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library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.5.3## Warning: package 'tidyr' was built under R version 4.5.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## in your R script or .Rprofile.library(tidycensus)## Warning: package 'tidycensus' was built under R version 4.5.3library(jsonlite)##
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## extractCOLLECTING POI DATA USING GOOGLE PLACES API
Choose two place types for the “includedTypes” parameter in the Google Places Nearby Search API. Refer to the API documentation for options.
Select a U.S. city. Use the “tigris” package to obtain an “sf” object of the city boundary. Avoid large cities (e.g., Atlanta) to prevent excessive API calls.
Divide the city into small areas using one of the following methods and prepare “locationRestriction” parameter values (longitude, latitude, and radius) for each area: Method 1: Census Block Groups (BGs) that intersect the city polygon Method 2: Fishnet grid cells based on the city polygon
Verify coverage to ensure there are no uncovered areas by visualizing your locationRestriction parameters on a map: Create a POINT “sf” object using longitude, latitude, and sf::st_as_sf(). Create buffers using radius and sf::st_buffer(). Build an interactive map displaying (1) the city boundary and (2) the buffer objects with the tmap package.
Collect POI data using the Nearby Search in the Google Places API. Include two place types in the includedTypes parameter. For FieldMask, include these 8 fields: places.id; places.displayName; places.formattedAddress; places.location; places.types; places.priceLevel; places.rating; places.userRatingCount You may also add other fields of interest, but these 8 must be included. Parse the API response into a data frame. Export the POI dataset into an RData or RDS file.
Map the collected POIs Convert the POI dataset into an sf object using sf::st_as_sf(). Create an interactive map showing both the POIs and the city boundary.
Answer the following questions at the end of your script: What city did you choose? Which two place types did you select? How many rows does your dataset contain? Upon visual inspection, do you notice any spatial patterns in how POIs are distributed across the city? (Optional) Did you observe any other interesting findings?
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# Get block groups for DeKalb County (Stone Mountain)
bg <- suppressMessages(
tidycensus::get_acs(
geography = "block group",
state = "GA",
county = c("DeKalb County"),
variables = c(hhincome = "B19013_001"),
year = 2023,
survey = "acs5",
geometry = TRUE,
output = "wide"
)
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StoneMountain <- tigris::places("GA") %>%
filter(NAME == "Stone Mountain")## Retrieving data for the year 2024## | | | 0% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | 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| 93% | |================================================================= | 94% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%# Get BGs intersecting with the City of StoneMountain boundary
bg_StoneMountain <- bg[StoneMountain, ]Method 1: Census Block Groups (BGs) that intersect the city polygon Method 2: Fishnet grid cells based on the city polygon Verify coverage to ensure there are no uncovered areas by visualizing your locationRestriction parameters on a map:
# Function: Get XY coordinates and radius
getXYRadius <- function(polygon, gcs_id, pcs_id) {
# Transform the CRS to PCS.
if (st_crs(polygon) != st_crs(pcs_id)) {
polygon <- polygon %>% st_transform(pcs_id)
}
# Get bounding box of a given polygon
bb <- st_bbox(polygon)
# Get XY coordinates of any one corner of the bounding box.
bb_corner <- st_point(c(bb[1], bb[2])) %>%
st_sfc(crs = pcs_id)
# Get centroid of the bb
bb_center <- bb %>%
st_as_sfc() %>%
st_centroid()
# Get the distance between bb_center and bb_corner
r <- st_distance(bb_corner, bb_center)
# Convert the CRS of centroid to GCS.
bb_center <- bb_center %>% st_transform(gcs_id)
# Get longitude and latitude
xy <- bb_center %>% st_coordinates() %>% as.vector()
lon_lat_radius <- data.frame(x = xy[1],
y = xy[2],
r = r)
return(lon_lat_radius)
}We can apply this function to each BG.
