ptaxsim_explainer_update.qmd

---
title: "PTAXSIM Tax Explainer"
execute:
  echo: false
  warning: false
format:
  html:
    grid:
      sidebar-width: 0px
      margin-width: 400px
    theme: zephyr
params:
    pin_14:
        label: "PIN"
        value: "20114070180000"
        input: text
---

```{r setup, include=FALSE, cache=FALSE}
options(tigris_use_cache = TRUE)
options(bitmapType = "cairo")
library(scales)
library(ptaxsim)
library(gt)
library(DBI)
library(RSQLite)
library(RColorBrewer)
library(patchwork)
library(tigris)
library(tidyverse)
library(glue)
library(sf)

source("maps.R")
source("helper_funcs.R")
# set the following variables and run
db_path <- "ptaxsim-2023.0.0.db"
pin_14 <- as.character(params$pin_14)
# pin_14 <- "20114070180000" # Hyde Park, Chicago
pin_14 <- "15361000280000" # Riverside
# pin_14 <- "16291280010000" # Cicero
# pin_14 <- "14294070931001" # Wrightwood, Chicago - TIF
# pin_14 <- "16123090200000" # Kinzie - TIF
year <- 2023
prior_year <- year - 1

ptaxsim_db_conn <- dbConnect(SQLite(), db_path)
```

```{r ptaxsim-data}
pin_details <- lookup_pin(year, pin_14)
pin_details_prior_year <- lookup_pin(prior_year, pin_14)

render_pin14 <- glue("Parcel ({render_pin(pin_14)})")

pin_exe_details <- pin_details %>%
    select(starts_with("exe")) %>%
    pivot_longer(cols = everything())

pin_exe_total <- pin_exe_details %>%
    pull(value) %>%
    sum()

tax_bill_data <- tax_bill(
    year_vec = year,
    pin_vec = pin_14,
    simplify = F
) %>% process_tax_bill()

tax_bill_data_prior_year <- tax_bill(
    year_vec = prior_year,
    pin_vec = pin_14,
    simplify = F
) %>% process_tax_bill()

current_year_agency <- lookup_agency(year = year, tax_code = lookup_tax_code(year = year, pin = pin_14))

prior_year_agency <- lookup_agency(year = prior_year, tax_code = lookup_tax_code(year = prior_year, pin = pin_14))

prior_eav_current_levy <- current_year_agency %>%
    select(!c(year, agency_total_eav)) %>%
    full_join(., prior_year_agency %>% select(!c(agency_total_ext))) %>%
    select(all_of(names(current_year_agency)))

current_eav_prior_levy <- prior_year_agency %>%
    select(!c(year, agency_total_eav)) %>%
    full_join(., current_year_agency %>% select(!c(agency_total_ext))) %>%
    select(all_of(names(current_year_agency)))

tax_bill_data_prior_eav_current_levy <- tax_bill(
    year_vec = prior_year,
    pin_vec = pin_14,
    agency_dt = prior_eav_current_levy,
    simplify = F
) %>% process_tax_bill()

tax_bill_data_current_eav_prior_levy <- tax_bill(
    year_vec = year,
    pin_vec = pin_14,
    agency_dt = current_eav_prior_levy,
    simplify = F
) %>% process_tax_bill()

cook_county_max_ext <- tax_bill_data %>%
    filter(agency_major_type == "Cook County") %>%
    slice_max(agency_total_ext) %>%
    mutate(
        agency_tax_amount = label_dol_amt(agency_tax_rate * (eav - exe_total)),
        agency_total_eav = label_dol_amt(agency_total_eav),
        agency_total_ext = label_dol_amt(agency_total_ext),
    )

tif_agency_nums <- tax_bill_data %>%
    select(tif_agency_num) %>%
    filter(!is.na(tif_agency_num)) %>%
    unique() %>%
    pull()

```

```{r ptaxsim-geo-data}
cook_roads <- roads("17", "031")

shp_bnd_pin <- get_shp_bnd_pin(pin_14, year)

shp_buf_pin <- shp_bnd_pin %>%
    st_centroid() %>%
    st_transform(3857) %>%
    st_buffer(650) %>%
    st_transform(4326)

shp_bnd_cook <- get_shp_bnd_cook()

shp_bnd_twn_muni <- tax_bill_data %>%
    filter(agency_minor_type %in% c("MUNI", "TOWNSHIP")) %>%
    select(agency_num, agency_minor_type, agency_name) %>%
    get_shp_twn_muni() %>%
    correct_shp_names() %>%
    mutate(agency_name = as.factor(agency_name))

shp_bnd_all_schools <- tax_bill_data %>%
    filter(agency_major_type == "School") %>%
    pull(agency_num) %>%
    get_shp_all_schools() %>%
    correct_shp_names() %>%
    mutate(agency_name = as.factor(agency_name))

shp_bnd_all_others <- tax_bill_data %>%
    filter(agency_major_type == "Miscellaneous") %>%
    pull(agency_num) %>%
    get_shp_all_others() %>%
    correct_shp_names() %>%
    mutate(agency_name = as.factor(agency_name))

shp_bnd_tifs <- get_shp_tifs(c(tif_agency_nums, "0")) %>%
    left_join(
        tax_bill_data %>%
            select(tif_agency_num, tif_agency_name) %>%
            filter(!is.na(tif_agency_num)) %>%
            unique(),
        by = join_by(AGENCY == tif_agency_num)
    ) %>%
    mutate(agency_name = str_to_title(tif_agency_name))

missing_dis_school <- get_missing_dis(shp_bnd_all_schools, "School")
missing_dis_others <- get_missing_dis(shp_bnd_all_others, "Miscellaneous")

missing_dis <- c(missing_dis_school, missing_dis_others)

first_school <- shp_bnd_all_schools %>%
    slice_max(shape_area) %>%
    left_join(tax_bill_data, by = join_by(AGENCY == agency_num, agency_name))

other_schools <- shp_bnd_all_schools %>%
    filter(!AGENCY_DESC %in% first_school$AGENCY_DESC)

