Section 1. Choose your Census Tracts.
The walkable census tract chosen was Cabbagetown, GEOID “13121003200”. I recently went for the Chomp and Stomp festival and found it to be highly walkable, and well connected to train and bus services. There was additional connectivity due to the beltline proximity, and it was bikeable as well. There was a large bicycle parking, in front of the Krog Tunnel.
The unwalkable census tract chosen was Druid Hills. While this is an affluent neighborhood, it is very car dependant, and amenities are spaced out. The housing is consists of single family units, on large lots of land. There isn’t much variety in terms of housing or alternative transit mode access.
Section 2. OSM, GSV, and computer vision.
Fill out the template to complete the script.
library(tidyverse)
library(tidycensus)
library(osmdata)
library(sfnetworks)
library(units)
library(sf)
library(tidygraph)
library(tmap)
library(here)
library(leaflet)
library(ggmap)Step 1. Get OSM data and clean it.
The getbb() function, which we used in the class to
download OSM data, isn’t suitable for downloading just two Census
Tracts. We will instead use an alternative method.
- Using tidycensus package, download the Census Tract polygon for Fulton and DeKalb counties.
- Extract two Census Tracts, each of which will be your most walkable and least walkable Census Tracts.
- Using their bounding boxes, get OSM data.
- Convert them into sfnetwork object and clean it.
# =========== NO MODIFICATION ZONE STARTS HERE ===============================
# Download Census Tract polygon for Fulton and DeKalb
tract <- get_acs("tract",
variables = c('tot_pop' = 'B01001_001'),
year = 2022,
state = "GA",
county = c("Fulton", "DeKalb"),
geometry = TRUE)## Getting data from the 2018-2022 5-year ACS## Warning: • You have not set a Census API key. Users without a key are limited to 500
## queries per day and may experience performance limitations.
## ℹ For best results, get a Census API key at
## http://api.census.gov/data/key_signup.html and then supply the key to the
## `census_api_key()` function to use it throughout your tidycensus session.
## This warning is displayed once per session.## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.## | | | 0% | | | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |======= | 9% | |======= | 10% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |=========== | 15% | |=========== | 16% | |============ | 18% | |============== | 19% | |============== | 20% | |=============== | 22% | |================ | 23% | |================== | 25% | |================== | 26% | |=================== | 27% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |====================== | 31% | |====================== | 32% | |======================= | 33% | |========================= | 35% | |============================= | 41% | |============================= | 42% | |============================== | 43% | |=============================== | 44% | |=============================== | 45% | |================================ | 46% | |================================= | 47% | |================================== | 49% | |=================================== | 50% | |==================================== | 51% | |==================================== | 52% | |===================================== | 53% | |====================================== | 54% | |====================================== | 55% | |======================================= | 56% | |======================================== | 57% | |========================================= | 58% | |========================================= | 59% | |========================================== | 60% | |=========================================== | 61% | |============================================ | 63% | |============================================= | 64% | |============================================== | 66% | |=============================================== | 67% | |================================================ | 68% | |================================================ | 69% | |================================================= | 70% | |================================================= | 71% | |================================================== | 71% | |=================================================== | 72% | |=================================================== | 73% | |==================================================== | 74% | |====================================================== | 77% | |======================================================= | 78% | |======================================================= | 79% | |======================================================== | 80% | |========================================================= | 82% | |=========================================================== | 84% | |=============================================================== | 90% | |================================================================ | 91% | |================================================================ | 92% | |================================================================= | 93% | |================================================================== | 94% | |=================================================================== | 95% | |==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |======================================================================| 100%# =========== NO MODIFY ZONE ENDS HERE ========================================
# TASK ////////////////////////////////////////////////////////////////////////
# The purpose of this TASK is to create one bounding box for walkable Census Tract and another bounding box for unwalkable Census Tract.
# As long as you generate what's needed for the subsequent codes, you are good. The numbered list of tasks below is to provide some hints.
