基于运输时间的热图/等高线(反向等时轮廓)
M--*_*M-- 9 google-maps r google-api googleway travel-time
注意: python中的解决方案也适用于我.
我试图根据运输时间绘制轮廓.为了更清楚,我想将具有相似行程时间(比如说10分钟间隔)的点聚类到特定点(目的地)并将它们映射为轮廓或热图.
现在,我唯一的想法就是gmapsdistance找到不同来源的旅行时间,然后将它们聚类并在地图上绘制.但是,正如您所知,这绝不是一个强大的解决方案.
该线在GIS社区和这一次的蟒蛇说明了类似的问题,但对于内特定的时间范围原点目的地.我想找到我可以在一定时间内前往目的地的起源.
现在,下面的代码显示了我的基本想法:
library(gmapsdistance)
set.api.key("YOUR.API.KEY")
mdestination <- "40.7+-73"
morigin1 <- "40.6+-74.2"
morigin2 <- "40+-74"
gmapsdistance(origin = morigin1,
destination = mdestination,
mode = "transit")
gmapsdistance(origin = morigin2,
destination = mdestination,
mode = "transit")
此地图也可能有助于理解这个问题:
更新I:
使用这个答案,我可以从原点得到我可以去的点数,但是我需要反转它并找到旅行时间等于某一时间到目的地的点数;
library(httr)
library(googleway)
library(jsonlite)
appId <- "TravelTime_APP_ID"
apiKey <- "TravelTime_API_KEY"
mapKey <- "GOOGLE_MAPS_API_KEY"
location <- c(40, -73)
CommuteTime <- (5 / 6) * 60 * 60
url <- "http://api.traveltimeapp.com/v4/time-map"
requestBody <- paste0('{
"departure_searches" : [
{"id" : "test",
"coords": {"lat":', location[1], ', "lng":', location[2],' },
"transportation" : {"type" : "driving"} ,
"travel_time" : ', CommuteTime, ',
"departure_time" : "2017-05-03T07:20:00z"
}
]
}')
res <- httr::POST(url = url,
httr::add_headers('Content-Type' = 'application/json'),
httr::add_headers('Accept' = 'application/json'),
httr::add_headers('X-Application-Id' = appId),
httr::add_headers('X-Api-Key' = apiKey),
body = requestBody,
encode = "json")
res <- jsonlite::fromJSON(as.character(res))
pl <- lapply(res$results$shapes[[1]]$shell, function(x){
googleway::encode_pl(lat = x[['lat']], lon = x[['lng']])
})
df <- data.frame(polyline = unlist(pl))
df_marker <- data.frame(lat = location[1], lon = location[2])
google_map(key = mapKey) %>%
add_markers(data = df_marker) %>%
add_polylines(data = df, polyline = "polyline")
更新II:
此外,旅行时间地图平台的文档讨论了具有到达时间的多起源,这正是我想要做的事情.但我需要为公共交通和驾驶(对于通勤时间不到一小时的地方)这样做,我认为既然公共交通很棘手(基于你所接近的车站),也许热图是比轮廓更好的选择.
该答案基于获得（大致）等距点的网格之间的原点-目的地矩阵。这是一项计算机密集型操作，不仅因为它需要大量的api调用来映射服务，而且还因为服务器必须为每个调用计算一个矩阵。所需调用的数量沿网格中的点数呈指数增长。
为了解决此问题，我建议您考虑在本地计算机或本地服务器上运行映射服务器。Project OSRM提供了一个相对简单，免费和开放源代码的解决方案，使您能够将OpenStreetMap服务器运行到Linux docker（https://github.com/Project-OSRM/osrm-backend）。拥有自己的本地映射服务器将使您能够根据需要进行尽可能多的API调用。R的osrm软件包允许您与OpenStreetMaps的api进行交互。包括放置在本地服务器上的内容。
library(raster) # Optional
library(sp)
library(ggmap)
library(tidyverse)
library(osrm)
devtools::install_github("cmartin/ggConvexHull") # Needed to quickly draw the contours
library(ggConvexHull)
我围绕布鲁塞尔市（比利时）城市创建了96个大致相等距离的点的网格。该网格未考虑地球曲率，在城市距离的水平上可以忽略不计。
为了方便起见，我使用栅格数据包下载比利时的ShapeFile并提取布鲁塞尔市的节点。
BE <- raster::getData("GADM", country = "BEL", level = 1)
Bruxelles <- BE[BE$NAME_1 == "Bruxelles", ]
df_grid <- makegrid(Bruxelles, cellsize = 0.02) %>%
SpatialPoints() %>%
as.data.frame() %>% ## I convert the SpatialPoints object into a simple data.frame
rownames_to_column() %>% ## create a unique id for each point in the data.frame
rename(id = rowname, lat = x2, lon = x1) # rename variables of the data.frame with more explanatory names.
