This project consists of three main sections.

In the first section, I selected one Census Tract that I think is the most walkable and another one that I think is least walkable within Fulton and DeKalb Counties, GA. If the area I want to use as walkable/unwalkable area is not well-covered by a single Census Tract, I select multiple adjacent tracts to cover the area.

The second section is the main part of this assignment in which I prepare OSM data, download GSV images, apply computer vision technique learned in class (i.e., semantic segmentation).

In the third section, I will summarise and analyze the output and provide your findings. After I apply computer vision to the images, I will have the number of pixels in each image that represent 150 categories in your data. I will focus on the following categories in my analysis: building, sky, tree, road, and sidewalk. Specifically, I 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.

Step 1. Get OSM data and clean it.

The getbb() function isn’t suitable for downloading just two Census Tracts. I will instead use an alternative method.

1. Using tidycensus package, download the Census Tract polygon for Fulton and DeKalb counties.
2. Extract two Census Tracts, each of which will be your most walkable and least walkable Census Tracts.
3. Using their bounding boxes, get OSM data.
4. Convert them into sfnetwork object and clean it.

# TASK ////////////////////////////////////////////////////////////////////////
# 1. Set up your api key
tidycensus::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 = 2022,
                 state = "GA", 
                 county = c("Fulton", "DeKalb"), 
                 geometry = TRUE)

## Getting data from the 2018-2022 5-year ACS

## Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.

## 
  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |=                                                                     |   1%
  |                                                                            
  |==                                                                    |   2%
  |                                                                            
  |==                                                                    |   3%
  |                                                                            
  |===                                                                   |   4%
  |                                                                            
  |====                                                                  |   5%
  |                                                                            
  |====                                                                  |   6%
  |                                                                            
  |=====                                                                 |   7%
  |                                                                            
  |======                                                                |   8%
  |                                                                            
  |======                                                                |   9%
  |                                                                            
  |=======                                                               |  10%
  |                                                                            
  |========                                                              |  11%
  |                                                                            
  |========                                                              |  12%
  |                                                                            
  |=========                                                             |  12%
  |                                                                            
  |=========                                                             |  13%
  |                                                                            
  |==========                                                            |  15%
  |                                                                            
  |===========                                                           |  15%
  |                                                                            
  |===========                                                           |  16%
  |                                                                            
  |============                                                          |  17%
  |                                                                            
  |============                                                          |  18%
  |                                                                            
  |=============                                                         |  18%
  |                                                                            
  |=============                                                         |  19%
  |                                                                            
  |==============                                                        |  20%
  |                                                                            
  |==============                                                        |  21%
  |                                                                            
  |===============                                                       |  21%
  |                                                                            
  |================                                                      |  23%
  |                                                                            
  |==================                                                    |  26%
  |                                                                            
  |===================                                                   |  26%
  |                                                                            
  |===================                                                   |  27%
  |                                                                            
  |====================                                                  |  29%
  |                                                                            
  |======================                                                |  32%
  |                                                                            
  |=======================                                               |  33%
  |                                                                            
  |========================                                              |  35%
  |                                                                            
  |=========================                                             |  35%
  |                                                                            
  |==========================                                            |  38%
  |                                                                            
  |===========================                                           |  39%
  |                                                                            
  |============================                                          |  40%
  |                                                                            
  |=============================                                         |  41%
  |                                                                            
  |==============================                                        |  43%
  |                                                                            
  |================================                                      |  46%
  |                                                                            
  |=================================                                     |  48%
  |                                                                            
  |===================================                                   |  49%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |====================================                                  |  51%
  |                                                                            
  |=====================================                                 |  52%
  |                                                                            
  |======================================                                |  54%
  |                                                                            
  |========================================                              |  57%
  |                                                                            
  |=========================================                             |  58%
  |                                                                            
  |==========================================                            |  60%
  |                                                                            
  |===========================================                           |  61%
  |                                                                            
  |============================================                          |  63%
  |                                                                            
  |=============================================                         |  65%
  |                                                                            
  |===============================================                       |  67%
  |                                                                            
  |================================================                      |  69%
  |                                                                            
  |=================================================                     |  71%
  |                                                                            
  |==================================================                    |  71%
  |                                                                            
  |===================================================                   |  73%
  |                                                                            
  |=====================================================                 |  75%
  |                                                                            
  |======================================================                |  77%
  |                                                                            
  |=======================================================               |  79%
  |                                                                            
  |========================================================              |  80%
  |                                                                            
  |=========================================================             |  82%
  |                                                                            
  |==========================================================            |  83%
  |                                                                            
  |============================================================          |  85%
  |                                                                            
  |============================================================          |  86%
  |                                                                            
  |=============================================================         |  87%
  |                                                                            
  |=============================================================         |  88%
  |                                                                            
  |===============================================================       |  90%
  |                                                                            
  |================================================================      |  92%
  |                                                                            
  |==================================================================    |  94%
  |                                                                            
  |====================================================================  |  97%
  |                                                                            
  |===================================================================== |  98%
  |                                                                            
  |===================================================================== |  99%
  |                                                                            
  |======================================================================| 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 <- c("13121003600", "13121003700")

