This function is design to provide an exploratory point pattern analysis. Is base on spatstat package a to do a basic point pattern analysis of Homogeneous an Inhomogeneous Poisson.
Usage
tspan(geotweets,bw, cont, acontour)
Arguments
geotweets
Geotagged tweets as a SpatialPointsDataFrame or a SpatialPointsDataFrame.
bw
Bandwith for Kernel Smoothed Intensity. Note that if you are using directly geotweets comming from tweet2r and t2SpatialPointDataFrame units are degrees.
cont
FALSE by default, geotweets bounding box provide the countour. If TRUE a countour must be provaided
acontour
Optional. A Spatial object with a defined bbox.
Details
In order to do a wider point pattern analysis is better to use directly the spatstat package
Value
tweetspphp
Simpliest Object of class"ppp"representing a point pattern dataset in the two-dimensional plane with no marks, (ppp)
hp
Homogeneous Poisson fitted point process model to an observed point pattern (ppm).
ihp
Inhomogeneous Poisson fitted point process model to an observed point pattern (ppm).
int
Computed kernel smoothed intensity function from a point pattern. (density.ppp).
Author(s)
Pau Arag<c3><83><c2><b3>
References
Baddeley, Adrian, y Rolf Turner. <c3><82><c2><ab>Spatstat: An R Package for Analyzing Spatial Point Patterns<c3><82><c2><bb>. Journal of Statistical Software 12, n.<c3><82><c2><ba> 6 (2005). doi:10.18637/jss.v012.i06. http://www.jstatsoft.org/v12/i06/
See Also
spatstat
Examples
library(sp)
library(spatstat)
#loada a SpatialPointsDataFrame
data("meuse.grid_ll")
# run function without contour
tspan(meuse.grid_ll,bw=0.0005)
#providing a contour as SpatialPointDataFrame
data("meuse.area")
#build the acontour layer
cont<-SpatialPoints(meuse.area, proj4string = CRS("+init=epsg:28992"))
#transform to meuse.grid_ll reference system
cont<-spTransform(cont, CRS("+init=epsg:4326"))
# run function with contour
tspan(meuse.grid_ll,bw=0.0005, cont = TRUE, acontour=cont)
{
}