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depth_interactive_map_alpha.R

@@ -91,14 +91,17 @@ PAM_PAL    <- c("#ffffd9", "#edf8b1", "#c7e9b4", "#7fcdbb",
 ENVIPE_PAL <- c("#fef0d9", "#fdcc8a", "#fc8d59", "#e34a33",
                 "#b30000", "#7f0000")                             # OrRd 6
 
+# GIIM keys MUST match the Spanish category strings stored in
+# dim_remesas_mun.giim_mun (Muy bajo / Bajo / Medio / Alto / Muy alto) so the
+# MapLibre `match` paint expression actually pairs each muni with a colour.
+# "No aplica" and "Nulo" are intentionally absent here -- they paint via the
+# match-fallback to fully-transparent and are filtered out of the legend.
 GIIM_COLORS <- c(
-  "Nulo"      = "#f8fafc",
-  "Muy bajo"  = "#dbeafe",
-  "Bajo"      = "#93c5fd",
-  "Medio"     = "#3b82f6",
-  "Alto"      = "#1d4ed8",
-  "Muy alto"  = "#1e3a8a",
-  "No aplica" = "#94a3b8"
+  "Muy bajo" = "#dbeafe",
+  "Bajo"     = "#93c5fd",
+  "Medio"    = "#3b82f6",
+  "Alto"     = "#1d4ed8",
+  "Muy alto" = "#1e3a8a"
 )
 
 MIGRANTS_PAL <- c("#ffffb2", "#fecc5c", "#fd8d3c",
@@ -224,13 +227,13 @@ POP_COLS <- list(
 
 # PATP sub-types (kind controls paint family: "num" = step, "cat" = match).
 PATP_COLS <- list(
-  "t_part"     = list(col = "t_part",     kind = "num", label = "Participation rate",
+  "t_part"     = list(col = "t_part",     kind = "num", label = "Economic participation rate",
                       breaks = c(35, 45, 50, 55, 60, 70)),
   "t_ocup"     = list(col = "t_ocup",     kind = "num", label = "Occupation rate",
                       breaks = c(92, 95, 97, 98, 99, 99.5)),
-  "pr"         = list(col = "pr",         kind = "num", label = "PR indicator",
+  "pr"         = list(col = "pr",         kind = "num", label = "Remittance receipt per capita",
                       breaks = c(0.5, 2, 5, 10, 25, 50)),
-  "ind_conec"  = list(col = "ind_conec",  kind = "num", label = "Connectivity index",
+  "ind_conec"  = list(col = "ind_conec",  kind = "num", label = "Road connectivity index",
                       breaks = c(0.15, 0.25, 0.35, 0.45, 0.6, 0.75)),
   "pr_pred_loc_rur"  = list(col = "pr_pred_loc_rur",  kind = "cat",
                             label = "Predominant rural problem",
@@ -261,13 +264,16 @@ POBREZA_COLS <- list(
 # step through one month at a time. Decode: `year = ym %/% 12`, `month = ym %% 12 + 1`.
 # Example: Jan 2010 = 24120, Dec 2026 = 24323.
 TEMPORAL_LAYERS <- list(
-  "migrants-pts" = list(label = "Missing migrants", color = COL_YEL,
+  "migrants-pts" = list(label = "Death during migration", color = COL_YEL,
                         table = "migrants",
                         # ym_expr drives both the chart cache and the point
                         # loader; same DuckDB expression in both places so the
-                        # chart axis and the map filter agree.
+                        # chart axis and the map filter agree. agg switches the
+                        # strip from raw incidents (COUNT(*)) to confirmed
+                        # deaths (SUM n_dead) -- the indicator of interest.
                         ym_expr        = "incident_year * 12 + CAST(SUBSTR(month, 6, 2) AS INTEGER) - 1",
                         point_ym_expr  = "incident_year * 12 + CAST(SUBSTR(month, 6, 2) AS INTEGER) - 1",
+                        agg            = "SUM(COALESCE(n_dead, 0))",
                         kind = "point"),
   "ocved-pts"    = list(label = "OCVED violence",  color = COL_AMBER,
                         table = "ocved_events",
@@ -286,6 +292,15 @@ TEMPORAL_LAYERS <- list(
                         table = "dim_pam_mun",
                         ym_expr   = "CAST(date_part('year', date) AS INTEGER) * 12 + CAST(date_part('month', date) AS INTEGER) - 1",
                         agg       = "SUM(pam_n)",
+                        kind      = "polygon"),
+  # Clandestine graves are recorded at annual resolution (one (CVEGEO, year)
+  # row per muni). Encode year as ym = year * 12 (Jan of that year) so the
+  # bars land cleanly on year tick marks even though the slider counts months.