# 1. Write the GEOID of walkable & unwalkable Census Tracts. e.g., tr1_ID <- c("13121001205", "13121001206")
# 2. Extract the selected Census Tracts using tr1_ID & tr2_ID
# 3. Create their bounding boxes using st_bbox(), and
# 4. assign them to tract_1_bb and tract_1_bb, respectively.
# For the walkable Census Tract(s)
# 1. Cabbagetown
tr1_ID <- "13121003200"
# 2~4
tract_1_bb <- tract %>%
filter(GEOID %in% tr1_ID) %>%
st_bbox()
# For the unwalkable Census Tract(s)
# 1. Druid Hills
tr2_ID <- "13089020200"
# 2~4
tract_2_bb <- tract %>%
filter(GEOID %in% tr2_ID) %>%
st_bbox()
# =========== NO MODIFICATION ZONE STARTS HERE ===============================
# Get OSM data for the two bounding box
osm_1 <- opq(bbox = tract_1_bb) %>%
add_osm_feature(key = 'highway',
value = c("motorway", "trunk", "primary",
"secondary", "tertiary", "unclassified",
"residential")) %>%
osmdata_sf() %>%
osm_poly2line()
osm_2 <- opq(bbox = tract_2_bb) %>%
add_osm_feature(key = 'highway',
value = c("motorway", "trunk", "primary",
"secondary", "tertiary", "unclassified",
"residential")) %>%
osmdata_sf() %>%
osm_poly2line()
# =========== NO MODIFY ZONE ENDS HERE ========================================
# TASK ////////////////////////////////////////////////////////////////////////
# 1. Convert osm_1 and osm_2 to sfnetworks objects (set directed = FALSE)
# 2. Clean the network by (1) deleting parallel lines and loops, (2) create missing nodes, and (3) remove pseudo nodes,
# 3. Add a new column named length using edge_length() function.
net1 <- osm_1$osm_lines %>%
# Drop redundant columns
select(osm_id, highway) %>%
sfnetworks::as_sfnetwork(directed = FALSE) %>%
activate("edges") %>%
filter(!edge_is_multiple()) %>% # remove duplicated edges
filter(!edge_is_loop()) %>% # remove loops
convert(., sfnetworks::to_spatial_subdivision) %>% # subdivide edges
convert(., sfnetworks::to_spatial_smooth) %>% # delete pseudo nodes
mutate(length = edge_length())## Warning: to_spatial_subdivision assumes attributes are constant over geometriesnet2 <- osm_2$osm_lines %>%
# Drop redundant columns
select(osm_id, highway) %>%
select(osm_id, highway) %>%
sfnetworks::as_sfnetwork(directed = FALSE) %>%
activate("edges") %>%
filter(!edge_is_multiple()) %>% # remove duplicated edges
filter(!edge_is_loop()) %>% # remove loops
convert(., sfnetworks::to_spatial_subdivision) %>% # subdivide edges
convert(., sfnetworks::to_spatial_smooth) %>% # delete pseudo nodes
mutate(length = edge_length())## Warning: to_spatial_subdivision assumes attributes are constant over geometries# //TASK //////////////////////////////////////////////////////////////////////
# =========== NO MODIFICATION ZONE STARTS HERE ===============================
# OSM for the walkable part
edges_1 <- net1 %>%
# Extract 'edges'
st_as_sf("edges") %>%
# Drop segments that are too short (100m)
mutate(length = as.vector(length)) %>%
filter(length > 100) %>%
# Add a unique ID for each edge
mutate(edge_id = seq(1,nrow(.)),
is_walkable = "walkable")
# OSM for the unwalkable part
edges_2 <- net2 %>%
# Extract 'edges'
st_as_sf("edges") %>%
# Drop segments that are too short (100m)
mutate(length = as.vector(length)) %>%
filter(length > 100) %>%
# Add a unique ID for each edge
mutate(edge_id = seq(1,nrow(.)),
is_walkable = "unwalkable")
# Merge the two
edges <- bind_rows(edges_1, edges_2)
# =========== NO MODIFY ZONE ENDS HERE ========================================Step 2. Define getAzimuth() function.
getAzimuth <- function(line){
# This function takes one edge (i.e., a street segment) as an input and
# outputs a data frame with four points (start, mid1, mid2, and end) and their azimuth.
# TASK ////////////////////////////////////////////////////////////////////////
# 1. From `line` object, extract the coordinates using st_coordinates() and extract the first two rows.
# 2. Use atan2() function to calculate the azimuth in degree.
# Make sure to adjust the value such that 0 is north, 90 is east, 180 is south, and 270 is west.
# 1
start_p <- line %>%
st_coordinates() %>%
.[1:2,1:2]
