Downloaded the City of Austin jurisdictions shapefile here: https://data.austintexas.gov/City-Government/BOUNDARIES_jurisdictions/3pzb-6mbr
Downloaded Texas census tracts shapefile from TIGER/line here: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html
I am filtering for only full and limited purpose jurisdictions.
I decided to do this analysis in R because performing an intersecting overlay or spatial join in ArcGIS would have cost 84 credits.
library(sf)Linking to GEOS 3.13.1, GDAL 3.11.0, PROJ 9.6.0; sf_use_s2() is TRUElibrary(readr)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, union# read Austin jurisdictions shapefile
austin_jur <- st_read("BOUNDARIES_jurisdictions_20260205/geo_export_5db1be39-2392-451a-8502-b3021a29112a.shp")Reading layer `geo_export_5db1be39-2392-451a-8502-b3021a29112a' from data source `C:\Users\kaitl\OneDrive\Documents\Every Texan\R\City of Austin Equity Index\Full and LTD census tracts\BOUNDARIES_jurisdictions_20260205\geo_export_5db1be39-2392-451a-8502-b3021a29112a.shp'
using driver `ESRI Shapefile'
Simple feature collection with 352 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -98.01504 ymin: 30.04298 xmax: -97.48034 ymax: 30.51685
Geodetic CRS: WGS 84# quick check
print(unique(austin_jur$JURISDICTION_LABEL))NULL# inspect Austin shapefile
names(austin_jur) [1] "objectid" "jurisdicti" "city_name" "jurisdic_2" "modified_f"
[6] "jurisdic_3" "jurisdic_4" "shape_area" "shape_leng" "globalid"
[11] "geometry" head(austin_jur)Simple feature collection with 6 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -97.92222 ymin: 30.22973 xmax: -97.77758 ymax: 30.3428
Geodetic CRS: WGS 84
objectid jurisdicti city_name jurisdic_2 modified_f
1 312 401472386 CITY OF AUSTIN AUSTIN 2 MILE ETJ 202
2 230 401472355 CITY OF AUSTIN AUSTIN 2 MILE ETJ 202
3 231 401472356 CITY OF AUSTIN AUSTIN 2 MILE ETJ 202
4 258 401472326 CITY OF AUSTIN AUSTIN 2 MILE ETJ 202
5 193 10 CITY OF AUSTIN AUSTIN LTD 211
6 222 401472320 CITY OF AUSTIN AUSTIN 2 MILE ETJ 202
jurisdic_3 jurisdic_4 shape_area shape_leng
1 DISANNEXED PER SB 1844 2 MILE ETJ 7263.191 467.3437
2 DISANNEXED PER SB 1844 2 MILE ETJ 43172.439 1007.8005
3 DISANNEXED PER SB 1844 2 MILE ETJ 61576.410 1022.9614
4 DISANNEXED PER SB 1844 2 MILE ETJ 43322.393 837.6653
5 LIMITED PURPOSE PLANNING ZONING LTD 7173169.650 16879.2008
6 DISANNEXED PER SB 1844 2 MILE ETJ 67123.084 1494.6134
globalid geometry
1 2644acde-7560-4f95-9336-d04480bb6ba6 MULTIPOLYGON (((-97.88191 3...
2 b5fef16f-58ac-4f6d-8757-bc27ce043d4c MULTIPOLYGON (((-97.78378 3...
3 eb5ea736-435c-49d0-8318-5c2cb4f49fb7 MULTIPOLYGON (((-97.8187 30...
4 150cc82f-8b67-4924-a6e8-0d0f07d97ddd MULTIPOLYGON (((-97.92192 3...
5 dabb5d59-05a0-4ea2-96a0-a69303af5c83 MULTIPOLYGON (((-97.899 30....
