Albany Park 311

R Markdown

Loading Packages

library(tidyverse)

## Warning: package 'ggplot2' was built under R version 4.3.1

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## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.1
## ✔ purrr     1.0.1     
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library(janitor)

## 
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library(lubridate)

Importing Data

setwd("~/Library/CloudStorage/OneDrive-Personal/Graduate School/2 - Year Two/3 - Spring - Housing and Social Impact Lab/HSI CMHDC Team OneDrive")

albany_311 = read.csv("311 Service Requests.csv")
albany_311 = janitor::clean_names(albany_311)

Tidying Data

albany_311 = albany_311 %>%
  mutate(street_address = toupper(street_address)) |>
  relocate(street_address, .before = sr_number) |>
  mutate(
    dt_created = mdy_hms(created_date),
    dt_last_modified = mdy_hms(last_modified_date),
    dt_closed = mdy_hms(closed_date),
    date_created = as.Date(dt_created),
    time_created = format(dt_created, "%H:%M:%S"),
    date_last_modified = as.Date(dt_last_modified),
    time_last_modified = format(dt_last_modified, "%H:%M:%S"),
    date_closed = as.Date(dt_closed),
    time_closed = format(dt_closed, "%H:%M:%S")
  ) %>%
  select(-dt_created, -dt_last_modified, -dt_closed) |>
  relocate(starts_with("date"), .after = street_address)

albany_311 |>
  group_by(street_address) |>
  tally() |>
  arrange(desc(n))

albany_311 |>
  group_by(zip_code) |>
  tally() |>
  arrange(desc(n))

albany_311 |>
  filter(street_number == 3104 &
           street_name == "AINSLIE") |>
  arrange(date_created)

albany_311 |>
  group_by(street_address, sr_type) |>
  tally() |>
  arrange(desc(n))

Uploading the Addresses in Simplified Format

library(dplyr)
library(lubridate)
library(readxl)
library(forcats)
library(scales)

## Warning: package 'scales' was built under R version 4.3.1

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
## 
##     discard

## The following object is masked from 'package:readr':
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##     col_factor

# Replace "your_file.xlsx" with actual file name/path
dirty_data1 <- read_excel("Master Rental Property List2.xlsx", sheet = "Simple Address")

properties = dirty_data1 |>
  mutate(
    # Convert dates (assuming MMDDYYYY format - adjust if different)
    buy_date = ymd(unit_buy_date),
    
    # Convert to factors
    unit_ID = as.factor(unit_ID),
    ZIP_code = as.factor(ZIP_code),
    region = as.factor(region),
    
    # Convert to dollars
    unit_buy_usd = dollar(unit_buy_price),
    
    # Convert entity_* columns to factors
    across(starts_with("entity"), as.factor),
    
    # Convert albany_* columns to logical
    across(starts_with("albany"), as.logical)
  ) |>
  relocate(unit_buy_usd, .after = unit_buy_price)

properties |>
  sample_n(5)

A Quick Summary of the Properties Data

summary(properties)

##  street_address        unit_ID    street_number      street_direction  
##  Length:225         P      : 27   Length:225         Length:225        
##  Class :character   C      : 24   Class :character   Class :character  
##  Mode  :character   E      : 24   Mode  :character   Mode  :character  
##                     S      : 15                                        
##                     Z      : 15                                        
##                     M      : 10                                        
##                     (Other):110                                        
##  street_name        street_type            city              state          
##  Length:225         Length:225         Length:225         Length:225        
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##     ZIP_code           region    unit_buy_price    unit_buy_usd      
##  60625  :36   city_north  :141   Min.   :      0   Length:225        
##  60647  :30   city_south  : 67   1st Qu.: 450000   Class :character  
##  60639  :29   suburb_north:  8   Median :1480000   Mode  :character  
##  60615  :28   suburb_west :  9   Mean   :1594628                     
##  60645  :25                      3rd Qu.:2100000                     
##  60620  :18                      Max.   :5100000                     
##  (Other):59                                                          
##  unit_buy_date                   unit_units                   entity_owner
##  Min.   :1998-12-01 00:00:00   Min.   : 2.00   CMHDC Properties, LLC:86   
##  1st Qu.:2002-08-02 00:00:00   1st Qu.:10.00   CMHDC                :40   
##  Median :2004-01-15 00:00:00   Median :20.00   Diversey Manor, LLC  :24   
##  Mean   :2008-01-04 08:00:00   Mean   :26.08   CDS Cicero NFP LLC   :16   
##  3rd Qu.:2011-10-26 00:00:00   3rd Qu.:42.00   6221 Wabansia, LLC   :15   
##  Max.   :2024-12-31 00:00:00   Max.   :51.00   4848 Wrightwood LLC  : 9   
##                                                (Other)              :35   
##        entity_mortgage         entity_manage albany_llc      albany_park    
##  Bank Financial:106    Kass Management:171   Mode :logical   Mode :logical  
##  Lakeside Bank : 40    Urban Equities : 54   FALSE:213       FALSE:189      
##  CIC           : 25                          TRUE :12        TRUE :36       
##  PNC           : 18                                                         
##  IHDA          : 17                                                         
##  (Other)       : 18                                                         
##  NA's          :  1                                                         
##     buy_date         
##  Min.   :1998-12-01  
##  1st Qu.:2002-08-02  
##  Median :2004-01-15  
##  Mean   :2008-01-04  
##  3rd Qu.:2011-10-26  
##  Max.   :2024-12-31  
## 

As visible above, there are 225 unique addresses across 51 ‘units’.