# 2
start_azi <- atan2(start_p[2,"X"] - start_p[1, "X"],
start_p[2,"Y"] - start_p[1, "Y"])*180/pi
# TASK ////////////////////////////////////////////////////////////////////////
# Repeat what you did above, but for last two rows (instead of the first two rows).
# Remember to flip the azimuth so that the camera would be looking at the street that's being measured
end_p <- line %>%
st_coordinates() %>%
.[(nrow(.)-1):nrow(.),1:2]
end_azi <- atan2(end_p[2,"X"] - end_p[1, "X"],
end_p[2,"Y"] - end_p[1, "Y"])*180/pi
end_azi <- if (end_azi < 180) {end_azi + 180} else {end_azi - 180}
# TASK ////////////////////////////////////////////////////////////////////////
# 1. From `line` object, use st_line_sample() function to generate points at 0.45 and 0.55 locations. These two points will be used to calculate the azimuth.
# 2. Use st_case() function to convert 'MULTIPOINT' object to 'POINT' object.
# 3. Extract coordinates using st_coordinates().
# 4. Use atan2() functino to Calculate azimuth.
# 5. Use st_line_sample() again to generate a point at 0.5 location and get its coordinates. This point will be the location at which GSV image will be downloaded.
mid_p <- line %>%
st_line_sample(sample = c(0.45, 0.55)) %>%
st_cast("POINT") %>%
st_coordinates()
mid_azi <- atan2(mid_p[2,"X"] - mid_p[1, "X"],
mid_p[2,"Y"] - mid_p[1, "Y"])*180/pi
mid_p <- line %>%
st_line_sample(sample = 0.5) %>%
st_cast("POINT") %>%
st_coordinates()
# =========== NO MODIFICATION ZONE STARTS HERE ===============================
return(tribble(
~type, ~X, ~Y, ~azi,
"start", start_p[1,"X"], start_p[1,"Y"], start_azi,
"mid1", mid_p[1, "X"], mid_p[1, "Y"], mid_azi,
"mid2", mid_p[1, "X"], mid_p[1, "Y"], ifelse(mid_azi < 180, mid_azi + 180, mid_azi - 180),
"end", end_p[2,"X"], end_p[2,"Y"], end_azi))
# =========== NO MODIFY ZONE ENDS HERE ========================================
}Step 3. Apply the function to all street segments
We can apply getAzimuth() function to the edges object.
We finally append edges object to make use of the columns
in edges object (e.g., is_walkable column).
When you are finished with this code chunk, you will be ready to
download GSV images.
# TASK ////////////////////////////////////////////////////////////////////////
# Apply getAzimuth() function to all edges.
# Remember that you need to pass edges object to st_geometry() before you apply getAzimuth()
edges_azi <- edges %>%
st_geometry() %>%
map_df(getAzimuth, .progress = T)
# =========== NO MODIFICATION ZONE STARTS HERE ===============================
edges_azi <- edges_azi %>%
bind_cols(edges %>%
st_drop_geometry() %>%
slice(rep(1:nrow(edges),each=4))) %>%
st_as_sf(coords = c("X", "Y"), crs = 4326, remove=FALSE) %>%
mutate(node_id = seq(1, nrow(.)))
# =========== NO MODIFY ZONE ENDS HERE ========================================Step 4. Define a function that formats request URL and download images.
getImage <- function(iterrow){
# This function takes one row of edges_azi and downloads GSV image using the information from edges_azi.
# TASK ////////////////////////////////////////////////////////////////////////
# Finish this function definition.
# 1. Extract required information from the row of edges_azi, including
# type (i.e., start, mid1, mid2, end), location, heading, edge_id, node_id, and key.
# 2. Format the full URL and store it in `request`. Refer to this page: https://developers.google.com/maps/documentation/streetview/request-streetview
# 3. Format the full path (including the file name) of the image being downloaded and store it in `fpath`
type <- iterrow$type
location <- #paste0(iterrow$Y %>% round(5), ",", iterrow$X %>% round(5))
heading <- iterrow$azi %>% round(1)
edge_id <- iterrow$edge_id
node_id <- iterrow$node_id
highway <- iterrow$highway
key <- Sys.getenv("Google_API_KEY")
endpoint <- "https://maps.googleapis.com/maps/api/streetview"
request <- glue::glue("{endpoint}?size=640x640&location={location}&heading={heading}&fov=90&pitch=0&key={key}")
fname <- glue::glue("GSV-nid_{node_id}-eid_{edge_id}-type_{type}-Location_{location}-heading_{heading}.jpg") # Don't change this code for fname
fpath <- here("downloaded_image", fname)
download.file(request, fpath, mode = 'wb')
# //TASK //////////////////////////////////////////////////////////////////////
# =========== NO MODIFICATION ZONE STARTS HERE ===============================
# Download images
if (!file.exists(fpath)){
download.file(furl, fpath, mode = 'wb')
}
# =========== NO MODIFY ZONE ENDS HERE ========================================
}Step 5. Download GSV images
Before you download GSV images, make sure
the row number of edges_azi is not too large! The row
number of edges_azi will be the number of GSV images you
will be downloading. Before you download images, always double-check
your Google Cloud Console’s Billing tab to make sure that you will not
go above the free credit of $200 each month. The price is $7 per 1000
images.
