Help for package spotoroo

Title:

Spatiotemporal Clustering of Satellite Hot Spot Data

Version:

0.1.5

Description:

An algorithm to cluster satellite hot spot data spatially and temporally.

License:

MIT + file LICENSE

URL:

https://tengmcing.github.io/spotoroo/, https://github.com/TengMCing/spotoroo/

BugReports:

https://github.com/TengMCing/spotoroo/issues

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.2.1

Depends:

R (≥ 3.3.0)

Imports:

geodist (≥ 0.0.4), progress (≥ 1.2.2), dplyr (≥ 1.0.0), cli (≥ 2.3.0), stats, patchwork, ggrepel, ggExtra (≥ 0.9), ggbeeswarm (≥ 0.7.2), ggplot2 (≥ 3.0.0)

Suggests:

sf (≥ 0.7-3), testthat (≥ 3.0.0), covr, knitr, rmarkdown, markdown

Config/testthat/edition:

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2024-11-19 02:11:15 UTC; patrickli

Author:

Weihao Li

[aut, cre], Di Cook

[ctb], Emily Dodwell [ctb]

Maintainer:

Weihao Li <llreczx@gmail.com>

Repository:

CRAN

Date/Publication:

2024-11-19 03:20:02 UTC

spotoroo: Spatiotemporal Clustering of Satellite Hot Spot Data

Description

An algorithm to cluster satellite hot spot data spatially and temporally.

Author(s)

Maintainer: Weihao Li llreczx@gmail.com (ORCID)

Other contributors:

Di Cook dicook@monash.edu (ORCID) [contributor]
Emily Dodwell emdodwell@gmail.com [contributor]

Calculation of the geodesic of a point to multiple points

Description

This function calculates the geodesic of a point to multiple points given the coordinate information. It is a wrapper of geodist::geodist_vec().

Usage

dist_point_to_vector(plon, plat, vlon, vlat)

Arguments

plon

Numeric. The longitude of a point.

plat

Numeric. The latitude of a point.

vlon

Numeric. A vector of longitude values.

vlat

Numeric. A vector of latitude values.

Value

Numeric. The geodesic of a point to multiple points in meters.

Examples


# Define vlon and vlat
vlon <- c(141.12, 141.13)
vlat <- c(-37.1, -37.0)

# Calculate the geodesic
dist_point_to_vector(141.12, -37.1, vlon, vlat)

Extracting fires from the spatiotemporal clustering results

Description

This function takes a spotoroo object to produce a data frame which contains information about the fire.

Usage

extract_fire(result, cluster = "all", noise = FALSE)

Arguments

result

spotoroo object. A result of a call to hotspot_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



  # 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)


  # Extract all fires
  all_fires <- extract_fire(result)
  head(all_fires, 3)

  # Extract cluster 4
  fire_4 <- extract_fire(result, 4)
  head(fire_4, 3)

Calculation of the fire movement

Description

This function calculates the movement of a single fire per step time indexes. It collects hot spots per step time indexes, then takes the mean or median of the longitude and latitude as the centre of the fire.

Usage

get_fire_mov(result, cluster, step = 1, method = "mean")

Arguments

result

spotoroo object. A result of a call to hotspot_cluster().

cluster

Integer. The membership label of the cluster.

step

Integer (>0). Step size used in the calculation of the fire movement.

method

Character. Either "mean" or "median", method of the calculation of the centre of the fire.

Value

A data.frame. The fire movement.

membership : Membership labels.
lon : Longitude of the centre of the fire.
lat : Latitude of the centre of the fire.
timeID : Time indexes.
obsTime : Observed time (approximated).
ignition : Whether or not it is a ignition point.

Examples




  # 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)

  # Get fire movement of the first cluster
  mov1 <- get_fire_mov(result, cluster = 1, step = 3, method = "mean")
  mov1

  # Get fire movement of the second cluster
  mov2 <- get_fire_mov(result, cluster = 2, step = 6, method = "median")
  mov2

Clustering hot spots spatially and temporally

Description

This function clusters hot spots spatially and temporally.

Usage

global_clustering(lon, lat, timeID, activeTime, adjDist)

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.

Value

Integer. A vector of membership labels.