```

```{r defaults}
column_colors <- brewer.pal(3, "Pastel1")
column_alpha <- 0.5

map_road_alpha <- 0.3
map_road_lwd <- 0.3
map_fg_fill_alpha <- 0.33
map_bg_fill_alpha <- 0.33
map_color_alpha <- 0.5
map_fg_lwd <- 1.1
map_bg_lwd <- 1.05

map_colors <- c(
    "#88CCEE", "#CC6677", "#DDCC77", "#117733", "#332288", "#AA4499",
    "#44AA99", "#999933", "#882255", "#661100", "#6699CC", "#888888"
)
map_names <- c(
    render_pin14, "Cook County",
    shp_bnd_twn_muni$agency_name %>% as.character(),
    shp_bnd_all_schools$agency_name %>% as.character(),
    shp_bnd_all_others$agency_name %>% as.character(),
    shp_bnd_tifs$agency_name %>% as.character()
)
css_names <- c(map_names, "increased", "decreased")
map_names <- factor(map_names, levels = map_names, ordered = TRUE)
map_colors <- name_map_colors(map_colors, map_names)
css_colors <- paste0(map_colors, "44")
css_colors <- c(css_colors, "#ff696144", "#61ffb844")
css_colors <- name_map_colors(css_colors, css_names)
```

## What are property taxes?

Property tax is owed by property owners annually, for both residential and commercial properties. 

These liabilities are decided per `r render_name("property parcel", render_pin14)`, and each parcel is uniquely identifiable with a 14-digit `r render_name("Property Index Number", render_pin14)` or `r render_name("PIN", render_pin14)`

Every `r render_name("PIN", render_pin14)` belongs to multiple tax districts (local government bodies), that need funds for their purposes. 

The Cook County Assessor's Office, the State, and county taxing districts (schools, parks, etc.) work together to calculate fair property taxes for residents.

The property taxes are collected centrally and then distributed to each district

## Breaking the bill down - Your local school district!

One of these districts is your local school district - `r render_name(first_school$agency_name)`! A portion of your taxes is sent to the agency to ensure it can continue operations.


::: {.column-margin}

> Generally, these districts fund the local elementary, middle and high schools, along with community colleges in the area.
>
`r if(!"Board Of Education" == first_school$agency_name) {"<!--"}`
> The `r render_name("Board Of Education")` is partially funded by property taxes and runs the Chicago Public Schools system, a K-12 system in the City of Chicago.
> 
> [Chicago Public Schools, Revenue, 2023](https://www.cps.edu/about/finance/budget/budget-2023/revenue/)
`r if(!"Board Of Education" == first_school$agency_name) {"-->"}`
`r if(!"Chicago Community College District 508" == first_school$agency_name) {"<!--"}`
> `r render_name("Chicago Community College District 508")` is partially funded by property taxes and runs the City Colleges of Chicago, a system of seven accredited colleges across the City of Chicago
> 
> [City Colleges of Chicago, Final Budget, FY 22](https://www.ccc.edu/departments/Documents/Finance%20Documents/FY2022%20Final%20Annual%20Operating%20Budget%20Book.pdf)
`r if(!"Chicago Community College District 508" == first_school$agency_name) {"-->"}`

:::

```{r init-map}
first_school %>%
    map_dist()
```

Now, let's look at the line item of the bill for `r render_name(render_pin14)` in `r year` for `r render_name(first_school$agency_name)` -

```{r increment-tax-bill-1}
tax_bill_data %>%
    filter(agency_num == first_school$AGENCY) %>%
    render_bill(
        c("School")
    ) %>%
    tab_style(
        style = cell_text(weight = "bold"),
        locations = list(
            cells_column_labels(columns = agency_name),
            cells_body(columns = agency_name),
            cells_row_groups()
        )
    ) %>%
    tab_style(
        style = cell_fill(color = css_colors[first_school$agency_name]),
        locations = cells_body()
    )
```

For the purpose of operating `r render_name(first_school$agency_name)`, in `r year`, it needs to collect `r first_school %>% pull(agency_total_ext) %>% label_dol_amt() %>% render_name(first_school$agency_name)` in order to provide services.

```{r increment-tax-bill-1-1}
tax_bill_data %>%
    filter(agency_name == first_school$agency_name) %>%
    render_bill(
        c("School")
    ) %>%
    tab_style(
        style = cell_text(weight = "bold"),
        locations = list(
            cells_column_labels(columns = agency_total_ext),
            cells_body(columns = agency_total_ext)
        )
    ) %>%
    tab_style(
        style = cell_fill(color = css_colors[first_school$agency_name]),
        locations = cells_body()
    )
```

But where can `r render_name(first_school$agency_name)` raise the money from? From all the properties within the district boundaries!  
The total taxable value of all properties falling in `r render_name(first_school$agency_name)` make up its **tax base**. 
For `r year`, this was `r first_school %>% pull(agency_total_eav) %>% label_dol_amt() %>% render_name(first_school$agency_name)`

```{r increment-tax-bill-1-2}
tax_bill_data %>%
    filter(agency_name == first_school$agency_name) %>%
    render_bill(
        c("School")
    ) %>%
    tab_style(
        style = cell_text(weight = "bold"),
        locations = list(
            cells_column_labels(columns = agency_total_eav),
            cells_body(columns = agency_total_eav)
        )
    ) %>%
    tab_style(
        style = cell_fill(color = css_colors[first_school$agency_name]),
        locations = cells_body()
    )
```

The cost of services of `r render_name(first_school$agency_name)` is therefore divided amongst all the property owners, and `r render_name(first_school$agency_name)` sets its agency tax rate equal to: 

$$
\mathrm{{Levy \over Base} = Tax \hspace{0.1cm} rate}
$$

Using real numbers, the tax rate equals: 
$$
\mathrm{`r label_dol_amt(first_school$agency_total_ext)` \over `r label_dol_amt(first_school$agency_total_eav)`} = `r label_p_ltx(first_school$agency_tax_rate)`
$$

```{r increment-tax-bill-1-3}
tax_bill_data %>%
    filter(agency_num == first_school$AGENCY) %>%
    render_bill(
        c("School")
    ) %>%
    tab_style(
        style = cell_text(weight = "bold"),
        locations = list(
            cells_column_labels(columns = agency_tax_rate),
            cells_body(columns = agency_tax_rate)
        )
    ) %>%
    tab_style(
        style = cell_fill(color = css_colors[first_school$agency_name]),
        locations = cells_body()
    )
```

#### How much do you owe `r render_name(first_school$agency_name)`?

As the owner of `r render_name(render_pin14)`, in the year `r year` your property is worth `r render_name(label_dol_amt(pin_details$av*10), render_pin14)`, of that, your taxable amount (or addition to the tax base) is `r render_name(label_dol_amt(pin_details$eav - pin_exe_total), render_pin14)`[^exemptionnote].

