importEnviroCar = function(file) {
require(rjson) # fromJSON
require(maptools) # spCbind
# read data as spatial object:
features = readOGR(file, layer = "OGRGeoJSON")
# convert time from text to POSIXct:
features$time = as.POSIXct(features$time, format = "%Y-%m-%dT%H:%M:%SZ")
# the third column is JSON, we want it in a table (data.frame) form: 1.
# form a list of lists
l1 = lapply(as.character(features$phenomenons), fromJSON)
# 2. parse the $value elements in the sublist:
l2 = lapply(l1, function(x) as.data.frame(lapply(x, function(X) X$value)))
# bind these value elements, row by row, together
ret = do.call(rbind, l2)
# read the units:
units = lapply(l1[1], function(x) as.data.frame(lapply(x, function(X) X$unit)))
# add a units attribute to layer
features$phenomenons = NULL
# add the table as attributes to the spatial object
if (length(features) == nrow(ret)) {
features = spCbind(features, ret)
attr(features, "units") = units[[1]]
features
} else NULL
}
require(RCurl)
## Loading required package: RCurl
## Loading required package: bitops
require(rgdal)
## Loading required package: rgdal
## Loading required package: sp
## rgdal: version: 0.8-11, (SVN revision 479M)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 1.10.0, released 2013/04/24
## Path to GDAL shared files: /usr/share/gdal/1.10
## Loaded PROJ.4 runtime: Rel. 4.8.0, 6 March 2012, [PJ_VERSION: 480]
## Path to PROJ.4 shared files: (autodetected)
require(maptools) #snapPointsToLines function
## Loading required package: maptools
## Checking rgeos availability: TRUE
url <- "https://giv-car.uni-muenster.de/stable/rest/tracks/5207d871e4b058cd3d669afe"
layer = readOGR(getURL(url, ssl.verifypeer = FALSE), layer = "OGRGeoJSON")
## OGR data source with driver: GeoJSON
## Source: "{"type":"FeatureCollection","properties":{"id":"5207d871e4b058cd3d669afe","sensor":{"type":"car","properties":{"id":"51cdb01ae4b0f76308194c4a","model":"525d","fuelType":"diesel","manufacturer":"BMW","constructionYear":2012}}},"features":[{"type":"Feature","geometry":{"type":"Point","coordinates":[7.59584,51.9691]},"properties":{"id":"5207d871e4b058cd3d669b00","time":"2013-08-11T16:56:06Z","phenomenons":{"Intake Temperature":{"value":38.0,"unit":"c"},"Intake Pressure":{"value":126.0,"unit":"kPa"},"CO2":{"value":0.005874942805039788,"unit":"kg/h"},"Calculated MAF":{"value":26.7454,"unit":"g/s"},"Speed":{"value":4.0,"unit":"km/h"},"Rpm":{"value":1339.0,"unit":"u/min"},"Consumption":{"value":0.002216959549071618,"unit":"l/h"}}}},{"type":"Feature","geometry":{"type":"Point","coordinates":[7.59578,51.9692]},"properties":{"id":"5207d871e4b058cd3d669b02","time":"2013-08-11T16:56:12Z","phenomenons":{"Consumption":{"value":0.001133023872679045,"unit":"l/h"},"CO2":{"value":0.003002513262599469,"unit":"kg/h"},"Intake Temperature":{"value":38.0,"unit":"c"},"Calculated MAF":{"value":13.6688,"unit":"g/s"},"Rpm":{"value":784.0,"unit":"u/min"},"Speed":{"value":10.0,"unit":"km/h"},"Intake Pressure":{"value":110.0,"unit":"kPa"}}}},{"type":"Feature","geometry":{"type":"Point","coordinates":[7.59573,51.9691]},"properties":{"id":"5207d871e4b058cd3d669b04","time":"2013-08-11T16:56:18Z","phenomenons":{"Consumption":{"value":0.0010798491379310346,"unit":"l/h"},"Intake Pressure":{"value":105.0,"unit":"kPa"},"Intake 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Pressure":{"value":107.0,"unit":"kPa"},"Rpm":{"value":789.0,"unit":"u/min"},"Consumption":{"value":0.001108479774535809,"unit":"l/h"}}}},{"type":"Feature","geometry":{"type":"Point","coordinates":[7.59602,51.9689]},"properties":{"id":"5207d871e4b058cd3d669b08","time":"2013-08-11T16:56:31Z","phenomenons":{"CO2":{"value":0.0047259578083554375,"unit":"kg/h"},"Consumption":{"value":0.0017833803050397877,"unit":"l/h"},"Speed":{"value":13.0,"unit":"km/h"},"Calculated MAF":{"value":21.5147,"unit":"g/s"},"Intake Pressure":{"value":111.0,"unit":"kPa"},"Intake Temperature":{"value":36.0,"unit":"c"},"Rpm":{"value":1215.0,"unit":"u/min"}}}},{"type":"Feature","geometry":{"type":"Point","coordinates":[7.59624,51.9688]},"properties":{"id":"5207d871e4b058cd3d669b0a","time":"2013-08-11T16:56:37Z","phenomenons":{"Intake Temperature":{"value":37.0,"unit":"c"},"Speed":{"value":5.0,"unit":"km/h"},"Calculated MAF":{"value":12.8299,"unit":"g/s"},"Intake 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## with 173 features and 3 fields
## Feature type: wkbPoint with 2 dimensions
# feature extracted OSM shape file containing subset of roads in Muenster
shape <- readOGR(dsn = ".", layer = "road_selection")
## OGR data source with driver: ESRI Shapefile
## Source: ".", layer: "road_selection"
## with 4261 features and 8 fields
## Feature type: wkbLineString with 2 dimensions
# points
points <- importEnviroCar(url)
## Loading required package: rjson
## OGR data source with driver: GeoJSON
## Source: "https://giv-car.uni-muenster.de/stable/rest/tracks/5207d871e4b058cd3d669afe", layer: "OGRGeoJSON"
## with 173 features and 3 fields
## Feature type: wkbPoint with 2 dimensions
res <- snapPointsToLines(points, shape)
## Loading required package: rgeos
## rgeos version: 0.3-1, (SVN revision 413M)
## GEOS runtime version: 3.3.8-CAPI-1.7.8
## Polygon checking: TRUE
## Warning: Spatial object 1 is not projected; GEOS expects planar coordinates
## Warning: Spatial object 2 is not projected; GEOS expects planar coordinates
# plotting raw points
plot(points, col = "deepskyblue")
# adding OSM roads
plot(shape, add = TRUE)
# adding map matched points
plot(res, add = TRUE, col = "red")
# sample code for using OSM and R (OSMAR) package library('osmar') src <-
# osmsource_api() center of Muenster with a 2km * 2km bounding box bb <-
# center_bbox(7.667724, 51.928308, 1500, 1500) bb <-
# corner_bbox(7.650558,51.927938,7.690977,51.935649)
# 7.557603,52.004258,7.689218,51.923227 bbb <-
# corner_bbox(7.473833,52.06002,7.774362,51.840151) bbbb <-
# corner_bbox(7.557603,52.004258,7.689218,51.923227) creating osmar object
# from bounding box ms <- get_osm(bb, source = src) subset data: choose
# only ms_hw_ids <- find(ms, way(tags(k == 'highway'))) transform from
# osmar object to sp object ms_as_sp <- as_sp(ms, what = c('points',
# 'lines', 'polygons'), crs = osm_crs(), simplify = TRUE) plot(ms_as_sp)
# create spatial lines data frame from ms_as_sp Sldf <- ms_as_sp$lines url
# to track in geosjon format