Init.

bin/find-small-winter-cities.R

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+
+# This project is to try to figure out similarity among
+# Grand Forks/East Grand Forks and US cities of similar size
+# in similar climate. The results will then be used to reseearch
+# what those cities are doing for bike infra and how it is
+# working out for them.
+
+# TODO make this Rmd file
+
+library(tidycensus)
+library(tidyverse)
+library(ggplot2)
+library(tigris)
+library(viridis)
+library(ini)
+library(sf)
+# Load the census API key, the ini file should be of format
+# [keys]
+# key = your_key_here
+census_api_key(read.ini('census_api_key.ini')$keys$key)
+
+# Get a list of variables
+vars <- load_variables("2018", "acs5")
+
+# This includes a broader area
+# us_metro <- get_acs(geography = "metropolitan statistical area/micropolitan statistical area",
+#                      variables = "B01001_001",
+#                     geometry = T)
+# This is just urban
+# Code is for total population, all ages
+# https://www.census.gov/data/developers/data-sets/acs-1year/notes-on-acs-api-variable-formats.html
+us_urban <- get_acs(geography = "urban area",
+                    variables = "B01001_001")
+
+# Geometry is not in tidycensus yet, so we have to get it this way
+# Download it once and save locally
+#urban_areas_geom <- urban_areas(class = "sf")
+#save(urban_areas_geom, file = "data/urban_areas_tigris.RData")
+# Load saved variable
+load("data/urban_areas_tigris.RData")
+
+us_urban_joined <- left_join(urban_areas_geom,
+                    us_urban,
+                    by = c("GEOID10" = "GEOID"))
+
+# Prove you can plot the largest cities
+ggplot(us_urban_joined %>% filter(estimate > 5000000)) +
+    geom_sf(aes(fill = estimate),
+            color = NA) +
+    theme_minimal() +
+    scale_fill_viridis()
+
+# Define range of populations to include
+gfk_pop <- us_urban_clean %>%
+                filter(NAME == "Grand Forks, ND--MN Urbanized Area (2010)") %>%
+                mutate(high = estimate + (estimate * 0.1),
+                       low = estimate - (estimate * 0.1))
+
+# Clean data
+us_urban_clean <- us_urban_joined %>%
+                    select(NAME, variable, estimate, moe) %>%
+                    st_centroid() %>%
+                    filter(estimate < gfk_pop[['high']],
+                           estimate > gfk_pop[['low']])
+
+# TODO bring in climate data
+
+
+
+
+
+
+