Sequential cluster algorithm of location data

Description

Applies sequential clustering algorithm to location data based on user-defined parameters and appends results to the dataframe. Provides a summary dataframe with attributes for each cluster commonly used as covariates in subsequent modeling efforts. Plots interactive cluster maps.

Usage

GPSeq_clus(
  dat,
  search_radius_m,
  window_days,
  clus_min_locs = 2,
  centroid_calc = "mean",
  show_plots = c(TRUE, "mean"),
  scale_plot_clus = TRUE,
  store_plots = FALSE,
  season_breaks_jul = NA,
  daylight_hrs = NA,
  prbar = TRUE
)

Arguments

Value

Returns a list containing two dataframes. The first contains the original location dataframe with "clus_ID" column assigning each row a cluster ID if applicable. The second dataframe in the list contains a summary of sequential clusters and common cluster attributes (descriptions below) for subsequent modeling. If 'show_plots' argument is active, returns interactive maps of locations and clusters by animal.

AID

Animal identification

clus_ID

Sequential cluster ID number

clus_start

Timestamp of first location in cluster

clus_end

Timestamp of last location in cluster

clus_status

"Closed" if the time window (window_days) has expired for the cluster according to users Sys.time() output. These clusters are therefore solidified and should not change if appending new location data. "Open" if the time window remains open at the time the function was run. "Open" clusters have the ability to shift sequence, combine with other clusters, emerge as a new cluster, etc. This attribute becomes relevant when appending new satellite data to the location dataframe, and may serve as an index of whether an animal continues to actively visit the cluster site within the time window.

g_c_Long

Geometic centroid longitude value calculated using the mean

g_c_Lat

Geometic centroid latitude value calculated using the mean

g_med_Long

Geometic centroid longitude value calculated using the median

g_med_Lat

Geometic centroid latitude value calculated using the median

clus_dur_hr

Hours from the first to last locations of the cluster

n_clus_locs

Number of locations within the cluster

visits

Number of visits/revisits to the cluster based on the number of times locations fall outside the search radius and return to add locations to the cluster

fix_succ_clus_dur

Fix rate success during the duration of the cluster

adj_clus_locs

Adjusted number of cluster locations accounting for missed fixes (number cluster locations / fix success of cluster duration)

fid

Fidelity to the cluster during cluster duration (number locations on cluster - number locations off cluster)

max_foray

Maximum location distance (meters) from centroid during cluster duration for all locations

clus_radius

Maximum location distance (meters) from centroid during cluster duration for cluster-attributed locations

avg_clus_dist

Mean distance from all cluster locations to centroid

n_24_per

Number of unique 24 hr periods during the cluster duration that hold at least one cluster location

bin_24hr

Binary output for cluster duration (0 == less or equal to 24hr, 1 == greater than 24hr)

season

Nominal attribute for user defined seasons based on 'season_breaks_jul' argument

night_pts

Number of night cluster locations based on 'daylight_hrs' argument

night_prop

Proportion of night cluster locations

Examples

GPSeq_clus(dat = ML_ex_dat[1:50,], search_radius_m = 200, window_days = 6,
           clus_min_locs = 3, show_plots = c(FALSE, "mean"))


GPSeq_clus(dat = ML_ex_dat, search_radius_m = 50, window_days = 2.5, clus_min_locs = 12,
           centroid_calc = "median", show_plots = c(TRUE, "median"), scale_plot_clus = FALSE,
           season_breaks_jul = c(120, 240, 300), daylight_hrs = c(8, 16), prbar=FALSE)

Sample Data for Sequential Clustering Routine

Description

A dataframe containing a subset of GPS location data from 2 male and 1 female mountain lions used for testing and running sequential cluster function examples.Example data provided by Wyoming Game and Fish Department, 2020.

Usage

ML_ex_dat

Format

A dataframe containing 4 columns:

AID

animal identification

TelemDate

location timestamp in POSIXct format

Lat

latitude coordinates

Long

longitude coordinates

label plots

Description

label plots

Usage

addTitle(
  object,
  text,
  color = "black",
  fontSize = "20px",
  fontFamily = "Sans",
  leftPosition = 50,
  topPosition = 2
)

Arguments

Value

title on plots

Export cluster .gpx file

Description

Uses results from 'GPSeq_clus" to export .gpx file from specified AID and vector of desired cluster numbers for navigation during field site investigations.

Usage

exp_clus_gpx(AID, cn = "all", locs, cs, centroid_calc = "mean", dir = NULL)

Arguments

Value

.gpx file

Examples

exp_clus_gpx(AID = "ML1605M", cn = 4,
             locs = GPSeq_clus(dat = ML_ex_dat[1:50,], search_radius_m = 200, window_days = 6,
                    clus_min_locs = 3, show_plots = c(FALSE, "mean"))[[1]],
             cs = GPSeq_clus(dat = ML_ex_dat[1:50,], search_radius_m = 200, window_days = 6,
                  clus_min_locs = 3, show_plots = c(FALSE, "mean"))[[2]],
             dir= tempdir()
)

Plot individual cluster .kml

Description

Uses results from 'GPSeq_clus" to plot individual cluster .kmls

Usage

ind_clus_kml(
  AID,
  cn,
  locs,
  cs,
  centroid_calc = "mean",
  overwrite = TRUE,
  dir = NULL
)

Arguments

Value

Opens the cluster locations and centroid .kml for assessment.

Julian Conversion

Description

Julian Conversion

Usage

julian_conv(x)

Arguments

Value

vector of julian days

arrange columns

Description

arrange columns

Usage

moveMe(data, tomove, where = "last", ba = NULL)

Arguments

Value

Dataframe with new column order