calculate_geographic_overlap_and_segregation | R Documentation |

This analysis calculates measures of geographic overlap and geographic segregation between all the xtracks in one group and all the xtracks in another group. These groups could be gender groups (as done in Wood et al. 2021), or they could represent age groups, family groups, gender X age groups, all could be potentially interesting. This analysis should be done with two lists of xtrack objects, with each xtrack representing one day of travel. The lists can be of any length, but for an apples-to-apples empirical comparison, should be approx. equal in length.

calculate_geographic_overlap_and_segregation( xtrack_list_1, xtrack_list_2, cell_size_m = 10 )

`xtrack_list_1` |
a list of xtracks representing 'group 1' |

`xtrack_list_2` |
a list of xtracks representing 'group 1'. |

`cell_size_m` |
The resolution of the raster analysis – i.e. the height and width of each cell in the raster representation of the landscape, in meters. |

A list presenting analysis results

- square_meters_visited_1
square meters of unique landscape areas visited by all xtracks in group 1

- square_meters_visited_2
square meters of unique landscape areas visited by all xtracks in group 2

- square_meters_visited_1_or_2
square meters of unique landscape areas visited by 1 or 2 (geographic union)

- square_meters_visited_by_1_and_2
square meters of unique landscape areas visited by 1 and 2 (geographic intersection)

- percent_of_what_1_visited_that_was_visited_by_2
percent of land visited by group 1 that was visited by group 2

- percent_of_what_2_visited_that_was_visited_by_1
percent of land visited by group 2 that was visited by group 1

- percent_of_what_was_visited_by_1_or_2_that_was_visited_by_1
percent of unique land visited by 1 or 2 that was visited by 1

- percent_of_what_was_visited_by_1_or_2_that_was_visited_by_2
percent of unique land visited by 1 or 2 that was visited by 2

- percent_of_what_was_visited_by_1_or_2_that_was_visited_by_1_and_2
percent of unique land visited by 1 or 2 that was visited by both 1 and 2.

For example of geographic segregation and overlap analysis see figure 4 in Wood et al. 2021 publication see https://www.nature.com/articles/s41562-020-01002-7

data(d1,d2,d3,d4,d5,d6,d7,d8) xt_1 <- xtrack(lat=d1$lat, lon=d1$lon, elevation_m=d1$elevation_m, in_camp=d1$in_camp, unix_time=d1$unix_time, distance_from_camp_m=d1$distance_from_camp_m, utm_epsg=32736) xt_2 <- xtrack(lat=d2$lat, lon=d2$lon, elevation_m=d2$elevation_m, in_camp=d2$in_camp, unix_time=d2$unix_time, distance_from_camp_m=d2$distance_from_camp_m, utm_epsg=32736) xt_3 <- xtrack(lat=d3$lat, lon=d3$lon, elevation_m=d3$elevation_m, in_camp=d3$in_camp, unix_time=d3$unix_time, distance_from_camp_m=d3$distance_from_camp_m, utm_epsg=32736) xt_4 <- xtrack(lat=d4$lat, lon=d4$lon, elevation_m=d4$elevation_m, in_camp=d4$in_camp, unix_time=d4$unix_time, distance_from_camp_m=d4$distance_from_camp_m, utm_epsg=32736) xt_5 <- xtrack(lat=d5$lat, lon=d5$lon, elevation_m=d5$elevation_m, in_camp=d5$in_camp, unix_time=d5$unix_time, distance_from_camp_m=d5$distance_from_camp_m, utm_epsg=32736) xt_6 <- xtrack(lat=d6$lat, lon=d6$lon, elevation_m=d6$elevation_m, in_camp=d6$in_camp, unix_time=d6$unix_time, distance_from_camp_m=d6$distance_from_camp_m, utm_epsg=32736) xt_7 <- xtrack(lat=d7$lat, lon=d7$lon, elevation_m=d7$elevation_m, in_camp=d7$in_camp, unix_time=d7$unix_time, distance_from_camp_m=d7$distance_from_camp_m, utm_epsg=32736) xt_8 <- xtrack(lat=d8$lat, lon=d8$lon, elevation_m=d8$elevation_m, in_camp=d8$in_camp, unix_time=d8$unix_time, distance_from_camp_m=d8$distance_from_camp_m, utm_epsg=32736) list_1 <- list(xt_1, xt_2, xt_3, xt_4) list_2 <- list(xt_5, xt_6, xt_7, xt_8) geo_seg_results <- calculate_geographic_overlap_and_segregation(list_1, list_2, cell_size_m = 10)

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