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##### Document How to do & interpret results of metaanalysi

Meta-analysis is a systematic method for synthesizing quantitative results of different empirical studies
regarding the effect of an independent variable (or determinant, or intervention, or treatment) on a
defined outcome (or dependent variable). Mainly developed in medical and psychological research as a
tool for synthesizing empirical information about the outcomes of a treatment, meta-analysis is now
increasingly used in the social sciences as a tool for hypothesis testing. However, the assumptions
underlying meta-analytic hypothesis testing in the social sciences will usually not be met under real-life
conditions. This is the reason why meta-analysis is increasingly conducted with a different aim, based on
more realistic assumptions. That aim is to explore the dispersion of effect sizes.

##### Plot

melb

##### PhD - Validation Sites

#Distance between the stations: 105114.58
basel_row <- select_validalo_aggregate[select_validalo_aggregate$Site == "BASEL", ]
rovaniemi_row <- select_validalo_aggregate[select_validalo_aggregate$Site == "ROVANIEMI", ]
svurban_row <- select_validalo_aggregate_sv[select_validalo_aggregate_sv$Site == "Sv. Urban", ]
#random_integer1 = 26
min_distance = 49
random_integer <- sample(1:10000, 1)
random_integer1 = 6097
set.seed(random_integer1)
# Calculate the number of rows for the remaining 10% sampling
desired_sample_size <- round(0.1 * nrow(select_validalo_aggregate)) -3 # Subtract 3 for Basel, Rovaniemi and Sv. Urban
# Randomly select the remaining rows
remaining_rows <- select_validalo_aggregate[!(select_validalo_aggregate$Site %in% c("BASEL", "ROVANIEMI","Sv. Urban")), ]
random_indices <- sample(nrow(remaining_rows), size = desired_sample_size, replace = FALSE)
random_selection <- remaining_rows[random_indices, ]
# Combine the selected rows with Basel and Rovaniemi rows
final_selection <- rbind(basel_row, rovaniemi_row,svurban_row, random_selection)
final_selection_sp = final_selection
coordinates(final_selection_sp) <- ~Longitude + Latitude
proj4string(final_selection_sp) <- CRS("+init=epsg:4326")
# Assuming final_selection_sp is your SpatialPointsDataFrame
# Extract the coordinates from the SpatialPointsDataFrame
coords <- coordinates(final_selection_sp)
# Calculate the distance matrix in meters
distance_matrix <- distm(coords, fun = distVincentyEllipsoid)
# If you want to find the minimum non-zero distance
min_distance <- min(distance_matrix[distance_matrix > 0])
Stations = select_validalo_aggregate_sp
Validation = final_selection_sp
mapview(Stations, alpha = 0.1) + mapview(Validation,zcol = "Group")