This function clusters hot spots spatially and temporally.
Arguments
- lon
Numeric. A vector of longitude values.
- lat
Numeric. A vector of latitude values.
- timeID
Integer (>=1). A vector of time indexes.
- activeTime
Numeric (>=0). Time tolerance. Unit is time index.
- adjDist
Numeric (>0). Distance tolerance. Unit is metre.
Details
For more details about the clustering algorithm and the arguments
activeTime and adjDist, please check the documentation
of hotspot_cluster().
This function performs the first 3 steps of the clustering algorithm.
Examples
# Define lon, lat and timeID for 10 observations
lon <- c(141.1, 141.14, 141.12, 141.14, 141.16, 141.12, 141.14,
141.16, 141.12, 141.14)
lat <- c(-37.10, -37.10, -37.12, -37.12, -37.12, -37.14, -37.14,
-37.14, -37.16, -37.16)
timeID <- c(rep(1, 5), rep(26, 5))
# Cluster 10 hot spots with different values of activeTime and adjDist
global_clustering(lon, lat, timeID, 12, 1500)
#>
#> ── activeTime = 12 time indexes | adjDist = 1500 meters
#> ✔ Cluster
#> ℹ 10 clusters found (including noise)
#> [1] 1 2 3 4 5 6 7 8 9 10
global_clustering(lon, lat, timeID, 24, 3000)
#>
#> ── activeTime = 24 time indexes | adjDist = 3000 meters
#> ✔ Cluster
#> ℹ 2 clusters found (including noise)
#> [1] 1 1 1 1 1 2 2 2 2 2
global_clustering(lon, lat, timeID, 36, 6000)
#>
#> ── activeTime = 36 time indexes | adjDist = 6000 meters
#> ✔ Cluster
#> ℹ 1 cluster found (including noise)
#> [1] 1 1 1 1 1 1 1 1 1 1