Update penalty cals

penalty = dplyr::case_when(
    value < 1 ~ penalty_value[1],     # Penalty of penalty_value[1] when value < 1
    value >= 1 & value <= 50 ~ penalty_value[2], 
    value > 50 & value <= 200 ~ penalty_value[3],
    TRUE ~ 1  # Default case when none of the above conditions are met
)

R/corenet.R

@@ -399,7 +399,7 @@ coherent_network_group = function(coherent_network, key_attribute = "all_fastest
 #' @export
 
 prepare_network = function(network, key_attribute = "all_fastest_bicycle_go_dutch", 
-                           road_scores = list("A Road" = 1, "B Road" = 2, "Minor Road" = 100000),penalty_value = 1,
+                           road_scores = list("A Road" = 1, "B Road" = 2, "Minor Road" = 100000),penalty_value = [10000000, 10000, 1000],
                            transform_crs = 27700) {
     # Cast the network to LINESTRING and transform to the specified CRS
     network = network |>
@@ -409,9 +409,9 @@ prepare_network = function(network, key_attribute = "all_fastest_bicycle_go_dutc
         sfnetworks::activate("edges") |>
         dplyr::mutate(
             # Handle NA values and normalize using key_attribute
-            value = dplyr::if_else(is.na(!!rlang::sym(key_attribute)), 0, !!rlang::sym(key_attribute)),
+            value = dplyr::if_else(is.na(!!rlang::sym(key_attribute)), 0.01, !!rlang::sym(key_attribute)),
             max_value = max(value, na.rm = TRUE),
-            value = ifelse(value <= 50, 0, value),
+            value = ifelse(value <= 5, 0, value),
             value = ifelse(value == 0, 0.01, value),
             # Calculate logarithmic arterialness and round it
             arterialness = ((max_value / value) ^ 3) / max(((max_value / value) ^ 3), na.rm = TRUE),
@@ -425,7 +425,12 @@ prepare_network = function(network, key_attribute = "all_fastest_bicycle_go_dutc
             }),
             # Calculate weight considering the road type influence
             weight = round(0.95 * arterialness + 0.05 * road_score, 6), 
-            penalty = ifelse(value <= 200, penalty_value, 1),
+            penalty = dplyr::case_when(
+                value < 1 ~ penalty_value[1],     # Penalty of penalty_value[1] when value < 1
+                value >= 1 & value <= 50 ~ penalty_value[2], 
+                value > 50 & value <= 200 ~ penalty_value[3],
+                TRUE ~ 1  # Default case when none of the above conditions are met
+            ),
             weight = weight * penalty    
         )
     # network = sfnetworks::activate(network, "nodes")