The previously calculated `r render_name("rate", first_school$agency_name)` is then applied to your `r render_name("addition to the tax base", render_pin14)`:

$$
(`r label_dol_amt(pin_details$eav - pin_exe_total)` * `r label_p_ltx(first_school$agency_tax_rate)`) = `r label_dol_amt((pin_details$eav - pin_exe_total)*first_school$agency_tax_rate)`
$$

This is the total tax amount you owe to `r render_name(first_school$agency_name)`


[^exemptionnote]:  
    `r if(pin_exe_total == 0) {"<!--"}` 
    This is taking into account the exemptions that your property qualifies for.
    This property qualifies for the following - 

    ```{r exe-det}
    pin_exe_details %>%
        filter(value > 0) %>%
        select(name) %>%
        gt() %>%
        cols_label(
            name = "Exemption Type",
        #    value = "Exemption Amount"
        ) %>%
        #fmt_currency(value) %>%
        fmt(columns = name, fns = exe_clean)
    ```

    The total exemption value is `r render_name(label_dol_amt(pin_exe_total), render_pin14)`
    `r if(pin_exe_total == 0) {"-->"}`
    `r if(pin_exe_total != 0) {"<!--"}`  
    This house qualifies for no exemptions
    `r if(pin_exe_total != 0) {"-->"}`  

```{r increment-tax-bill-1-5}
tax_bill_data %>%
    filter(agency_name == first_school$agency_name) %>%
    render_bill(
        c("School")
    ) %>%
    tab_style(
        style = cell_text(weight = "bold"),
        locations = list(
            cells_column_labels(columns = tax_amt_post_exe),
            cells_body(columns = tax_amt_post_exe)
        )
    ) %>%
    tab_style(
        style = cell_fill(color = css_colors[first_school$agency_name]),
        locations = cells_body()
    )
```

That was the whole first line!

### Other School Districts

Now, the PIN, `r render_name(render_pin14)` also has other school districts that need their own funds, these are as follows -

::: {.column-margin}

`r if(!"Board Of Education" %in% other_schools$AGENCY_DESC) {"<!--"}`
> The `r render_name("Board Of Education")` is partially funded by property taxes and runs the Chicago Public Schools system, a K-12 system in the City of Chicago.
> 
> [Chicago Public Schools, Revenue, 2023](https://www.cps.edu/about/finance/budget/budget-2023/revenue/)
`r if(!"Board Of Education" %in% other_schools$AGENCY_DESC) {"-->"}`
`r if(!"Chicago Community College District 508" %in% other_schools$AGENCY_DESC) {"<!--"}`
> `r render_name("Chicago Community College District 508")` is partially funded by property taxes and runs the City Colleges of Chicago, a system of seven accredited colleges across the City of Chicago
> 
> [City Colleges of Chicago, Final Budget, FY 22](https://www.ccc.edu/departments/Documents/Finance%20Documents/FY2022%20Final%20Annual%20Operating%20Budget%20Book.pdf)
`r if(!"Chicago Community College District 508" %in% other_schools$AGENCY_DESC) {"-->"}`

:::

```{r school-map-indi}
plt_lst <- list()
for (i in 1:nrow(other_schools)) {
    plt_lst[[i]] <- map_dist(other_schools[i, ])
}

Reduce("+", plt_lst) +
    plot_layout(guides = "collect") &
    theme(legend.position = "bottom")
```

These school districts also add their own line items to the bill - 

```{r increment-tax-bill-5}
tax_bill_data %>%
    filter(
        agency_major_type == "School"
    ) %>%
    render_bill(
        c("School")
    ) %>%
    tab_style(
        style = cell_text(weight = "bold"),
        locations = cells_body(rows = agency_name %in% other_schools$AGENCY_DESC)
    ) %>%
    data_color(
        method = "factor",
        rows = agency_major_type == "School",
        columns = agency_name,
        target_columns = everything(),
        fn = css_get_colors,
        autocolor_text = FALSE
    )
```


But school districts aren't the only local government you must pay taxes to...

### The Township and the Municipality

Illinois is a state that also has a sub-county level of local government known as the township. This could be distinct from the municipality of your parcel!

::: {.column-margin}

`r if(!"CITY OF CHICAGO" %in% tax_bill_data$agency_name) {"<!--"}`
> The `r render_twn_muni()` uses the property tax levy for - 
> 
>  - Servicing long term debt (partially)
>  - The Annuity and Benefit Funds for the police, firefighters, municipal employees, and laborers and retirement board employees (partially)
> 
> [City of Chicago, Budget Overview, 2023](https://www.chicago.gov/content/dam/city/depts/obm/supp_info/2023Budget/2023-OVERVIEW.pdf)
`r if(!"CITY OF CHICAGO" %in% tax_bill_data$agency_name) {"-->"}`

`r if("CITY OF CHICAGO" %in% tax_bill_data$agency_name) {"<!--"}`
>  `r render_twn_muni()` could use these funds for generally running city services including water, police, street maintenance etc.
`r if("CITY OF CHICAGO" %in% tax_bill_data$agency_name) {"-->"}`

:::

`r render_name(render_pin14)` belongs to the following - 
```{r town-muni-data}
tax_bill_data %>%
    filter(agency_minor_type %in% c("MUNI", "TOWNSHIP")) %>%
    select(agency_minor_type, agency_name) %>%
    mutate(agency_minor_type = ifelse(agency_minor_type == "MUNI", "MUNICIPALITY", agency_minor_type)) %>%
    gt() %>%
    cols_label(
        agency_minor_type = "Local Government Level",
        agency_name = "Taxing Agency Name"
    ) %>%
    fmt(c(agency_name, agency_minor_type), fns = str_to_title) %>%
    data_color(
        method = "factor",
        columns = agency_name,
        target_columns = everything(),
        fn = css_get_colors,
        apply_to = "fill",
        autocolor_text = FALSE
    )
```

These are often named quite similarly, and are even coterminous in a few cases.