# =========== NO MODIFICATION ZONE STARTS HERE ===============================
# Loop!
for (i in seq(1,nrow(edges_azi))){
getImage(edges_azi[i,])
}
# =========== NO MODIFY ZONE ENDS HERE ========================================ZIP THE DOWNLOADED IMAGES AND NAME IT ‘gsv_images.zip’ FOR STEP 6.
Step 6. Apply computer vision
Now, use Google Colab to apply the semantic segmentation model. Zip your images and upload the images to your Colab session.
Step 7. Merging the processed data back to R
Once all of the images are processed and saved in your Colab session as a CSV file, download the CSV file and merge it back to edges.
# TASK ////////////////////////////////////////////////////////////////////////
seg_output <- read.csv("C:/Users/Hina/Downloads/seg_output (1).csv")%>%
mutate(node_id = img_id)
# =========== NO MODIFICATION ZONE STARTS HERE ===============================
# Join the seg_output object back to edges_azi object using node_id as the join key.
edges_seg_output <- edges_azi %>%
inner_join(seg_output, by=c("node_id" = "img_id")) %>%
select(type, X, Y, node_id, building, sky, tree, road, sidewalk, is_walkable) %>%
mutate(across(c(building, sky, tree, road, sidewalk), function(x) x/(640*640)))
# =========== NO MODIFY ZONE ENDS HERE ========================================Section 3. Summarise and analyze the results.
At the beginning of this assignment, you defined one Census Tract as walkable and the other as unwalkable. The key to the following analysis is the comparison between walkable and unwalkable Census Tracts.
Analysis 1 - Create interactive map(s) to visualize the spatial distribution of the streetscape.
You need to create maps of the proportion of building, sky, tree, road, and sidewalk for walkable and unwalkable areas. In total, you will have 10 maps.
Provide a brief description of your findings from the maps.
### Analysis 2 - Compare the means.
You need to calculate the mean of the proportion of building, sky, tree, road, and sidewalk for walkable and unwalkable areas. For example, you need to calculate the mean of building category for each of walkable and unwalkable Census Tracts. Then, you need to calculate the mean of sky category for each of walkable and unwalkable Census Tracts. In total, you will have 10 mean values. **<font color=pink> Provide a brief description of your findings. </font>**
```r
# TASK ////////////////////////////////////////////////////////////////////////
library(magrittr)##
## Attaching package: 'magrittr'## The following object is masked from 'package:ggmap':
##
## inset## The following object is masked from 'package:purrr':
##
## set_names## The following object is masked from 'package:tidyr':
##
## extractlibrary(tmap)
library(leaflet)
tmap_mode("view")## tmap mode set to interactive viewingt1 <- tm_basemap("OpenStreetMap") +
tm_shape(edges_seg_output %>% filter(is_walkable =="walkable")) +
tm_dots(col = 'building', style="jenks")
t2 <- tm_basemap("OpenStreetMap") +
tm_shape(edges_seg_output %>% filter(is_walkable=="unwalkable")) +
tm_dots(col = 'building', style="jenks")
t3 <- tm_basemap("OpenStreetMap") +
tm_shape(edges_seg_output %>% filter(is_walkable=="walkable")) +
tm_dots(col = 'sky', style="jenks")
t4 <- tm_basemap("OpenStreetMap") +
tm_shape(edges_seg_output %>% filter(is_walkable=="unwalkable")) +
tm_dots(col = 'sky', style="jenks")
t5 <- tm_basemap("OpenStreetMap") +
tm_shape(edges_seg_output %>% filter(is_walkable=="walkable")) +
tm_dots(col = 'tree', style="jenks")
t6 <- tm_basemap("OpenStreetMap") +
tm_shape(edges_seg_output %>% filter(is_walkable=="unwalkable")) +
tm_dots(col = 'tree', style="jenks")
t7 <- tm_basemap("OpenStreetMap") +
