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R How to convert MultiLineString GeoJson file to dataframe with long and lat columns?
How to sort a dataframe by multiple column(s)How to add a GeoJSON layer in GraphHopper?Openlayers: select a feature programmatically from GeoJsonAdding Maplines in HighMaps using geojson fileD3js Map : GeometryCollection Vs FeaturesCollectionTwo geojson files not superposinggeojson file formatting issue, mini-arrays(coordinates) inside larger array of coordinatesgeojson from mysql : convert a postgresql's query with aggregation function json_agg (for web mapping)How to append properties to geojson file in python?loading geojson into Mapbox object not valid
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;
I have a MultiLineString Geogeson file exported from gqig gis software.
A small example :
"type": "FeatureCollection",
"name": "route1",
"crs": "type": "name", "properties": "name": "urn:ogc:def:crs:EPSG::3857"
,
"features": [
"type": "Feature", "properties": "FID": 0 , "geometry": "type":
"MultiLineString", "coordinates": [ [ [ 1936131.287994222715497,
-4335318.772792792879045 ], [ -2633407.770391199737787,
1763382.609922708477825 ], [ -2922369.195528693497181,
4600947.908943663351238 ], [ -1640888.092745035886765,
5275789.498084637336433 ], [ -361201.781421858817339, 5970373.793290910311043
], [ -361201.781421858817339, 5970373.793290910311043 ] ] ]
]
How can I convert it in a dataframe binding nodes with long and lat colunms ?
Expected result :
node long lat
1 1936131.287994222715497 -4335318.772792792879045
2 -2633407.770391199737787 1763382.609922708477825
What I tried (creating a list) :
route1 <- jsonlite::fromJSON(readr::read_file("routes/route1.geojson"))
r geojson
asked Mar 9 at 6:55
WilcarWilcar
6301931
add a comment |
I have a MultiLineString Geogeson file exported from gqig gis software.
A small example :
"type": "FeatureCollection",
"name": "route1",
"crs": "type": "name", "properties": "name": "urn:ogc:def:crs:EPSG::3857"
,
"features": [
"type": "Feature", "properties": "FID": 0 , "geometry": "type":
"MultiLineString", "coordinates": [ [ [ 1936131.287994222715497,
-4335318.772792792879045 ], [ -2633407.770391199737787,
1763382.609922708477825 ], [ -2922369.195528693497181,
4600947.908943663351238 ], [ -1640888.092745035886765,
5275789.498084637336433 ], [ -361201.781421858817339, 5970373.793290910311043
], [ -361201.781421858817339, 5970373.793290910311043 ] ] ]
]
How can I convert it in a dataframe binding nodes with long and lat colunms ?
Expected result :
node long lat
1 1936131.287994222715497 -4335318.772792792879045
2 -2633407.770391199737787 1763382.609922708477825
What I tried (creating a list) :
route1 <- jsonlite::fromJSON(readr::read_file("routes/route1.geojson"))
r geojson
asked Mar 9 at 6:55
WilcarWilcar
6301931
The GeoJSON specification rfc7946 states "GeoJSON uses a geographic coordinate reference system, World Geodetic System 1984, and units of decimal degrees" - so the data you have here is technically not actual valid GeoJSON
– SymbolixAU
Mar 12 at 3:44
Other CRS values were permitted in previous specifications, but this rule has changed in rfc7946
– SymbolixAU
Mar 12 at 3:45
add a comment |
I have a MultiLineString Geogeson file exported from gqig gis software.
A small example :
"type": "FeatureCollection",
"name": "route1",
"crs": "type": "name", "properties": "name": "urn:ogc:def:crs:EPSG::3857"
,
"features": [
"type": "Feature", "properties": "FID": 0 , "geometry": "type":
"MultiLineString", "coordinates": [ [ [ 1936131.287994222715497,
-4335318.772792792879045 ], [ -2633407.770391199737787,
1763382.609922708477825 ], [ -2922369.195528693497181,
4600947.908943663351238 ], [ -1640888.092745035886765,
5275789.498084637336433 ], [ -361201.781421858817339, 5970373.793290910311043
], [ -361201.781421858817339, 5970373.793290910311043 ] ] ]
]
How can I convert it in a dataframe binding nodes with long and lat colunms ?
Expected result :
node long lat
1 1936131.287994222715497 -4335318.772792792879045
2 -2633407.770391199737787 1763382.609922708477825
What I tried (creating a list) :
route1 <- jsonlite::fromJSON(readr::read_file("routes/route1.geojson"))
r geojson
asked Mar 9 at 6:55
WilcarWilcar
6301931
I have a MultiLineString Geogeson file exported from gqig gis software.
