圖表分析繪製
##
## ── tmap v3 code detected ───────────────────────────────────────────────────────
## [v3->v4] `tm_tm_polygons()`: migrate the argument(s) related to the scale of
## the visual variable `fill` namely 'palette' (rename to 'values') to fill.scale
## = tm_scale(<HERE>).
## [v3->v4] `tm_polygons()`: migrate the argument(s) related to the legend of the
## visual variable `fill` namely 'title' to 'fill.legend = tm_legend(<HERE>)'
## [cols4all] color palettes: use palettes from the R package cols4all. Run
## `cols4all::c4a_gui()` to explore them. The old palette name "Greens" is named
## "brewer.greens"
## Multiple palettes called "greens" found: "brewer.greens", "matplotlib.greens". The first one, "brewer.greens", is returned.
##
## ── tmap v3 code detected ───────────────────────────────────────────────────────
## [v3->v4] `tm_tm_polygons()`: migrate the argument(s) related to the scale of
## the visual variable `fill` namely 'palette' (rename to 'values') to fill.scale
## = tm_scale(<HERE>).[v3->v4] `tm_polygons()`: migrate the argument(s) related to the legend of the
## visual variable `fill` namely 'title' to 'fill.legend = tm_legend(<HERE>)'[cols4all] color palettes: use palettes from the R package cols4all. Run
## `cols4all::c4a_gui()` to explore them. The old palette name "Reds" is named
## "brewer.reds"Multiple palettes called "reds" found: "brewer.reds", "matplotlib.reds". The first one, "brewer.reds", is returned.
library(sf)
library(dplyr)
library(stringr)
# 假設 park_landuse 為從 landuse_sf 篩選後的公園資料
# 過濾出「陽明山公園」多邊形
yangmingshan_poly <- park_landuse %>%
filter(str_detect(使用分區, "陽明山公園")) # 或使用其他辨識欄位，如使用分區 / 編號
# 計算每所學校到陽明山公園多邊形的最近距離
dist_to_yangmingshan <- st_distance(school_sf, yangmingshan_poly)
# 轉為數值並找出過近學校（小於 300 公尺）
dist_to_yangmingshan_numeric <- apply(dist_to_yangmingshan, 1, min) # 多個面積時取最近
school_sf <- school_sf %>%
mutate(靠近陽明山 = dist_to_yangmingshan_numeric < 300)
# 建立 500 公尺緩衝區
school_buffers <- school_sf %>%
st_buffer(dist = 500)
# 可視化檢查
tm_shape(school_buffers) + tm_borders(col = "blue") +
tm_shape(school_sf) + tm_dots(size = 0.2, col = "red") +
tm_layout(title = "學校500M緩衝區")
# 找出每所學校的緩衝區是否與任何公園相交
park_count <- st_intersects(school_buffers, park_facilities) %>% lengths()
medical_count <- st_intersects(school_buffers, medical_facilities) %>% lengths()
# 合併回學校資料
school_sf <- school_sf %>%
mutate(
park_count = park_count,
medical_count = medical_count
)
# 排序並選出前 25 筆（可視需求包含同分並列）
least_parks_sf <- school_sf %>%
arrange(park_count) %>%
slice_head(n = 25)
least_parks_df <- as.data.frame(least_parks_sf)
# 篩選出公園數量最少的25所學校
least_parks_sf <- school_sf %>%
arrange(park_count) %>%
slice_head(n = 25)
# 繪製地圖
ggplot() +
geom_sf(data = VILLAGE, fill = "gray90", color = "black", size = 0.3) + # 台北市行政區界線
geom_sf(data = least_parks_sf, aes(color = park_count), size = 3) + # 公園最少學校點
scale_color_viridis_c(option = "plasma", direction = -1, name = "公園數量") +