# Define EPSG codes for GCS (WGS 84) and PCS (WGS 84 / UTM zone 16N)
gcs_id <- 4326
pcs_id <- 32616
# Pre-allocate a data frame for StoneMountain BGs
bg_StoneMountain_xyr <- data.frame(
x = numeric(nrow(bg_StoneMountain)),
y = NA,
r = NA
)
# For-loop over BGs in StoneMountain
for (i in 1:nrow(bg_StoneMountain)) {
bg_StoneMountain_xyr[i, ] <- bg_StoneMountain[i, ] %>%
getXYRadius(gcs_id = gcs_id, pcs_id = pcs_id)
}tmap_mode("view")## ℹ tmap modes "plot" - "view"
## ℹ toggle with `tmap::ttm()`bg_StoneMountain_xyr %>%
st_as_sf(coords = c("x", "y"), crs = st_crs(bg_StoneMountain)) %>%
st_buffer(dist = .$r) %>%
tm_shape() +
tm_polygons(alpha = 0.3, col = "red") +
tm_shape(bg_StoneMountain) +
tm_borders(col = "blue")##
## ── tmap v3 code detected ───────────────────────────────────────────────────────
## [v3->v4] `tm_polygons()`: use 'fill' for the fill color of polygons/symbols
## (instead of 'col'), and 'col' for the outlines (instead of 'border.col').[v3->v4] `tm_polygons()`: use `fill_alpha` instead of `alpha`.# Get bbox for StoneMountain
StoneMountain_bb <- StoneMountain %>%
st_transform(pcs_id) %>%
st_bbox()
# Coordinates for bbox sides
west <- StoneMountain_bb[1]
east <- StoneMountain_bb[3]
south <- StoneMountain_bb[2]
north <- StoneMountain_bb[4]
# Split bbox into a grid
fishnet_n <- 4
dist <- abs(east - west) / fishnet_n
# Fishnet points
fish_x <- seq(from = west, to = east, by = dist)
fish_y <- seq(from = south, to = north, by = dist)
fishnet <- expand.grid(fish_x, fish_y) %>%
rename(x = Var1, y = Var2) %>%
st_as_sf(coords = c("x", "y"), crs = pcs_id)
fishnet$x <- fishnet %>%
st_transform(gcs_id) %>%
st_coordinates() %>%
.[, 1]
fishnet$y <- fishnet %>%
st_transform(gcs_id) %>%
st_coordinates() %>%
.[, 2]
fishnet$r <- dist * (2^(1/2) / 2)
# Visualize fishnet over StoneMountain
fishnet %>%
st_buffer(dist = .$r) %>%
tm_shape() +
tm_polygons(alpha = 0.1, col = "red") +
tm_shape(StoneMountain) +
tm_borders(lwd = 3)## ## ── tmap v3 code detected ───────────────────────────────────────────────────────## [v3->v4] `tm_polygons()`: use `fill_alpha` instead of `alpha`.library(httr)
google_api_key <- Sys.getenv("GOOGLE_API")
# Approximate center of Stone Mountain, GA (DeKalb County, Atlanta metro)
lat <- 33.8053 # Stone Mountain area
lon <- -84.1703
radius <- 500.0
endpoint <- "https://places.googleapis.com/v1/places:searchNearby"
body <- list(
includedTypes = list("bank", "school"),
locationRestriction = list(
circle = list(
center = list(latitude = lat, longitude = lon),
radius = radius
)
)
)
resp <- httr::POST(
endpoint,
httr::add_headers(
"Content-Type" = "application/json",
"X-Goog-Api-Key" = google_api_key,
"X-Goog-FieldMask" = "places.displayName,places.formattedAddress,places.types"
),
body = body,
encode = "json"
)
data <- httr::content(resp, as = "text") %>%
jsonlite::fromJSON(flatten = TRUE) %>%
as.data.frame()
print(data)## places.types
