Major-Assignment 2

Task Description

Exploring Walkability Through Street View and Computer Vision

This assignment is divided into three main sections.

In the first section, you will select two Census Tracts within Fulton and DeKalb Counties, GA — one that you believe is the most walkable and another that is the least walkable. You may choose any tracts within these two counties. If the area you want to analyze is not well represented by a single tract, you may select multiple adjacent tracts (e.g., two contiguous tracts as one “walkable area”). The definition of walkable is up to you — it can be based on your personal experience (e.g., places where you’ve had particularly good or bad walking experiences), Walk Score data, or any combination of criteria. After making your selections, provide a brief explanation of why you chose those tracts.

The second section is the core of this assignment. You will prepare OpenStreetMap (OSM) data, download Google Street View (GSV) images, and apply the computer vision technique covered in class — semantic segmentation.

In the third section, you will summarize and analyze the results. After applying computer vision to the images, you will obtain pixel counts for 19 different object categories. Using the data, you will:

• Create maps to visualize the spatial distribution of these objects,
• Draw boxplots to compare their distributions between the walkable and unwalkable tracts, and
• Perform t-tests to examine the differences in mean values and their statistical significance.

Section 0. Packages

Importing the necessary packages is part of this assignment. Add any required packages to the code chunk below as you progress through the tasks.

library(tidyverse)
library(magrittr)
library(osmdata)
library(sfnetworks)
library(units)
library(sf)
library(ggplot2)
library(tidygraph)
library(tidycensus)
library(tmap)
library(here)
library(progress)
library(nominatimlite)
ttm()

Section 1. Choose your Census Tracts.

Use the Census Tract map in the following code chunk to identify the GEOIDs of the tracts you consider walkable and unwalkable.

# TASK ////////////////////////////////////////////////////////////////////////
# Set up your api key here
census_api_key(Sys.getenv("CENSUS_API_KEY"))
# //TASK //////////////////////////////////////////////////////////////////////

# =========== NO MODIFICATION ZONE STARTS HERE ===============================
# Download Census Tract polygon for Fulton and DeKalb
tract <- get_acs("tract", 
                 variables = c('pop' = 'B01001_001'),
                 year = 2023,
                 state = "GA", 
                 county = c("Fulton", "DeKalb"), 
                 geometry = TRUE)

tmap_mode("view")
tm_basemap("OpenStreetMap") +
  tm_shape(tract) + 
  tm_polygons(fill_alpha = 0.2)
# =========== NO MODIFY ZONE ENDS HERE ========================================

Once you have the GEOIDs, create two Census Tract objects – one representing your most walkable area and the other your least walkable area.

# TASK ////////////////////////////////////////////////////////////////////////
# 1. Specify the GEOIDs of your walkable and unwalkable Census Tracts. 
#    e.g., tr_id_walkable <- c("13121001205", "13121001206")
# 2. Extract the selected Census Tracts using `tr_id_walkable` and `tr_id_unwalkable`

# For the walkable Census Tract(s)
tr_id_walkable <- c("13121001205")  

tract_walkable <- tract %>%
  filter(GEOID %in% tr_id_walkable)

# For the unwalkable Census Tract(s)
tr_id_unwalkable <- c("13121012000")

tract_unwalkable <- tract %>%
  filter(GEOID %in% tr_id_unwalkable)

# //TASK //////////////////////////////////////////////////////////////////////


# TASK ////////////////////////////////////////////////////////////////////////
# Create an interactive map showing `tract_walkable` and `tract_unwalkable`
tm_shape(tract) +
  tm_borders(col = "gray80") +
tm_shape(tract_walkable) +
  tm_fill(col = "green", alpha = 0.25) +
  tm_borders(col = "green", lwd = 2) +
tm_shape(tract_unwalkable) +
  tm_fill(col = "red", alpha = 0.25) +
  tm_borders(col = "red", lwd = 2) +
  tm_layout(title = "Walkable vs Unwalkable Census Tracts")

## 

## ── tmap v3 code detected ───────────────────────────────────────────────────────

## [v3->v4] `tm_polygons()`: use 'fill' for the fill color of polygons/symbols
## (instead of 'col'), and 'col' for the outlines (instead of 'border.col').
## [v3->v4] `tm_polygons()`: use `fill_alpha` instead of `alpha`.
## [v3->v4] `tm_polygons()`: use `fill_alpha` instead of `alpha`.
## [v3->v4] `tm_layout()`: use `tm_title()` instead of `tm_layout(title = )`
## This message is displayed once every 8 hours.

