boston: Corrected Boston Housing Data
In spdep: Spatial Dependence: Weighting Schemes, Statistics and Models
Description
Usage
Format
Note
Source
References
Examples
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
1
Format
This data frame contains the following columns:
- TOWN
a factor with levels given by town names
- TOWNNO
a numeric vector corresponding to TOWN
- TRACT
a numeric vector of tract ID numbers
- LON
a numeric vector of tract point longitudes in decimal degrees
- LAT
a numeric vector of tract point latitudes in decimal degrees
- MEDV
a numeric vector of median values of owner-occupied housing
in USD 1000
- CMEDV
a numeric vector of corrected median values of
owner-occupied housing in USD 1000
- CRIM
a numeric vector of per capita crime
- ZN
a numeric vector of proportions of residential land zoned
for lots over 25000 sq. ft per town (constant for all Boston tracts)
- INDUS
a numeric vector of proportions of non-retail business
acres per town (constant for all Boston tracts)
- CHAS
a factor with levels 1 if tract borders Charles River;
0 otherwise
- NOX
a numeric vector of nitric oxides concentration (parts per
10 million) per town
- RM
a numeric vector of average numbers of rooms per dwelling
- AGE
a numeric vector of proportions of owner-occupied units
built prior to 1940
- DIS
a numeric vector of weighted distances to five Boston
employment centres
- RAD
a numeric vector of an index of accessibility to radial
highways per town (constant for all Boston tracts)
- TAX
a numeric vector full-value property-tax rate per USD
10,000 per town (constant for all Boston tracts)
- PTRATIO
a numeric vector of pupil-teacher ratios per town
(constant for all Boston tracts)
- B
a numeric vector of 1000*(Bk - 0.63)^2 where Bk is the
proportion of blacks
- LSTAT
a numeric vector of percentage values of lower status
population
Note
Details of the creation of the tract shapefile given in final don't run block; tract boundaries for 1990: http://www.census.gov/geo/maps-data/data/cbf/cbf_tracts.html then choose 1990 Census and Massachusetts to download: tr25_d90_shp.zip, counties in the BOSTON SMSA https://www2.census.gov/programs-surveys/metro-micro/geographies/reference-files/1963/historical-delineation-files/63mfips.txt; tract conversion table 1980/1970: https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/7913?q=07913&permit[0]=AVAILABLE, http://www.icpsr.umich.edu/cgi-bin/bob/zipcart2?path=ICPSR&study=7913&bundle=all&ds=1&dups=yes
Source
Formerly at “http://lib.stat.cmu.edu/datasets/boston\_corrected.txt” which is now offline, also as data(BostonHousing2, package="mlbench")
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
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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))
## Not run:
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)
## End(Not run)
## Not run: 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$double.eps^(1/4)))
summary(gp1)
gp2 <- lagsarlm(log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2)
+ AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT),
data=boston.c, nb2listw(boston.soi), method="Matrix")
summary(gp2)
## End(Not run)
## Not run:
## Conversion table 1980/1970
# ICPSR_07913.zip
# 07913-0001-Data.txt
# http://dx.doi.org/10.3886/ICPSR07913.v1
# Provider: ICPSR
# Content: text/plain; charset="us-ascii"
#
# TY - DATA
# T1 - Census of Population and Housing 1980 [United States]:
# 1970-Pre 1980 Tract Relationships
# AU - United States Department of Commerce. Bureau of the Census
# DO - 10.3886/ICPSR07913.v1
# PY - 1984-06-28
# UR - http://dx.doi.org/10.3886/ICPSR07913.v1
# PB - Inter-university Consortium for Political and Social Research
# (ICPSR) [distributor]
# ER -
# widths <- c(ID=5L, FIPS70State=2L, FIPS70cty=3L, Tract70=6L, FIPS80State=2L,
# FIPS80cty=3L, f1=7L, CTC=6L, f2=2L, intersect1=3L, intersect2=3L, name=30L)
# dta0 <- read.fwf("07913-0001-Data.txt", unname(widths),
# col.names=names(widths), colClasses=rep("character", 12), as.is=TRUE)
# sub <- grep("25", dta0$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
## https://www.census.gov/population/metro/files/lists/historical/90nfips.txt
## 1123 Boston-Lawrence-Salem-Lowell-Brockton, MA NECMA
## 1123 25 009 Essex County
## 1123 25 017 Middlesex County
## 1123 25 021 Norfolk County
## 1123 25 023 Plymouth County
## 1123 25 025 Suffolk County
# 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$TRACTBASE, CTC4u)
## match 1980 tracts with 1990
# BOSTON_SMSA1 <- BOSTON_SMSA[!is.na(TB_CTC4u),]
## union Polygons objects with same 1970 tract code
#library(rgeos)
# BOSTON_SMSA2 <- gUnaryUnion(BOSTON_SMSA1,
# id=as.character(BOSTON_SMSA1$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))
## End(Not run)
spdep documentation built on Aug. 19, 2017, 3:01 a.m.