## 1 school, government_office, point_of_interest, educational_institution, establishment
## 2 bank, atm, insurance_agency, consultant, finance, service, point_of_interest, establishment
## 3 school, association_or_organization, health, service, point_of_interest, educational_institution, establishment
## 4 school, service, point_of_interest, educational_institution, establishment
## 5 school, point_of_interest, educational_institution, establishment
## places.formattedAddress
## 1 5265 Mimosa Dr, Stone Mountain, GA 30083, USA
## 2 5399 E Mountain St, Stone Mountain, GA 30083, USA
## 3 927 Main St, Stone Mountain, GA 30083, USA
## 4 5283 W Mountain St, Stone Mountain, GA 30083, USA
## 5 5312 W Mountain St, Stone Mountain, GA 30083, USA
## places.displayName.text places.displayName.languageCode
## 1 Champion Middle School en
## 2 Truist en
## 3 Kameo Production Studios, LLC en
## 4 Vastshades Music en
## 5 Valor Preparatory Academy endata <- content(resp, as = "text")
data <- jsonlite::fromJSON(data, flatten = TRUE) %>%
as.data.frame()print(resp) # metadata + body## Response [https://places.googleapis.com/v1/places:searchNearby]
## Date: 2026-10-02 21:53
## Status: 200
## Content-Type: application/json; charset=UTF-8
## Size: 1.82 kB
## {
## "places": [
## {
## "types": [
## "school",
## "government_office",
## "point_of_interest",
## "educational_institution",
## "establishment"
## ],
## ...print(data)## places.types
## 1 school, government_office, point_of_interest, educational_institution, establishment
## 2 bank, atm, insurance_agency, consultant, finance, service, point_of_interest, establishment
## 3 school, association_or_organization, health, service, point_of_interest, educational_institution, establishment
## 4 school, service, point_of_interest, educational_institution, establishment
## 5 school, point_of_interest, educational_institution, establishment
## places.formattedAddress
## 1 5265 Mimosa Dr, Stone Mountain, GA 30083, USA
## 2 5399 E Mountain St, Stone Mountain, GA 30083, USA
## 3 927 Main St, Stone Mountain, GA 30083, USA
## 4 5283 W Mountain St, Stone Mountain, GA 30083, USA
## 5 5312 W Mountain St, Stone Mountain, GA 30083, USA
## places.displayName.text places.displayName.languageCode
## 1 Champion Middle School en
## 2 Truist en
## 3 Kameo Production Studios, LLC en
## 4 Vastshades Music en
## 5 Valor Preparatory Academy ennearbySearch <- function(lat, lon, radius, types_vec, fieldmask_vec, google_api_key) {
endpoint <- "https://places.googleapis.com/v1/places:searchNearby"
body <- list(
includedTypes = as.list(types_vec),
locationRestriction = list(
circle = list(
center = list(latitude = lat, longitude = lon),
radius = radius
)
),
rankPreference = "DISTANCE"
)
resp <- httr::POST(
endpoint,
httr::add_headers(
"Content-Type" = "application/json",
"X-Goog-Api-Key" = google_api_key,
"X-Goog-FieldMask" = paste(fieldmask_vec, collapse = ",")
),
body = body,
encode = "json"
)
data <- httr::content(resp, as = "text") %>%
jsonlite::fromJSON(flatten = TRUE) %>%
as.data.frame()
if (nrow(data) == 20) {
print("WARNING: The response has 20 rows! Consider using a smaller spatial unit.")
