The document describes using R to analyze OpenStreetMap data for Asheville, North Carolina. It extracts different types of map features like streets and waterways from OSM and plots them on a map. Functions from libraries like osmdata, ggplot, and ggmap are used to retrieve the data, separate it by type, and overlay the features on a map of Asheville. The code generates a labeled map showing streets, rivers, and railroads to demonstrate visualizing OpenStreetMap data in R.

1. DAVV
School of Data Science and Forecasting
M.sc. (Data Science and Analytics) 2nd Sem
Name - Tanay Deshmukh
Roll Number - DS5B-2034
Subject - Statistical Programming in R
Subject Code - DS5B-508
Assignment Topic - OpenStreetMap(OSM)
Date - 30-06-2021
Submitted to – Mr. Vandit Hedau

2. Result

3. Libraries used –
Functions used –
• available_tags()
• getbb()
• add_osm_feature()
• osmdata_sf()
• geom_sf()
• coord_sf()
• etc
Procedure

4. available_tags("highway")
getbb("atlanta georgia")
big_streets <- getbb("Asheville United States")%>%
opq()%>%
add_osm_feature(key = "highway", value = c("motorway", "primary", "motorway_link", "primary_link"))%>%
osmdata_sf()
big_streets
med_streets <- getbb("Asheville United States")%>%
opq()%>%
add_osm_feature(key = "highway", value = c("Secondary", "tertiary", "secondary_link", "tertiary_link"))%>%
osmdata_sf()
small_streets <- getbb("Asheville United States")%>%
opq()%>%
add_osm_feature(key = "highway", value = c("residential", "living_street", "unclassified", "service", "footway"))%>%
osmdata_sf()
river <- getbb("Asheville United States")%>%
opq()%>%
add_osm_feature(key = "waterway", value = "river")%>%
osmdata_sf()
railway <- getbb("Asheville United States")%>%
opq()%>%
add_osm_feature(key = "railway", value = "rail")%>%
osmdata_sf()
available_tags("railway")
Importing Data

5. Console Code

6. ggplot() +
geom_sf(data = big_streets$osm_lines,
inherit.aes = FALSE,
colour = "yellowgreen")
ggplot() +
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
colour = "blue")
ggplot() +
geom_sf(data = big_streets$osm_lines,
inherit.aes = FALSE,
colour = "yellowgreen") +
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
colour = "blue")
Plotting Separate maps

7. ggplot() +
geom_sf(data = big_streets$osm_lines,
inherit.aes = FALSE,
colour = "yellowgreen") +
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
colour = "blue") +
coord_sf(xlim = c(-82.60, -82.51),
ylim = c(35.54, 35.63),
expand = TRUE)
Selected Area from Plot

8. ggplot() +
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
colour = "blue",
size = .8,
alpha = .3) +
geom_sf(data = railway$osm_lines,
inherit.aes = FALSE,
color = "white",
size = .2,
alpha = .5) +
geom_sf(data = med_streets$osm_lines,
inherit.aes = FALSE,
color = "yellow3",
size = .3,
alpha = .5) +
geom_sf(data = small_streets$osm_lines,
inherit.aes = FALSE,
color = "black",
size = .2,
alpha = .5) +
geom_sf(data = big_streets$osm_lines,
inherit.aes = FALSE,
colour = "yellowgreen",
size = .5,
alpha = .6) +
coord_sf(xlim = c(-82.60, -82.51),
ylim = c(35.54, 35.63),
expand = TRUE) +
theme_void() +
theme(plot.title = element_text(size = 20, family = "lato", face = "bold", hjust = .5),
plot.subtitle = element_text(family = "lato", size = 8, hjust = .5, margin = margin(2, 0, 5, 0))) +
labs(title = "Raccoon City", subtitle = "35.54°N/82.60°W")
Quick image of OSM

9. library(osmdata)
library(ggmap)
library(tidyverse)
library(remotes)
library(showtext)
library(rvest)
available_tags("highway")
getbb("atlanta georgia")
big_streets <- getbb("Asheville United States")%>%
opq()%>%
add_osm_feature(key = "highway", value = c("motorway", "primary", "motorway_link", "primary_link"))%>%
osmdata_sf()
big_streets
med_streets <- getbb("Asheville United States")%>%
opq()%>%
add_osm_feature(key = "highway", value = c("Secondary", "tertiary", "secondary_link", "tertiary_link"))%>%
osmdata_sf()
small_streets <- getbb("Asheville United States")%>%
opq()%>%
add_osm_feature(key = "highway", value = c("residential", "living_street", "unclassified", "service", "footway"))%>%
osmdata_sf()
river <- getbb("Asheville United States")%>%
opq()%>%
add_osm_feature(key = "waterway", value = "river")%>%
osmdata_sf()
railway <- getbb("Asheville United States")%>%
opq()%>%
add_osm_feature(key = "railway", value = "rail")%>%
osmdata_sf()
available_tags("railway")
Code(1)

10. ggplot() +
geom_sf(data = big_streets$osm_lines,
inherit.aes = FALSE,
colour = "yellowgreen")
ggplot() +
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
colour = "blue")
ggplot() +
geom_sf(data = big_streets$osm_lines,
inherit.aes = FALSE,
colour = "yellowgreen") +
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
colour = "blue")
ggplot() +
geom_sf(data = big_streets$osm_lines,
inherit.aes = FALSE,
colour = "yellowgreen") +
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
colour = "blue") +
coord_sf(xlim = c(-82.60, -82.51),
ylim = c(35.54, 35.63),
expand = TRUE)
font_add_google(name = "Lato", family = "lato")
showtext_auto()
ggplot() +
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
colour = "blue",
size = .8,
alpha = .3) +
geom_sf(data = railway$osm_lines,
inherit.aes = FALSE,
color = "white",
size = .2,
alpha = .5) +
geom_sf(data = med_streets$osm_lines,
inherit.aes = FALSE,
color = "yellow3",
size = .3,
alpha = .5) +
geom_sf(data = small_streets$osm_lines,
inherit.aes = FALSE,
color = "black",
size = .2,
alpha = .5) +
geom_sf(data = big_streets$osm_lines,
inherit.aes = FALSE,
colour = "yellowgreen",
size = .5,
alpha = .6) +
coord_sf(xlim = c(-82.60, -82.51),
ylim = c(35.54, 35.63),
expand = TRUE) +
theme_void() +
theme(plot.title = element_text(size = 20, family = "lato", face = "bold", hjust = .5),
plot.subtitle = element_text(family = "lato", size = 8, hjust = .5, margin =
margin(2, 0, 5, 0))) +
labs(title = "Raccoon City", subtitle = "35.54°N/82.60°W")
Code(2)
Code(3)

11. Final OpenStreetMap

13. Thank You