Metrics
We are going to look at how distressed urban communities change between 2000 and 2010. Median home value will be one of the primary variables we will use in the model since it captures a lot of information about the ‘neighborhood’ (census tract). We will also utilize some neighborhood health metrics as predictors and outcomes.
Gentrification Metrics
Gentrification, physical and demographic changes to a neighborhood that brings in wealthier residents, new businesses, investment and development in the area, is an important topic in social science. Gentrification also bring concerns related social-justice due to displacement and dislocation of low-income residents from the neighborhood. Traditionally low-income neighborhood across the United States gentrify. Identifying neighborhood changes related to gentrification can help in planning for negative by-products of gentrification and urban development of the area. City can plan for low-income amenities and rent-controlled-housing in recently developed and developing neighborhoods. To capture the neighborhood changes gentrification metrices are constructed. Gentrification is not caused by a single variable, but it is the result of pool of gentrifiers with cultural preference for urban living, better amenities, disposable income and urban housing. These variables can measure the trends in latent changes such as economic strength, human capital, community vulnerability, and community distress in the neighborhood among many other factors,allowing the city to intervene before the low-income population are severely affected.
The objective of this chapter is to capture initial picture of neighborhood change in the year 2000 using longitudinal tabulated database (LTDB) from census data containing data related to almost 72,000 unique census tracts. It contains almost 70 variables describing racial, socio-economic, housing, age, and marital status of the population. The key to observing changes is the conversion of Census tract data into useful ‘features’ or variables that help predict gentrification. In theory, neighborhood change suggests that low-priced neighborhoods adjacent to wealthy ones have the highest probability of gentrifying in the face of new housing demand.
This paper uses Median Home Value as one of the main variables to construct gentrification metrices. To measure the general dimension of community strength and vulnerability in the year 2000 three instruments are created measuring different latent construct. For the porpose of this paper neighorhood health is constructed from the entire available LTDB data.
Set-Up
Library
# load necessary packages ---- library(here) library(tidyverse) library(dplyr) library(psych) library(dplyr) # data wrangling library(xtable) # nice tables library(kableExtra) # nice tables library(psych) # calculate instrument reliability (alpha) library(ggplot2) # graphics library(tidyr) # wrangling
Functions
# Load helper functions for graphing purposes source(here("functions/lab-05_helper_functions_cs.R"))
Data
Load the LTDB data in raw form.
# load necessary data census_dat2000 <- read_csv(here("data/raw/Harmonized_Census_Tracts/ltdb_std_all_sample/ltdb_std_2000_sample.csv")) %>% # make all column names lower case rename_all(.funs = function(i) str_to_lower(i)) # replace -999 in data set with NA census_dat2000 %>% as_tibble() %>% mutate_all(function(i) ifelse(i == -999, NA, i)) %>% na.omit()
Note that we are comparing neighborhoods (census tracts) within cities, so comparisons with rural areas are not very meaningful. Before you begin your analysis drop all of the rural tracts located outside of metropolitan areas.
Add metro attributes and urban / rural status by merging the crosswalk data using county FIPS codes.
URL <- "https://data.nber.org/cbsa-msa-fips-ssa-county-crosswalk/cbsatocountycrosswalk.csv" crosswalk <- read.csv(URL, stringsAsFactors = F) # all metro areas in the country sort(unique(crosswalk$cbsaname)) crosswalk$urban <- ifelse(crosswalk$cbsaname == "", "rural", "urban") keep.these <- c("countyname", "state", "fipscounty", "msa", "msaname", "cbsa", "cbsaname", "urban") cw <- dplyr::select(crosswalk, keep.these) # merge into census data by county FIPS watch the leading zeros problem
Neighborhood Health Indices
Construction of three neighborhood indices and their reliability.
Three different instruments/metrics are constructed to measure the neighborhood health. The value of variables included in the instruments are converted to their Z-scores, this avoids overweighting one variable. After normalising each variable, the Cornbach Alpha Score is calculated which shows how closely each variable is related to each other as a group. An alpha score of 70% or higher is considered better for internal relialbility of the group.
Instrument 1
This Instrument measures economic strength in the neighborhood. The variables included in the matrix are percent population with a college degree, median household income, and median home value. Percent of population with a college degree or higher is calculated by dividing the number of people with a college degree by the of population 25 years and over.
