NYcity_subset: New York City CCES Respondents in 2008
In krige: Geospatial Kriging with Metropolis Sampling

NYcity_subset

R Documentation

New York City CCES Respondents in 2008

Description

These data are a subset of the 2008 Cooperative Congressional Election Survey (CCES) Common Content. Only 568 respondents from New York City are included, with predictors drawn from Gill's (2020) model of self-reported ideology. The CCES data are merged with predictors on geographic location based on ZIP codes (from ArcGIS & TomTom) and county ruralism (from the USDA).

Format

The NYcity_subset dataset has 568 observations and 26 variables.

state: The state abbreviation of the respondent's residence.
zip: The respondent's ZIP code.
age: The age of the respondent in years.
female: An indicator of whether the respondent is female.
ideology: The respondent's self-reported ideology on a scale of 0 (liberal) to 100 (conservative).
educ: The respondent's level of education. 0=No Highschool, 1=High School Graduate, 2=Some College, 3=2-year Degree, 4=4-year degree, 5=Post-Graduate.
race: The respondent's race. 1=White, 2=African American, 3=Nonwhite & nonblack.
empstat: The respondent's employment status. 1=employed, 2=unemployed, 3=not in workforce.
ownership: Indicator for whether the respondent owns his or her own home.
inc14: The respondent's self reported income. 1=Less than $10,000, 2=$10,000-$14,999, 3=$15,000-$19,000, 4=$20,000-$24,999, 5=$25,000-$29,999, 6=$30,000-$39,999, 7=$40,000-$49,999, 8=$50,000-$59,999, 9=$60,000-$69,999, 10=$70,000-$79,999, 11=$80,000-$89,999, 12=$100,000-$119,999, 13=$120,000-$149,999, 14=$150,000 or more.
catholic: Indicator for whether the respondent is Catholic.
mormon: Indicator for whether the respondent is Mormon.
orthodox: Indicator for whether the respondent is Orthodox Christian.
jewish: Indicator for whether the respondent is Jewish.
islam: Indicator for whether the respondent is Muslim.
mainline: Indicator for whether the respondent is Mainline Christian.
evangelical: Indicator for whether the respondent is Evangelical Christian.
FIPS_Code: FIPS code of the repondent's state.
rural: Nine-point USDA scale of the ruralism of each county, with 0 meaning the most urban and 8 meaning the most rural.
zipPop: Indicates the population of the repondent's ZIP code.
zipLandKM: Indicates the land area in square kilometers of the repondent's ZIP code.
weight: Survey weights created by the CCES.
cd: The congressional district the respondent resides in.
fipsCD: Index that fuses the state FIPS code in the first two digits and the congressional district number in the last two digits.
northings: Indicates the geographical location of the respondent in kilometer-based northings.
eastings: Indicates the geographical location of the respondent in kilometer-based eastings.

Source

Ansolabehere, Stephen. 2011. "CCES, Common Content, 2008." Ver. 4.

ArcGIS. 2012. "USA ZIP Code Areas." https://www.arcgis.com/home/item.html?id=8d2012a2016e484dafaac0451f9aea24

United States Department of Agriculture. 2013. "2013 Rural-Urban Continuum Codes." https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx

References

Jeff Gill. 2020. Measuring Constituency Ideology Using Bayesian Universal Kriging. State Politics & Policy Quarterly. doi:10.1177/1532440020930197

Examples

        
## Not run: 
nyc <- NYcity_subset

#data cleaning
nyc$cathOrth<-nyc$catholic+nyc$orthodox
nyc$consRelig<-nyc$mormon+nyc$evangelical
nyc$jewMus<-nyc$jewish+nyc$islam

# Explanatory Variable Matrix
psrm.data <-cbind(nyc$age, nyc$educ, I(nyc$age*nyc$educ), as.numeric(nyc$race==2), 
       as.numeric(nyc$race==3), nyc$female, I(as.numeric(nyc$race==2)*nyc$female), 
       I(as.numeric(nyc$race==3)*nyc$female), nyc$cathOrth, nyc$consRelig, 
       nyc$jewMus, nyc$mainline, nyc$rural, nyc$ownership, 
       as.numeric(nyc$empstat==2), as.numeric(nyc$empstat==3),nyc$inc14)

dimnames(psrm.data)[[2]] <- c("Age", "Education", "Age.education", 
                             "African.American", "Nonwhite.nonblack","Female", 
                             "African.American.female", "Nonwhite.nonblack.female", 
                             "Catholic.Orthodox", "Evang.Mormon", "Jewish.Muslim", 
                             "Mainline","Ruralism", "Homeowner", "Unemployed",
                             "Not.in.workforce","Income")

# Outcome Variable
ideo <- matrix(nyc$ideology,ncol=1)

# WARNING: This example was tuned on Amazon Web Services (EC2) over many hours
# with 150,000 iterations--a strain in 2020 for most desktop machines.
# A test with few iterations allows illustration.
#M<-150000 
M<-150 
set.seed(1,kind="Mersenne-Twister")

# Estimate the Model
nyc.fit <- metropolis.krige(formula = ideo ~ psrm.data, coords = cbind(nyc$eastings, nyc$northings),
          powered.exp=1, n.iter=M, spatial.share=0.31,range.share=0.23,beta.var=10,
          range.tol=0.01, b.tune=0.1, nugget.tune=20, psill.tune=5)		
      
# Discard first 20% of Iterations as Burn-In (User Discretion Advised).
nyc.fit <- burnin(nyc.fit, M/5)

# Summarize Results
summary(nyc.fit)

#Convergence Diagnostics: Geweke and Heidelberger-Welch
geweke(nyc.fit)
heidel.welch(nyc.fit)

# Draw Semivariogram
semivariogram(nyc.fit)

## End(Not run)

krige documentation built on May 1, 2022, 5:06 p.m.