Exploratory Data Analysis

Data Grouped by Unit

properties |>
  group_by(unit_ID) |>
  summarize(number_unique_addresses = nrow(street_address),
            number_units = median(unit_units, na.rm = T),
            property_price = median(unit_buy_price, na.rm = T),
            property_purchase_date = median(unit_buy_date, na.rm = T)) |>
  arrange(desc(number_units))

Creating a Map of Addresses

Creating a Geocoded Data Frame

library(tidyverse)
# install.packages("tidygeocoder")
library(tidygeocoder) # For geocoding

## Warning: package 'tidygeocoder' was built under R version 4.3.3

# install.packages("sf")
library(sf)           # For spatial data

## Warning: package 'sf' was built under R version 4.3.3

## Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE

# install.packages("tigris")
library(tigris)       # To get county boundaries

## Warning: package 'tigris' was built under R version 4.3.3

## To enable caching of data, set `options(tigris_use_cache = TRUE)`
## in your R script or .Rprofile.

# Geocode addresses (using Census geocoder as a free option)
properties_geo <- properties %>%
  geocode(
    street = street_address,
    city = city,    # Most properties are in Chicago; adjust if needed
    state = state,
    method = "census"
  ) |>
  mutate(
    entity_owner = as.character(entity_owner),
    entity_mortgage = as.character(entity_mortgage),
    entity_manage = as.character(entity_manage)
  )

## Passing 225 addresses to the US Census batch geocoder

## Query completed in: 1.3 seconds

# Check for failed geocodes
properties_geo %>% filter(is.na(lat) | is.na(long))

Get County Boundaries

Download county shapres for the Chicago metro area

counties <- counties(state = "IL", cb = TRUE, year = 2023) %>%
  filter(NAME %in% c("Cook", "Lake", "McHenry", "DuPage", "Will", "Kane")) %>%
  st_transform(4326) # Use WGS 84 coordinate system

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Create the Map

Plot properties on a map with county boundaries:

base_map = ggplot() +
  # Plot county boundaries
  geom_sf(data = counties, fill = "lightgray", color = "darkgray") +
  # Plot CMHDC properties
  geom_point(
    data = properties_geo,
    aes(x = long, y = lat),
    color = "#2A5C7D",
    size = 2,
    alpha = 0.7
  ) +
  # Labels and theme
  labs(
    title = "CMHDC Properties in Cook County and Collar Counties",
    caption = "Data: CMHDC Master Property List"
  ) +
  theme_void() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold"),
    legend.position = "bottom"
  )

base_map

## Warning: Removed 34 rows containing missing values (`geom_point()`).

Interactive Map with leaflet

For zoomable maps with pop-up property info

library(leaflet)

leaflet(properties_geo) %>%
  addTiles() %>%
  addPolygons(
    data = counties,
    color = "#444444",
    weight = 1,
    fillOpacity = 0.1
  ) %>%
  addCircleMarkers(
    lng = ~long,
    lat = ~lat,
    radius = 5,
    color = "#2A5C7D",
    popup = ~paste0(
      "<b>Address:</b> ", street_address, "<br>",
      "<b>Owner:</b> ", ifelse(is.na(entity_owner), "N/A", entity_owner), "<br>",
      "<b>Mortgage:</b> ", ifelse(is.na(entity_mortgage), "N/A", entity_mortgage), "<br>",
      "<b>Manager:</b> ", ifelse(is.na(entity_manage), "N/A", entity_manage), "<br>",
      "<b>Acquired:</b> ", format(unit_buy_date, "%m/%d/%Y"), " ($", 
      prettyNum(unit_buy_price, big.mark = ","), ")<br>",
      "<b>Units:</b> ", unit_units
    )
  )

## Warning in validateCoords(lng, lat, funcName): Data contains 34 rows with
## either missing or invalid lat/lon values and will be ignored

act_map = leaflet(properties_geo) %>%
  addTiles() %>%
  addPolygons(
    data = counties,
    color = "#444444",
    weight = 1,
    fillOpacity = 0.1
  ) %>%
  addCircleMarkers(
    lng = ~long,
    lat = ~lat,
    radius = 5,
    color = "#2A5C7D",
    popup = ~paste0(
      "<b>Address:</b> ", street_address, "<br>",
      "<b>Owner:</b> ", ifelse(is.na(entity_owner), "N/A", entity_owner), "<br>",
      "<b>Mortgage:</b> ", ifelse(is.na(entity_mortgage), "N/A", entity_mortgage), "<br>",
      "<b>Manager:</b> ", ifelse(is.na(entity_manage), "N/A", entity_manage), "<br>",
      "<b>Acquired:</b> ", format(unit_buy_date, "%m/%d/%Y"), " ($", 
      prettyNum(unit_buy_price, big.mark = ","), ")<br>",
      "<b>Units:</b> ", unit_units
    )
  )

