R Notebook

rm(list = ls())
library(readxl)
library(dplyr)
library(sf)
library(ggplot2)
library(tidyverse)
library(tmap)
library(pals)
library(cartography)
library(RColorBrewer)
library(reshape2)
library(grid)
library(units)
library(tidyr)
library(aspace)
library(spatstat)
library(spdep)
library(readr)

school_sf=st_read("C:/Users/User/Desktop/邱冠瑋/台大/大二下/空間分析/期末/SCHOOL/SCHOOL.shp",options="ENCODING=BIG5",quiet=T)

tpe_sf=st_read("C:/Users/User/Desktop/邱冠瑋/台大/大二下/空間分析/期末/Taipei_vill/Taipei_Vill.shp",options="ENCODING=BIG5",quiet=T)

# 讀檔：指定中文 BIG5 編碼 + 完整路徑
accident <- read.csv("C:/Users/User/Desktop/邱冠瑋/台大/大二下/空間分析/期末/113年臺北市道路交通事故斑點圖.csv",fileEncoding = "BIG5")

# 轉為 sf 點資料
accident_sf <- st_as_sf(accident, coords = c("座標.X", "座標.Y"), crs = 4326)

school_sf <- st_transform(school_sf, 3826)
accident_sf <- st_transform(accident_sf, 3826)

# 1. 建立 500 公尺 buffer
school_buffer <- st_buffer(school_sf, dist = 200)

# 2. 將事故點與 buffer 做空間交集
acc_in_buffer <- st_join(accident_sf, school_buffer, join = st_within, left = FALSE)

# 3. 計算每間學校 buffer 內的事故數量
acc_count <- acc_in_buffer %>%
  st_drop_geometry() %>%
  group_by(SID, Name) %>%
  summarise(acc_num = n(), .groups = "drop") %>%
  arrange(desc(acc_num))

# 4. 顯示事故數最多的前幾間學校
print(head(acc_count, 10))

NA

# 加上行政區資訊到 school_sf（空間 Join）
school_sf <- st_join(school_sf, tpe_sf[, c("TOWN", "VILLAGE")], join = st_within, left = FALSE)
# STEP 1：找出事故最多的學校
top_sid <- acc_count$SID[1]
top_school <- school_sf %>% filter(SID == top_sid)

# STEP 2：建立該校的 500 公尺 buffer
top_buffer <- st_buffer(top_school, dist = 200)

# STEP 3：找出 buffer 有相交的所有村里
focus_village <- tpe_sf[st_intersects(tpe_sf, top_buffer, sparse = FALSE), ]

# STEP 4：找出 buffer 包含的車禍點
top_acc <- accident_sf[top_buffer, ]

# STEP 5：繪圖
tm_shape(focus_village) + 
  tm_borders(col = "gray60", lwd = 1.5) +

tm_shape(top_buffer) + 
  tm_borders(lwd = 2, col = "blue") +

tm_shape(top_acc) +
  tm_dots(col = "black", size = 0.04, alpha = 0.6) +

tm_shape(top_school) +
  tm_symbols(size = 0.5, col = "red", shape = 19, border.col = "black") +  # 確保繪圖格式

tm_layout(main.title = paste0("事故最多學校周邊分布：", top_school$Name),
          legend.outside = TRUE)

── tmap v3 code detected ───────────────────────────────────────────────────
[v3->v4] `tm_dots()`: use `fill_alpha` instead of `alpha`.[v3->v4] `tm_layout()`: use `tm_title()` instead of `tm_layout(main.title =
)`