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`

.

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 blacksLSTAT: a numeric vector of percentage values of lower status population

Previously available from http://lib.stat.cmu.edu/datasets/boston_corrected.txt

Details of the creation of the tract GPKG file: tract boundaries for 1990 (formerly at: http://www.census.gov/geo/cob/bdy/tr/tr90shp/tr25_d90_shp.zip, counties in the BOSTON SMSA http://www.census.gov/population/metro/files/lists/historical/63mfips.txt); tract conversion table 1980/1970 (formerly at : 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). The shapefile contains corrections and extra variables (tract 3592 is corrected to 3593; the extra columns are:

units: number of single family houses

cu5k: count of units under USD 5,000

c5_7_5: counts USD 5,000 to 7,500

C*_*: interval counts

co50k: count of units over USD 50,000

median: recomputed median values

BB: recomputed black population proportion

censored: whether censored or not

NOXID: NOX model zone ID

POP: tract population

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.

Bivand, Roger. Revisiting the Boston data set - Changing the units of observation affects estimated willingness to pay for clean air. REGION, v. 4, n. 1, p. 109-127, 2017. https://openjournals.wu.ac.at/ojs/index.php/region/article/view/107.

```
if (requireNamespace("spdep", quietly = TRUE)) {
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)
spdep::lm.morantest(hr0, spdep::nb2listw(boston.soi))
}
#>
#> Global Moran I for regression residuals
#>
#> data:
#> model: lm(formula = 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)
#> weights: spdep::nb2listw(boston.soi)
#>
#> Moran I statistic standard deviate = 14.509, p-value < 2.2e-16
#> alternative hypothesis: greater
#> sample estimates:
#> Observed Moran I Expectation Variance
#> 0.4364296993 -0.0168870829 0.0009762383
#>
if (requireNamespace("sf", quietly = TRUE)) {
boston.tr <- sf::st_read(system.file("shapes/boston_tracts.gpkg",
package="spData")[1])
if (requireNamespace("spdep", quietly = TRUE)) {
boston_nb <- spdep::poly2nb(boston.tr)
}
}
#> Reading layer `boston_tracts' from data source
#> `/home/runner/work/_temp/Library/spData/shapes/boston_tracts.gpkg'
#> using driver `GPKG'
#> Simple feature collection with 506 features and 36 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -71.52311 ymin: 42.00305 xmax: -70.63823 ymax: 42.67307
#> Geodetic CRS: NAD27
```