MA maps

1.0 Introduction

This markdown file is working through the creation of a multiple-layer map of Massachusetts. This project is mostly about improving my map creation skills and maybe understand my home state better. I will be using data from 2023 tigerline shape files, MassGIS, and the American Community Survey.

This will be an on going project where I come back periodically to add more maps and refine the code. For right now there will be some maps of selected population statistics. The data will be maped by census tract and city/town borders. The main package that will be used for the map creation is the sf package.

2.0 Data Cleaning

The data is in relatively good condition, thanks to the American Community Survey (ACS), which typically does a good job of formatting and organizing. However, some minor cleaning is still necessary.

2.1 Reading Data

Each data table from the ACS comes with a metadata table that explains the column names. In this case, we are using the 2022 five-year summary tables for all of the data from the ACS. We Will print out the metadata table for each data set so that we can examine what variables we have.

The name of each variable includes the table name and starts with S. For example, S1902 is the ACS table name. If you would like to find the source of this data, you can search for it on the Census website.

inc_data <- read.csv("Data/ACSST5Y2022.S1902_2024-09-30T115303/ACSST5Y2022.S1902-Data.csv")

inc_MetaData <- read.csv("Data/ACSST5Y2022.S1902_2024-09-30T115303/ACSST5Y2022.S1902-Column-Metadata.csv")

# View Data definitions 
inc_MetaData |>
  filter(grepl("E$", Column.Name)) |>
  kable(Caption = "Income Data Variable Codes") |>
  kable_material(lightable_options = "striped") |>
  scroll_box(width = "100%", height = "400px") 

Column.Name	Label
NAME	Geographic Area Name
S1902_C01_001E	Estimate!!Number!!HOUSEHOLD INCOME!!All households
S1902_C01_002E	Estimate!!Number!!HOUSEHOLD INCOME!!All households!!With earnings
S1902_C01_003E	Estimate!!Number!!HOUSEHOLD INCOME!!All households!!With earnings!!With wages or salary income
S1902_C01_004E	Estimate!!Number!!HOUSEHOLD INCOME!!All households!!With earnings!!With self-employment income
S1902_C01_005E	Estimate!!Number!!HOUSEHOLD INCOME!!All households!!With interest, dividends, or net rental income
S1902_C01_006E	Estimate!!Number!!HOUSEHOLD INCOME!!All households!!With Social Security income
S1902_C01_007E	Estimate!!Number!!HOUSEHOLD INCOME!!All households!!With Supplemental Security Income (SSI)
S1902_C01_008E	Estimate!!Number!!HOUSEHOLD INCOME!!All households!!With cash public assistance income or Food Stamps/SNAP
S1902_C01_009E	Estimate!!Number!!HOUSEHOLD INCOME!!All households!!With cash public assistance income or Food Stamps/SNAP!!With cash public assistance
S1902_C01_010E	Estimate!!Number!!HOUSEHOLD INCOME!!All households!!With retirement income
S1902_C01_011E	Estimate!!Number!!HOUSEHOLD INCOME!!All households!!With other types of income
S1902_C01_012E	Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families
S1902_C01_013E	Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!No workers
S1902_C01_014E	Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!1 worker
S1902_C01_015E	Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!2 workers, both spouses worked
S1902_C01_016E	Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!2 workers, other
S1902_C01_017E	Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!3 or more workers, both spouses worked
S1902_C01_018E	Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!3 or more workers, other
S1902_C01_019E	Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population
S1902_C01_020E	Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!White
S1902_C01_021E	Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!Black or African American
S1902_C01_022E	Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!American Indian and Alaska Native
S1902_C01_023E	Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!Asian
S1902_C01_024E	Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!Native Hawaiian and Other Pacific Islander
S1902_C01_025E	Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!Some other race
S1902_C01_026E	Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!Two or more races
S1902_C01_027E	Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!Hispanic or Latino origin (of any race)
S1902_C01_028E	Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!White alone, not Hispanic or Latino
S1902_C02_001E	Estimate!!Percent Distribution!!HOUSEHOLD INCOME!!All households
S1902_C02_002E	Estimate!!Percent Distribution!!HOUSEHOLD INCOME!!All households!!With earnings
S1902_C02_003E	Estimate!!Percent Distribution!!HOUSEHOLD INCOME!!All households!!With earnings!!With wages or salary income
S1902_C02_004E	Estimate!!Percent Distribution!!HOUSEHOLD INCOME!!All households!!With earnings!!With self-employment income
S1902_C02_005E	Estimate!!Percent Distribution!!HOUSEHOLD INCOME!!All households!!With interest, dividends, or net rental income
S1902_C02_006E	Estimate!!Percent Distribution!!HOUSEHOLD INCOME!!All households!!With Social Security income
S1902_C02_007E	Estimate!!Percent Distribution!!HOUSEHOLD INCOME!!All households!!With Supplemental Security Income (SSI)
S1902_C02_008E	Estimate!!Percent Distribution!!HOUSEHOLD INCOME!!All households!!With cash public assistance income or Food Stamps/SNAP
S1902_C02_009E	Estimate!!Percent Distribution!!HOUSEHOLD INCOME!!All households!!With cash public assistance income or Food Stamps/SNAP!!With cash public assistance
S1902_C02_010E	Estimate!!Percent Distribution!!HOUSEHOLD INCOME!!All households!!With retirement income
S1902_C02_011E	Estimate!!Percent Distribution!!HOUSEHOLD INCOME!!All households!!With other types of income
S1902_C02_012E	Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families
S1902_C02_013E	Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!No workers
S1902_C02_014E	Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!1 worker
S1902_C02_015E	Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!2 workers, both spouses worked
S1902_C02_016E	Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!2 workers, other
S1902_C02_017E	Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!3 or more workers, both spouses worked
S1902_C02_018E	Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!3 or more workers, other
S1902_C02_019E	Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population
S1902_C02_020E	Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!White
S1902_C02_021E	Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!Black or African American
S1902_C02_022E	Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!American Indian and Alaska Native
S1902_C02_023E	Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!Asian
S1902_C02_024E	Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!Native Hawaiian and Other Pacific Islander
S1902_C02_025E	Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!Some other race
S1902_C02_026E	Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!Two or more races
S1902_C02_027E	Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!Hispanic or Latino origin (of any race)
S1902_C02_028E	Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!White alone, not Hispanic or Latino
S1902_C03_001E	Estimate!!Mean income (dollars)!!HOUSEHOLD INCOME!!All households
S1902_C03_002E	Estimate!!Mean income (dollars)!!HOUSEHOLD INCOME!!All households!!With earnings
S1902_C03_003E	Estimate!!Mean income (dollars)!!HOUSEHOLD INCOME!!All households!!With earnings!!With wages or salary income
S1902_C03_004E	Estimate!!Mean income (dollars)!!HOUSEHOLD INCOME!!All households!!With earnings!!With self-employment income
S1902_C03_005E	Estimate!!Mean income (dollars)!!HOUSEHOLD INCOME!!All households!!With interest, dividends, or net rental income
S1902_C03_006E	Estimate!!Mean income (dollars)!!HOUSEHOLD INCOME!!All households!!With Social Security income
S1902_C03_007E	Estimate!!Mean income (dollars)!!HOUSEHOLD INCOME!!All households!!With Supplemental Security Income (SSI)
S1902_C03_008E	Estimate!!Mean income (dollars)!!HOUSEHOLD INCOME!!All households!!With cash public assistance income or Food Stamps/SNAP
S1902_C03_009E	Estimate!!Mean income (dollars)!!HOUSEHOLD INCOME!!All households!!With cash public assistance income or Food Stamps/SNAP!!With cash public assistance
S1902_C03_010E	Estimate!!Mean income (dollars)!!HOUSEHOLD INCOME!!All households!!With retirement income
S1902_C03_011E	Estimate!!Mean income (dollars)!!HOUSEHOLD INCOME!!All households!!With other types of income
S1902_C03_012E	Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families
S1902_C03_013E	Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!No workers
S1902_C03_014E	Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!1 worker
S1902_C03_015E	Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!2 workers, both spouses worked
S1902_C03_016E	Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!2 workers, other
S1902_C03_017E	Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!3 or more workers, both spouses worked
S1902_C03_018E	Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!3 or more workers, other
S1902_C03_019E	Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population
S1902_C03_020E	Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!White
S1902_C03_021E	Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!Black or African American
S1902_C03_022E	Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!American Indian and Alaska Native
S1902_C03_023E	Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!Asian
S1902_C03_024E	Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!Native Hawaiian and Other Pacific Islander
S1902_C03_025E	Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race–!!Some other race
S1902_C03_026E	Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!Two or more races
S1902_C03_027E	Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!Hispanic or Latino origin (of any race)
S1902_C03_028E	Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!White alone, not Hispanic or Latino

pop_data <-  read.csv("Data/ACSST5Y2022.S0101_2024-10-08T171910/ACSST5Y2022.S0101-Data.csv")

pop_MetaData <- read.csv("Data/ACSST5Y2022.S0101_2024-10-08T171910/ACSST5Y2022.S0101-Column-Metadata.csv")

pop_MetaData |>
  filter(grepl("E$", Column.Name)) |>
  kable(Caption = "Income Data Variable Codes") |>
  kable_material(lightable_options = "striped") |>
  scroll_box(width = "100%", height = "400px") 