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)
global_clustering(lon, lat, timeID, 24, 3000)
global_clustering(lon, lat, timeID, 36, 6000)

Handling noise in the clustering results

Description

This function finds noise from the clustering results and label it with -1.

Usage

handle_noise(global_membership, timeID, minPts, minTime)

Arguments

global_membership

Integer. A vector of membership labels.

timeID

Integer. A vector of time indexes.

minPts

Numeric (>0). Minimum number of hot spots in a cluster.

minTime

Numeric (>=0). Minimum length of time of a cluster. Unit is time index.

Details

For more details about the clustering algorithm and the arguments minPts and minTime, please check the documentation of hotspot_cluster(). This function performs the step 4 of the clustering algorithm. It uses a given threshold (minimum number of points and minimum length of time) to find noise and label it with -1.

Value

Integer. A vector of membership labels.

Examples


# Define membership labels and timeID for 10 observations
global_membership <- c(1,1,1,2,2,2,2,2,2,3,3,3,3,3,3)
timeID <- c(1,2,3,2,3,3,4,5,6,3,3,3,3,3,3)

# Handle noise with different values of minPts and minTime
handle_noise(global_membership, timeID, 4, 0)
handle_noise(global_membership, timeID, 4, 1)
handle_noise(global_membership, timeID, 3, 3)

Spatiotemporal clustering of hot spot data

Description

This is the main function of the package.
This function clusters hot spots into fires. It can be used to reconstruct fire history and detect fire ignition points.

Usage

hotspot_cluster(
  hotspots,
  lon = "lon",
  lat = "lat",
  obsTime = "obsTime",
  activeTime = 24,
  adjDist = 3000,
  minPts = 4,
  minTime = 3,
  ignitionCenter = "mean",
  timeUnit = "n",
  timeStep = 1
)

Arguments

hotspots

List/Data frame. A list or a data frame which contains information of hot spots.

lon

Character. The name of the column of the list which contains numeric longitude values.

lat

Character. The name of the column of the list which contains numeric latitude values.

obsTime

Character. The name of the column of the list which contains the observed time of hot spots. The observed time has to be in date, datetime or numeric.

activeTime

Numeric (>=0). Time tolerance. Unit is time index.

adjDist

Numeric (>0). Distance tolerance. Unit is metre.

minPts

Numeric (>0). Minimum number of hot spots in a cluster.

minTime

Numeric (>=0). Minimum length of time of a cluster. Unit is time index.

ignitionCenter

Character. Method to calculate ignition points, either "mean" or "median".

timeUnit

Character. One of "s" (seconds), "m" (minutes), "h" (hours), "d" (days) and "n" (numeric).

timeStep

Numeric (>0). Number of units of timeUnit in a time step.

Details

Arguments timeUnit and timeStep need to be specified to convert date/datetime/numeric to time index. More details can be found in transform_time_id().

This clustering algorithm consisted of 5 steps:

In step 1, it defines T intervals using the time index

Interval(t) = [max(1, t - activeTime),t]

where t = 1, 2, ..., T, and T is the maximum time index. activeTime is an argument that needs to be specified. It represents the maximum time difference between two hot spots in the same local cluster. Please notice that a local cluster is different with a cluster in the final result. More details will be given in the next part.

In step 2, the algorithm performs spatial clustering on each interval. A local cluster is a cluster found in an interval. Argument adjDist is used to control the spatial clustering. If the distance between two hot spots is smaller or equal to adjDist, they are directly-connected. If hot spot A is directly-connected with hot spot B and hot spot B is directly-connected with hot spot C, hot spot A, B and C are connected. All connected hot spots become a local cluster.

In step 3, the algorithm starts from interval 1. It marks all hot spots in this interval and records their membership labels. Then it moves on to interval 2. Due to a hot spot could exist in multiple intervals, it checks whether any hot spot in interval 2 has been marked. If there is any, their membership labels will be carried over from the record. Unmarked hot spots in interval 2, which share the same local cluster with marked hot spots, their membership labels are carried over from marked hot spots. If a unmarked hot spot shares the same local cluster with multiple marked hot spots, the algorithm will carry over the membership label from the nearest one. All other unmarked hot spots in interval 2 that do not share the same cluster with any marked hot spot, their membership labels will be adjusted such that the clusters they belong to are considered to be new clusters. Finally, all hot spots in interval 2 are marked and their membership labels are recorded. This process continues for interval 3, 4, ..., T. After finishing step 3, all hot spots are marked and their membership labels are recorded.