labs(title = "台北市半徑500公尺內公園數量最少的25所學校") +
theme_minimal() +
theme(
legend.position = "right",
plot.title = element_text(hjust = 0.5, size = 16)
)
# 計算每所學校周邊 500m / 1km 內的速食店數量
buffer_distance <- 1000 # 1km 範圍
school_buffers <- st_buffer(school_sf, buffer_distance)
school_sf$fastfood_count <- sapply(seq_len(nrow(school_buffers)), function(i) {
sum(st_intersects(school_buffers[i, ], fastfood, sparse = FALSE))
})
# 找出速食店最多的學校 (前10名)
top_schools <- school_sf %>%
arrange(desc(fastfood_count)) %>%
slice_head(n = 10)
print(top_schools[, c("Name", "fastfood_count")])
## Simple feature collection with 10 features and 2 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 301315.7 ymin: 2769158 xmax: 307610.6 ymax: 2771943
## Projected CRS: TWD97 / TM2 zone 121
## Name fastfood_count geometry
## 1 福星國小 13 POINT (301315.7 2771005)
## 2 光復國小 10 POINT (306291.9 2770354)
## 3 日新國小 10 POINT (302114.8 2771913)
## 4 敦化國小 9 POINT (305347.2 2771329)
## 5 信義國小 9 POINT (306835.9 2769425)
## 6 吉林國小 8 POINT (303437.6 2771943)
## 7 忠孝國小 8 POINT (302458.4 2770546)
## 8 仁愛國小 7 POINT (305738.6 2769854)
## 9 三興國小 6 POINT (306338.6 2769158)
## 10 博愛國小 6 POINT (307610.6 2769928)
# 進行K、G、F 函數分析
# 轉換成 spatstat 格式
school_ppp <- as.ppp(st_coordinates(school_sf), owin(range(st_coordinates(school_sf)[,1]), range(st_coordinates(school_sf)[,2])))
fastfood_ppp <- as.ppp(st_coordinates(fastfood), owin(range(st_coordinates(fastfood)[,1]), range(st_coordinates(fastfood)[,2])))
## Warning: data contain duplicated points
K_school <- Kest(school_ppp)
G_school <- Gest(school_ppp)
F_school <- Fest(school_ppp)
K_fastfood <- Kest(fastfood_ppp)
G_fastfood <- Gest(fastfood_ppp)
F_fastfood <- Fest(fastfood_ppp)
# 可視化學校周邊速食店密度
ggplot(school_sf, aes(x = fastfood_count)) +
geom_histogram(binwidth = 1, fill = "blue", alpha = 0.7) +
labs(title = "學校周邊速食店數量分布", x = "速食店數量", y = "學校數") +
theme_minimal()
# 計算每所學校周邊 500m / 1km 內的速食店數量
buffer_distance <- 1000 # 1km 範圍
school_buffers <- st_buffer(school_sf, buffer_distance)
school$fastfood_count <- sapply(seq_len(nrow(school_buffers)), function(i) {
sum(st_intersects(school_buffers[i, ], fastfood, sparse = FALSE))
})
school_stats <- school_sf
# 找出速食店最多的前 10 所學校
top_schools <- school_sf %>%
arrange(desc(fastfood_count)) %>%
slice_head(n = 10)
# Coordinates
school_coords <- st_coordinates(school_sf)
fastfood_coords <- st_coordinates(fastfood)
# 定義owin空間窗口
xrange <- range(c(school_coords[,1], fastfood_coords[,1]))
yrange <- range(c(school_coords[,2], fastfood_coords[,2]))
win <- owin(xrange, yrange)
school_ppp <- as.ppp(school_coords, W = win)
fastfood_ppp <- as.ppp(fastfood_coords, W = win)
## Warning: data contain duplicated points
K_school <- Kest(school_ppp)
G_school <- Gest(school_ppp)
F_school <- Fest(school_ppp)
K_fastfood <- Kest(fastfood_ppp)
G_fastfood <- Gest(fastfood_ppp)
F_fastfood <- Fest(fastfood_ppp)
# 地圖視覺化
# 5-1: 繪製速食店最多的前10所學校位置
ggplot() +