Let's see boundaries of `r render_twn_muni()`, and where the parcel falls in them!

```{r town-muni-map}
plt_lst <- list()
for (i in 1:nrow(shp_bnd_twn_muni)) {
    plt_lst[[i]] <- map_dist(shp_bnd_twn_muni[i, ])
}

Reduce("+", plt_lst) +
    plot_layout(guides = "collect") &
    theme(legend.position = "bottom")
```

These add other line items to your tax bill...

```{r increment-tax-bill-3}
tax_bill_data %>%
    filter(
        agency_major_type == "School" |
            agency_minor_type == "MUNI" |
            agency_minor_type == "TOWNSHIP"
    ) %>%
    render_bill(
        c("School", "Municipality/Township")
    ) %>%
    tab_style(
        style = cell_text(weight = "bold"),
        locations = cells_body(rows = agency_major_type != "School")
    ) %>%
    data_color(
        method = "factor",
        rows = agency_major_type != "School",
        columns = agency_name,
        target_columns = everything(),
        fn = css_get_colors,
        autocolor_text = FALSE
    )

```

Within `r render_twn_muni()`, there are also other districts that your property belongs to. These are run by the respective agency but collect tax separately. These are as follows -

```{r increment-tax-bill-4, echo=F}
tax_bill_data %>%
    filter(
        agency_major_type %in% c("School", "Municipality/Township")
    ) %>%
    render_bill(
        c("School", "Municipality/Township")
    ) %>%
    tab_style(
        style = cell_text(weight = "bold"),
        locations = cells_body(
            rows =
                (agency_major_type != "School") &
                    !(agency_name %in% (tax_bill_data %>%
                        filter(agency_minor_type %in% c("MUNI", "TOWNSHIP")) %>%
                        pull(agency_name))
                    )
        )
    ) %>%
    data_color(
        method = "factor",
        rows = (agency_major_type != "School") &
            !(agency_name %in% (tax_bill_data %>%
                filter(agency_minor_type %in% c("MUNI", "TOWNSHIP")) %>%
                pull(agency_name))
            ),
        columns = agency_name,
        target_columns = everything(),
        fn = css_get_colors,
        autocolor_text = FALSE
    )
```

::: {.column-margin}

`r if(!"City Of Chicago" %in% tax_bill_data$agency_name) {"<!--"}`
> Here, there is a more direct funding of the Chicago Public Library System through the City of Chicago Library Fund, and of the Chicago Public School System through the City Of Chicago School Building & Improvement Fund.
`r if(!"City Of Chicago" %in% tax_bill_data$agency_name) {"-->"}`

`r if("City Of Chicago" %in% tax_bill_data$agency_name) {"<!--"}`
> Here, there is a more direct funding of other systems in a municipality like its libraries, roads, etc.
`r if("City Of Chicago" %in% tax_bill_data$agency_name) {"-->"}`

:::

Similarly, there are also other special purpose tax districts as follows - 

### Special districts 

Similarly, there are a few other miscellaneous districts

```{r others-map-indi}
plt_lst <- list()
for (i in 1:nrow(shp_bnd_all_others)) {
    plt_lst[[i]] <- map_dist(shp_bnd_all_others[i, ])
}

Reduce("+", plt_lst) +
    plot_layout(guides = "collect") &
    theme(legend.position = "bottom")
```

All of these also add their own line items to the tax bill - 

```{r increment-tax-bill-6}
tax_bill_data %>%
    filter(agency_major_type != "Cook County") %>%
    render_bill(
        c("School", "Municipality/Township", "Miscellaneous")
    ) %>%
    tab_style(
        style = cell_text(weight = "bold"),
        locations = cells_body(rows = !(agency_major_type %in% c("Municipality/Township", "School")))
    ) %>%
    data_color(
        method = "factor",
        rows = !(agency_major_type %in% c("Municipality/Township", "School")),
        columns = agency_name,
        target_columns = everything(),
        fn = css_get_colors,
        autocolor_text = FALSE
    )
```

::: {.column-margin}

`r if(!"Metro Water Reclamation District Of Greater Chicago" %in% tax_bill_data$agency_name) {"<!--"}`
> The `r render_name("Metro Water Reclamation District Of Greater Chicago")` is funded substantially by property taxes, and it uses the funds towards sewage, wastewater, and stormwater management. It maintains the "TARP" underneath Chicago, and reversed the flow of the rivers away from Lake Michigan.
`r if(!"Metro Water Reclamation District Of Greater Chicago" %in% tax_bill_data$agency_name) {"-->"}`
`r if(!"Chicago Park District" %in% tax_bill_data$agency_name) {"<!--"}`
> The `r render_name("Chicago Park District")` is also substantially funded by property taxes. Along with running all Chicago Park District parks across the City of Chicago, it further funds the operations of -
> 
>  - The maintenance and upkeep of Soldier Field and, Northerly Island
>  - Remittance to Lincoln Park Zoo, and Shedd Aquarium
>  - Remittance to museums located in CPD land including Adler Planetarium, The Art Institute of Chicago, Chicago History Museum, DuSable Museum of African American History, The Field Museum, Museum of Contemporary Art, and Museum of Science and Industry amongst others
>   - The Garfield Park Conservatory Alliance 
>   - The management of MLK Center
`r if(!"Chicago Park District" %in% tax_bill_data$agency_name) {"-->"}`

:::