A small example :
"type": "FeatureCollection",
"name": "route1",
"crs": "type": "name", "properties": "name": "urn:ogc:def:crs:EPSG::3857"
,
"features": [
"type": "Feature", "properties": "FID": 0 , "geometry": "type":
"MultiLineString", "coordinates": [ [ [ 1936131.287994222715497,
-4335318.772792792879045 ], [ -2633407.770391199737787,
1763382.609922708477825 ], [ -2922369.195528693497181,
4600947.908943663351238 ], [ -1640888.092745035886765,
5275789.498084637336433 ], [ -361201.781421858817339, 5970373.793290910311043
], [ -361201.781421858817339, 5970373.793290910311043 ] ] ]
]
How can I convert it in a dataframe binding nodes with long and lat colunms ?
Expected result :
node long lat
1 1936131.287994222715497 -4335318.772792792879045
2 -2633407.770391199737787 1763382.609922708477825
What I tried (creating a list) :
route1 <- jsonlite::fromJSON(readr::read_file("routes/route1.geojson"))
r geojson
r geojson
asked Mar 9 at 6:55
WilcarWilcar
6301931
asked Mar 9 at 6:55
WilcarWilcar
6301931
edited Mar 9 at 7:07
Wilcar
asked Mar 9 at 6:55
WilcarWilcar
6301931
asked Mar 9 at 6:55
WilcarWilcar
6301931
asked Mar 9 at 6:55
WilcarWilcar
6301931
6301931
The GeoJSON specification rfc7946 states "GeoJSON uses a geographic coordinate reference system, World Geodetic System 1984, and units of decimal degrees" - so the data you have here is technically not actual valid GeoJSON
– SymbolixAU
Mar 12 at 3:44
Other CRS values were permitted in previous specifications, but this rule has changed in rfc7946
– SymbolixAU
Mar 12 at 3:45
add a comment |
The GeoJSON specification rfc7946 states "GeoJSON uses a geographic coordinate reference system, World Geodetic System 1984, and units of decimal degrees" - so the data you have here is technically not actual valid GeoJSON
– SymbolixAU
Mar 12 at 3:44
Other CRS values were permitted in previous specifications, but this rule has changed in rfc7946
– SymbolixAU
Mar 12 at 3:45
The GeoJSON specification rfc7946 states "GeoJSON uses a geographic coordinate reference system, World Geodetic System 1984, and units of decimal degrees" - so the data you have here is technically not actual valid GeoJSON
– SymbolixAU
Mar 12 at 3:44
The GeoJSON specification rfc7946 states "GeoJSON uses a geographic coordinate reference system, World Geodetic System 1984, and units of decimal degrees" - so the data you have here is technically not actual valid GeoJSON
– SymbolixAU
Mar 12 at 3:44
Other CRS values were permitted in previous specifications, but this rule has changed in rfc7946
– SymbolixAU
Mar 12 at 3:45
Other CRS values were permitted in previous specifications, but this rule has changed in rfc7946
– SymbolixAU
Mar 12 at 3:45
add a comment |
2 Answers
2
active
oldest
votes
library(sf) can read GeoJSON. This will give you an sf object. If you want the coordinates you can use the st_coordinates() function.
library(sf)
sf <- sf::st_read( geo, quiet = T )
df <- as.data.frame( sf::st_coordinates( sf ) )
# X Y L1 L2
# 1 1936131.3 -4335319 1 1
# 2 -2633407.8 1763383 1 1
# 3 -2922369.2 4600948 1 1
# 4 -1640888.1 5275789 1 1
# 5 -361201.8 5970374 1 1
# 6 -361201.8 5970374 1 1
This extra L1 and L2 columns tell you to which linestring within the MULTILINESTRING each coordinate pair belong.
answered Mar 11 at 20:52
SymbolixAUSymbolixAU
17k33292
add a comment |
If you examine the structure of the obtained list with str(route1) you can see that the data is stored in an array, which you can extract.
a <- route1$features$geometry$coordinates[[1]]
a
# , , 1
#
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] 1936131 -2633408 -2922369 -1640888 -361201.8 -361201.8
#
# , , 2
#
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] -4335319 1763383 4600948 5275789 5970374 5970374
Now, just do a cbind() to get what you want.