Column.Name	Label
NAME	Geographic Area Name
S0101_C01_001E	Estimate!!Total!!Total population
S0101_C01_002E	Estimate!!Total!!Total population!!AGE!!Under 5 years
S0101_C01_003E	Estimate!!Total!!Total population!!AGE!!5 to 9 years
S0101_C01_004E	Estimate!!Total!!Total population!!AGE!!10 to 14 years
S0101_C01_005E	Estimate!!Total!!Total population!!AGE!!15 to 19 years
S0101_C01_006E	Estimate!!Total!!Total population!!AGE!!20 to 24 years
S0101_C01_007E	Estimate!!Total!!Total population!!AGE!!25 to 29 years
S0101_C01_008E	Estimate!!Total!!Total population!!AGE!!30 to 34 years
S0101_C01_009E	Estimate!!Total!!Total population!!AGE!!35 to 39 years
S0101_C01_010E	Estimate!!Total!!Total population!!AGE!!40 to 44 years
S0101_C01_011E	Estimate!!Total!!Total population!!AGE!!45 to 49 years
S0101_C01_012E	Estimate!!Total!!Total population!!AGE!!50 to 54 years
S0101_C01_013E	Estimate!!Total!!Total population!!AGE!!55 to 59 years
S0101_C01_014E	Estimate!!Total!!Total population!!AGE!!60 to 64 years
S0101_C01_015E	Estimate!!Total!!Total population!!AGE!!65 to 69 years
S0101_C01_016E	Estimate!!Total!!Total population!!AGE!!70 to 74 years
S0101_C01_017E	Estimate!!Total!!Total population!!AGE!!75 to 79 years
S0101_C01_018E	Estimate!!Total!!Total population!!AGE!!80 to 84 years
S0101_C01_019E	Estimate!!Total!!Total population!!AGE!!85 years and over
S0101_C01_020E	Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years
S0101_C01_021E	Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years
S0101_C01_022E	Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years
S0101_C01_023E	Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years
S0101_C01_024E	Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years
S0101_C01_025E	Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!16 years and over
S0101_C01_026E	Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!18 years and over
S0101_C01_027E	Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!21 years and over
S0101_C01_028E	Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!60 years and over
S0101_C01_029E	Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!62 years and over
S0101_C01_030E	Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!65 years and over
S0101_C01_031E	Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!75 years and over
S0101_C01_032E	Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Median age (years)
S0101_C01_033E	Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)
S0101_C01_034E	Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Age dependency ratio
S0101_C01_035E	Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio
S0101_C01_036E	Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Child dependency ratio
S0101_C01_037E	Estimate!!Total!!Total population!!PERCENT ALLOCATED!!Sex
S0101_C01_038E	Estimate!!Total!!Total population!!PERCENT ALLOCATED!!Age
S0101_C02_001E	Estimate!!Percent!!Total population
S0101_C02_002E	Estimate!!Percent!!Total population!!AGE!!Under 5 years
S0101_C02_003E	Estimate!!Percent!!Total population!!AGE!!5 to 9 years
S0101_C02_004E	Estimate!!Percent!!Total population!!AGE!!10 to 14 years
S0101_C02_005E	Estimate!!Percent!!Total population!!AGE!!15 to 19 years
S0101_C02_006E	Estimate!!Percent!!Total population!!AGE!!20 to 24 years
S0101_C02_007E	Estimate!!Percent!!Total population!!AGE!!25 to 29 years
S0101_C02_008E	Estimate!!Percent!!Total population!!AGE!!30 to 34 years
S0101_C02_009E	Estimate!!Percent!!Total population!!AGE!!35 to 39 years
S0101_C02_010E	Estimate!!Percent!!Total population!!AGE!!40 to 44 years
S0101_C02_011E	Estimate!!Percent!!Total population!!AGE!!45 to 49 years
S0101_C02_012E	Estimate!!Percent!!Total population!!AGE!!50 to 54 years
S0101_C02_013E	Estimate!!Percent!!Total population!!AGE!!55 to 59 years
S0101_C02_014E	Estimate!!Percent!!Total population!!AGE!!60 to 64 years
S0101_C02_015E	Estimate!!Percent!!Total population!!AGE!!65 to 69 years
S0101_C02_016E	Estimate!!Percent!!Total population!!AGE!!70 to 74 years
S0101_C02_017E	Estimate!!Percent!!Total population!!AGE!!75 to 79 years
S0101_C02_018E	Estimate!!Percent!!Total population!!AGE!!80 to 84 years
S0101_C02_019E	Estimate!!Percent!!Total population!!AGE!!85 years and over
S0101_C02_020E	Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years
S0101_C02_021E	Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years
S0101_C02_022E	Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years
S0101_C02_023E	Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years
S0101_C02_024E	Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years
S0101_C02_025E	Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!16 years and over
S0101_C02_026E	Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!18 years and over
S0101_C02_027E	Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!21 years and over
S0101_C02_028E	Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!60 years and over
S0101_C02_029E	Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!62 years and over
S0101_C02_030E	Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!65 years and over
S0101_C02_031E	Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!75 years and over
S0101_C02_032E	Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Median age (years)
S0101_C02_033E	Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)
S0101_C02_034E	Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Age dependency ratio
S0101_C02_035E	Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio
S0101_C02_036E	Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Child dependency ratio
S0101_C02_037E	Estimate!!Percent!!Total population!!PERCENT ALLOCATED!!Sex
S0101_C02_038E	Estimate!!Percent!!Total population!!PERCENT ALLOCATED!!Age
S0101_C03_001E	Estimate!!Male!!Total population
S0101_C03_002E	Estimate!!Male!!Total population!!AGE!!Under 5 years
S0101_C03_003E	Estimate!!Male!!Total population!!AGE!!5 to 9 years
S0101_C03_004E	Estimate!!Male!!Total population!!AGE!!10 to 14 years
S0101_C03_005E	Estimate!!Male!!Total population!!AGE!!15 to 19 years
S0101_C03_006E	Estimate!!Male!!Total population!!AGE!!20 to 24 years
S0101_C03_007E	Estimate!!Male!!Total population!!AGE!!25 to 29 years
S0101_C03_008E	Estimate!!Male!!Total population!!AGE!!30 to 34 years
S0101_C03_009E	Estimate!!Male!!Total population!!AGE!!35 to 39 years
S0101_C03_010E	Estimate!!Male!!Total population!!AGE!!40 to 44 years
S0101_C03_011E	Estimate!!Male!!Total population!!AGE!!45 to 49 years
S0101_C03_012E	Estimate!!Male!!Total population!!AGE!!50 to 54 years
S0101_C03_013E	Estimate!!Male!!Total population!!AGE!!55 to 59 years
S0101_C03_014E	Estimate!!Male!!Total population!!AGE!!60 to 64 years
S0101_C03_015E	Estimate!!Male!!Total population!!AGE!!65 to 69 years
S0101_C03_016E	Estimate!!Male!!Total population!!AGE!!70 to 74 years
S0101_C03_017E	Estimate!!Male!!Total population!!AGE!!75 to 79 years
S0101_C03_018E	Estimate!!Male!!Total population!!AGE!!80 to 84 years
S0101_C03_019E	Estimate!!Male!!Total population!!AGE!!85 years and over
S0101_C03_020E	Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years
S0101_C03_021E	Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years
S0101_C03_022E	Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years
S0101_C03_023E	Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years
S0101_C03_024E	Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years
S0101_C03_025E	Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!16 years and over
S0101_C03_026E	Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!18 years and over
S0101_C03_027E	Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!21 years and over
S0101_C03_028E	Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!60 years and over
S0101_C03_029E	Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!62 years and over
S0101_C03_030E	Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!65 years and over
S0101_C03_031E	Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!75 years and over
S0101_C03_032E	Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Median age (years)
S0101_C03_033E	Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)
S0101_C03_034E	Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Age dependency ratio
S0101_C03_035E	Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio
S0101_C03_036E	Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Child dependency ratio
S0101_C03_037E	Estimate!!Male!!Total population!!PERCENT ALLOCATED!!Sex
S0101_C03_038E	Estimate!!Male!!Total population!!PERCENT ALLOCATED!!Age
S0101_C04_001E	Estimate!!Percent Male!!Total population
S0101_C04_002E	Estimate!!Percent Male!!Total population!!AGE!!Under 5 years
S0101_C04_003E	Estimate!!Percent Male!!Total population!!AGE!!5 to 9 years
S0101_C04_004E	Estimate!!Percent Male!!Total population!!AGE!!10 to 14 years
S0101_C04_005E	Estimate!!Percent Male!!Total population!!AGE!!15 to 19 years
S0101_C04_006E	Estimate!!Percent Male!!Total population!!AGE!!20 to 24 years
S0101_C04_007E	Estimate!!Percent Male!!Total population!!AGE!!25 to 29 years
S0101_C04_008E	Estimate!!Percent Male!!Total population!!AGE!!30 to 34 years
S0101_C04_009E	Estimate!!Percent Male!!Total population!!AGE!!35 to 39 years
S0101_C04_010E	Estimate!!Percent Male!!Total population!!AGE!!40 to 44 years
S0101_C04_011E	Estimate!!Percent Male!!Total population!!AGE!!45 to 49 years
S0101_C04_012E	Estimate!!Percent Male!!Total population!!AGE!!50 to 54 years
S0101_C04_013E	Estimate!!Percent Male!!Total population!!AGE!!55 to 59 years
S0101_C04_014E	Estimate!!Percent Male!!Total population!!AGE!!60 to 64 years
S0101_C04_015E	Estimate!!Percent Male!!Total population!!AGE!!65 to 69 years
S0101_C04_016E	Estimate!!Percent Male!!Total population!!AGE!!70 to 74 years
S0101_C04_017E	Estimate!!Percent Male!!Total population!!AGE!!75 to 79 years
S0101_C04_018E	Estimate!!Percent Male!!Total population!!AGE!!80 to 84 years
S0101_C04_019E	Estimate!!Percent Male!!Total population!!AGE!!85 years and over
S0101_C04_020E	Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years
S0101_C04_021E	Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years
S0101_C04_022E	Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years
S0101_C04_023E	Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years
S0101_C04_024E	Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years
S0101_C04_025E	Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!16 years and over
S0101_C04_026E	Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!18 years and over
S0101_C04_027E	Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!21 years and over
S0101_C04_028E	Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!60 years and over
S0101_C04_029E	Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!62 years and over
S0101_C04_030E	Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!65 years and over
S0101_C04_031E	Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!75 years and over
S0101_C04_032E	Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Median age (years)
S0101_C04_033E	Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)
S0101_C04_034E	Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Age dependency ratio
S0101_C04_035E	Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio
S0101_C04_036E	Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Child dependency ratio
S0101_C04_037E	Estimate!!Percent Male!!Total population!!PERCENT ALLOCATED!!Sex
S0101_C04_038E	Estimate!!Percent Male!!Total population!!PERCENT ALLOCATED!!Age
S0101_C05_001E	Estimate!!Female!!Total population
S0101_C05_002E	Estimate!!Female!!Total population!!AGE!!Under 5 years
S0101_C05_003E	Estimate!!Female!!Total population!!AGE!!5 to 9 years
S0101_C05_004E	Estimate!!Female!!Total population!!AGE!!10 to 14 years
S0101_C05_005E	Estimate!!Female!!Total population!!AGE!!15 to 19 years
S0101_C05_006E	Estimate!!Female!!Total population!!AGE!!20 to 24 years
S0101_C05_007E	Estimate!!Female!!Total population!!AGE!!25 to 29 years
S0101_C05_008E	Estimate!!Female!!Total population!!AGE!!30 to 34 years
S0101_C05_009E	Estimate!!Female!!Total population!!AGE!!35 to 39 years
S0101_C05_010E	Estimate!!Female!!Total population!!AGE!!40 to 44 years
S0101_C05_011E	Estimate!!Female!!Total population!!AGE!!45 to 49 years
S0101_C05_012E	Estimate!!Female!!Total population!!AGE!!50 to 54 years
S0101_C05_013E	Estimate!!Female!!Total population!!AGE!!55 to 59 years
S0101_C05_014E	Estimate!!Female!!Total population!!AGE!!60 to 64 years
S0101_C05_015E	Estimate!!Female!!Total population!!AGE!!65 to 69 years
S0101_C05_016E	Estimate!!Female!!Total population!!AGE!!70 to 74 years
S0101_C05_017E	Estimate!!Female!!Total population!!AGE!!75 to 79 years
S0101_C05_018E	Estimate!!Female!!Total population!!AGE!!80 to 84 years
S0101_C05_019E	Estimate!!Female!!Total population!!AGE!!85 years and over
S0101_C05_020E	Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years
S0101_C05_021E	Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years
S0101_C05_022E	Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years
S0101_C05_023E	Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years
S0101_C05_024E	Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years
S0101_C05_025E	Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!16 years and over
S0101_C05_026E	Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!18 years and over
S0101_C05_027E	Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!21 years and over
S0101_C05_028E	Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!60 years and over
S0101_C05_029E	Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!62 years and over
S0101_C05_030E	Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!65 years and over
S0101_C05_031E	Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!75 years and over
S0101_C05_032E	Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Median age (years)
S0101_C05_033E	Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)
S0101_C05_034E	Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Age dependency ratio
S0101_C05_035E	Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio
S0101_C05_036E	Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Child dependency ratio
S0101_C05_037E	Estimate!!Female!!Total population!!PERCENT ALLOCATED!!Sex
S0101_C05_038E	Estimate!!Female!!Total population!!PERCENT ALLOCATED!!Age
S0101_C06_001E	Estimate!!Percent Female!!Total population
S0101_C06_002E	Estimate!!Percent Female!!Total population!!AGE!!Under 5 years
S0101_C06_003E	Estimate!!Percent Female!!Total population!!AGE!!5 to 9 years
S0101_C06_004E	Estimate!!Percent Female!!Total population!!AGE!!10 to 14 years
S0101_C06_005E	Estimate!!Percent Female!!Total population!!AGE!!15 to 19 years
S0101_C06_006E	Estimate!!Percent Female!!Total population!!AGE!!20 to 24 years
S0101_C06_007E	Estimate!!Percent Female!!Total population!!AGE!!25 to 29 years
S0101_C06_008E	Estimate!!Percent Female!!Total population!!AGE!!30 to 34 years
S0101_C06_009E	Estimate!!Percent Female!!Total population!!AGE!!35 to 39 years
S0101_C06_010E	Estimate!!Percent Female!!Total population!!AGE!!40 to 44 years
S0101_C06_011E	Estimate!!Percent Female!!Total population!!AGE!!45 to 49 years
S0101_C06_012E	Estimate!!Percent Female!!Total population!!AGE!!50 to 54 years
S0101_C06_013E	Estimate!!Percent Female!!Total population!!AGE!!55 to 59 years
S0101_C06_014E	Estimate!!Percent Female!!Total population!!AGE!!60 to 64 years
S0101_C06_015E	Estimate!!Percent Female!!Total population!!AGE!!65 to 69 years
S0101_C06_016E	Estimate!!Percent Female!!Total population!!AGE!!70 to 74 years
S0101_C06_017E	Estimate!!Percent Female!!Total population!!AGE!!75 to 79 years
S0101_C06_018E	Estimate!!Percent Female!!Total population!!AGE!!80 to 84 years
S0101_C06_019E	Estimate!!Percent Female!!Total population!!AGE!!85 years and over
S0101_C06_020E	Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years
S0101_C06_021E	Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years
S0101_C06_022E	Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years
S0101_C06_023E	Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years
S0101_C06_024E	Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years
S0101_C06_025E	Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!16 years and over
S0101_C06_026E	Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!18 years and over
S0101_C06_027E	Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!21 years and over
S0101_C06_028E	Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!60 years and over
S0101_C06_029E	Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!62 years and over
S0101_C06_030E	Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!65 years and over
S0101_C06_031E	Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!75 years and over
S0101_C06_032E	Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Median age (years)
S0101_C06_033E	Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)
S0101_C06_034E	Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Age dependency ratio
S0101_C06_035E	Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio
S0101_C06_036E	Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Child dependency ratio
S0101_C06_037E	Estimate!!Percent Female!!Total population!!PERCENT ALLOCATED!!Sex
S0101_C06_038E	Estimate!!Percent Female!!Total population!!PERCENT ALLOCATED!!Age

com_data <- read.csv("Data/ACSST5Y2022.S0801_2024-10-09T003539/ACSST5Y2022.S0801-Data.csv")|>
  dplyr::select(-NAME)

com_MetaData <- read.csv("Data/ACSST5Y2022.S0801_2024-10-09T003539/ACSST5Y2022.S0801-Column-Metadata.csv") 

# Filter out Margins of Error to make viewing easier
com_MetaData |>
  filter(grepl(pattern = "E$", Column.Name)) |>
  kable(Caption = "Comm Data Variable Codes") |>
  kable_material(lightable_options = "striped") |>
  scroll_box(width = "100%", height = "400px") 