In step 4, it checks each cluster. If there is any cluster contains less than minPts hot spots, or lasts shorter than minTime, it will not be considered to be a cluster any more, and their hot spots will be assigned with -1 as their membership labels. A hot spot with membership label -1 is noise. Arguments minPts and minTime need to be specified.

In step 5, the algorithm finds the earliest observed hot spots in each cluster and records them as ignition points. If there are multiple earliest observed hot spots in a cluster, the mean or median of the longitude values and the latitude values will be used as the coordinate of the ignition point. This needs to be specified in argument ignitionCenter.

Value

A spotoroo object. The clustering results. It is also a list:

hotspots : A data frame contains information of hot spots.
- lon : Longitude.
- lat : Latitude.
- obsTime : Observed time.
- timeID : Time index.
- membership : Membership label.
- 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.
ignition : A data frame contains information of ignition points.
- lon : Longitude.
- lat : Latitude.
- obsTime : Observed time.
- timeID : Time index.
- obsInCluster : Number of observations in the cluster.
- clusterTimeLen : Length of time of the cluster.
- clusterTimeLenUnit : Unit of length of time of the cluster.
setting : A list contains the clustering settings.

Examples



  # 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)

  # Make a summary of the clustering results
  summary(result)

  # Make a plot of the clustering results
  plot(result, bg = plot_vic_map())

1070 observations of satellite hot spots

Description

A dataset containing the 1070 observations of satellite hot spots in Victoria, Australia during the 2019-2020 Australian bushfire season.

Usage

hotspots

Format

A data frame with 1070 rows and 3 variables:

lon: longitude
lat: latitude
obsTime: observed time

Source

https://www.eorc.jaxa.jp/ptree/

Calculation of the ignition points

Description

This function calculates ignition points for all clusters.

Usage

ignition_point(lon, lat, obsTime, timeUnit, timeID, membership, ignitionCenter)

Arguments

lon

Numeric. A vector of longitude values.

lat

Numeric. A vector of latitude values.

obsTime

Date/Datetime/Numeric. A vector of observed time.

timeUnit

Character. One of "s" (seconds), "m"(minutes), "h"(hours), "d"(days) and "n"(numeric).

timeID

Integer (>=1). A vector of time indexes.

membership

Integer. A vector of membership labels.

ignitionCenter

Character. Method of calculating ignition points, one of "mean" and "median".

Details

For more details about the clustering algorithm and the argument timeUnit, timeID and ignitionCenter, please check the documentation of hotspot_cluster(). This function performs the step 5 of the clustering algorithm. It calculates ignition points. For a cluster, when there are multiple earliest hot spots, if ignitionCenter is "mean", the centroid of these hot spots will be used as the ignition point. If ignitionCenter is "median", median longitude and median latitude of these hot spots will be used.

Value

A data frame of ignition points

membership : Membership labels.
lon : Longitude of ignition points.
lat : Latitude of ignition points.
obsTime : Observed time of ignition points.
timeID : Time indexes.
obsInCluster : Number of observations in the cluster.
clusterTimeLen : Length of time of the cluster.
clusterTimeLenUnit : Unit of length of time of the cluster.

Examples


# Define lon, lat, obsTime, timeID and membership 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)
obsTime <- c(rep(1, 5), rep(26, 5))
timeUnit <- "n"
timeID <- c(rep(1, 5), rep(26, 5))
membership <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2)

# Calculate the ignition points using different methods
ignition_point(lon, lat, obsTime, timeUnit, timeID, membership, "mean")
ignition_point(lon, lat, obsTime, timeUnit, timeID, membership, "median")

Clustering hot spots spatially

Description

This function clusters hot spots spatially.

Usage

local_clustering(lon, lat, adjDist)

Arguments

lon

Numeric. A vector of longitude values.

lat

Numeric. A vector of latitude values.

adjDist

Numeric (>0). Distance tolerance. Unit is metre.

