BostonHousing: BostonHousing
In nodeHarvest: Node Harvest for Regression and Classification

Description Usage Format Details References

Housing data for 506 census tracts of Boston from the 1970 census. The dataframe 'BostonHousing' contains the original data by Harrison and Rubinfeld (1979), the dataframe 'BostonHousing2' the corrected version with additional spatial information (see references below).

1	data(BostonHousing)

The original data are 506 observations on 14 variables, 'medv' being the target variable:

crim: per capita crime rate by town
zn: proportion of residential land zoned for lots over 25,000 sq.ft
indus: proportion of non-retail business acres per town
chas: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
nox: nitric oxides concentration (parts per 10 million)
rm: average number of rooms per dwelling
age: proportion of owner-occupied units built prior to 1940
dis: weighted distances to five Boston employment centres
rad: index of accessibility to radial highways
tax: full-value property-tax rate per USD 10,000
ptratio: pupil-teacher ratio by town
b: 1000(B - 0.63)^2 where B is the proportion of blacks by town
lstat: percentage of lower status of the population
medv: median value of owner-occupied homes in USD 1000's

The original data have been taken from the UCI Repository Of Machine Learning Databases at

http://www.ics.uci.edu/~mlearn/MLRepository.html,

See Statlib and references there for details on the corrections. Converted to R format by Friedrich Leisch.

Harrison, D. and Rubinfeld, D.L. (1978). Hedonic prices and the demand for clean air. _Journal of Environmental Economics and Management_, *5*, 81-102.

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

Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.

Pace, R. Kelley, and O.W. Gilley (1997). Using the Spatial Configuration of the Data to Improve Estimation. _Journal of the Real Estate Finance and Economics_, *14*, 333-340. [Added georeferencing and spatial estimation.]

nodeHarvest documentation built on May 2, 2019, 2:45 a.m.