boston: Corrected Boston Housing Data

Description

The boston.c data frame has 506 rows and 20 columns. It contains the Harrison and Rubinfeld (1978) data corrected for a few minor errors and augmented with the latitude and longitude of the observations. Gilley and Pace also point out that MEDV is censored, in that median values at or over USD 50,000 are set to USD 50,000. The original data set without the corrections is also included in package mlbench as BostonHousing. In addition, a matrix of tract point coordinates projected to UTM zone 19 is included as boston.utm, and a sphere of influence neighbours list as boston.soi.

Usage

data(boston)

Arguments

source

http://lib.stat.cmu.edu/datasets/boston_corrected.txt

References

Harrison, David, and Daniel L. Rubinfeld, Hedonic Housing Prices and the Demand for Clean Air, Journal of Environmental Economics and Management, Volume 5, (1978), 81-102. Original data.

Gilley, O.W., and R. Kelley Pace, On the Harrison and Rubinfeld Data, Journal of Environmental Economics and Management, 31 (1996), 403-405. Provided corrections and examined censoring.

Pace, R. Kelley, and O.W. Gilley, Using the Spatial Configuration of the Data to Improve Estimation, Journal of the Real Estate Finance and Economics, 14 (1997), 333-340.

Examples

Run this code

data(boston)
hr0 <- lm(log(MEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2) +
 AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT), data=boston.c)
summary(hr0)
logLik(hr0)
gp0 <- lm(log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2) +
 AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT), data=boston.c)
summary(gp0)
logLik(gp0)
lm.morantest(hr0, nb2listw(boston.soi))
require(maptools)
boston.tr <- readShapePoly(system.file("etc/shapes/boston_tracts.shp",
  package="spdep")[1], ID="poltract",
  proj4string=CRS(paste("+proj=longlat +datum=NAD27 +no_defs +ellps=clrk66",
  "+nadgrids=@conus,@alaska,@ntv2_0.gsb,@ntv1_can.dat")))
boston_nb <- poly2nb(boston.tr)
gp1 <- errorsarlm(log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2)
 + I(RM^2) +  AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT),
 data=boston.c, nb2listw(boston.soi), method="Matrix", 
 control=list(tol.opt = .Machine$double.eps^(1/4)))
summary(gp1)
gp2 <- lagsarlm(log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2)
 +  AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT),
 data=boston.c, nb2listw(boston.soi), method="Matrix")
summary(gp2)
## Conversion table 1980/1970
# ICPSR_07913.zip
# 07913-0001-Data.txt
# http://dx.doi.org/10.3886/ICPSR07913.v1
# Provider: ICPSR
# Content: text/plain; charset="us-ascii"
# 
# TY  - DATA
# T1  - Census of Population and Housing 1980 [United States]:
# 1970-Pre 1980 Tract Relationships
# AU  - United States Department of Commerce. Bureau of the Census
# DO  - 10.3886/ICPSR07913.v1
# PY  - 1984-06-28
# UR  - http://dx.doi.org/10.3886/ICPSR07913.v1
# PB  - Inter-university Consortium for Political and Social Research
# (ICPSR) [distributor]
# ER  -
# widths <- c(ID=5L, FIPS70State=2L, FIPS70cty=3L, Tract70=6L, FIPS80State=2L,
#  FIPS80cty=3L, f1=7L, CTC=6L, f2=2L, intersect1=3L, intersect2=3L, name=30L)
# dta0 <- read.fwf("07913-0001-Data.txt", unname(widths),
#  col.names=names(widths), colClasses=rep("character", 12), as.is=TRUE)
# sub <- grep("25", dta0$FIPS80State)
# MA <- dta0[sub,]
## match against boston data set
# library(spdep)
# data(boston)
# bTR <- boston.c$TRACT
# x1 <- match(as.integer(MA$Tract70), bTR)
# BOSTON <- MA[!is.na(x1),]
## MA 1990 tracts
# library(rgdal)
# MAtr90 <- readOGR(".", "tr25_d90")
## counties in the BOSTON SMSA
# BOSTON_SMSA <- MAtr90[MAtr90$CO# proj4string(BOSTON_SMSA) <- CRS(paste("+proj=longlat +datum=NAD27 +no_defs",
#   "+ellps=clrk66 +nadgrids=@conus,@alaska,@ntv2_0.gsb,@ntv1_can.dat"))
# CTC4 <- substring(BOSTON$CTC, 1, 4)
# CTC4u <- unique(CTC4)
# TB_CTC4u <- match(BOSTON_SMSA$TRACTBASE, CTC4u)
## match 1980 tracts with 1990
# BOSTON_SMSA1 <- BOSTON_SMSA[!is.na(TB_CTC4u),]
## union Polygons objects with same 1970 tract code
#library(rgeos)
# BOSTON_SMSA2 <- unionSpatialPolygons(BOSTON_SMSA1,
#  id=as.character(BOSTON_SMSA1$TRACTBASE))
#BOSTON_SMSA2 <- gUnaryUnion(BOSTON_SMSA1,
# id=as.character(BOSTON_SMSA1$TRACTBASE))
## reorder data set
# mm <- match(as.integer(as.character(row.names(BOSTON_SMSA2))), boston.c$TRACT)
# df <- boston.c[mm,]
# row.names(df) <- df$TRACT
# row.names(BOSTON_SMSA2) <- as.character(as.integer(row.names(BOSTON_SMSA2)))
## create SpatialPolygonsDataFrame
# BOSTON_SMSA3 <- SpatialPolygonsDataFrame(BOSTON_SMSA2,
#  data=data.frame(poltract=row.names(BOSTON_SMSA2),
#  row.names=row.names(BOSTON_SMSA2)))
# BOSTON_SMSA4 <- spCbind(BOSTON_SMSA3, df)
# mm1 <- match(boston.c$TRACT, row.names(BOSTON_SMSA4))
# BOSTON_SMSA5 <- BOSTON_SMSA4[mm1,]
#writeOGR(BOSTON_SMSA5, ".", "boston_tracts", driver="ESRI Shapefile",
# overwrite_layer=TRUE)
# moran.test(boston.c$CMEDV, nb2listw(boston.soi))
# moran.test(BOSTON_SMSA5$CMEDV, nb2listw(boston.soi))