Details

For more details about the clustering algorithm and the argument adjDist, please check the documentation of hotspot_cluster(). This function performs the step 2 of the clustering algorithm. It clusters hot spots in a given interval.

Value

Integer. A vector of membership labels.

Examples


# Define lon and lat 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)

# Cluster 10 hot spots with different values of adjDist
local_clustering(lon, lat, 2000)
local_clustering(lon, lat, 3000)
local_clustering(lon, lat, 4000)

Plotting spatiotemporal clustering result

Description

plot.spotoroo() is the plot method of the class spotoroo. It is a simple wrapper of plot_spotoroo().

Usage

## S3 method for class 'spotoroo'
plot(x, ...)

Arguments

x

spotoroo object. A result of a call to hotspot_cluster().

...

Additional arguments pass to plot_spotoroo()

Value

A ggplot object. The plot of the clustering results.

Examples




  # 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)



  # Different types of plots

  # Default plot
  plot(result, "def", bg = plot_vic_map())

  # Fire movement plot
  plot(result, "mov", cluster = 1:3, step = 3, bg = plot_vic_map())

Default method of plotting the clustering results

Description

This function plots the clustering result spatially as a scatter plot.

Usage

plot_def(
  result,
  cluster = "all",
  hotspot = TRUE,
  noise = FALSE,
  ignition = TRUE,
  from = NULL,
  to = NULL,
  bg = NULL
)

Arguments

result

spotoroo object. A result of a call to hotspot_cluster().

cluster

Character/Integer. If "all", plot all clusters. If an integer vector is given, plot corresponding clusters.

hotspot

Logical. If TRUE, plot the hot spots.

noise

Logical. If TRUE, plot the noise points.

ignition

Logical. If TRUE, plot the ignition points.

from

OPTIONAL. Date/Datetime/Numeric. Start time. The data type needs to be the same as the provided observed time.

to

OPTIONAL. Date/Datetime/Numeric. End time. The data type needs to be the same as the provided observed time.

bg

OPTIONAL. ggplot object. If specified, plot onto this object.

Value

A ggplot object. The plot of the clustering results.

Examples



  # 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)

  # Plot a subset of clusters
  plot_def(result, cluster = 1:3)

  # Plot all clusters
  plot_def(result, cluster = "all")

Plotting the fire movement

Description

This function plots the fire movement. The fire movement is calculated from get_fire_mov().

Usage

plot_fire_mov(
  result,
  cluster = "all",
  hotspot = TRUE,
  from = NULL,
  to = NULL,
  step = 1,
  bg = NULL
)

Arguments

result

spotoroo object. A result of a call to hotspot_cluster().

cluster

Character/Integer. If "all", plot all clusters. If an integer vector is given, plot corresponding clusters.

hotspot

Logical. If TRUE, plot the hot spots.

from

OPTIONAL. Date/Datetime/Numeric. Start time. The data type needs to be the same as the provided observed time.

to

OPTIONAL. Date/Datetime/Numeric. End time. The data type needs to be the same as the provided observed time.

step

Integer (>0). Step size used in the calculation of the fire movement.

bg

OPTIONAL. ggplot object. If specified, plot onto this object.

Value

A ggplot object. The plot of the fire movements.

Examples



  # 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)

  # Plot cluster 1 to 4
  plot_fire_mov(result, cluster = 1:4)

  # Plot cluster 1 to 4, set step = 6
  plot_fire_mov(result, cluster = 1:4, step = 6)

Plotting spatiotemporal clustering result

Description

This function takes a spotoroo object to produce a plot of the clustering results. It can be called by plot.spotoroo().

Usage

plot_spotoroo(
  result,
  type = "def",
  cluster = "all",
  hotspot = TRUE,
  noise = FALSE,
  ignition = TRUE,
  from = NULL,
  to = NULL,
  step = 1,
  mainBreak = NULL,
  minorBreak = NULL,
  dateLabel = NULL,
  bg = NULL
)

Arguments

result

spotoroo object. A result of a call to hotspot_cluster().

type

Character. Type of the plot. One of "def" (default), "timeline" (timeline) and "mov" (fire movement).

cluster

Character/Integer. If "all", plot all clusters. If an integer vector is given, plot corresponding clusters. Unavailable in plot_timeline().

