Heatmap/Contours 基于运输时间（反向等时等高线）

Question

注意： 需要r, python, java, or if necessary, c++ or c#中的解决方案。

我正在尝试根据运输时间绘制等高线。更清楚地说，我想将具有相似旅行时间（比如 10 分钟间隔）的点聚集到特定点（目的地）并将它们映射为等高线或热图。

现在，我唯一的想法是使用 R 包 gmapsdistance 来查找不同的旅行时间起源，然后将它们聚类并绘制在地图上。但是，如您所知，这绝不是一个可靠的解决方案。

这个 thread on GIS-community and this one for python 说明了类似的问题，但对于起点到目的地在特定的时间。我想找到可以在一定时间内到达目的地的起点。

现在，下面的代码展示了我的初步想法（使用 R）：

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")

这张地图也可能有助于理解问题：

使用这个answer我可以得到我可以从原点去但我需要的点反转它并找到到我的目的地的旅行时间小于特定时间的点；

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")

此外，Documentation of Travel Time Map Platform谈到Multi Origins with Arrival time这这正是我想做的事情。但我需要为 public 交通和驾驶（对于通勤时间不到一小时的地方）做这件事，我认为因为 public 交通很棘手（取决于你靠近哪个车站）也许热图是比等高线更好的选择。

Answer 1

此答案基于获得（大致）等距点网格之间的起点-终点矩阵。这是一项计算机密集型操作，不仅因为它需要对地图服务进行大量 API 次调用，还因为服务器必须为每次调用计算一个矩阵。所需调用的数量随着网格中点的数量呈指数增长。

为了解决这个问题，我建议您考虑运行ning 在您的本地计算机或本地服务器上的映射服务器上。 Project OSRM 提供了一个相对简单、免费和开源的解决方案，使您能够将 OpenStreetMap 服务器运行转换为 Linux docker (https://github.com/Project-OSRM/osrm-backend)。拥有自己的本地映射服务器将使您可以根据需要进行任意数量的 API 调用。 R 的 osrm 包允许您与 OpenStreetMaps 的 APIs 交互，包括那些放置在本地服务器上的。

library(raster) # Optional
library(sp)
library(ggmap)
library(tidyverse)
library(osrm)
devtools::install_github("cmartin/ggConvexHull") # Needed to quickly draw the contours
library(ggConvexHull)

我在布鲁塞尔（比利时）大都市周围创建了一个由 96 个大致等距的点组成的网格。此网格未考虑地球曲率，在城市距离水平上可以忽略不计。

为方便起见，我使用栅格包下载了比利时的ShapeFile并提取了布鲁塞尔市的节点。

  BE <- raster::getData("GADM", country = "BEL", level = 1)
  Bruxelles <- BE[BE$NAME_1 == "Bruxelles", ]

  df_grid <- makegrid(Bruxelles, cellsize = 0.02) %>% 
        SpatialPoints() %>%
        ## I convert the SpatialPoints object into a simple data.frame 
        as.data.frame() %>% 
        ## create a unique id for each point in the data.frame
        rownames_to_column() %>% 
        ## rename variables of the data.frame with more explanatory names.
        rename(id = rowname, lat = x2, lon = x1) 

 ## 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.
 options(osrm.server = "http://127.0.0.1:5000/") 

 ## I obtain a list with distances (Origin Destination Matrix in ...
 ### ... minutes, origins and destinations)
 Distance_Tables <- osrmTable(loc = df_grid) 

 OD_Matrix <- Distance_Tables$durations %>% ## subset the previous list
                ## convert the Origin Destination Matrix into a tibble
                as_data_frame() %>%  
                rownames_to_column() %>% 
                ## make sure we have an id column for the OD tibble
                rename(origin_id = rowname) %>% 
                ## transform the tibble into long/tidy format
                gather(key = destination_id, value = distance_time, -origin_id) %>% 
                left_join(df_grid, by = c("origin_id" = "id")) %>% 
                ## set origin coordinates
                rename(origin_lon = lon, origin_lat = lat) %>% 
                left_join(df_grid, by = c("destination_id" = "id")) %>% 
                ## set destination coordinates
                rename(destination_lat = lat, destination_lon = lon)

 ## 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 %>% 
                ## Here I selected point_id 42 as the desired target, ...
                ## ... just because it is not far from the City Center.
                filter(destination_id == 42), 
                size = 0.5) + 
   ## Draw a diamond around point_id 42                                      
   geom_point(aes(x = origin_lon, y = origin_lat), 
              data = OD_Matrix %>% 
                filter(destination_id == 42, origin_id == 42),
              shape = 5, size = 3) +  
   ## Countour marking a distance of up to 8 minutes
   geom_convexhull(alpha = 0.2, 
                   fill = "blue", 
                   colour = "blue",
                   data = OD_Matrix %>% 
                            filter(destination_id == 42, 
                            distance_time <= 8), 
                   aes(x = origin_lon, y = origin_lat)) + 
   ## Countour marking a distance of up to 16 minutes
   geom_convexhull(alpha = 0.2, 
                   fill = "red",
                   colour = "red",
                   data = OD_Matrix %>% 
                            filter(destination_id == 42, 
                                   distance_time <= 15), 
                   aes(x = origin_lon, y = origin_lat))

结果

蓝色等高线表示到市中心最多 8 分钟的距离。红色等高线表示最多 15 分钟的距离。

Answer 2

我想出了一个比进行多次 api 调用更适用的方法。

这个想法是找到你在特定时间可以到达的地方（看看这个 thread）。可以通过将时间从早上更改为晚上来模拟交通。您最终会得到一个可以从两个地方到达的重叠区域。

然后您可以使用并在该重叠区域内绘制一些点，并为您的目的地绘制热图。这样，您需要覆盖的区域（点）就会更少，因此您的 api 调用会少得多（请记住为此问题使用适当的时间）。

在下文中，我试图证明我的意思，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。

这显示了如何绘制相交区域。

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")

这只是相交区域的示意图。

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 idea/answer 的其他用户，我没有将它包括在我的中，只是引用它。

Heatmap/Contours 基于运输时间（反向等时等高线）

Heatmap/Contours based on Transportation Time (Reverse Isochronic Contours)

python

java

r

osrm

travel-time

结果