gtwr: Geographically and Temporally Weighted Regression

In GWmodel: Geographically-Weighted Models

Geographically and Temporally Weighted Regression

Description

A function for calibrating a Geographically and Temporally Weighted Regression (GTWR) model.

Usage

gtwr(formula, data, regression.points, obs.tv, reg.tv, st.bw, kernel="bisquare",
     adaptive=FALSE, p=2, theta=0, longlat=F,lamda=0.05,t.units = "auto",ksi=0,
     st.dMat)

Arguments

formula	Regression model formula of a formula object
data	a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp
regression.points	a Spatial*DataFrame object, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp; Note that no diagnostic information will returned if it is assigned
obs.tv	a vector of time tags for each observation, which could be numeric or of POSIXlt class
reg.tv	a vector of time tags for each regression location, which could be numeric or of POSIXlt class
st.bw	spatio-temporal bandwidth used in the weighting function, possibly calculated by bw.gwr;fixed (distance) or adaptive bandwidth(number of nearest neighbours)
kernel	function chosen as follows: gaussian: wgt = exp(-.5*(vdist/bw)^2); exponential: wgt = exp(-vdist/bw); bisquare: wgt = (1-(vdist/bw)^2)^2 if vdist < bw, wgt=0 otherwise; tricube: wgt = (1-(vdist/bw)^3)^3 if vdist < bw, wgt=0 otherwise; boxcar: wgt=1 if dist < bw, wgt=0 otherwise
adaptive	if TRUE calculate an adaptive kernel where the bandwidth (bw) corresponds to the number of nearest neighbours (i.e. adaptive distance); default is FALSE, where a fixed kernel is found (bandwidth is a fixed distance)
p	the power of the Minkowski distance, default is 2, i.e. the Euclidean distance
theta	an angle in radians to rotate the coordinate system, default is 0
longlat	if TRUE, great circle distances will be calculated
lamda	an parameter between 0 and 1 for calculating spatio-temporal distance
t.units	character string to define time unit
ksi	an parameter between 0 and PI for calculating spatio-temporal distance, see details in Wu et al. (2014)
st.dMat	a pre-specified spatio-temporal distance matrix, and can be calculated via the function st.dist

Value

A list of class “gtwrm”:

GTW.arguments	a list class object including the model fitting parameters for generating the report file
GTW.diagnostic	a list class object including the diagnostic information of the model fitting
lm	an object of class inheriting from “lm”, see lm.
SDF	a SpatialPointsDataFrame (may be gridded) or SpatialPolygonsDataFrame object (see package “sp”) integrated with fit.points, GTWR coefficient estimates, y value,predicted values, coefficient standard errors and t-values in its "data" slot.
timings	starting and ending time.
this.call	the function call used.

Note

The function implements GTWR model proposed by Huang et al. (2010) and Wu et al. (2014).

Author(s)

Binbin Lu [e-mail]

References

Huang, B., Wu, B., & Barry, M. (2010). Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24, 383-401.

Wu, B., Li, R., & Huang, B. (2014). A geographically and temporally weighted autoregressive model with application to housing prices. International Journal of Geographical Information Science, 28, 1186-1204.

Fotheringham, A. S., Crespo, R., & Yao, J. (2015). Geographical and Temporal Weighted Regression (GTWR). Geographical Analysis, 47, 431-452.

GWmodel documentation built on July 9, 2023, 5:52 p.m.