Instrument 1 measures a Cronbach Alpha Score of 87%
economic_strength <- census_dat2000 %>% mutate(pcol00 = (census_dat2000$col00/census_dat2000$ag25up00)) %>% select(pcol00, hinc00, mhmval00) %>% mutate(pcol00zscore = scale(pcol00, center = TRUE, scale = TRUE), hinc00zscore = scale(hinc00, center = TRUE, scale = TRUE), mhmval00zscore = scale(mhmval00, center = TRUE, scale = TRUE)) %>% select(pcol00zscore, hinc00zscore, mhmval00zscore) %>% rename(`percent with college degree` = pcol00zscore, `median household income` = hinc00zscore, `median home value` = mhmval00zscore, ) %>% data.frame() # pairs( instrument1, lower.panel= panel.smooth, upper.panel= panel.cor ) alpha1 <- psych::alpha(economic_strength, check.keys = TRUE)$total$raw_alpha alpha1
[1] 0.8699186
Instrument 2
This Instrument measures community vulnerability in the neighborhood. The variables included in the matrix are percent unemployed population, percentage of population with a high school degree or less, and per capita income.
Instrument 2 measures a Cornbach Alpha Score of 77%
community_vulnerability <- census_dat2000 %>% mutate(punemp00 = (census_dat2000$unemp00/census_dat2000$clf00), phs00 = (census_dat2000$hs00/census_dat2000$ag25up00)) %>% select(punemp00, phs00, incpc00) %>% mutate(punemp00zscore = scale(punemp00, center = TRUE, scale = TRUE), phs00zscore = scale(phs00, center = TRUE, scale = TRUE), incpc00zscore = scale(incpc00, center = TRUE, scale = TRUE)) %>% select(punemp00zscore, phs00zscore, incpc00zscore) %>% rename(`percent unemployed` = punemp00zscore, `percent with high school degree or less` = phs00zscore, `per capita income` = incpc00zscore, ) %>% data.frame() # pairs( instrument1, lower.panel= panel.smooth, upper.panel= panel.cor ) alpha2 <- psych::alpha(community_vulnerability, check.keys = TRUE)$total$raw_alpha alpha2
[1] 0.7732793
Instrument 3
This Instrument measures community distress in the neighborhood. The variables included in the matrix are median home value, median household income, percent unemployed, and percent widowed, divorced and separated.
Instrument 3 measures a Cornbach Alpha Score of 74%
community_distress <- census_dat2000 %>% mutate(punemp00 = (census_dat2000$unemp00/census_dat2000$clf00), pwds00 = (census_dat2000$wds00/census_dat2000$ag15up00)) %>% select(mhmval00, hinc00, punemp00, pwds00) %>% mutate(mhmval00zscore = scale(mhmval00, center = TRUE, scale = TRUE), hinc00zscore = scale(hinc00, center = TRUE, scale = TRUE), punemp00zscore = scale(punemp00, center = TRUE, scale = TRUE), pwds00zscore = scale(pwds00, center = TRUE, scale = TRUE)) %>% select(mhmval00zscore, hinc00zscore, punemp00zscore, pwds00zscore) %>% rename(`median home value` = mhmval00zscore, `median household income` = hinc00zscore, `percent unemployed` = punemp00zscore, `percent widowed, divorced and separated` = pwds00zscore) %>% data.frame() # pairs( instrument1, lower.panel= panel.smooth, upper.panel= panel.cor ) alpha3 <- psych::alpha(community_distress, check.keys = TRUE)$total$raw_alpha alpha3
[1] 0.7468594
Descriptive Statistics
Filter out urban county data from crosswalk data and keep entries for unique CBSA. Then look for those CBSA in LTGB_2000 data base and keep only common entires.
Present descriptive statistics on all of the metrics for all urban census tracts.