6 4fac79bf-020e-4d9b-b655-dc8574a7852b MULTIPOLYGON (((-97.77912 3...Here I am filtering to include only limited and full-purpose jurisdictions in Austin.
austin_jur_filtered <- austin_jur %>%
filter(jurisdic_2 %in% c("AUSTIN LTD", "AUSTIN FULL PURPOSE"))
print(unique(austin_jur_filtered$jurisdic_2))[1] "AUSTIN LTD" "AUSTIN FULL PURPOSE"# read the Texas Census tracts shapefile
tx_census_tracts <- st_read("tl_2025_48_tract/tl_2025_48_tract.shp")Reading layer `tl_2025_48_tract' from data source
`C:\Users\kaitl\OneDrive\Documents\Every Texan\R\City of Austin Equity Index\Full and LTD census tracts\tl_2025_48_tract\tl_2025_48_tract.shp'
using driver `ESRI Shapefile'
Simple feature collection with 6896 features and 13 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -106.6456 ymin: 25.83705 xmax: -93.50804 ymax: 36.5007
Geodetic CRS: NAD83# check columns
print(names(tx_census_tracts)) [1] "STATEFP" "COUNTYFP" "TRACTCE" "GEOID" "GEOIDFQ" "NAME"
[7] "NAMELSAD" "MTFCC" "FUNCSTAT" "ALAND" "AWATER" "INTPTLAT"
[13] "INTPTLON" "geometry"# rebuild a clean GEOID (I was getting mostly NA values for some reason for GEOID)
tx_census_tracts <- tx_census_tracts %>%
mutate(
GEOID_clean = paste0(STATEFP, COUNTYFP, TRACTCE)
)
# check
head(tx_census_tracts$GEOID_clean)[1] "48201342001" "48201421101" "48201423301" "48381020400" "48029171926"
[6] "48113017819"# double check
#unique(census_tracts$GEOID_clean)
# inspect new GEOIDs in Excel
tract_ids <- unique(tx_census_tracts$GEOID_clean)
write.csv(tract_ids, "All_Census_Tract_GEOIDs.csv", row.names = FALSE)
# there are 6,896 tracts in Texas according to this list# make sure both layers have valid geometry
austin_jur_filtered <- st_make_valid(austin_jur_filtered)
tx_census_tracts <- st_make_valid(tx_census_tracts)# match coordinate reference systems
if (st_crs(tx_census_tracts) != st_crs(austin_jur_filtered)) {
tx_census_tracts <- st_transform(tx_census_tracts, st_crs(austin_jur_filtered))
}# spatially join tracts to any Austin jurisdiction polygon they intersect
selected_tracts <- st_join(tx_census_tracts, austin_jur_filtered, join = st_intersects)# keep only those that actually intersect Austin (non-NA jurisdiction label)
selected_tracts <- selected_tracts %>%
filter(!is.na(jurisdic_2))# check for NA values
table(is.na(selected_tracts$GEOID_clean))
FALSE
383 head(selected_tracts$GEOID_clean, 20) [1] "48453002112" "48453002113" "48453002201" "48453041300" "48453031600"
[6] "48453032500" "48453045600" "48453045600" "48453043000" "48453043000"
[11] "48453043000" "48453034200" "48453042000" "48453042000" "48453042000"
[16] "48453045100" "48453045100" "48453041400" "48453032100" "48453043100"# quick check that the selected tracts look reasonable
print(paste("# of tracts intersecting Austin:", nrow(selected_tracts)))[1] "# of tracts intersecting Austin: 383"print(paste("# of unique GEOIDs:", length(unique(selected_tracts$GEOID_clean))))[1] "# of unique GEOIDs: 271"There are 271 intersecting census tracts, meaning there are 271 tracts in Austin full and limited purpose jurisdictions. This makes sense, as Travis County alone has 291 census tracts, though not all of Travis County is in Austin. There are a total of 493 census tracts in all of Bastrop, Hays, Travis, and Williamson Counties combined.
The reason there are 383 rows, but only 271 distinct tracts, could be because some tracts may touch multiple Austin jurisdiction polygons since Austin’s boundary is split into parts.
