jshannon75

Creating a road elevation map in R

Jerry Shannon

This gist describes the process of calculating elevation change for roads in Athens-Clarke County, Georgia. This analysis is part of a process of creating a bike map for the county. As part of identifying the most bike (and walk) friendly routes, I and a student wanted to identify roads with the greatest elevation change.

Downloading OSM data

jshannon75

library(tidyverse)
library(osmdata)
library(sf)
library(tigris)
library(wordcloud2)
library(tmap)

#Load city outline
city="Atlanta"

jshannon75

library(tidyverse)
library(osmdata)
library(sf)
library(tigris)
library(wordcloud2)
library(tmap)

#Load city outline
city="Atlanta"

jshannon75

library(tidyverse)
library(sf)

#Join population data to tract boundaries and convert to point coordinates
tractdata<-read_csv("nhgis0105_2017_tract_racepov.csv") #Data on population downloaded from NHGIS
tracts<-st_read("nhgis0101_shapefile_tl2017_us_tract_2017/US_tract_2017.shp",stringsAsFactors=FALSE) %>% #Tract boundaries downloaded from NHGIS
  st_transform(4326) 
tract_center<-tracts %>%
  st_centroid() %>%
  st_coordinates() %>%

jshannon75

from os import path
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import os

from wordcloud import WordCloud, STOPWORDS

d = path.dirname(__file__) if "__file__" in locals() else os.getcwd()

jshannon75

library(tidyverse)
library(ggrepel)
library(gghighlight)
library(tidycensus)
library(sf)

dailydata<-read_csv("https://github.com/nytimes/covid-19-data/raw/master/us-counties.csv")

#This next step requires a census API 
cty_pop<-get_acs(geography="county",variables = "B01001_001",geometry=TRUE) %>%

jshannon75

library(tidyverse)
library(covdata)
library(tmap)
library(TTR)

ga_mobility<-apple_mobility %>%
  filter(sub_region=="Georgia" & geo_type=="county" &
           is.na(index)==FALSE) %>% 
  group_by(region) %>% 
  mutate(ra = runMean(index, 5)) %>%  ###Create a five day moving average

jshannon75

library(tidycensus)
library(tidyverse)
library(sf)

#Select variables
v18<-load_variables(2018,"acs5",cache=TRUE)
vars<-v18 %>%
  filter(substr(name,1,6) %in% c("B25064","B03002","B25106","B19013"))
#write_csv(vars,"data/census_vars.csv") #Select just those variables I want
vars_sel<-read_csv("data/census_vars.csv")

jshannon75

#Code adapted from https://github.com/datawookie/travelling-salesman-map/blob/master/travelling-salesman-demo.R

library(tidyverse)
library(purrr)
library(ggmap)
library(gmapsdistance)
library(TSP)
library(sf)

# Due to changes in the Google Maps Terms of Service, this code will not work without first registering an API key.

jshannon75

library(tidyverse)
library(extrafont)
library(sf)

ebird<-read_csv("ebird_GA_Sep2019.csv") #Individual observation data

species<-ebird %>%
  distinct(`COMMON NAME`)
species_blue<-species %>%
  filter(str_detect(tolower(`COMMON NAME`),"blue"))