Patterns and Trends in Data - Exercise 6

Import and visualize data

library("readr")
library("sf")
library(tidyverse)


wildschwein_BE <- read_delim("data/wildschwein_BE_2056.csv", ",") |>
    st_as_sf(coords = c("E", "N"), crs = 2056, remove = FALSE)

Fanel <- read_sf("data/Feldaufnahmen_Fanel.gpkg")

# Information dataset incl. georeference-system:
Fanel

Simple feature collection with 975 features and 2 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 2568099 ymin: 1199766 xmax: 2578824 ymax: 1207836
Projected CRS: CH1903+ / LV95
# A tibble: 975 × 3
   FieldID Frucht                                                           geom
     <dbl> <chr>                                                   <POLYGON [m]>
 1       1 Roggen ((2570914 1202743, 2570917 1202749, 2570985 1202833, 2571294 …
 2       0 <NA>   ((2570893 1202758, 2570893 1202758, 2570959 1202845, 2570985 …
 3       0 <NA>   ((2570868 1202776, 2570872 1202781, 2570913 1202828, 2570946 …
 4       2 Wiese  ((2570882 1203234, 2570641 1202974, 2570630 1202983, 2570606 …
 5       3 Weide  ((2570249 1203116, 2570371 1203328, 2570481 1203197, 2570390 …
 6       5 Weide  ((2570378 1203320, 2570466 1203436, 2570552 1203289, 2570481 …
 7       6 Weide  ((2570466 1203436, 2570572 1203495, 2570659 1203433, 2570659 …
 8       4 Weide  ((2569706 1203278, 2569706 1203342, 2570199 1203198, 2570223 …
 9       7 Wiese  ((2570804 1203310, 2570805 1203312, 2570900 1203608, 2571208 …
10       0 Wald   ((2571004 1202990, 2571041 1203029, 2571073 1203003, 2571035 …
# ℹ 965 more rows

# Information types data of all collumns:
str(Fanel)

sf [975 × 3] (S3: sf/tbl_df/tbl/data.frame)
 $ FieldID: num [1:975] 1 0 0 2 3 5 6 4 7 0 ...
 $ Frucht : chr [1:975] "Roggen" NA NA "Wiese" ...
 $ geom   :sfc_POLYGON of length 975; first list element: List of 1
  ..$ : num [1:12, 1:2] 2570914 2570917 2570985 2571294 2571719 ...
  ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
 - attr(*, "sf_column")= chr "geom"
 - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA
  ..- attr(*, "names")= chr [1:2] "FieldID" "Frucht"

plot(Fanel[2])

Annotate Trajectories from vector data

boars_summer <- wildschwein_BE |> 
  mutate(DatetimeUTC = as.POSIXct(DatetimeUTC)) |> 
  mutate(Monat = month(DatetimeUTC)) |> 
  filter(Monat == c(5,6))
    
boars_summer_j <- st_join(boars_summer, Fanel)

Explore annotated trajectories

# Für visualisierung als erstes die Geometry rauskicken:
boars_spatio_crops <- boars_summer_j |> 
  st_drop_geometry() |> 
  mutate(
    hour = hour(round_date(DatetimeUTC, "hour")),
    Frucht = fct_lump_n(Frucht, 5)
  ) |> 
  count(TierName, hour, Frucht) |> 
  group_by(TierName, hour) |> 
  mutate(perc = n/sum(n))

boars_spatio_crops |> 
  ggplot(aes(hour, perc, fill = Frucht)) +
  geom_col() +
  facet_wrap(~TierName)

Import and visualize vegetationindex (raster data)

library(terra)
library(tmap)

terra <- rast("data/vegetationshoehe_LFI.tif")
plot(terra)

tmap_mode("plot")

tm_shape(terra) +
  tm_raster()

Annotate Trajectories from raster data

boar_veg <- extract(terra, vect(boars_summer_j))

boars_summer_j$veg <- boar_veg$vegetationshoehe_LFI

glimpse(boars_summer_j)

Rows: 7,780
Columns: 11
$ TierID      <chr> "002A", "002A", "002A", "002A", "002A", "002A", "002A", "0…
$ TierName    <chr> "Sabi", "Sabi", "Sabi", "Sabi", "Sabi", "Sabi", "Sabi", "S…
$ CollarID    <dbl> 12275, 12275, 12275, 12275, 12275, 12275, 12275, 12275, 12…
$ DatetimeUTC <dttm> 2015-05-01 00:00:17, 2015-05-01 00:30:15, 2015-05-01 01:0…
$ E           <dbl> 2570093, 2570091, 2570078, 2570079, 2570044, 2569999, 2569…
$ N           <dbl> 1205256, 1205253, 1205246, 1205247, 1205212, 1205213, 1205…
$ geometry    <POINT [m]> POINT (2570093 1205256), POINT (2570091 1205253), PO…
$ Monat       <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5…
$ FieldID     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Frucht      <chr> "Wald", "Wald", "Wald", "Wald", "Wald", "Wald", NA, NA, "W…
$ veg         <dbl> 8.74, 11.06, 17.48, 15.90, 21.94, 20.95, 0.18, 0.45, 13.09…