distRandCum: R function to test the significance of the spatial...
In gianmarcoalberti/GmAMisc: Gianmarco Alberti Miscellaneous

Description Usage Arguments Details See Also Examples

The function allows to assess if there is a significant spatial association between a point pattern and the features of another pattern. For instance, users may want to assess if the features of a point pattern tend to lie close to some features represented by polylines.

1 2	distRandCum(from.feat, to.feat, studyplot = NULL, buffer = 0, B = 200, type = "rand")

`from.feat:`	feature (of point type; SpatialPointsDataFrame class) whose spatial association with the to-feature has to be assessed.
`to.feat:`	feature (point, polyline, or polygon type; SpatialPointsDataFrame, SpatialLinesDataFrame, SpatialPolygonsDataFrame class) in relation to which the spatial association of the from-feature has to be assessed.
`studyplot:`	feature (of polygon type; SpatialPolygonsDataFrame class) representing the study area; if not provided, the study area is internally worked out as the bounding polygon based on the union the convex hulls of the from- and of the to-feature.
`buffer:`	add a buffer to the convex hull of the study area (0 by default); the unit depends upon the units of the input data.
`B:`	number of randomizations to be used (200 by default).
`type:`	by default is set to "rand", which performs the randomization-based analysis; if both the from.feature and the to.feature dataset are of point type, setting the parameter to "perm" allows to opt for the permutation-based approach.

Given a from-feature (event for which we want to estimate the spatial association with the to-feature) and a to-feature (event in relation to which we want to estimate the spatial association for the from-feature), the assessment is performed by means of a randomized procedure:

-keeping fixed the location of the to-feature, random from-features are drawn B times (the number of randomized from-features is equal to the number of observed from-features);
-for each draw, the minimum distance to the to-features is calculated; if the to-feature is made up of polygons, the from-features falling within a polygon will have a distance of 0;
-a cumulative distribution of random minimum distances is thus obtained;
-the cumulative random minimum distances are used to work out an acceptance interval (with significance level equal to 0.05; sensu Baddeley et al., "Spatial Point Patterns. Methodology and Applications with R", CRC Press 2016, 208) that allows to assess the statistical significance of the cumulative distribution of the observed minimum distances, and that is built using the above-mentioned B realizations of a Complete Spatial Random process.

The from-feature must be a point feature, whilst the to-feature can be a point or a polyline or a polygon feature.

The rationale of the procedure is that, if there indeed is a spatial association between the two features, the from-feature should be closer to the to-feature than randomly generated from-features. If the studyplot shapefile is not provided, the random locations are drawn within a bounding polygon based on the union the convex hulls of the from- and of the to-feature.

If both the from-feature and the to-feature are of point type (SpatialPointsDataFrame class), the user may opt for the randomized procedure described above (parameter 'type' set to 'rand'), or for a permutation-based procedure (parameter 'type' set to 'perm'). Unlike the procedure described above, whereby random points are drawn within the study area, the permutation-based routine builds a cumulative distribution of minimum distances keeping the points location unchanged and randomly assigning the points to either of the two patterns. The re-assigment is performed B times (200 by default) and each time the minimum distance is calculated.

The function produces a cumulative distribution chart in which the distribution of the observed minimum distances is represented by a black line, and acceptance interval is represented in grey. The number of iteration used and the type of analysis (whether randomization-based or permutation-based) are reported in the chart's title.

For an example of the use of the analysis, see for instance Carrero-Pazos, M. (2018). Density, intensity and clustering patterns in the spatial distribution of Galician megaliths (NW Iberian Peninsula). Archaeological and Anthropological Sciences. https://doi.org/10.1007/s12520-018-0662-2, fig. 6.

distRandSign

data(springs)
data(faults)
distRandCum(from.feat=springs, to.feat=faults) #perform the analysis using the default 200 iterations and the default randomization-based approach

data(malta_polyg)
distRandCum(from.feat=springs, to.feat=faults, studyplot=malta_polyg, B=100) #same as above, but using a polygon (SpatialPolygonsDataFrame class) as studyplot and using 100 iteration

data("malta_polyg") # load a sample polygon
pA <- spsample(malta_polyg, n=30, type='random')  #create a set of 30 random points within the polygon
pB <- spsample(malta_polyg, n=40, type='random')  #create a set of 40 random points within the polygon
distRandCum(pA, pB, studyplot=malta_polyg) #perform the analysis; since both patterns are of point type but the 'type' parameter is left in its default value ('rand'), the
randomization-based approach is used

distRandCum(pA, pB, studyplot=malta_polyg, type="perm") #same as above, but using the permutation-based approach