Add user specified classification of territory areas

Opportunity to add a new column 'user_class' which enables the user to confirm or reject the automated assessment of territory areas.

Usage

user_classify(
  territory_poly,
  territory = NULL,
  possible = NULL,
  activity = NULL
)

Arguments

territory_poly: a territory polygon generated with beavertools::estimate_territories()
territory: numeric vector containing the ID numbers for areas to be reclassified as 'Territory'. e.g. c(10, 28)
possible: numeric vector containing the ID numbers for areas to be reclassified as 'Possible'
activity: numeric vector containing the ID numbers for areas to be reclassified as 'Activity'

Value

territory_poly is returned with the an additional column 'user_class'

Examples

# Here we filter the filter the built in 2019-2020 ROBT feeding sign data `RivOtter_FeedSigns`
# Then pipe this 'sf' object to forage_density.
ROBT_201920 <- RivOtter_FeedSigns %>%
dplyr::filter(SurveySeason == "2019 - 2020")%>%
  forage_density(., 'FeedCat')
#> No value supplied for "kd_extent" argument: default extent will be used
#> 
#> calculating weighted kde

# Now we load the ROBT `RivOtter_OtherSigns` dataset and filter to the same
# year as the forage density raster.

CS_201920 <- RivOtter_OtherSigns %>%
dplyr::filter(SurveySeason == "2019 - 2020")

# run territory classification
otter_poly <- estimate_territories(ROBT_201920, confirm_signs = CS_201920)

# create the map for checking automated territory classification
check_auto_terr(otter_poly, basemap=FALSE, label=TRUE)
#> Warning: st_point_on_surface may not give correct results for longitude/latitude data


user_classify(otter_poly, territory = c(10, 28))
#> Simple feature collection with 19 features and 9 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 305066.8 ymin: 82922.7 xmax: 323174.2 ymax: 114417.3
#> Projected CRS: OSGB 1936 / British National Grid
#> First 10 features:
#>     quant quantf id Upper_Thresh Confirm_signs terr_status user_class
#> 1    0.04   0.04  1          Yes           Yes   Territory  Territory
#> 1.1  0.04   0.04  2          Yes           Yes   Territory  Territory
#> 1.2  0.04   0.04  3          Yes           Yes   Territory  Territory
#> 1.3  0.04   0.04  4           No            No    Activity   Activity
#> 1.4  0.04   0.04  5           No            No    Activity   Activity
#> 1.5  0.04   0.04  6          Yes            No    Possible   Possible
#> 1.6  0.04   0.04  7           No            No    Activity   Activity
#> 1.7  0.04   0.04  8          Yes           Yes   Territory  Territory
#> 1.8  0.04   0.04  9           No            No    Activity   Activity
#> 1.9  0.04   0.04 10          Yes           Yes   Territory  Territory
#>          mean_fd       sum_fd                       geometry
#> 1   6.446271e+00 1.931303e+04 POLYGON ((306363.5 85553.01...
#> 1.1 3.760834e+00 3.855607e+04 POLYGON ((307984.4 103446.1...
#> 1.2 9.883062e+00 1.021019e+05 POLYGON ((308968.5 87075.83...
#> 1.3 7.160177e-06 6.365397e-03 POLYGON ((308760.1 98358.51...
#> 1.4 1.015215e-05 1.273080e-02 POLYGON ((309188.5 104207.5...
#> 1.5 1.093554e+00 1.285254e+04 POLYGON ((309466.3 93201.71...
#> 1.6 7.120130e-06 6.365397e-03 POLYGON ((309362.1 96904.92...
#> 1.7 3.611413e+00 1.276273e+04 POLYGON ((309721 95485.93, ...
#> 1.8 3.286781e-05 3.819240e-02 POLYGON ((310010.5 96524.21...
#> 1.9 2.393606e+00 1.276271e+04 POLYGON ((311943.9 99016.09...