## Warning in validateCoords(lng, lat, funcName): Data contains 34 rows with
## either missing or invalid lat/lon values and will be ignored

act_map

Analyzing 311 Call Data in CMHDC Albany Park Properties

albany_311 |>
  group_by(sr_type) |>
  tally() |>
  arrange(desc(n)) |>
  rename(number_of_calls = n)

Checking 311 Call Earliest and Latest Recording Dates

message("Earliest 311 call: ", min(albany_311$date_created))

## Earliest 311 call: 2018-07-01

message("Most recent 311 call: ", max(albany_311$date_created))

## Most recent 311 call: 2025-05-14

message("Earliest Albany Park property purchase date: ",
        min(properties$unit_buy_date[properties$albany_park == T]))

## Earliest Albany Park property purchase date: 2000-05-09

message("Most eecent Albany Park property purchase date: ",
        max(properties$unit_buy_date[properties$albany_park == T]))

## Most eecent Albany Park property purchase date: 2016-12-06

This should prove that all 311 calls from CMHDC’s Albany Park properties came during their time of ownership.

Filter albany_311 to only include addresses from CMHDC’s Albany Park portfolio.

Extract Albany Park Addresses from properties Data

albany_park_addresses = properties |>
  filter(albany_park) |>
  distinct(street_address) |>
  pull(street_address)

albany_park_addresses

##  [1] "3252 W LELAND AVE"   "3253 W LELAND AVE"   "3254 W LELAND AVE"  
##  [4] "3255 W LELAND AVE"   "3256 W LELAND AVE"   "4957 N ALBANY AVE"  
##  [7] "4958 N ALBANY AVE"   "4959 N ALBANY AVE"   "3051 W ARGYLE ST"   
## [10] "3052 W ARGYLE ST"    "3053 W ARGYLE ST"    "3054 W ARGYLE ST"   
## [13] "3055 W ARGYLE ST"    "3056 W ARGYLE ST"    "3057 W ARGYLE ST"   
## [16] "5337 N DAMEN AVE"    "4412 N ST LOUIS AVE" "4413 N ST LOUIS AVE"
## [19] "4414 N ST LOUIS AVE" "4415 N ST LOUIS AVE" "4416 N ST LOUIS AVE"
## [22] "4417 N ST LOUIS AVE" "4418 N ST LOUIS AVE" "4419 N ST LOUIS AVE"
## [25] "4420 N ST LOUIS AVE" "4421 N ST LOUIS AVE" "4422 N ST LOUIS AVE"
## [28] "4423 N ST LOUIS AVE" "4424 N ST LOUIS AVE" "4425 N ST LOUIS AVE"
## [31] "4426 N ST LOUIS AVE" "2244 W FARRAGUT AVE" "2734 W GREGORY ST"  
## [34] "3104 W AINSLIE ST"   "4633 N LAWNDALE AVE" "4948 N HARDING AVE"

Filter 311 Data for These Addresses

albany_311 |>
  filter(toupper(street_address) %in% toupper(albany_park_addresses))

albany_311_filtered = albany_311 |>
  filter(toupper(street_address) %in% toupper(albany_park_addresses))

message(nrow(albany_311_filtered), " 311 requests linked to CMHDC Albany Park properties")

## 141 311 requests linked to CMHDC Albany Park properties

Aggregate by sr_type

albany_311_filtered |>
  count(street_address, sr_type, name = "call_count") |>
  arrange(desc(call_count)) |>
  arrange(street_address)

Addresses that Have Made an Associated 311 Call

albany_311_filtered |>
  count(street_address, name = "call_count") |>
  arrange(desc(call_count)) |>
  arrange(street_address)

message(nrow(albany_311_filtered |>
  count(street_address, name = "call_count") |>
  arrange(desc(call_count)) |>
  arrange(street_address)), " CMHDC Albany Park addresses have made 311 requests linked to said properties")

## 26 CMHDC Albany Park addresses have made 311 requests linked to said properties

Most Frequent Callers within the CMHDC Albany Park Portfolio

albany_311_filtered |>
  count(street_address, name = "call_count") |>
  arrange(street_address) |>
  arrange(desc(call_count))

Addresses that Have Not Made an Associated 311 Call

properties %>%
  filter(albany_park) %>%
  anti_join(albany_311, by = "street_address") %>%
  select(street_address)

message(nrow(properties %>%
  filter(albany_park) %>%
  anti_join(albany_311, by = "street_address") %>%
  select(street_address)), " CMHDC Albany Park addresses have not made any 311 requests in the recording window")

## 10 CMHDC Albany Park addresses have not made any 311 requests in the recording window

Most Common Call Types

albany_311_filtered |>
  count(sr_type, name = "call_count") |>
  arrange(desc(call_count))

Comparing 311 Call Volumes between CMHDC and non-CMHDC Properties

total = nrow(albany_311 |>
  group_by(street_address) |>
  tally() |>
  arrange(n))

total_calls = albany_311 |>
  group_by(street_address) |>
  tally() |>
  summarize(total_calls = sum(n, na.rm = T)) |>
  pull()

CMHDC = nrow(albany_311_filtered |>
  group_by(street_address) |>
  tally() |>
  arrange(n))

