Extracting fires from the spatiotemporal clustering results
Source:R/extract_fire.R
extract_fire.RdThis function takes a spotoroo object to produce a data frame which
contains information about the fire.
Arguments
- result
spotorooobject. A result of a call tohotspot_cluster().- cluster
Character/Integer. If "all", extract all clusters. If an integer vector is given, extract corresponding clusters.
- noise
Logical. Whether or not to include noise.
Value
A data.frame. The fire information
lon: Longitude.lat: Latitude.obsTime: Observed time.timeID: Time indexes.membership: Membership labels.noise: Whether it is a noise point.distToIgnition: Distance to the ignition location.distToIgnitionUnit: Unit of distance to the ignition location.timeFromIgnition: Time from ignition.timeFromIgnitionUnit: Unit of time from ignition.type: Type of the entry, either "hotspot", "noise" or "ignition"obsInCluster: Number of observations in the cluster.clusterTimeLen: Length of time of the cluster.clusterTimeLenUnit: Unit of length of time of the cluster.
Examples
# \donttest{
# Time consuming functions (>5 seconds)
# Get clustering results
result <- hotspot_cluster(hotspots,
lon = "lon",
lat = "lat",
obsTime = "obsTime",
activeTime = 24,
adjDist = 3000,
minPts = 4,
minTime = 3,
ignitionCenter = "mean",
timeUnit = "h",
timeStep = 1)
#>
#> ──────────────────────────────── SPOTOROO 0.1.6 ────────────────────────────────
#>
#> ── Calling Core Function : `hotspot_cluster()` ──
#>
#> ── "1" time index = 1 hour
#> ✔ Transform observed time → time indexes
#> ℹ 970 time indexes found
#>
#> ── activeTime = 24 time indexes | adjDist = 3000 meters
#> ✔ Cluster
#> ℹ 16 clusters found (including noise)
#>
#> ── minPts = 4 hot spots | minTime = 3 time indexes
#> ✔ Handle noise
#> ℹ 6 clusters left
#> ℹ noise proportion : 0.935 %
#>
#> ── ignitionCenter = "mean"
#> ✔ Compute ignition points for clusters
#> ℹ average hot spots : 176.7
#> ℹ average duration : 131.9 hours
#>
#> ── Time taken = 0 mins 1 sec for 1070 hot spots
#> ℹ 0.001 secs per hot spot
#>
#> ────────────────────────────────────────────────────────────────────────────────
# Extract all fires
all_fires <- extract_fire(result)
head(all_fires, 3)
#> lon lat obsTime timeID membership noise distToIgnition
#> 1 149.30 -37.75999 2019-12-29 13:10:00 1 1 FALSE 1111.885
#> 2 149.30 -37.78000 2019-12-29 13:10:00 1 1 FALSE 1111.885
#> 3 149.32 -37.78000 2019-12-29 13:30:00 1 1 FALSE 2080.914
#> distToIgnitionUnit timeFromIgnition timeFromIgnitionUnit type obsInCluster
#> 1 m 0.0000000 hours h hotspot 146
#> 2 m 0.0000000 hours h hotspot 146
#> 3 m 0.3333333 hours h hotspot 146
#> clusterTimeLen clusterTimeLenUnit
#> 1 116.1667 hours h
#> 2 116.1667 hours h
#> 3 116.1667 hours h
# Extract cluster 4
fire_4 <- extract_fire(result, 4)
head(fire_4, 3)
#> lon lat obsTime timeID membership noise distToIgnition
#> 1 149.16 -37.25999 2020-01-10 04:10:00 280 4 FALSE 3334.822
#> 2 149.16 -37.28000 2020-01-10 04:10:00 280 4 FALSE 1111.052
#> 3 149.16 -37.30000 2020-01-10 04:10:00 280 4 FALSE 1111.607
#> distToIgnitionUnit timeFromIgnition timeFromIgnitionUnit type obsInCluster
#> 1 m 0 hours h hotspot 256
#> 2 m 0 hours h hotspot 256
#> 3 m 0 hours h hotspot 256
#> clusterTimeLen clusterTimeLenUnit
#> 1 124.1667 hours h
#> 2 124.1667 hours h
#> 3 124.1667 hours h
# }