时间戳

首页 > 解决方案 > R - 在城市地图上拟合网格并将数据输入到网格方块中

r - R - 在城市地图上拟合网格并将数据输入到网格方块中

问题描述

我正在尝试像这样在圣何塞上放置一个网格：

圣何塞网格

您可以使用以下代码直观地制作网格：

  ca_cities = tigris::places(state = "CA") #using tigris package to get shape file of all CA cities

  sj = ca_cities[ca_cities$NAME == "San Jose",] #specifying to San Jose

  UTM_ZONE = "10" #the UTM zone for San Jose, will be used to convert the proj4string of sj into UTM

  main_sj = sj@polygons[[1]]@Polygons[[5]] #the portion of the shape file I focus on. This is the boundary of san jose

  #converting the main_sj polygon into a spatialpolygondataframe using the sp package
  tst_ps = sp::Polygons(list(main_sj), 1)
  tst_sps = sp::SpatialPolygons(list(tst_ps))
  proj4string(tst_sps) = proj4string(sj)
  df = data.frame(f = 99.9)
  tst_spdf = sp::SpatialPolygonsDataFrame(tst_sps, data = df)

  #transforming the proj4string and declaring the finished map as "map"
  map = sp::spTransform(tst_sps, CRS(paste0("+proj=utm +zone=",UTM_ZONE," ellps=WGS84")))

  #designates the number of horizontal and vertical lines of the grid
  NUM_LINES_VERT = 25
  NUM_LINES_HORZ = 25
  #getting bounding box of map
  bbox = map@bbox
  #Marking the x and y coordinates for each of the grid lines.
  x_spots = seq(bbox[1,1], bbox[1,2], length.out = NUM_LINES_HORZ)
  y_spots = seq(bbox[2,1], bbox[2,2], length.out = NUM_LINES_VERT)

  #creating the coordinates for the lines. top and bottom connect to each other. left and right connect to each other
  top_vert_line_coords = expand.grid(x = x_spots, y = y_spots[1])
  bottom_vert_line_coords = expand.grid(x = x_spots, y = y_spots[length(y_spots)])
  left_horz_line_coords = expand.grid(x = x_spots[1], y = y_spots)
  right_horz_line_coords = expand.grid(x = x_spots[length(x_spots)], y = y_spots)

  #creating vertical lines and adding them all to a list
  vert_line_list = list()
  for(n in 1 : nrow(top_vert_line_coords)){
    vert_line_list[[n]] = sp::Line(rbind(top_vert_line_coords[n,], bottom_vert_line_coords[n,]))
  }

  vert_lines = sp::Lines(vert_line_list, ID = "vert") #creating Lines object of the vertical lines

  #creating horizontal lines and adding them all to a list
  horz_line_list = list()
  for(n in 1 : nrow(top_vert_line_coords)){
    horz_line_list[[n]] = sp::Line(rbind(left_horz_line_coords[n,], right_horz_line_coords[n,]))
  }

  horz_lines = sp::Lines(horz_line_list, ID = "horz") #creating Lines object of the horizontal lines

  all_lines = sp::Lines(c(horz_line_list, vert_line_list), ID = 1) #combining horizontal and vertical lines into a single grid format

  grid_lines = sp::SpatialLines(list(all_lines)) #converting the lines object into a Spatial Lines object
  proj4string(grid_lines) = proj4string(map) #ensuring the projections are the same between the map and the grid lines.

  trimmed_grid = intersect(grid_lines, map) #grid that shapes to the san jose map

  plot(map) #plotting the map of San Jose
  lines(trimmed_grid) #plotting the grid

但是，我正在努力将每个网格“正方形”（一些网格块不是正方形，因为它们适合圣何塞地图的形状）变成我可以输入数据的 bin。换句话说，如果每个网格“正方形”的编号为 1:n，那么我可以制作这样的数据框：

  grid_id num_assaults num_thefts
1       1          100         89
2       2           55        456
3       3           12       1321
4       4           48        498
5       5           66          6

