Upload depth_interactive_map_alpha.R

depth_interactive_map_alpha.R

@@ -88,7 +88,7 @@ COL_INFRA  <- "#006B82"
 COL_COUNT  <- "#374151"
 
 COL_RED    <- "#9E2A3A"   # muted brick red
-COL_YEL    <- "#B89307"   # muted ochre (was bright #DEB406)
+COL_YEL    <- "#F5CB5C"   # muted ochre (was bright #DEB406)
 COL_AMBER  <- "#3A4B5C"   # cool slate (point colour)
 COL_PURPLE <- "#7E4A86"   # toned-down mauve
 COL_GREEN  <- "#2D6E54"   # forest, lower chroma
@@ -188,11 +188,11 @@ PATP_DELITO_PAL <- c(
 # threaded through acc_legend_from_spec().
 PATP_RURAL_LABELS_EN <- c(
   "Sociales y económicos"                                              = "Social and economic",
-  "Carencia o deficiencia en servicios básicos"                        = "Lack / deficient basic services",
+  "Carencia o deficiencia en servicios básicos"                        = "Lack/deficient basic services",
   "Caminos, transportes y telecomunicaciones"                          = "Roads, transport and telecom",
   "Inseguridad, delincuencia y adicciones"                             = "Insecurity, crime and addictions",
-  "Carencia o deficiencia en la infraestructura educativa y de salud"  = "Lack / deficient education and health infrastructure",
-  "Municipios sin localidades objetivo"                                = "Municipalities without target localities",
+  "Carencia o deficiencia en la infraestructura educativa y de salud"  = "Lack/deficient education and health services",
+  "Municipios sin localidades objetivo"                                = "No target localities (rural)",
   "Sin problema"                                                       = "No problem",
   "Falta de información / Otro problema"                               = "Missing information / other",
   "Riesgos ambientales"                                                = "Environmental risk",
@@ -201,7 +201,7 @@ PATP_RURAL_LABELS_EN <- c(
 PATP_DELITO_LABELS_EN <- c(
   "El patrimonio"                      = "Property",
   "otros bienes"                       = "Other property",
-  "La vida y la Integridad corporal"   = "Life and bodily integrity",
+  "La vida y la Integridad corporal"   = "Life and integrity",
   "múltiples bienes"                   = "Multiple assets",
   "La familia"                         = "Family",
   "Sin delito"                         = "No crime",
@@ -216,14 +216,11 @@ PATP_DELITO_LABELS_EN <- c(
 # silently lumped into an existing bucket. SQL CASE in
 # WEB_SQL_TEMPLATES$load_infra_pts maps the raw values to these buckets.
 INFRA_CATS <- c(
-  "Shelter"        = "#2D6E54",   # forest green
-  "Food & Lodging" = "#B89307",   # ochre
-  "Government"     = "#3A4B5C",   # slate
-  "Embassy"        = "#1F4A6B",   # deep navy (international protection)
-  "Legal & Aid"    = "#7E4A86",   # mauve
-  "Police"         = "#9E2A3A",   # brick red
-  "Health"         = "#0987A8",   # teal
-  "Religious"      = "#6B5640",   # warm brown
+  "Shelter"              = "#3C6E71",   # forest green
+  "Support (NGO)"      = "#F5CB5C",   # ochre
+  "Support (GOV)"        = "#3A4B5C",   # slate
+  "Embassy / Consulate"  = "#4D194D",   # deep navy (international protection)
+  "Migration office"         = "#9E2A3A",   # brick red
   "Other"          = "#94a3b8"    # neutral slate (fallback)
 )
 
