“spotoroo” stands for spatiotemporal clustering in R of hot spot data. It is an algorithm to cluster satellite hot spots, detect ignition points and reconstruct fire movement.
You can install the released version of spotoroo from CRAN with:
install.packages("spotoroo")You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("TengMCing/spotoroo")library(spotoroo)The below examples use the built-in dataset hotspots.
The hot spot data needs to has at least three columns: the longitude,
the latitude, and the observed time.
str(hotspots)
#> 'data.frame': 1070 obs. of 3 variables:
#> $ lon : num 147 146 143 149 142 ...
#> $ lat : num -37.5 -37.9 -37.8 -37.4 -37.1 ...
#> $ obsTime: POSIXct, format: "2020-02-01 05:20:00" "2020-01-02 06:30:00" ...Perform spatiotemporal clustering on this dataset. You need to specify which columns correspond to the spatial variables (“lon”, “lat”), and which to observed time (“obsTime”).
There is a choice of options for the algorithm.
“activeTime” sets the time to consider that a fire can be active, and longer than this between hot spots will create a new cluster
“adjDist” sets the maximum intra-cluster spatial distance between nearest hot spots beyond which they would be considered part of a different cluster
“minPts” sets the minimum number of hot spots in a cluster
“minTime” sets the minimum length of time of a cluster
“ignitionCenter” sets the method to calculate the ignition points
“timeUnit” and “timeStep” set the length of time between successive time indexes
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 0.1.4 ────────────────────────────────
#>
#> ── 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
#>
#> ────────────────────────────────────────────────────────────────────────────────
result
#> ℹ spotoroo object: 6 clusters | 1070 hot spots (including noise points)You can make a summary of the clustering results.
summary(result)
#>
#> ──────────────────────────────── SPOTOROO 0.1.4 ────────────────────────────────
#>
#> ── Calling Core Function : `summary_spotoroo()` ──
#>
#> CLUSTERS: ALL
#> OBSERVATIONS: 1070
#> FROM: 2019-12-29 13:10:00
#> TO: 2020-02-07 22:50:00
#>
#> ── Clusters
#> ℹ Number of clusters: 6
#>
#> Observations in cluster
#> Min. 1st Qu. Mean 3rd Qu. Max.
#> 111.0 131.0 176.7 233.2 256.0
#> Duration of cluster (hours)
#> Min. 1st Qu. Mean 3rd Qu. Max.
#> 111.2 118.2 131.9 146.1 148.3
#>
#> ── Hot spots (excluding noise)
#> ℹ Number of hot spots: 1060
#>
#> Distance to ignition points (m)
#> Min. 1st Qu. Mean 3rd Qu. Max.
#> 0.0 2840.3 5058.2 6981.6 13452.7
#> Time from ignition (hours)
#> Min. 1st Qu. Mean 3rd Qu. Max.
#> 0.0 25.2 62.5 98.2 148.3
#>
#> ── Noise
#> ℹ Number of noise points: 10 (0.93 %)
#>
#> ────────────────────────────────────────────────────────────────────────────────You can extract a subset of clusters from the results.
fire_1_and_2 <- extract_fire(result, 1:2)
head(fire_1_and_2, 2)
#> lon lat obsTime timeID membership noise distToIgnition
#> 1 149.3 -37.75999 2019-12-29 13:10:00 1 1 FALSE 1111.885
#> 2 149.3 -37.78000 2019-12-29 13:10:00 1 1 FALSE 1111.885
#> distToIgnitionUnit timeFromIgnition timeFromIgnitionUnit type obsInCluster
#> 1 m 0 hours h hotspot 146
#> 2 m 0 hours h hotspot 146
#> clusterTimeLen clusterTimeLenUnit
#> 1 116.1667 hours h
#> 2 116.1667 hours hPlot of the result. In this example, there is a total of 6 clusters, so all can be displayed.
plot(result, bg = plot_vic_map())
You can also choose a subset of clusters, and this will plot without a map, so that you can see a zoomed in view of the hot spot clusters and their ignition points.
plot(result, cluster = c(1,2,3,4))
To examine the fire movements, use the option “mov”, and the movement will be shown as connected lines between centroids at each time step, for each cluster.
plot(result,
type = "mov",
cluster = 1:3,
step = 6,
bg = plot_vic_map())
To examine the time line of clusters and learn about the intensity of fire periods, use the option “timeline”.
plot(result, "timeline",
dateLabel = "%b %d",
mainBreak = "1 week")