CMHDC_calls = albany_311_filtered |>
  group_by(street_address) |>
  tally() |>
  summarize(total_CMHDC_calls = sum(n, na.rm = T)) |>
  pull()

nonCMHDC = total - CMHDC

nonCMHDC_calls = total_calls - CMHDC_calls

Statistical Tests

# Contingency table
contingency_table <- matrix(c(
  CMHDC_calls,  # CMHDC calls
  nonCMHDC_calls,  # Non-CMHDC calls
  CMHDC,   # CMHDC properties
  nonCMHDC  # Non-CMHDC properties
), nrow = 2, byrow = TRUE)

# Two-proportion z-test
prop_test <- prop.test(
  x = contingency_table,
  alternative = "two.sided",
  conf.level = 0.95
)

# Results
contingency_table

##      [,1]  [,2]
## [1,]  141 80272
## [2,]   26 14215

prop_test

## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  contingency_table
## X-squared = 0.0065755, df = 1, p-value = 0.9354
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  -0.0008720132  0.0007274799
## sample estimates:
##      prop 1      prop 2 
## 0.001753448 0.001825714

Adding Overlays from Census Data to Maps

Downloading Data with tidycensus

# install.packages("tidycensus")
library(tidycensus)

## Warning: package 'tidycensus' was built under R version 4.3.3

Setting my Census API Key

census_api_key("33b955bc5b3dd067ac4f38a3ea59304661e08e2d", overwrite = TRUE)

## To install your API key for use in future sessions, run this function with `install = TRUE`.

readRenviron("~/.Renviron")

Downloading Median Household Income by ZIP for Illinois

# Get all ZCTAs nationally, then filter for Illinois
all_zcta_income <- get_acs(
  geography = "zcta",
  variables = "B19013_001",
  year = 2023,
  survey = "acs5"
)

## Getting data from the 2019-2023 5-year ACS

# Filter for Illinois ZCTAs (e.g., 60625, 60630 in Albany Park)
il_zcta_income <- all_zcta_income %>%
  filter(str_sub(GEOID, 1, 2) == "17")  # Illinois FIPS code is 17

il_zcta_income

library(tidycensus)
library(tigris)
library(leaflet)
library(dplyr)
library(sf)

# Set Census API key (if not already done)
# census_api_key("YOUR_KEY", install = TRUE)

# Define variables of interest
acs_vars <- c(
                # Demographic Information
                age_med = "B01002_001",
                pop_total = "B01003_001",
                pop_under18 = "B09001_001",
                race_white = "B02008_001",
                race_black = "B02009_001",
                race_native = "B02010_001",
                race_asian = "B02011_001",
                race_nhpi = "B02012_001",
                race_other = "B02013_001",
                
                # Economic Impact
                income_med = "B19013_001",
                income_wages = "B19052_002",
                income_self = "B19053_002",
                income_public_assistance = "B19058_002",
                poverty_ratio_under50 = "C17002_002",
                poverty_ratio_50to99 = "C17002_003",
                poverty_ratio_over199 = "C17002_008",
                gini = "B19083_001",
                
                # Employment Impact
                labor_employed = "B23025_004",
                labor_unemployed = "B23025_005",
                labor_out_of_force = "B23025_007",
                commute_under5 = "B08303_001",
                commute_5to9 = "B08303_002",
                commute_10to14 = "B08303_003",
                commute_15to19 = "B08303_004",
                commute_20to24 = "B08303_005",
                commute_25to29 = "B08303_006",
                commute_30to34 = "B08303_007",
                commute_35to39 = "B08303_008",
                commute_40to44 = "B08303_009",
                commute_45to59 = "B08303_010",
                commute_60to89 = "B08303_011",
                commute_over89 = "B08303_012",
                
                # Social Impact
                edu_bachelor = "B15003_022",
                
                # Housing Stability
                tot_occupied = "B25002_002",
                tot_vacant = "B25002_003",
                vacant_4rent = "B25004_001",
                vacant_4sale = "B25004_003",
                occupied_owner = "B25003_002",
                occupied_renter = "B25003_003",
                homeownership = "B25003_002",
                median_year_built = "B25035_001",
                median_home_value = "B25077_001",
                rent_burden_30to35 = "B25070_007",
                rent_burden_35to40 = "B25070_008",
                rent_burden_40to49 = "B25070_009",
                rent_burden_over50 = "B25070_010",
                housing_cost_under100 = "B25104_002",
                housing_cost_100to199 = "B25104_003",
                housing_cost_200to299 = "B25104_004",
                housing_cost_300to399 = "B25104_005",
                housing_cost_400to499 = "B25104_006",
                housing_cost_500to599 = "B25104_007",
                housing_cost_600to699 = "B25104_008",
                housing_cost_700to799 = "B25104_009",
                housing_cost_800to899 = "B25104_010",
                housing_cost_900to999 = "B25104_011",
                housing_cost_1000to1499 = "B25104_012",
                housing_cost_1500to1999 = "B25104_013",
                housing_cost_2000to2499 = "B25104_014",
                housing_cost_2500to2999 = "B25104_015",
                housing_cost_over2999 = "B25104_016",
                rent_median_0bd = "B25031_002",
                rent_median_1bd = "B25031_003",
                rent_median_2bd = "B25031_004",
                rent_median_3bd = "B25031_005"
                )

