides complete documentation of the development of the Final FY 2011 Fair Market Rents (FMRs) for any area of the country selected by the user. After selecting the desired geography, the user is provided a page containing a summary of how the FY 2011 FMRs were developed and updated starting with the formation of the FY 2011 FMR Areas from the metropolitan Core-Based Statistical Areas (CBSAs) as established by the Office of Management and Budget, the 2000 Census benchmark, the newly available 2008 American Community Survey (ACS) 1 year data and the newly available 2006-2008 3-year data, incorporating information from Final FY 2010 FMRs, and updating to FY 2011 including information from local Random Digit Dialing (RDD) survey data. The tables on the summary page include links…[continue]

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"City Of Alexandria -- Time Series Data" (2011, November 10) Retrieved December 3, 2016, from http://www.paperdue.com/essay/city-of-alexandria-time-series-data-116368

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"City Of Alexandria -- Time Series Data", 10 November 2011, Accessed.3 December. 2016, http://www.paperdue.com/essay/city-of-alexandria-time-series-data-116368


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