cbsa <- cw %>% filter(urban == "urban") %>% select(cbsa, urban) cbsa <- unique(cbsa)
options(scipen = 999) cbsa.id <- cbsa$cbsa keep.these <- census_dat2000$cbsa10 %in% cbsa.id dat_2000_urban <- filter(census_dat2000, keep.these) summary(dat_2000_urban) %>% kable() %>% kable_styling() %>% scroll_box(height = "400px", width = "100%", fixed_thead = TRUE)
| trtid10 | state | county | tract | placefp10 | cbsa10 | metdiv10 | ccflag10 | pop00sf3 | ruanc00 | itanc00 | geanc00 | iranc00 | scanc00 | rufb00 | itfb00 | gefb00 | irfb00 | scfb00 | fb00 | nat00 | n10imm00 | ag5up00 | olang00 | lep00 | ag25up00 | hs00 | col00 | ag15up00 | mar-00 | wds00 | clf00 | unemp00 | dflabf00 | flabf00 | empclf00 | prof00 | manuf00 | semp00 | ag18cv00 | vet00 | cni16u00 | dis00 | dpov00 | npov00 | n65pov00 | dfmpov00 | nfmpov00 | dwpov00 | nwpov00 | dbpov00 | nbpov00 | dnapov00 | nnapov00 | dhpov00 | nhpov00 | dapov00 | napov00 | incpc00 | hu00sp | h30old00 | ohu00sp | h10yrs00 | dmulti00 | multi00 | hinc00 | hincw00 | hincb00 | hinch00 | hinca00 | mhmval00 | mrent00 | hh00 | hhw00 | hhb00 | hhh00 | hha00 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. : 1001020100 | Length:40351 | Length:40351 | Length:40351 | Min. : 100 | Min. :10180 | Min. :99999 | Min. :0.0000 | Min. : 0 | Min. : 0.00 | Min. : 0.0 | Min. : 0.0 | Min. : 0.0 | Min. : 0.00 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.0 | Min. : 0.0 | Min. : 0.0 | Min. : 0 | Min. : 0.0 | Min. : 0.0 | Min. : 0 | Min. : 0 | Min. : 0.0 | Min. : 0 | Min. : 0 | Min. : 0.0 | Min. : 0 | Min. : 0.00 | Min. : 0 | Min. : 0.0 | Min. : 0 | Min. : 0.0 | Min. : 0.0 | Min. : 0.0 | Min. : 0 | Min. : 0.0 | Min. : 0 | Min. : 0.0 | Min. : 0 | Min. : 0.0 | Min. : 0.00 | Min. : 0.0 | Min. : 0.00 | Min. : 0 | Min. : 0.00 | Min. : 0.0 | Min. : 0.0 | Min. : 0.00 | Min. : 0.000 | Min. : 0.0 | Min. : 0.00 | Min. : 0.000 | Min. : 0.00 | Min. : 0 | Min. : 0 | Min. : 0 | Min. : 0 | Min. : 0.0 | Min. : 0 | Min. : 0.0 | Min. : 2499 | Min. : 2499 | Min. : 2499 | Min. : 2499 | Min. : 5000 | Min. : 0 | Min. : 0 | Min. : 0 | Min. : 0 | Min. : 0.000 | Min. : 0.00 | Min. : 0.0000 | |
| 1st Qu.:12101030404 | Class :character | Class :character | Class :character | 1st Qu.:28968 | 1st Qu.:19740 | 1st Qu.:99999 | 1st Qu.:0.0000 | 1st Qu.: 2697 | 1st Qu.: 0.00 | 1st Qu.: 36.0 | 1st Qu.: 162.0 | 1st Qu.: 124.0 | 1st Qu.: 15.00 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 62.0 | 1st Qu.: 27.0 | 1st Qu.: 16.0 | 1st Qu.: 2517 | 1st Qu.: 134.8 | 1st Qu.: 14.0 | 1st Qu.:1718 | 1st Qu.: 671 | 1st Qu.: 229.0 | 1st Qu.: 2124 | 1st Qu.: 1039 | 1st Qu.: 362.0 | 1st Qu.:1279 | 1st Qu.: 50.00 | 1st Qu.:1072 | 1st Qu.: 594.0 | 1st Qu.:1186 | 1st Qu.: 292.9 | 1st Qu.: 110.0 | 1st Qu.: 86.0 | 1st Qu.: 1992 | 1st Qu.: 