# 2~4
tract_1_bb <- tract %>% 
  filter(GEOID %in% tr1_ID) %>%
  st_bbox()
  
# For the unwalkable Census Tract(s)  
# 1.
tr2_ID <- c("13137000601", "13121001001")

# 2~4
tract_2_bb <- tract %>% 
  filter(GEOID %in% tr2_ID) %>%
  # st_union() %>%
  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 %>% 
  # Drop redundant columns 
  select(osm_id, highway) %>% 
  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

net2 <- osm_2$osm_lines %>% 
  # Drop redundant columns 
  select(osm_id, highway) %>% 
  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 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 //////////////////////////////////////////////////////////////////////

    
    
  
  # 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() function 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_azi <- (mid_azi + 360) %% 360
  
  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 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) 

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

# =========== 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
  key <- Sys.getenv("Google_API")
  
  endpoint <- "https://maps.googleapis.com/maps/api/streetview"
  
  furl <- 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") 
  fpath <- paste0("/Users/katherinelosada/MSUA/", 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 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 ////////////////////////////////////////////////////////////////////////
# Read the downloaded CSV file from Google Colab
seg_output <- read.csv(
  "seg_output.csv"
)
seg_output <- seg_output %>% rename(node_id = img_id)

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


# =========== 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="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 ========================================

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.

# TASK ////////////////////////////////////////////////////////////////////////
# Create interactive map(s) to visualize the `edges_seg_output` objects. 
# As long as you can deliver the message clearly, you can use any format/package you want.

tmap_mode("view")

## tmap mode set to interactive viewing

walkable <- edges_azi %>% 
  filter(is_walkable == "walkable") %>% 
  pull(node_id)

unwalkable <- edges_azi %>% 
  filter(is_walkable == "unwalkable") %>% 
  pull(node_id)

categories <- c("building", "sky", "tree", "road", "sidewalk")

common_palette <- magma(5)

maps_list <- list()

for (category in categories) {
  map_walkable <- tm_shape(edges_seg_output %>% filter(node_id %in% walkable)) +
    tm_dots(col = category, style = "equal", palette = common_palette, 
            title = paste0(category, " - Walkable Tract"))
  
  map_unwalkable <- tm_shape(edges_seg_output %>% filter(node_id %in% unwalkable)) +
    tm_dots(col = category, style = "equal", palette = common_palette, 
            title = paste0(category, " - Unwalkable Tract"))
  
  maps_list[[paste0(category, "_walkable")]] <- map_walkable
  maps_list[[paste0(category, "_unwalkable")]] <- map_unwalkable
}

tmap_arrange(maps_list[["building_walkable"]], maps_list[["building_unwalkable"]],
             maps_list[["sky_walkable"]], maps_list[["sky_unwalkable"]],
             maps_list[["tree_walkable"]], maps_list[["tree_unwalkable"]],
             maps_list[["road_walkable"]], maps_list[["road_unwalkable"]],
             maps_list[["sidewalk_walkable"]], maps_list[["sidewalk_unwalkable"]],
             ncol = 2)