::: {.column-margin}

> As of FY23, `r render_name(cook_county_max_ext$agency_name,"Cook County")` uses this levy to entirely fund - 
> 
>  - The Election Fund - This in turn funds the Board of Election Commissioners (wholly) and the Cook County Clerk (partially)
>  - The Debt Service Fund - This in turn funds Bond Principal and Interest payments (wholly)
> 
> As of FY23, `r render_name(cook_county_max_ext$agency_name,"Cook County")` also uses property taxes to partially fund - 
> 
>  - General Fund - This in turn pays for the day-to-day operations of the Cook County along with funding the Court System, the Department of Corrections, and the Sheriff Operations
>  - Health Enterprise Fund - This in turn funds Cook County Health
>  - The Capital Projects Fund
>  - Annuity & Benefits Fund
> 
> [Cook County Annual Appropriation, FY23](https://www.cookcountyil.gov/sites/g/files/ywwepo161/files/documents/2023-01/Volume%20I%20-%20Budget%20Overview%20FY23%20Annual%20Appropriation.pdf)

:::

### Cook County tax district

Similarly, `r render_name("Cook County")` is one of the local government bodies that `r render_name(render_pin14)` falls in, with it's own set of tax districts. 

```{r init-map-old}
pin_bbox <- st_bbox(shp_bnd_pin)
pin_xlim <- c(pin_bbox$xmin - 0.02, pin_bbox$xmax + 0.02)
pin_ylim <- c(pin_bbox$ymin - 0.015, pin_bbox$ymax + 0.015)

road_map <- ggplot() +
    geom_sf(data = cook_roads, color = alpha("#888888", map_road_alpha), lwd = map_road_lwd)

main_map <- road_map +
    geom_sf(
        data = shp_bnd_cook,
        aes(fill = "Cook County", color = "Cook County"),
        alpha = map_fg_fill_alpha, lwd = map_fg_lwd
    ) +
    geom_rect(
        aes(xmin = pin_xlim[1], xmax = pin_xlim[2], ymin = pin_ylim[1], ymax = pin_ylim[2]),
        color = map_colors[1], fill = NA, lwd = 1.2
    ) +
    geom_sf(
        data = shp_buf_pin,
        alpha = 1,
        lwd = map_fg_lwd,
        fill = "white", color = "white"
    ) +
    geom_sf(
        data = shp_bnd_pin,
        alpha = map_fg_fill_alpha,
        lwd = map_fg_lwd,
        aes(fill = render_pin14, color = render_pin14)
    ) +
    scale_fill_manual(values = map_colors, name = "Extent", drop = FALSE, breaks = map_names) +
    scale_color_manual(values = alpha(map_colors, map_color_alpha), name = "Extent", drop = FALSE, breaks = map_names) +
    theme_void() +
    theme(
        plot.margin = margin(0, 1, 0, 1, unit = "cm"),
        legend.position = c(0.1, 0.6)
    )

inset_map <- main_map +
    geom_sf(data = cook_roads, color = alpha("#888888", map_road_alpha), lwd = map_road_lwd + 0.2) +
    coord_sf(xlim = pin_xlim, ylim = pin_ylim, expand = FALSE) +
    theme_void() +
    guides(colour = "none", fill = "none", alpha = "none")

main_map +
    inset_element(inset_map, -0.1, 0, 0.37, 0.4, align_to = "full")
```

Now, `r render_name("Cook County")` and its tax districts add the following lines to the tax bill, resulting in the complete bill! -

```{r increment-tax-bill-2}
tax_bill_data %>%
    render_bill(
        c("School", "Municipality/Township", "Miscellaneous", "Cook County")
    ) %>%
    tab_style(
        style = cell_text(weight = "bold"),
        locations = cells_body(rows = (agency_major_type %in% c("Cook County")))
    ) %>%
    data_color(
        method = "factor",
        rows = (agency_major_type %in% c("Cook County")),
        columns = agency_name,
        target_columns = everything(),
        fn = css_get_colors,
        autocolor_text = FALSE
    )
```

::: {.column-margin}

> The `r render_name("Forest Preserve District Of Cook County", "Cook County")`, mostly funded by property taxes, further funds, amongst other things, the following -  
> 
>  - The Chicago Zoological Society (Brookefield Zoo)
>  - The Chicago Horticulture Society (Chicago Botanic Garden)
> 
> [FPCC Budget Appropriation, FY23](https://fpdcc.com/downloads/budget/2023/FPCC-2023-Budget-Appropriation-Ordinance-012523.pdf)

:::

`r if(!isTRUE(nchar(tif_agency_nums) > 0)) {"<!--"}`

## TIFs!

TIFs or Tax Increment Financing districts are special vehicles that redirect your funding towards other purposes

Your parcel - `r render_name(render_pin14)` - belongs to the following TIFs - 

```{r tif-data}
tax_bill_data %>%
    select(tif_agency_name, tif_share) %>%
    unique() %>%
    gt() %>%
    cols_label(
        tif_agency_name = "TIF Name",
        tif_share = "Share of TIF"
    ) %>%
    fmt(tif_agency_name, fns = str_to_title) %>%
    fmt_percent(tif_share) %>%
    data_color(
        method = "factor",
        columns = tif_agency_name,
        target_columns = everything(),
        fn = css_get_colors,
        apply_to = "fill",
        autocolor_text = FALSE
    )

```

```{r tif-map-indi, eval = isTRUE(nchar(tif_agency_nums) > 0)}
plt_lst <- list()
for (i in 1:nrow(shp_bnd_tifs)) {
    plt_lst[[i]] <- map_dist(shp_bnd_tifs[i, ])
}

Reduce("+", plt_lst) +
    plot_layout(guides = "collect") &
    theme(legend.position = "bottom")
```

The way TIFs work is by funnelling a share of all your (post-exemption) taxes to the special purpose of the TIF.  

The share of the TIF differs every year. It works by freezing the tax base of the Municipality to the year that the TIF was enacted, and any increase in the Tax Base is redirected to the TIF.  