}
return(data)
}nearbySearch(
lat = 33.8053,
lon = -84.1703,
radius = radius,
types_vec = c("bank", "school"),
fieldmask_vec = c(
"places.id",
"places.displayName",
"places.formattedAddress",
"places.location",
"places.types",
"places.priceLevel",
"places.rating",
"places.userRatingCount"
),
google_api_key = Sys.getenv("GOOGLE_API")
)## places.id
## 1 ChIJh_QLfzuv9YgRGWYoRfULfnw
## 2 ChIJwZxCxjqv9YgRSnZHAanPp_s
## 3 ChIJD2Xmajyv9YgRY0lWo8JoyQo
## 4 ChIJUZHfEgGv9YgRARArrwpbQB4
## 5 ChIJJQQ1JsKv9YgRnQjfaoFseAs
## places.types
## 1 school, association_or_organization, health, service, point_of_interest, educational_institution, establishment
## 2 school, government_office, point_of_interest, educational_institution, establishment
## 3 bank, atm, insurance_agency, consultant, finance, service, point_of_interest, establishment
## 4 school, service, point_of_interest, educational_institution, establishment
## 5 school, point_of_interest, educational_institution, establishment
## places.formattedAddress places.rating
## 1 927 Main St, Stone Mountain, GA 30083, USA NA
## 2 5265 Mimosa Dr, Stone Mountain, GA 30083, USA NA
## 3 5399 E Mountain St, Stone Mountain, GA 30083, USA 3.6
## 4 5283 W Mountain St, Stone Mountain, GA 30083, USA 5.0
## 5 5312 W Mountain St, Stone Mountain, GA 30083, USA NA
## places.userRatingCount places.location.latitude places.location.longitude
## 1 NA 33.80676 -84.17016
## 2 NA 33.80519 -84.17398
## 3 40 33.80817 -84.16881
## 4 4 33.80756 -84.17331
## 5 NA 33.80863 -84.17199
## places.displayName.text places.displayName.languageCode
## 1 Kameo Production Studios, LLC en
## 2 Champion Middle School en
## 3 Truist en
## 4 Vastshades Music en
## 5 Valor Preparatory Academy enNow that we have the Nearby Search API function, we can repeat this
process for all the Census BGs using the
bg_StoneMountain_xyr object we created earlier.
# Pre-allocate list
data_list <- vector("list", nrow(bg_StoneMountain_xyr))
for (i in seq_len(nrow(bg_StoneMountain_xyr))) {
data_list[[i]] <- nearbySearch(
lat = bg_StoneMountain_xyr$y[i],
lon = bg_StoneMountain_xyr$x[i],
radius = bg_StoneMountain_xyr$r[i],
types_vec = c("bank", "school"),
fieldmask_vec = c(
"places.id",
"places.displayName",
"places.formattedAddress",
"places.location",
"places.types",
"places.priceLevel",
"places.rating",
"places.userRatingCount"
),
google_api_key = Sys.getenv("GOOGLE_API")
)
Sys.sleep(1)
}## [1] "WARNING: The response has 20 rows! Consider using a smaller spatial unit."# Combine all data frames
data_all <- dplyr::bind_rows(data_list)saveRDS(data_all, here("mini_assignment_1_T_otoo_NEW.rds"))# Convert the data to an sf object using XY coordinates
data_all_sf <- data_all %>%
dplyr::rename(
x = places.location.longitude,
y = places.location.latitude
) %>%
dplyr::filter(!is.na(x) & !is.na(y)) %>%
sf::st_as_sf(coords = c("x", "y"), crs = 4326)
# Map POIs and Stone Mountain boundary
tmap_mode("view")## ℹ tmap modes "plot" - "view"tm_shape(data_all_sf) +
tm_dots(
col = "places.rating",
size = "places.userRatingCount",
palette = "magma",
popup.vars = c(
"Name" = "places.displayName.text",
"Rating" = "places.rating",
"Rating Count" = "places.userRatingCount"
)
) +
tm_shape(StoneMountain) +
tm_borders()##
## ── tmap v3 code detected ───────────────────────────────────────────────────────
## [v3->v4] `tm_tm_dots()`: migrate the argument(s) related to the scale of the
## visual variable `fill` namely 'palette' (rename to 'values') to fill.scale =
## tm_scale(<HERE>).Answer: I chose a city called Stone Mountain. It is located in DeKalb County in the State of Georgia, USA.
Answer: The place types selected were banks and schools
Answer: the dataset contains one row
Answer: There are medium ratings in terms of place ratings and high ratings on place user rating counts in the northern part of the city compared to the southern part. Again, the central part of the city does not show any form of rating in the city, using banks and schools as a reference point. The distribution of the POIs in the city could be highly affected when there are missing values to report from the Nearby search.
Answer: Nearby search also showed place ratings and place user ratings of a location that is well beyond the boundary of the study location (Stone Mountain).