Density2d绘图使用另一个填充变量（类似于geom_tile）？
我正在尝试为我的最终项目绘制一张地图，我正试图在美国制作BLock的犯罪热图。
对于每个街区，我都有Lat，Lon和犯罪率的预测。它遵循这种结构：
Lat / Lon / Prediction
-76.0 / 40.0 / 125
-76.120 / 40.5 / 145
-75.98 / 41.001 / 95
等等。
有没有办法绘制热图，显示预测为填充？
我认为这就是geom_tiles所做的，但是geom不起作用（可能是因为点间距不均匀）
任何帮助都会受到欢迎。请！
修改
这是我到目前为止所尝试的：
-geom_density2d：
ggplot(ny2,aes(x=GEO_CENTROID_LON,y=GEO_CENTROID_LON,fill=prediction))+geom_density2d()
给我错误：“单位错误（tic_pos.c，”mm“）：'x'和'units'的长度必须为＆gt; 0”
-geom_tiles：
ggplot(ny2,aes(x=GEO_CENTROID_LON,y=GEO_CENTROID_LON,fill=prediction))+geom_tile()
生成具有适当比例的绘图，但不生成地图上显示的数据。
关于chloropeth，如果我发生整个美国的块级信息，它会起作用，但我找不到这样的数据。
可以找到数据的SUBSAMPLE here
2 个答案:
答案 0 :(得分：27)
首先，让我们加载数据：
data<-read.csv(file = "NY subsample.csv")
数据点
然后，让我们尝试绘制数据的基本位置和值：
require('ggplot2')
# start with points
pred.points <- ggplot(data = data,
aes(x = GEO_CENTROID_LON,
y = GEO_CENTROID_LAT,
colour = prediction)) +
geom_point()
print(pred.points)
ggsave(filename = "NYSubsamplePredPoints.png",
plot = p2,
scale = 1,
width = 5, height = 3,
dpi = 300)
给了我们这个：
分类数据
然后，您可以尝试使用stat_summary2d()：
pred.stat <- ggplot(data = data,
aes(x = GEO_CENTROID_LON,
y = GEO_CENTROID_LAT,
z = prediction)) +
stat_summary2d(fun = mean)
print(pred.stat)
ggsave(filename = "NYSubsamplePredStat.png",
plot = pred.stat,
scale = 1,
width = 5, height = 3,
dpi = 300)
给出了每个方框中预测平均值的这个图。
Binned并使用自定义色彩映射和正确投影
接下来，我们可以设置容器大小，色标和修正投影：
# refine breaks and palette ----
require('RColorBrewer')
YlOrBr <- c("#FFFFD4", "#FED98E", "#FE9929", "#D95F0E", "#993404")
pred.stat.bin.width <- ggplot(data = data,
aes(x = GEO_CENTROID_LON,
y = GEO_CENTROID_LAT,
z = prediction)) +
stat_summary2d(fun = median, binwidth = c(.05, .05)) +
scale_fill_gradientn(name = "Median",
colours = YlOrBr,
space = "Lab") +
coord_map()
print(pred.stat.bin.width)
ggsave(filename = "NYSubsamplePredStatBinWidth.png",
plot = pred.stat.bin.width,
scale = 1,
width = 5, height = 3,
dpi = 300)
给了我们这个：
在基本地图上绘制
最后，这里的数据覆盖在地图上。
require('ggmap')
map.in <- get_map(location = c(min(data$GEO_CENTROID_LON),
min(data$GEO_CENTROID_LAT),
max(data$GEO_CENTROID_LON),
max(data$GEO_CENTROID_LAT)),
source = "osm")
theme_set(theme_bw(base_size = 8))
pred.stat.map <- ggmap(map.in) %+% data +
aes(x = GEO_CENTROID_LON,
y = GEO_CENTROID_LAT,
z = prediction) +
stat_summary2d(fun = median,
binwidth = c(.05, .05),
alpha = 0.5) +
scale_fill_gradientn(name = "Median",
colours = YlOrBr,
space = "Lab") +
labs(x = "Longitude",
y = "Latitude") +
coord_map()
print(pred.stat.map)
ggsave(filename = "NYSubsamplePredStatMap.png",
plot = pred.stat.map,
scale = 1,
width = 5, height = 3,
dpi = 300)
设置色彩映射
最后，要将色彩映射设置为http://www.cadmaps.com/images/HeatMapImage.jpg，我们可以猜测色彩映射：
colormap <- c("Violet","Blue","Green","Yellow","Red","White")
再次进行绘图：
pred.stat.map.final <- ggmap(map.in) %+% data +
aes(x = GEO_CENTROID_LON,
y = GEO_CENTROID_LAT,
z = prediction) +
stat_summary2d(fun = median,
binwidth = c(.05, .05),
alpha = 1.0) +
scale_fill_gradientn(name = "Median",
colours = colormap,
space = "Lab") +
labs(x = "Longitude",
y = "Latitude") +
coord_map()
print(pred.stat.map.final)
答案 1 :(得分：2)