This means that for `r render_name(cook_county_max_ext$agency_name, "Cook County")`, the following amount is siphoned off to the TIF 

$$
`r cook_county_max_ext$agency_tax_amount` * `r label_p_ltx(cook_county_max_ext$tif_share)` = `r label_dol_amt(sum(cook_county_max_ext$final_tax_to_tif + cook_county_max_ext$rpm_tif_to_dist))`
$$

Similarly, each tax district contributes - 

```{r tif-bill-1, eval = isTRUE(nchar(tif_agency_nums) > 0)}
tax_bill_data %>%
    mutate(final_tif_tax = final_tax_to_tif + rpm_tif_to_dist) %>%
    mutate(final_dist_tax = tax_amt_post_exe - final_tif_tax) %>%
    select(agency_name, tax_amt_post_exe, tif_share, final_tif_tax, final_dist_tax, agency_major_type) %>%
    group_by(agency_major_type) %>%
    gt() %>%
    cols_label(
        agency_name = "Tax District",
        tax_amt_post_exe = "Final Tax Owed",
        tif_share = "TIF Share",
        final_tif_tax = "Amount directed to TIF",
        final_dist_tax = "Amount directed to Agency"
    ) %>%
    row_group_order(c("Cook County", "Municipality/Township", "School", "Miscellaneous")) %>%
    fmt(agency_name, fns = str_to_title) %>%
    fmt_currency(c(final_tif_tax, final_dist_tax, tax_amt_post_exe)) %>%
    fmt_percent(tif_share) %>%
    tab_options(row_group.as_column = T) %>%
    grand_summary_rows(
        fns = list(
            label = "Amount directed to TIF",
            id = "sum",
            fn = "sum"
        ),
        columns = final_tif_tax,
        fmt = ~ fmt_currency(., rows = "sum")
    ) %>%
    tab_style(
        style = list(
            cell_fill(color = css_colors[shp_bnd_tifs$agency_name])
        ),
        locations = list(
            cells_body(columns = final_tif_tax),
            cells_column_labels(columns = final_tif_tax),
            cells_grand_summary(columns = final_tif_tax)
        )
    ) %>%
    tab_style(
        style = list(
            cell_text(weight = "bold")
        ),
        locations = list(
            cells_body(columns = final_dist_tax),
            cells_column_labels(columns = final_dist_tax),
            cells_stub_grand_summary(),
            cells_grand_summary(columns = final_tif_tax)
        )
    )
```

The total property tax diverted to the `r render_name(shp_bnd_tifs$agency_name)` is `r render_name(label_d(sum(tax_bill_data$final_tax_to_tif, tax_bill_data$rpm_tif_to_dist)) ,shp_bnd_tifs$agency_name)`

The column **"Amount directed to Agency"** is the share of your total property tax contributions that actually go to said agencies!

`r if(!"030210900" %in% tif_agency_nums) {"<!--"}`
### Special TIF 

The Red-Purple Modernisation TIF is a special one!  

Of it's collections, it redirects some money back to the districts, and a chunk of money to the Chicago Public Schools! 

In our case, of the `r render_name(label_d(sum(tax_bill_data$final_tax_to_tif, tax_bill_data$rpm_tif_to_dist)) ,shp_bnd_tifs$agency_name)` directed to the TIF -  

 - `r render_name(label_d(sum(tax_bill_data$rpm_tif_to_rpm)), shp_bnd_tifs$agency_name)` - To the `r render_name(shp_bnd_tifs$agency_name)`
 - `r render_name(label_d(sum(tax_bill_data$rpm_tif_to_cps)), "Board Of Education")` - To the `r render_name("Chicago Public Schools", "Board Of Education")`
 - `r label_d(sum(tax_bill_data$rpm_tif_to_dist))` - Back to the districts 

Therefore, the final tax collection for each tax district (adding back the share for the `r render_name("Chicago Public Schools", "Board Of Education")`, and the amount back to the other districts) looks as follows - 

```{r tif-final, eval = isTRUE(nchar(tif_agency_nums) > 0)}
tax_bill_data %>%
    mutate(final_tif_tax = final_tax_to_tif + rpm_tif_to_dist) %>%
    mutate(final_dist_tax = tax_amt_post_exe - final_tif_tax) %>%
    mutate(rpm_tif_to_dist = ifelse(agency_name == "Board Of Education", sum(tax_bill_data$rpm_tif_to_cps), rpm_tif_to_dist)) %>%
    mutate(total_tax_agency = final_dist_tax + rpm_tif_to_dist) %>%
    select(agency_name, final_dist_tax, agency_major_type, rpm_tif_to_dist, total_tax_agency) %>%
    group_by(agency_major_type) %>%
    gt() %>%
    cols_label(
        agency_name = "Tax District",
        final_dist_tax = "Amount directed to Agency",
        rpm_tif_to_dist = "TIF back to Agency",
        # rpm_tif_back = "TIF to CPS",
        total_tax_agency = "Total amount to Agency"
    ) %>%
    row_group_order(c("Cook County", "Municipality/Township", "School", "Miscellaneous")) %>%
    fmt(agency_name, fns = str_to_title) %>%
    fmt_currency(c(final_dist_tax, rpm_tif_to_dist, total_tax_agency)) %>%
    tab_options(row_group.as_column = T) %>%
    tab_style(
        style = list(
            cell_text(weight = "bold")
        ),
        locations = list(
            cells_body(columns = total_tax_agency),
            cells_column_labels(columns = total_tax_agency)
        )
    )
```

`r if((!isTRUE(nchar(tif_agency_nums) > 0)) || (!"030210900" %in% tif_agency_nums)) {"-->"}`


