combine_geographies.qmd

---
title: "Combining Map Geographies"
format: html
editor: visual
---

```{r setup,  warning = FALSE, message = FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)


library(tidyverse)
library(DBI)
library(data.table)
library(ggspatial)
library(gstat)
library(here)
library(httr)
library(jsonlite)
library(ptaxsim)
library(sf)
library(stars)
library(glue)

# Create the DB connection with the default name expected by PTAXSIM functions
ptaxsim_db_conn <- DBI::dbConnect(RSQLite::SQLite(), "./ptaxsim.db/ptaxsim-2021.0.4.db")


options(digits=4, scipen = 999)

library(sf)
library(jsonlite)
library(httr)

# link to the API output as a JSON file
muni_shp <- read_sf("https://gis.cookcountyil.gov/traditional/rest/services/politicalBoundary/MapServer/2/query?outFields=*&where=1%3D1&f=geojson")


township_shp <- read_sf("https://gis.cookcountyil.gov/traditional/rest/services/politicalBoundary/MapServer/3/query?outFields=*&where=1%3D1&f=geojson")

wards_shp <- read_sf("https://data.cityofchicago.org/resource/p293-wvbd.geojson")
```

```{r}

base_url <- "https://datacatalog.cookcountyil.gov/resource/tx2p-k2g9.json"

# Grab all PINs with townsip variables

pins_2021 <- GET(
  base_url,
  query = list(
    year = 2021,
  #  property_city != "CHICAGO",
   # property_city = "ROGERS PARK",
    `$select` = paste0(c("pin", "class", "township_code", "township_name", "cook_municipality_name",
                         "ward_num",
                         "nbhd_code", "census_puma_geoid"),
   collapse = ","),
    `$limit` = 500000000L
  )
)

# chi_pins_2021 <- GET(
#   base_url,
#   query = list(
#     year = 2021,
#     property_city = "CHICAGO",
#     `$select` = paste0(c("pin", "class", "township_code", "township_name", "property_city", 
#                          "ward_num",
#                          "nbhd_code", "census_puma_geoid"),
#    collapse = ","),
#     `$limit` = 500000000L
#   )
# )

pins_2021 <- fromJSON(rawToChar(pins_2021$content))

#chi_pins_2021 <- fromJSON(rawToChar(chi_pins_2021$content))

# table(pins_2021$nbhd_code)
```

12,397 pins in Rogers Park in tax year 2021.

```{r}

source("./scripts/helper_tc_muninames.R")

joined_pins <- read_csv("./Output/4C_joined_PINs_bills_and_exemptions.csv") %>% mutate(tax_code_num = as.character(tax_code_num))

joined_pins <- left_join(joined_pins, pins_2021, by = c("pin")) 

joined_pins <- joined_pins %>% left_join(tc_muninames, by = "tax_code_num")

joined_pins <- joined_pins %>% 
  mutate(area_name = ifelse(!is.na(ward_num), ward_num, shpfile_name) )

# table(joined_pins$area_name)


pct_exempt_MC <- joined_pins %>% 
  group_by(area_name) %>%
  rename(tax_code_rate = tax_code_rate.x) %>%
  mutate(total_eav = sum(eav), # eav from pin table. "original" eav before exemptions or tifs
         nonTIF_EAV_post_exemps = sum(final_tax_to_dist/(tax_code_rate/100)),
         pin_count  = n(),
         township_exemptEAV = sum(all_exemptions)) %>%
  ungroup() %>%
  group_by(area_name, major_class_code, total_eav, pin_count, township_exemptEAV, nonTIF_EAV_post_exemps) %>%
  summarize(MC_eav =  sum(eav),
            mc_pc = n(),
            MC_exemptions = sum(all_exemptions),
            MC_nonTIF_EAV_post_exemps = sum(final_tax_to_dist / (tax_code_rate/100) ),
            
) %>%
  mutate(
         pct_eav_MC = MC_eav / total_eav,
       #  pct_pins_w_exe = mc_pc / pin_count,
        exemps_per_resPIN =  MC_exemptions / mc_pc)

# <- pct_exempt_MC %>% filter(!is.na(area_name))

# wardmap <- pct_exempt_MC %>% 
#   full_join(muni_ward, by = c( "ward_num" = "ward_id") ) %>%
#   ggplot(aes(fill = exemps_per_resPIN)) + 
#   geom_sf(aes(geometry = geometry), color = "black") + 
# 
#   labs(title = "Average Exempt EAV for Residential PINs", 
#      #  caption = "The median township has 67.2% of its EAV from Class 2 Residential Properties"
#      ) +
#   theme_void() + 
#   theme(axis.ticks = element_blank(), axis.text = element_blank())+# +#+
#   scale_fill_steps2(
#     high = "darkblue", low = "black",  #  guide = "legend",
#     #midpoint = median(pct_residential$pct_eav_MC),
#     show.limits=TRUE,
#     na.value = NA,
#     name = "Exempt EAV",
#     labels = scales::dollar)
# 
# wardmap
```