# quick view of unique GEOIDs
print(sort(unique(selected_tracts$GEOID_clean))[1:20]) [1] "48021950303" "48209010912" "48209010923" "48453000101" "48453000102"
[6] "48453000203" "48453000204" "48453000205" "48453000206" "48453000302"
[11] "48453000304" "48453000305" "48453000307" "48453000308" "48453000309"
[16] "48453000401" "48453000402" "48453000500" "48453000601" "48453000605"# to save only distinct GEOIDs with no duplicates
selected_unique <- selected_tracts %>%
distinct(GEOID_clean, .keep_all = TRUE)# save the intersected tracts shapefile
st_write(selected_tracts, "Austin_LTD_FULL_Tracts.shp")Warning in abbreviate_shapefile_names(obj): Field names abbreviated for ESRI
Shapefile driverWriting layer `Austin_LTD_FULL_Tracts' to data source
`Austin_LTD_FULL_Tracts.shp' using driver `ESRI Shapefile'
Writing 383 features with 24 fields and geometry type Polygon.# Also export a CSV list of tract GEOIDs and names
tract_list <- selected_tracts %>%
st_drop_geometry() %>%
arrange(GEOID_clean)
write_csv(tract_list, "Austin_LTD_FULL_Tract_List.csv")
###################################################################################
# files without duplicate GEOIDs
st_write(selected_unique, "Austin_LTD_FULL_Tracts_Unique.shp")Warning in abbreviate_shapefile_names(obj): Field names abbreviated for ESRI
Shapefile driverWriting layer `Austin_LTD_FULL_Tracts_Unique' to data source
`Austin_LTD_FULL_Tracts_Unique.shp' using driver `ESRI Shapefile'
Writing 271 features with 24 fields and geometry type Polygon.unique_tract_list <- selected_unique %>%
st_drop_geometry() %>%
arrange(GEOID_clean)
write_csv(unique_tract_list, "Austin_LTD_FULL_Tract_List_Unique.csv")Checking to see if I still get 271 tracts when I join my shapefile to Coda’s shapefile. Coda’s shapefile contains all census tracts in Bastrop, Hays, Travis, and Williamson counties.
# read CRG’s shapefile
CRG_tracts <- st_read("CRG_Census_Tracts_Final_All_Counties_Shape/MergedFeatures.shp")Reading layer `MergedFeatures' from data source
`C:\Users\kaitl\OneDrive\Documents\Every Texan\R\City of Austin Equity Index\Full and LTD census tracts\CRG_Census_Tracts_Final_All_Counties_Shape\MergedFeatures.shp'
using driver `ESRI Shapefile'
Simple feature collection with 492 features and 13 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -10942440 ymin: 3471769 xmax: -10800710 ymax: 3620342
Projected CRS: WGS 84 / Pseudo-Mercatornames(CRG_tracts) [1] "STATE_ABBR" "STATE_FIPS" "COUNTY_FIP" "STCOFIPS" "TRACT_FIPS"
[6] "FIPS" "POPULATION" "POP_SQMI" "SQMI" "POPULATI_1"
[11] "POP20_SQMI" "Shape__Are" "Shape__Len" "geometry" # reconstruct clean GEOID
CRG_tracts <- CRG_tracts %>%
mutate(GEOID_clean = paste0(STATE_FIPS, COUNTY_FIP, TRACT_FIPS))
head(CRG_tracts$GEOID_clean)[1] "48453001910" "48453002105" "48453035900" "48453000305" "48453000608"
[6] "48453033300"# check which of my 271 unique GEOIDs are in Coda's file
common_geo <- intersect(unique(selected_unique$GEOID_clean),
unique(CRG_tracts$GEOID_clean))
setdiff(unique(selected_unique$GEOID_clean), unique(CRG_tracts$GEOID_clean)) # not in CRG's filecharacter(0)length(CRG_tracts$GEOID_clean)[1] 492# how many unique GEOID's in Coda's file
length(unique(CRG_tracts$GEOID_clean))[1] 492length(common_geo)[1] 271Verified that all 271 tracts are in Coda’s file.
After talking to Coda, we realized some tracts are biasing the analysis, as they barely fall partially within the bounds of Austin. This revised analysis excludes tracts that are less than 10% inside the Austin boundaries.
