Data Analytics using crime dataset

This is a set of maps that show which parts of Cameron County have the highest proportion of people aged 65 and older; these would be most relevant to tertiary prevention of ADRD, though also tie to secondary prevention to some extent. The top map, "Percentage (%) of Population age 65 and over", illustrates the proportion of people who are 65 and older as divided by the population in that tract, multiplied by 100. The bottom map, "Raw # of Population age 65 and over", shows the numerator for that proportion calculation; essentially, how many people 65 and older are *actually* living in that Census tract. .. (you'll need 'tidyverse', 'tm', 'tidycensus', 'sf', and 'cwi' from DataHaven for ACS labeling) Code:: get_acs("tract", table = "B01001", state = "TX", county = "Cameron", year= 2022, survey = "acs5", geometry = TRUE) %>% label_acs(.) %>% separate_wider_delim(., cols = "label", delim = "!!", names_sep = "", too_few = "align_start") %>% separate_wider_delim(., cols = "variable", delim = "_", names_sep = "", too_few = "align_start") %>% mutate(variable2 = as.numeric(variable2)) %>% filter(variable2 != "1" & variable2 != "2" & variable2!= "26") %>% filter(variable2 > 6 & variable2 < 26 | variable2 > 30 & variable2 <=49) %>% transform(., tract_pop_sum = ave(.$estimate, .$NAME, FUN=sum)) %>% mutate(tract_pop_prop = estimate/tract_pop_sum, test = if_else(variable2 > 14 & variable2 < 26 | variable2>38 & variable2<=49,"atrisk","norm")) %>% group_by(test, NAME) %>% mutate(atrisk_sum = sum(estimate)) %>% transform(., tract_pop_sum = ave(.$estimate, .$NAME, FUN=sum)) %>% mutate(tract_pop_prop = estimate/tract_pop_sum, test = if_else(variable2 > 19 & variable2 < 26 | variable2>43 & variable2<=49,"65plus","norm")) %>% group_by(test, NAME) %>% mutate(atrisk_sum = sum(estimate), prop_atrisk = 100 * atrisk_sum/tract_pop_sum) -> cam_pop_tracts_65plus cam_pop_tracts_65plus %>% filter(test == "65plus") %>% st_as_sf(.) %>% dplyr::select(prop_atrisk, test, atrisk_sum, geometry, NAME) %>% drop_na(.) %>% unique(.) %>% mutate(prop_atrisk = round(prop_atrisk, 1)) %>% dplyr::rename(., "Percentage (%) of Population age 65 and over"=1, "Raw # of Population age 65 and over"=3) %>% tm_shape(.) + tm_polygons(col=c("Percentage (%) of Population age 65 and over", "Raw # of Population age 65 and over"), alpha=0.6, style="jenks", palette="Reds") + tm_layout(legend.outside = "TRUE") + tm_facets(nrow = 2, sync = TRUE)

EDA for Silk_Textile in Government data set