Download ZCTA data for Illinois

il_zcta <- get_acs(
  geography = "zcta",
  variables = acs_vars,
  year = 2021,
  survey = "acs5",
  geometry = TRUE,
  output = "wide"
) %>%
  filter(str_sub(GEOID, 1, 2) %in% c("60", "61", "62")) %>%  # Illinois ZCTAs
  st_transform(4326) |>
  mutate(edu_bachelor_pct = (edu_bachelorE / (pop_totalE - pop_under18E)) * 100,
         rent_burdened = (rent_burden_30to35E + rent_burden_35to40E +
                            rent_burden_40to49E + rent_burden_over50E),
         rent_burdened_pct = (rent_burdened / pop_totalE) * 100,
         poverty_ratio_under50_pct = (poverty_ratio_under50E / pop_totalE) * 100) |>
  relocate(edu_bachelor_pct, .after = edu_bachelorE) |>
  relocate(rent_burdened, rent_burdened_pct, .before = rent_burden_30to35E) |>
  relocate(poverty_ratio_under50_pct, .before = poverty_ratio_under50E)

## Getting data from the 2017-2021 5-year ACS

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Verifying Data Structure

class(il_zcta)  # Should show "sf" and "data.frame"

## [1] "sf"         "data.frame"

st_crs(il_zcta)  # Should show coordinate system (e.g., 4326)

## Coordinate Reference System:
##   User input: EPSG:4326 
##   wkt:
## GEOGCRS["WGS 84",
##     ENSEMBLE["World Geodetic System 1984 ensemble",
##         MEMBER["World Geodetic System 1984 (Transit)"],
##         MEMBER["World Geodetic System 1984 (G730)"],
##         MEMBER["World Geodetic System 1984 (G873)"],
##         MEMBER["World Geodetic System 1984 (G1150)"],
##         MEMBER["World Geodetic System 1984 (G1674)"],
##         MEMBER["World Geodetic System 1984 (G1762)"],
##         MEMBER["World Geodetic System 1984 (G2139)"],
##         MEMBER["World Geodetic System 1984 (G2296)"],
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]],
##         ENSEMBLEACCURACY[2.0]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["geodetic latitude (Lat)",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["geodetic longitude (Lon)",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     USAGE[
##         SCOPE["Horizontal component of 3D system."],
##         AREA["World."],
##         BBOX[-90,-180,90,180]],
##     ID["EPSG",4326]]

il_zcta |>
  sample_n(5)

# Merge with CMHDC property data (albany_park = TRUE/FALSE)
properties_sf <- properties_geo %>%
  filter(!c(is.na(lat) | is.na(long))) |>
  mutate(zip = str_sub(ZIP_code, 1, 5)) %>%
  left_join(il_zcta, by = c("zip" = "GEOID")) %>%
  st_as_sf(coords = c("long", "lat"),
           crs = 4326
           )

# Create map layers
create_choropleth <- function(data, var, palette = "Blues", title) {
  # Custom bins (adjust as needed)
  bins <- c(0, 40000, 60000, 80000, 100000, 150000, Inf)
  
  color_data <- colorQuantile(palette, domain = data[[var]])
  
  leaflet() %>%
    addPolygons(
      data = data,
      fillColor = ~color_data(get(var)),
      fillOpacity = 0.7,
      weight = 0.5,
      color = "white",
      label = ~paste(title, ": $", prettyNum(round(get(var), -3), big.mark = ",")),
      group = title  # Critical for layer control
    ) %>%
    addLegend(
      position = "bottomright",
      pal = color_data,
      values = data[[var]],
      title = title,
      labFormat = labelFormat(prefix = "$")
    )
}

# Generate layers
layers <- list(
  create_choropleth(il_zcta, "income_medE", "YlGn", "Median Income"),
  create_choropleth(il_zcta, "edu_bachelor_pct", "Purples", "Bachelor's Degree+"),
  create_choropleth(il_zcta, "poverty_ratio_under50_pct", "Reds", "Poverty Ratio"),
  create_choropleth(il_zcta, "rent_burdened_pct", "Oranges", "Severe Rent Burden")
)

# Create base map
final_map <- leaflet() %>%
  addTiles()

# Define color palettes for each variable
pal_income <- colorNumeric("YlGn", domain = il_zcta$income_medE)
pal_edu <- colorNumeric("Purples", domain = il_zcta$edu_bachelor_pct)
pal_poverty = colorNumeric("Reds", domain = il_zcta$poverty_ratio_under50_pct)
pal_burdened = colorNumeric("Oranges", domain = il_zcta$rent_burdened_pct)

