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?
The most walkable census tract that I have experienced was in Fulton County, next to Piedmont Park (tract 13121000400, Census Tract 4). The most unwalkable tract I chose is in South East Point and East of College Park, Fulton County, right above the Hartsfield-Jackson Atlanta International Airport (tract ID 13121012300, Census Tract 123).
I have several standards for a place to be ‘walkable.’ First, the area should be near community space like parks or shopping complex. Second, the sidewalks should have enough vegetation to make the streets aesthetic and to give the impression of coziness and safety. Third, there should be not a lot of cars passing by, which could be mean less driving lanes, less routes that trespassing the area.
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)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.
# TASK ////////////////////////////////////////////////////////////////////////
# 1. Set up your api key here
census_api_key(Sys.getenv("census_api"))## To install your API key for use in future sessions, run this function with `install = TRUE`.# //TASK //////////////////////////////////////////////////////////////////////
# =========== 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 <- "13121000400"
# 2~4
tract_1_bb <- tract %>%
subset(GEOID == tr1_ID) %>%
st_bbox()
# For the unwalkable Census Tract(s)
# 1.
tr2_ID <- c("13121012300")
# 2~4
tract_2_bb <- tract %>%
subset(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$osm_lines %>%
sfnetworks::as_sfnetwork(directed = FALSE) %>%
activate("edges") %>%
filter(!edge_is_multiple()) %>%
filter(!edge_is_loop()) %>%
convert(sfnetworks::to_spatial_subdivision) %>%
convert(sfnetworks::to_spatial_smooth) %>%
mutate(length = edge_length())## Warning: to_spatial_subdivision assumes attributes are constant over geometriesnet2 <- osm_2$osm_lines %>%
sfnetworks::as_sfnetwork(directed = FALSE) %>%
activate("edges") %>%
filter(!edge_is_multiple()) %>%
filter(!edge_is_loop()) %>%
convert(sfnetworks::to_spatial_subdivision) %>%
convert(sfnetworks::to_spatial_smooth) %>%
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 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() %>%
.[1:2,1:2]
# 2
start_azi <- atan2(start_p[2,"X"] - start_p[1, "X"],
start_p[2,"Y"] - start_p[1, "Y"])*180/pi
# //TASK //////////////////////////////////////////////////////////////////////
# 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_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_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 <- Sys.getenv("google_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("image", 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("csv","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) %>%
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.
# building
building_pal <- colorNumeric("Greys", domain = pspnet_nodes$building)
buildingmap1 <- leaflet(pspnet_nodes %>% st_as_sf('building')) %>%
addProviderTiles("CartoDB.Positron") %>%
setView(-84.37819, 33.78877, 14)%>%
addCircles(color = ~building_pal(building), fillOpacity = 0.9) %>%
addLegend("bottomright", pal = building_pal, values = ~building, title = "Buildings",
labFormat = labelFormat(prefix="%"), opacity = 0.9)
buildingmap1buildingmap2 <- leaflet(pspnet_nodes %>% st_as_sf('building')) %>%
addProviderTiles("CartoDB.Positron") %>%
setView(-84.43808, 33.66072, 13)%>%
addCircles(color = ~building_pal(tree), fillOpacity = 0.9) %>%
addLegend("bottomright", pal = building_pal, values = ~building, title = "Buildings",
labFormat = labelFormat(prefix="%"), opacity = 0.9)## Warning in building_pal(tree): Some values were outside the color scale and will
## be treated as NA
## Warning in building_pal(tree): Some values were outside the color scale and will
## be treated as NAbuildingmap2In my selected walkable tract,which is near Ansley Park and Piedmont Park, most streets have buildings percentage near zero. The left part of the tract has buildings densely located. Compared to that, East Point has much higher buildings ratio.
# sky
sky_pal <- colorNumeric("Blues", domain = pspnet_nodes$sky)
skymap1 <- leaflet(pspnet_nodes %>% st_as_sf('sky')) %>%
addProviderTiles("CartoDB.Positron") %>%
setView(-84.37819, 33.78877, 14)%>%
addCircles(color = ~sky_pal(sky), fillOpacity = 0.9) %>%
addLegend("bottomright", pal = sky_pal, values = ~sky, title = "sky",
labFormat = labelFormat(prefix="%"), opacity = 0.9)
skymap1skymap2 <- leaflet(pspnet_nodes %>% st_as_sf('sky')) %>%
addProviderTiles("CartoDB.Positron") %>%
setView(-84.43808, 33.66072, 13)%>%
addCircles(color = ~sky_pal(sky), fillOpacity = 0.9) %>%
addLegend("bottomright", pal = sky_pal, values = ~sky, title = "sky",
labFormat = labelFormat(prefix="%"), opacity = 0.9)
skymap2Compared to the walkable tract, East Point streets are more exposed to bare sky. Especially following the highways, almost 50% from the each image is bare sky.
