pointdensity: Point density function for geospatial data
In pointdensityP: Point Density for Geospatial Data
Description
Usage
Arguments
Details
Author(s)
References
Examples
View source: R/pointdensity_0.3.5.R
Description
This function maps a dataset of geospatial points to a regular grid and calculates the density and temporal average of the points.
Usage
1
pointdensity(df, lat_col, lon_col, date_col = NULL, grid_size, radius)
Arguments
df
Data frame minimally containing latitude and longitude of spatial point data
lat_col
name of column in df that contains latitude or vertical dimension of data
lon_col
name of column in df that contains longitude or horizontal dimension of data
date_col
name of column in df that contains date associated with the event
grid_size
distance in kilometers between the grid lines that will support discretization of data and density reference
radius
distance in kilometers that represents the local neighborhood where an event adds density
Details
pointdensity returns a density count and the temporal average for every point in the original list. The dataframe returned includes four columns: lat, lon, count, and date_avg. The "lat" column is the original latitude data; the "lon" column is the original longitude data; the "count" is the density count of the number of points within a defined radius (the neighborhood); and the date_avg column includes the average date of each point in the neighborhood. Designed specifically for geospatial point processes and originally developed for military applications, this technique applies to any geospatial point process where there is a desire for an explainable measurement of density and maintaining fidelity of the original point locations. Typical spatial density plotting algorithms, such as kernel density estimation, implement some type of smoothing function that often results in a density value that is difficult to interpret. pointdensity was designed for ease of interpretation. Potential applications include analysis of military events, crime, and real estate transactions. An example follows with the Arigon data using ggmap (recommended) for visualization:
Arigon_density <- pointdensity(df = Arigon, lat_col = "latitude", lon_col = "longitude",
date_col = "date", grid_size = 1, radius = 2)
map_base <- qmap(location="44.12,-120.83", zoom = 7, darken=0.3)
map_base + geom_point(aes(x = lon, y = lat, colour = count), shape = 16, size = 2,
data = Arigon_density) + scale_colour_gradient(low = "green", high = "red")
Here is another example using the crime dataset from ggmap:
H_crime <- pointdensity(df = clean_crime, lat_col = "lat", lon_col = "lon",
grid_size = 1, radius = 4)
map_base <- qmap(location="29.76,-95.42", zoom = 11, darken=0.3)
map_base + geom_point(aes(x = lon, y = lat, colour = count), shape = 16, size = 2,
data = H_crime) + scale_colour_gradient(low = "green", high = "red")
Author(s)
Paul Evangelista paul.evangelista@usma.edu
David Beskow david.beskow@usma.edu
References
Wand, M. P. (1994). Fast Computation of Multivariate Kernel Estimators. Journal of Computational and Graphical Statistics, 3, 433-445.
Examples
1
2
3
Arigon_test <- Arigon[1:1000,]
Arigon_density <- pointdensity(df = Arigon_test, lat_col = "latitude",
lon_col = "longitude", date_col = "date", grid_size = 1, radius = 2)
Example output
The radius was adjusted to 2.0016 km in order to accomodate the grid size
algorithm grid step radius is 2
binning data...
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Data length is 1000 ; reduced to 789 bins. Density calculation starting.
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pointdensityP documentation built on Dec. 15, 2020, 5:36 p.m.
R Package Documentation
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Description Usage Arguments Details Author(s) References Examples
View source: R/pointdensity_0.3.5.R
Description
This function maps a dataset of geospatial points to a regular grid and calculates the density and temporal average of the points.
Usage
1 | pointdensity(df, lat_col, lon_col, date_col = NULL, grid_size, radius)
|
Arguments
df |
Data frame minimally containing latitude and longitude of spatial point data |
lat_col |
name of column in |
lon_col |
name of column in |
date_col |
name of column in |
grid_size |
distance in kilometers between the grid lines that will support discretization of data and density reference |
radius |
distance in kilometers that represents the local neighborhood where an event adds density |
Details
pointdensity returns a density count and the temporal average for every point in the original list. The dataframe returned includes four columns: lat, lon, count, and date_avg. The "lat" column is the original latitude data; the "lon" column is the original longitude data; the "count" is the density count of the number of points within a defined radius (the neighborhood); and the date_avg column includes the average date of each point in the neighborhood. Designed specifically for geospatial point processes and originally developed for military applications, this technique applies to any geospatial point process where there is a desire for an explainable measurement of density and maintaining fidelity of the original point locations. Typical spatial density plotting algorithms, such as kernel density estimation, implement some type of smoothing function that often results in a density value that is difficult to interpret. pointdensity was designed for ease of interpretation. Potential applications include analysis of military events, crime, and real estate transactions. An example follows with the Arigon data using ggmap (recommended) for visualization:
Arigon_density <- pointdensity(df = Arigon, lat_col = "latitude", lon_col = "longitude",
date_col = "date", grid_size = 1, radius = 2)
map_base <- qmap(location="44.12,-120.83", zoom = 7, darken=0.3)
map_base + geom_point(aes(x = lon, y = lat, colour = count), shape = 16, size = 2,
data = Arigon_density) + scale_colour_gradient(low = "green", high = "red")
Here is another example using the crime dataset from ggmap:
H_crime <- pointdensity(df = clean_crime, lat_col = "lat", lon_col = "lon",
grid_size = 1, radius = 4)
map_base <- qmap(location="29.76,-95.42", zoom = 11, darken=0.3)
map_base + geom_point(aes(x = lon, y = lat, colour = count), shape = 16, size = 2,
data = H_crime) + scale_colour_gradient(low = "green", high = "red")
Author(s)
Paul Evangelista paul.evangelista@usma.edu
David Beskow david.beskow@usma.edu
References
Wand, M. P. (1994). Fast Computation of Multivariate Kernel Estimators. Journal of Computational and Graphical Statistics, 3, 433-445.
Examples
1 2 3 | Arigon_test <- Arigon[1:1000,]
Arigon_density <- pointdensity(df = Arigon_test, lat_col = "latitude",
lon_col = "longitude", date_col = "date", grid_size = 1, radius = 2)
|
Example output
The radius was adjusted to 2.0016 km in order to accomodate the grid size
algorithm grid step radius is 2
binning data...
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Data length is 1000 ; reduced to 789 bins. Density calculation starting.
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