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Description Usage Format Note Source References Examples
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
1 |
Format
This data frame contains the following columns:
- TOWN
a factor with levels given by town names
- TOWNNO
a numeric vector corresponding to TOWN
- TRACT
a numeric vector of tract ID numbers
- LON
a numeric vector of tract point longitudes in decimal degrees
- LAT
a numeric vector of tract point latitudes in decimal degrees
- MEDV
a numeric vector of median values of owner-occupied housing in USD 1000
- CMEDV
a numeric vector of corrected median values of owner-occupied housing in USD 1000
- CRIM
a numeric vector of per capita crime
- ZN
a numeric vector of proportions of residential land zoned for lots over 25000 sq. ft per town (constant for all Boston tracts)
- INDUS
a numeric vector of proportions of non-retail business acres per town (constant for all Boston tracts)
- CHAS
a factor with levels 1 if tract borders Charles River; 0 otherwise
- NOX
a numeric vector of nitric oxides concentration (parts per 10 million) per town
- RM
a numeric vector of average numbers of rooms per dwelling
- AGE
a numeric vector of proportions of owner-occupied units built prior to 1940
- DIS
a numeric vector of weighted distances to five Boston employment centres
- RAD
a numeric vector of an index of accessibility to radial highways per town (constant for all Boston tracts)
- TAX
a numeric vector full-value property-tax rate per USD 10,000 per town (constant for all Boston tracts)
- PTRATIO
a numeric vector of pupil-teacher ratios per town (constant for all Boston tracts)
- B
a numeric vector of
1000*(Bk - 0.63)^2where Bk is the proportion of blacks- LSTAT
a numeric vector of percentage values of lower status population
Note
Details of the creation of the tract shapefile given in final don't run block; tract boundaries for 1990: http://www.census.gov/geo/maps-data/data/cbf/cbf_tracts.html then choose 1990 Census and Massachusetts to download: tr25_d90_shp.zip, counties in the BOSTON SMSA https://www2.census.gov/programs-surveys/metro-micro/geographies/reference-files/1963/historical-delineation-files/63mfips.txt; tract conversion table 1980/1970: https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/7913?q=07913&permit[0]=AVAILABLE, http://www.icpsr.umich.edu/cgi-bin/bob/zipcart2?path=ICPSR&study=7913&bundle=all&ds=1&dups=yes
Source
Formerly at “http://lib.stat.cmu.edu/datasets/boston\_corrected.txt” which is now offline, also as data(BostonHousing2, package="mlbench")
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | 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))
## Not run:
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)
## End(Not run)
## Not run: 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)
## End(Not run)
## Not run:
## 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$Tract70), bTR)
# BOSTON <- MA[!is.na(x1),]
## MA 1990 tracts
# library(rgdal)
# MAtr90 <- readOGR(".", "tr25_d90")
## counties in the BOSTON SMSA
## https://www.census.gov/population/metro/files/lists/historical/90nfips.txt
## 1123 Boston-Lawrence-Salem-Lowell-Brockton, MA NECMA
## 1123 25 009 Essex County
## 1123 25 017 Middlesex County
## 1123 25 021 Norfolk County
## 1123 25 023 Plymouth County
## 1123 25 025 Suffolk County
# BOSTON_SMSA <- MAtr90[MAtr90$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 <- 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))
## End(Not run)
|
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