238.0 | 1st Qu.:1679 | 1st Qu.: 262.0 | 1st Qu.: 2602 | 1st Qu.: 155.5 | 1st Qu.: 11.63 | 1st Qu.: 673.3 | 1st Qu.: 18.00 | 1st Qu.: 1481 | 1st Qu.: 78.05 | 1st Qu.: 23.0 | 1st Qu.: 0.0 | 1st Qu.: 0.00 | 1st Qu.: 0.000 | 1st Qu.: 38.0 | 1st Qu.: 0.00 | 1st Qu.: 8.008 | 1st Qu.: 0.00 | 1st Qu.: 15583 | 1st Qu.:1111 | 1st Qu.: 248 | 1st Qu.:1015 | 1st Qu.: 633.0 | 1st Qu.:1111 | 1st Qu.: 58.0 | 1st Qu.: 32068 | 1st Qu.: 34125 | 1st Qu.: 22688 | 1st Qu.: 26585 | 1st Qu.: 30000 | 1st Qu.: 74100 | 1st Qu.: 387 | 1st Qu.:1018 | 1st Qu.: 639 | 1st Qu.: 8.145 | 1st Qu.: 10.08 | 1st Qu.: 0.3678 | |
| Median :27145010102 | Mode :character | Mode :character | Mode :character | Median :55000 | Median :31340 | Median :99999 | Median :0.0000 | Median : 3743 | Median : 10.33 | Median : 89.0 | Median : 341.0 | Median : 232.0 | Median : 45.00 | Median : 0.000 | Median : 0.000 | Median : 5.842 | Median : 0.000 | Median : 0.000 | Median : 157.5 | Median : 69.0 | Median : 55.0 | Median : 3489 | Median : 269.0 | Median : 36.0 | Median :2397 | Median :1072 | Median : 453.0 | Median : 2936 | Median : 1576 | Median : 535.0 | Median :1843 | Median : 84.04 | Median :1490 | Median : 862.7 | Median :1738 | Median : 515.0 | Median : 202.0 | Median :149.0 | Median : 2756 | Median : 358.0 | Median :2366 | Median : 402.0 | Median : 3645 | Median : 312.0 | Median : 28.68 | Median : 962.4 | Median : 43.00 | Median : 2606 | Median : 157.00 | Median : 108.0 | Median : 12.0 | Median : 10.00 | Median : 0.000 | Median : 109.7 | Median : 12.00 | Median : 38.000 | Median : 0.00 | Median : 19696 | Median :1532 | Median : 627 | Median :1412 | Median : 912.1 | Median :1532 | Median : 239.0 | Median : 41580 | Median : 43249 | Median : 34316 | Median : 37680 | Median : 48750 | Median : 100900 | Median : 489 | Median :1413 | Median :1098 | Median : 41.000 | Median : 31.86 | Median : 11.3812 | |
| Mean :27767098651 | NA | NA | NA | Mean :56261 | Mean :30268 | Mean :99999 | Mean :0.4274 | Mean : 3844 | Mean : 24.43 | Mean : 156.8 | Mean : 454.4 | Mean : 261.5 | Mean : 98.92 | Mean : 2.921 | Mean : 3.868 | Mean : 9.364 | Mean : 1.177 | Mean : 1.661 | Mean : 316.6 | Mean : 120.7 | Mean : 140.4 | Mean : 3581 | Mean : 528.8 | Mean : 117.2 | Mean :2464 | Mean :1152 | Mean : 604.5 | Mean : 3013 | Mean : 1643 | Mean : 561.6 | Mean :1905 | Mean : 105.29 | Mean :1524 | Mean : 893.7 | Mean :1799 | Mean : 602.9 | Mean : 248.8 | Mean :170.5 | Mean : 2828 | Mean : 383.6 | Mean :2443 | Mean : 439.8 | Mean : 3738 | Mean : 442.7 | Mean : 39.49 | Mean : 991.0 | Mean : 67.65 | Mean : 2681 | Mean : 206.81 | Mean : 433.3 | Mean : 107.7 | Mean : 28.35 | Mean : 6.461 | Mean : 424.8 | Mean : 98.03 | Mean : 111.896 | Mean : 14.95 | Mean : 21240 | Mean :1575 | Mean : 699 | Mean :1448 | Mean : 965.9 | Mean :1575 | Mean : 388.3 | Mean : 44766 | Mean : 46447 | Mean : 41561 | Mean : 43713 | Mean : 57489 | Mean : 120462 | Mean : 533 | Mean :1449 | Mean :1114 | Mean : 160.742 | Mean : 113.52 | Mean : 34.6014 | |