@@ -1172,7 +1169,7 @@ WEB_SPEC <- list(
          geom = "polygon", color = "envipe_step", value = "insecurity_perc",
          opacity = 0.6,  visible = FALSE),
     list(id = "routes-lines",    type = "line",   src = "src_routes",
-         geom = "line",    color = COL_AMBER,      opacity = 0.1, visible = FALSE),
+         geom = "line",    color = "#FFDF9E",      opacity = 0.1, visible = FALSE),
     list(id = "viaferrea-lines", type = "line",   src = "src_via",
          geom = "line",    color = "#990f0f",      opacity = 0.9,  visible = FALSE,
          dash = c(4, 2)),
@@ -1267,11 +1264,11 @@ WEB_SPEC <- list(
       list(kind = "layer", value = "routes-lines",
            label = "Main migration routes through Mexico",
            tooltip = "Key routes migrants use to travel through Mexico towards the northern border.",
-           legend = list(type = "line", color = COL_AMBER, label = "Route")),
+           legend = list(type = "line", color = COL_AMBER, label = "Potential routes following national roads")),
       list(kind = "layer", value = "viaferrea-lines",
            label = "Railway network",
-           tooltip = "Railway lines used by migrants, including freight routes known as 'La Bestia'.",
-           legend = list(type = "line", color = "#9E2A3A", label = "Railway", dashed = TRUE)),
+           tooltip = "Railway lines used by migrants, including freight routes",
+           legend = list(type = "line", color = "#9E2A3A", label = "Potential routes thought railway", dashed = TRUE)),
       list(kind = "layer", value = "pop-choro",
            label = "Population (Census 2020)",
            tooltip = "Census 2020 population counts. With the grid view off, each municipality shows the sum of its CPV cells (3 sub-types: total, born abroad, resident abroad in 2015). With the grid view on, each rHEALPix cell shows its own value at the active nivel (n4-n7).",
@@ -4132,7 +4129,7 @@ server <- function(input, output, session) {
         add_line_layer(
           id           = "routes-lines",
           source       = routes_sf,
-          line_color   = COL_AMBER,
+          line_color   = "#FFDF9E",
           line_width   = list("interpolate", list("linear"), list("zoom"),
                               5, 0.5, 10, 2.0),
           line_opacity = 0.1, visibility = "none"
@@ -4354,7 +4351,7 @@ server <- function(input, output, session) {
       if ("routes-lines"    %in% active)
         lazy_add_fill("routes",  "src_routes",  "routes-lines",  function(prx, d)
           prx |> add_line_layer("routes-lines",   source = d,
-            line_color = COL_AMBER,
+            line_color = "#FFDF9E",
             # Zoom-interpolated width so the network reads at a national view
             # without bloating into a solid mass when zoomed in on a city.
             line_width = list("interpolate", list("linear"), list("zoom"),
@@ -4705,15 +4702,15 @@ server <- function(input, output, session) {
   # tile the bottom row. Avoids the treemapify dependency.
   # Re-toned to the muted Spatial data palette so the treemaps speak the same
   # colour language as the map points (no Tailwind-bright sky / red / purple).
-  CAUSE_PAL <- c("Other / unknown"   = "#374151",   # neutral slate (COL_COUNT)
-                 "Drowning"          = "#0987A8",   # teal-blue / water
-                 "Harsh environment" = "#B89307",   # ochre / heat
-                 "Vehicle accident"  = "#7E4A86",   # mauve
+  CAUSE_PAL <- c("Other / unknown"   = "#3A4B5C",   # neutral slate (COL_COUNT)
+                 "Drowning"          = "#1B9AAA",   # teal-blue / water
+                 "Harsh environment" = "#DDDBCB",   # ochre / heat
+                 "Vehicle accident"  = "#F5F1E3",   # mauve
                  "Violence"          = "#9E2A3A")   # brick red
   # Demographics are nominal (not ordered) and carry no risk valence -- three
   # distinct muted hues, avoiding the pink = female / blue = male cliche.
-  DEMO_PAL  <- c(Children = "#B89307",   # ochre
-                 Female   = "#7E4A86",   # mauve
+  DEMO_PAL  <- c(Children = "#DDDBCB",   # ochre
+                 Female   = "#1B9AAA",   # mauve
                  Male     = "#3A4B5C")   # slate
 
   # Stacked-bar layout: one horizontal row of segments, widths proportional
@@ -4751,7 +4748,7 @@ server <- function(input, output, session) {
                           paste0(substr(laid$label, 1L, pmax(1L, max_chars - 1L)), "."),
                           laid$label)
     laid$show_label <- laid$w >= 0.10                # ~ 33 px segments and up
-    laid$show_pct   <- laid$w >= 0.06
+    laid$show_pct   <- laid$w >= 0.08
     label_df <- laid[laid$show_label, , drop = FALSE]
     pct_df   <- laid[laid$show_pct,   , drop = FALSE]
     pct_only <- !pct_df$show_label
@@ -4805,7 +4802,7 @@ server <- function(input, output, session) {
   output$migrants_treemap_cause <- renderPlot({
     df <- migrants_filtered()
     if (is.null(df) || nrow(df) == 0) return(NULL)
-    df$cause_cat[is.na(df$cause_cat) | df$cause_cat == ""] <- "Other / unknown"
+    df$cause_cat[is.na(df$cause_cat) | df$cause_cat == ""] <- "Other/unknown"
     agg <- aggregate(n_dead ~ cause_cat, data = df, FUN = function(x) sum(x, na.rm = TRUE))
     summary_df <- data.frame(label = agg$cause_cat,
                               value = agg$n_dead,