# tree
tree_pal <- colorNumeric("Greens", domain = pspnet_nodes$tree)
treemap1 <- leaflet(pspnet_nodes %>% st_as_sf('tree')) %>%
addProviderTiles("CartoDB.Positron") %>%
setView(-84.37819, 33.78877, 14)%>%
addCircles(color = ~tree_pal(tree), fillOpacity = 0.9) %>%
addLegend("bottomright", pal = tree_pal, values = ~tree, title = "Trees",
labFormat = labelFormat(prefix="%"), opacity = 0.9)
treemap1treemap2 <- leaflet(pspnet_nodes %>% st_as_sf('tree')) %>%
addProviderTiles("CartoDB.Positron") %>%
setView(-84.43808, 33.66072, 13)%>%
addCircles(color = ~tree_pal(tree), fillOpacity = 0.9) %>%
addLegend("bottomright", pal = tree_pal, values = ~tree, title = "Trees",
labFormat = labelFormat(prefix="%"), opacity = 0.9)
treemap2Tree percentage did not show much variance between walkable and unwalkable tracts.Left side of the unwalkable tract shows high tree percentage.
# road
road_pal <- colorNumeric("Reds", domain = pspnet_nodes$road)
roadmap1 <- leaflet(pspnet_nodes %>% st_as_sf('road')) %>%
addProviderTiles("CartoDB.Positron") %>%
setView(-84.37819, 33.78877, 14)%>%
addCircles(color = ~road_pal(road), fillOpacity = 0.9) %>%
addLegend("bottomright", pal = road_pal, values = ~road, title = "road",
labFormat = labelFormat(prefix="%"), opacity = 0.9)
roadmap1roadmap2 <- leaflet(pspnet_nodes %>% st_as_sf('road')) %>%
addProviderTiles("CartoDB.Positron") %>%
setView(-84.43808, 33.66072, 13)%>%
addCircles(color = ~road_pal(road), fillOpacity = 0.9) %>%
addLegend("bottomright", pal = road_pal, values = ~road, title = "road",
labFormat = labelFormat(prefix="%"), opacity = 0.9)
roadmap2Percentage of road is also similar within tracts. The walkable tract has more variation between streets. Unwalkable tract has high road percentage almost in all points.
# sidewalk
sidewalk_pal <- colorNumeric("Spectral", domain = pspnet_nodes$sidewalk)
sidewalkmap1 <- leaflet(pspnet_nodes %>% st_as_sf('sidewalk')) %>%
addProviderTiles("CartoDB.Positron") %>%
setView(-84.37819, 33.78877, 14)%>%
addCircles(color = ~sidewalk_pal(sidewalk), fillOpacity = 0.9) %>%
addLegend("bottomright", pal = sidewalk_pal, values = ~sidewalk, title = "sidewalk",
labFormat = labelFormat(prefix="%"), opacity = 0.9)
sidewalkmap1sidewalkmap2 <- leaflet(pspnet_nodes %>% st_as_sf('sidewalk')) %>%
addProviderTiles("CartoDB.Positron") %>%
setView(-84.43808, 33.66072, 13)%>%
addCircles(color = ~sidewalk_pal(sidewalk), fillOpacity = 0.9) %>%
addLegend("bottomright", pal = sidewalk_pal, values = ~sidewalk, title = "sidewalk",
labFormat = labelFormat(prefix="%"), opacity = 0.9)
sidewalkmap2# //TASK //////////////////////////////////////////////////////////////////////Similar with road percentage, sidewalk percentage also shows similar result but more variation is presented in the walkable tract. Almost all points in the unwalkable tract have very low sidewalk percentage while walkable tracts have some points where sidewalk percentage is high.