options(osrm.server = "http://127.0.0.1:5000/") ## I point osrm.server to the OpenStreet docker running in my Linux machine. Do not run this if you are getting your data from OpenStreet public servers.
Distance_Tables <- osrmTable(loc = df_grid) ## I obtain a list with distances (Origin Destination Matrix in minutes, origins and destinations)
OD_Matrix <- Distance_Tables$durations %>% ## Subset the previous list and
as_data_frame() %>% ## ...convert the Origin Destination Matrix into a tibble
rownames_to_column() %>%
rename(origin_id = rowname) %>% ## make sure we have an id column for the OD tibble
gather(key = destination_id, value = distance_time, -origin_id) %>% # transform the tibble into long/tidy format
left_join(df_grid, by = c("origin_id" = "id")) %>%
rename(origin_lon = lon, origin_lat = lat) %>% ## set origin coordinates
left_join(df_grid, by = c("destination_id" = "id")) %>%
rename(destination_lat = lat, destination_lon = lon) ## set destination coordinates
## Obtain a nice looking road map of Brussels
Brux_map <- get_map(location = "bruxelles, belgique",
zoom = 11,
source = "google",
maptype = "roadmap")
ggmap(Brux_map) +
geom_point(aes(x = origin_lon, y = origin_lat),
data = OD_Matrix %>%
filter(destination_id == 42), ## Here I selected point_id 42 as the desired target, just because it is not far from the City Center.
size = 0.5) +
geom_point(aes(x = origin_lon, y = origin_lat),
data = OD_Matrix %>%
filter(destination_id == 42, origin_id == 42),
shape = 5, size = 3) + ## Draw a diamond around point_id 42
geom_convexhull(alpha = 0.2,
fill = "blue",
colour = "blue",
data = OD_Matrix %>%
filter(destination_id == 42,
distance_time <= 8), ## Countour marking a distance of up to 8 minutes
aes(x = origin_lon, y = origin_lat)) +
geom_convexhull(alpha = 0.2,
fill = "red",
colour = "red",
data = OD_Matrix %>%
filter(destination_id == 42,
distance_time <= 15), ## Countour marking a distance of up to 16 minutes
aes(x = origin_lon, y = origin_lat))
结果
蓝色轮廓代表距市中心最多8分钟的距离。红色轮廓表示最长15分钟的距离。
我希望这可以帮助您获得反向等时线。
我想出了一种与进行大量 api 调用相比适用的方法。
这个想法是找到你可以在特定时间到达的地方（看看这个线程）。可以通过将时间从早上更改为晚上来模拟交通。您最终会得到一个重叠区域，您可以从两个地方到达该区域。
然后，您可以使用Nicolas 答案并在该重叠区域内绘制一些点，并为您拥有的目的地绘制热图。这样，您将需要覆盖的区域（点）更少，因此您将进行更少的 api 调用（请记住为此使用适当的时间）。
下面，我试图证明我所说的这些是什么意思，并让您明白您可以制作另一个答案中提到的网格，以使您的估计更加可靠。
这显示了如何映射相交区域。
library(httr)
library(googleway)
library(jsonlite)
appId <- "Travel.Time.ID"
apiKey <- "Travel.Time.API"
mapKey <- "Google.Map.ID"
locationK <- c(40, -73) #K
locationM <- c(40, -74) #M
CommuteTimeK <- (3 / 4) * 60 * 60
CommuteTimeM <- (0.55) * 60 * 60
url <- "http://api.traveltimeapp.com/v4/time-map"
requestBodyK <- paste0('{
"departure_searches" : [
{"id" : "test",
"coords": {"lat":', locationK[1], ', "lng":', locationK[2],' },
"transportation" : {"type" : "public_transport"} ,
"travel_time" : ', CommuteTimeK, ',
"departure_time" : "2018-06-27T13:00:00z"
}
]
}')
requestBodyM <- paste0('{
"departure_searches" : [
{"id" : "test",
"coords": {"lat":', locationM[1], ', "lng":', locationM[2],' },
