Package 'stdbscan'

GPS pings from the GeoLife GPS Trajectories dataset

Description

Extraction of the GeoLife GPS Trajectories dataset. The selected trajectory id is 000-20081023025304.

Data manipulation applied to the raw data :

• Conversion to EPSG:4586

• Manual selection of the pings

• Selection of relevant variables

Usage

geolife_traj

Format

A data.frame with one row per ping and the following columns:

• date (chr): The date

• time (chr): The time

• x (dbl): Longitude (EPSG:4586)

• y (dbl): Latitude (EPSG:4586)

Source

https://www.microsoft.com/en-us/download/details.aspx?id=52367

Examples

data(geolife_traj)
head(geolife_traj)

---

Predict newdata

Description

Assigns each new observation to an existing cluster from a fitted stdbscan object, or marks it as noise if it falls outside any cluster.

Usage

## S3 method for class 'stdbscan'
predict(object, data, newdata, ...)

Arguments

object	An object of class stdbscan.
data	matrix. The data set used to create the clustering object.
newdata	matrix. New data points for which the cluster membership should be predicted. The data must be in the same format as the input data.
...	Additional arguments are passed on to dbscan::frNN().

Value

An integer vector of cluster labels, matching the labels of the input stdbscan object.

Examples

data(geolife_traj)

geolife_traj$date_time <- as.POSIXct(
  paste(geolife_traj$date, geolife_traj$time),
  format = "%Y-%m-%d %H:%M:%S",
  tz = "GMT"
)
geolife_traj$t <- as.numeric(
  geolife_traj$date_time - min(geolife_traj$date_time)
)
data <- cbind(geolife_traj$x, geolife_traj$y, geolife_traj$t)

res <- st_dbscan(
  data = data,
  eps_spatial = 3,
  eps_temporal = 30,
  min_pts = 5
)

newdata <- cbind(
  c(440160,  440165,  440144,  440130,  440160),
  c(4428129, 4428135, 4428120, 4428123, 4428122),
  c(4617,    4620,    4629,    4635,    4640)
)
predict(res, data, newdata)

---

Spatio-Temporal DBSCAN

Description

Perform ST-DBSCAN clustering on points with spatial and temporal coordinates. This algorithm identifies clusters of points that are close both in space and time.

Usage

st_dbscan(data, eps_spatial, eps_temporal, min_pts, ...)

Arguments

data	matrix. A matrix containing, in that order, x, y and t. x (longitude) and y (latitude) are the spatial coordinates and t is the cumulative time since a common origin (e.g. c(0, 6, 10)). t must be sorted.
eps_spatial	Numeric. The spatial radius threshold. Points closer than this in space may belong to the same cluster.
eps_temporal	Numeric. The temporal threshold. Points closer than this in time may belong to the same cluster.
min_pts	Integer. Minimum number of points required to form a core point.
...	Additional arguments are passed on to dbscan::frNN() and dbscan::dbscan().

Details

ST-DBSCAN extends classical DBSCAN by incorporating a temporal constraint. Two points are considered neighbors if they are within eps_spatial in space and within eps_temporal in time. Clusters are expanded from core points recursively following the DBSCAN algorithm.

ST-DBSCAN is implemented using the following approach:

1. Find the spatial neighbors using Fixed Radius Nearest Neighbors (dbscan::frNN())

2. Filter the spatial neighbors by the temporal constraint

3. Apply DBSCAN on the filtered neighbors using dbscan::dbscan()

Value

st_dbscan() returns an object of class stdbscan with the following components:

cluster	Integer vector with cluster assignments. Zero indicates noise points.
eps	Value of the eps_spatial parameter.
minPts	Value of the minPts parameter.
metric	Used distance metric.
borderPoints	Whether border points are considered as noise (FALSE) or not (TRUE).
eps_temporal	Value of the eps_temporal parameter.

This class is a simple extension of the dbscan class. For more details, see dbscan documentation.

References

Birant, D., & Kut, A. (2007). ST-DBSCAN: An algorithm for clustering spatial–temporal data. Data & Knowledge Engineering, 60(1), 208–221. https://doi.org/10.1016/j.datak.2006.01.013

Examples

data(geolife_traj)

geolife_traj$date_time <- as.POSIXct(
  paste(geolife_traj$date, geolife_traj$time),
  format = "%Y-%m-%d %H:%M:%S",
  tz = "GMT"
)
geolife_traj$t <- as.numeric(
  geolife_traj$date_time - min(geolife_traj$date_time)
)
data <- cbind(geolife_traj$x, geolife_traj$y, geolife_traj$t)

st_dbscan(
  data = data,
  eps_spatial = 3,
  eps_temporal = 30,
  min_pts = 3,
  # Extra arguments
  splitRule = "STD",
  search = "kdtree",
  approx = 1
)

---

Check if points are core points

Description

Check if data points are core points. A core point is a point with more than min_pts points in its neighborhood.

Usage

st_dbscan_corepoint(data, eps_spatial, eps_temporal, min_pts, ...)

Arguments

data	matrix. A matrix containing, in that order, x, y and t. x (longitude) and y (latitude) are the spatial coordinates and t is the cumulative time since a common origin (e.g. c(0, 6, 10)).
eps_spatial	Numeric. The spatial radius threshold. Points closer than this in space may belong to the same cluster.
eps_temporal	Numeric. The temporal threshold. Points closer than this in time may belong to the same cluster.
min_pts	Integer. Minimum number of points required to form a core point.
...	Additional arguments are passed on to dbscan::frNN().

Value

A boolean vector indicating if data points are core points.

Examples

data(geolife_traj)

geolife_traj$date_time <- as.POSIXct(
  paste(geolife_traj$date, geolife_traj$time),
  format = "%Y-%m-%d %H:%M:%S",
  tz = "GMT"
)
geolife_traj$t <- as.numeric(
  geolife_traj$date_time - min(geolife_traj$date_time)
)
data <- cbind(geolife_traj$x, geolife_traj$y, geolife_traj$t)

res <- st_dbscan_corepoint(
  data = data,
  eps_spatial = 3,
  eps_temporal = 30,
  min_pts = 3
)
head(res)

---