| 3rd Qu.:42003564300 | NA | NA | NA | 3rd Qu.:84250 | 3rd Qu.:40140 | 3rd Qu.:99999 | 3rd Qu.:1.0000 | 3rd Qu.: 4924 | 3rd Qu.: 30.71 | 3rd Qu.: 189.0 | 3rd Qu.: 611.2 | 3rd Qu.: 360.0 | 3rd Qu.: 109.00 | 3rd Qu.: 0.000 | 3rd Qu.: 2.439 | 3rd Qu.: 13.000 | 3rd Qu.: 0.000 | 3rd Qu.: 0.000 | 3rd Qu.: 368.0 | 3rd Qu.: 153.0 | 3rd Qu.: 154.0 | 3rd Qu.: 4585 | 3rd Qu.: 587.8 | 3rd Qu.: 100.5 | 3rd Qu.:3174 | 3rd Qu.:1546 | 3rd Qu.: 835.1 | 3rd Qu.: 3858 | 3rd Qu.: 2173 | 3rd Qu.: 733.0 | 3rd Qu.:2482 | 3rd Qu.: 133.01 | 3rd Qu.:1955 | 3rd Qu.:1164.0 | 3rd Qu.:2356 | 3rd Qu.: 824.8 | 3rd Qu.: 337.8 | 3rd Qu.:230.8 | 3rd Qu.: 3623 | 3rd Qu.: 502.0 | 3rd Qu.:3152 | 3rd Qu.: 577.0 | 3rd Qu.: 4812 | 3rd Qu.: 590.0 | 3rd Qu.: 55.00 | 3rd Qu.:1289.2 | 3rd Qu.: 90.00 | 3rd Qu.: 3753 | 3rd Qu.: 276.00 | 3rd Qu.: 427.1 | 3rd Qu.: 83.0 | 3rd Qu.: 29.00 | 3rd Qu.: 3.284 | 3rd Qu.: 372.0 | 3rd Qu.: 63.08 | 3rd Qu.: 109.693 | 3rd Qu.: 9.00 | 3rd Qu.: 24913 | 3rd Qu.:2017 | 3rd Qu.:1040 | 3rd Qu.:1864 | 3rd Qu.:1251.2 | 3rd Qu.:2017 | 3rd Qu.: 558.0 | 3rd Qu.: 54047 | 3rd Qu.: 55670 | 3rd Qu.: 52625 | 3rd Qu.: 53985 | 3rd Qu.: 72398 | 3rd Qu.: 142900 | 3rd Qu.: 623 | 3rd Qu.:1865 | 3rd Qu.:1555 | 3rd Qu.: 161.000 | 3rd Qu.: 106.53 | 3rd Qu.: 34.6893 | |
| Max. :56025001800 | NA | NA | NA | Max. :99999 | Max. :49740 | Max. :99999 | Max. :1.0000 | Max. :36206 | Max. :1108.03 | Max. :3841.4 | Max. :4251.5 | Max. :2958.1 | Max. :2815.00 | Max. :753.008 | Max. :355.951 | Max. :258.000 | Max. :114.000 | Max. :185.510 | Max. :5827.0 | Max. :2346.0 | Max. :4061.0 | Max. :31960 | Max. :8121.0 | Max. :4529.0 | Max. :9448 | Max. :6994 | Max. :4644.7 | Max. :28171 | Max. :12555 | Max. :4450.0 | Max. :7139 | Max. :6405.33 | Max. :6162 | Max. :3057.4 | Max. :5358 | Max. :3524.7 | Max. :1934.0 | Max. :983.5 | Max. :11543 | Max. :8128.5 | Max. :9793 | Max. :2149.0 | Max. :20188 | Max. :5157.0 | Max. :438.00 | Max. :5722.3 | Max. :949.00 | Max. :10726 | Max. :4208.00 | Max. :7523.0 | Max. :4150.0 | Max. :5837.29 | Max. :2665.000 | Max. :9450.0 | Max. :4326.00 | Max. :6423.000 | Max. :1293.00 | Max. :147633 | Max. :7717 | Max. :4975 | Max. :5850 | Max. :5834.4 | Max. :7717 | Max. :4768.0 | Max. :200001 | Max. :200001 | Max. :200001 | Max. :200001 | Max. :250000 | Max. :1000001 | Max. :2001 | Max. :5869 | Max. :3744 | Max. :2706.000 | Max. :2196.00 | Max. :1799.0000 | |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA's :137 | NA's :319 | NA's :4455 | NA's :2847 | NA's :8480 | NA's :136 | NA's :115 | NA | NA | NA | NA | NA |