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# 分析目標：建立「學校 500 公尺內之速食店，應至少對應一間素食店」之空間對應策略


library(sf)
library(dplyr)
library(ggplot2)
library(tmap)

school <- st_read("C:/Users/13569/Downloads/SCHOOL (1)/SCHOOL.shp", options = "ENCODING=big5")

options:        ENCODING=big5 
Reading layer `SCHOOL' from data source `C:\Users\13569\Downloads\SCHOOL (1)\SCHOOL.shp' using driver `ESRI Shapefile'
Simple feature collection with 148 features and 4 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 297078.6 ymin: 2763290 xmax: 312516.7 ymax: 2784542
Projected CRS: TWD97 / TM2 zone 121

tpe_fastfood <- st_read("C:/Users/13569/Downloads/Tpe_Fastfood (1)/Tpe_Fastfood.shp", options = "ENCODING=big5")

options:        ENCODING=big5 
Reading layer `Tpe_Fastfood' from data source `C:\Users\13569\Downloads\Tpe_Fastfood (1)\Tpe_Fastfood.shp' using driver `ESRI Shapefile'
Simple feature collection with 98 features and 8 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 297198.9 ymin: 2763885 xmax: 312205.7 ymax: 2781148
Projected CRS: TWD97 / TM2 zone 121

tpe_vill <- st_read("C:/Users/13569/Downloads/Taipei_Vill/Taipei_Vill.shp", options = "ENCODING=big5")

options:        ENCODING=big5 
Reading layer `Taipei_Vill' from data source `C:\Users\13569\Downloads\Taipei_Vill\Taipei_Vill.shp' using driver `ESRI Shapefile'
Simple feature collection with 456 features and 8 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 296094.4 ymin: 2761518 xmax: 317198.9 ymax: 2789180
Projected CRS: TWD97 / TM2 zone 121

health_food <- st_read("C:/Users/13569/Downloads/Healthy_Food_Locations/Healthy_Food_Locations.shp")

Reading layer `Healthy_Food_Locations' from data source `C:\Users\13569\Downloads\Healthy_Food_Locations\Healthy_Food_Locations.shp' using driver `ESRI Shapefile'
Simple feature collection with 50 features and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 121.4432 ymin: 24.9735 xmax: 121.5807 ymax: 25.1475
Geodetic CRS:  WGS 84

school <- st_transform(school, 3826)
tpe_fastfood <- st_transform(tpe_fastfood, 3826)
tpe_vill <- st_transform(tpe_vill, 3826)
health_food <- st_transform(health_food, 3826)

school_buffer <- st_buffer(school, dist = 500)

fastfood_near_school <- st_join(tpe_fastfood, school_buffer, join = st_within, left = FALSE)

fastfood_buffer <- st_buffer(fastfood_near_school, dist = 500)

intersections <- st_intersects(fastfood_buffer, health_food)

fastfood_buffer$has_healthfood <- lengths(intersections) > 0
fastfood_buffer$need_policy <- ifelse(fastfood_buffer$has_healthfood, "已有健康點", "需設置健康店")


tmap_mode("plot")

ℹ tmap mode set to "plot".

tm_shape(tpe_vill) +
  tm_borders() +
  tm_shape(fastfood_buffer) +
  tm_fill(col = "need_policy", palette = c("green", "red"), title = "500m共存圈狀態") +
  tm_shape(tpe_fastfood) +
  tm_dots(col = "blue", size = 0.1, title = "速食店") +
  tm_shape(health_food) +
  tm_dots(col = "darkgreen", size = 0.1, title = "健康飲食據點") +
  tm_layout(title = "學校周邊的速食店附近是否有素食餐廳")

── 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>)'[tm_dots()] Argument `title` unknown.[tm_dots()] Argument `title` unknown.[v3->v4] `tm_layout()`: use `tm_title()` instead of `tm_layout(title = )`Multiple palettes called "green" found: "kovesi.green", "tableau.green". The first one, "kovesi.green", is returned.

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