# //TASK //////////////////////////////////////////////////////////////////////

Provide a brief description of your selected Census Tracts. Why do you consider these tracts walkable or unwalkable? What factors do you think contribute to their walkability?

• Response: The walkable Census Tract I selected is located near Georgia Tech and within the neighborhood where I ilve. This area has a dense street network, well-connected sidewalks, and a mix of residential and commercial land uses. Everyday destinations such as grocery stores, cafés, and public transit stops are within short walking distance. In contrast, the unwalkable Census Tract is dominated by major transportation infrastructure, including highways and large interchanges. These wide roadways and limited crossings act as physical barriers for pedestrains, and sidewalks are often absent or discontinuous. I therefore expect this tract to have very low pedestrian accessibility and street-level walkability.

Section 2. OSM, GSV, and Computer Vision.

Step 1. Get and clean OSM data.

To obtain the OSM network for your selected Census Tracts: (1) Create bounding boxes. (2) Use the bounding boxes to download OSM data. (3) Convert the data into an sfnetwork object and clean it.

# TASK ////////////////////////////////////////////////////////////////////////
# Create one bounding box (`tract_walkable_bb`) for your walkable Census Tract(s) and another (`tract_unwalkable_bb`) for your unwalkable Census Tract(s).

# For the walkable Census Tract(s)
tract_walkable_bb <- tract_walkable %>%
  st_bbox()

# For the unwalkable Census Tract(s)  
tract_unwalkable_bb <- tract_unwalkable %>%
  st_bbox()

# //TASK //////////////////////////////////////////////////////////////////////


# =========== NO MODIFICATION ZONE STARTS HERE ===============================
# Get OSM data for the two bounding boxes
osm_walkable <- opq(bbox = tract_walkable_bb) %>%
  add_osm_feature(key = 'highway', 
                  value = c("primary", "secondary", "tertiary", "residential")) %>%
  osmdata_sf() %>% 
  osm_poly2line()

osm_unwalkable <- opq(bbox = tract_unwalkable_bb) %>%
  add_osm_feature(key = 'highway', 
                  value = c("primary", "secondary", "tertiary", "residential")) %>%
  osmdata_sf() %>% 
  osm_poly2line()
# =========== NO MODIFY ZONE ENDS HERE ========================================


# TASK ////////////////////////////////////////////////////////////////////////
# 1. Convert `osm_walkable` and `osm_unwalkable` into sfnetwork objects (as undirected networks),
# 2. Clean the network by (1) deleting parallel lines and loops, (2) creating missing nodes, and (3) removing pseudo nodes (make sure the `summarise_attributes` argument is set to 'first' when doing so).

net_walkable <- osm_walkable$osm_lines %>% 
  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, summarise_attributes = "first") # delete pseudo nodes

## Warning: to_spatial_subdivision assumes attributes are constant over geometries

net_unwalkable <- osm_unwalkable$osm_lines %>% 
  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, summarise_attributes = "first") # delete pseudo nodes

## Warning: to_spatial_subdivision assumes attributes are constant over geometries

# //TASK //////////////////////////////////////////////////////////////////////
  
  
# TASK //////////////////////////////////////////////////////////////////////
# Using `net_walkable` and`net_unwalkable`,
# 1. Activate the edge component of each network.
# 2. Create a `length` column.
# 3. Filter out short (<300 feet) segments.
# 4. Randomly Sample 100 rows per road type.
# 5. Assign the results to `edges_walkable` and `edges_unwalkable`, respectively.

# OSM for the walkable part
set.seed(123) # for reproducibility
edges_walkable <- net_walkable %>% 
  st_as_sf("edges") %>% 
  select(osm_id, highway) %>% 
  # Add a length column
  mutate(length = st_length(.) %>% unclass()) %>% 
  # Drop short segments (< 300 feet)
  filter(length >= 91) %>% 
  # Sample 100 rows per road type
  group_by(highway) %>% 
  slice_sample(n = 100) %>% 
  # Assign a unique ID for each edge
  ungroup() %>% 
  mutate(edge_id = seq(1,nrow(.)))