+  "fosas-choro"  = list(label = "Clandestine graves",
+                        color = "#fec44f",            # FOSAS_PAL mid (amber)
+                        table = "dim_fosas_mun",
+                        ym_expr   = "year * 12",
+                        agg       = "SUM(fosas_total)",
                         kind      = "polygon")
 )
 # Outer bounds for the slider in ym encoding: Jan 2010 .. Dec 2026.
@@ -619,6 +634,26 @@ WEB_SQL_TEMPLATES <- list(
        GROUP BY CVEGEO
      ) p ON c.CVEGEO = p.CVEGEO",
 
+  # ym-window-filtered fosas aggregate. dim_fosas_mun is annual (one row per
+  # CVEGEO with that muni's recorded year of activity), so the ym filter
+  # operates on year * 12. fosas_total / cuerpos_total are SUMmed in case
+  # any muni ever ends up with multiple rows.
+  src_fosas_ym =
+    "SELECT c.municipio, c.entidad, c.CVEGEO,
+            COALESCE(CAST(f.fosas_sum   AS INTEGER), 0) AS fosas_total,
+            COALESCE(CAST(f.cuerpos_sum AS INTEGER), 0) AS cuerpos_total,
+            c.wkt
+     FROM crime_mun c
+     LEFT JOIN (
+       SELECT CVEGEO,
+              SUM(fosas_total)   AS fosas_sum,
+              SUM(cuerpos_total) AS cuerpos_sum
+       FROM dim_fosas_mun
+       WHERE year IS NOT NULL
+         AND (year * 12) BETWEEN {ym_lo} AND {ym_hi}
+       GROUP BY CVEGEO
+     ) f ON c.CVEGEO = f.CVEGEO",
+
   temporal_yr_all =
     "SELECT {yr_expr} AS yr, COUNT(*) AS n FROM {tbl}
      WHERE {yr_col} IS NOT NULL
@@ -831,6 +866,13 @@ ddb_minmax <- function(con, tbl, col, filter_zero = TRUE) {
     as.numeric(DBI::dbGetQuery(con, web_sql_qbreaks(tbl, col, n))[1L, ]),
     error = function(e) seq_len(n - 1L)
   )
+  # Arbitrary-SQL variant: lets a layer compute its breaks on a derived view
+  # (e.g. per-muni averages) so the colour ramp aligns with the painted
+  # values rather than raw row-level distribution.
+  qbq_sql <- function(sql) tryCatch(
+    as.numeric(DBI::dbGetQuery(con, sql)[1L, ]),
+    error = function(e) NULL
+  )
   list(
     crime_sf = tryCatch(
       DBI::dbGetQuery(con, web_sql_simplified("src_crime", tol = 0.005)) |>
@@ -841,7 +883,18 @@ ddb_minmax <- function(con, tbl, col, filter_zero = TRUE) {
     qb = list(
       crime = lapply(CRIME_COLS, function(info)
         qbq("crime_mun", info$col, length(CRIME_STEP_PAL))),
-      desap = qbq("dim_desap_mun", "final_desap_nl_per_100k", length(DESAP_PAL)),
+      # desap is averaged per-CVEGEO in src_desap, so the ramp also has to be
+      # computed on per-muni averages -- not on the raw row distribution
+      # (which collapses 5 of 6 buckets into a narrow low-end window).
+      desap = qbq_sql(paste0(
+        "WITH m AS (SELECT CVEGEO, AVG(final_desap_nl_per_100k) AS r ",
+        "           FROM dim_desap_mun GROUP BY CVEGEO HAVING r > 0) ",
+        "SELECT ",
+        paste0("PERCENTILE_CONT(", round(seq_len(length(DESAP_PAL) - 1L) / length(DESAP_PAL), 8),
+               ") WITHIN GROUP (ORDER BY r)",
+               collapse = ", "),
+        " FROM m"
+      )) %||% qbq("dim_desap_mun", "final_desap_nl_per_100k", length(DESAP_PAL)),
       pam   = qbq("dim_pam_mun",   "pam_n",                   length(PAM_PAL)),
       pop   = qbq("crime_mun",     "poblacion",               length(POP_PAL))
     ),
@@ -1048,51 +1101,55 @@ WEB_SPEC <- list(
     list(id = "hazards", label = "Hazards", open = TRUE, items = list(
       list(kind = "layer", value = "crime-choro",
            label = "Criminal incidence by municipality",
-           tooltip = "Rate of crimes per 100,000 people in each municipality. Select crime type using the buttons below.",
-           legend = list(type = "ramp", pal = "crime", unit = " /100k"),
+           tooltip = "Rate of crimes per 100,000 people in each municipality. Select crime type using the buttons below. Source: Registro Nacional de Incidencia Delictiva (RNID)",
+           legend = list(type = "ramp", pal = "crime", unit = " /100k",
+                         labels = "qualitative"),
            checked = TRUE),
       list(kind = "radios", group = "crime"),
       list(kind = "layer", value = "migrants-pts",
-           label = "Missing migrant incidents by Internation Organization of Migration",
-           tooltip = "Each dot is a location where migrants went missing or died, recorded by the International Organization for Migration.",
-           legend = list(type = "dot", color = COL_YEL, label = "Incident location")),
+           label = "Deaths during migration",
+           tooltip = "Each dot is a location where migrants went died, recorded by the International Organization for Migration.",
+           legend = list(type = "dot", color = COL_YEL, label = "Death location")),
       list(kind = "layer", value = "desap-choro",
-           label = "Missing and no located persons per 100k (RNPDNO)",
-           tooltip = "People officially reported as disappeared and no located in each municipality, shown per 100,000 residents (2010 to 2024)",
+           label = "Missing and no located persons per 100k",
+           tooltip = "People officially reported as disappeared and no located in each municipality, shown per 100,000 residents (2010 to 2024). Source:
+           Registro Nacional de Personas Desaparecidas y No Localizadas (RNPDNO)",
            legend = list(type = "ramp", pal = "desap", unit = " /100k")),
       list(kind = "layer", value = "fosas-choro",
            label = "Clandestine graves by municipality",