geom_sf(data = VILLAGE, fill = "gray90", color = "black", size = 0.3) +
geom_sf(data = top_schools, aes(color = fastfood_count), size = 3) +
scale_color_viridis_c(option = "plasma", direction = -1, name = "速食店數量") +
labs(title = "台北市速食店數量最多的前10所學校（1公里範圍內）") +
theme_minimal() +
theme(
legend.position = "right",
plot.title = element_text(hjust = 0.5, size = 16)
)
# 5-2: 所有學校速食店分布（點大小代表數量）
ggplot() +
geom_sf(data = school_stats, aes(size = fastfood_count, color = fastfood_count), alpha = 0.7) +
scale_color_viridis_c(option = "plasma", name = "速食店數量") +
scale_size(range = c(1, 10), name = "速食店數量") +
labs(title = "各學校 1 公里內的速食店數量") +
theme_minimal()
# 5-3: 直方圖
ggplot(school_stats, aes(x = fastfood_count)) +
geom_histogram(binwidth = 1, fill = "steelblue", color = "white") +
labs(
title = "各學校 1 公里內的速食店數量分布",
x = "速食店數量",
y = "學校數量"
) +
theme_minimal()
library(sf)
library(dplyr)
top_n <- 5
# 計算距離矩陣
distance_matrix <- st_distance(school_sf, park_facilities)
distance_numeric <- as.matrix(distance_matrix)
# 取得最近5個公園的 index
nearest_park_indexes <- apply(distance_numeric, 1, function(row) {
order(row)[1:top_n]
})
# 初始化列表用來存儲資訊
nearest_park_ids <- list()
nearest_park_coords_x <- list()
nearest_park_coords_y <- list()
# 逐校紀錄
for (i in seq_len(nrow(school_sf))) {
indexes <- nearest_park_indexes[, i]
nearest_park_ids[[i]] <- park_facilities$編號[indexes]
coords <- st_coordinates(park_facilities[indexes, ])
nearest_park_coords_x[[i]] <- coords[, 1]
nearest_park_coords_y[[i]] <- coords[, 2]
}
# 整理為資料框
nearest_park_ids_df <- do.call(rbind, nearest_park_ids) %>% as.data.frame()
colnames(nearest_park_ids_df) <- paste0("nearest_park_", 1:top_n, "_id")
nearest_park_x_df <- do.call(rbind, nearest_park_coords_x) %>% as.data.frame()
## Warning in (function (..., deparse.level = 1) : number of columns of result is
## not a multiple of vector length (arg 1)
colnames(nearest_park_x_df) <- paste0("nearest_park_", 1:top_n, "_x")
nearest_park_y_df <- do.call(rbind, nearest_park_coords_y) %>% as.data.frame()
## Warning in (function (..., deparse.level = 1) : number of columns of result is
## not a multiple of vector length (arg 1)
colnames(nearest_park_y_df) <- paste0("nearest_park_", 1:top_n, "_y")
# 距離也整理
nearest_park_dists <- t(apply(distance_numeric, 1, function(row) {
sort(row, partial = 1:top_n)[1:top_n]
}))
nearest_park_dists_df <- as.data.frame(nearest_park_dists)
colnames(nearest_park_dists_df) <- paste0("distance_to_park_", 1:top_n)
# 檢查原 sf 是否已包含這些欄位，若有重名則先移除
cols_to_remove <- intersect(names(school_sf), c(
colnames(nearest_park_ids_df),
colnames(nearest_park_dists_df),
colnames(nearest_park_x_df),
colnames(nearest_park_y_df)
))
school_sf <- school_sf %>% select(-all_of(cols_to_remove))
# 合併所有資訊
school_sf <- bind_cols(
school_sf,
nearest_park_ids_df,
nearest_park_dists_df,
nearest_park_x_df,
nearest_park_y_df
)
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# 查看結果（只取前五筆）