# union Austin boundary into a single polygon
austin_union <- st_union(austin_jur_filtered)
# project both Austin union and unique tracts into an equal-area CRS
# using EPSG:3083 (Texas Centric Albers) — same as Coda used
austin_a <- st_transform(austin_union, 3083)
tracts_a <- st_transform(selected_unique, 3083)
# intersection pieces representing portion of each tract inside Austin
inside_pieces <- st_intersection(
tracts_a %>% select(GEOID_clean, COUNTYFP),
st_sf(geometry = austin_a)
)Warning: attribute variables are assumed to be spatially constant throughout
all geometries# compute overlap share (area percent inside Austin)
overlap_share <- inside_pieces %>%
mutate(piece_area = as.numeric(st_area(.))) %>%
st_drop_geometry() %>%
group_by(GEOID_clean) %>%
summarise(in_austin_area = sum(piece_area), .groups = "drop") %>%
left_join(
tracts_a %>%
mutate(tract_area = as.numeric(st_area(.))) %>%
st_drop_geometry() %>%
select(GEOID_clean, tract_area),
by = "GEOID_clean"
) %>%
mutate(
pct_in_austin = 100 * in_austin_area / tract_area
)
# attach percent overlap back on
selected_strict <- selected_unique %>%
left_join(overlap_share %>% select(GEOID_clean, pct_in_austin),
by = "GEOID_clean")# exclude tracts with less than 10% of area inside Austin
selected_10pct <- selected_strict %>%
filter(pct_in_austin >= 10)
# check how many are excluded and how many remain
n_before <- nrow(selected_unique)
n_after <- nrow(selected_10pct)
n_excluded <- n_before - n_after
print(paste("Original unique tracts:", n_before))[1] "Original unique tracts: 271"print(paste("Tracts after excluding <10% overlaps:", n_after))[1] "Tracts after excluding <10% overlaps: 250"print(paste("Number of tracts excluded:", n_excluded))[1] "Number of tracts excluded: 21"# ensure 21 were excluded
if (n_excluded >= 20 & n_excluded <= 22) {
print("exclusion test passed: 21 tracts removed")
} else {
warning("you got something different from coda!!!!")
}[1] "exclusion test passed: 21 tracts removed"# save new outputs
# save the 10% filtered shapefile
st_write(selected_10pct, "Austin_LTD_FULL_Tracts_10pct_Intersect.shp")Warning in abbreviate_shapefile_names(obj): Field names abbreviated for ESRI
Shapefile driverWriting layer `Austin_LTD_FULL_Tracts_10pct_Intersect' to data source
`Austin_LTD_FULL_Tracts_10pct_Intersect.shp' using driver `ESRI Shapefile'
Writing 250 features with 25 fields and geometry type Polygon.# export csv of the remaining tracts
strict_tract_list <- selected_10pct %>%
st_drop_geometry() %>%
select(GEOID_clean, NAME, NAMELSAD, pct_in_austin) %>%
arrange(GEOID_clean)
write_csv(strict_tract_list, "Austin_LTD_FULL_Tract_List_10pct.csv")# identify excluded tracts (those with < 10% area in Austin)
excluded_tracts <- selected_strict %>%
filter(pct_in_austin < 10)
# check count
print(paste("Excluded tracts:", nrow(excluded_tracts)))[1] "Excluded tracts: 21"# export excluded tracts to CSV
excluded_tracts_list <- excluded_tracts %>%
st_drop_geometry() %>%
arrange(pct_in_austin)
write_csv(excluded_tracts_list, "KW_Excluded_Less_Than_10pct.csv")# creating a map to highlight Williamson & Hays Tracts
# I need a better idea of where these tracts are located in relation to Travis County tracts. For our evictions indicator, we only have Travis County data. We may need to take averages of surrounding Travis County tracts to use for Williamson and Hays.
library(tmap)Warning: package 'tmap' was built under R version 4.5.2# static mode for Quarto embedding
tmap_mode("plot")ℹ tmap modes "plot" - "view"
ℹ toggle with `tmap::ttm()`# classify counties
selected_10pct_map <- selected_10pct %>%
mutate(
county_name = case_when(
COUNTYFP == "453" ~ "Travis",
COUNTYFP == "491" ~ "Williamson",
COUNTYFP == "209" ~ "Hays",
TRUE ~ "Other"
)
)
# # keep relevant counties
# selected_10pct_map <- selected_10pct_map %>%
# filter(county_name %in% c("Travis", "Williamson", "Hays"))
# build map
final_map <- tm_shape(austin_union) +
tm_borders(col = "black", lwd = 2) +
tm_shape(selected_10pct_map) +
tm_fill(
col = "county_name",
palette = c(
"Travis" = "lightgray",
"Williamson" = "#E41A1C",
"Hays" = "#377EB8"
),
alpha = 0.7,
title = "County"
) +
tm_borders(col = "white", lwd = 0.4) +
tm_layout(
main.title = "Austin Census Tracts (≥10% in City)\nWilliamson & Hays Highlighted",
legend.outside = TRUE,
main.title.size = 1.1
)
── tmap v3 code detected ───────────────────────────────────────────────────────