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

Brief Description of Maps: The interactive maps should reveal clear spatial differences between walkable and unwalkable areas in terms of infrastructure and urban design. I expect walkable areas to show higher densities of buildings, roads, and sidewalks, with strategically placed trees to enhance pedestrian experience. In contrast, unwalkable areas often have more open spaces, less developed pedestrian infrastructure, and more visible sky and trees scattered in less urbanized settings. However, it looks like the census tracts I though were unwalkable actually aren’t that bad because the results are similar throughout the plots. Both areas lack sidewalks, are densely road oriented, and have few trees. The walkable tract has more sky and less buildingss which contradict my prediction. After analyzing further, it looks like the GEOIDs I inputted don’t accurately showcase the census tracts I had planned.

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. 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.

mean_values <- data.frame(Category = character(),
                          Walkable_Mean = numeric(),
                          Unwalkable_Mean = numeric(),
                          stringsAsFactors = FALSE)

for (category in categories) {
  walkable_mean <- edges_seg_output %>%
    filter(node_id %in% walkable) %>%
    summarise(mean_value = mean(get(category), na.rm = TRUE)) %>%
    pull(mean_value)
  
  unwalkable_mean <- edges_seg_output %>%
    filter(node_id %in% unwalkable) %>%
    summarise(mean_value = mean(get(category), na.rm = TRUE)) %>%
    pull(mean_value)
  
  mean_values <- rbind(mean_values, data.frame(Category = category,
                                               Walkable_Mean = walkable_mean,
                                               Unwalkable_Mean = unwalkable_mean))
}

mean_values

##   Category Walkable_Mean Unwalkable_Mean
## 1 building    0.07856162      0.15815490
## 2      sky    0.30773027      0.19620022
## 3     tree    0.13089113      0.16494084
## 4     road    0.36140662      0.35676969
## 5 sidewalk    0.03023149      0.04580379

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

Brief Description of Mean Values: After averaging the scores for each category for both the walkable and unwalkable census tracts, I can easily compare and rank the results. The results are similar between the census tracts with relatively low scores across all the categories. These results may suggest that walkable areas tend to have a more balanced mix of infrastructure, but the values are not what one may expect because walkable areas should have more sidewalks than unwalkable areas.

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 ////////////////////////////////////////////////////////////////////////

categories <- c("building", "sky", "tree", "road", "sidewalk")

category_data <- edges_seg_output %>%
  st_drop_geometry() %>% 
  mutate(walkable_tract = case_when(node_id %in% walkable ~ "Walkable", TRUE ~ "Unwalkable")) %>%
  gather(key = "Category", value = "Proportion", all_of(categories))

ggplot(category_data, aes(x = walkable_tract, y = Proportion, fill = walkable_tract)) + 
  geom_boxplot() +
  facet_wrap(~ Category, scales = "free_y") +  
  theme_minimal() +
  labs(title = "Comparison of Proportions for Walkable vs Unwalkable Areas",
       y = "Proportion",
       x = "Area Type (Walkable vs Unwalkable)") +
  scale_fill_manual(values = c("Walkable" = "lightblue", "Unwalkable" = "salmon")) +  
  theme(legend.position = "none")  

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

Brief Description of Box Plots: The box plots reveal valuable insights about the distribution of proportions for each category in walkable versus unwalkable census tracts, especially when considering outliers. The walkable areas (in blue) reveal more outliers. Overall, the outliers suggest that while most areas fit general patterns, certain tracts deviate significantly particularly in terms of built infrastructure and green space.