"transportation" : {"type" : "driving"} ,
"travel_time" : ', CommuteTimeM, ',
"departure_time" : "2018-06-27T13:00:00z"
}
]
}')
resKi <- httr::POST(url = url,
httr::add_headers('Content-Type' = 'application/json'),
httr::add_headers('Accept' = 'application/json'),
httr::add_headers('X-Application-Id' = appId),
httr::add_headers('X-Api-Key' = apiKey),
body = requestBodyK,
encode = "json")
resMi <- httr::POST(url = url,
httr::add_headers('Content-Type' = 'application/json'),
httr::add_headers('Accept' = 'application/json'),
httr::add_headers('X-Application-Id' = appId),
httr::add_headers('X-Api-Key' = apiKey),
body = requestBodyM,
encode = "json")
resK <- jsonlite::fromJSON(as.character(resKi))
resM <- jsonlite::fromJSON(as.character(resMi))
plK <- lapply(resK$results$shapes[[1]]$shell, function(x){
googleway::encode_pl(lat = x[['lat']], lon = x[['lng']])
})
plM <- lapply(resM$results$shapes[[1]]$shell, function(x){
googleway::encode_pl(lat = x[['lat']], lon = x[['lng']])
})
dfK <- data.frame(polyline = unlist(plK))
dfM <- data.frame(polyline = unlist(plM))
df_markerK <- data.frame(lat = locationK[1], lon = locationK[2], colour = "#green")
df_markerM <- data.frame(lat = locationM[1], lon = locationM[2], colour = "#lavender")
iconK <- "red"
df_markerK$icon <- iconK
iconM <- "blue"
df_markerM$icon <- iconM
google_map(key = mapKey) %>%
add_markers(data = df_markerK,
lat = "lat", lon = "lon",colour = "icon",
mouse_over = "K_K") %>%
add_markers(data = df_markerM,
lat = "lat", lon = "lon", colour = "icon",
mouse_over = "M_M") %>%
add_polygons(data = dfM, polyline = "polyline", stroke_colour = '#461B7E',
fill_colour = '#461B7E', fill_opacity = 0.6) %>%
add_polygons(data = dfK, polyline = "polyline",
stroke_colour = '#F70D1A',
fill_colour = '#FF2400', fill_opacity = 0.4)
您可以像这样提取相交区域：
# install.packages(c("rgdal", "sp", "raster","rgeos","maptools"))
library(rgdal)
library(sp)
library(raster)
library(rgeos)
library(maptools)
Kdata <- resK$results$shapes[[1]]$shell
Mdata <- resM$results$shapes[[1]]$shell
xyfunc <- function(mydf) {
xy <- mydf[,c(2,1)]
return(xy)
}
spdf <- function(xy, mydf){
sp::SpatialPointsDataFrame(
coords = xy, data = mydf,
proj4string = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))}
for (i in (1:length(Kdata))) {Kdata[[i]] <- xyfunc(Kdata[[i]])}
for (i in (1:length(Mdata))) {Mdata[[i]] <- xyfunc(Mdata[[i]])}
Kshp <- list(); for (i in (1:length(Kdata))) {Kshp[i] <- spdf(Kdata[[i]],Kdata[[i]])}
Mshp <- list(); for (i in (1:length(Mdata))) {Mshp[i] <- spdf(Mdata[[i]],Mdata[[i]])}
Kbind <- do.call(bind, Kshp)
Mbind <- do.call(bind, Mshp)
#plot(Kbind);plot(Mbind)
x <- intersect(Kbind,Mbind)
#plot(x)
xdf <- data.frame(x)
xdf$icon <- "https://i.stack.imgur.com/z7NnE.png"
google_map(key = mapKey,
location = c(mean(latmax,latmin), mean(lngmax,lngmin)), zoom = 8) %>%
add_markers(data = xdf, lat = "lat", lon = "lng", marker_icon = "icon")
这只是相交区域的说明。
现在，您可以从xdf数据框中获取坐标并围绕这些点构建网格，最终得出热图。为了尊重提出该想法/答案的其他用户，我没有将其包含在我的内容中，而只是参考了它。
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