[v3->v4] `tm_tm_polygons()`: migrate the argument(s) related to the scale of
the visual variable `fill` namely 'palette' (rename to 'values') to fill.scale
= tm_scale(<HERE>).[v3->v4] `tm_polygons()`: use 'fill' for the fill color of polygons/symbols
(instead of 'col'), and 'col' for the outlines (instead of 'border.col').[v3->v4] `tm_polygons()`: use `fill_alpha` instead of `alpha`.[v3->v4] `tm_polygons()`: migrate the argument(s) related to the legend of the
visual variable `fill` namely 'title' to 'fill.legend = tm_legend(<HERE>)'[v3->v4] `tm_layout()`: use `tm_title()` instead of `tm_layout(main.title = )`final_map
# save map as PNG
tmap_save(
final_map,
filename = "Austin_full_limited.png",
width = 10,
height = 8,
units = "in",
dpi = 300
)Map saved to C:\Users\kaitl\OneDrive\Documents\Every Texan\R\City of Austin Equity Index\Full and LTD census tracts\Austin_full_limited.pngResolution: 3000 by 2400 pixelsSize: 10 by 8 inches (300 dpi)library(sf)
library(dplyr)
library(readr)
library(stringr)
library(FNN)Warning: package 'FNN' was built under R version 4.5.2##### 1. Load eviction data
evictions_raw <- read_csv(
"evictions_travis_tract_basta_CLEANED.csv",
col_types = cols(.default = "c")
)
##### 2. Clean eviction rate + GEOID
evictions_raw <- evictions_raw %>%
mutate(
# parse numbers safely
eviction_clean = readr::parse_number(`Eviction filing rate`),
# normalize GEOID
GEOID_clean = str_pad(
str_extract(GEOID, "\\d+"),
width = 11,
pad = "0"
)
)Warning: There was 1 warning in `mutate()`.
ℹ In argument: `eviction_clean = readr::parse_number(`Eviction filing rate`)`.
Caused by warning:
! 20 parsing failures.
row col expected actual
2 -- a number -
26 -- a number -
33 -- a number -
35 -- a number -
79 -- a number -
... ... ........ ......
See problems(...) for more details.##### 3. Keep only usable rows
evictions_clean <- evictions_raw %>%
filter(
!is.na(GEOID_clean),
!is.na(eviction_clean)
) %>%
select(GEOID_clean, eviction_clean)
##### 4. Join to Austin tracts
tracts_with_evictions <- selected_10pct %>%
left_join(evictions_clean, by = "GEOID_clean")
##### 5. Split Travis vs non-Travis
evict_col <- "eviction_clean"
travis_tracts <- tracts_with_evictions %>%
filter(COUNTYFP == "453")
non_travis_tracts <- tracts_with_evictions %>%
filter(COUNTYFP != "453")
# keep only Travis tracts with real data
travis_valid <- travis_tracts %>%
filter(!is.na(.data[[evict_col]]))
##### 6. Find touching neighbors
touch_neighbors <- st_touches(
non_travis_tracts,
travis_tracts
)
##### 7. Compute centroids in projected CRS (for distance)
non_proj <- st_transform(non_travis_tracts, 3083)
travis_proj <- st_transform(travis_valid, 3083)
non_cent <- st_centroid(non_proj)Warning: st_centroid assumes attributes are constant over geometriestravis_cent <- st_centroid(travis_proj)Warning: st_centroid assumes attributes are constant over geometriesnon_coords <- st_coordinates(non_cent)
travis_coords <- st_coordinates(travis_cent)
##### 8. k-nearest neighbors (only valid data)
k <- 3
knn <- get.knnx(
data = travis_coords,
query = non_coords,
k = k
)
nearest_k <- knn$nn.index
##### 9. Hybrid averaging function
get_hybrid_avg <- function(i) {
## touching neighbors
touch_ids <- touch_neighbors[[i]]
if (length(touch_ids) > 0) {
vals <- travis_tracts[[evict_col]][touch_ids]
vals <- vals[!is.na(vals)]
if (length(vals) > 0) {
return(mean(vals))
}
}
## nearest valid neighbors
nn_ids <- nearest_k[i, ]
vals2 <- travis_valid[[evict_col]][nn_ids]
vals2 <- vals2[!is.na(vals2)]
if (length(vals2) > 0) {
return(mean(vals2))
}
## nNo data available
return(NA_real_)
}
##### 10. Apply function
non_travis_tracts$neighbor_eviction_avg <-
sapply(seq_len(nrow(non_travis_tracts)), get_hybrid_avg)
##### 11. Combine back with Travis
final_eviction_proxy <- bind_rows(
travis_tracts %>%
mutate(
neighbor_eviction_avg = .data[[evict_col]]
),
non_travis_tracts
)
##### 12. Quality checks
cat(
"Missing values:",
sum(is.na(final_eviction_proxy$neighbor_eviction_avg)),
"\n"
)Missing values: 8 final_eviction_proxy %>%
count(COUNTYFP, is.na(neighbor_eviction_avg))Simple feature collection with 4 features and 3 fields
Geometry type: GEOMETRY
Dimension: XY
Bounding box: xmin: -98.0104 ymin: 30.08853 xmax: -97.45043 ymax: 30.52177
Geodetic CRS: WGS 84
COUNTYFP is.na(neighbor_eviction_avg) n geometry
1 209 FALSE 1 POLYGON ((-98.01009 30.1316...
