h1: close proximity
h2: greater travel
h3: socioeconomic status
h4: race
other controls
case6 <- read.csv("~/Dropbox (ASU)/Papers/Neighborhood COVID/Data/FullData.csv")
## reading in shapefile
tractLA <- shapefile('~/Dropbox (ASU)/Papers/Neighborhood COVID/Data/tl_2019_22_tract/tl_2019_22_tract.shp')
tractLA2 <- merge(tractLA, case6, by.x="GEOID", by.y="Tract.ID")
f1 <- cpcln~ log(popdens)+pubtransit+ outsidecounty+tourismemp+collegepct+povpct+noinsurance+blackpct+hisppct+asianpct+otherracepct+DiversityIndex+over70pct+male
summary(ols <-lm(f1, data=tractLA2@data))##
## Call:
## lm(formula = f1, data = tractLA2@data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0124460 -0.0028313 -0.0004756 0.0018495 0.0270728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0081419 0.0020279 -4.015 6.37e-05 ***
## log(popdens) 0.0006356 0.0001082 5.874 5.72e-09 ***
## pubtransit 0.0368281 0.0040390 9.118 < 2e-16 ***
## outsidecounty 0.0099831 0.0008814 11.326 < 2e-16 ***
## tourismemp 0.0057477 0.0027620 2.081 0.037674 *
## collegepct -0.0001419 0.0012095 -0.117 0.906606
## povpct -0.0097230 0.0017027 -5.710 1.47e-08 ***
## noinsurance -0.0112068 0.0036191 -3.097 0.002010 **
## blackpct 0.0117483 0.0007934 14.807 < 2e-16 ***
## hisppct 0.0258909 0.0032164 8.050 2.24e-15 ***
## asianpct 0.0197914 0.0045690 4.332 1.62e-05 ***
## otherracepct 0.0228950 0.0060638 3.776 0.000169 ***
## DiversityIndex -0.0037527 0.0010379 -3.616 0.000314 ***
## over70pct 0.0261171 0.0038315 6.816 1.57e-11 ***
## male 0.0026486 0.0033073 0.801 0.423411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.004514 on 1048 degrees of freedom
## (85 observations deleted due to missingness)
## Multiple R-squared: 0.488, Adjusted R-squared: 0.4811
## F-statistic: 71.34 on 14 and 1048 DF, p-value: < 2.2e-16covid_nbq <- poly2nb(tractLA2) #Queenโs neighborhood
covid_nbq_w <- nb2listw(covid_nbq, style="W")
## Spatial Lag Model
summary(covid_LAG <- lagsarlm(f1, data = tractLA2@data, covid_nbq_w))## Warning: Function lagsarlm moved to the spatialreg package##
## Call:spatialreg::lagsarlm(formula = formula, data = data, listw = listw,
## na.action = na.action, Durbin = Durbin, type = type, method = method,
## quiet = quiet, zero.policy = zero.policy, interval = interval,
## tol.solve = tol.solve, trs = trs, control = control)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.01002621 -0.00182169 -0.00037226 0.00107065 0.02506775
##
## Type: lag
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.0020e-03 1.6087e-03 -3.7309 0.0001908
## log(popdens) 2.3010e-04 8.6929e-05 2.6469 0.0081229
## pubtransit 1.2966e-02 3.2825e-03 3.9501 7.813e-05
## outsidecounty 4.2763e-03 7.1425e-04 5.9872 2.135e-09
## tourismemp 2.8212e-03 2.1896e-03 1.2885 0.1975850
## collegepct -4.3052e-04 9.5868e-04 -0.4491 0.6533798
## povpct -5.4476e-03 1.3631e-03 -3.9965 6.428e-05
## noinsurance -2.8818e-03 2.8746e-03 -1.0025 0.3160992
## blackpct 7.4628e-03 6.7420e-04 11.0691 < 2.2e-16
## hisppct 1.2617e-02 2.5962e-03 4.8596 1.176e-06
## asianpct 7.0571e-03 3.6476e-03 1.9347 0.0530232
## otherracepct 1.3886e-02 4.8127e-03 2.8852 0.0039118
## DiversityIndex -2.3554e-03 8.2829e-04 -2.8437 0.0044599
## over70pct 2.0683e-02 3.0447e-03 6.7932 1.097e-11
## male 3.3583e-03 2.6210e-03 1.2813 0.2000941
##
## Rho: 0.60481, LR test value: 393.48, p-value: < 2.22e-16
## Asymptotic standard error: 0.026903
## z-value: 22.481, p-value: < 2.22e-16
## Wald statistic: 505.4, p-value: < 2.22e-16
##
## Log likelihood: 4436.856 for lag model
## ML residual variance (sigma squared): 1.2792e-05, (sigma: 0.0035766)
## Number of observations: 1063
## Number of parameters estimated: 17
## AIC: -8839.7, (AIC for lm: -8448.2)
## LM test for residual autocorrelation
## test value: 32.283, p-value: 1.3327e-08## Spatial Error Model
summary(covid_error <- errorsarlm(f1, data = tractLA2@data, covid_nbq_w))## Warning: Function errorsarlm moved to the spatialreg package##
## Call:
## spatialreg::errorsarlm(formula = formula, data = data, listw = listw,
## na.action = na.action, Durbin = Durbin, etype = etype, method = method,
## quiet = quiet, zero.policy = zero.policy, interval = interval,
## tol.solve = tol.solve, trs = trs, control = control)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00995327 -0.00179853 -0.00045815 0.00087272 0.02608968
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.00161963 0.00179112 -0.9043 0.3658577
## log(popdens) 0.00008065 0.00011509 0.7008 0.4834401
## pubtransit 0.00721094 0.00398289 1.8105 0.0702215
## outsidecounty 0.00096851 0.00100465 0.9640 0.3350352
## tourismemp 0.00201946 0.00220289 0.9167 0.3592830
## collegepct -0.00185477 0.00132748 -1.3972 0.1623506
## povpct -0.00494857 0.00151181 -3.2733 0.0010631
## noinsurance -0.00151196 0.00286296 -0.5281 0.5974231
## blackpct 0.00957644 0.00078153 12.2535 < 2.2e-16
## hisppct 0.00964557 0.00297314 3.2442 0.0011777
## asianpct 0.00607079 0.00475970 1.2755 0.2021473
## otherracepct 0.01074468 0.00529611 2.0288 0.0424801
## DiversityIndex -0.00333548 0.00093104 -3.5825 0.0003403
## over70pct 0.02183397 0.00321778 6.7854 1.158e-11
## male 0.00570073 0.00242669 2.3492 0.0188151
##
## Lambda: 0.75563, LR test value: 361.01, p-value: < 2.22e-16
## Asymptotic standard error: 0.023652
## z-value: 31.948, p-value: < 2.22e-16
## Wald statistic: 1020.7, p-value: < 2.22e-16
##
## Log likelihood: 4420.624 for error model
## ML residual variance (sigma squared): 1.2408e-05, (sigma: 0.0035225)
## Number of observations: 1063
## Number of parameters estimated: 17
## AIC: -8807.2, (AIC for lm: -8448.2)