CP8883-BK-major2
There can be multi-step instructions, like shown below. You may use pipe (%>%) to link multiple functions to perform the task in the instruction. Make sure that you assign the output of your task into an object with the specified name. This is to make sure that your code will run smoothly - if you change the name of the object (i.e., subset_car in the example below), all the subsequent code will NOT run properly.
I expect you to repalce where it says # **YOUR CODE HERE..** with your answer, like below.
There will also be multiple ‘NO MODIFICATION ZONE’. Do not modify code in the No Modification Zone.
You will need to knit it, publish it on Rpubs, and submit the link. If there is any question about this template, do not hesitate to reach out to Bonwoo.
Introduction to the assignment
This assignment consists of three main sections.
In the first section, you need to select one Census Tract that you think is the most walkable and another one that you think is least walkable within Fulton and DeKalb Counties, GA. As long as the two Census Tracts are within the two counties, you can pick any two you want. If the area you want to use as walkable/unwalkable area is not well-covered by one Census Tract, you can select multiple tracts (e.g., selecting three adjacent tracts as one walkable area). The definition of ‘walkable’ can be your own - you can choose solely based on your experience (e.g., had best/worst walking experience), refer to Walk Score, or any other mix of criteria you want. After you make the selection, provide a short write-up of why you chose those Census Tracts.
The second section is the main part of this assignment in which you prepare OSM data, download GSV images, apply computer vision.
In the third section, you will summarise and analyze the output and provide your findings. After you apply computer vision to the images, you will have the number of pixels in each image that represent 150 categories in your data. You will focus on the following categories in your analysis: building, sky, tree, road, and sidewalk. Specifically, you will (1) create maps to visualize the spatial distribution of different objects, (2) compare the mean of each category between the two Census Tract and (3) draw boxplots to compare the distributions.
Section 1. Choose your Census Tracts.
Provide a brief description of your census tracts. Why do you think the Census Tracts are walkable and unwalkable? What were the contributing factors?
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(jsonlite)
library(httr)
library(reshape2)
library(yelpr)
library(knitr)Step 1. Get OSM data and clean it.
The getbb() function, which we used in the class material 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 sfnetworks data and clean it.
## To install your API key for use in future sessions, run this function with `install = TRUE`.
## To install your API key for use in future sessions, run this function with `install = TRUE`.# =========== NO MODIFICATION ZONE STARTS HERE ===============================
# Download Census Tract polygon for Fulton and DeKalb
tract <- get_acs("tract",
variables = c('tot_pop' = 'B01001_001'),
year = 2020,
state = "GA",
county = c("Fulton", "DeKalb"),
geometry = TRUE)## Getting data from the 2016-2020 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.##
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|======================================================================| 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.
tr1_ID <- '13121003800'
# 2~4
tract_1_bb <- tract %>%
filter(GEOID == tr1_ID) %>%
st_bbox()
# For the unwalkable Census Tract(s)
# 1.
tr2_ID <- '13121009407'
# 2~4
tract_2_bb <- tract %>%
filter(GEOID == tr2_ID) %>%
st_bbox()
# //TASK //////////////////////////////////////////////////////////////////////
# =========== 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$osm_lines %>%
as_sfnetwork(directed = FALSE) %>%
activate('edges') %>%
filter(!edge_is_multiple()) %>%
filter(!edge_is_loop()) %>%
convert(to_spatial_subdivision) %>%
convert(to_spatial_smooth) %>%
mutate('length' = edge_length() %>% unclass())## Warning: to_spatial_subdivision assumes attributes are constant over geometriesnet2 <- osm_2$osm_lines %>%
as_sfnetwork(directed = FALSE) %>%
activate('edges') %>%
filter(!edge_is_multiple()) %>%
filter(!edge_is_loop()) %>%
convert(to_spatial_subdivision) %>%
convert(to_spatial_smooth) %>%
mutate('length' = edge_length() %>% unclass())## 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 redundant columns
