update growth map to use indirect landfall states

app/queries.R

@@ -30,6 +30,8 @@ fips.counties <- tbl(con, I("fips.counties"))
 fips.states <- tbl(con, I("fips.states"))
 
 gis.affected_area_landfalls <- tbl(con, I("gis.affected_area_landfalls"))
+gis.indirect_landfalls <- tbl(con, I("gis.indirect_landfalls"))
+gis.us_states_boundary <- tbl(con, I("gis.us_states_boundary"))
 
 hurdat.best_track <- tbl(con, I("hurdat.best_track"))
 hurdat.hurdat_storms <- tbl(con, I("hurdat.hurdat_storms"))
@@ -54,6 +56,7 @@ view.all_lf_type_landfalls <- tbl(con, "all_lf_type_landfalls")
 view.lf_id_location <- tbl(con, "lf_id_location")
 view.lf_landfall_gis <- tbl(con, "lf_landfall_gis")
 view.best_track_summary <- tbl(con, "best_track_summary")
+view.yearly_state_metrics <- tbl(con, "yearly_state_metrics")
 #qry <- econ.storm_base_loss %>%
 #  left_join(view.hurdat_track, by = c("storm_basin", "storm_year", "storm_name"))
 
@@ -417,6 +420,7 @@ get_hurdat_track <- function(storm) {
 get_normalized_metric_growth <- function(storm, full_lf_id) {
   lf_id_parts <- split_full_lf_id(full_lf_id)
 
+  if (lf_id_parts$lf_type == 'LF') {
     affected_counties <- gis.affected_area_landfalls %>%
       filter(
         storm_basin == storm$storm_basin,
@@ -482,26 +486,71 @@ get_normalized_metric_growth <- function(storm, full_lf_id) {
         geom_wkt
       ) %>%
       arrange(state_fips, county_fips, year)
+  } else {
+    affected_state <- gis.indirect_landfalls %>%
+      filter(
+        storm_basin == storm$storm_basin,
+        storm_year == storm$storm_year,
+        storm_name == storm$storm_name,
+        lf_type == lf_id_parts$lf_type,
+        lf_id == lf_id_parts$lf_id
+      ) %>%
+      select(
+        state_fips
+      )
 
-  #query <- normalized_metrics %>%
-  #  inner_join(
-  #    public.counties,
-  #    by = c("state_fips" = "statefp", "county_fips" = "countyfp")
-  #  ) %>%
-  #  mutate(
-  #    geom_wkt = sql("ST_AsText(ST_Transform(geom, 4326))")
-  #  ) %>%
-  #  select(
-  #    state_fips,
-  #    county_fips,
-  #    year,
-  #    population,
-  #    housing_units,
-  #    normalized_population,
-  #    normalized_housing,
-  #    geom_wkt
-  #  ) %>%
-  #  arrange(state_fips, county_fips, year)
+    baseline_metrics <- view.yearly_state_metrics %>%
+      filter(year == storm$storm_year) %>%
+      inner_join(affected_state, by = "state_fips") %>%
+      select(
+        state_fips,
+        baseline_population = state_population,
+        baseline_housing = state_housing_units
+      )
+
+    normalized_metrics <- view.yearly_state_metrics %>%
+      filter(
+        year >= storm$storm_year
+      ) %>%
+      inner_join(affected_state, by = "state_fips") %>%
+      inner_join(baseline_metrics, by = "state_fips") %>%
+      mutate(
+        normalized_population = as.numeric(state_population) /
+          as.numeric(baseline_population),
+        normalized_housing = as.numeric(state_housing_units) /
+          as.numeric(baseline_housing)
+      ) %>%
+      select(
+        state_fips,
+        year,
+        population = state_population,
+        housing_units = state_housing_units,
+        normalized_population,
+        normalized_housing
+      )
+
+    query <- normalized_metrics %>%
+      inner_join(
+        gis.us_states_boundary %>%
+          rename(state_fips = statefp),
+        by = "state_fips"
+      ) %>%
+      mutate(
+        geom_wkt = sql(
+          "ST_AsText(ST_SimplifyPreserveTopology(ST_Transform(wkb_geometry, 4326), .001))"
+        )
+      ) %>%
+      select(
+        state_fips,
+        year,
+        population,
+        housing_units,
+        normalized_population,
+        normalized_housing,
+        geom_wkt
+      ) %>%
+      arrange(state_fips, year)
+  }
 
   result <- query %>% collect()