## Final Tax Bill

This is your final, total tax bill for `r render_name(render_pin14)` for the year `r year`

```{r}
tax_bill_data %>%
    replace(is.na(.), 0) %>%
    mutate(final_tax = ifelse(agency_name == "Board Of Education", final_tax_to_dist + sum(tax_bill_data$rpm_tif_to_cps), final_tax_to_dist)) %>%
    add_row(
        agency_major_type = "TIF",
        agency_name = tax_bill_data %>%
            select(tif_agency_name) %>%
            unique() %>% pull(),
        final_tax = ifelse(sum(tax_bill_data$rpm_tif_to_rpm) > 0, sum(tax_bill_data$rpm_tif_to_rpm), sum(tax_bill_data$final_tax_to_tif))
    ) %>%
    filter(final_tax > 0) %>%
    select(agency_major_type, agency_name, final_tax) %>%
    group_by(agency_major_type) %>%
    gt() %>%
    grand_summary_rows(
        fns = list(
            label = "Total Tax Owed",
            id = "sum",
            fn = "sum"
        ),
        columns = final_tax,
        fmt = ~ fmt_currency(., rows = "sum")
    ) %>%
    cols_label(
        agency_name = "Tax District",
        final_tax = "Tax amount"
    ) %>%
    fmt_currency(final_tax) %>%
    data_color(
        method = "factor",
        columns = agency_name,
        target_columns = everything(),
        fn = css_get_colors,
        apply_to = "fill",
        autocolor_text = FALSE
    ) %>%
    tab_style(
        style = list(
            cell_text(weight = "bold")
        ),
        locations = list(
            cells_stub_grand_summary(),
            cells_grand_summary()
        )
    ) %>%
    tab_options(row_group.as_column = T)
```

`r if(length(missing_dis) > 0) {paste0("_NOTE: The following districts were not shown on the maps above - ", glue("{paste(str_to_title(missing_dis), collapse = '; ')}"), "_")}`

## Prior Tax Bill Comparison

Compared to this, your final, total tax bill, for `r render_name(render_pin14)` for the year `r year-1` looks as follows 

```{r}
tax_bill_data_prior_year %>%
    replace(is.na(.), 0) %>%
    mutate(final_tax = ifelse(agency_name == "Board Of Education", final_tax_to_dist + sum(tax_bill_data_prior_year$rpm_tif_to_cps), final_tax_to_dist)) %>%
    add_row(
        agency_major_type = "TIF",
        agency_name = tax_bill_data_prior_year %>%
            select(tif_agency_name) %>%
            unique() %>% pull(),
        final_tax = ifelse(sum(tax_bill_data_prior_year$rpm_tif_to_rpm) > 0, sum(tax_bill_data_prior_year$rpm_tif_to_rpm), sum(tax_bill_data_prior_year$final_tax_to_tif))
    ) %>%
    filter(final_tax > 0) %>%
    select(agency_major_type, agency_name, final_tax) %>%
    group_by(agency_major_type) %>%
    gt() %>%
    grand_summary_rows(
        fns = list(
            label = "Total Tax Owed",
            id = "sum",
            fn = "sum"
        ),
        columns = final_tax,
        fmt = ~ fmt_currency(., rows = "sum")
    ) %>%
    cols_label(
        agency_name = "Tax District",
        final_tax = "Tax amount"
    ) %>%
    fmt_currency(final_tax) %>%
    data_color(
        method = "factor",
        columns = agency_name,
        target_columns = everything(),
        fn = css_get_colors,
        apply_to = "fill",
        autocolor_text = FALSE
    ) %>%
    tab_style(
        style = list(
            cell_text(weight = "bold")
        ),
        locations = list(
            cells_stub_grand_summary(),
            cells_grand_summary()
        )
    ) %>%
    tab_options(row_group.as_column = T)
```

```{r}
if (sum(tax_bill_data$tax_amt_post_exe) > sum(tax_bill_data_prior_year$tax_amt_post_exe)) {
    bill_inc <- TRUE
    bill_change_text <- "increased"
} else {
    bill_inc <- FALSE
    bill_change_text <- "decreased"
}

```

That is, your tax bill `r {render_name(bill_change_text)}` by `r (sum(tax_bill_data$tax_amt_post_exe) - sum(tax_bill_data_prior_year$tax_amt_post_exe)) %>% abs() %>% label_dol_amt() %>% render_name(bill_change_text)` from `r prior_year` to `r year`

Given what we now know about how property taxes are calculated, let us break out the change into the a few different sections.

### Change due to change in the EAV

```{r}
if (pin_details$eav > pin_details_prior_year$eav) {
    eav_inc <- TRUE
    eav_change_text <- "increased"
} else if (pin_details$eav < pin_details_prior_year$eav) {
    eav_inc <- FALSE
    eav_change_text <- "decreased"
} else {
    eav_change_text <- "stayed the same"
}

```

For `r render_name(render_pin14)`, the EAV in **`r prior_year`** was `r pin_details_prior_year %>% pull(eav) %>% label_dol_amt() %>% render_name(render_pin14)`, and the EAV in **`r year`** was `r pin_details %>% pull(eav) %>% label_dol_amt() %>% render_name(render_pin14)`.

That is, the EAV `r {render_name(eav_change_text)}` by `r (pin_details_prior_year$eav - pin_details$eav) %>% abs() %>% label_dol_amt() %>% render_name(eav_change_text)`, or about `r ((pin_details_prior_year$eav - pin_details$eav) %>% abs() / pin_details$eav) %>% label_p_ltx() %>% render_name(eav_change_text)`

Due to just the change in EAV, the tax bill amount also changes! If the EAV from **`r year`**, `r pin_details %>% pull(eav) %>% label_dol_amt() %>% render_name(render_pin14)` was instead applied to the tax bill for **`r prior_year`**, the new tax bill would look as follows -- 

```{r}
if (sum(tax_bill_data_prior_year$tax_amt_post_exe) < sum(tax_bill_data_current_eav_prior_levy$tax_amt_post_exe)) {
    tax_amt_inc <- TRUE
    tax_amt_change_text <- "increased"
} else {
    tax_amt_inc <- FALSE
    tax_amt_change_text <- "decreased"
}

display_two_bills(tax_bill_data_prior_year, tax_bill_data_current_eav_prior_levy, "Tax amount (prior year)", "Tax amount (prior year, current EAV)")
```


That is, the total tax bill would have `r render_name(tax_amt_change_text)` from **`r sum(tax_bill_data_prior_year$tax_amt_post_exe) %>% label_dol_amt`** to **`r sum(tax_bill_data_current_eav_prior_levy$tax_amt_post_exe) %>% label_dol_amt`**. A change of `r abs(sum(tax_bill_data_current_eav_prior_levy$tax_amt_post_exe) - sum(tax_bill_data_prior_year$tax_amt_post_exe)) %>% label_dol_amt %>% render_name(tax_amt_change_text)` or `r (abs(sum(tax_bill_data_current_eav_prior_levy$tax_amt_post_exe) - sum(tax_bill_data_prior_year$tax_amt_post_exe)) / sum(tax_bill_data_prior_year$tax_amt_post_exe)) %>% label_p_ltx %>% render_name(tax_amt_change_text)` due to the change in your PIN's EAV.  The reason this change is not the same as the change in the EAV is due to the other PINs being reassessed and their EAV changing as well, leading to a different load on your PIN!

