在R中创建LISA空间簇的地图 [英] Create a map of spatial clusters LISA in R

问题描述

我想创建一个显示现象的局部空间簇的地图，最好使用Local Moran(LISA).

I would like to create a map showing local spatial cluster of a phenomenon, preferably using Local Moran (LISA).

在下面的可重现示例中，我使用spdep计算局部莫兰指数，但我想知道是否有简单的方法(最好是使用ggplot2)来绘制团簇.帮助吗?

In the reproducible example below, I calculate the local moran's index using spdep but I would like to know if there is as simple way to map the clustes, prefebly using ggplot2. Help ?

library(UScensus2000tract)
library(ggplot2)
library(spdep)

# load data
data("oregon.tract")

# plot Census Tract map
plot(oregon.tract)

# create  Queens contiguity matrix
spatmatrix <- poly2nb(oregon.tract)

#calculate the local moran of the distribution of black population
lmoran <- localmoran(oregon.tract@data$black, nb2listw(spatmatrix))

现在要使该示例与真实数据集更加相似，我的形状文件中有一些NA值，它们表示多边形中的孔，因此不应在计算中使用这些区域.

Now to make this example more similar to my real dataset, I have some NA values in my shape file, which represent holes in the polygon, so these areas shouldn't be used in the calculation.

oregon.tract@data$black[3:5] <- NA

推荐答案

这是一种策略:

library(UScensus2000tract)
library(spdep)
library(ggplot2)
library(dplyr)

# load data
data("oregon.tract")
# plot Census Tract map
plot(oregon.tract)

# create Queens contiguity matrix
spatmatrix <- poly2nb(oregon.tract)

# create a neighbours list with spatial weights
listw <- nb2listw(spatmatrix)

# calculate the local moran of the distribution of white population
lmoran <- localmoran(oregon.tract$white, listw)
summary(lmoran)

# padronize the variable and save it to a new column
oregon.tract$s_white <- scale(oregon.tract$white)  %>% as.vector()

# create a spatially lagged variable and save it to a new column
oregon.tract$lag_s_white <- lag.listw(listw, oregon.tract$s_white)

# summary of variables, to inform the analysis
summary(oregon.tract$s_white)
summary(oregon.tract$lag_s_white)

# moran scatterplot, in basic graphics (with identification of influential observations)
x <- oregon.tract$s_white
y <- oregon.tract$lag_s_white %>% as.vector()
xx <- data.frame(x, y)

moran.plot(x, listw)

# moran sccaterplot, in ggplot 
# (without identification of influential observations - which is possible but requires more effort)
ggplot(xx, aes(x, y)) + geom_point() + geom_smooth(method = 'lm', se = F) + geom_hline(yintercept = 0, linetype = 'dashed') + geom_vline(xintercept = 0, linetype = 'dashed') 

# create a new variable identifying the moran plot quadrant for each observation, dismissing the non-significant ones
oregon.tract$quad_sig <- NA

# high-high quadrant
oregon.tract[(oregon.tract$s_white >= 0 & 
                 oregon.tract$lag_s_white >= 0) & 
                (lmoran[, 5] <= 0.05), "quad_sig"] <- "high-high"
# low-low quadrant
oregon.tract[(oregon.tract$s_white <= 0 & 
                 oregon.tract$lag_s_white <= 0) & 
                (lmoran[, 5] <= 0.05), "quad_sig"] <- "low-low"
# high-low quadrant
oregon.tract[(oregon.tract$s_white >= 0 & 
                 oregon.tract$lag_s_white <= 0) & 
                (lmoran[, 5] <= 0.05), "quad_sig"] <- "high-low"
# low-high quadrant
oregon.tract@data[(oregon.tract$s_white <= 0 
               & oregon.tract$lag_s_white >= 0) & 
                (lmoran[, 5] <= 0.05), "quad_sig"] <- "low-high"
# non-significant observations
oregon.tract@data[(lmoran[, 5] > 0.05), "quad_sig"] <- "not signif."  

oregon.tract$quad_sig <- as.factor(oregon.tract$quad_sig)
oregon.tract@data$id <- rownames(oregon.tract@data)

# plotting the map
df <- fortify(oregon.tract, region="id")
df <- left_join(df, oregon.tract@data)
df %>% 
  ggplot(aes(long, lat, group = group, fill = quad_sig)) + 
  geom_polygon(color = "white", size = .05)  + coord_equal() + 
  theme_void() + scale_fill_brewer(palette = "Set1")

此答案基于此页面，由 Eli Knaap在Twitter上，并且还从@timelyportfolio的回答中借用了这个问题.

This answer was based on this page, suggested by Eli Knaap on twitter, and also borrowed from the answer by @timelyportfolio to this question.

我使用变量white代替了black，因为black的显式结果较少.

I used the variable white instead of black because black had less explicit results.

关于NA，localmoran()包含参数na.action，文档对此进行了说明:

Concerning NAs, localmoran() includes the argument na.action, about which the documentation says:

na.action是一个函数(默认为na.fail)，也可以为na.omit或> na.exclude-在这些情况下，权重列表将被子集化以删除数据中的NA.可能有必要将zero.policy设置为TRUE，因为此子集可能创建无邻居观察.请注意，只有在不使用nb2listw的glist参数的情况下创建的权重列表可以被子集化.如果使用na.pass，则在计算空间滞后时将NA值替换为零.

na.action is a function (default na.fail), can also be na.omit or > na.exclude - in these cases the weights list will be subsetted to remove NAs in the data. It may be necessary to set zero.policy to TRUE because this subsetting may create no-neighbour observations. Note that only weights lists created without using the glist argument to nb2listw may be subsetted. If na.pass is used, zero is substituted for NA values in calculating the spatial lag.

我尝试过:

oregon.tract@data$white[3:5] <- NA
lmoran <- localmoran(oregon.tract@data$white, listw, zero.policy = TRUE, 
                 na.action = na.exclude)

但是在lag.listw中遇到了问题，但是没有时间研究它.抱歉.

But run into problems in lag.listw but did not have time to look into it. Sorry.

这篇关于在R中创建LISA空间簇的地图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！