r - Openstreetmap/iGraph - 从 center_bbox osmar 对象创建一个 center_bbox/使其高效

Question

电话get_osm(muc_bbox, src)从大 osm 文件加载时需要相当多的时间(46.151 秒)。
我想知道是否可以从 center_bbox 创建一个 center_bbox 以便按需说？将一个大文件加载到内存中并根据要求从中创建小“盒子”？或者对这个问题有不同的方法吗？也许可以将大文件以不同的结构加载到内存中并根据需要从中创建 bbox，这样总体上会更快？
我在这里上传了一个更大的 osm 文件 OSM File
先感谢您!
BR。

library(osmar)
library(igraph)

### Get data ----
src <- osmsource_osmosis(file = "~/streets_bayern.osm")

muc_bbox <- center_bbox(11.575278, 48.137222, 41242.57, 41242.57)
muc <- get_osm(muc_bbox, src)

### Reduce to highways: ----
hways <- subset(muc, way_ids = find(muc, way(tags(k == "highway"))))
hways <- find(hways, way(tags(k == "name")))
hways <- find_down(muc, way(hways))
hways <- subset(muc, ids = hways)

#### Plot data ----
## Plot complete data and highways on top:
plot(muc)
plot_ways(muc, col = "lightgrey")
plot_ways(hways, col = "coral", add = TRUE)

### Define route start and end nodes: ----
id<-find(muc, node(tags(v %agrep% "Sendlinger Tor")))[1]
hway_start_node <-find_nearest_node(muc, id, way(tags(k == "highway"))) 
hway_start <- subset(muc, node(hway_start_node))

id <- find(muc, node(attrs(lon > 11.58 & lat > 48.15)))[1]
hway_end_node <- find_nearest_node(muc, id, way(tags(k == "highway")))
hway_end <- subset(muc, node(hway_end_node))

## Add the route start and and nodes to the plot:
plot_nodes(hway_start, add = TRUE, col = "red", pch = 19, cex = 2)
plot_nodes(hway_end, add = TRUE, col = "red", pch = 19, cex = 2)

### Create street graph ----
gr <- as.undirected(as_igraph(hways))

### Compute shortest route: ----
# Calculate route
route <- function(start_node,end_node) {
  get.shortest.paths(gr,
                     from = as.character(start_node),
                     to = as.character(end_node), 
                     mode = "all")[[1]][[1]]}
# Plot route

plot.route <- function(r,color) {
  r.nodes.names <- as.numeric(V(gr)[r]$name)
  r.ways <- subset(hways, ids = osmar::find_up(hways, node(r.nodes.names)))
  plot_ways(r.ways, add = TRUE, col = color, lwd = 2)
}




# Number of new ways to look for 
nways <- 10
# Weight factor applied to already found way
weightfactor <- 2


for (numway in 1:nways) {
  r <- route(hway_start_node,hway_end_node)
  color <- colorRampPalette(c("springgreen","royalblue"))(nways)[numway]
  plot.route(r,color)
  # Modify current route weight
  
  route_nodes <- as.numeric(V(gr)[r]$name)
  #We construct a newosmarobject containing only elements related to the nodes defining the route:
  route_ids <- find_up(hways, node(route_nodes))
  route_muc <- subset(hways, ids = route_ids)
  
  #Route details.
  #In order to present route details like street names,
  #distances, and directions we have to work directly on the internals of the osmar objects.
  #We start by extracting the route’s node IDs (which are in the correct order) and the way IDs (which we have to order)
  #where the nodes are members:
  
  node_ids <- route_muc$nodes$attrs$id
  
  # to delete node ids.
  # gr_muc<-delete_vertices(gr_muc, as.character(node_ids[20]))
  
  way_ids <- local({
    w <- match(node_ids, route_muc$ways$refs$ref)
    route_muc$ways$refs$id[w]
  })
  
  #Then we extract the names of the ways in the correct order:>
  way_names <- local({
    n <- subset(route_muc$ways$tags, k == "name")
    n[match(way_ids, n$id), "v"]
  })
  
  #The next step is to extract the nodes’ coordinates,>
  node_coords <- route_muc$nodes$attrs[, c("lon", "lat")]
  
  #and to compute the distances (meters) and the bearings (degrees) between successive nodes (using thepackagegeosphere):
  node_dirs <- local({
    n <- nrow(node_coords)
    from <- 1:(n - 1)
    to <- 2:n
    cbind(dist = c(0, distHaversine(node_coords[from,], node_coords[to,])),
          bear = c(0, bearing(node_coords[from,], node_coords[to,])))
  })
  
  #Finally, we pack together all the information, and additionally compute the cumulative distance
  #and a16-point compass rose direction (thecompass()function is available in the “navigator ” demo from theosmarpackage):
  
  route_details <- data.frame(way_names, node_dirs)
  route_details$cdist <- cumsum(route_details$dist)
  route_details$coord <- node_coords
  route_details$id <- node_ids
  print(route_details)
 

Answer 1

你首先要download和 install计算机上的渗透。
以下脚本允许您定义一个框并查询您提供的大 map :

library(osmar)

# Set Osmosis source
src <- osmsource_osmosis(file = here::here("streets_bayern.osm"))

# Define box
muc_bbox <- center_bbox(11.575278, 48.137222, 1000, 1000)

# normally this should work, but didn't :
# muc  <- get_osm(muc_bbox, src)

# As I had trouble using directly get_osm, used modified source of it instead :
destination <- tempfile(tmpdir = here::here(),pattern = "tmp",fileext=".tmp")
request <- osmar:::osm_request(src, muc_bbox, destination)

# Folder where Osmosis.bat is located
osmosis.path <- 'c:/RDev/Osmosis/bin/'

request <- paste0(osmosis.path,request)

# Run request and check if it worked OK
if (system(request)==127) {stop('Osmosis request failed')}

# get response
response <- readLines(destination)
unlink(destination)

# Parse response
muc <- as_osmar(xmlParse(response))

plot(muc)

响应时间在 10 秒左右，比从 url 下载 map 要快得多。