# OSM for the unwalkable part
edges_unwalkable <- net_unwalkable %>% 
  st_as_sf("edges") %>% 
  select(osm_id, highway) %>% 
  # Add a length column
  mutate(length = st_length(.) %>% unclass()) %>% 
  # Drop short segments (< 300 feet)
  filter(length >= 91) %>% 
  # Sample 100 rows per road type
  group_by(highway) %>% 
  slice_sample(n = 100) %>% 
  # Assign a unique ID for each edge
  ungroup() %>% 
  mutate(edge_id = seq(1,nrow(.)))

# //TASK //////////////////////////////////////////////////////////////////////
  
# =========== NO MODIFICATION ZONE STARTS HERE ===============================
# Merge the two
edges <- bind_rows(edges_walkable %>% mutate(is_walkable = TRUE), 
                   edges_unwalkable %>% mutate(is_walkable = FALSE)) %>% 
  mutate(edge_id = seq(1,nrow(.)))
# =========== NO MODIFY ZONE ENDS HERE ========================================

Step 2. Define getAzimuth() function.

In this assignment, you will collect two GSV images per road segment, as illustrated in the figure below. To do this, you will define a function that extracts the coordinates of the midpoint and the azimuths in both directions.

If you can’t see this image, try changing the markdown editing mode from ‘Source’ to ‘Visual’ (you can find the buttons in the top-left corner of this source pane).

getAzimuth <- function(line){

  # TASK ////////////////////////////////////////////////////////////////////////
  # 1. Use the `st_line_sample()` function to sample three points at locations 0.48, 0.5, and 0.52 along the line. These points will be used to calculate the azimuth.
  # 2. Use `st_cast()` function to convert the 'MULTIPOINT' object into a 'POINT' object.
  # 3. Extract coordinates using `st_coordinates()`.
  # 4. Assign the coordinates of the midpoint to `mid_p`.
  # 5. Calculate the azimuths from the midpoint in both directions and save them as `mid_azi_1` and `mid_azi_2`, respectively.
  
  # 1-3
  mid_p3 <- line %>% 
    st_line_sample(sample = c(0.48, 0.5, 0.52)) %>% 
    st_cast("POINT") %>% 
    st_coordinates()
  
  # 4
  mid_p <- mid_p3[2,]
  
  # 5
  mid_azi_1 <- atan2(mid_p3[1,"X"] - mid_p3[2, "X"],
                     mid_p3[1,"Y"] - mid_p3[2, "Y"])*180/pi
  
  mid_azi_2 <- atan2(mid_p3[3,"X"] - mid_p3[2, "X"],
                     mid_p3[3,"Y"] - mid_p3[2, "Y"])*180/pi
  
  # //TASK //////////////////////////////////////////////////////////////////////
 
  
  # =========== NO MODIFICATION ZONE STARTS HERE ===============================
  return(tribble(
    ~type,    ~X,            ~Y,             ~azi,
    "mid1",    mid_p["X"],   mid_p["Y"],      mid_azi_1,
    "mid2",    mid_p["X"],   mid_p["Y"],      mid_azi_2))
  # =========== NO MODIFY ZONE ENDS HERE ========================================

}

Step 3. Apply the function to all street segments

Apply the getAzimuth() function to the edges object. Once this step is complete, your data will be ready for downloading 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)

# //TASK //////////////////////////////////////////////////////////////////////

# =========== NO MODIFICATION ZONE STARTS HERE ===============================
edges_azi <- edges_azi %>% 
  bind_cols(edges %>% 
              st_drop_geometry() %>% 
              slice(rep(1:nrow(edges),each=2))) %>% 
  st_as_sf(coords = c("X", "Y"), crs = 4326, remove=FALSE) %>% 
  mutate(img_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 the row.
  
  # TASK ////////////////////////////////////////////////////////////////////////
  # 1. Extract required information from the row of `edges_azi`
  # 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$edge_id
  img_id <- iterrow$img_id
  highway <- iterrow$highway
  key <- Sys.getenv("GOOGLE_API_KEY") # your 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_{img_id}-eid_{edge_id}-type_{type}-Location_{location}-heading_{heading}-highway_{highway}.jpg")
  fpath <- "gsv_images" # Set your own directory for the downloaded images
  # //TASK //////////////////////////////////////////////////////////////////////

  
  
  # =========== NO MODIFICATION ZONE STARTS HERE ===============================
  # Download images
  # if (!file.exists(fpath)){
    # download.file(furl, fpath, mode = 'wb') 
  download.file(request, destfile = file.path(fpath, fname), mode = 'wb')
  
  Sys.sleep(1)
  # =========== NO MODIFY ZONE ENDS HERE ========================================
}

Step 5. Download GSV images

Before you download GSV images, make sure the row number in edges_azi is not too large! Each row corresponds to one GSV image, so if the row count exceeds your API quota, consider selecting different Census Tracts.

