use Seattle Development Capacity to show impact of DADU policy thresholds

dadu-analysis.R

# SETUP ----
library(tidyverse)
library(treemapify) 
library(RSocrata)
library(snakecase)
library(forcats)
library(waffle)
library(rcartocolor)
# DATA ----

d <- read.socrata("https://data.seattle.gov/resource/unmu-zvzk.csv") %>% 
  rename_all(to_screaming_snake_case)

data <- 
  d %>% 
  filter(CLASS %in% "SF") %>% 
  filter(!RESSTAT %in% "MIO") %>% 
  filter(LAND_SQFT >= 3200) %>% 
  mutate(MAX_LOT_CVRG = case_when(
    ZONELUT %in% c("SF 5000", "SF 7200","SF 9600") && LAND_SQFT < 5000 ~ 1000 + .15*LAND_SQFT,
    ZONELUT %in% c("SF 5000", "SF 7200","SF 9600") && LAND_SQFT >= 5000 ~ .35*LAND_SQFT,
    ZONELUT %in% "RSL" ~ as.double(LAND_SQFT),
    TRUE ~ NA_real_
  )) %>% 
  mutate(AVAIL_SF = MAX_LOT_CVRG - BLDG_RES_GRSSQF,
         BACKYARD_EST_SF = .75*(LAND_SQFT - BLDG_RES_GRSSQF)) %>%
  filter(AVAIL_SF > 250,
         .6*BACKYARD_EST_SF > 250) %>% 
  mutate_if(is_double,as.integer) %>% 
  select(PIN,
         PROP_NAME,
         ZONELUT,
         CLASS,
         RESSTAT,
         LAND_SQFT,
         PARCEL_DEV_SQFT,
         BLDG_RES_GRSSQF,
         MAX_LOT_CVRG,
         AVAIL_SF,
         BACKYARD_EST_SF)


data_sum <- 
  data %>% 
  mutate(GT_8000_LGL = PARCEL_DEV_SQFT>=8000,
         GT_8000_CHR = if_else(GT_8000_LGL,"8000sqft or Larger", "Less than 8000sqft"),
         GT_8000_FCT = fct_infreq(GT_8000_CHR, TRUE) %>% fct_rev,
         ZONELUT_FCT =  fct_infreq(ZONELUT, TRUE) %>% fct_rev,
         ZONELUT_FCT_SIZE = if_else(GT_8000_LGL, 
                                    str_c(ZONELUT,"GT_8000",sep = "_"),
                                    str_c(ZONELUT,"LT_8000",sep = "_")),
         ZONELUT_FCT_SIZE = str_replace_all(ZONELUT_FCT_SIZE, "\\s","_")) %>% 
  mutate(ZONELUT_FCT_SIZE = factor(ZONELUT_FCT_SIZE, 
                                   levels= c(
                                     "SF_5000_GT_8000",
                                     "SF_7200_GT_8000",
                                     "SF_9600_GT_8000",
                                     "RSL_GT_8000",
                                     "SF_5000_LT_8000",
                                     "SF_7200_LT_8000",
                                     "SF_9600_LT_8000",
                                     "RSL_LT_8000"), 
                                   ordered = TRUE) %>% fct_rev) %>% 
  {bind_rows(.,
             filter(.,GT_8000_LGL), 
             .id = "ID")} %>% 
  group_by(ID) %>% 
  nest %>% 
  transmute(GROUP = if_else(ID %in% "1","ALL","GT_8000"),
            DATA = data) %>% 
  unnest %>% 
  group_by(GROUP,ZONELUT_FCT_SIZE) %>%  
  summarise(PARCEL_DEV_SQFT = sum(PARCEL_DEV_SQFT),
          N = n()) %>%  
  arrange(GROUP)

d_waffle <- 
  data_sum %>% 
  transmute(ZONELUT_FCT_SIZE,
           N = N *1e-2) %>% 
  nest %>%  
  transpose %>% 
  map(2)

d_w1 <- d_waffle[[1]]
d_w2 <- 
  d_w1 %>% mutate(N = if_else(str_detect(as.character(ZONELUT_FCT_SIZE),"LT"),0,N))
  

cols <- carto_pal(n = 7,"Geyser")[c(2:5)]
cols_rep <- rep(cols,2)

w1 <- waffle(d_w1, rows = 10,size = 2,legend_pos="none",  reverse = TRUE,colors = cols_rep)
w2 <- waffle(d_w2, rows = 10,size = 2,pad = 29, legend_pos="bottom",  reverse = TRUE, colors = cols_rep)
iron(w1,w2)