2 453 FALSE 225 POLYGON ((-97.83473 30.2966...
3 453 TRUE 8 MULTIPOLYGON (((-97.6419 30...
4 491 FALSE 16 POLYGON ((-97.73813 30.5059...##### 13. Export for composite index
final_eviction_table <- final_eviction_proxy %>%
st_drop_geometry() %>%
select(
GEOID_clean,
COUNTYFP,
all_of(evict_col),
neighbor_eviction_avg
) %>%
arrange(GEOID_clean)
write_csv(
final_eviction_table,
"Austin_Evictions_Hybrid_Spatial_Proxy.csv"
)# INSPECT TOUCHING TRAVIS NEIGHBORS + EVICTION RATES
library(purrr)
library(tidyr)
# Build table of touching relationships
touch_table <- map_df(
seq_along(touch_neighbors),
function(i) {
non_id <- non_travis_tracts$GEOID_clean[i]
travis_ids <- touch_neighbors[[i]]
# skip if no touching neighbors
if (length(travis_ids) == 0) return(NULL)
tibble(
non_travis_geoid = non_id,
travis_geoid = travis_tracts$GEOID_clean[travis_ids],
travis_eviction_rate = travis_tracts[[evict_col]][travis_ids]
)
}
)
# Add assigned hybrid value
touch_table <- touch_table %>%
left_join(
non_travis_tracts %>%
st_drop_geometry() %>%
select(
GEOID_clean,
neighbor_eviction_avg
),
by = c("non_travis_geoid" = "GEOID_clean")
)
# Calculate neighbor mean (for verification)
touch_table <- touch_table %>%
group_by(non_travis_geoid) %>%
mutate(
neighbor_mean_check = mean(travis_eviction_rate, na.rm = TRUE),
n_neighbors = n()
) %>%
ungroup()
# View in console (first few rows)
print(head(touch_table, 20))# A tibble: 18 × 6
non_travis_geoid travis_geoid travis_eviction_rate neighbor_eviction_avg
<chr> <chr> <dbl> <dbl>
1 48491020405 48453033800 0.0101 0.0314
2 48491020405 48453034300 NA 0.0314
3 48491020405 48453033700 0.0215 0.0314
4 48491020405 48453032700 0.0626 0.0314
5 48491020503 48453043000 0.0657 0.0501
6 48491020503 48453034100 0.0344 0.0501
7 48209010912 48453033300 0.00837 0.0390
8 48209010912 48453033200 0.0696 0.0390
9 48491020513 48453043000 0.0657 0.0657
10 48491020410 48453034200 0.0208 0.0393
11 48491020410 48453032700 0.0626 0.0393
12 48491020410 48453034100 0.0344 0.0393
13 48491020404 48453035800 0.0837 0.0842
14 48491020404 48453034300 NA 0.0842
15 48491020404 48453034400 0.0848 0.0842
16 48491020411 48453043000 0.0657 0.0403
17 48491020411 48453034200 0.0208 0.0403
18 48491020411 48453034100 0.0344 0.0403
# ℹ 2 more variables: neighbor_mean_check <dbl>, n_neighbors <int># save for documentation
write_csv(
touch_table,
"Austin_NonTravis_Touching_Travis_Evictions.csv"
)
################################################################################summary(tracts_with_evictions$`Eviction filing rate`)Length Class Mode
0 NULL NULL sum(is.na(travis_tracts$`Eviction filing rate`))[1] 0nrow(travis_tracts)[1] 233