Column.Name	Label
NAME	Geographic Area Name
S0801_C01_001E	Estimate!!Total!!Workers 16 years and over
S0801_C01_002E	Estimate!!Total!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van
S0801_C01_003E	Estimate!!Total!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Drove alone
S0801_C01_004E	Estimate!!Total!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Carpooled
S0801_C01_005E	Estimate!!Total!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Carpooled!!In 2-person carpool
S0801_C01_006E	Estimate!!Total!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Carpooled!!In 3-person carpool
S0801_C01_007E	Estimate!!Total!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Carpooled!!In 4-or-more person carpool
S0801_C01_008E	Estimate!!Total!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Workers per car, truck, or van
S0801_C01_009E	Estimate!!Total!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Public transportation (excluding taxicab)
S0801_C01_010E	Estimate!!Total!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Walked
S0801_C01_011E	Estimate!!Total!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Bicycle
S0801_C01_012E	Estimate!!Total!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Taxicab, motorcycle, or other means
S0801_C01_013E	Estimate!!Total!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Worked from home
S0801_C01_014E	Estimate!!Total!!Workers 16 years and over!!PLACE OF WORK!!Worked in state of residence
S0801_C01_015E	Estimate!!Total!!Workers 16 years and over!!PLACE OF WORK!!Worked in state of residence!!Worked in county of residence
S0801_C01_016E	Estimate!!Total!!Workers 16 years and over!!PLACE OF WORK!!Worked in state of residence!!Worked outside county of residence
S0801_C01_017E	Estimate!!Total!!Workers 16 years and over!!PLACE OF WORK!!Worked outside state of residence
S0801_C01_018E	Estimate!!Total!!Workers 16 years and over!!PLACE OF WORK!!Living in a place
S0801_C01_019E	Estimate!!Total!!Workers 16 years and over!!PLACE OF WORK!!Living in a place!!Worked in place of residence
S0801_C01_020E	Estimate!!Total!!Workers 16 years and over!!PLACE OF WORK!!Living in a place!!Worked outside place of residence
S0801_C01_021E	Estimate!!Total!!Workers 16 years and over!!PLACE OF WORK!!Not living in a place
S0801_C01_022E	Estimate!!Total!!Workers 16 years and over!!PLACE OF WORK!!Living in 12 selected states
S0801_C01_023E	Estimate!!Total!!Workers 16 years and over!!PLACE OF WORK!!Living in 12 selected states!!Worked in minor civil division of residence
S0801_C01_024E	Estimate!!Total!!Workers 16 years and over!!PLACE OF WORK!!Living in 12 selected states!!Worked outside minor civil division of residence
S0801_C01_025E	Estimate!!Total!!Workers 16 years and over!!PLACE OF WORK!!Not living in 12 selected states
S0801_C01_026E	Estimate!!Total!!Workers 16 years and over who did not work from home
S0801_C01_027E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!12:00 a.m. to 4:59 a.m.
S0801_C01_028E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!5:00 a.m. to 5:29 a.m.
S0801_C01_029E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!5:30 a.m. to 5:59 a.m.
S0801_C01_030E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!6:00 a.m. to 6:29 a.m.
S0801_C01_031E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!6:30 a.m. to 6:59 a.m.
S0801_C01_032E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!7:00 a.m. to 7:29 a.m.
S0801_C01_033E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!7:30 a.m. to 7:59 a.m.
S0801_C01_034E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!8:00 a.m. to 8:29 a.m.
S0801_C01_035E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!8:30 a.m. to 8:59 a.m.
S0801_C01_036E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!9:00 a.m. to 11:59 p.m.
S0801_C01_037E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!Less than 10 minutes
S0801_C01_038E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!10 to 14 minutes
S0801_C01_039E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!15 to 19 minutes
S0801_C01_040E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!20 to 24 minutes
S0801_C01_041E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!25 to 29 minutes
S0801_C01_042E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!30 to 34 minutes
S0801_C01_043E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!35 to 44 minutes
S0801_C01_044E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!45 to 59 minutes
S0801_C01_045E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!60 or more minutes
S0801_C01_046E	Estimate!!Total!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!Mean travel time to work (minutes)
S0801_C01_047E	Estimate!!Total!!VEHICLES AVAILABLE!!Workers 16 years and over in households
S0801_C01_048E	Estimate!!Total!!VEHICLES AVAILABLE!!Workers 16 years and over in households!!No vehicle available
S0801_C01_049E	Estimate!!Total!!VEHICLES AVAILABLE!!Workers 16 years and over in households!!1 vehicle available
S0801_C01_050E	Estimate!!Total!!VEHICLES AVAILABLE!!Workers 16 years and over in households!!2 vehicles available
S0801_C01_051E	Estimate!!Total!!VEHICLES AVAILABLE!!Workers 16 years and over in households!!3 or more vehicles available
S0801_C01_052E	Estimate!!Total!!PERCENT ALLOCATED!!Means of transportation to work
S0801_C01_053E	Estimate!!Total!!PERCENT ALLOCATED!!Private vehicle occupancy
S0801_C01_054E	Estimate!!Total!!PERCENT ALLOCATED!!Place of work
S0801_C01_055E	Estimate!!Total!!PERCENT ALLOCATED!!Time of departure to go to work
S0801_C01_056E	Estimate!!Total!!PERCENT ALLOCATED!!Travel time to work
S0801_C01_057E	Estimate!!Total!!PERCENT ALLOCATED!!Vehicles available
S0801_C02_001E	Estimate!!Male!!Workers 16 years and over
S0801_C02_002E	Estimate!!Male!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van
S0801_C02_003E	Estimate!!Male!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Drove alone
S0801_C02_004E	Estimate!!Male!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Carpooled
S0801_C02_005E	Estimate!!Male!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Carpooled!!In 2-person carpool
S0801_C02_006E	Estimate!!Male!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Carpooled!!In 3-person carpool
S0801_C02_007E	Estimate!!Male!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Carpooled!!In 4-or-more person carpool
S0801_C02_008E	Estimate!!Male!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Workers per car, truck, or van
S0801_C02_009E	Estimate!!Male!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Public transportation (excluding taxicab)
S0801_C02_010E	Estimate!!Male!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Walked
S0801_C02_011E	Estimate!!Male!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Bicycle
S0801_C02_012E	Estimate!!Male!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Taxicab, motorcycle, or other means
S0801_C02_013E	Estimate!!Male!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Worked from home
S0801_C02_014E	Estimate!!Male!!Workers 16 years and over!!PLACE OF WORK!!Worked in state of residence
S0801_C02_015E	Estimate!!Male!!Workers 16 years and over!!PLACE OF WORK!!Worked in state of residence!!Worked in county of residence
S0801_C02_016E	Estimate!!Male!!Workers 16 years and over!!PLACE OF WORK!!Worked in state of residence!!Worked outside county of residence
S0801_C02_017E	Estimate!!Male!!Workers 16 years and over!!PLACE OF WORK!!Worked outside state of residence
S0801_C02_018E	Estimate!!Male!!Workers 16 years and over!!PLACE OF WORK!!Living in a place
S0801_C02_019E	Estimate!!Male!!Workers 16 years and over!!PLACE OF WORK!!Living in a place!!Worked in place of residence
S0801_C02_020E	Estimate!!Male!!Workers 16 years and over!!PLACE OF WORK!!Living in a place!!Worked outside place of residence
S0801_C02_021E	Estimate!!Male!!Workers 16 years and over!!PLACE OF WORK!!Not living in a place
S0801_C02_022E	Estimate!!Male!!Workers 16 years and over!!PLACE OF WORK!!Living in 12 selected states
S0801_C02_023E	Estimate!!Male!!Workers 16 years and over!!PLACE OF WORK!!Living in 12 selected states!!Worked in minor civil division of residence
S0801_C02_024E	Estimate!!Male!!Workers 16 years and over!!PLACE OF WORK!!Living in 12 selected states!!Worked outside minor civil division of residence
S0801_C02_025E	Estimate!!Male!!Workers 16 years and over!!PLACE OF WORK!!Not living in 12 selected states
S0801_C02_026E	Estimate!!Male!!Workers 16 years and over who did not work from home
S0801_C02_027E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!12:00 a.m. to 4:59 a.m.
S0801_C02_028E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!5:00 a.m. to 5:29 a.m.
S0801_C02_029E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!5:30 a.m. to 5:59 a.m.
S0801_C02_030E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!6:00 a.m. to 6:29 a.m.
S0801_C02_031E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!6:30 a.m. to 6:59 a.m.
S0801_C02_032E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!7:00 a.m. to 7:29 a.m.
S0801_C02_033E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!7:30 a.m. to 7:59 a.m.
S0801_C02_034E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!8:00 a.m. to 8:29 a.m.
S0801_C02_035E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!8:30 a.m. to 8:59 a.m.
S0801_C02_036E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!9:00 a.m. to 11:59 p.m.
S0801_C02_037E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!Less than 10 minutes
S0801_C02_038E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!10 to 14 minutes
S0801_C02_039E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!15 to 19 minutes
S0801_C02_040E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!20 to 24 minutes
S0801_C02_041E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!25 to 29 minutes
S0801_C02_042E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!30 to 34 minutes
S0801_C02_043E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!35 to 44 minutes
S0801_C02_044E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!45 to 59 minutes
S0801_C02_045E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!60 or more minutes
S0801_C02_046E	Estimate!!Male!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!Mean travel time to work (minutes)
S0801_C02_047E	Estimate!!Male!!VEHICLES AVAILABLE!!Workers 16 years and over in households
S0801_C02_048E	Estimate!!Male!!VEHICLES AVAILABLE!!Workers 16 years and over in households!!No vehicle available
S0801_C02_049E	Estimate!!Male!!VEHICLES AVAILABLE!!Workers 16 years and over in households!!1 vehicle available
S0801_C02_050E	Estimate!!Male!!VEHICLES AVAILABLE!!Workers 16 years and over in households!!2 vehicles available
S0801_C02_051E	Estimate!!Male!!VEHICLES AVAILABLE!!Workers 16 years and over in households!!3 or more vehicles available
S0801_C02_052E	Estimate!!Male!!PERCENT ALLOCATED!!Means of transportation to work
S0801_C02_053E	Estimate!!Male!!PERCENT ALLOCATED!!Private vehicle occupancy
S0801_C02_054E	Estimate!!Male!!PERCENT ALLOCATED!!Place of work
S0801_C02_055E	Estimate!!Male!!PERCENT ALLOCATED!!Time of departure to go to work
S0801_C02_056E	Estimate!!Male!!PERCENT ALLOCATED!!Travel time to work
S0801_C02_057E	Estimate!!Male!!PERCENT ALLOCATED!!Vehicles available
S0801_C03_001E	Estimate!!Female!!Workers 16 years and over
S0801_C03_002E	Estimate!!Female!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van
S0801_C03_003E	Estimate!!Female!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Drove alone
S0801_C03_004E	Estimate!!Female!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Carpooled
S0801_C03_005E	Estimate!!Female!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Carpooled!!In 2-person carpool
S0801_C03_006E	Estimate!!Female!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Carpooled!!In 3-person carpool
S0801_C03_007E	Estimate!!Female!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Carpooled!!In 4-or-more person carpool
S0801_C03_008E	Estimate!!Female!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Car, truck, or van!!Workers per car, truck, or van
S0801_C03_009E	Estimate!!Female!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Public transportation (excluding taxicab)
S0801_C03_010E	Estimate!!Female!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Walked
S0801_C03_011E	Estimate!!Female!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Bicycle
S0801_C03_012E	Estimate!!Female!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Taxicab, motorcycle, or other means
S0801_C03_013E	Estimate!!Female!!Workers 16 years and over!!MEANS OF TRANSPORTATION TO WORK!!Worked from home
S0801_C03_014E	Estimate!!Female!!Workers 16 years and over!!PLACE OF WORK!!Worked in state of residence
S0801_C03_015E	Estimate!!Female!!Workers 16 years and over!!PLACE OF WORK!!Worked in state of residence!!Worked in county of residence
S0801_C03_016E	Estimate!!Female!!Workers 16 years and over!!PLACE OF WORK!!Worked in state of residence!!Worked outside county of residence
S0801_C03_017E	Estimate!!Female!!Workers 16 years and over!!PLACE OF WORK!!Worked outside state of residence
S0801_C03_018E	Estimate!!Female!!Workers 16 years and over!!PLACE OF WORK!!Living in a place
S0801_C03_019E	Estimate!!Female!!Workers 16 years and over!!PLACE OF WORK!!Living in a place!!Worked in place of residence
S0801_C03_020E	Estimate!!Female!!Workers 16 years and over!!PLACE OF WORK!!Living in a place!!Worked outside place of residence
S0801_C03_021E	Estimate!!Female!!Workers 16 years and over!!PLACE OF WORK!!Not living in a place
S0801_C03_022E	Estimate!!Female!!Workers 16 years and over!!PLACE OF WORK!!Living in 12 selected states
S0801_C03_023E	Estimate!!Female!!Workers 16 years and over!!PLACE OF WORK!!Living in 12 selected states!!Worked in minor civil division of residence
S0801_C03_024E	Estimate!!Female!!Workers 16 years and over!!PLACE OF WORK!!Living in 12 selected states!!Worked outside minor civil division of residence
S0801_C03_025E	Estimate!!Female!!Workers 16 years and over!!PLACE OF WORK!!Not living in 12 selected states
S0801_C03_026E	Estimate!!Female!!Workers 16 years and over who did not work from home
S0801_C03_027E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!12:00 a.m. to 4:59 a.m.
S0801_C03_028E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!5:00 a.m. to 5:29 a.m.
S0801_C03_029E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!5:30 a.m. to 5:59 a.m.
S0801_C03_030E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!6:00 a.m. to 6:29 a.m.
S0801_C03_031E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!6:30 a.m. to 6:59 a.m.
S0801_C03_032E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!7:00 a.m. to 7:29 a.m.
S0801_C03_033E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!7:30 a.m. to 7:59 a.m.
S0801_C03_034E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!8:00 a.m. to 8:29 a.m.
S0801_C03_035E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!8:30 a.m. to 8:59 a.m.
S0801_C03_036E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TIME OF DEPARTURE TO GO TO WORK!!9:00 a.m. to 11:59 p.m.
S0801_C03_037E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!Less than 10 minutes
S0801_C03_038E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!10 to 14 minutes
S0801_C03_039E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!15 to 19 minutes
S0801_C03_040E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!20 to 24 minutes
S0801_C03_041E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!25 to 29 minutes
S0801_C03_042E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!30 to 34 minutes
S0801_C03_043E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!35 to 44 minutes
S0801_C03_044E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!45 to 59 minutes
S0801_C03_045E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!60 or more minutes
S0801_C03_046E	Estimate!!Female!!Workers 16 years and over who did not work from home!!TRAVEL TIME TO WORK!!Mean travel time to work (minutes)
S0801_C03_047E	Estimate!!Female!!VEHICLES AVAILABLE!!Workers 16 years and over in households
S0801_C03_048E	Estimate!!Female!!VEHICLES AVAILABLE!!Workers 16 years and over in households!!No vehicle available
S0801_C03_049E	Estimate!!Female!!VEHICLES AVAILABLE!!Workers 16 years and over in households!!1 vehicle available
S0801_C03_050E	Estimate!!Female!!VEHICLES AVAILABLE!!Workers 16 years and over in households!!2 vehicles available
S0801_C03_051E	Estimate!!Female!!VEHICLES AVAILABLE!!Workers 16 years and over in households!!3 or more vehicles available
S0801_C03_052E	Estimate!!Female!!PERCENT ALLOCATED!!Means of transportation to work
S0801_C03_053E	Estimate!!Female!!PERCENT ALLOCATED!!Private vehicle occupancy
S0801_C03_054E	Estimate!!Female!!PERCENT ALLOCATED!!Place of work
S0801_C03_055E	Estimate!!Female!!PERCENT ALLOCATED!!Time of departure to go to work
S0801_C03_056E	Estimate!!Female!!PERCENT ALLOCATED!!Travel time to work
S0801_C03_057E	Estimate!!Female!!PERCENT ALLOCATED!!Vehicles available