我仍然不确定你的目标是什么，但是如果你想要一张带有一些点和轮廓的地图，那是可能的。输出看起来像下面的截图;显然有很多方法可以调整。 （显示的州是康涅狄格州。）
代码如下：
library(ggplot2)
library(maps)
mytext <- "GEOGRAPHY_ID GEO_CENTROID_LAT GEO_CENTROID_LON prediction
90012003024 41.364744 -73.373055 90.1
90012052001 41.488312 -73.381211 97.5
90012452002 41.300794 -73.467077 70.8
90012452003 41.319154 -73.479132 95.7
90012453001 41.293613 -73.456416 94.9
90012453003 41.284093 -73.493029 84.6
90012454001 41.263554 -73.462508 133.7
90010716002 41.181796 -73.196349 264.1
90010714003 41.183947 -73.198804 157.9
90010714004 41.185397 -73.200341 133.1
90010716001 41.184585 -73.195373 245.4
90010719001 41.195616 -73.203213 241.3
90010719002 41.191719 -73.20067 201.4
90010810001 41.222401 -73.155751 99.8
90012053002 41.437178 -73.396647 88.9
90012053003 41.451281 -73.413168 87.2
90012101001 41.400968 -73.458019 95.8
90012101002 41.394998 -73.449385 94.9
90012455002 41.270781 -73.52177 130
90012456001 41.360282 -73.530252 76
90012456002 41.288121 -73.507822 72.7
90012456003 41.313295 -73.528298 99.5
90010720002 41.187461 -73.206022 183.8
90010721001 41.189995 -73.216666 122.1
90010721003 41.180815 -73.216566 146.6
90010812001 41.233187 -73.117965 104.3
90012102003 41.393535 -73.440432 157.3
90012456004 41.296988 -73.522458 79.8
90012571001 41.613474 -73.503408 100.3
90012572001 41.203247 -73.202377 137.5
90012572002 41.194794 -73.192015 140
90010722002 41.195945 -73.210519 151.4
90010722003 41.193973 -73.214259 136.6
90010723001 41.203358 -73.20714 143.9
90010723002 41.198932 -73.206447 142.4
90010723003 41.203037 -73.212283 186.3
90010724001 41.208966 -73.201209 165.8
90010813001 41.246832 -73.131348 72.6
90010813003 41.246515 -73.111748 155.4
90012103003 41.402606 -73.440486 167.3
90012104002 41.390956 -73.434375 94.8
90012104004 41.381899 -73.431034 102.2
90012105001 41.369942 -73.437917 123.2
90012572003 41.195887 -73.197341 173.9
90012572004 41.198997 -73.199825 162.1
90010724002 41.209585 -73.20547 148.2
90010725001 41.212546 -73.215288 91.6
90010725002 41.208281 -73.212059 98.6
90010901002 41.287263 -73.250588 102.3
90010902003 41.266822 -73.23491 92
90012105002 41.353974 -73.458436 99
90012106001 41.383904 -73.465779 73.8
90012106002 41.382667 -73.457351 83.2
90012106003 41.387105 -73.458921 189.7
90010603003 41.211193 -73.280762 90.6
90010604001 41.1995 -73.306179 92.5
90010604002 41.181699 -73.295321 88.5
90010604003 41.171775 -73.283657 74.7
90010726004 41.222752 -73.235936 95.1
90010726005 41.211959 -73.228826 161.5
90010727001 41.225968 -73.207215 131.2
90010727002 41.214759 -73.208042 132.3
90012106004 41.390604 -73.45726 162.3
90012107012 41.399524 -73.464468 133.7
90012107013 41.396556 -73.471967 99.7
90012107021 41.386497 -73.468571 79
90010604004 41.167694 -73.309524 84.1
90010604005 41.195134 -73.325438 83.8
90010605001 41.155009 -73.2852 84.2
90010606001 41.139112 -73.283955 110.6
90010607002 41.147077 -73.261239 94.2
90010728001 41.224439 -73.198242 99.1
90010728002 41.217875 -73.197045 202.6
90010728003 41.210812 -73.195182 190.9
90010729001 41.216106 -73.185495 102.2
90010729002 41.221654 -73.19115 120.5
