This is a map detailing the incorporated and unincorporated communities throughout Cameron County using data spanning from 2022 to 2023; this represents the most up-to-date information available publicly, as far as I know. A Census-Designated Place (CDP), per the U.S. Census Bureau, is "...[a] statistical equivalent of [an] incorporated place and represent[s] unincorporated communities that do not have a legally defined boundary or an active, functioning governmental structure." Source: https://www.census.gov/programs-surveys/bas/information/cdp.html ... code: library(easypackages) libraries(c("readxl", "ggmap", "ggiraph", "ggforce", "ggcorrplot", "ggthemes", "ggsignif", "ggsflabel", "ggrepel", "ggpubr", "ggsci", "glue", "gt", "janitor", "maptools", "mapview", "magrittr", "plyr", "prettyunits", "progress", "progressr", "psych", "rgeos", "rio", "rms", "Hmisc", "robustbase", "rspat", "s2", "sfheaders", "sfweight", "snakecase", "smoothr", "sp", "spatial", "spatialEco", "spatstat", "spatstat.linnet", "spatstat.model", "rpart", "spatstat.explore", "nlme", "spatstat.random", "spatstat.geom", "spatstat.data", "spdep", "sf", "spData", "abind", "summarytools", "terra", "tidycensus", "tidylog", "tidyselect", "lubridate", "forcats", "stringr", "dplyr", "purrr", "readr", "tidyr", "tibble", "ggplot2", "tidyverse", "tigris", "tmap", "vctrs", "viridis", "viridisLite", "vroom", "waldo", "wk", "stats", "graphics", "grDevices", "utils", "datasets", "methods", "base", "haven", "foreign", "survey", "srvyr", "sitrep", "questionr", "srvyr", "stringr")) ##tigris package, pulls Census data TX_places<- places(state = "TX", cb = FALSE, year = 2023) cam_pop_tracts<- get_acs("tract", table = "B01001", state = "TX", county = "Cameron", year= 2022, survey = "acs5", geometry = TRUE) # colonias data available through the Texas OAG database via filtering # https://texasoag.maps.arcgis.com/apps/webappviewer/index.html?id=1bc9c4f7b1da47dd8fc535fbd17dc060 # the file I'm using comes from the Cameron County DOT though, I'm pretty sure, circa 05/2022 colonias_sf<- read_sf(dsn = "E:/COLONIAS/COLONIAS.shp") ## Step 1: (after loading everything in) make a "cookie cutter" to filter the places data cam_pop_tracts %>% st_as_sf(.) %>% st_union(.) -> cam_union ## Step 2: Cookie Cut! st_intersection(TX_places, cam_union) -> cam_places ## Step 3: Make the map of places (unincorporated) cam_places %>% mutate(TYPE = str_remove(NAMELSAD, paste0(NAME)) %>% str_squish(.) %>% toupper(.)) %>% st_as_sf(.) %>% tm_shape(.) + tm_polygons(col = "TYPE", popup.vars = c("NAME", "TYPE"), alpha = 0.6) + tm_basemap("OpenStreetMap") -> bigboss_CDP_map ## Step 4 (optional): Show relevant colonias too based on size using "tm_bubbles" bigboss_CDP_map + tm_shape(st_make_valid(st_as_sf(colonias_sf))) + tm_bubbles(size="EST_POP", fill = "COLONIA_NM", col="purple", alpha = 0.4, popup.vars=c("COLONIA_NM", "EST_POP", "INCORPORAT"))

Average ride for members appears to be stable during the weekdays Monday to Friday. Average ride duration for causal riders is higher on Thursday compared to other day,there must definitely be a reason for that.

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