# Add layers
final_map <- final_map %>%
  # Median Income Layer
  addPolygons(
    data = il_zcta,
    fillColor = ~pal_income(income_medE),
    fillOpacity = 0.7,
    weight = 0.5,
    color = "white",
    group = "Median Income",
    label = ~paste("Median Income: $", scales::comma(income_medE))
  ) %>%
  # Bachelor's Degree+ Layer
  addPolygons(
    data = il_zcta,
    fillColor = ~pal_edu(edu_bachelor_pct),
    fillOpacity = 0.7,
    weight = 0.5,
    color = "white",
    group = "Bachelor's Degree+",
    label = ~paste("Bachelor's+: ", round(edu_bachelor_pct, 3), "%")
  ) |>
  # Income-to-Poverty-Level Ratio Layer
  addPolygons(
    data = il_zcta,
    fillColor = ~pal_poverty(poverty_ratio_under50_pct),
    fillOpacity = 0.7,
    weight = 0.5,
    color = "white",
    group = "Percent Income-to-Poverty-Level Ratio < 0.5",
    label = ~paste("Percent in Poverty: ", round(poverty_ratio_under50_pct, 3), "%")
  ) |>
  # Rent-Burdened Layer
  addPolygons(
    data = il_zcta,
    fillColor = ~pal_burdened(rent_burdened_pct),
    fillOpacity = 0.7,
    weight = 0.5,
    color = "white",
    group = "Percent Rent Burdened",
    label = ~paste("Percent Rent Burdened: ", round(rent_burdened_pct, 3), "%")
  )

# Load Chicago community areas (replace with your Albany Park shapefile if needed)
chi_comm <- tigris::county_subdivisions(state = "IL", county = "Cook", year = 2023) %>%
  st_transform(4326) %>%
  filter(NAME == "Chicago")  # Verify NAME matches your data

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Add Boundary for Albany Park

library(tigris)
library(leaflet)

# Download ZCTA shapefile for 60625
albany_zcta <- zctas(cb = TRUE, year = 2020) %>%
  filter(ZCTA5CE20 == "60625") %>%  # Filter for 60625
  st_transform(4326)  # Convert to WGS84

## ZCTAs can take several minutes to download.  To cache the data and avoid re-downloading in future R sessions, set `options(tigris_use_cache = TRUE)`

Add controls and CMHDC properties

final_map %>%
  addPolygons(
    data = albany_zcta,
    color = "blue",
    weight = 3,
    fillOpacity = 0,
    group = "Albany Park Boundary"
  ) |>
  addPolygons(
    data = chi_comm,
    color = "red",
    weight = 3,
    fillOpacity = 0,
    group = "Chicago Boundary"
  ) %>%
  addLayersControl(
    overlayGroups = c("CMHDC Properties", "Chicago Boundary",
                      "Albany Park Boundary"),
    baseGroups = c("Median Income", "Bachelor's Degree+",
                   "Percent Income-to-Poverty-Level Ratio < 0.5",
                   "Percent Rent Burdened"),
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  addLegend(
    position = "bottomright",
    pal = pal_income,
    values = il_zcta$income_medE,
    title = "Median Income",
    group = "Median Income"
  ) %>%
  addLegend(
    position = "bottomright",
    pal = pal_edu,
    values = il_zcta$edu_bachelor_pct,
    title = "Bachelor's Degree+",
    group = "Bachelor's Degree+"
  ) |>
  addLegend(
    position = "bottomleft",
    pal = pal_poverty,
    values = il_zcta$poverty_ratio_under50_pct,
    title = "Percent Income-to-Poverty-Level Ratio < 0.5",
    group = "Percent Income-to-Poverty-Level Ratio < 0.5"
  ) |>
  addLegend(
    position = "bottomleft",
    pal = pal_burdened,
    values = il_zcta$rent_burdened_pct,
    title = "Percent Rent Burdened",
    group = "Percent Rent Burdened"
  ) |>
  addCircleMarkers(
    data = properties_sf,
    radius = 5,
    color = "#2A5C7D",
    group = "CMHDC Properties"
  )

Potentially irrelevant nationwide, county-level data

counties = get_acs(
  geography = "county",
  variables = c(
                # Demographic Information
                age_med = "B01002_001",
                pop_total = "B01003_001",
                pop_under18 = "B09001_001",
                race_white = "B02008_001",
                race_black = "B02009_001",
                race_native = "B02010_001",
                race_asian = "B02011_001",
                race_nhpi = "B02012_001",
                race_other = "B02013_001",
                
                # Economic Impact
                income_med = "B19013_001",
                income_wages = "B19052_002",
                income_self = "B19053_002",
                income_public_assistance = "B19058_002",
                poverty_ratio_under50 = "C17002_002",
                poverty_ratio_50to99 = "C17002_003",
                poverty_ratio_over199 = "C17002_008",
                gini = "B19083_001",
                
                # Employment Impact
                labor_employed = "B23025_004",
                labor_unemployed = "B23025_005",
                labor_out_of_force = "B23025_007",
                commute_under5 = "B08303_001",
                commute_5to9 = "B08303_002",
                commute_10to14 = "B08303_003",
                commute_15to19 = "B08303_004",
                commute_20to24 = "B08303_005",
                commute_25to29 = "B08303_006",
                commute_30to34 = "B08303_007",
                commute_35to39 = "B08303_008",
                commute_40to44 = "B08303_009",
                commute_45to59 = "B08303_010",
                commute_60to89 = "B08303_011",
                commute_over89 = "B08303_012",
                
