| BostonHousing {mlbench} | R Documentation |
Boston Housing Data
Description
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).
Usage
data("BostonHousing", package = "mlbench")
data("BostonHousing2", package = "mlbench")
Format
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 corrected data set has the following additional columns:
| cmedv | corrected median value of owner-occupied homes in USD 1000's |
| town | name of town |
| tract | census tract |
| lon | longitude of census tract |
| lat | latitude of census tract |
Source
The original data were taken from the UCI Repository Of Machine Learning Databases (Blake & Merz 1998) and no longer seem to be available from the UC Irvine Machine Learning Repository (now at https://archive.ics.uci.edu/). The corrected data were taken from Statlib at http://lib.stat.cmu.edu/datasets/. See Statlib and references there for details on the corrections. Both were converted to R format by Friedrich Leisch.
References
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.]
Blake, C.L. & Merz, C.J. (1998). UCI Repository of Machine Learning Databases. Irvine, CA: University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘http://www.ics.uci.edu/~mlearn/MLRepository.html’.
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.]
Examples
data("BostonHousing", package = "mlbench")
summary(BostonHousing)
data("BostonHousing2", package = "mlbench")
summary(BostonHousing2)