You do not want to run the following code chunk more than once, so the code chunk option eval=FALSE is set to prevent the API call from executing again when knitting the script.

# =========== NO MODIFICATION ZONE STARTS HERE ===============================
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

Use this Google Colab script to apply the pretrained semantic segmentation model to your GSV images.

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_azi.

# TASK ////////////////////////////////////////////////////////////////////////
# Read the downloaded CSV file containing the semantic segmentation results.
seg_output <- read.csv("seg_output_walkable.csv")

# //TASK ////////////////////////////////////////////////////////////////////////

# TASK ////////////////////////////////////////////////////////////////////////  
# 1. Join the `seg_output` data to `edges_azi`.
# 2. Calculate the proportion of predicted pixels for the following categories: `building`, `sky`, `road`, and `sidewalk`. If there are other categories you are interested in, feel free to include their proportions as well.
# 3. Calculate the proportion of greenness using the `vegetation` and `terrain` categories.
# 4. Calculate the building-to-street ratio. For the street, use `road` and `sidewalk` pixels; including `car` pixels is optional.

edges_seg_output <- edges_azi %>% 
  inner_join(seg_output, by=c("img_id"="img_id"))

edges_seg_output %<>% 
  mutate(pct_building = building/(768*768),
         pct_sky = sky/(768*768),
         pct_road = road/(768*768),
         pct_sidewalk = sidewalk/(768*768))

edges_seg_output %<>%
  mutate(pct_greenness = (vegetation+terrain)/(768*768))

edges_seg_output %<>% 
  mutate(b2s_ratio = building/(road+sidewalk+car)) %>% 
  mutate(b2s_ratio = case_when(
    b2s_ratio >= 1 ~ 1, # Set the upper limit as 1.
    TRUE ~ b2s_ratio))
# //TASK ////////////////////////////////////////////////////////////////////////

Section 3. Summarize and analyze the results.

At the beginning of this assignment, you specified walkable and unwalkable Census Tracts. The key focus of this section is the comparison between these two types of tracts.

Analysis 1 - Visualize Spatial Distribution

Create interactive maps showing the proportion of sidewalk, greenness, and the building-to-street ratio for both walkable and unwalkable areas. In total, you will produce 6 maps. Provide a brief description of your findings.

# TASK ////////////////////////////////////////////////////////////////////////
# Plot interactive map(s)
# As long as you can deliver the message clearly, you can use any format/package you want.

# Ensure CRS consistency
walk_bb  <- st_transform(tract_walkable,  st_crs(edges_seg_output))
unwalk_bb <- st_transform(tract_unwalkable, st_crs(edges_seg_output))

# Subset SVI points by bounding boxes
pts_walk   <- edges_seg_output[lengths(st_intersects(edges_seg_output, walk_bb))   > 0, ]
pts_unwalk <- edges_seg_output[lengths(st_intersects(edges_seg_output, unwalk_bb)) > 0, ]

# Label group
pts_walk$area_type   <- "Walkable"
pts_unwalk$area_type <- "Unwalkable"

# Combine
pts_all <- rbind(pts_walk, pts_unwalk)

tm_shape(tract_walkable) +
  tm_borders(col = "green", lwd = 2) +
  tm_shape(pts_walk) +
  tm_dots(size = 0.7,
          fill = "pct_sidewalk", 
          fill.scale = tm_scale(values = c("#F7FBFF", "#6BAED6", "#3182BD", "#08519C")))

tm_shape(tract_walkable) +
  tm_borders(col = "green", lwd = 2) +
  tm_shape(pts_walk) +
  tm_dots(size = 0.7,
          fill = "pct_greenness", 
          fill.scale = tm_scale(values = c("darkgray", "yellow", "green", "darkgreen")))