            tooltip = "Number of clandestine graves and bodies found per municipality. Source: Plataforma Ciudadana de Fosas (2006-2024)",
-           legend = list(type = "ramp", pal = "fosas", unit = " pits")),
+           legend = list(type = "ramp", pal = "fosas", unit = " pits/bodies",
+                         labels = "qualitative")),
       list(kind = "radios", group = "fosas"),
       list(kind = "layer", value = "graves-pts",
            label = "Clandestine burial sites",
            tooltip = "Locations where clandestine grave sites have been discovered across Mexico.",
-           legend = list(type = "dot", color = COL_RED, label = "Burial site")),
+           legend = list(type = "dot", color = COL_RED, label = "Site location")),
       list(kind = "layer", value = "ocved-pts",
-           label = "Organised criminal violence events (OCVED 2.0)",
-           tooltip = "Incidents of organised crime violence such as confrontations, attacks and criminal activities.",
+           label = "Organised criminal violence events",
+           tooltip = "Incidents of organised crime violence such as confrontations, attacks and criminal activities. Source: (OCVED 2.0)",
            legend = list(type = "dot", color = COL_AMBER, label = "Violence event")),
       list(kind = "layer", value = "ged-pts",
-           label = "Armed conflict events (UCDP-GED)",
-           tooltip = "Armed conflict events recorded by Uppsala University's global conflict research dataset.",
+           label = "Armed conflict events",
+           tooltip = "Armed conflict events recorded by Uppsala University's global conflict research dataset. Source: (UCDP-GED)",
            legend = list(type = "dot", color = COL_LIGHT_BLUE, label = "Conflict event")),
       list(kind = "layer", value = "envipe-state",
-           label = "Crime victimisation survey by state (ENVIPE)",
-           tooltip = "State-level survey on how safe people feel and how many crimes go unreported. Select metric below.",
-           legend = list(type = "ramp", pal = "envipe", unit = " %")),
+           label = "Crime victimisation survey by state",
+           tooltip = "State-level survey on how safe people feel and how many crimes go unreported. Select metric below. Source: National Survey of Victimization and Perception of Public Safety (ENVIPE)",
+           legend = list(type = "ramp", pal = "envipe", unit = " %",
+                         labels = "qualitative")),
       list(kind = "radios", group = "envipe"),
       list(kind = "soon", label = "Heat stress index")
     )),
     list(id = "exposure", label = "Exposure", open = FALSE, items = list(
       list(kind = "layer", value = "pam-choro",
-           label = "Irregular migration events by municipality (PAM)",
-           tooltip = "Number of people who arrived at migrant assistance points or shelters in each municipality.",
+           label = "Irregular migration events by municipality",
+           tooltip = "Events involving people who entered Mexican territory without immigration documents proving their legal stay and were subjected to an Administrative Immigration Procedure (PAM). Source: Unidad de Política Migratoria. Gobierno de México",
            legend = list(type = "ramp", pal = "pam", unit = " events")),
       list(kind = "layer", value = "remesas-choro",
-           label = "Migration intensity index MEX-USA (GIIM)",
-           tooltip = "Index measuring the intensity of migration from Mexico to the United States, from none to very high.",
-           legend = list(type = "cats", pal = "giim")),
+           label = "Degree of Migration Intensity MEX-USA (GIIM)",
+           tooltip = "Index measuring the intensity of migration from Mexico to the United States, from none to very high. Source: CONAPO",
+           legend = list(type = "cats", pal = "giim", filterable = "remesas")),
       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.",
@@ -1102,9 +1159,10 @@ WEB_SPEC <- list(
            tooltip = "Railway lines used by migrants, including freight routes known as 'La Bestia'.",
            legend = list(type = "line", color = "#9E2A3A", label = "Railway", dashed = TRUE)),
       list(kind = "layer", value = "pop-choro",
-           label = "Population (CPV 2020)",
+           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).",
-           legend = list(type = "ramp", pal = "pop", unit = " people")),
+           legend = list(type = "ramp", pal = "pop", unit = " people",
+                         labels = "qualitative")),
       list(kind = "radios", group = "pop")
     )),
     list(id = "vulnerability", label = "Vulnerability", open = FALSE, items = list(
@@ -1113,9 +1171,10 @@ WEB_SPEC <- list(
            tooltip = "Shelters, food, health, government, legal and religious assistance points along migrant routes. Click a swatch in the legend to filter.",
            legend = list(type = "cats", pal = "infra_cats", filterable = "infra")),
       list(kind = "layer", value = "patp-choro",
-           label = "PATP-CONEVAL municipal indicators",
-           tooltip = "Multi-dimensional CONEVAL PATP indicators. Numeric chips render as percentile choropleths; categorical chips (rural problem / crime focus) render with a fixed palette. Works both at municipal level and on the grid.",
-           legend = list(type = "ramp", pal = "patp", unit = ""),
+           label = "Territorial analysis of poverty",
+           tooltip = "Multi-dimensional poverty analysis, including sociodemographics, infrastructure and government. Source: CONEVAL",