### Change due to change in levy

Now, let's instead focus on the levy being changed!  

Assuming your PIN's EAV stayed the same as in `r prior_year`, at `r pin_details_prior_year %>% pull(eav) %>% label_dol_amt() %>% render_name(render_pin14)`, but instead of last year's levies, the agencies had this year's levies, what would the total bill look like?

```{r}
if (sum(tax_bill_data_prior_year$tax_amt_post_exe) < sum(tax_bill_data_prior_eav_current_levy$tax_amt_post_exe)) {
    tax_amt_inc <- TRUE
    tax_amt_change_text <- "increased"
} else {
    tax_amt_inc <- FALSE
    tax_amt_change_text <- "decreased"
}

display_two_bills(tax_bill_data_prior_year, tax_bill_data_prior_eav_current_levy, "Tax amount (prior year)", "Tax amount (current year, prior EAV)")
```


That is, the total tax bill would have `r render_name(tax_amt_change_text)` from **`r sum(tax_bill_data_prior_year$tax_amt_post_exe) %>% label_dol_amt`** to **`r sum(tax_bill_data_prior_eav_current_levy$tax_amt_post_exe) %>% label_dol_amt`**. A change of `r abs(sum(tax_bill_data_prior_eav_current_levy$tax_amt_post_exe) - sum(tax_bill_data_prior_year$tax_amt_post_exe)) %>% label_dol_amt %>% render_name(tax_amt_change_text)` or `r (abs(sum(tax_bill_data_prior_eav_current_levy$tax_amt_post_exe) - sum(tax_bill_data_prior_year$tax_amt_post_exe)) / sum(tax_bill_data_prior_year$tax_amt_post_exe)) %>% label_p_ltx %>% render_name(tax_amt_change_text)` due to the change in your taxing districts' levies. 

### Putting the increases together

Therefore, we can now see that the summary of the tax bills look as follows - 

```{r}
tibble(
    Category = c("Prior Levy", "Current Levy"),
    Prior_EAV = c(
        sum(tax_bill_data_prior_year$tax_amt_post_exe) %>% label_dol_amt(),
        sum(tax_bill_data_prior_eav_current_levy$tax_amt_post_exe) %>% label_dol_amt()
    ),
    Current_EAV = c(
        sum(tax_bill_data_current_eav_prior_levy$tax_amt_post_exe) %>% label_dol_amt(),
        sum(tax_bill_data$tax_amt_post_exe) %>% label_dol_amt()
    )
) %>%
    gt() %>%
    cols_label(
        Category = "",
        Prior_EAV = "Prior EAV",
        Current_EAV = "Current EAV"
    ) %>%
    tab_style(
        style = list(
            cell_text(weight = "bold"),
            cell_fill(color = css_colors[render_pin14])
        ),
        locations = cells_body(
            columns = c(2),
            rows = c(1)
        )
    ) %>%
    tab_style(
        style = list(
            cell_text(weight = "bold"),
            cell_fill(color = css_colors[render_pin14])
        ),
        locations = cells_body(
            columns = c(3),
            rows = c(2)
        )
    )
```

If we look at only the changes from the prior bill, we can see the following --

```{r}
inc_table <- tibble(
    Category = c("Prior Levy", "Current Levy"),
    Prior_EAV = c(
        NA,
        (sum(tax_bill_data_prior_eav_current_levy$tax_amt_post_exe) - sum(tax_bill_data_prior_year$tax_amt_post_exe))
    ),
    Current_EAV = c(
        (sum(tax_bill_data_current_eav_prior_levy$tax_amt_post_exe) - sum(tax_bill_data_prior_year$tax_amt_post_exe)),
        (sum(tax_bill_data$tax_amt_post_exe) - sum(tax_bill_data_prior_year$tax_amt_post_exe))
    )
)

inc_table %>%
    gt() %>%
    cols_label(
        Category = "",
        Prior_EAV = "Prior EAV",
        Current_EAV = "Current EAV"
    ) %>%
    tab_style(
        style = list(
            cell_text(weight = "bold"),
            cell_fill(color = css_colors[render_pin14])
        ),
        locations = cells_body(
            columns = c(3),
            rows = c(2)
        )
    ) %>%
    tab_style(
        style = list(
            cell_text(style = "italic")
        ),
        locations = cells_body(
            columns = c(2),
            rows = c(2)
        )
    ) %>%
    tab_style(
        style = list(
            cell_text(style = "italic")
        ),
        locations = cells_body(
            columns = c(3),
            rows = c(1)
        )
    ) %>%
    fmt_currency(columns = c(2,3)) %>%
    fmt_missing(missing_text = "-")
```

That is, of the total `r inc_table[[2, 3]] %>% label_dol_amt() %>% render_name(render_pin14)` change in the property tax bill from last year, `r inc_table[[1, 3]] %>% label_dol_amt() %>% render_name(render_pin14)` can be attributed to a change in the PIN's EAV and  `r inc_table[[2, 2]] %>% label_dol_amt() %>% render_name(render_pin14)` can be attributed to a change in the taxing district's levies. The remaining  `r (inc_table[[2, 3]] - inc_table[[2, 2]] - inc_table[[1, 3]]) %>% label_dol_amt() %>% render_name(render_pin14)` is due to a combination of the two changes.

`r Sys.getenv("EXPLAINER_VERSION")`