qmpattern: Quick mapping for point pattern data
In qspatialR/qspatial: Areal and Point Pattern Data Visualization and Analysis

Description Usage Arguments Details Examples

View source: R/qmpattern.R

Generates the visualization and analysis of first and second order properties of point pattern data.

qmpattern(
  shapefile,
  longitude = longitude,
  latitude = latitude,
  fun = c("G", "F"),
  sigma = 0.01,
  nsim = 99,
  palette = "RdYlBu",
  psize = 0.75,
  pcolor = "black"
)

`shapefile`	A shapefile of the study region.
`longitude`	The name of the variable containing the longitude.
`latitude`	The name of the variable containing the latitude.
`fun`	A vector with two summary functions. "G" for nearest neighbor function, "F" for empty space function, "K" for Ripley's K function and "J" for the J function.
`sigma`	Optional. The radius used to estimate the Intensity, if a radius is not chosen it will be calculated using the bw.diggle function.
`nsim`	The number of simulations generated when computing the envelope. The default is 99.
`palette`	The color pallete used in the Intensity plot.

The function generates a quick visualization and analysis of point pattern data with ggplot2 and spatstat's summary functions. The default option is to analyze using the G and F functions, they work well for larger datasets and the simulation does not take much time to complete. There are options included to pick two functions between K, G, F and J summary functions, but it's important to notice that the runtime of the K function is considerably bigger.

The function will return a grid with two maps and two summary function plots: A map with the location of each registered event, an Intensity map, and two plots with the simulated envelopes of the chosen summary functions.

There is some degree of customization allowed for the maps. It is possible to change the size and color of the points in the point pattern map and the palette of the Intensity map. It's also possible to change the radius used in the intensity estimate. A bigger value will find more ocurrences while estimating which in turn will result in a higher intensity for the area.

The summary functions interpretation is made the following way:

For the G and the K functions, if the observed curve is above the Complete Spatial Randomness (CSR) envelope it's a suggestion of spatial clustering, if it's below it suggests a spatial regularity behavior. The spatial behavior will be complete random if the observed curve is inside the envelope.

The F function behavior is inverse to the G function. If the curve is above the CSR envelope it suggests a spatial regularity behavior, if it's below it suggests spatial clustering. If it's inside the simulated envelope, it's a sign that the spatial behavior of the data is completely random.

For the J function, the J(r) = 1 line marks the CSR behavior. If the observed curve is above the envelope it suggests a spatial regularity behavior, if it's below it suggests spatial clustering.

By default the function always plots two of the summary functions, so the user can validate the results with both of them.

# Loading the example data and the included shapefile

accidents.data = acidentes
recife = recife

# This example data contains a Recife city shapefile and the
# coordinates of transit accidents that happened in the city
# in 2016. With the shapefile and the latitude and longitude
# coordinates the qmpattern can generate a visualization of
# the point pattern data and check it's spatial behavior.

accidents.map = qmpattern(shapefile = recife,
longitude = accidents.data$longitude, accidents.data$latitude,
fun = c("G", "F"), sigma = 0.01, nsim = 99)