+           legend = list(type = "ramp", pal = "patp", unit = "",
+                         labels = "qualitative"),
            # Variant legends shown when the matching categorical chip is active.
            # data-variant-key on each wrap is matched by setPATPType in JS.
            legend_extras = list(
@@ -1128,7 +1187,8 @@ WEB_SPEC <- list(
       list(kind = "layer", value = "pobreza-choro",
            label = "Poverty (CONEVAL 2020)",
            tooltip = "CONEVAL 2020 poverty / extreme poverty share by municipality. Enable the grid view to see this layer.",
-           legend = list(type = "ramp", pal = "pobreza", unit = " %")),
+           legend = list(type = "ramp", pal = "pobreza", unit = " %",
+                         labels = "qualitative")),
       list(kind = "radios", group = "pobreza")
     ))
   ),
@@ -1151,14 +1211,14 @@ WEB_SPEC <- list(
     ),
     pop = list(
       list(label = "Total population",        value = "POBTOT"),
-      list(label = "Born abroad",             value = "PNACOP"),
-      list(label = "Resident abroad (2015)",  value = "PRESOP2015")
+      list(label = "Foreign population",             value = "PNACOP"),
+      list(label = "Recent resident (abroad in 2015)",  value = "PRESOP2015")
     ),
     patp = list(
-      list(label = "Participation rate",          value = "t_part"),
+      list(label = "Economic Participation rate",          value = "t_part"),
       list(label = "Occupation rate",             value = "t_ocup"),
       list(label = "PR indicator",                value = "pr"),
-      list(label = "Connectivity index",          value = "ind_conec"),
+      list(label = "connectivity index",          value = "ind_conec"),
       list(label = "Predominant rural problem",   value = "pr_pred_loc_rur"),
       list(label = "Common-jurisdiction crime",   value = "delitos_fc_b_j_a")
     ),
@@ -1174,10 +1234,16 @@ WEB_SPEC <- list(
     subtitle = "Summary of main indicators for migrants across Mexico.",
     groups = list(
       list(id = "hazards", label = "Hazards", open = TRUE, cards = list(
-        list(title = "Missing Migrants Incidents (IOM)", kind = "stat", chart = "temporal_plot",
+        list(title = "Missing Migrants Incidents (IOM)", kind = "stat",
              stats = list(
                list(id = "n_incidents",  label = "Recorded incidents", color = COL_YEL),
                list(id = "n_fatalities", label = "Confirmed dead",     color = COL_RED)
+             ),
+             charts = list(
+               list(id = "migrants_treemap_cause", label = "Cause of death",
+                    height = "70px"),
+               list(id = "migrants_treemap_demo",  label = "Demographics (children / female / male)",
+                    height = "44px")
              )),
         list(title = "Missing persons and not located (RNPDNO)", kind = "stat",
              stats = list(list(id = "n_desap", label = "Disappearances & no located", color = COL_RISK))),
@@ -1523,7 +1589,8 @@ LUCIDE_JS <- tags$head(
     // first). The chart follows only the most-recent one. ymLo/ymHi drive the
     // map filter (client-side for points, server for PAM). ym encoding:
     // ym = year * 12 + month - 1  (Jan 2010 = 24120, Dec 2026 = 24323).