school_sf %>%
select(Name, starts_with("nearest_park_"), starts_with("distance_to_park_"),靠近陽明山) %>%
head()
school_sf = school_sf %>%
select(Name, starts_with("nearest_park_"), starts_with("distance_to_park_"),靠近陽明山)
print(school_sf)
## Simple feature collection with 148 features and 22 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 297078.6 ymin: 2763290 xmax: 312516.7 ymax: 2784542
## Projected CRS: TWD97 / TM2 zone 121
## First 10 features:
## Name nearest_park_1_id nearest_park_2_id nearest_park_3_id
## 1 松山國小 758 12032 2816
## 2 西松國小 487 486 597
## 3 敦化國小 702 161 147
## 4 民生國小 460 459 461
## 5 民權國小 502 491 481
## 6 民族國小 462 121 2169
## 7 三民國小 480 481 478
## 8 健康國小 498 473 486
## 9 興雅國小 582 571 600
## 10 永春國小 580 574 14875
## nearest_park_4_id nearest_park_5_id nearest_park_1_x nearest_park_2_x
## 1 12055 1855 308259.3 308258.8
## 2 598 570 306942.2 306939.2
## 3 707 146 305435.9 305435.5
## 4 458 457 305747.9 305744.1
## 5 475 478 306430.9 306302.3
## 6 122 457 305708.7 305700.0
## 7 2762 491 307184.3 307172.8
## 8 481 2761 306818.8 306817.0
## 9 581 585 307555.4 307556.4
## 10 766 690 308240.5 308240.5
## nearest_park_3_x nearest_park_4_x nearest_park_5_x nearest_park_1_y
## 1 308276.7 308282.4 308282.9 2771394
## 2 306952.7 306953.2 306953.2 2771764
## 3 305435.2 305435.1 305434.8 2771241
## 4 305740.4 305735.9 305731.3 2772153
## 5 306211.4 306204.7 306204.9 2772690
## 6 305699.9 305699.7 305699.6 2772459
## 7 307169.5 307169.0 307169.0 2772996
## 8 306815.6 306814.4 306813.6 2772401
## 9 307559.3 307594.3 307598.6 2770976
## 10 308228.7 308241.8 308240.5 2770895
## nearest_park_2_y nearest_park_3_y nearest_park_4_y nearest_park_5_y
## 1 2771432 2771433 2771429 2771396
## 2 2771770 2771780 2771780 2771780
## 3 2771242 2771243 2771243 2771244
## 4 2772155 2772156 2772158 2772160
## 5 2772670 2772657 2772656 2772656
## 6 2772458 2772458 2772458 2772458
## 7 2773072 2773094 2773097 2773098
## 8 2772403 2772406 2772409 2772412
## 9 2771013 2771015 2771015 2771015
## 10 2770895 2770922 2770922 2770895
## distance_to_park_1 distance_to_park_2 distance_to_park_3 distance_to_park_4
## 1 63.43128 125.85702 137.85526 165.15428
## 2 115.25267 136.04299 175.74864 196.27399
## 3 75.71269 249.04590 305.07207 328.05105
## 4 18.17680 27.46306 48.41266 89.09668
## 5 220.24398 220.81074 280.24091 379.82961
## 6 32.74393 85.93970 120.47399 168.22366
## 7 266.34619 288.58493 292.08348 299.56804
## 8 223.16616 313.45602 317.56575 360.99062
## 9 74.71672 173.47225 294.25273 346.16003
## 10 196.86848 200.36690 230.43362 246.48837
## distance_to_park_5 靠近陽明山 geometry
## 1 181.3046 FALSE POINT (308286.4 2771496)
## 2 386.8265 FALSE POINT (306854.9 2771689)
## 3 343.4932 FALSE POINT (305347.2 2771329)
## 4 184.1529 FALSE POINT (305712 2772151)
## 5 379.8884 FALSE POINT (306729.6 2772821)
## 6 172.6898 FALSE POINT (305664.1 2772492)
## 7 402.3463 FALSE POINT (306930.4 2772894)
## 8 380.9857 FALSE POINT (306782 2772172)
## 9 381.9655 FALSE POINT (307516.8 2771077)
## 10 282.6541 FALSE POINT (308431.8 2770848)
#包含離陽明山國家公園較近的國小