> need to create an area_name variable that all observations have, group and summarize by that variable. join to combined shapefile using area_name

```{r combo-munis-wards-map}
#| code-fold: true
#| eval: false


muni_shp_small <- muni_shp %>% 
  group_by(OBJECTID) %>%
  mutate(MUNICIPALITY = ifelse(is.na(MUNICIPALITY), "Unincorporated", MUNICIPALITY)) %>%
  filter(MUNICIPALITY != "Chicago") %>%
  select(MUNICIPALITY, geometry, AGENCY_DESC, OBJECTID) %>%
    rename(area_name = MUNICIPALITY) %>%
    mutate(area_name = ifelse(is.na(area_name), AGENCY_DESC, area_name) )

wards_shp_small <- wards_shp %>%  
  select(ward, geometry) %>%
  rename(area_name = ward) %>%
  mutate(AGENCY_DESC = NA, 
         OBJECTID=NA)

muni_ward <- rbind(muni_shp_small, wards_shp_small) 


pct_exempt_MC %>% 
 # filter(major_class_code == 2) %>%
  anti_join(muni_ward, by = c( "area_name" = "area_name") )



wardmap <- pct_exempt_MC %>% 
  filter(major_class_code == 2) %>%
  full_join(muni_ward, by = c( "area_name" = "area_name") ) %>%
  ggplot(aes(fill = exemps_per_resPIN)) + 
  geom_sf(aes(geometry = geometry), color = "black") + 

  labs(title = "Average Exempt EAV for Residential PINs", 
     #  caption = "The median township has 67.2% of its EAV from Class 2 Residential Properties"
     ) +
  theme_void() + 
  theme(axis.ticks = element_blank(), axis.text = element_blank())+# +#+
  scale_fill_steps2(
    high = "#001E62", low = "black",  #  guide = "legend",
    #midpoint = median(pct_residential$pct_eav_MC),
    show.limits=TRUE,
    na.value = "#F2F7EB",
    name = "Exempt EAV",
    labels = scales::dollar)

wardmap

ggsave(plot = wardmap, "./avg_exe_EAV_forResPINs.png")

```

![](images/combo-munis-wards-map-1.png)

Missing:

Evanston

```{r eval=FALSE}
forest_shp <- read_sf("https://gis.cookcountyil.gov/traditional/rest/services/fpdcc/MapServer/4/query?outFields=*&where=1%3D1&f=geojson")


forest_shp %>% 
  #filter(major_class_code == 2) %>%
  #full_join(muni_ward, by = c( "area_name" = "area_name") ) %>%
  ggplot(
   aes()
    ) + 
  geom_sf(aes(geometry = geometry),fill = "darkgreen", color = "black") + theme_void()

rivers_shp <- read_sf("https://gis.cookcountyil.gov/traditional/rest/services/planimetry/MapServer/6/query?outFields=*&where=1%3D1&f=geojson")

rivers_shp %>% 
  ggplot(
   aes()
    ) + 
  geom_sf(aes(geometry = geometry),color = "blue") + theme_void()
```