-    var TEMPORAL_LAYER_IDS = ['migrants-pts','ocved-pts','ged-pts','pam-choro'];
+    var TEMPORAL_LAYER_IDS = ['migrants-pts','ocved-pts','ged-pts',
+                              'pam-choro','fosas-choro'];
     // Point layers carry a unified 'ym' feature property via load_pts_with_ym
     // so the client filter doesn't need to know each table's date format.
     var TEMPORAL_POINT_COL = {
@@ -2416,17 +2483,32 @@ dp_stat <- function(val_id, label, color = "#1e293b") {
 }
 
 ind_card <- function(title, body, chart_id = NULL, note = NULL, soon = FALSE,
-                     half = FALSE, chart_height = "55px") {
+                     half = FALSE, chart_height = "55px",
+                     charts = NULL) {
   cls <- paste("ind-card", if (isTRUE(half)) "ind-card--half" else "")
+  # `charts` (plural) is a list of list(id, label, height) for cards that need
+  # more than one plot slot (e.g. the migrants card pairs a cause-of-death
+  # treemap with a demographic-split treemap). If both `chart_id` and `charts`
+  # are NULL the card renders body-only.
+  chart_block <- if (!is.null(charts)) {
+    tagList(lapply(charts, function(c) {
+      div(class = "ind-card-chart",
+          if (!is.null(c$label))
+            tags$div(class = "ind-card-chart-label", c$label),
+          plotOutput(c$id, width = "100%",
+                     height = c$height %||% chart_height))
+    }))
+  } else if (!is.null(chart_id)) {
+    div(class = "ind-card-chart",
+        plotOutput(chart_id, width = "100%", height = chart_height))
+  }
   div(class = cls,
     div(class = "ind-card-hdr",
       span(class = "ind-card-title", title),
       if (soon) tags$span(class = "acc-coming-tag", "Soon")
     ),
     if (!is.null(body)) div(class = "ind-card-body", body),
-    if (!is.null(chart_id))
-      div(class = "ind-card-chart",
-          plotOutput(chart_id, width = "100%", height = chart_height)),
+    chart_block,
     if (!is.null(note))
       div(class = "ind-card-note", note)
   )
@@ -2448,12 +2530,22 @@ acc_legend_from_spec <- function(lg) {
   pal <- if (!is.null(lg$pal)) WEB_SPEC$palettes[[lg$pal]] else NULL
   switch(lg$type,
     ramp = {
-      # Prefer hardcoded lo/hi when provided (back-compat); otherwise pull the
-      # real min/max from the cached data and format with the unit.
-      bnds <- legend_bounds(lg$pal, lg$unit %||% "")
-      acc_legend_ramp(pal,
-                      lo = lg$lo %||% bnds$lo,
-                      hi = lg$hi %||% bnds$hi)
+      # Layers that switch the painted column via a chip (crime, fosas, ...)
+      # can't show a single column's min/max -- the bounds change with every
+      # chip pick. `labels = "qualitative"` opts those layers into static
+      # "Low"/"High" labels.
+      if (identical(lg$labels, "qualitative")) {
+        acc_legend_ramp(pal,
+                        lo = lg$lo %||% "Low",
+                        hi = lg$hi %||% "High")
+      } else {
+        # Prefer hardcoded lo/hi when provided (back-compat); otherwise pull
+        # the real min/max from the cached data and format with the unit.
+        bnds <- legend_bounds(lg$pal, lg$unit %||% "")
+        acc_legend_ramp(pal,
+                        lo = lg$lo %||% bnds$lo,
+                        hi = lg$hi %||% bnds$hi)
+      }
     },
     cats = acc_legend_cats(pal, filterable_key = lg$filterable),
     dot  = acc_legend_dot(lg$color, lg$label %||% "Event / point"),
@@ -2522,6 +2614,7 @@ render_panel_card <- function(card) {
   )
   ind_card(card$title, body,
            chart_id     = card$chart,
+           charts       = card$charts,
            note         = card$note,
            half         = isTRUE(card$half),
            chart_height = "55px")
@@ -4250,6 +4343,28 @@ server <- function(input, output, session) {
                           as.character(pl$cats %||% character(0)))
   }, ignoreInit = TRUE, ignoreNULL = TRUE)
 
+  # --- Legend filter: remesas-choro by GIIM category --------------------------
+  # Same click-a-swatch-to-toggle pattern; column is `giim_mun` (Spanish
+  # category strings stored in dim_remesas_mun.giim_mun). "No aplica" and
+  # "Nulo" are not in GIIM_COLORS at all, so they paint transparent via
+  # match-fallback and never appear in the legend.