select(osm_id, highway, length) %>%
# Drop segments that are too short (100m)
mutate(length = as.vector(length)) %>%
filter(length > 50) %>%
# 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 redundant columns
select(osm_id, highway, length) %>%
# Drop segments that are too short (100m)
mutate(length = as.vector(length)) %>%
filter(length > 50) %>%
# 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 a function that performs Step 3.
get_azi <- 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,]
# 2
start_azi <- atan2(start_p[2,"X"] - start_p[1, "X"],
start_p[2,"Y"] - start_p[1, "Y"])*180/pi # 180/pi
# //TASK //////////////////////////////////////////////////////////////////////
# 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 //////////////////////////////////////////////////////////////////////
# 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_geometry() %>%
.[[1]] %>%
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_geometry() %>%
.[[1]] %>%
st_line_sample(sample = 0.5) %>%
st_coordinates() %>%
.[1,1:2]
# //TASK //////////////////////////////////////////////////////////////////////
# =========== 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["X"], mid_p["Y"], mid_azi,
"mid2", mid_p["X"], mid_p["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 get_azi() 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 get_azi() function to all edges.
# Remember that you need to pass edges object to st_geometry()
# before you apply get_azi()
endp_azi <- edges %>%
st_geometry() %>%
map_df(get_azi)
# //TASK //////////////////////////////////////////////////////////////////////
# =========== NO MODIFICATION ZONE STARTS HERE ===============================
endp <- endp_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.
get_image <- function(iterrow){
# This function takes one row of endp and downloads GSV image using the information from endp.
# TASK ////////////////////////////////////////////////////////////////////////
# Finish this function definition.
# 1. Extract required information from the row of endp, including
# type (i.e., start, mid1, mid2, end), location, heading, edge_id, node_id, source (i.e., outdoor vs. default) and key.
# 2. Format the full URL and store it in furl. 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(4), ",", iterrow$X %>% round(4))
heading <- iterrow$azi %>% round(1)
edge_id <- iterrow$edge_id
node_id <- iterrow$node_id
key <- API
furl <- glue::glue("https://maps.googleapis.com/maps/api/streetview?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("C:/Users/11969/Desktop/Intro to Urban_Analytics/googlepic", fname)
# //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 endp is not too large! The row number of endp 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(endp))){
get_image(endp[i,])
}
# =========== NO MODIFY ZONE ENDS HERE ========================================Step 6. Apply computer vision
Now, you need to upload the images you downloaded to Google Drive. You should upload the images to the same folder that we used in class - the ‘demo_images’ folder in the root directory of Google Drive. Then, use Google Colab to apply a semantic segmentation model called Pyramid Scene Parsing Network.
Step 7. Merging the processed data back to R
Once all of the images are processed and saved in your Google Drive as a CSV file, download the CSV file and merge it back to edges.
# Read the downloaded CSV file from Google Drive
pspnet <- read.csv(here("C:/Users/11969/Desktop/Intro to Urban_Analytics/googlepic", "seg_output.csv"))
# =========== NO MODIFICATION ZONE STARTS HERE ===============================
# Join the pspnet object back to endp object using node_id as the join key.
pspnet_nodes <- endp %>% inner_join(pspnet, by="node_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/unwalkable Census Tracts.
Analysis 1 - Create 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 (two Census Tracts times five categories).
Below the maps, provide a brief description of your findings from the maps.