# Read tract shapes
TractShapes <- st_read(dsn =  "Data/tl_2023_25_tract/tl_2023_25_tract.shp") 

## Reading layer `tl_2023_25_tract' from data source 
##   `/Users/avery/Documents/Rpubs_Projects/Maps/MA/Data/tl_2023_25_tract/tl_2023_25_tract.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 1620 features and 13 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -73.50821 ymin: 41.18705 xmax: -69.85886 ymax: 42.88678
## Geodetic CRS:  NAD83

# City shapes comes from MASSGIS in a slightly different format. 
CityShapes <- st_read(dsn = "Data/townssurvey_shp/TOWNSSURVEY_POLYM.shp")

## Reading layer `TOWNSSURVEY_POLYM' from data source 
##   `/Users/avery/Documents/Rpubs_Projects/Maps/MA/Data/townssurvey_shp/TOWNSSURVEY_POLYM.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 351 features and 18 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 33863.73 ymin: 777606.4 xmax: 330837 ymax: 959743
## Projected CRS: NAD83 / Massachusetts Mainland

# rename and numeric-ify Long and Lat
TractShapes <- TractShapes |> 
  mutate(INTPTLAT = as.numeric(INTPTLAT),
         INTPTLON = as.numeric(INTPTLON)) |> 
  rename(Latitude = INTPTLAT,
         Longitude = INTPTLON)

CityShapes <- CityShapes |> 
  st_as_sf()

2.2 Join data

We can now jump right into merging the data. We will do this using the dplyr::inner_join function. This function returns all of the rows that match in each data frame. We will join together pop_data, inc_data, and com_data into a single data frame called inc_map_data.

# Join Population and Income data, Tract Level
inc_map_data <- inner_join(pop_data, inc_data,  by = c("GEO_ID" ))


# Join Pop/inc data with Commute data, Tract Level 
inc_map_data <- inner_join(inc_map_data ,com_data, by = c("GEO_ID"))

Next, we must coerce the data into a numeric format. For this, we will use base::as.numeric. This function has the useful side effect of converting observations that can’t be interpreted as numbers into NA values. This is beneficial because we have some missing values are represented as blanks, '-', and 'N'. We are leveraging this side effect to ensure consistency in our missing values.

# we want to coerce NAs so that missing values are consistent
inc_map_data <- inc_map_data |>
  slice(-1) |>
  mutate(across(c(-GEO_ID, -NAME.x, -NAME.y), ~as.numeric(.)))

## Warning: There were 704 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `across(c(-GEO_ID, -NAME.x, -NAME.y), ~as.numeric(.))`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 703 remaining warnings.

Finally, we have one more join to complete. This will involve the inc_map_data, which contains most of the data we want to map, and the TractShapes, which contains the shapes of Census tracts. After this step, we will have our complete map data named map_data.

# join incomplete map data with the Tract shapes 
map_data <- inner_join(inc_map_data, TractShapes,by = c('GEO_ID' = "GEOIDFQ")) 


map_data |>
  head() |>
  kable(caption = 'Map Data') |>
  kable_material(lightable_options = "striped") |>
  scroll_box(width = "100 |> %", height = "400px") 