                # Social Impact
                edu_bachelor = "B15003_022",
                commute_time = "B08303_001",
                
                # Housing Stability
                tot_occupied = "B25002_002",
                tot_vacant = "B25002_003",
                vacant_4rent = "B25004_001",
                vacant_4sale = "B25004_003",
                occupied_owner = "B25003_001",
                occupied_renter = "B25003_002",
                homeownership = "B25003_002",
                median_year_built = "B25035_001",
                median_home_value = "B25077_001",
                rent_burden = "B25070_001",
                housing_cost_under100 = "B25104_002",
                housing_cost_100to199 = "B25104_003",
                housing_cost_200to299 = "B25104_004",
                housing_cost_300to399 = "B25104_005",
                housing_cost_400to499 = "B25104_006",
                housing_cost_500to599 = "B25104_007",
                housing_cost_600to699 = "B25104_008",
                housing_cost_700to799 = "B25104_009",
                housing_cost_800to899 = "B25104_010",
                housing_cost_900to999 = "B25104_011",
                housing_cost_1000to1499 = "B25104_012",
                housing_cost_1500to1999 = "B25104_013",
                housing_cost_2000to2499 = "B25104_014",
                housing_cost_2500to2999 = "B25104_015",
                housing_cost_over2999 = "B25104_016",
                rent_median_0bd = "B25031_002",
                rent_median_1bd = "B25031_003",
                rent_median_2bd = "B25031_004",
                rent_median_3bd = "B25031_005"),
  year = 2023,
  survey = "acs5",
  output = "wide"
)

## Getting data from the 2019-2023 5-year ACS

counties

Subsetting the Counties Data for Only Illinois Counties

counties_il = counties |>
  filter(str_sub(GEOID, 1, 2) == "17")  # Illinois Counties

counties_il

Statistical Analysis

Getting IL ZCTA Data from 2016

il_zcta_pre <- get_acs(
  geography = "zcta",
  variables = acs_vars,
  year = 2016,
  survey = "acs5",
  geometry = TRUE,
  output = "wide"
) %>%
  filter(str_sub(GEOID, 1, 2) %in% c("60", "61", "62")) %>%  # Illinois ZCTAs
  st_transform(4326) |>
  mutate(edu_bachelor_pct = (edu_bachelorE / (pop_totalE - pop_under18E)) * 100,
         rent_burdened = (rent_burden_30to35E + rent_burden_35to40E +
                            rent_burden_40to49E + rent_burden_over50E),
         rent_burdened_pct = (rent_burdened / pop_totalE) * 100,
         poverty_ratio_under50_pct = (poverty_ratio_under50E / pop_totalE) * 100) |>
  relocate(edu_bachelor_pct, .after = edu_bachelorE) |>
  relocate(rent_burdened, rent_burdened_pct, .before = rent_burden_30to35E) |>
  relocate(poverty_ratio_under50_pct, .before = poverty_ratio_under50E)

## Getting data from the 2012-2016 5-year ACS

## Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.

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Comparing Changes in Albany Park

library(tidycensus)
library(dplyr)

# Get 2016 ACS data for 60625
albany_2016 <- get_acs(
  geography = "zcta",
  state = "IL",
  variables = acs_vars,
  year = 2016,
  survey = "acs5",
  zcta = "60625",
  output = "wide"
) %>%
  mutate(year = 2016) |>
  mutate(edu_bachelor_pct = (edu_bachelorE / (pop_totalE - pop_under18E)) * 100,
         rent_burdened = (rent_burden_30to35E + rent_burden_35to40E +
                            rent_burden_40to49E + rent_burden_over50E),
         rent_burdened_pct = (rent_burdened / pop_totalE) * 100,
         poverty_ratio_under50_pct = (poverty_ratio_under50E / pop_totalE) * 100) |>
  relocate(edu_bachelor_pct, .after = edu_bachelorE) |>
  relocate(rent_burdened, rent_burdened_pct, .before = rent_burden_30to35E) |>
  relocate(poverty_ratio_under50_pct, .before = poverty_ratio_under50E)

## Getting data from the 2012-2016 5-year ACS

# Get 2021 ACS data for 60625
albany_2021 <- get_acs(
  geography = "zcta",
  variables = acs_vars,
  year = 2021,
  survey = "acs5",
  zcta = "60625",
  output = "wide"
) %>%
  mutate(year = 2021) |>
  mutate(edu_bachelor_pct = (edu_bachelorE / (pop_totalE - pop_under18E)) * 100,
         rent_burdened = (rent_burden_30to35E + rent_burden_35to40E +
                            rent_burden_40to49E + rent_burden_over50E),
         rent_burdened_pct = (rent_burdened / pop_totalE) * 100,
         poverty_ratio_under50_pct = (poverty_ratio_under50E / pop_totalE) * 100) |>
  relocate(edu_bachelor_pct, .after = edu_bachelorE) |>
  relocate(rent_burdened, rent_burdened_pct, .before = rent_burden_30to35E) |>
  relocate(poverty_ratio_under50_pct, .before = poverty_ratio_under50E)

## Getting data from the 2017-2021 5-year ACS

Getting Similar Data for Jefferson Park and Avondale

# Get 2021 ACS data for 60630 (Jefferson Park)
jeffpark_2021 <- get_acs(
  geography = "zcta",
  variables = acs_vars,
  year = 2021,
  survey = "acs5",
  zcta = "60630",
  output = "wide"
) %>%
  mutate(year = 2021) |>
  mutate(edu_bachelor_pct = (edu_bachelorE / (pop_totalE - pop_under18E)) * 100,
         rent_burdened = (rent_burden_30to35E + rent_burden_35to40E +
                            rent_burden_40to49E + rent_burden_over50E),
         rent_burdened_pct = (rent_burdened / pop_totalE) * 100,
         poverty_ratio_under50_pct = (poverty_ratio_under50E / pop_totalE) * 100) |>
  relocate(edu_bachelor_pct, .after = edu_bachelorE) |>
  relocate(rent_burdened, rent_burdened_pct, .before = rent_burden_30to35E) |>
  relocate(poverty_ratio_under50_pct, .before = poverty_ratio_under50E)