# 1. 整理距離資料
distance_df <- school_sf %>%
st_drop_geometry() %>%
select(Name,
distance_to_park_1,
distance_to_park_2,
distance_to_park_3,
distance_to_park_4,
distance_to_park_5) %>%
pivot_longer(
cols = starts_with("distance_to_park_"),
names_to = "k_order",
values_to = "distance"
) %>%
mutate(k = as.numeric(gsub("distance_to_park_", "", k_order)))
# 2. 使用 Tukey’s Rule 計算離群值閾值
distance_outlier_info <- distance_df %>%
group_by(k) %>%
summarise(
Q1 = quantile(distance, 0.25, na.rm = TRUE),
Q3 = quantile(distance, 0.75, na.rm = TRUE),
IQR = Q3 - Q1,
upper = Q3 + 1.5 * IQR
)
# 3. 加入是否為離群值的標註
distance_df <- distance_df %>%
left_join(distance_outlier_info, by = "k") %>%
mutate(outlier = distance > upper)
# 4. 繪圖：K-order Nearest Neighbor Distance 曲線
ggplot(distance_df, aes(x = k, y = distance, group = Name)) +
geom_line(alpha = 0.5, color = "gray") +
geom_point(aes(color = outlier), size = 2) +
geom_text(data = filter(distance_df, outlier),
aes(label = Name),
size = 3, hjust = -0.1, vjust = 0.5) +
scale_x_continuous(breaks = 1:5) +
scale_color_manual(values = c("black", "red")) +
labs(
title = "K-order Nearest Park Distance per School",
x = "K-th Nearest Park",
y = "Distance (m)",
color = "Outlier"
) +
theme_minimal()
#不包含離陽明山國家公園較近的國小
# 1. 整理距離資料
distance_df <- school_sf %>%
filter(靠近陽明山 == FALSE) %>%
st_drop_geometry() %>%
select(Name,
distance_to_park_1,
distance_to_park_2,
distance_to_park_3,
distance_to_park_4,
distance_to_park_5) %>%
pivot_longer(
cols = starts_with("distance_to_park_"),
names_to = "k_order",
values_to = "distance"
) %>%
mutate(k = as.numeric(gsub("distance_to_park_", "", k_order)))
# 2. 使用 Tukey’s Rule 計算離群值閾值
distance_outlier_info <- distance_df %>%
group_by(k) %>%
summarise(
Q1 = quantile(distance, 0.25, na.rm = TRUE),
Q3 = quantile(distance, 0.75, na.rm = TRUE),
IQR = Q3 - Q1,
upper = Q3 + 1.5 * IQR
)
# 3. 加入是否為離群值的標註
distance_df <- distance_df %>%
left_join(distance_outlier_info, by = "k") %>%
mutate(outlier = distance > upper)
# 4. 繪圖：K-order Nearest Neighbor Distance 曲線
ggplot(distance_df, aes(x = k, y = distance, group = Name)) +
geom_line(alpha = 0.5, color = "gray") +
geom_point(aes(color = outlier), size = 2) +
geom_text(data = filter(distance_df, outlier),
aes(label = Name),
size = 3, hjust = -0.1, vjust = 0.5) +
scale_x_continuous(breaks = 1:5) +
scale_color_manual(values = c("black", "red")) +
labs(
title = "K-order Nearest Park Distance per School",
x = "K-th Nearest Park",
y = "Distance (m)",
color = "Outlier"
) +
theme_minimal()
# 5. 輸出 outliers 的資料
outlier_df <- distance_df %>%
filter(outlier == TRUE)
# 印出離群資料（或寫入 CSV 檔）
print(outlier_df)
## # A tibble: 28 × 9
## Name k_order distance k Q1 Q3 IQR upper outlier
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 福德國小 distance_to_park_5 642. 5 228. 386. 158. 624. TRUE