90010904004 41.228466 -73.191144 129.8
90010905001 41.245708 -73.153402 94.4
90012107022 41.390499 -73.47572 122
90012108003 41.395869 -73.494013 169
90010607004 41.162216 -73.266692 108.4
90010729003 41.208007 -73.192118 119.6
90010731002 41.209897 -73.173502 200.1
90010732001 41.199521 -73.157307 104
90010907002 41.292969 -73.213286 97.2
90012109002 41.422006 -73.509898 130.3
90012109003 41.408571 -73.469713 96.7
90012110002 41.427622 -73.478071 71
90010732002 41.199779 -73.15968 129.9
90010733001 41.194838 -73.161245 220.4
90010733002 41.196553 -73.167201 223.9
90010734001 41.200568 -73.177405 167.6
90010734003 41.202783 -73.185394 132.6
90011002001 41.320343 -73.199107 84.5
90011002002 41.3086 -73.220255 88
90011002004 41.331716 -73.221468 78.4
90012113001 41.445838 -73.445831 79.8
90010612001 41.179606 -73.224896 81.8
90010612002 41.181751 -73.232609 87.5
90010613001 41.175294 -73.231124 101
90010613002 41.173877 -73.239615 81.5
90010735001 41.196816 -73.182355 201
90010735002 41.193029 -73.183216 175.8
90010735003 41.19529 -73.184959 231.1
90010736001 41.194455 -73.177717 198.3
90011003002 41.341963 -73.192365 77.7
90011051001 41.233168 -73.263024 123.6
90011052002 41.285673 -73.287781 111.5
90012113003 41.441636 -73.436266 116.9
90012114001 41.430746 -73.428211 78.7
90012114002 41.447087 -73.427341 108.3
90012114003 41.422319 -73.420856 65.4
90010614002 41.167362 -73.237612 95.1
90010615001 41.154024 -73.24035 146
90010616001 41.137264 -73.264706 72.9
90010737003 41.190196 -73.163451 174.4
90010737004 41.191454 -73.168643 136.9
90010737005 41.188779 -73.168872 125.4
90010738001 41.190175 -73.175898 149.3
90010738002 41.188112 -73.176438 104.1
90011102011 41.30204 -73.07854 93.2
90011102021 41.292759 -73.089266 221.6
90011102022 41.277148 -73.094239 209
90012201003 41.450671 -73.524399 95.3
90012201004 41.464062 -73.509055 91.4
90012202001 41.494752 -73.495944 71.2
90010616002 41.131092 -73.255091 114.8
90010616003 41.126782 -73.269031 116.1
90010701001 41.155218 -73.22345 222
90010701003 41.1547 -73.228366 121.2
90010701004 41.158021 -73.229301 119.4
90010738003 41.189804 -73.178993 120.1
90010739001 41.190496 -73.1826 120
90010739002 41.187738 -73.18267 173.6
90010739003 41.186232 -73.18633 218.8
90010739004 41.190055 -73.185757 115.7
90010740001 41.182438 -73.180944 187.2
90010740002 41.181587 -73.17741 145.9
90012203001 41.521653 -73.470862 103.1
90012203002 41.487018 -73.45847 95.8
90012301002 41.442759 -73.311749 103.8
90010702001 41.161921 -73.22484 130.6
90010702002 41.163873 -73.22144 137.8
90010702003 41.15867 -73.219191 181.1
90010703001 41.160754 -73.21476 215.2
90010705001 41.167282 -73.191851 175
90010743001 41.18634 -73.159732 166.9
90010743002 41.182201 -73.158335 109
90010743003 41.180505 -73.162622 152.2
90010743004 41.178543 -73.168002 157.3
90010743005 41.181 -73.168064 143.4
90010743006 41.182831 -73.16868 122.4
90011103022 41.269651 -73.130548 88.6
90011104001 41.294908 -73.161871 111.3
90012302001 41.413873 -73.304276 93.1
90012303003 41.387196 -73.321197 118.5
90010705002 41.167522 -73.196366 213.2