Map Data

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1400000US25001010100	Census Tract 101; Barnstable County; Massachusetts	3630	43	116	104	80	72	43	40	119	80	63	62	128	101	202	131	157	95	263	142	224	139	311	94	558	160	337	123	271	110	344	147	174	104	126	74	114	85	123	92	61	54	300	202	121	81	932	189	3359	194	3330	210	3272	216	1366	242	1265	221	1029	210	414	149	56.1	2.0	167.9	35.5	57.8	15.0	44.7	11.9	13.0	9.5	NA	NA	NA	NA	NA	NA	3.2	2.9	2.2	2.0	1.2	1.1	3.3	2.2	1.7	1.7	3.5	2.8	5.6	3.6	4.3	2.6	7.2	3.9	6.2	3.8	8.6	2.6	15.4	4.4	9.3	3.4	7.5	3.0	9.5	4.0	4.8	2.9	3.5	2.0	3.1	2.3	3.4	2.5	1.7	1.5	8.3	5.6	3.3	2.2	25.7	5.2	92.5	5.1	91.7	5.6	90.1	5.7	37.6	6.6	34.8	6.1	28.3	5.8	11.4	4.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0	NA	0.6	NA	2275	181	116	104	61	67	35	32	101	66	28	31	126	101	121	79	96	87	179	123	166	122	212	67	390	158	212	90	103	62	136	83	99	79	42	39	52	56	96	77	43	40	255	180	86	63	651	155	2031	228	2020	233	1962	238	644	168	602	157	432	135	193	101	52.8	5.0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5.1	4.5	2.7	2.9	1.5	1.4	4.4	3.0	1.2	1.4	5.5	4.5	5.3	3.4	4.2	3.8	7.9	5.2	7.3	5.3	9.3	2.9	17.1	6.5	9.3	4.0	4.5	2.7	6.0	3.6	4.4	3.5	1.8	1.7	2.3	2.5	4.2	3.4	1.9	1.8	11.2	7.8	3.8	2.8	28.6	6.7	89.3	7.6	88.8	7.8	86.2	7.9	28.3	7.4	26.5	6.9	19.0	5.9	8.5	4.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1355	182	0	14	19	18	8	15	18	17	35	54	2	4	81	64	61	42	84	63	58	56	99	57	168	100	125	66	168	70	208	113	75	54	84	58	62	68	27	29	18	17	45	45	35	54	281	106	1328	180	1310	180	1310	180	722	145	663	138	597	145	221	106	61.7	4.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.0	3.1	1.4	1.3	0.6	1.1	1.3	1.3	2.6	4.0	0.1	0.3	6.0	4.9	4.5	3.0	6.2	4.6	4.3	4.0	7.3	4.0	12.4	6.7	9.2	4.8	12.4	5.4	15.4	8.1	5.5	4.0	6.2	4.1	4.6	4.9	2.0	2.1	1.3	1.3	3.3	3.3	2.6	4.0	20.7	7.7	98.0	2.1	96.7	3.3	96.7	3.3	53.3	9.3	48.9	9.2	44.1	9.7	16.3	7.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Census Tract 101; Barnstable County; Massachusetts	1996	193	1516	153	1307	156	467	134	917	187	796	190	95	67	277	112	67	51	448	139	343	128	674	123	81	41	217	68	238	94	92	65	11	18	35	43	3630	43	2932	248	27	28	29	46	80	78	0	14	204	255	358	193	419	269	2894	248	1996	193	76.0	6.9	65.5	7.9	23.4	6.6	45.9	8.0	39.9	7.6	4.8	3.2	13.9	5.4	3.4	2.6	22.4	6.4	17.2	6.2	674	123	12.0	6.4	32.2	9.1	35.3	11.3	13.6	9.3	1.6	2.6	5.2	6.2	3630	43	80.8	6.7	0.7	0.8	0.8	1.3	2.2	2.2	0	1.2	5.6	7.0	9.9	5.3	11.5	7.4	79.7	6.7	153475	26633	158299	29936	168706	33084	41719	24859	35694	10298	20134	2232	8983	1528	NA	NA	4276	5494	27386	6783	12211	3060	183230	36945	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	86915	16421	101029	17651	NA	NA	NA	NA	15549	19372	NA	NA	NA	NA	33153	11648	36322	33983	99199	17569	NA	2007	229	33.9	10.1	25.9	7.8	8.1	5.6	6.1	4.9	2.0	2.9	0	2.1	1.14	0.10	1.9	1.9	15.7	7.2	9.6	5.1	2.4	2.2	36.4	8.9	95.7	2.9	79.7	6.6	16.0	6.4	4.3	2.9	96.0	3.3	70.4	7.4	25.6	6.8	4.0	3.3	100	2.1	72.8	7.2	27.2	7.2	0	2.1	1277	245	2.8	2.8	0.0	3.3	2.3	2.2	2.3	2.7	2.4	2.1	4.7	3.6	16.4	6.5	18.6	7.6	4.2	3.8	46.2	12.3	53.4	10.6	18.7	8.7	3.1	2.6	5.5	4.9	2.2	2.7	6.3	5.5	2.2	2.6	0.6	1.0	8.0	4.4	18.9	5.7	2002	229	3.9	2.9	51.0	9.8	33.4	9.6	11.6	5.8	17.4	NA	10.6	NA	10.1	NA	31.5	NA	15.2	NA	0.0	NA	1381	202	30.1	10.8	22.9	10.1	7.2	6.7	4.3	4.8	2.9	4.3	0	3.0	1.16	0.16	2.8	2.7	12.5	6.7	12.4	6.5	2.5	2.8	39.7	10.3	94.5	4.0	76.1	8.9	18.4	8.7	5.5	4.0	95.1	4.0	65.0	9.8	30.1	9.3	4.9	4.0	100	3.0	68.6	9.5	31.4	9.5	0	3.0	833	175	3.0	3.7	0.0	5.0	2.2	2.5	3.5	4.2	2.4	2.5	6.4	4.9	11.4	6.8	23.3	11.2	1.1	1.8	46.8	13.8	48.0	11.9	15.8	8.0	3.6	3.5	7.1	7.0	3.4	4.1	9.6	8.6	3.4	3.9	0.0	5.0	9.1	5.9	22.7	8.1	1376	203	3.4	3.2	56.5	11.1	26.6	9.9	13.4	7.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	626	162	42.3	16.2	32.4	13.8	9.9	11.2	9.9	11.2	0.0	6.6	0	6.6	1.13	0.15	0.0	6.6	22.8	15.6	3.5	5.2	2.2	3.4	29.1	13.6	98.2	2.9	87.5	7.8	10.7	6.6	1.8	2.9	97.8	3.4	82.1	8.8	15.7	8.1	2.2	3.4	100	6.6	82.1	8.8	17.9	8.8	0	6.6	444	154	2.5	4.1	0.0	9.1	2.7	4.1	0.0	9.1	2.5	4.1	1.6	2.7	25.9	13.5	9.9	8.5	9.9	10.1	45.0	17.6	63.5	20.3	24.1	19.5	2.3	3.6	2.5	4.1	0.0	9.1	0.0	9.1	0.0	9.1	1.8	3.0	5.9	6.3	11.8	5.4	626	162	5.1	6.0	39.0	15.5	48.2	16.5	7.7	7.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	25	001	010100	25001010100	101	Census Tract 101	G5020	S	25046216	12765872	42.05983	-70.20041	MULTIPOLYGON (((-70.25001 4…
1400000US25001010206	Census Tract 102.06; Barnstable County; Massachusetts	4352	333	130	72	241	145	209	139	92	44	107	107	105	104	46	51	421	271	163	78	155	101	305	287	247	113	735	202	350	153	477	169	317	133	163	117	89	49	450	259	55	42	635	273	144	110	934	253	3745	322	3717	328	3680	318	2131	368	1779	344	1396	289	569	208	59.1	4.7	91.6	13.3	87.5	20.1	60.1	17.6	27.4	12.2	NA	NA	NA	NA	NA	NA	3.0	1.6	5.5	3.2	4.8	3.0	2.1	1.0	2.5	2.5	2.4	2.4	1.1	1.2	9.7	6.0	3.7	1.9	3.6	2.3	7.0	6.5	5.7	2.5	16.9	4.6	8.0	3.5	11.0	3.9	7.3	3.1	3.7	2.7	2.0	1.1	10.3	5.6	1.3	1.0	14.6	5.9	3.3	2.6	21.5	5.8	86.1	5.7	85.4	5.9	84.6	5.8	49.0	8.7	40.9	8.1	32.1	6.9	13.1	4.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0	NA	1.3	NA	2081	196	86	53	218	139	65	59	51	42	107	107	85	99	29	30	141	129	83	59	41	42	208	195	62	65	338	132	89	62	216	104	110	75	119	93	33	26	283	141	42	42	411	141	116	107	496	176	1685	193	1670	196	1661	196	905	202	780	199	567	166	262	118	53.1	7.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	4.1	2.5	10.5	6.4	3.1	2.9	2.5	2.1	5.1	5.3	4.1	4.7	1.4	1.4	6.8	6.1	4.0	2.7	2.0	2.0	10.0	9.3	3.0	3.1	16.2	6.1	4.3	3.1	10.4	5.2	5.3	3.6	5.7	4.4	1.6	1.3	13.6	6.5	2.0	2.0	19.8	6.3	5.6	5.3	23.8	8.2	81.0	6.2	80.2	6.3	79.8	6.4	43.5	9.5	37.5	9.7	27.2	8.1	12.6	5.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2271	260	44	52	23	26	144	131	41	30	0	14	20	31	17	25	280	160	80	69	114	82	97	96	185	94	397	118	261	129	261	111	207	117	44	51	56	36	167	142	13	22	224	167	28	33	438	156	2060	246	2047	248	2019	241	1226	228	999	213	829	182	307	120	61.0	2.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1.9	2.2	1.0	1.1	6.3	5.5	1.8	1.3	0.0	1.9	0.9	1.4	0.7	1.1	12.3	6.9	3.5	3.0	5.0	3.5	4.3	4.2	8.1	4.0	17.5	5.3	11.5	5.5	11.5	4.9	9.1	5.2	1.9	2.3	2.5	1.6	7.4	5.9	0.6	1.0	9.9	6.9	1.2	1.4	19.3	6.4	90.7	6.8	90.1	6.9	88.9	6.8	54.0	10.1	44.0	9.1	36.5	8.0	13.5	5.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Census Tract 102.06; Barnstable County; Massachusetts	1975	231	1353	195	1208	202	513	184	1000	226	940	183	73	52	89	61	8	14	762	169	259	109	1195	197	352	142	282	126	450	204	51	40	60	48	0	14	4352	333	3793	347	11	13	0	14	36	46	0	14	69	74	443	348	53	72	3757	345	1975	231	68.5	7.1	61.2	8.1	26.0	8.8	50.6	9.1	47.6	7.8	3.7	2.8	4.5	3.2	0.4	0.7	38.6	7.6	13.1	5.2	1195	197	29.5	10.6	23.6	9.9	37.7	14.3	4.3	3.5	5.0	4.4	0.0	3.5	4352	333	87.2	7.6	0.3	0.3	0.0	1.0	0.8	1.1	0	1.0	1.6	1.7	10.2	7.6	1.2	1.6	86.3	7.8	130711	20921	103136	16801	87469	12629	66043	28051	40977	30861	28542	3637	8989	3852	NA	NA	NA	NA	57902	13638	21743	9005	169144	34226	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	60001	11058	65503	12000	NA	NA	NA	NA	NA	NA	NA	NA	38009	30629	16946	10499	NA	NA	65759	12131	NA	1949	275	74.5	8.8	65.7	7.8	8.8	6.1	8.8	6.1	0.0	2.2	0	2.2	1.06	0.04	0.0	2.2	4.2	3.8	0.0	2.2	0.0	2.2	21.3	8.8	98.0	3.1	93.2	4.8	4.8	3.7	2.0	3.1	0.0	2.2	0.0	2.2	0.0	2.2	100.0	2.2	100	2.2	41.3	9.6	58.7	9.6	0	2.2	1534	267	0.0	2.7	2.5	3.9	11.2	10.2	4.7	4.0	4.0	3.6	13.4	6.7	11.9	5.9	3.0	3.7	7.1	5.3	42.1	9.6	24.6	10.8	13.4	7.7	3.3	2.7	9.1	6.9	10.4	7.8	15.2	9.1	3.1	2.9	2.3	2.4	18.6	11.9	35.7	13.5	1948	275	5.6	4.3	19.9	8.5	52.6	14.1	21.9	13.0	21.5	NA	25.7	NA	19.7	NA	43.9	NA	30.4	NA	2.9	NA	757	166	74.4	17.6	65.7	15.2	8.7	8.1	8.7	8.1	0.0	5.5	0	5.5	1.06	0.06	0.0	5.5	5.3	7.0	0.0	5.5	0.0	5.5	20.3	16.1	97.2	4.3	94.8	5.6	2.4	3.6	2.8	4.3	0.0	5.5	0.0	5.5	0.0	5.5	100.0	5.5	100	5.5	54.2	15.8	45.8	15.8	0	5.5	603	189	0.0	6.8	3.5	5.3	0.0	6.8	11.9	9.9	10.1	9.2	15.8	11.1	12.4	8.8	1.0	1.3	8.1	9.1	37.1	14.3	25.4	13.5	18.1	13.5	6.6	6.4	5.8	8.4	10.1	9.2	22.6	13.2	2.5	3.5	3.2	4.7	5.8	6.7	24.1	9.2	756	166	0.0	5.5	9.4	8.0	57.3	18.8	33.3	19.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1192	250	74.6	10.1	65.7	11.1	8.9	6.4	8.9	6.4	0.0	3.5	0	3.5	1.06	0.05	0.0	3.5	3.5	4.0	0.0	3.5	0.0	3.5	21.9	10.5	98.5	2.4	92.1	5.3	6.4	4.8	1.5	2.4	0.0	3.5	0.0	3.5	0.0	3.5	100.0	3.5	100	3.5	33.1	13.0	66.9	13.0	0	3.5	931	234	0.0	4.5	1.9	3.1	18.5	15.5	0.0	4.5	0.0	4.5	11.9	9.1	11.6	8.5	4.3	5.9	6.4	5.7	45.3	16.1	24.1	13.4	10.4	8.9	1.1	1.4	11.2	10.9	10.6	8.9	10.4	8.8	3.5	4.4	1.8	2.7	26.9	16.7	43.2	18.5	1192	250	9.2	7.0	26.5	11.2	49.7	15.0	14.6	11.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	25	001	010206	25001010206	102.06	Census Tract 102.06	G5020	S	51240906	18828934	41.92264	-70.01537	MULTIPOLYGON (((-70.08245 4…
1400000US25001010208	Census Tract 102.08; Barnstable County; Massachusetts	1627	332	0	14	31	31	16	16	33	44	38	49	1	4	0	14	0	14	17	17	143	93	114	115	119	78	302	208	332	140	284	147	150	97	36	49	11	21	47	47	0	14	47	47	71	91	89	86	1580	330	1580	330	1547	321	1115	264	900	222	813	199	197	126	65.0	3.3	117.5	36.6	112.1	52.8	106.0	49.7	6.1	7.0	NA	NA	NA	NA	NA	NA	0.0	2.6	1.9	2.0	1.0	1.0	2.0	2.7	2.3	3.0	0.1	0.2	0.0	2.6	0.0	2.6	1.0	1.1	8.8	5.7	7.0	6.6	7.3	4.9	18.6	11.2	20.4	8.7	17.5	8.2	9.2	6.3	2.2	2.9	0.7	1.3	2.9	2.9	0.0	2.6	2.9	2.9	4.4	5.6	5.5	5.2	97.1	2.9	97.1	2.9	95.1	2.9	68.5	8.5	55.3	10.3	50.0	10.5	12.1	8.0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0	NA	0.0	NA	879	238	0	14	16	16	16	16	33	44	1	3	1	4	0	14	0	14	0	14	79	63	99	112	30	45	224	209	125	73	170	90	74	71	0	14	11	21	32	32	0	14	32	32	34	44	35	44	847	240	847	240	814	235	604	205	467	137	380	121	85	73	64.3	6.2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.0	4.7	1.8	1.9	1.8	1.9	3.8	5.1	0.1	0.3	0.1	0.4	0.0	4.7	0.0	4.7	0.0	4.7	9.0	7.6	11.3	11.8	3.4	5.1	25.5	19.9	14.2	8.9	19.3	9.8	8.4	8.7	0.0	4.7	1.3	2.5	3.6	3.8	0.0	4.7	3.6	3.8	3.9	5.1	4.0	5.1	96.4	3.8	96.4	3.8	92.6	4.9	68.7	13.6	53.1	15.3	43.2	16.1	9.7	9.0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	748	174	0	14	15	16	0	14	0	14	37	49	0	14	0	14	0	14	17	17	64	63	15	24	89	54	78	69	207	97	114	80	76	75	36	49	0	14	15	16	0	14	15	16	37	49	54	44	733	171	733	171	733	171	511	130	433	131	433	131	112	100	66.2	1.0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.0	5.5	2.0	2.1	0.0	5.5	0.0	5.5	4.9	6.4	0.0	5.5	0.0	5.5	0.0	5.5	2.3	2.3	8.6	8.0	2.0	3.1	11.9	6.6	10.4	9.2	27.7	12.6	15.2	10.4	10.2	9.6	4.8	6.1	0.0	5.5	2.0	2.1	0.0	5.5	2.0	2.1	4.9	6.4	7.2	5.7	98.0	2.1	98.0	2.1	98.0	2.1	68.3	9.6	57.9	11.5	57.9	11.5	15.0	12.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Census Tract 102.08; Barnstable County; Massachusetts	1031	243	594	232	513	217	180	122	562	219	563	139	66	77	91	90	52	59	516	216	161	98	420	148	226	148	107	63	64	50	0	14	23	30	0	14	1627	332	1626	332	0	14	0	14	0	14	0	14	1	3	0	14	1	3	1626	332	1031	243	57.6	14.5	49.8	16.7	17.5	10.2	54.5	15.9	54.6	14.3	6.4	7.4	8.8	8.6	5.0	5.6	50.0	15.6	15.6	9.9	420	148	53.8	21.7	25.5	18.3	15.2	11.5	0.0	9.6	5.5	7.5	0.0	9.6	1627	332	99.9	0.2	0.0	2.6	0.0	2.6	0.0	2.6	0	2.6	0.1	0.2	0.0	2.6	0.1	0.2	99.9	0.2	129342	35085	94420	32128	85807	31672	67036	26993	27346	10522	28420	5704	NA	NA	NA	NA	NA	NA	79384	52178	25286	27264	142995	45206	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	82607	25319	82657	25364	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	82657	25364	NA	769	271	55.0	19.3	55.0	19.3	0.0	5.4	0.0	5.4	0.0	5.4	0	5.4	1.00	0.02	0.0	5.4	0.3	0.5	0.0	5.4	5.7	5.7	39.0	19.7	93.2	5.8	91.0	6.4	2.2	2.5	6.8	5.8	0.0	5.4	0.0	5.4	0.0	5.4	100.0	5.4	100	5.4	79.5	11.8	20.5	11.8	0	5.4	469	183	0.0	8.7	0.0	8.7	0.0	8.7	0.0	8.7	3.6	4.0	1.7	3.9	41.8	26.7	32.2	22.6	0.0	8.7	20.7	15.6	49.9	19.2	8.1	8.4	9.8	12.0	22.8	7.4	0.0	8.7	0.0	8.7	0.0	8.7	0.0	8.7	9.4	7.9	23.7	13.2	766	271	2.7	4.4	37.1	19.5	35.1	18.6	25.1	12.6	9.0	NA	0.0	NA	3.5	NA	21.7	NA	26.2	NA	0.0	NA	435	218	35.9	23.6	35.9	23.6	0.0	9.3	0.0	9.3	0.0	9.3	0	9.3	1.01	0.03	0.0	9.3	0.2	0.6	0.0	9.3	4.4	6.9	59.5	25.1	95.6	6.9	95.6	6.9	0.0	9.3	4.4	6.9	0.0	9.3	0.0	9.3	0.0	9.3	100.0	9.3	100	9.3	93.1	7.6	6.9	7.6	0	9.3	176	89	0.0	21.3	0.0	21.3	0.0	21.3	0.0	21.3	0.0	21.3	0.0	21.3	44.3	29.1	31.8	25.8	0.0	21.3	23.9	22.7	56.8	27.6	13.1	16.5	13.1	16.1	6.3	11.3	0.0	21.3	0.0	21.3	0.0	21.3	0.0	21.3	10.8	15.7	26.5	27.3	433	217	4.8	7.8	52.2	26.5	35.3	25.2	7.6	7.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	334	124	79.9	13.3	79.9	13.3	0.0	11.9	0.0	11.9	0.0	11.9	0	11.9	1.01	0.03	0.0	11.9	0.3	1.0	0.0	11.9	7.5	10.8	12.3	11.6	90.1	12.4	85.0	13.1	5.1	5.4	9.9	12.4	0.0	11.9	0.0	11.9	0.0	11.9	100.0	11.9	100	11.9	61.7	21.2	38.3	21.2	0	11.9	293	114	0.0	13.5	0.0	13.5	0.0	13.5	0.0	13.5	5.8	6.2	2.7	6.2	40.3	28.2	32.4	25.1	0.0	13.5	18.8	16.4	45.7	17.7	5.1	8.1	7.8	10.0	32.8	10.3	0.0	13.5	0.0	13.5	0.0	13.5	0.0	13.5	8.5	8.4	22.0	13.0	333	124	0.0	12.0	17.4	12.9	34.8	16.7	47.7	18.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	25	001	010208	25001010208	102.08	Census Tract 102.08	G5020	S	54268861	11461462	42.01356	-70.06415	MULTIPOLYGON (((-70.15464 4…