并用数据填充每个网格“正方形”，每个犯罪事件的点位置，希望使用包中的over()功能sp。

我已经尝试解决这个问题好几个星期了，但我无法弄清楚。我一直在寻找一个简单的解决方案，但我似乎找不到它。任何帮助，将不胜感激。

标签： rgeospatialspatialrastersp

解决方案

此外，这是一个基于 sf 和 tidyverse 的解决方案：

使用 sf，您可以使用该st_make_grid()功能制作一个正方形网格。在这里，我将在圣何塞的边界框上创建一个 2 公里的网格，然后将其与圣何塞的边界相交。请注意，我正在投影到 UTM 区域 10N，因此我可以以米为单位指定网格大小。 

library(tigris)
library(tidyverse)
library(sf)
options(tigris_class = "sf", tigris_use_cache = TRUE)
set.seed(1234)

sj <- places("CA", cb = TRUE) %>%
  filter(NAME == "San Jose") %>%
  st_transform(26910)

g <- sj %>%
  st_make_grid(cellsize = 2000) %>%
  st_intersection(sj) %>%
  st_cast("MULTIPOLYGON") %>%
  st_sf() %>%
  mutate(id = row_number())

接下来，我们可以生成一些随机犯罪数据st_sample()并绘制它以查看我们正在处理的内容。 

thefts <- st_sample(sj, size = 500) %>%
  st_sf()

assaults <- st_sample(sj, size = 200) %>%
  st_sf()

plot(g$geometry)
plot(thefts, add = TRUE, col = "red")

在此处输入图像描述

然后犯罪数据可以在空间上加入到网格中st_join()。我们可以绘图来检查我们的结果。 

theft_grid <- g %>%
  st_join(thefts) %>%
  group_by(id) %>%
  summarize(num_thefts = n())

plot(theft_grid["num_thefts"])

在此处输入图像描述

然后我们可以对攻击数据执行相同的操作，然后将两个数据集连接在一起以获得所需的结果。如果您有大量犯罪数据集，则可以修改这些数据集以在purrr::map(). 

assault_grid <- g %>%
  st_join(assaults) %>%
  group_by(id) %>%
  summarize(num_assaults = n()) 

st_geometry(assault_grid) <- NULL

crime_data <- left_join(theft_grid, assault_grid, by = "id")

crime_data

Simple feature collection with 190 features and 3 fields
geometry type:  GEOMETRY
dimension:      XY
bbox:           xmin: 584412 ymin: 4109499 xmax: 625213.2 ymax: 4147443
epsg (SRID):    26910
proj4string:    +proj=utm +zone=10 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
# A tibble: 190 x 4
      id num_thefts num_assaults                                                     geometry
   <int>      <int>        <int>                                               <GEOMETRY [m]>
 1     1          2            1 POLYGON ((607150.3 4111499, 608412 4111499, 608412 4109738,…
 2     2          4            1 POLYGON ((608412 4109738, 608412 4111499, 609237.8 4111499,…
 3     3          3            1 POLYGON ((608412 4113454, 608412 4111499, 607150.3 4111499,…
 4     4          2            2 POLYGON ((609237.8 4111499, 608412 4111499, 608412 4113454,…
 5     5          1            1 MULTIPOLYGON (((610412 4112522, 610412 4112804, 610597 4112…
 6     6          1            1 POLYGON ((616205.4 4113499, 616412 4113499, 616412 4113309,…
 7     7          1            1 MULTIPOLYGON (((617467.1 4113499, 618107.9 4113499, 617697.…
 8     8          2            1 POLYGON ((605206.8 4115499, 606412 4115499, 606412 4114617,…
 9     9          5            1 POLYGON ((606412 4114617, 606412 4115499, 608078.2 4115499,…
10    10          1            1 POLYGON ((609242.7 4115499, 610412 4115499, 610412 4113499,…
# ... with 180 more rows

推荐阅读