90010706001 41.177971 -73.193067 236.6
90010706002 41.167732 -73.187421 227.7
90010709001 41.170459 -73.203877 159.7
90010709002 41.17138 -73.198856 186.7
90010710001 41.171339 -73.218595 126.6
90010744001 41.179957 -73.157483 135.6
90010744002 41.177069 -73.162353 133.4
90010744003 41.172159 -73.168866 185.4
90010744004 41.176011 -73.16636 169.5
90010801001 41.200673 -73.150037 105.6
90010801003 41.193386 -73.151688 140.3
90011105004 41.309581 -73.141151 77.3
90012304001 41.384815 -73.294462 118.4
90012304002 41.36671 -73.293527 94.8
90012304003 41.368872 -73.348572 194.7
90012305011 41.406865 -73.223661 89.3
90010710002 41.172463 -73.214353 184.7
90010712001 41.180827 -73.207841 133.3
90010712002 41.179002 -73.205237 206.9
90010801004 41.199783 -73.15404 140.2
90010802001 41.190012 -73.142391 106.3
90010802002 41.189527 -73.147431 86.6
90010802003 41.188494 -73.152008 121.6
90010804001 41.182524 -73.140233 153.5
90010804002 41.177371 -73.137682 151.9
90012002001 41.381013 -73.410319 87.5
90012305021 41.375831 -73.251305 105.6
90012305023 41.379858 -73.219021 96.8
90010712003 41.177009 -73.202378 168.4
90010712004 41.173824 -73.203364 197.4
90010713001 41.18072 -73.201927 184
90010713002 41.178332 -73.198693 183.5
90010714001 41.188573 -73.196834 115.9
90010714002 41.185972 -73.201774 118.1
90010805002 41.152953 -73.123918 71.8
90010806001 41.185641 -73.129554 112.4
90012003021 41.36255 -73.396786 86.7
90012003022 41.351419 -73.408962 154
90012402002 41.317929 -73.400807 91.5
90012402003 41.300946 -73.348506 126.8
90010202001 41.134991 -73.600739 118.3
90010202002 41.114341 -73.58158 84.7
90010203001 41.154604 -73.545957 90.1
90010203003 41.15133 -73.581789 109.2
90010353001 41.126492 -73.477104 191.2
90010353002 41.129396 -73.513016 221
90010354001 41.192382 -73.482245 120.4
90010354002 41.160459 -73.474248 114.7
90010354003 41.144611 -73.464233 216.9
90010354004 41.186241 -73.495502 88.6
90010503004 41.149218 -73.336642 192.9
90010503005 41.169512 -73.351917 140.6
90010504002 41.116467 -73.375013 85.9
90010505002 41.116352 -73.352404 85.4
90010203004 41.136534 -73.573863 86.2
90010205002 41.066638 -73.562444 77.2
90010425001 41.160707 -73.404989 125.9
90010425003 41.144366 -73.403332 121.1
90010426003 41.133452 -73.385111 81.7
90010505003 41.111412 -73.358066 80.8
90010505004 41.129533 -73.36223 82.9
90010551001 41.233676 -73.362879 108.9
90010206001 41.103581 -73.554734 85.2
90010206003 41.085439 -73.552623 82.5
90010426004 41.12787 -73.392559 136.8
90010427001 41.149039 -73.424079 107
90010428002 41.131288 -73.416989 91
90010551003 41.21145 -73.381718 77.7
90010551004 41.247205 -73.406188 77.1
90010552001 41.213714 -73.338138 108.3
90010552003 41.201885 -73.365519 115.7
90010208003 41.087838 -73.544622 77.2
90010209001 41.098298 -73.515074 83.6
90010428004 41.127102 -73.399138 85.3
90010429001 41.149545 -73.437497 77
90010430002 41.12977 -73.433261 106.9
90010602003 41.193534 -73.252047 60.8
90010602004 41.187197 -73.257167 141.4
90010210001 41.086533 -73.521131 112.1
90010211001 41.072121 -73.517654 101.2