1400000US25001010304	Census Tract 103.04; Barnstable County; Massachusetts	2739	315	42	37	50	41	99	63	102	76	133	90	57	40	92	71	35	47	36	34	97	60	243	105	289	110	391	150	260	110	214	98	142	86	117	70	340	194	149	86	81	66	272	105	154	96	455	133	2542	293	2467	299	2428	304	1464	252	1248	258	1073	265	599	209	60.6	2.1	73.9	16.5	96.5	31.9	77.0	30.1	19.5	7.6	NA	NA	NA	NA	NA	NA	1.5	1.3	1.8	1.5	3.6	2.2	3.7	2.8	4.9	3.2	2.1	1.5	3.4	2.5	1.3	1.7	1.3	1.2	3.5	2.2	8.9	3.7	10.6	3.7	14.3	5.2	9.5	3.6	7.8	3.5	5.2	3.1	4.3	2.5	12.4	7.2	5.4	3.0	3.0	2.4	9.9	3.6	5.6	3.4	16.6	4.3	92.8	3.3	90.1	3.6	88.6	4.0	53.5	7.7	45.6	9.0	39.2	9.2	21.9	7.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0	NA	0.6	NA	1164	188	7	14	21	19	14	17	35	39	35	28	37	38	28	33	35	47	10	15	31	28	111	78	135	64	214	115	151	59	65	41	88	74	58	37	89	75	35	24	14	16	56	30	56	44	180	77	1116	178	1108	178	1069	183	665	149	533	126	451	118	235	98	60.7	2.0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.6	1.2	1.8	1.6	1.2	1.5	3.0	3.4	3.0	2.5	3.2	3.2	2.4	2.7	3.0	3.9	0.9	1.3	2.7	2.4	9.5	6.5	11.6	5.6	18.4	8.7	13.0	5.1	5.6	3.6	7.6	6.1	5.0	3.3	7.6	6.3	3.0	2.0	1.2	1.4	4.8	2.4	4.8	3.8	15.5	6.1	95.9	2.6	95.2	2.4	91.8	4.3	57.1	7.9	45.8	10.0	38.7	9.2	20.2	7.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1575	251	35	36	29	34	85	58	67	64	98	87	20	19	64	50	0	14	26	32	66	56	132	80	154	86	177	86	109	77	149	82	54	35	59	39	251	156	114	78	67	64	216	105	98	87	275	96	1426	240	1359	239	1359	239	799	195	715	208	622	201	364	160	60.2	5.1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2.2	2.2	1.8	2.1	5.4	3.7	4.3	4.0	6.2	5.2	1.3	1.2	4.1	3.0	0.0	2.7	1.7	2.0	4.2	3.4	8.4	4.7	9.8	5.2	11.2	5.5	6.9	4.7	9.5	4.9	3.4	2.2	3.7	2.4	15.9	9.8	7.2	4.9	4.3	4.0	13.7	6.4	6.2	5.2	17.5	5.1	90.5	5.3	86.3	6.4	86.3	6.4	50.7	10.9	45.4	12.1	39.5	11.6	23.1	10.0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Census Tract 103.04; Barnstable County; Massachusetts	1363	222	914	169	820	174	192	90	550	156	686	201	0	14	48	43	22	33	563	174	208	114	764	129	185	83	178	70	200	83	136	75	65	56	0	14	2739	315	2507	285	0	14	0	14	102	79	0	14	0	14	130	94	10	17	2497	287	1363	222	67.1	9.9	60.2	11.5	14.1	6.0	40.4	9.2	50.3	11.0	0.0	3.1	3.5	3.1	1.6	2.4	41.3	9.7	15.3	8.4	764	129	24.2	9.8	23.3	8.3	26.2	9.9	17.8	9.7	8.5	7.1	0.0	5.4	2739	315	91.5	4.4	0.0	1.5	0.0	1.5	3.7	2.8	0	1.5	0.0	1.5	4.7	3.4	0.4	0.6	91.2	4.6	120217	18886	117523	22741	115888	23422	64519	51019	25166	7814	27356	3977	NA	NA	NA	NA	NA	NA	32557	10018	26360	11603	153755	27293	72738	26722	138498	53812	192021	45072	232916	75715	142746	17709	NA	NA	60625	9570	61614	11242	NA	NA	NA	NA	23841	15278	NA	NA	NA	NA	70425	48012	NA	NA	61275	11290	NA	1265	281	77.6	10.3	74.9	10.9	2.8	3.2	0.7	1.1	2.1	3.0	0	3.3	1.02	0.03	1.2	1.9	0.0	3.3	0.0	3.3	0.0	3.3	21.2	10.1	98.8	1.9	94.1	4.8	4.7	4.8	1.2	1.9	15.3	7.7	3.2	2.7	12.1	6.4	84.7	7.7	100	3.3	31.0	10.7	69.0	10.7	0	3.3	997	260	1.7	2.7	1.2	2.0	7.4	5.7	11.8	8.4	6.7	5.1	17.0	8.2	6.5	4.5	24.9	10.2	7.9	5.9	14.8	7.5	16.9	7.2	18.3	7.5	15.8	8.4	5.6	4.8	2.4	2.6	10.8	5.9	7.7	6.8	1.5	2.5	21.0	16.4	36.2	14.7	1265	281	0.0	3.3	22.0	10.3	45.6	12.8	32.4	14.1	10.0	NA	17.6	NA	16.4	NA	32.3	NA	28.1	NA	0.0	NA	619	165	70.9	15.7	68.7	16.5	2.3	3.3	0.0	6.6	2.3	3.3	0	6.6	1.02	0.03	2.4	4.0	0.0	6.6	0.0	6.6	0.0	6.6	26.7	15.3	97.6	4.0	94.8	4.0	2.7	4.2	2.4	4.0	16.0	9.4	4.5	5.1	11.5	8.2	84.0	9.4	100	6.6	34.9	15.8	65.1	15.8	0	6.6	454	115	3.7	5.8	2.6	4.1	6.8	7.5	18.9	13.0	6.2	7.0	18.7	9.8	3.7	4.8	13.4	11.7	14.3	12.8	11.5	8.5	6.6	5.7	26.9	13.1	18.9	11.0	4.4	5.4	3.1	4.6	15.4	10.5	6.2	6.7	3.3	5.6	15.2	15.2	36.5	22.0	619	165	0.0	6.6	22.1	15.8	47.7	14.4	30.2	14.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	646	190	84.1	9.5	80.8	10.4	3.3	3.6	1.4	2.2	1.9	2.9	0	6.4	1.02	0.03	0.0	6.4	0.0	6.4	0.0	6.4	0.0	6.4	15.9	9.5	100.0	6.4	93.3	8.2	6.7	8.2	0.0	6.4	14.6	10.7	1.9	2.9	12.7	10.4	85.4	10.7	100	6.4	27.2	11.4	72.8	11.4	0	6.4	543	191	0.0	7.5	0.0	7.5	7.9	8.2	5.9	9.2	7.2	8.8	15.5	12.5	8.8	7.1	34.4	13.0	2.6	4.4	17.7	10.2	25.4	12.3	11.0	7.7	13.3	10.6	6.6	7.0	1.8	2.9	7.0	6.8	9.0	10.6	0.0	7.5	25.8	20.2	35.9	15.7	646	190	0.0	6.4	21.8	10.6	43.7	14.4	34.5	16.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	25	001	010304	25001010304	103.04	Census Tract 103.04	G5020	S	18347803	7830614	41.82511	-69.97620	MULTIPOLYGON (((-70.0148 41…
1400000US25001010306	Census Tract 103.06; Barnstable County; Massachusetts	2985	312	92	82	116	113	132	101	78	53	142	94	173	116	95	105	139	120	81	60	165	98	163	82	224	130	254	88	234	93	582	198	132	94	110	80	73	78	248	94	66	43	406	150	154	95	708	160	2619	291	2579	282	2488	274	1385	290	1318	292	1131	285	315	200	57.6	5.7	91.2	18.7	106.1	32.8	78.1	29.0	28.0	10.8	NA	NA	NA	NA	NA	NA	3.1	2.7	3.9	3.7	4.4	3.4	2.6	1.7	4.8	3.1	5.8	3.9	3.2	3.5	4.7	3.9	2.7	2.0	5.5	3.3	5.5	2.8	7.5	4.3	8.5	2.9	7.8	3.1	19.5	6.5	4.4	3.0	3.7	2.6	2.4	2.6	8.3	2.9	2.2	1.5	13.6	4.6	5.2	3.1	23.7	4.8	87.7	5.0	86.4	4.6	83.4	4.4	46.4	8.4	44.2	8.5	37.9	8.5	10.6	6.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0	NA	4.4	NA	1424	220	58	69	63	84	39	45	61	43	108	81	89	64	68	86	77	63	40	38	62	71	58	45	78	67	133	64	68	38	213	104	94	85	42	33	73	78	102	73	49	40	209	130	120	83	443	122	1238	183	1215	183	1124	175	623	143	604	145	490	132	209	136	51.0	13.9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	4.1	4.5	4.4	5.6	2.7	3.3	4.3	3.2	7.6	5.6	6.3	4.6	4.8	5.7	5.4	4.5	2.8	2.6	4.4	5.0	4.1	3.2	5.5	4.5	9.3	4.7	4.8	2.7	15.0	7.7	6.6	5.9	2.9	2.4	5.1	5.4	7.2	4.7	3.4	3.0	14.7	8.1	8.4	5.6	31.1	6.9	86.9	8.8	85.3	8.1	78.9	7.6	43.8	10.9	42.4	11.0	34.4	9.8	14.7	9.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1561	218	34	49	53	53	93	71	17	22	34	46	84	74	27	27	62	63	41	40	103	61	105	60	146	80	121	49	166	76	369	140	38	31	68	69	0	14	146	69	17	22	197	88	34	46	265	83	1381	189	1364	186	1364	186	762	182	714	183	641	179	106	75	59.3	5.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2.2	3.1	3.4	3.4	6.0	4.4	1.1	1.4	2.2	2.9	5.4	4.6	1.7	1.8	4.0	3.8	2.6	2.6	6.6	3.8	6.7	4.1	9.4	5.3	7.8	3.1	10.6	4.9	23.6	7.8	2.4	1.9	4.4	4.3	0.0	2.7	9.4	4.0	1.1	1.4	12.6	4.9	2.2	2.9	17.0	4.9	88.5	5.1	87.4	4.9	87.4	4.9	48.8	8.7	45.7	8.9	41.1	9.0	6.8	4.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Census Tract 103.06; Barnstable County; Massachusetts	1361	172	878	161	770	145	225	113	427	136	601	164	36	31	39	37	14	23	625	148	225	117	944	143	365	136	250	94	190	90	107	57	22	24	10	15	2985	312	2605	391	223	248	10	17	18	27	0	14	0	14	129	125	0	14	2605	391	1361	172	64.5	9.2	56.6	9.6	16.5	7.7	31.4	8.3	44.2	10.2	2.6	2.3	2.9	2.8	1.0	1.6	45.9	8.9	16.5	8.3	944	143	38.7	11.6	26.5	9.6	20.1	9.1	11.3	6.3	2.3	2.5	1.1	1.7	2985	312	87.3	10.0	7.5	8.2	0.3	0.6	0.6	0.9	0	1.4	0.0	1.4	4.3	4.2	0.0	1.4	87.3	10.0	105017	15951	91399	21664	89291	22500	51087	20411	30088	11390	28126	5909	NA	NA	NA	NA	NA	NA	47207	9377	13924	7798	118031	13800	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	48023	6921	48391	6533	54398	82995	NA	NA	NA	NA	NA	NA	NA	NA	20884	20371	NA	NA	48391	6533	NA	1154	253	82.2	6.4	80.9	6.1	1.3	2.1	1.3	2.1	0.0	3.6	0	3.6	1.01	0.01	1.3	2.1	0.0	3.6	1.0	1.7	1.9	3.2	13.6	5.4	97.4	2.9	92.3	5.2	5.1	4.7	2.6	2.9	48.8	14.3	6.0	4.1	42.8	12.6	51.2	14.3	100	3.6	38.3	9.4	61.7	9.4	0	3.6	997	235	9.8	7.8	0.0	4.2	2.3	2.7	1.1	1.7	5.4	5.1	14.7	7.3	15.3	9.3	13.4	6.2	8.6	6.9	29.2	9.0	19.9	8.7	7.1	4.6	17.4	8.2	21.3	9.9	7.4	5.4	17.6	9.5	4.2	3.8	0.0	4.2	5.2	4.0	21.3	3.1	1154	253	2.2	3.3	24.2	10.6	38.7	12.7	34.9	14.4	14.1	NA	20.5	NA	18.5	NA	31.4	NA	24.6	NA	4.9	NA	615	166	81.8	9.4	81.8	9.4	0.0	6.7	0.0	6.7	0.0	6.7	0	6.7	1.00	0.02	2.4	4.0	0.0	6.7	0.0	6.7	1.6	2.8	14.1	9.1	95.1	5.6	89.6	8.0	5.5	6.2	4.9	5.6	44.9	16.0	7.0	6.0	37.9	15.1	55.1	16.0	100	6.7	42.9	13.6	57.1	13.6	0	6.7	528	154	12.3	12.5	0.0	7.7	4.4	5.1	2.1	3.1	10.2	10.0	17.0	11.9	14.6	14.2	9.1	8.0	9.1	9.9	21.2	13.3	22.7	12.4	2.3	4.3	11.9	10.1	23.9	14.6	8.7	8.1	20.6	10.0	8.0	7.3	0.0	7.7	1.9	3.3	21.0	4.0	615	166	2.0	3.2	21.5	15.0	46.7	14.3	29.9	14.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	539	147	82.7	9.1	80.0	8.4	2.8	4.5	2.8	4.5	0.0	7.6	0	7.6	1.01	0.03	0.0	7.6	0.0	7.6	2.0	3.7	2.2	3.8	13.0	9.6	100.0	7.6	95.4	5.0	4.6	5.0	0.0	7.6	53.2	16.3	4.8	5.8	48.4	14.4	46.8	16.3	100	7.6	33.0	12.5	67.0	12.5	0	7.6	469	139	7.0	8.2	0.0	8.7	0.0	8.7	0.0	8.7	0.0	8.7	12.2	7.7	16.2	11.2	18.3	9.8	8.1	7.4	38.2	15.4	16.6	13.5	12.6	9.2	23.5	12.5	18.3	10.0	6.0	5.9	14.1	14.2	0.0	8.7	0.0	8.7	9.0	6.6	21.6	4.4	539	147	2.4	3.6	27.3	11.4	29.7	13.7	40.6	15.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	25	001	010306	25001010306	103.06	Census Tract 103.06	G5020	S	17828291	1730719	41.85938	-69.98263	MULTIPOLYGON (((-70.01083 4…
1400000US25001010400	Census Tract 104; Barnstable County; Massachusetts	3342	421	108	70	96	56	88	60	151	93	87	81	87	80	54	63	241	126	127	113	143	101	162	82	167	90	217	132	486	244	465	192	330	156	134	77	199	81	184	91	151	93	443	125	87	81	747	228	3001	447	2899	449	2876	451	1831	492	1776	492	1614	451	663	204	64.6	7.0	64.4	15.8	160.1	50.0	125.6	47.3	34.5	11.4	NA	NA	NA	NA	NA	NA	3.2	2.1	2.9	1.8	2.6	1.9	4.5	2.8	2.6	2.4	2.6	2.3	1.6	1.9	7.2	3.9	3.8	3.4	4.3	3.1	4.8	2.6	5.0	2.7	6.5	3.8	14.5	6.2	13.9	4.8	9.9	4.6	4.0	2.2	6.0	2.5	5.5	2.9	4.5	2.8	13.3	4.2	2.6	2.4	22.4	7.1	89.8	3.8	86.7	4.2	86.1	4.6	54.8	10.0	53.1	10.0	48.3	9.3	19.8	5.8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0	NA	0.0	NA	1309	312	37	42	0	14	37	32	76	62	87	81	22	35	27	47	113	54	15	25	57	45	49	41	69	67	71	50	323	265	54	43	174	107	23	25	75	60	37	32	76	62	150	82	87	81	340	108	1193	301	1159	299	1136	297	720	294	699	292	649	292	272	127	64.9	10.3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2.8	3.2	0.0	3.2	2.8	2.5	5.8	4.5	6.6	5.9	1.7	2.7	2.1	3.8	8.6	4.9	1.1	1.9	4.4	3.4	3.7	3.2	5.3	5.1	5.4	3.9	24.7	16.9	4.1	3.3	13.3	8.3	1.8	1.9	5.7	4.4	2.8	2.5	5.8	4.5	11.5	6.0	6.6	5.9	26.0	9.2	91.1	5.0	88.5	6.0	86.8	6.5	55.0	13.6	53.4	13.7	49.6	14.4	20.8	9.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2033	232	71	67	96	56	51	42	75	64	0	14	65	58	27	31	128	93	112	107	86	59	113	63	98	67	146	120	163	73	411	182	156	76	111	71	124	61	147	81	75	64	293	128	0	14	407	202	1808	239	1740	235	1740	235	1111	236	1077	237	965	196	391	116	64.5	5.7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	3.5	3.2	4.7	2.8	2.5	2.1	3.7	3.1	0.0	2.1	3.2	2.8	1.3	1.5	6.3	4.4	5.5	5.1	4.2	3.0	5.6	3.2	4.8	3.3	7.2	5.7	8.0	3.8	20.2	8.5	7.7	3.7	5.5	3.4	6.1	3.0	7.2	3.9	3.7	3.1	14.4	6.1	0.0	2.1	20.0	9.1	88.9	6.1	85.6	6.1	85.6	6.1	54.6	10.0	53.0	10.0	47.5	8.6	19.2	5.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Census Tract 104; Barnstable County; Massachusetts	1587	178	914	165	843	175	369	153	532	148	898	197	72	65	127	65	12	20	666	143	71	52	808	172	229	121	192	119	215	71	74	75	68	54	30	35	3342	421	3198	431	10	16	0	14	53	70	0	14	0	14	81	89	71	84	3174	433	1587	178	57.6	8.3	53.1	9.0	23.3	9.4	33.5	8.9	56.6	9.6	4.5	4.2	8.0	4.1	0.8	1.3	42.0	8.6	4.5	3.2	808	172	28.3	12.4	23.8	12.6	26.6	10.0	9.2	9.0	8.4	6.7	3.7	4.5	3342	421	95.7	3.6	0.3	0.5	0.0	1.3	1.6	2.2	0	1.3	0.0	1.3	2.4	2.6	2.1	2.5	95.0	3.7	104850	17870	87499	19831	83945	20291	24956	15574	57228	26965	28084	4185	15900	4760	NA	NA	NA	NA	41952	8768	22214	8517	153043	27245	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	52173	9215	51099	9424	NA	NA	NA	NA	194091	14469	NA	NA	NA	NA	4895	3162	16710	20274	51217	9488	NA	1296	237	83.3	5.3	73.3	8.9	10.0	5.9	5.6	4.9	4.4	4.5	0	3.2	1.07	0.05	0.8	1.3	2.2	2.1	0.0	3.2	0.0	3.2	13.6	5.1	100.0	3.2	96.1	3.1	3.9	3.1	0.0	3.2	57.3	14.7	16.0	8.3	41.3	14.8	42.7	14.7	100	3.2	34.2	12.3	65.8	12.3	0	3.2	1120	225	0.0	3.7	0.0	3.7	3.7	3.3	10.5	9.3	6.1	4.9	3.4	3.4	18.8	9.4	13.9	7.5	8.8	5.9	34.8	10.1	23.3	10.8	18.8	8.8	13.1	7.9	8.0	5.4	5.8	5.8	15.4	9.8	6.2	3.4	3.3	3.2	6.2	8.4	21.8	6.1	1249	237	2.2	3.5	13.3	6.3	45.1	14.9	39.5	12.9	8.1	NA	9.7	NA	23.5	NA	16.3	NA	13.8	NA	0.0	NA	532	122	81.6	6.8	66.5	14.7	15.0	11.2	6.6	7.6	8.5	10.4	0	7.7	1.13	0.11	0.0	7.7	3.2	3.8	0.0	7.7	0.0	7.7	15.2	7.0	100.0	7.7	97.6	3.7	2.4	3.7	0.0	7.7	57.0	16.7	22.7	11.2	34.2	17.6	43.0	16.7	100	7.7	44.2	16.5	55.8	16.5	0	7.7	451	113	0.0	9.0	0.0	9.0	7.5	7.7	12.2	11.2	7.5	8.5	6.2	7.2	19.5	10.7	0.9	1.4	2.9	4.5	43.2	20.2	21.5	13.3	26.8	13.0	22.4	15.1	2.4	3.9	0.0	9.0	16.2	11.3	0.0	9.0	2.2	3.7	8.4	13.3	20.8	10.0	485	122	2.7	4.5	2.7	4.0	45.2	13.1	49.5	14.0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	764	209	84.6	8.3	78.0	11.4	6.5	6.2	5.0	5.5	1.6	2.5	0	5.4	1.04	0.05	1.4	2.3	1.6	2.5	0.0	5.4	0.0	5.4	12.4	7.0	100.0	5.4	95.0	4.8	5.0	4.8	0.0	5.4	57.6	16.7	11.4	8.0	46.2	16.9	42.4	16.7	100	5.4	27.2	12.8	72.8	12.8	0	5.4	669	199	0.0	6.2	0.0	6.2	1.0	2.1	9.4	14.0	5.1	5.8	1.5	2.5	18.2	14.2	22.7	13.3	12.9	9.1	29.1	13.7	24.5	13.2	13.3	13.1	6.9	7.9	11.8	8.8	9.7	8.8	14.8	14.3	10.3	6.2	4.0	4.8	4.6	5.8	22.5	4.7	764	209	1.8	3.1	20.0	10.8	45.0	19.5	33.1	15.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	25	001	010400	25001010400	104	Census Tract 104	G5020	S	15587094	8476393	41.75415	-69.98612	MULTIPOLYGON (((-70.02372 4…