tm_shape(tract_walkable) +
  tm_borders(col = "green", lwd = 2) +
  tm_shape(pts_walk) +
  tm_dots(size = 0.7,
          fill = "b2s_ratio", 
          fill.scale = tm_scale(values = c("#EED8AE", "#C4A484", "#8B5A2B", "brown")))

tm_shape(tract_unwalkable) +
  tm_borders(col = "red", lwd = 2) +
  tm_shape(pts_unwalk) +
  tm_dots(size = 0.7,
          fill = "pct_sidewalk", 
          fill.scale = tm_scale(values = c("#F7FBFF", "#6BAED6", "#3182BD", "#08519C")))

tm_shape(tract_unwalkable) +
  tm_borders(col = "red", lwd = 2) +
  tm_shape(pts_unwalk) +
  tm_dots(size = 0.7,
          fill = "pct_greenness", 
          fill.scale = tm_scale(values = c("darkgray", "yellow", "green", "darkgreen")))

tm_shape(tract_unwalkable) +
  tm_borders(col = "red", lwd = 2) +
  tm_shape(pts_unwalk) +
  tm_dots(size = 0.7,
          fill = "b2s_ratio", 
          fill.scale = tm_scale(values = c("#EED8AE", "#C4A484", "#8B5A2B", "brown")))

# //TASK //////////////////////////////////////////////////////////////////////

• Response In the walkable Census Tract, the distribution of greenness, sidewalk coverage, and building-to-street ratio appears uneven. Some street segments show high sidewalk and dense building edges, while others exhibit lower values. This suggests that even within generally walkable neighborhoods, the physical environment varies locally—likely reflecting differences in land use, street width, or vegetation density. In contrast, the unwalkable Census Tract displays a more consistent pattern of low walkability features. Nearly half of the area shows low sidewalk proportions, and most segments have very low building-to-street ratios, indicating sparse built form and limited pedestrian infrastructure. The visual environment is dominated by open road surfaces and wide spaces.

Analysis 2 - Boxplot

Create boxplots for the proportion of each category (building, sky, road, sidewalk, greenness, and any additional categories of interest) and the building-to-street ratio for walkable and unwalkable tracts. Each plot should compare walkable and unwalkable tracts. In total, you will produce 6 or more boxplots. Provide a brief description of your findings.

# TASK ////////////////////////////////////////////////////////////////////////
# Create boxplot(s) using ggplot2 package.
# Select key variables for boxplots
vars <- c("pct_building", "pct_sky", "pct_road", "pct_sidewalk", "pct_greenness", "b2s_ratio")

df_long <- pts_all %>%
  st_drop_geometry() %>%
  select(area_type, all_of(vars)) %>%
  tidyr::pivot_longer(
    cols = all_of(vars),
    names_to = "category",
    values_to = "proportion"
  )

# Building
ggplot(filter(df_long, category == "pct_building"),
       aes(x = area_type, y = proportion, fill = area_type)) +
  geom_boxplot(alpha = 0.7, width = 0.6) +
  scale_fill_manual(values = c("Walkable" = "#4CAF50", "Unwalkable" = "#E64A19")) +
  labs(title = "Building proportion", x = "", y = "Proportion") +
  theme_minimal(base_size = 14) +
  theme(legend.position = "none")

# Sky
ggplot(filter(df_long, category == "pct_sky"),
       aes(x = area_type, y = proportion, fill = area_type)) +
  geom_boxplot(alpha = 0.7, width = 0.6) +
  scale_fill_manual(values = c("Walkable" = "#4CAF50", "Unwalkable" = "#E64A19")) +
  labs(title = "Sky proportion", x = "", y = "Proportion") +
  theme_minimal(base_size = 14) +
  theme(legend.position = "none")

# Road
ggplot(filter(df_long, category == "pct_road"),
       aes(x = area_type, y = proportion, fill = area_type)) +
  geom_boxplot(alpha = 0.7, width = 0.6) +
  scale_fill_manual(values = c("Walkable" = "#4CAF50", "Unwalkable" = "#E64A19")) +
  labs(title = "Road proportion", x = "", y = "Proportion") +
  theme_minimal(base_size = 14) +
  theme(legend.position = "none")