cbind(a[, , 1], a[, , 2])
# [,1] [,2]
# [1,] 1936131.3 -4335319
# [2,] -2633407.8 1763383
# [3,] -2922369.2 4600948
# [4,] -1640888.1 5275789
# [5,] -361201.8 5970374
# [6,] -361201.8 5970374
Or as data frame:
d <- data.frame(long=a[, , 1], lat=a[, , 2])
d <- cbind(node=rownames(d), d)
d
# node long lat
# 1 1 1936131.3 -4335319
# 2 2 -2633407.8 1763383
# 3 3 -2922369.2 4600948
# 4 4 -1640888.1 5275789
# 5 5 -361201.8 5970374
# 6 6 -361201.8 5970374
answered Mar 9 at 8:37
jay.sfjay.sf
7,38831842
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
library(sf) can read GeoJSON. This will give you an sf object. If you want the coordinates you can use the st_coordinates() function.
library(sf)
sf <- sf::st_read( geo, quiet = T )
df <- as.data.frame( sf::st_coordinates( sf ) )
# X Y L1 L2
# 1 1936131.3 -4335319 1 1
# 2 -2633407.8 1763383 1 1
# 3 -2922369.2 4600948 1 1
# 4 -1640888.1 5275789 1 1
# 5 -361201.8 5970374 1 1
# 6 -361201.8 5970374 1 1
This extra L1 and L2 columns tell you to which linestring within the MULTILINESTRING each coordinate pair belong.
answered Mar 11 at 20:52
SymbolixAUSymbolixAU
17k33292
add a comment |
library(sf) can read GeoJSON. This will give you an sf object. If you want the coordinates you can use the st_coordinates() function.
library(sf)
sf <- sf::st_read( geo, quiet = T )
df <- as.data.frame( sf::st_coordinates( sf ) )
# X Y L1 L2
# 1 1936131.3 -4335319 1 1
# 2 -2633407.8 1763383 1 1
# 3 -2922369.2 4600948 1 1
# 4 -1640888.1 5275789 1 1
# 5 -361201.8 5970374 1 1
# 6 -361201.8 5970374 1 1
This extra L1 and L2 columns tell you to which linestring within the MULTILINESTRING each coordinate pair belong.
answered Mar 11 at 20:52
SymbolixAUSymbolixAU
17k33292
add a comment |
library(sf) can read GeoJSON. This will give you an sf object. If you want the coordinates you can use the st_coordinates() function.
library(sf)
sf <- sf::st_read( geo, quiet = T )
df <- as.data.frame( sf::st_coordinates( sf ) )
# X Y L1 L2
# 1 1936131.3 -4335319 1 1
# 2 -2633407.8 1763383 1 1
# 3 -2922369.2 4600948 1 1
# 4 -1640888.1 5275789 1 1
# 5 -361201.8 5970374 1 1
# 6 -361201.8 5970374 1 1
This extra L1 and L2 columns tell you to which linestring within the MULTILINESTRING each coordinate pair belong.
answered Mar 11 at 20:52
SymbolixAUSymbolixAU
17k33292
library(sf) can read GeoJSON. This will give you an sf object. If you want the coordinates you can use the st_coordinates() function.
library(sf)
sf <- sf::st_read( geo, quiet = T )
df <- as.data.frame( sf::st_coordinates( sf ) )
# X Y L1 L2
# 1 1936131.3 -4335319 1 1
# 2 -2633407.8 1763383 1 1
# 3 -2922369.2 4600948 1 1
# 4 -1640888.1 5275789 1 1
# 5 -361201.8 5970374 1 1
# 6 -361201.8 5970374 1 1
This extra L1 and L2 columns tell you to which linestring within the MULTILINESTRING each coordinate pair belong.
answered Mar 11 at 20:52
SymbolixAUSymbolixAU
17k33292
edited Mar 12 at 3:45
answered Mar 11 at 20:52
SymbolixAUSymbolixAU
17k33292
answered Mar 11 at 20:52
SymbolixAUSymbolixAU
17k33292
answered Mar 11 at 20:52
SymbolixAUSymbolixAU
17k33292
17k33292
add a comment |
add a comment |
If you examine the structure of the obtained list with str(route1) you can see that the data is stored in an array, which you can extract.
a <- route1$features$geometry$coordinates[[1]]
a
# , , 1
#
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] 1936131 -2633408 -2922369 -1640888 -361201.8 -361201.8
#
# , , 2
#
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] -4335319 1763383 4600948 5275789 5970374 5970374
Now, just do a cbind() to get what you want.
cbind(a[, , 1], a[, , 2])
# [,1] [,2]
# [1,] 1936131.3 -4335319
# [2,] -2633407.8 1763383
# [3,] -2922369.2 4600948
# [4,] -1640888.1 5275789
# [5,] -361201.8 5970374
# [6,] -361201.8 5970374
Or as data frame:
d <- data.frame(long=a[, , 1], lat=a[, , 2])
d <- cbind(node=rownames(d), d)
d
# node long lat
# 1 1 1936131.3 -4335319
# 2 2 -2633407.8 1763383
# 3 3 -2922369.2 4600948
# 4 4 -1640888.1 5275789
# 5 5 -361201.8 5970374
# 6 6 -361201.8 5970374
answered Mar 9 at 8:37
jay.sfjay.sf
7,38831842
add a comment |
If you examine the structure of the obtained list with str(route1) you can see that the data is stored in an array, which you can extract.
