m <- leaflet() |>

addProviderTiles(providers$CartoDB.Positron) |>

addLayersControl(baseGroups = c("Base Map"),

overlayGroups = c("Industrial Lands", ex_nm),

options = layersControlOptions(collapsed = TRUE)) |>

addPolygons(data = il_lyr,

fillColor = "#EB4584",

weight = 1,

opacity = 1.0,

color = "#EB4584",

dashArray = "1",

fillOpacity = 1.0,

group="Industrial Lands")

if (!(is.null(ex_lyr))) {

m <- m |>

addPolygons(data = ex_lyr,

fillColor = "#00A7A0",

weight = 1,

opacity = 1.0,

color = "#00A7A0",

dashArray = "1",

fillOpacity = 1.0,

group=ex_nm)

}

# Add in Region Button

m <- m |>

addEasyButton(easyButton(

icon="fa-globe", title="Region",

onClick=JS("function(btn, map){map.setView([47.615,-122.257],8.5); }"))) |>

setView(-122.257, 47.615, zoom = 8.5)

return(m)

tbl_by_county <- lyr |>

st_drop_geometry() |>

group_by(county_name) |>

summarise(acres = round(sum(total_acreage), 0)) |>

as_tibble()

region <- tbl_by_county |>

mutate(county_name = "Region") |>

group_by(county_name) |>

summarise(acres = round(sum(acres), 0)) |>

as_tibble()

tbl_by_county <- bind_rows(tbl_by_county, region) |>

mutate(county_name = factor(county_name, levels=county_ord)) |>

arrange(county_name) |>

rename(County = "county_name", `Acres` = "acres")

return(tbl_by_county)

final_tbl <- datatable(tbl,

#container = summary_container,

#colnames = c('Group', rep(c('Estimate', 'Share'), num_years)),

options = list(pageLength = 15,

dom = 'rt',

#buttons = c('csv', 'excel'),

columnDefs = list(list(className = 'dt-center', targets=1:1))

),

#extensions = 'Buttons',

filter = 'none',

rownames = FALSE) |>

formatCurrency("Acres", "", digits = 0)

return(final_tbl)

final_tbl <- datatable(tbl,

options = list(pageLength = 15,

initComplete = htmlwidgets::JS(

"function(settings, json) {",

paste0("$(this.api().table().container()).css({'font-size': '", fs, "'});"),

"}"),

dom = 'rt',

columnDefs = list(list(className = 'dt-center', targets=1:8))

),

filter = 'none',

rownames = FALSE) |>

formatCurrency(num_cols, "", digits = 0)

return(final_tbl)

parcel_file <- file.path("data/county-files", p_file)

parcel_threshold <- p_thresh

parcels <- st_read(parcel_file) |>

st_make_valid() |>

st_transform(crs = spn)

parcels <- parcels |>

mutate(parcel_acreage = as.numeric(set_units(st_area(parcels), "acre")))

# Use Parcel Points for City, Regional Geography and MIC assignment

parcel_points <- st_centroid(parcels) |> select(all_of(p_id))

# Add MIC name

p <- st_intersection(parcel_points, mic) |> select(all_of(p_id), mic) |> st_drop_geometry()

parcels <- left_join(parcels, p, by = p_id) |> mutate(mic = replace_na(mic, "Not in a MIC"))

# Add Rgeo name

p <- st_intersection(parcel_points, rgeo) |> select(all_of(p_id), rgeo) |> st_drop_geometry()

parcels <- left_join(parcels, p, by = p_id) |> mutate(rgeo = replace_na(rgeo, "Rural"))

# Add City name

p <- st_intersection(parcel_points, city) |> select(all_of(p_id), city) |> st_drop_geometry()

parcels <- left_join(parcels, p, by = p_id) |> mutate(city = replace_na(city, "Unincorporated"))

industrial_zones <- il_lyr |>

filter(county_name == p_county) |>

st_transform(crs = spn) |>

select("county_name") |>

st_union() |>

st_sf() |>

mutate(county_name = p_county)

industrial <- st_intersection(parcels, industrial_zones) |>

select(parcel_id = all_of(p_id), "parcel_acreage", "county_name", "mic", "rgeo", "city")

industrial_parcels <- industrial |>

mutate(total_acreage = as.numeric(set_units(st_area(industrial), "acre"))) |>

mutate(parcel_share = total_acreage/parcel_acreage) |>

filter(total_acreage >= parcel_threshold) |>

st_drop_geometry() |>

select("parcel_id", "county_name", "total_acreage", "parcel_share", "mic", "rgeo", "city") |>

mutate(county_id = case_when(

county_name == "King" ~ 33,

county_name == "Kitsap" ~ 35,

county_name == "Pierce" ~ 53,

county_name == "Snohomish" ~ 61)) |>

mutate(parcel_integer = as.numeric(parcel_id)) |>

drop_na() |>

mutate(parcel_fips = paste0(county_id, parcel_integer)) |>

select("parcel_fips", "total_acreage", "parcel_share", "mic", "rgeo", "city")

return(industrial_parcels)

parcel_file <- file.path("data/county-files", p_file)

# Read in Parcel file

print(str_glue("Opening {p_file} parcel file."))

parcels <- st_read(parcel_file) |>

st_make_valid() |>

st_transform(crs = spn)