## 2 公館國小 distance_to_park_2 472. 2 119. 257. 138. 463. TRUE
## 3 公館國小 distance_to_park_3 759. 3 160. 313. 153. 543. TRUE
## 4 公館國小 distance_to_park_4 778. 4 190. 360. 171. 616. TRUE
## 5 公館國小 distance_to_park_5 787. 5 228. 386. 158. 624. TRUE
## 6 東門國小 distance_to_park_2 555. 2 119. 257. 138. 463. TRUE
## 7 東門國小 distance_to_park_3 560. 3 160. 313. 153. 543. TRUE
## 8 忠孝國小 distance_to_park_1 358. 1 62.9 168. 105. 325. TRUE
## 9 新和國小 distance_to_park_4 629. 4 190. 360. 171. 616. TRUE
## 10 新和國小 distance_to_park_5 635. 5 228. 386. 158. 624. TRUE
## # ℹ 18 more rows
library(sf)
library(dplyr)
library(ggplot2)
# === 1. 找出第1或第5近公園為離群的學校名稱 ===
outlier_names <- distance_df %>%
filter(k %in% c(1, 5), outlier == TRUE) %>%
pull(Name) %>%
unique()
# === 2. 取得第1與第5距離資訊 ===
outlier_distances <- distance_df %>%
filter(Name %in% outlier_names, k %in% c(1, 5)) %>%
select(Name, k, distance) %>%
tidyr::pivot_wider(names_from = k, names_prefix = "k", values_from = distance)
# === 3. 合併到學校資料 ===
outlier_school_sf <- school_sf %>%
filter(Name %in% outlier_names, 靠近陽明山 == FALSE) %>%
left_join(outlier_distances, by = "Name")
# === 4. 建立以第1近與第5近距離為半徑的緩衝區 ===
buffer_k1 <- st_buffer(outlier_school_sf, dist = outlier_school_sf$k1)
buffer_k5 <- st_buffer(outlier_school_sf, dist = outlier_school_sf$k5)
# 加入標記以便辨識是哪種緩衝區
buffer_k1$type <- "1st Nearest"
buffer_k5$type <- "5th Nearest"
# 合併兩種緩衝區資料
school_buffers <- rbind(buffer_k1, buffer_k5)
# === 5. 繪圖 ===
library(ggrepel) # 用來美化標籤（可選）
## Warning: 套件 'ggrepel' 是用 R 版本 4.4.3 來建造的
ggplot() +
geom_sf(data = park_facilities, color = "lightgray", size = 0.5) +
geom_sf(data = school_sf, color = "gray", shape = 1) +
geom_sf(data = outlier_school_sf, color = "red", size = 0.5) +
geom_sf(data = school_buffers, aes(linetype = type), fill = NA, color = "red") +
# 標示離群學校名稱
ggrepel::geom_text_repel(
data = outlier_school_sf,
aes(geometry = geometry, label = Name),
stat = "sf_coordinates",
min.segment.length = 0,
size = 4,
color = "red"
) +
labs(title = "Buffers for 1st and 5th Nearest Parks of Outlier Schools",
linetype = "Buffer Type") +
theme_minimal()
library(sf)
library(dplyr)
library(leaflet)
# 假設 outlier_schools_sf 已經有了
# 轉成經緯度系統 (WGS84)
schools_wgs <- st_transform(outlier_school_sf, 4326)
# 建立緩衝區 (例如半徑 1000 公尺)
# 注意：經緯度單位是度，不適合直接用於距離
# 所以先轉成公尺單位的投影系統 (例如EPSG:3857)，做buffer，再轉回4326
schools_proj <- st_transform(schools_wgs, 3857)
buffer_radius <- 500 # 公尺
schools_buffer <- st_buffer(schools_proj, dist = buffer_radius)
# 再轉回經緯度
schools_buffer_wgs <- st_transform(schools_buffer, 4326)
# Leaflet 地圖繪製
leaflet() %>%
addTiles() %>%
addPolygons(data = schools_buffer_wgs,
color = "blue", weight = 2, fill = TRUE, fillOpacity = 0.2,
popup = ~schools_wgs$Name) %>%
addMarkers(data = schools_wgs,
lng = ~st_coordinates(geometry)[,1],
lat = ~st_coordinates(geometry)[,2],
popup = ~Name)