# Sidewalk
ggplot(filter(df_long, category == "pct_sidewalk"),
       aes(x = area_type, y = proportion, fill = area_type)) +
  geom_boxplot(alpha = 0.7, width = 0.6) +
  scale_fill_manual(values = c("Walkable" = "#4CAF50", "Unwalkable" = "#E64A19")) +
  labs(title = "Sidewalk proportion", x = "", y = "Proportion") +
  theme_minimal(base_size = 14) +
  theme(legend.position = "none")

# Greenness
ggplot(filter(df_long, category == "pct_greenness"),
       aes(x = area_type, y = proportion, fill = area_type)) +
  geom_boxplot(alpha = 0.7, width = 0.6) +
  scale_fill_manual(values = c("Walkable" = "#4CAF50", "Unwalkable" = "#E64A19")) +
  labs(title = "Greenness", x = "", y = "Proportion") +
  theme_minimal(base_size = 14) +
  theme(legend.position = "none")

# Building-to-street ratio
ggplot(filter(df_long, category == "b2s_ratio"),
       aes(x = area_type, y = proportion, fill = area_type)) +
  geom_boxplot(alpha = 0.7, width = 0.6) +
  scale_fill_manual(values = c("Walkable" = "#4CAF50", "Unwalkable" = "#E64A19")) +
  labs(title = "Building-to-street ratio", x = "", y = "Ratio") +
  theme_minimal(base_size = 14) +
  theme(legend.position = "none")

# //TASK //////////////////////////////////////////////////////////////////////

• Response The boxplots reveal clear differences in the visual composition of street view images between the walkable and unwalkable Census Tracts. The walkable tract shows a higher proportion of building pixels and a much higher building-to-street ratio, suggesting a denser built environment and stronger street enclosure. It also has a higher share of sidewalk pixels, reflecting better pedestrian infrastructure. In contrast, the unwalkable tract exhibits a larger proportion of sky pixels, indicating more open or highway-type spaces with fewer vertical elements, and a slightly higher share of road pixels. Interestingly, the walkable tract’s greenness proportion is slightly lower, implying that pedestrian-friendly areas in this context are more urban and compact rather than green or park-like.

Analysis 3 - Mean Comparison (t-test)

Perform t-tests on the mean proportion of each category (building, sky, road, sidewalk, greenness, and any additional categories of interest) as well as the building-to-street ratio between street segments in the walkable and unwalkable tracts. This will result in 6 or more t-test results. Provide a brief description of your findings.

# TASK ////////////////////////////////////////////////////////////////////////
# Perform t-tests and report both the differences in means and their statistical significance.
# As long as you can deliver the message clearly, you can use any format/package you want.

df_test <- pts_all %>%
  st_drop_geometry() %>%
  select(area_type, all_of(vars))

t_results <- lapply(vars, function(v) {
  ttest <- t.test(df_test[[v]] ~ df_test$area_type)
  data.frame(
    variable = v,
    mean_walkable   = mean(df_test[[v]][df_test$area_type == "Walkable"], na.rm = TRUE),
    mean_unwalkable = mean(df_test[[v]][df_test$area_type == "Unwalkable"], na.rm = TRUE),
    t_statistic = ttest$statistic,
    p_value = ttest$p.value
  )
})

t_results <- bind_rows(t_results)
print(t_results)

##            variable mean_walkable mean_unwalkable t_statistic      p_value
## t...1  pct_building    0.20496931      0.05140554  -9.3628532 1.744899e-12
## t...2       pct_sky    0.12505622      0.24669755   6.5964544 1.070017e-08
## t...3      pct_road    0.33701945      0.36370849   2.0926506 4.146221e-02
## t...4  pct_sidewalk    0.05155872      0.04069135  -2.3709535 2.084979e-02
## t...5 pct_greenness    0.24147797      0.25961427   0.8785614 3.827748e-01
## t...6     b2s_ratio    0.51444138      0.12448134  -9.3061900 2.316283e-12

# //TASK //////////////////////////////////////////////////////////////////////

• Response The t-test results show clear visual differences between the walkable and unwalkable Census Tracts. The walkable tract has a significantly higher proportion of building pixels, sidewalk coverage, and a much higher building-to-street ratio, indicating a denser and more enclosed street environment. In contrast, the unwalkable tract shows significantly more visible sky and slightly higher road coverage, reflecting open, auto-oriented spaces with limited pedestrian infrastructure. The proportion of greenness does not differ significantly between the two areas, suggesting that walkability in this comparison is primarily influenced by built form rather than vegetation.