a <- route1$features$geometry$coordinates[[1]]
a
# , , 1
#
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] 1936131 -2633408 -2922369 -1640888 -361201.8 -361201.8
#
# , , 2
#
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] -4335319 1763383 4600948 5275789 5970374 5970374
Now, just do a cbind() to get what you want.
cbind(a[, , 1], a[, , 2])
# [,1] [,2]
# [1,] 1936131.3 -4335319
# [2,] -2633407.8 1763383
# [3,] -2922369.2 4600948
# [4,] -1640888.1 5275789
# [5,] -361201.8 5970374
# [6,] -361201.8 5970374
Or as data frame:
d <- data.frame(long=a[, , 1], lat=a[, , 2])
d <- cbind(node=rownames(d), d)
d
# node long lat
# 1 1 1936131.3 -4335319
# 2 2 -2633407.8 1763383
# 3 3 -2922369.2 4600948
# 4 4 -1640888.1 5275789
# 5 5 -361201.8 5970374
# 6 6 -361201.8 5970374
answered Mar 9 at 8:37
jay.sfjay.sf
7,38831842
add a comment |
If you examine the structure of the obtained list with str(route1) you can see that the data is stored in an array, which you can extract.
a <- route1$features$geometry$coordinates[[1]]
a
# , , 1
#
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] 1936131 -2633408 -2922369 -1640888 -361201.8 -361201.8
#
# , , 2
#
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] -4335319 1763383 4600948 5275789 5970374 5970374
Now, just do a cbind() to get what you want.
cbind(a[, , 1], a[, , 2])
# [,1] [,2]
# [1,] 1936131.3 -4335319
# [2,] -2633407.8 1763383
# [3,] -2922369.2 4600948
# [4,] -1640888.1 5275789
# [5,] -361201.8 5970374
# [6,] -361201.8 5970374
Or as data frame:
d <- data.frame(long=a[, , 1], lat=a[, , 2])
d <- cbind(node=rownames(d), d)
d
# node long lat
# 1 1 1936131.3 -4335319
# 2 2 -2633407.8 1763383
# 3 3 -2922369.2 4600948
# 4 4 -1640888.1 5275789
# 5 5 -361201.8 5970374
# 6 6 -361201.8 5970374
answered Mar 9 at 8:37
jay.sfjay.sf
7,38831842
If you examine the structure of the obtained list with str(route1) you can see that the data is stored in an array, which you can extract.
a <- route1$features$geometry$coordinates[[1]]
a
# , , 1
#
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] 1936131 -2633408 -2922369 -1640888 -361201.8 -361201.8
#
# , , 2
#
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] -4335319 1763383 4600948 5275789 5970374 5970374
Now, just do a cbind() to get what you want.
cbind(a[, , 1], a[, , 2])
# [,1] [,2]
# [1,] 1936131.3 -4335319
# [2,] -2633407.8 1763383
# [3,] -2922369.2 4600948
# [4,] -1640888.1 5275789
# [5,] -361201.8 5970374
# [6,] -361201.8 5970374
Or as data frame:
d <- data.frame(long=a[, , 1], lat=a[, , 2])
d <- cbind(node=rownames(d), d)
d
# node long lat
# 1 1 1936131.3 -4335319
# 2 2 -2633407.8 1763383
# 3 3 -2922369.2 4600948
# 4 4 -1640888.1 5275789
# 5 5 -361201.8 5970374
# 6 6 -361201.8 5970374
answered Mar 9 at 8:37
jay.sfjay.sf
7,38831842
edited Mar 9 at 8:42
answered Mar 9 at 8:37
jay.sfjay.sf
7,38831842
answered Mar 9 at 8:37
jay.sfjay.sf
7,38831842
answered Mar 9 at 8:37
jay.sfjay.sf
7,38831842
7,38831842
add a comment |
add a comment |
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Required, but never shown
Required, but never shown
The GeoJSON specification rfc7946 states "GeoJSON uses a geographic coordinate reference system, World Geodetic System 1984, and units of decimal degrees" - so the data you have here is technically not actual valid GeoJSON
– SymbolixAU
Mar 12 at 3:44
Other CRS values were permitted in previous specifications, but this rule has changed in rfc7946
– SymbolixAU
Mar 12 at 3:45