Different elevation sources may bring better or worse results, when
computing slopes for a road network.
In many cases, the resolution of raster digital elevation models (DEM)
does not matter much, when we assume that they will be small enough for
large segments - for instance, if we need to know the gradient of a 2km
length highway, planned for motorized vehicles.
But when physical effort matters, such as for active travel (walking,
cycling), a 50m road with a 2% gradient might be very different from an
8% gradient.
For smaller road segments, getting an accurate gradient value might be
an issue, in particular when the free and open data sources do not
provide a good resolution.
In this example, we will see and compare the results of slopes R
package for the same road network
sample (in Lisbon, Portugal), using five different elevation data
sources, with different resolution.
In active modes of transportation, such as walking or cycling, movement occurs along a network of elements that set up the pedestrian or the cycling infrastructure. Modelling such networks plays an important role in the planning of transportation, and the related policy design. Thus, spatial data on the natural and built environment is essential to effectively estimate the generic costs or efforts involved in active modes. Topography is a key component of the environment with impact on the physical effort of active travel modes: Rodrı́guez and Joo (2004) explored the direct relation between topography and people’s propensity to walk or cycle. As such, to effectively reflect active transportation effort, transportation planning analysis for active modes must take into account a generalized cost of travel reflecting the topography. Slope, the key derived data from topography that most impacts active transportation, has been demonstrated to be one of the factors that bicyclists are sensitive to when deciding on paths (Broach, Dill, and Gliebe 2012). Iseki and Tingstrom (2014) have examined the extent to which topography influences the determination of bikesheds, while Macias (2016) explored methods for the calculation of pedestrian catchment areas for public transit incorporating topography data and energy expenditure estimates. Despite its importance, topography, and its derived slope quantification, has been identified as a major data limitation in many studies concerning active accessibility in active modes (Vale, Saraiva, and Pereira 2016). Slope can be estimated using elevation data sources at various scales and obtained using distinct techniques. Slope itself can be calculated using several formulae and algorithms, especially for grid digital elevation models (DEM) (J. Tang and Pilesjö 2011; Jing Tang, Pilesjö, and Persson 2013), but for the case of the linear segments of a transportation network, longitudinal slope is to be assigned.
- Mostrar exemplos de route planners (quantos/que %) usa o declive? E como é que este é obtido?
- Mostrar as fontes (texto abaixo, RF)
Google Maps Terrain is improving every year (Harris 2014; El-Ashmawy 2016; Wang 2017). It started by using SRTM data, but now at some places, it recurs to LIDAR technology (Scott 2010), although not available yet for all places (see: https://en.wikipedia.org/wiki/National_lidar_dataset). It requires an API key for elevation access.
OpenTopoData provides a nice comparison of the available topographic data, regarding its resolution and coverage. Unfortunately, the open ones, with a good resolution, only cover USA and New Zeland.
Is their quality, resolution, etc. enough to approximate the reality?
In open datasets, they don’t always say how the slope was computed of which information was used as base.
Open data sources.
Road Network: OpenstreetMap…
For this example, we will use and compare four elevation sources:
- NASA Digital Elevation Model, with 27m cell resolution
- MapBox-Terrain tiles, with 0.1 meter height increments (ref)
- Google Elevation, with resolution of 1.9m
- Copernicus European DEM, with 25m cell resolution
- Instituto Superior Técnico Digital Elevation Model, with 10m cell resolution
The SRTM NASA’s mission Os dados do SRTM (Shuttle Radar Topography Mission), uma missão da NASA, estão disponíveis gratuitamente, mas para uma resolução de ~30m, com erro da altimetria vertical de 16m. Para fazer donwload do tile correcto, pode-se também recorrer a um outro plugin do QGIS, o SRTM-Donwloader, e pedir para guardar o raster que cobre a shapefile da rede viária - é uma opção no QGIS, e é necessário registar uma conta.
This extracted with QGIS SRTM Downloader plugin and clipped)
Package ceramic https://github.com/hypertidy/ceramic
https://docs.mapbox.com/help/troubleshooting/access-elevation-data/
Requires an API key The slopes_3d() function from slopes package
retrieves the z-values information for each vertice, storing an xy
linestring as a xyz linestring.
It varies from place to place. In Lisbon the resolution is of 1.93m.
Requires an API key
The European Land Monitoring Service (“EU-DEM V1.1”
2019)
25m resolution and vertical accuracy of +/- 7m RMSE (“Copernicus Land
Monitoring Service - Reference Data: EU-DEM (Tech.)”
2017), for all Europe. The slopes_xyz()
function from slopes package retrieves the average weighted slope from a
xyz linestring.
This DEM was acquired by Instituto Superior Técnico (University of Lisbon) by 2012, covers all the Northern Metropolitan Area of Lisbon, and has a 10m cell resolution, when projected at the official Portuguese EPSG: 3763 - TM06/ETRS89. No more is known about this raster, and it has been used in several projects at CERIS Research Center. Modelo de Helena Rua das Linhas de Torres Vedras: digitalizou-se as curvas de nível e converteu-se para raster.
A sample of Lisbon’s Road Network, available on OpenStreetMap.
