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计算 shapefile 中的密度

[英]Calculating density within a shapefile

I'm trying to calculate density within a shapefile, but I'm fairly confident I'm doing it wrong.我正在尝试计算 shapefile 中的密度,但我很有信心我做错了。 The idea is to figure out which geographical regions there have been the most sales by density.这个想法是通过密度找出哪些地理区域的销售额最高。

Here is a link to the file that I use (testdata.shp)这是我使用的文件的链接(testdata.shp)

library(sf)

sample <- st_read("testdata.shp")

sample$area <- st_area(sample$geometry)

density_calc <-sample %>% st_buffer(0) %>% group_by(areas) %>% summarise(`Sales (density)` = sum(sales)/sum(area))

Here are the details of the shapefile:以下是 shapefile 的详细信息:

Geometry set for 2106 features 
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -120.0065 ymin: 35.00184 xmax: -114.0396 ymax: 42.00221
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs

I guess my issue is, I don't really know what is right and wrong, so I have no clue if I did it correctly.我想我的问题是,我真的不知道什么是对什么是错,所以我不知道我是否做对了。

Sorry if it's not the most extensive question, I just don't remember my high school geometry that well!对不起,如果这不是最广泛的问题,我只是不太记得我的高中几何!

the raster package helps make this calculation very easy and just like working with a data.frame in R : raster包有助于使这个计算变得非常简单,就像在R使用 data.frame 一样:

library(raster)
list.files(workDir)
test_shp <- shapefile(file.path(workDir, 'testdata.shp'))
names(test_shp)
#[1] "distrct"       "sbdstrc"       "terrtry"      
#[4] "region"        "turf"          "sales"        
#[7] "leads"         "cnvrsns"       "areas" 

sum(is.na(test_shp$sales)) #note that 346 polygons have no sales data

#get the area as square kilometers
test_shp$km2 <- area(test_shp) / 10000

#calc the sales density
test_shp$sales_density <- test_shp$sales / test_shp$km2

#calculate the 25th, 50th, and 75th percentile of all polygons
quartiles <- quantile(test_shp$sales_density, probs=c(0.25, 0.5, 0.75), na.rm=TRUE) 

#plot the result, coloring by which percentile the sales density is for a given polygon 
plot(test_shp, col=ifelse(is.na(test_shp$sales_density), 'gray', ifelse(test_shp$sales_density >= quartiles[3], 'dark green', ifelse(test_shp$sales_density >= quartiles[2], 'light green', ifelse(test_shp$sales_density >= quartiles[1], 'yellow', 'red')))), border='transparent')  (eg. >75th, 50-75th, etc.)

#add the legend
legend('bottomleft', legend=c('Q4', 'Q3', 'Q2', 'Q1', 'No data'), pch=15, col=c('dark green', 'light green', 'yellow', 'red', 'gray'))

销售密度结果

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