2.3 Name cleaning and Column Selection

Here we will go in and rename all of the most usefull conlumns. Not every single one will map but the step will make the data much easier to work with.

# Dropp and rename columns
map_data <- map_data |>
  rename( 
    MeanIncome_fullPooled = S1902_C03_001E, 
    MeanIncome_White = S1902_C03_020E,
    MeanIncome_Black = S1902_C03_021E, 
    MeanIncome_AmericanIndian = S1902_C03_022E,
    MeanIncome_Asian = S1902_C03_023E,  
    MeanIncome_API = S1902_C03_024E, 
    MeanIncome_OtherRaces = S1902_C03_025E,
    MeanIncome_TwoOrMore = S1902_C03_026E,
    MeanIncome_Hispanic = S1902_C03_027E,
    MedianAge_fullPop = S0101_C01_032E,
    CommuteTotal_pooled = S0801_C01_001E, # aka the total number of workers
    CommuteTotal_Car = S0801_C01_002E,
    CommuteTotal_Public = S0801_C01_009E,
    CommuteTotal_InState = S0801_C01_014E,
    CommuteTotal_InCounty = S0801_C01_015E, 
    WorkedHome_Total = S0801_C01_013E,
  ) 

Since we will want to select a lot of columns, it’s a good idea to find a shortcut. Instead of typing out all of the names and creating a large, hard-to-read, and error-prone block of text, we can use pattern matching. We will use the base::grep function and a regular expression to grab the column names we want and place them in a list.