+  observeEvent(input$remesas_filter, {
+    pl <- input$remesas_filter
+    if (is.null(pl) || !isTRUE(lazy_added$remesas)) return()
+    visible <- as.character(pl$cats %||% character(0))
+    prx <- maplibre_proxy("map")
+    if (length(visible) == 0) {
+      prx |> set_filter("remesas-choro",
+        list("==", list("get", "giim_mun"), "__none__"))
+    } else if (length(visible) >= length(GIIM_COLORS)) {
+      prx |> set_filter("remesas-choro", NULL)
+    } else {
+      prx |> set_filter("remesas-choro",
+        list("in", list("get", "giim_mun"),
+             list("literal", as.list(visible))))
+    }
+  }, ignoreInit = TRUE, ignoreNULL = TRUE)
+
   # --- Nav-bar search: fly to picked result -----------------------------------
   observeEvent(input$search_flyto, {
     pt <- input$search_flyto
@@ -4391,62 +4506,114 @@ server <- function(input, output, session) {
     )
   })
 
-  output$temporal_plot <- renderPlot({
-    req(con)
-    # IOM temporal: incidents (COL_YEL) + confirmed dead (COL_RED) overlay
-    yr_expr_ts   <- "CAST(date_part('year', incident_date_ts) AS INTEGER)"
-    yr_expr_raw  <- "CAST(date_part('year', CAST(incident_date AS TIMESTAMP)) AS INTEGER)"
-    df_all <- tryCatch(
-      DBI::dbGetQuery(con, web_sql("temporal_mig_all", yr_expr = yr_expr_ts)),
-      error = function(e) tryCatch(
-        DBI::dbGetQuery(con, web_sql("temporal_mig_all", yr_expr = yr_expr_raw)),
-        error = function(e2) { message("temporal: ", e2$message); NULL }
-      )
-    )
-    if (is.null(df_all) || nrow(df_all) == 0) return(NULL)
+  # --- Migrants treemaps (replaces the old single time-series plot) ----------
+  # Two slot card: cause-of-death and demographic split. Both honour the
+  # current panel selection (whole-country when no polygon is drawn, restricted
+  # to the in-polygon subset when one is). Treemap geometry is a 2-row slice-
+  # and-dice -- the dominant category gets the top row, smaller categories
+  # tile the bottom row. Avoids the treemapify dependency.
+  CAUSE_PAL <- c("Other / unknown"   = "#6b7280",
+                 "Drowning"          = "#0ea5e9",
+                 "Harsh environment" = "#f59e0b",
+                 "Vehicle accident"  = "#a855f7",
+                 "Violence"          = "#ef4444")
+  DEMO_PAL  <- c(Children = "#f97316",
+                 Female   = "#ec4899",
+                 Male     = "#0987A8")
+
+  treemap_layout <- function(df) {
+    # df: data.frame(label, value, color); returns df with xmin/xmax/ymin/ymax
+    df <- df[!is.na(df$value) & df$value > 0, , drop = FALSE]
+    if (nrow(df) == 0) return(NULL)
+    df <- df[order(-df$value), , drop = FALSE]
+    total <- sum(df$value)
+    # Top row holds the leader; remainder tiles the bottom row.
+    leader <- df[1, , drop = FALSE]
+    rest   <- if (nrow(df) > 1) df[-1, , drop = FALSE] else NULL
+    leader$xmin <- 0; leader$xmax <- 1
+    leader_share <- leader$value / total
+    leader$ymax <- 1; leader$ymin <- 1 - leader_share
+    if (!is.null(rest) && nrow(rest) > 0) {
+      rest_share <- rest$value / sum(rest$value)
+      rest$xmax <- cumsum(rest_share)
+      rest$xmin <- c(0, head(rest$xmax, -1))
+      rest$ymin <- 0; rest$ymax <- 1 - leader_share
+      rbind(leader, rest)
+    } else {
+      leader$ymin <- 0; leader$ymax <- 1
+      leader
+    }
+  }
 
-    filter_wkt <- rv$temporal_filter_wkt
-    df_sel <- if (!is.null(filter_wkt)) {
-      tryCatch(DBI::dbGetQuery(con, web_sql("temporal_mig_sel",
-        yr_expr = yr_expr_ts, poly_wkt = filter_wkt)), error = function(e) NULL)
-    } else NULL
-
-    COL_INC  <- COL_YEL   # incidents
-    COL_DEAD <- COL_RED   # confirmed dead
-
-    p <- ggplot(df_all, aes(x = yr)) +
-      # Incidents area + line
-      geom_area(aes(y = n), fill = COL_INC, alpha = 0.10, color = NA) +
-      geom_line(aes(y = n), color = COL_INC, linewidth = 0.7, alpha = 0.55) +
-      # Confirmed dead overlay
-      geom_line(aes(y = n_dead), color = COL_DEAD, linewidth = 0.7,
-                alpha = 0.6, linetype = "22")
-    if (!is.null(df_sel) && nrow(df_sel) > 0) {
-      p <- p +
-        geom_line(data = df_sel, aes(y = n),
-                  color = COL_INC, linewidth = 1.1) +
-        geom_point(data = df_sel, aes(y = n),
-                   color = COL_INC, size = 1.5, shape = 21,
-                   fill = "white", stroke = 0.7) +
-        geom_line(data = df_sel, aes(y = n_dead),
-                  color = COL_DEAD, linewidth = 1.0, linetype = "22") +
-        geom_point(data = df_sel, aes(y = n_dead),
-                   color = COL_DEAD, size = 1.3, shape = 21,
-                   fill = "white", stroke = 0.7)
+  treemap_plot <- function(df, pal, base_size = 7) {
+    laid <- treemap_layout(df)
+    if (is.null(laid)) return(NULL)
+    laid$pct <- round(100 * laid$value / sum(laid$value))
+    laid$label_full <- paste0(laid$label, "\n", laid$pct, "%")
+    ggplot(laid, aes(xmin = xmin, xmax = xmax,
+                     ymin = ymin, ymax = ymax,
+                     fill = label)) +
+      geom_rect(color = "#0f172a", linewidth = 0.4) +
+      geom_text(aes(x = (xmin + xmax) / 2,
+                    y = (ymin + ymax) / 2,
+                    label = label_full),
+                size = base_size / .pt * 2.4, color = "white",
+                fontface = "bold", lineheight = 0.85) +
+      scale_fill_manual(values = pal, na.value = "#334155") +
+      coord_cartesian(expand = FALSE, xlim = c(0, 1), ylim = c(0, 1)) +
+      theme_void() +
+      theme(legend.position  = "none",
+            plot.background  = element_rect(fill = "transparent", color = NA),
+            panel.background = element_rect(fill = "transparent", color = NA),
+            plot.margin      = margin(0, 0, 0, 0))
+  }
+
+  # Source data: the whole migrants table by default, or just the in-polygon
+  # subset when the user has drawn a selection (mirrors n_incidents handling).