# Calculate acreage for parcels

print(str_glue("Calculating parcel acreage for {p_file}."))

parcels <- parcels |> mutate(parcel_acreage = as.numeric(set_units(st_area(parcels), "acre")))

# Clean up Parcel ID using County ID to ensure unique values when counties are combined

print(str_glue("Creating unique parcel_fips_id for {p_file} to use in joins."))

parcels <- parcels |>

select(parcel_id_fips = all_of(p_id), "parcel_acreage") |>

mutate(county_id = p_county, parcel_integer = as.numeric(parcel_id_fips)) |>

drop_na() |>

mutate(parcel_id_fips = as.numeric(paste0(county_id, parcel_integer))) |>

mutate(county_name = case_when(

county_id == 33 ~ "King",

county_id == 35 ~ "Kitsap",

county_id == 53 ~ "Pierce",

county_id == 61 ~ "Snohomish")) |>

select(-"parcel_integer")

# Use Parcel Points for City, Regional Geography and MIC assignment

print(str_glue("Creating centroid file for {p_file} to join names to."))

parcel_points <- st_centroid(parcels) |> select("parcel_id_fips")

# Add MIC name

print(str_glue("Adding MIC names to {p_file}."))

p <- st_intersection(parcel_points, mic) |> select("parcel_id_fips", mic) |> st_drop_geometry()

parcels <- left_join(parcels, p, by = c("parcel_id_fips")) |> mutate(mic = replace_na(mic, "Not in a MIC"))

# Add Rgeo name

print(str_glue("Adding Regional Geographies to {p_file}."))

p <- st_intersection(parcel_points, rgeo) |> select("parcel_id_fips", rgeo) |> st_drop_geometry()

parcels <- left_join(parcels, p, by = c("parcel_id_fips")) |> mutate(rgeo = replace_na(rgeo, "Rural"))

# Add City name

print(str_glue("Adding City names to {p_file}."))

p <- st_intersection(parcel_points, city) |> select("parcel_id_fips", city) |> st_drop_geometry()

parcels <- left_join(parcels, p, by = c("parcel_id_fips")) |> mutate(city = replace_na(city, "Unincorporated"))

# Remove any duplicate parcel ids

print(str_glue("Removing any duplicate parcel ids from {p_file}."))

parcels <- parcels |> distinct(parcel_id_fips, .keep_all = TRUE)

return(parcels)

data_file <- file.path("data", p_data)

buildings_file <- file.path("data", p_buildings)

# Get Land Use Code and Parcel SQFT

print(str_glue("Getting Parcel Use Code and Size from UrbanSim Parcel Data."))

d <- read_tsv(data_file) |>

mutate(parcel_integer = as.numeric(parcel_id_fips)) |>

drop_na() |>

mutate(parcel_id_fips = as.numeric(paste0(county_id, parcel_integer))) |>

select("parcel_id_fips", "land_use_type_id", "gross_sqft")

# Get Improvement Value by Parcel

print(str_glue("Getting Parcel Improvement values from UrbanSim Buildings Data."))

b <- read_tsv(buildings_file) |>

mutate(parcel_integer = as.numeric(parcel_id_fips)) |>

drop_na() |>

mutate(parcel_id_fips = as.numeric(paste0(county_id, parcel_integer))) |>

mutate(improvement_value = as.integer(improvement_value)) |>

mutate(improvement_value = replace_na(improvement_value, 0)) |>

group_by(parcel_id_fips) |>

summarise(improvement_value = sum(improvement_value)) |>

as_tibble()

# Combine Land Use Code, Size and Improvement Value

print(str_glue("Combining Use Code, Size and Improvment Values for Parcels."))

parcels <- left_join(d, b, by="parcel_id_fips") |>

mutate(improvement_value = replace_na(improvement_value, 0))

# Add Parcel Data to Combined Region Parcels file

print(str_glue("Adding parcel data to regional parcel file and cleaning up"))

p <- left_join(p_file, parcels, by="parcel_id_fips") |>

drop_na() |>

mutate(gross_sqft = as.numeric(gross_sqft)) |>

mutate(impval = improvement_value / gross_sqft) |>

mutate(development_flag = case_when(

impval < vacant_threshold ~ "Vacant",

impval < redevel_threshold ~ "Redevelopable",

impval >= redevel_threshold ~ "Not Redevelopable")) |>

mutate(urban = case_when(

rgeo == "Metro" & city != "Bremerton" ~ "Y",

rgeo == "Metro" & city == "Bremerton" ~ "N")) |>

mutate(urban = replace_na(urban, "N")) |>

mutate(adjustement_factor = case_when(

urban == "Y" ~ urban_deduction,

urban == "N" ~ suburban_deduction)) |>

mutate(land_use_type_id = as.integer(land_use_type_id)) |>

mutate(land_use_type_id = replace_na(land_use_type_id, 0)) |>

mutate(use_code = case_when(

mic != "Not in a MIC" & land_use_type_id == 6 ~ "Y",

mic == "Not in a MIC" & land_use_type_id == 6 ~ "N",

!(land_use_type_id %in% c(0, 2, 6, 7, 8, 19, 22, 23, 29)) ~ "Y",

land_use_type_id %in% c(0, 2, 6, 7, 8, 19, 22, 23, 29) ~ "N")) |>

filter(use_code == "Y")

return(p)