hotspot

Logical. If TRUE, plot the hot spots. Unavailable in plot_timeline().

noise

Logical. If TRUE, plot the noise. Only used in plot_def().

ignition

Logical. If TRUE, plot the ignition points. Only used in plot_def().

from

OPTIONAL. Date/Datetime/Numeric. Start time. The data type needs to be the same as the provided observed time.

to

OPTIONAL. Date/Datetime/Numeric. End time. The data type needs to be the same as the provided observed time.

step

Integer (>=0). Step size used in the calculation of the fire movement. Only used in plot_fire_mov().

mainBreak

OPTIONAL. Character/Numeric. A string/value giving the difference between major breaks. If the observed time is in date/datetime format, this value will be passed to ggplot2::scale_x_date() or ggplot2::scale_x_datetime() as date_breaks. Only used in plot_timeline().

minorBreak

OPTIONAL. Character/Numeric. A string/value giving the difference between minor breaks. If the observed time is in date/datetime format, this value will be passed to ggplot2::scale_x_date() or ggplot2::scale_x_datetime() as date_minor_breaks. Only used in plot_timeline().

dateLabel

OPTIONAL. Character. A string giving the formatting specification for the labels. If the observed time is in date/datetime format, this value will be passed to ggplot2::scale_x_date() or ggplot2::scale_x_datetime() as date_labels. Unavailable if the observed time is in numeric format. Only used in plot_timeline().

bg

OPTIONAL. ggplot object. If specified, plot onto this object. Unavailable in plot_timeline().

Details

if type is "def", the clustering results will be plotted spatially. See also plot_def(). Available arguments:

result
type
cluster
ignition
hotspot
noise
from (OPTIONAL)
to (OPTIONAL)
bg (OPTIONAL)

if type is "mov", plot of the fire movement will be made. See also plot_fire_mov(). Available arguments:

result
type
cluster
hotspot
from (OPTIONAL)
to (OPTIONAL)
step
bg (OPTIONAL)

if type is "timeline", plot of the timeline will be made. See also plot_timeline(). Available arguments:

result
type
from (OPTIONAL)
to (OPTIONAL)
mainBreak (OPTIONAL)
minorBreak (OPTIONAL)
dateLabel (OPTIONAL)

Value

A ggplot object. The plot of the clustering results.

Examples



  # Time consuming functions (>5 seconds)


  # Get clustering result
  result <- hotspot_cluster(hotspots,
                          lon = "lon",
                          lat = "lat",
                          obsTime = "obsTime",
                          activeTime = 24,
                          adjDist = 3000,
                          minPts = 4,
                          minTime = 3,
                          ignitionCenter = "mean",
                          timeUnit = "h",
                          timeStep = 1)

  # Different types of plots

  # Default plot
  plot_spotoroo(result, "def", bg = plot_vic_map())


  # Fire movement plot
  plot_spotoroo(result, "mov", cluster = 1:3, step = 3,
                bg = plot_vic_map())

Plotting the timeline of the fire and the noise

Description

This function plots the timeline of the fires and the noise points.

Usage

plot_timeline(
  result,
  from = NULL,
  to = NULL,
  mainBreak = NULL,
  minorBreak = NULL,
  dateLabel = NULL
)

Arguments

result

spotoroo object. A result of a call to hotspot_cluster().

from

OPTIONAL. Date/Datetime/Numeric. Start time. The data type needs to be the same as the provided observed time.

to

OPTIONAL. Date/Datetime/Numeric. End time. The data type needs to be the same as the provided observed time.

mainBreak

minorBreak

dateLabel

Value

A ggplot object. The plot of the timeline.

Examples



  # 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)

  # Plot timeline
  plot_timeline(result,
              mainBreak = "1 week",
              minorBreak = "1 day",
              dateLabel = "%b %d")

Plotting map of Victoria, Australia

Description

This function plots the map of Victoria, Australia.

Usage

plot_vic_map(...)

Arguments

...

All arguments will be ignored.

Details

Require package sf installed.

Value

A ggplot object. The map of Victoria, Australia.

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  plot_vic_map()
}

Printing spatiotemporal clustering result

Description

print.spotoroo() is the print method of the class spotoroo.