+  migrants_filtered <- reactive({
+    poly_wkt <- rv$temporal_filter_wkt
+    if (is.null(poly_wkt)) {
+      tryCatch(DBI::dbGetQuery(con,
+        "SELECT cause_cat, n_dead, n_children, n_females, n_males
+         FROM migrants"),
+        error = function(e) { message("migrants_filtered: ", e$message); NULL })
+    } else {
+      sql <- glue::glue(
+        "SELECT m.cause_cat, m.n_dead, m.n_children, m.n_females, m.n_males
+         FROM migrants m
+         WHERE m.lon IS NOT NULL AND m.lat IS NOT NULL
+           AND ST_Within(ST_Point(m.lon, m.lat), ST_GeomFromText('{poly_wkt}'))",
+        .open = "{", .close = "}")
+      tryCatch(DBI::dbGetQuery(con, sql),
+               error = function(e) { message("migrants_filtered poly: ", e$message); NULL })
     }
-    p + theme_minimal(base_size = 8) +
-      theme(
-        plot.background    = element_rect(fill = "transparent", color = NA),
-        panel.background   = element_rect(fill = "transparent", color = NA),
-        panel.grid         = element_blank(),
-        panel.grid.major.y = element_line(color = "#e2e8f0", linewidth = 0.3),
-        axis.title         = element_blank(),
-        axis.text.x        = element_text(size = 7, color = "#64748b"),
-        axis.text.y        = element_blank(),
-        axis.ticks         = element_blank(),
-        plot.margin        = margin(1, 4, 1, 4)
-      )
-  }, bg = "transparent", res = 96)
+  })
+
+  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"
+    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,
+                              color = unname(CAUSE_PAL[agg$cause_cat]),
+                              stringsAsFactors = FALSE)
+    treemap_plot(summary_df, CAUSE_PAL, base_size = 8)
+  }, bg = "transparent", res = 110)
+
+  output$migrants_treemap_demo <- renderPlot({
+    df <- migrants_filtered()
+    if (is.null(df) || nrow(df) == 0) return(NULL)
+    summary_df <- data.frame(
+      label = c("Children", "Female", "Male"),
+      value = c(sum(df$n_children, na.rm = TRUE),
+                sum(df$n_females,  na.rm = TRUE),
+                sum(df$n_males,    na.rm = TRUE)),
+      color = unname(DEMO_PAL),
+      stringsAsFactors = FALSE
+    )
+    treemap_plot(summary_df, DEMO_PAL, base_size = 8)
+  }, bg = "transparent", res = 110)
 
   # Shared helper: temporal bar chart by year for point event tables
   make_temporal_yr <- function(tbl, yr_col, line_color) {
@@ -4561,45 +4728,45 @@ server <- function(input, output, session) {
     ))
   }, ignoreInit = TRUE)
 
-  # PAM month-window re-aggregation: when the strip range changes and PAM is
-  # active, re-query src_pam_ym + push the new fill via the proxy. The current
-  # layer is removed and re-added with the ym-filtered source. The observer is
-  # JS-debounced (200ms) so dragging the slider doesn't queue dozens of heavy
-  # re-aggregations. Full-range windows short-circuit -- the unfiltered layer
-  # already on the map IS the answer for the full range, no re-query needed.
+  # Generic ym-window re-aggregation for polygon temporal layers. Each entry
+  # below pairs a layer id with: the lazy-load key (so we don't try to swap a
+  # source on a layer that hasn't been added yet) and the SQL template that
+  # rebuilds the per-muni aggregate filtered to [ym_lo, ym_hi]. The
+  # short-circuit, the JS debounce, and the set_source swap from the PAM fix
+  # all apply uniformly.