90010211003 41.075939 -73.52826 78.2
90010431001 41.102395 -73.446269 83.8
90010431002 41.1173 -73.459513 73.3
90010432002 41.110378 -73.442314 92.3
90010212002 41.070902 -73.543556 89.6
90010213001 41.070755 -73.553697 110.1
90010213003 41.057543 -73.550212 98.6
90010433002 41.120549 -73.433645 95.1
90010434001 41.124754 -73.414431 91.3
90010434002 41.122855 -73.419197 87.9
90010434003 41.129448 -73.422258 98.3
90010435001 41.125176 -73.386176 144.2
90010214001 41.053676 -73.554293 202.3
90010214002 41.050584 -73.555773 176.2
90010214003 41.04669 -73.557427 103
90010214004 41.053666 -73.562879 172.1
90010215001 41.053793 -73.54926 145.9
90010215002 41.050154 -73.548156 139.8
90010435002 41.120456 -73.394265 87.9
90010435003 41.117986 -73.388516 78.1
90010436002 41.111907 -73.404062 183.2
90010437001 41.117371 -73.414574 202.5
90010215003 41.046584 -73.550538 148.7
90010215004 41.050229 -73.551746 126
90010216001 41.063583 -73.538563 185.4
90010216002 41.060054 -73.543822 132.2
90010216004 41.062798 -73.535453 124.6
90010437002 41.11312 -73.413212 213.7
90010438001 41.114109 -73.420907 167.6
90010438002 41.114952 -73.424774 122.8
90010438003 41.109739 -73.423166 149.8
90010438004 41.10928 -73.42707 137.6
90010101023 41.117859 -73.646129 87.8
90010102013 41.061299 -73.62279 157
90010217001 41.056839 -73.527159 171.4
90010217003 41.055427 -73.533343 129.1
90010217004 41.057926 -73.534384 138.6
90010217005 41.057235 -73.529757 104.5
90010218011 41.067416 -73.5255 144.2
90010439001 41.097703 -73.435218 153.8
90010439004 41.08607 -73.443137 68.4
90010440001 41.10246 -73.422491 199.8
90010102022 41.05194 -73.591855 97.6
90010103002 41.03803 -73.628652 87.4
90010218012 41.063118 -73.529794 118.4
90010218013 41.062331 -73.523699 120.1
90010218023 41.059329 -73.520307 107.8
90010219001 41.057436 -73.508325 96.8
90010440002 41.102279 -73.426726 201.9
90010440004 41.096429 -73.424339 166.7
90010440005 41.091685 -73.428074 265.9
90010441001 41.09657 -73.419621 174.2
90010441002 41.095089 -73.417375 165.8
90010103005 41.029176 -73.648761 130.3
90010220002 41.053379 -73.518227 148.5
90010221001 41.051607 -73.522784 157.5
90010442002 41.104538 -73.408893 154.1
90010442003 41.100721 -73.407695 121.1
90010105004 41.018874 -73.641155 144.3
90010221002 41.046457 -73.522742 133.3
90010221003 41.039903 -73.52882 126.5
90010223001 41.038742 -73.54947 135.6
90010223002 41.0331 -73.548214 78.3"
ny2 <- read.table(textConnection(mytext), sep = " ",
check.names = FALSE,
strip.white = TRUE,
header = TRUE)
names(ny2) <- c('id','lat','long','prediction')
us.states <- map_data("state")
plot.states <- c('connecticut') # subset to Connecticut for ease of use in this example
p <- ggplot() +
geom_polygon(data = us.states[us.states$region %in% plot.states, ],
aes(x = long, y = lat, group = group),
colour = "white", fill = "lightblue") +
geom_point(data = ny2, aes(x = long, y = lat,
colour = prediction), group = NULL) +
stat_density2d(data = ny2, aes(x = long, y = lat), size = 0.02) +
coord_map() +
theme()
print(p)
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