Package 'ODMeans'

First Hierarchy Function

Description

First Hierarchy Function

Usage

first_hierarchy(data, numK, limitsSeparation, maxDist, kmeans_pp = FALSE)

Arguments

data	A data frame with four columns: Initial Latitude | Initial Longitude | Final Latitude | Final Longitude
numK	Initial number of clusters in the first call of k-means in the global hierarchy.
limitsSeparation	Within cluster distance threshold to determine if a global cluster must be separated into two new clusters.
maxDist	Meter distance threshold used to re-estimate centroids in global hierarchy.
kmeans_pp	Boolean value, if TRUE it initialize centroids using kmeans++.

Value

Returns an S3 class object similar to kmeans S3 Class, with eight properties.

Examples

data(ODMeansTaxiData)
first_hierarchy_data = first_hierarchy(ODMeansTaxiData, 10, 300, 1000)

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ODMeans Function

Description

ODMeans Function

Usage

odmeans(
  data,
  numKGlobal,
  limitSeparationGlobal,
  maxDistGlobal,
  distHierarchical,
  numKLocal,
  limitSeparationLocal,
  maxDistLocal,
  kmeans_pp = FALSE
)

Arguments

data	A data frame with four columns: Initial Latitude | Initial Longitude | Final Latitude | Final Longitude
numKGlobal	Initial number of clusters in the first call of k-means in the global hierarchy.
limitSeparationGlobal	Within cluster distance threshold to determine if a global cluster must be separated into two new clusters.
maxDistGlobal	Meter distance threshold used to re-estimate centroids in global hierarchy.
distHierarchical	Meter distance threshold between origin and destination to generate new local clusters from a first layer cluster
numKLocal	Initial number of clusters in the first call of k-means in the local hierarchy.
limitSeparationLocal	Within cluster distance threshold to determine if a local cluster must be separated into two new clusters.
maxDistLocal	Meter distance threshold used to re-estimate centroids in local hierarchy.
kmeans_pp	Boolean value, if TRUE it initialize centroids using kmeans++.

Value

Returns an S3 class object similar to kmeans S3 Class, with eight properties.

Examples

data(ODMeansTaxiData)
odmeans_data = odmeans(ODMeansTaxiData, 10, 300, 1000, 2200, 3, 50, 100)

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Graph ODMeans Function

Description

Graph ODMeans Function

Usage

odmeans_graph(
  odmeans_data,
  title = "ODMeans Graph",
  maptype = "roadmap",
  zoom = 4,
  add_cluster = TRUE
)

Arguments

odmeans_data	It receives an object from S3 ODMeans class. However, it can also work with objects from similar classes like S3 k-Means
title	It receives an string, and corresponds to the title of the plot.
maptype	It receives a string with the type of the map. Depending on the map selected, it will change the background of it. The possible values are: “terrain”, “satellite”, “roadmap”, “hybrid”.
zoom	An integer from 3 (continent) to 21 (building), which controls the level of zoom applied to the map.
add_cluster	Receives TRUE or FALSE value. When True, it adds the number of the cluster to the arrows.

Value

A ggplot graph showing a map with the centers of the clusters.

Examples

data(ODMeansTaxiData)
odmeans_data = odmeans(ODMeansTaxiData, 10, 300, 1000, 2200, 3, 50, 100)
odmeans_plot = odmeans_graph(odmeans_data, "ODMeans Taxi Graph", "roadmap", 11, FALSE)

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Origin-Destination points

Description

A synthetic data frame containing 1700 Origin-Destination points.

Usage

data(ODMeansSampleData)

Format

A data frame with 10000 rows and 5 variables:

OriginLatitude

Consists of the origin latitude dimension

OriginLongitude

Consists of the origin longitude dimension

DestinationLatitude

Consists of the destination latitude dimension

DestinationLongitude

Consists of the destination longitude dimension

original_cluster

Original cluster of the points when it was created

...

Source

Synthetic data

---

Origin-Destination Taxi data

Description

The data frame contains the 452,166 trips collected for the months of March (2014 to 2016), July (2014 to 2016), and November (2014 and 2015). The data points are the taxi's initial and ending location based on latitude and longitude.

Usage

data(ODMeansTaxiData)

Format

A data frame with 452,166 rows and 4 variables:

OriginLatitude

Origin latitude dimension

OriginLongitude

Origin longitude dimension

DestinationLatitude

Destination latitude dimension

DestinationLongitude

Destination longitude dimension

...

Source

Fantaxico, Fermanti Servicios de Ingeniería S.A, Santiago, Chile.

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Second Hierarchy Clusters

Description

Second Hierarchy Clusters

Usage

second_hierarchy(
  data,
  Kcluster,
  distHierarchical,
  numKLocal,
  limitSeparationLocal,
  maxDistLocal
)

Arguments

data	A data frame with four columns: Initial Latitude | Initial Longitude | Final Latitude | Final Longitude
Kcluster	An ODMeans structure, result of function first_hierarchy.
distHierarchical	Meter distance threshold between origin and destination to generate new local clusters from a first layer cluster.
numKLocal	Initial number of clusters in the first call of k-means in the local hierarchy.
limitSeparationLocal	Within cluster distance threshold to determine if a local cluster must be separated into two new clusters.
maxDistLocal	Meter distance threshold used to re-estimate centroids in local hierarchy.

Value

Returns an S3 class object similar to kmeans S3 Class, with eight properties.

Examples

data(ODMeansTaxiData)
first_hierarchy_data = first_hierarchy(ODMeansTaxiData, 10, 500, 1000)
second_hierarchy_data = second_hierarchy(ODMeansTaxiData, first_hierarchy_data, 2200, 3, 50, 100)

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