+  TEMPORAL_POLY_SOURCES <- list(
+    "pam-choro"   = list(lazy_key = "pam",   sql = "src_pam_ym"),
+    "fosas-choro" = list(lazy_key = "fosas", sql = "src_fosas_ym")
+  )
   observeEvent(input$temporal_filter, {
     pl <- input$temporal_filter
     if (is.null(pl)) return()
     active <- pl$active %||% character(0)
-    if (!("pam-choro" %in% active) || !isTRUE(lazy_added$pam)) return()
     ym_lo <- pl$start %||% TEMPORAL_RANGE[1]
     ym_hi <- pl$end   %||% TEMPORAL_RANGE[2]
-    # Short-circuit: full-range (relative to PAM's actual data span) = no
-    # filtering = leave the unfiltered layer alone. This is what prevents
-    # PAM-on freezing the app: the very first syncTemporalStrip after
-    # lazy_add_fill fires temporal_filter with the default full range, and
-    # without this guard the heavy 2475-row aggregate ran a second time
-    # and triggered a remove+re-add on a layer still being committed to
-    # the MapLibre source.
-    df_pam <- .APP_CACHE$temporal[["pam-choro"]]
-    pam_data_lo <- if (!is.null(df_pam)) min(df_pam$ym) else TEMPORAL_RANGE[1]
-    pam_data_hi <- if (!is.null(df_pam)) max(df_pam$ym) else TEMPORAL_RANGE[2]
-    if (ym_lo <= pam_data_lo && ym_hi >= pam_data_hi) return()
-    sf_data <- tryCatch({
-      DBI::dbGetQuery(con,
-        web_sql("src_pam_ym", ym_lo = ym_lo, ym_hi = ym_hi)) |>
-        sf::st_as_sf(wkt = "wkt", crs = 4326) |>
-        dplyr::select(-wkt)
-    }, error = function(e) { message("PAM ym re-query: ", e$message); NULL })
-    if (is.null(sf_data)) return()
-    # Swap the source data on the existing pam-choro layer instead of
-    # remove + re-add. mapgl's add_fill_layer creates a source named after
-    # the layer ID; remove_layer doesn't drop the source, so the previous
-    # pattern was hitting "source already exists" on the second activation
-    # and leaving the map in a broken state -- this is what was making the
-    # app appear to freeze whenever PAM was re-enabled.
-    maplibre_proxy("map") |>
-      set_source(layer_id = "pam-choro", source = sf_data)
+    for (lid in intersect(active, names(TEMPORAL_POLY_SOURCES))) {
+      spec <- TEMPORAL_POLY_SOURCES[[lid]]
+      if (!isTRUE(lazy_added[[spec$lazy_key]])) next
+      # Full-range = unfiltered layer already on the map IS the answer.
+      df_cache <- .APP_CACHE$temporal[[lid]]
+      data_lo <- if (!is.null(df_cache)) min(df_cache$ym) else TEMPORAL_RANGE[1]
+      data_hi <- if (!is.null(df_cache)) max(df_cache$ym) else TEMPORAL_RANGE[2]
+      if (ym_lo <= data_lo && ym_hi >= data_hi) next
+      sf_data <- tryCatch({
+        DBI::dbGetQuery(con,
+          web_sql(spec$sql, ym_lo = ym_lo, ym_hi = ym_hi)) |>
+          sf::st_as_sf(wkt = "wkt", crs = 4326) |>
+          dplyr::select(-wkt)
+      }, error = function(e) {
+        message(lid, " ym re-query: ", e$message); NULL
+      })
+      if (is.null(sf_data)) next
+      # set_source swaps the source data on the existing layer without
+      # touching the paint expression / tooltip -- avoids the source-already-
+      # exists collision that the previous remove + re-add pattern caused.
+      maplibre_proxy("map") |>
+        set_source(layer_id = lid, source = sf_data)
+    }
   }, ignoreNULL = TRUE, ignoreInit = TRUE)
 
   # --- PAM temporal plot (irregular migration events over time) ---------------
@@ -4711,7 +4878,8 @@ server <- function(input, output, session) {
   # (otherwise hidden outputs stay suspended and read "---"). Cost: one extra
   # render per output per session -- negligible (cached strings + small plots).
   for (.oid in c(
-    "temporal_pam", "temporal_plot", "temporal_ocved", "temporal_ged",
+    "temporal_pam", "migrants_treemap_cause", "migrants_treemap_demo",
+    "temporal_ocved", "temporal_ged",
     "breakdown_giim", "breakdown_infra",
     "n_incidents", "n_fatalities", "n_graves", "n_facilities", "n_events",
     "ocved_n", "n_ged", "n_mun", "n_desap", "n_pam", "n_fosas", "n_cuerpos",