Basics

Setup

The following analysis requires several packages to be loaded:

library(tidyverse)
library(raster)
library(polyCheck)
library(rgeos)

Datasets

Within the package, there are a few datasets which are included to represent the dataset which is being validated. These do not represent actual areas surveyed, but were randomly selected for the purpose of explaining and validating the model. The three datasets are as follows:

settlement_points_sample_min.shp: these provide the settlement classifications types.
survey_points_sample.shp: the locations which have been selected for surveying and require delineation.
ornl.shp: the location of previously surveyed areas within the study region.

# Load filepath from repository
dataPath <- system.file("extdata", package = "polyCheck")

# Settlement types
settlements <- shapefile(file.path(dataPath, "settlement_points.shp"))

# Survey Points
survey_points <-  shapefile(file.path(dataPath, "survey_points_sample.shp"))

# We must mimic how the FID is generated 
survey_points$FID <-  0:(nrow(survey_points)-1)

# load the ORNL data
ornl <- shapefile(file.path(dataPath, "ornl.shp"))

Polygons to be Validated

A selection of polygons are provided within the extdata/polygons subdirectory of the package. These are the objects which we are wishing to validate within the model. Note, these do not represent survey areas and have been designed to highlight the functionality of the package. We can use the function list_polygons to list the file paths for polygons. This will by default search within subdirectories in the directory specified, but we can override this by specifying recursive = FALSE.

polygons <- list_polygons(dataPath, "polygons")
basename(polygons)
#> [1] "000_js_polygon.shp" "001_js_polygon.shp" "002_mh_polygon.shp"
#> [4] "003_mh_polygon.shp" "004_dc_polygon.shp" "005_dc_polygon.shp"
#> [7] "006_ss_polygon.shp" "007_ss_polygon.shp"

To check whether any of the delineated polygons conflict, we can merge the polygons into a single shapefile using the merge_delineated_polygons function:

polygons_all <- merge_delineated_polygons(polygons)
#> Loading Polygons
#> Merging Polygons
#> Complete
polygons_all
#> class       : SpatialPolygonsDataFrame 
#> features    : 8 
#> extent      : 7.670478, 7.745, 11.07066, 11.13065  (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
#> variables   : 1
#> names       : ID 
#> min values  :  0 
#> max values  :  7

Diagnosing Polygons

To diagnose polygons, we can use the assess_polygons function as shown below:

diagnostics <- 
  assess_polygons(polygons[1],
                survey_points,
                settlement_points,
                ornl,
                polygons_all,
                progress = FALSE)

The full set of parameters is displayed below:

Diagnostic Values
Parameter	Value
filename	000_js_polygon.shp
num_features	1
Id	1
Pt_ids	0
image_ID	NA
dig_date	10-OCT-2018
SRC_DATE2	31-DEC-2015
digitiser	js
id	0
crs_match	TRUE
no_points	542
dist_from_survey	2135
prop_settlement_type	0
ornl_overlap	FALSE
self_intersect_id	No Intersection
self_intersect_prop	NA
no_sides	7
shp_area	31568
shp_area_hect	3.16
Polsby_popper	0.72
perimeter	743

The function is designed to work with either a single polygon or a list of polygons. If provided multiple values, the function will return a data.table If we provide it the full set above:

diagnostics <- 
  assess_polygons(polygons,
                survey_points,
                settlement_points,
                ornl,
                polygons_all)

The results are presented below:

Setup

Datasets

Polygons to be Validated

Diagnosing Polygons

Contents