## Getting data from the 2017-2021 5-year ACS

# Get 2021 ACS data for 60618 (Avondale)
avondale_2021 <- get_acs(
  geography = "zcta",
  variables = acs_vars,
  year = 2021,
  survey = "acs5",
  zcta = "60618",
  output = "wide"
) %>%
  mutate(year = 2021) |>
  mutate(edu_bachelor_pct = (edu_bachelorE / (pop_totalE - pop_under18E)) * 100,
         rent_burdened = (rent_burden_30to35E + rent_burden_35to40E +
                            rent_burden_40to49E + rent_burden_over50E),
         rent_burdened_pct = (rent_burdened / pop_totalE) * 100,
         poverty_ratio_under50_pct = (poverty_ratio_under50E / pop_totalE) * 100) |>
  relocate(edu_bachelor_pct, .after = edu_bachelorE) |>
  relocate(rent_burdened, rent_burdened_pct, .before = rent_burden_30to35E) |>
  relocate(poverty_ratio_under50_pct, .before = poverty_ratio_under50E)

## Getting data from the 2017-2021 5-year ACS

# Combine and analyze changes
albany_changes <- bind_rows(albany_2016, albany_2021) %>%
  select(-NAME, -GEOID) %>%
  pivot_longer(
    cols = -year,
    names_to = c("variable", "type"),
    names_sep = "(?<=[A-Za-z])_?(?=[EM]$)",  # Split between variable name and E/M suffix
    values_to = "value"
  ) %>%
  pivot_wider(
    names_from = c(type, year),
    values_from = value,
    names_sep = "_"
  ) %>%
  mutate(
    # Calculate changes and significance
    estimate_change = E_2021 - E_2016,
    se_2016 = M_2016 / 1.645,
    se_2021 = M_2021 / 1.645,
    se_diff = sqrt(se_2016^2 + se_2021^2),
    z_score = estimate_change / se_diff,
    p_value = 2 * pnorm(-abs(z_score)),
    significant = p_value <= 0.05
  ) %>%
  rename(
    estimate_2016 = E_2016,
    estimate_2021 = E_2021,
    moe_2016 = M_2016,
    moe_2021 = M_2021
  )

## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 74 rows [5, 6, 27, 28,
## 29, 30, 31, 32, 33, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, ...].

# Filter significant changes
significant_changes <- albany_changes %>%
  filter(significant) %>%
  select(variable, estimate_2016, estimate_2021, estimate_change, p_value)

Statistical Results

# View results
significant_changes %>%
  arrange(p_value) %>%
  mutate(across(where(is.numeric), ~round(., 6))) %>%
  knitr::kable()

variable	estimate_2016	estimate_2021	estimate_change	p_value
rent_median_2bd	1143	1460.0	317.0	0.000000
rent_median_1bd	905	1092.0	187.0	0.000000
rent_median_0bd	738	952.0	214.0	0.000000
income_med	59601	76088.0	16487.0	0.000000
race_other	9586	16306.0	6720.0	0.000000
rent_median_3bd	1275	1599.0	324.0	0.000000
median_home_value	312100	358700.0	46600.0	0.000012
income_wages	24774	26794.0	2020.0	0.000434
tot_occupied	29157	30888.0	1731.0	0.002315
age_med	34	35.3	1.3	0.020366
race_native	531	1315.0	784.0	0.021508

Comparing Albany Park to Two Neighboring Communities

comparison_data <- bind_rows(
  albany_2021 %>% mutate(neighborhood = "Albany Park"),
  jeffpark_2021 %>% mutate(neighborhood = "Jefferson Park"),
  avondale_2021 %>% mutate(neighborhood = "Avondale")
) %>%
  select(neighborhood, income_medE, edu_bachelor_pct, poverty_ratio_under50_pct,
         rent_burdened_pct)

comparison_data

# ANOVA for median income
aov_income <- aov(income_medE ~ neighborhood, data = comparison_data)
summary(aov_income)

##              Df    Sum Sq  Mean Sq
## neighborhood  2 121170056 60585028

# Kruskal-Wallis for non-normal variables
kruskal.test(edu_bachelor_pct ~ neighborhood, data = comparison_data)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  edu_bachelor_pct by neighborhood
## Kruskal-Wallis chi-squared = 2, df = 2, p-value = 0.3679

# Pairwise comparisons with Tukey's HSD
TukeyHSD(aov_income)

## Warning in qtukey(conf.level, length(means), x$df.residual): NaNs produced

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = income_medE ~ neighborhood, data = comparison_data)
## 
## $neighborhood
##                             diff lwr upr p adj
## Avondale-Albany Park       15562 NaN NaN   NaN
## Jefferson Park-Albany Park  8132 NaN NaN   NaN
## Jefferson Park-Avondale    -7430 NaN NaN   NaN