The regular expression we are using is "^[a-z]+_[a-z]+$". This will match the pattern of the names we created for the important variables. The expression can be read as ^ (start), [a-z]+ (any letters) with a single _ in between, more [a-z]+ (letters), and $ (end). We have turned the ignore.case option on because the names we want use camel case.

# custom function to select conlumn names with the pattern we name them with.

ColnumSelect <- function(x) {
  names <- grep("^[a-z]+_[a-z]+$",colnames(x), ignore.case = TRUE , value = TRUE)
  
  return(names)
}

We now use the dplyr::all_of function within the dplyr::select function to call our custom fuction that will return a list of the column names we want. ahh the joys of funcional programing lol

map_data <- map_data |>
  dplyr::select(GEOID,
                all_of(ColnumSelect(map_data)), # use Custom function
                STATEFP, COUNTYFP,TRACTCE, NAME, NAMELSAD, MTFCC,
                FUNCSTAT,ALAND, AWATER, Latitude, Longitude, geometry )

map_data |>
  head() |>
  kable(Caption = "Full Map Data") |>
  kable_material(lightable_options = "striped") |>
  scroll_box(width = "100%", height = "400px") 

GEOID	GEO_ID	MedianAge_fullPop	MeanIncome_fullPooled	MeanIncome_White	MeanIncome_Black	MeanIncome_AmericanIndian	MeanIncome_Asian	MeanIncome_API	MeanIncome_OtherRaces	MeanIncome_TwoOrMore	MeanIncome_Hispanic	CommuteTotal_pooled	CommuteTotal_Car	CommuteTotal_Public	WorkedHome_Total	CommuteTotal_InState	CommuteTotal_InCounty	STATEFP	COUNTYFP	TRACTCE	NAME	NAMELSAD	MTFCC	FUNCSTAT	ALAND	AWATER	Latitude	Longitude	geometry
25001010100	1400000US25001010100	56.1	153475	101029	NA	NA	15549	NA	NA	33153	36322	2007	33.9	1.9	36.4	95.7	79.7	25	001	010100	101	Census Tract 101	G5020	S	25046216	12765872	42.05983	-70.20041	MULTIPOLYGON (((-70.25001 4…
25001010206	1400000US25001010206	59.1	130711	65503	NA	NA	NA	NA	38009	16946	NA	1949	74.5	0.0	21.3	98.0	93.2	25	001	010206	102.06	Census Tract 102.06	G5020	S	51240906	18828934	41.92264	-70.01537	MULTIPOLYGON (((-70.08245 4…
25001010208	1400000US25001010208	65.0	129342	82657	NA	NA	NA	NA	NA	NA	NA	769	55.0	0.0	39.0	93.2	91.0	25	001	010208	102.08	Census Tract 102.08	G5020	S	54268861	11461462	42.01356	-70.06415	MULTIPOLYGON (((-70.15464 4…
25001010304	1400000US25001010304	60.6	120217	61614	NA	NA	23841	NA	NA	70425	NA	1265	77.6	1.2	21.2	98.8	94.1	25	001	010304	103.04	Census Tract 103.04	G5020	S	18347803	7830614	41.82511	-69.97620	MULTIPOLYGON (((-70.0148 41…
25001010306	1400000US25001010306	57.6	105017	48391	54398	NA	NA	NA	NA	20884	NA	1154	82.2	1.3	13.6	97.4	92.3	25	001	010306	103.06	Census Tract 103.06	G5020	S	17828291	1730719	41.85938	-69.98263	MULTIPOLYGON (((-70.01083 4…
25001010400	1400000US25001010400	64.6	104850	51099	NA	NA	194091	NA	NA	4895	16710	1296	83.3	0.8	13.6	100.0	96.1	25	001	010400	104	Census Tract 104	G5020	S	15587094	8476393	41.75415	-69.98612	MULTIPOLYGON (((-70.02372 4…

The last step in the data cleaning process is to turn the merged map data into an sf object. This allows the data to be used in the geom_sf function that we will use for plotting earlier on. Also we are coercing the Mean Income variable to numeric so that we can map it.

map <- map_data |>
  mutate(MeanIncome_fullPooled = as.numeric(MeanIncome_fullPooled)) |>
  st_as_sf()

# garabage collect the data we no longer need. 

rm(list  = c('inc_data','inc_map_data', 'inc_MetaData', 'pop_data', 'pop_MetaData'))

3.0 Make Maps

3.1 City/ Town Names

The First map we will make is The census tracts and city borders overlay-ed. This is just a visual check that everything is working. Its important to note that some census tracts lineup with town/city borders. This is especially prevalent in central and western mass where populations are less dense.

# Plot geometries of census tracts and city/town borders to check they work
ggplot() +
  geom_sf(data = TractShapes, 
          fill = NA, 
          color = "red",
          size = 0.5,
          alpha = 0.5) +  
  geom_sf(data = CityShapes, 
          fill = NA, 
          color = "black", 
          size = 0.5, 
          alpha = 0.5 ) + 
  theme_minimal() +
  labs(title = "Census Tracts with City/Town Borders")

3.2 Population Change 2010 to 2020

Note: Hyperbolic Transformation used on population maps.

The Inverse Hyperbolic Sine transformation is used here to better visualize the differences between municipalities. Population change and population density can vary on a large scale, making subtle differences hard to distinguish on a map. By applying this transformation, small values are expanded, and large values are compressed, allowing for a more informative map. However, this does make the legend harder to interpret, as the scale becomes less intuitive. Despite this, the map provides a clearer comparison of municipalities.

The Inverse hyperbolic sine function is defined as:

\[asinh(x) = ln(x+ \sqrt{x^2+1})\] A good example of this is the towns of Gloucester and Essex. Gloucester, while not densely populated overall, has a highly dense downtown area. Meanwhile, Essex lacks a downtown area and is much less dense overall. Without the transformation, the difference between these two places was almost imperceptible.

# create main map 
map_plot <- ggplot() +
  geom_sf(data = CityShapes, 
          aes(fill = asinh(POPCH10_20) ), 
          color = "black", 
          size = 1 ) + 
  # Fill citys
  scale_fill_viridis_c(option = "A",
                       na.value = "grey90") +  
  # Labels
  labs(title = "Population Change 2010 to 2020", 
       fill = "Population Change") + 
  # add city names
  geom_sf_text(data = CityShapes, 
                 aes(label = TOWN),
                 size = 2, 
                 color = "white") +
  theme_classic() +
  theme(
    axis.text = element_blank(),      
    axis.ticks = element_blank(),
    axis.line = element_blank(),
    plot.title = element_text(size = 20)
  )

map_plot

3.2 Population Density

# create main map 
map_plot <- ggplot() +
  geom_sf(data = CityShapes, 
          aes(fill = asinh(POP2020 / AREA_SQMI)),
          color = "black", 
          size = 1 ) + 
  # City Name Labels
  geom_sf_text(data = CityShapes, 
               aes(label = TOWN),
               size = 2, 
               color = "white") +
  # City Fill 
  scale_fill_viridis_c(option = "D",
                       na.value = "grey90") +  
  labs(title = "2020 Population Density",
       fill = "Population Denisty") + 
  theme_classic() +
  theme(axis.text = element_blank(),      
        axis.ticks = element_blank(),
        axis.line = element_blank(),
        plot.title = element_text(size = 40)
  )

map_plot

3.3 Houshold Income

# create main map 
map_plot <- ggplot() +
  geom_sf(data = map,
          aes(fill = MeanIncome_fullPooled)) +  
  geom_sf(data = CityShapes, 
          fill = NA, 
          color = "black",
          size = 1 ) + 
  geom_sf_text(data = CityShapes, 
               aes(label = TOWN), 
               size = 2, 
               color = "white") +
  scale_fill_viridis_c(option = "plasma",
                       na.value = "grey90") +  
  labs(title = "MA Census Tract Household Income",
       fill = "Income") + 
  theme_classic() +
  theme(
    axis.text = element_blank(),      
    axis.ticks = element_blank(),
    axis.line = element_blank(),
    plot.title = element_text(size = 40)
  )

map_plot

The map of Mean income by census tract has come out looking great. Some things that immediately stand out is that the census tracts in the Boston area are very small making it very hard to look at. The solution will be to make a map inset that is a blown up version of the Boston area.

# Define the limits for the Boston area zoom
boston_x_limits <- c(-71.28, -70.95)  
boston_y_limits <- c(42.23, 42.48)    

# Create the inset for Boston zoom
boston_inset <- ggplot() +
  geom_sf(data = map,
          aes(fill = MeanIncome_fullPooled)) + 
  geom_sf(data = CityShapes, 
          fill = NA,
          color = "black",
          size = 1) +  
  geom_sf_text(data = CityShapes, 
               aes(label = TOWN),
               size = 3, 
               color = "white") +
  coord_sf(xlim = boston_x_limits, 
           ylim = boston_y_limits, 
           expand = FALSE) + 
  scale_fill_viridis_c(option = "plasma", 
                       na.value = "grey90") +  
  theme_void() +  # No axes for the inset
  theme(legend.position = "none")  


boston_inset

# Combine the main plot with the inset
final_plot <- map_plot +
  annotation_custom(ggplotGrob(boston_inset), 
                    xmin = -70.4, xmax = -69.0,
                    ymin = 42.2, ymax = 43.4) 

final_plot

Unfortunately, the inset of the Boston area is being cut off when I knit this document. It renders perfectly in RStudio, but shifts when I knit to HTML. I will need to put in more work on this going forward.

Otherwise, the map turned out beautifully. One of the key takeaways is that the MetroWest area is the wealthiest in the state. This area is roughly located outside I-95 and inside I-495, highlighted by towns like Dover, Wellesley, Weston, Sudbury, Concord, and Carlisle.

Some other standout areas of the state include the Lowell suburbs, such as Andover, Boxford, and North Reading.

Another major takeaway is that the eastern part of the state is much wealthier overall than the western part. This is likely due to communities’ proximity to the economic engine of the region in Boston. There are more opportunities in the eastern part of the state, with several world-class colleges and universities, as well as a thriving healthcare industry.

3.4 Transportation Maps

These are my two favorite maps in this project. They both use the same coloring system and show an almost perfect inverse of each other. The results are quite obvious but informative nonetheless.

The inner core of the Boston area sees low levels of car usage and high levels of public transport usage. Generally, the public transport users in this map are customers of the MBTA.

# Define the limits for the Boston area zoom
boston_x_limits <- c(-71.35, -70.85)  
boston_y_limits <- c(42.13, 42.58)  

# create main map 
public <- ggplot() +
  geom_sf(data = map,
          aes(fill = CommuteTotal_Public)) +  
  geom_sf(data = CityShapes, 
          fill = NA, 
          color = "black",
          size = 1 ) + 
  coord_sf(xlim = boston_x_limits, 
           ylim = boston_y_limits, 
           expand = FALSE) + 
  geom_sf_text(data = CityShapes, 
               aes(label = TOWN), 
               size = 3, 
               color = "white") +
  scale_fill_viridis_c(option = "H",
                       na.value = "grey90") +  
  labs(title = "Commute Via Public Transport",
       fill = "Population Preportion") + 
  theme_classic() +
  theme(
    axis.text = element_blank(),      
    axis.ticks = element_blank(),
    axis.line = element_blank(),
    plot.title = element_text(size = 30)
  )

public

In the future, I should layer the T and commuter rail paths onto these maps to illustrate how proximity to the train impacts the usage of public transport. It seems that proximity to the train has a significant impact on how many people use public transportation. This is most clearly seen in Malden. Western Malden is closest to the MBTA stop at Malden Center, while eastern Malden is quite far from the station. The census tracts in that area show a much lower usage of public transport. There is a corridor of green that extends north of downtown Boston, closely hugging the route that the Orange Line takes. This corridor is strongest in Charlestown, Somerville, Malden, Medford, and stops in Melrose.

# create main map 
Car <- ggplot() +
  geom_sf(data = map,
          aes(fill = CommuteTotal_Car)) +  
  geom_sf(data = CityShapes, 
          fill = NA, 
          color = "black",
          size = 3 ) + 
  coord_sf(xlim = boston_x_limits, 
           ylim = boston_y_limits, 
           expand = FALSE) + 
  geom_sf_text(data = CityShapes, 
               aes(label = TOWN), 
               size = 3, 
               color = "black") +
  scale_fill_viridis_c(option = "H",
                       na.value = "grey90") +  
  labs(title = "Commute Via Car",
       fill = "Population Preportion") + 
  theme_classic() +
  theme(
    axis.text = element_blank(),      
    axis.ticks = element_blank(),
    axis.line = element_blank(),
    plot.title = element_text(size = 30)
  )

Car

The area around the Charles River has the lowest usage of cars to get to work. This is likely due to the high levels of traffic and the presence of many students in that area. Numerous colleges are located there, and many students likely walk or use public transit to get to class or work.

The North and South Shore use cars more often than MetroWest, which could be due to people on either shore working in gateway cities or in New Hampshire or Rhode Island. Surprisingly, the North Shore (Peabody, Lynn, Saugus, …) uses Cars much more than the South Shore (Quincy, Braintree, Hingham, …).