Usage

## S3 method for class 'spotoroo'
print(x, ...)

Arguments

x

spotoroo object. A result of a call to hotspot_cluster().

...

Additional arguments will be ignored.

Value

No return value, called for side effects

Examples



  # 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)


  # print the results
  print(result)

Summarizing spatiotemporal clustering result

Description

summary.spotoroo() is the summary method of the class spotoroo. It is a simple wrapper of summary_spotoroo().

Usage

## S3 method for class 'spotoroo'
summary(object, ...)

Arguments

object

spotoroo object. A result of a call to hotspot_cluster().

...

Additional arguments pass to summary_spotoroo()

Value

No return value, called for side effects

Examples



  # 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)


  # Make a summary
  summary(result)

Summarizing spatiotemporal clustering results

Description

This function takes a spotoroo object to produce a summary of the clustering results. It can be called by summary.spotoroo().

Usage

summary_spotoroo(result, cluster = "all")

Arguments

result

spotoroo object. A result of a call to hotspot_cluster().

cluster

Character/Integer. If "all", summarize all clusters. If an integer vector is given, summarize corresponding clusters.

Value

No return value, called for side effects

Examples



  # 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)


  # Make a summary of all clusters
  summary_spotoroo(result)

  # Make a summary of cluster 1 to 3
  summary_spotoroo(result, 1:3)

Transforming a series of time or datetime to time indexes

Description

This function transforms a series of time or datetime to time indexes.

Usage

transform_time_id(obsTime, timeUnit, timeStep)

Arguments

obsTime

Date/Datetime/Numeric. A vector of observed time of hot spots. If timeUnit is "n", obsTime needs to be a numeric vector, otherwise, it needs to be in date or datetime format.

timeUnit

Character. The unit of time, one of "s" (seconds), "m"(minutes), "h"(hours), "d"(days) and "n"(numeric).

timeStep

Numeric (>0). Number of units of timeUnit in a time step.

Details

The earliest time is assigned with a time index 1. The difference between any other time to the earliest time is transformed using the timeUnit and divided by the timeStep. These differences are floored to integer and used as the time indexes.

Value

Integer. A vector of time indexes.

Examples


# Define obsTime
obsTime <- as.Date(c("2020-01-01",
                     "2020-01-02",
                     "2020-01-04"))

# Transform it to time index under different settings
transform_time_id(obsTime, "h", 1)
transform_time_id(obsTime, "m", 60)
transform_time_id(obsTime, "s", 3600)

# Define numeric obsTime
obsTime <- c(1,
             1.5,
             4.5,
             6)

# Transform it to time index under different settings
transform_time_id(obsTime, "n", 1)
transform_time_id(obsTime, "n", 1.5)

simple features map of Victoria

Description

A dataset containing the simple features of Victoria, Australia.

Usage

vic_map

Format

A "sf" object with 1 row.

Details

The dataset is obtained via the following codes:
library(rnaturalearth)
au_map <- ne_states(country = "Australia", returnclass = "sf")
vic_map <- au_map[7,]$geometry

Source

https://www.naturalearthdata.com/

spotoroo: Spatiotemporal Clustering of Satellite Hot Spot Data

Description

Author(s)

See Also

Calculation of the geodesic of a point to multiple points

Description

Usage

Arguments

Value

Examples

Extracting fires from the spatiotemporal clustering results

Description

Usage

Arguments

Value

Examples

Calculation of the fire movement

Description

Usage

Arguments

Value

Examples

Clustering hot spots spatially and temporally

Description

Usage

Arguments

Details

Value

Examples

Handling noise in the clustering results

Description

Usage

Arguments

Details

Value

Examples

Spatiotemporal clustering of hot spot data

Description

Usage

Arguments

Details

Value

Examples

1070 observations of satellite hot spots

Description

Usage

Format

Source

Calculation of the ignition points

Description

Usage

Arguments

Details

Value

Examples

Clustering hot spots spatially

Description

Usage

Arguments

Details

Value

Examples

Plotting spatiotemporal clustering result

Description

Usage

Arguments

Value

Examples

Default method of plotting the clustering results

Description

Usage

Arguments

Value

Examples

Plotting the fire movement

Description

Usage

Arguments

Value

Examples