tm_shape(edges_seg_output %>% filter(is_walkable=="walkable")) +
tm_dots(col = 'road', style="jenks")
t8 <- tm_basemap("OpenStreetMap") +
tm_shape(edges_seg_output %>% filter(is_walkable=="unwalkable")) +
tm_dots(col = 'road', style="jenks")
t9 <- tm_basemap("OpenStreetMap") +
tm_shape(edges_seg_output %>% filter(is_walkable=="walkable")) +
tm_dots(col = 'sidewalk', style="jenks")
t10 <- tm_basemap("OpenStreetMap") +
tm_shape(edges_seg_output %>% filter(is_walkable=="unwalkable")) +
tm_dots(col = 'sidewalk', style="jenks")
tmap_arrange(t1, t2, sync = F, ncol=2)tmap_arrange(t3, t4, sync = F, ncol=2)tmap_arrange(t5, t6, sync = F, ncol=2)tmap_arrange(t7, t8, sync = F, ncol=2)tmap_arrange(t9, t10, sync = F, ncol=2)# //TASK //////////////////////////////////////////////////////////////////////Analysis 3 - Draw boxplot
You need to calculate the mean of the proportion of building, sky, tree, road, and sidewalk for walkable and unwalkable areas. For example, you need to calculate the mean of building category for each of walkable and unwalkable Census Tracts. Then, you need to calculate the mean of sky category for each of walkable and unwalkable Census Tracts. In total, you will have 10 mean values. Provide a brief description of your findings.
# TASK ////////////////////////////////////////////////////////////////////////
library(gridExtra)## Warning: package 'gridExtra' was built under R version 4.3.3##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(grid)
edges_seg_walkable <- edges_seg_output %>%
filter(is_walkable == "walkable")
edges_seg_unwalkable <- edges_seg_output %>%
filter(is_walkable == "unwalkable")
# Walkable
non_spatial_data_walkable <- st_drop_geometry(edges_seg_walkable)
mean_table_walkable <- data.frame(
Column = names(non_spatial_data_walkable)[5:9],
Mean = colMeans(non_spatial_data_walkable[, 5:9], na.rm = TRUE)
)
# Unwalkable
non_spatial_data_unwalkable <- st_drop_geometry(edges_seg_unwalkable)
mean_table_unwalkable <- data.frame(
Column = names(non_spatial_data_unwalkable)[5:9],
Mean = colMeans(non_spatial_data_unwalkable[, 5:9], na.rm = TRUE)
)
# create titles
walkable_title <- textGrob("Walkable Tract Means", gp = gpar(fontsize = 14, fontface = "bold"))
unwalkable_title <- textGrob("Unwalkable Tract Means", gp = gpar(fontsize = 14, fontface = "bold"))
# Combine the two tables in one panel using grid.arrange
grid.arrange(
walkable_title, tableGrob(mean_table_walkable),
unwalkable_title, tableGrob(mean_table_unwalkable),
ncol = 2 # Adjust heights so the titles don't crowd the tables
)
building_box <- ggplot(data = edges_seg_output) +
geom_boxplot(aes(x = is_walkable, y = (building*100)), color="black",fill="white") +
labs(y= "Buildings", x = "Walkability", title = "Building % by Walkability")
tree_box <- ggplot(data = edges_seg_output) +
geom_boxplot(aes(x = is_walkable, y = (tree*100)), color="black",fill="white") +
labs(y= "Trees", x = "Walkability", title = "Tree % by Walkability")
road_box <- ggplot(data = edges_seg_output) +
geom_boxplot(aes(x = is_walkable, y = (road*100)), color="black",fill="white") +
labs(y= "Roads", x = "Walkability", title = "Road % by Walkability")
sidewalk_box <- ggplot(data = edges_seg_output) +
geom_boxplot(aes(x = is_walkable, y = (sidewalk*100)), color="black",fill="white") +
labs(y= "Sidewalks", x = "Walkability", title = "Sidewalk % by Walkability")
sky_box <- ggplot(data = edges_seg_output) +
geom_boxplot(aes(x = is_walkable, y = (sky*100)), color="black",fill="white") +
labs(y= "Sky", x = "Walkability", title = "Sky % by Walkability")
grid.arrange(building_box, tree_box, road_box, sidewalk_box, sky_box, ncol=3)