# TASK ////////////////////////////////////////////////////////////////////////
# Create map(s) to visualize the `pspnet_nodes` objects.
# As long as you can deliver the message clearly, you can use any format/package you want.
pspnet1 <- pspnet_nodes %>%
mutate(pct_building = building*100) %>%
mutate(pct_sky = sky*100) %>%
mutate(pct_tree = tree*100) %>%
mutate(pct_road = road*100) %>%
mutate(pct_sidewalk = sidewalk*100)
walkable <- pspnet1 %>%
filter(is_walkable == "walkable")
unwalkable <- pspnet1 %>%
filter(is_walkable == "unwalkable")
# //TASK //////////////////////////////////////////////////////////////////////tmap_mode("view")## tmap mode set to interactive viewingo1 <- tm_shape(pspnet1 %>%
filter(is_walkable == "walkable"))+
tm_dots(col = "pct_building") + tm_layout+ tm_layout(title = "Percent of Buildings")
o2 <- tm_shape(pspnet1 %>%
filter(is_walkable == "unwalkable"))+
tm_dots(col = "pct_building")+ tm_layout+ tm_layout(title = "Percent of Buildings")
#map both together
tmap_arrange(o1,o2) o3 <- tm_shape(pspnet1 %>%
filter(is_walkable == "walkable"))+
tm_dots(col = "pct_sky") + tm_layout+ tm_layout(title = "Percent of Sky")
o4 <- tm_shape(pspnet1 %>%
filter(is_walkable == "unwalkable"))+
tm_dots(col = "pct_sky")+ tm_layout+ tm_layout(title = "Percent of Sky")
#map both together
tmap_arrange(o3,o4)o5 <- tm_shape(pspnet1 %>%
filter(is_walkable == "walkable"))+
tm_dots(col = "pct_tree") + tm_layout+ tm_layout(title = "Percent of Tree")
o6 <- tm_shape(pspnet1 %>%
filter(is_walkable == "unwalkable"))+
tm_dots(col = "pct_tree")+ tm_layout+ tm_layout(title = "Percent of Tree")
#map both together
tmap_arrange(o5,o6)o7 <- tm_shape(pspnet1 %>%
filter(is_walkable == "walkable"))+
tm_dots(col = "pct_road") + tm_layout+ tm_layout(title = "Percent of Roads")
o8 <- tm_shape(pspnet1 %>%
filter(is_walkable == "unwalkable"))+
tm_dots(col = "pct_road")+ tm_layout+ tm_layout(title = "Percent of Roads")
#map both together
tmap_arrange(o7,o8) o9 <- tm_shape(pspnet1 %>%
filter(is_walkable == "walkable"))+
tm_dots(col = "pct_sidewalk") + tm_layout+ tm_layout(title = "Percent of Sidewalk")
lo <- tm_shape(pspnet1 %>%
filter(is_walkable == "unwalkable"))+
tm_dots(col = "pct_sidewalk")+ tm_layout+ tm_layout(title = "Percent of Sidewalk")
#map both together
tmap_arrange(o9,lo) 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. After the calculation, provide a brief description of your findings.
# TASK ////////////////////////////////////////////////////////////////////////
# Perform the calculation as described above.
# As long as you can deliver the message clearly, you can use any format/package you want.
#walkable
buildm <- mean(walkable$pct_building)
skeym<- mean(walkable$pct_sky)
treem <- mean(walkable$pct_tree)
roadm <- mean(walkable$pct_road)
sidewm <- mean(walkable$pct_sidewalk)
#unwalkable
ubuildm <- mean(unwalkable$pct_building)
uskym <- mean(unwalkable$pct_sky)
utreem <- mean(unwalkable$pct_tree)
uroadm <- mean(unwalkable$pct_road)
usidewm <- mean(unwalkable$pct_sidewalk)
# //TASK //////////////////////////////////////////////////////////////////////a <- c("building", "sky", "tree", "road", "sidewalk")
walkable_means <- c(buildm, skeym, treem, roadm, sidewm)
unwalkable_means <- c(ubuildm, uskym, utreem, uroadm, usidewm)
df_means <- data.frame(a, walkable_means, unwalkable_means)
print(df_means)## a walkable_means unwalkable_means
## 1 building 5.475865 7.402457
## 2 sky 30.617074 23.790442
## 3 tree 15.186208 14.948306
## 4 road 33.342277 25.105151
## 5 sidewalk 4.549649 3.176255Analysis 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. After the calculation, provide a brief description of your findings.
pspnet_longer <- pspnet1 %>%
pivot_longer(
cols = pct_building:pct_sidewalk,
names_to = "Image_Elements",
values_to = "Percentages"
)
ggplot(data = pspnet_longer %>%
separate(col = "Image_Elements", into=c("Image_Elements"),sep=", ") %>%
drop_na(Image_Elements)
) +
geom_boxplot(mapping = aes(x = is_walkable, y = Percentages), color="Red", outlier.size = 1, lwd=0.5) +
labs(x = "Walkability",
y = "Percentage") +
facet_wrap(~Image_Elements, scales = "free_y",
labeller = labeller(Image_Elements =
c("pct_building" = "Building",
"pct_sky" = "Sky",
"pct_tree" = "Trees",
"pct_road" = "Roads",
"pct_sidewalk" = "Sidewalks"))) 
The number and distribution of buildings in the two census tracts are similar. Where walkability is low, there are a lot of roads, which I don’t quite understand (I feel my data is wrong, but I don’t know which step is wrong).The median of walkable and unwalkable is similar, but the area with more trees is less walkable. I don’t understand. (I think the more trees, the better walkability). The only figure I found correct was sidewalks, where the number and median of walkable were higher than unwalkable.