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.
tract1_sf <- tract_1_bb %>% st_as_sfc() %>% st_set_crs(4326)## Warning: st_crs<- : replacing crs does not reproject data; use st_transform for
## thattract2_sf <- tract_2_bb %>% st_as_sfc() %>% st_set_crs(4326)## Warning: st_crs<- : replacing crs does not reproject data; use st_transform for
## thatwalkable_nodes <- st_filter(pspnet_nodes, tract1_sf) %>%
summarise(node = n(),
pct_building = mean(building, na.rm = T),
pct_sky = mean(sky, na.rm = T),
pct_tree = mean(tree, na.rm = T),
pct_road = mean(road, na.rm = T),
pct_sidewalk = mean(sidewalk, na.rm = T))
unwalkable_nodes <- st_filter(pspnet_nodes, tract2_sf) %>%
summarise(node = n(),
pct_building = mean(building, na.rm = T),
pct_sky = mean(sky, na.rm = T),
pct_tree = mean(tree, na.rm = T),
pct_road = mean(road, na.rm = T),
pct_sidewalk = mean(sidewalk, na.rm = T))
walkable_nodes## Simple feature collection with 1 feature and 6 fields
## Geometry type: MULTIPOINT
## Dimension: XY
## Bounding box: xmin: -84.3878 ymin: 33.78176 xmax: -84.36859 ymax: 33.79576
## Geodetic CRS: WGS 84
## # A tibble: 1 x 7
## node pct_building pct_sky pct_tree pct_road pct_sidewalk
## <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 548 0.136 0.214 0.172 0.359 0.0379
## # ... with 1 more variable: geometry <MULTIPOINT [arc_degree]>unwalkable_nodes## Simple feature collection with 1 feature and 6 fields
## Geometry type: MULTIPOINT
## Dimension: XY
## Bounding box: xmin: -84.45301 ymin: 33.64201 xmax: -84.42316 ymax: 33.67936
## Geodetic CRS: WGS 84
## # A tibble: 1 x 7
## node pct_building pct_sky pct_tree pct_road pct_sidewalk
## <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2483 0.0335 0.279 0.201 0.339 0.0165
## # ... with 1 more variable: geometry <MULTIPOINT [arc_degree]># //TASK //////////////////////////////////////////////////////////////////////First of all, the selected unwalkable tract has much more detection points than the walkable tract. Different from what I expected, the mean percentage of buildings were lower in the unwalkable tract.The percentage of sky was higher in the unwalkable site, as I expected from the map.The percentage of tree was lower in the walkable tract and the percentage of road was surprisingly slightly higher in the walkable tract. The percentage of sidewalk was lower in unwalkable tract, as expected from the map.
Analysis 3 - Draw boxplot
# TASK ////////////////////////////////////////////////////////////////////////
# Create boxplot(s) using geom_boxplot() function from ggplot2 package.
# You may find the code from mini-assignment 4 useful here.
pspnet_nodes_classified <- pspnet_nodes %>%
mutate(tract = case_when(lengths(st_intersects(pspnet_nodes, tract1_sf))>0~'walkable',
lengths(st_intersects(pspnet_nodes, tract2_sf))>0~'unwalkable')) %>%
pivot_longer(cols = c('building','sky','tree','road','sidewalk'),
names_to = 'element',
values_to = 'pct',
values_drop_na = TRUE) %>%
drop_na(tract)
ggplot(data = pspnet_nodes_classified) +
aes(x = factor(element), y = pct, fill = element) +
xlab("Image Elements") + ylab("Percentage of Elements in an Image") +
ggtitle("Comparing Percentage of Street View Image Elements \nin Walkable and Unwalkable Tracts") +
geom_boxplot() +
facet_wrap(~tract)
# //TASK //////////////////////////////////////////////////////////////////////Based on the box plot, walkable tracts have more variance in percentage of buildings in streets. There will be places where people can see a lot of buildings, and places where people can see not much buildings but something else. From the map above, I can suspect that streets in residential area, near the park has low percentage of buildings but near the busy area in midtown, more buildings are seen.
The percentage of road looks similar in walkable and unwalkable tracts. The walkable tract has slightly higher mean and variance in the distribution. However, it’s hard to use percentage of road as a determining factor of walkability because google street view images are taken in roads in the first place.
The percentage of sidewalk is higher in the walkable tract, as expected. Walkable areas would have wider sidewalks, which makes the street safer and more pedestrian-friendly.
The percentage of sky is higher in the unwalkable tract. I suspect that this is because some streets in the tract has low percentage of trees. Too much exposure to sky should be concerning, since it could mean more exposure to weather condition, such as heat, rain, or snow.
The percentage of tree is slightly higher in the unwalkable tract. From the previous maps, I found that the unwalkable tract has an adjacent park as well in the left, which provides much exposure to trees. However, since it is only the left part, the variation of the distribution is also larger.