After retrieving the data from “portugal” - the only dataset available
at the moment for the case study -, we will make a buffer of 2000m
around “Campo Martires Patria,” right in the center of Lisbon, and
collect a sample that contains variability regarding:
- types of highways, from large avenues to small stairs
- orthogonal and organic highways or streets
- flat and hilly highways
- flat and hilly areas
- long and short highways
- Retrieve the OSM road network and filter by highway classes, removing pathways
portugal_osm = oe_get("Portugal", provider = "geofabrik", stringsAsFactors = FALSE,
quiet = FALSE, force_download = TRUE, force_vectortranslate = TRUE) #218 MB!portugal_osm_filtered = portugal_osm %>%
dplyr::filter(
highway %in% c(
'primary',"primary_link",'secondary',"secondary_link",
'tertiary',"tertiary_link","trunk","trunk_link",
"motorway","motorway_link","service","track",
"residential","cycleway","living_street","pedestrian",
"steps", "unclassified"
)
)-
Create a buffer area with 2km around a point in the center of Lisbon, and clip the road network with it
-
Clean the road netwok, by removing unconnected segments
-
Filter from the OSM original network, the segments in the clean one
-
Breake up the road segments at their internal vertices, but leaving brunels (bridges and tunnels) intact
- Import the DEM and make sure the road network dataset has the same projection
- Estimate the gradient
RoadNetworkNASA = RoadNetwork
RoadNetworkNASA$slope = slope_raster(RoadNetworkNASA, dem = demNASA)
RoadNetworkNASA$slope_pct = RoadNetworkNASA$slope*100 #percentage- Estimate the gradient, directly with
slopes
The slopes_3d() function from slopes package retrieves the z-values
information for each vertice, storing an xy linestring as a xyz
linestring.
RoadNetworkMBox = slope_3d(r= RoadNetwork)## Preparing to download: 9 tiles at zoom = 13 from
## https://api.mapbox.com/v4/mapbox.terrain-rgb/
RoadNetworkMBox$slope = slope_xyz(RoadNetworkMBox)
RoadNetworkMBox$slope_pct = RoadNetworkMBox$slope*100 #percentage- Import the DEM and make sure the road network dataset has the same projection
- Estimate the gradient
RoadNetworkEU$slope = slope_raster(RoadNetworkEU, dem = demEU)
RoadNetworkEU$slope_pct = RoadNetworkEU$slope*100 #percentage- Estimate the gradient, directly with
slopes
The slopes_xyz function from slopes package retrieves the average
weighted slope from a xyz linestring.
RoadNetworkGMAP = readRDS("Benchmark_files/google.Rds")
RoadNetworkGMAP$slope = slope_xyz(RoadNetworkGMAP)
RoadNetworkGMAP$slope_pct = RoadNetworkGMAP$slope*100 #percentage- Import the DEM and make sure the road network dataset has the same projection
- Estimate the gradient
RoadNetworkIST$slope = slope_raster(RoadNetworkIST, dem = demIST)
RoadNetworkIST$slope_pct = RoadNetworkIST$slope*100 #percentage| resolution | Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | NA’s | |
|---|---|---|---|---|---|---|---|---|
| STRM NASA | 27.78 | -29.00 | 17.00 | 82.00 | 82.63 | 121.00 | 299.00 | -29 |
| EU-DEM Copernicus | 25.00 | -3.18 | 53.30 | 95.40 | 105.83 | 147.95 | 353.67 | 181269 |
| IST DEM | 10.00 | -0.22 | 54.96 | 87.48 | 93.85 | 122.62 | 293.51 | 695616 |
DEM information for the area
| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | |
|---|---|---|---|---|---|---|
| STRM NASA | 0.000 | 3.172 | 6.207 | 7.764 | 10.583 | 51.354 |
| Ceramic MapBox | 0.003 | 2.915 | 5.692 | 7.232 | 9.965 | 43.584 |
| EU-DEM Copernicus | 0.000 | 2.015 | 4.251 | 5.681 | 7.902 | 33.542 |
| Google Elevation | 0.000 | 1.331 | 3.449 | 5.581 | 7.175 | 127.011 |
| IST DEM | 0.000 | 1.518 | 3.711 | 5.673 | 7.523 | 77.639 |
Result summaries
Remove segments with gradient above 60%?
- Adopt a simplistic qualitative classification for cycling effort uphill, and compare the number of segments in each class
| 0-3: flat | 3-5: mild | 5-8: medium | 8-10: hard | 10-20: extreme | >20: impossible | |
|---|---|---|---|---|---|---|
| STRM NASA | 23.2 | 17.4 | 22.3 | 9.6 | 22.5 | 5.1 |
| Ceramic MapBox | 25.9 | 18.0 | 22.1 | 9.1 | 20.7 | 4.2 |
| EU-DEM Copernicus | 37.1 | 19.6 | 18.9 | 7.6 | 14.8 | 2.0 |
| Google Elevation | 45.7 | 17.2 | 15.2 | 6.0 | 11.9 | 4.0 |
| IST DEM | 42.2 | 18.8 | 16.3 | 6.9 | 12.1 | 3.8 |
Percentage of road segments in each gradient interval and qualitative class
- View maps side by side
Which statistics to use?
Check where they have a higher variation, if on flat or hilly areas,
if on organic or orthogonal streets areas…
Variation patterns
We can see that even with the finer resolution (assuming google), it brings up some errors, when road segments cross tunnels or bridges.
Which methodology we recommend, facing these results? What is the range of “resolution”, or segment length, or another variable, that best fits for slopes processing in active transportation?
This is more oriented towards bike route planners being more reliable to give a better experience to the cyclist (as opposed to a bad and demotivating experience)?
With a sample of some streets in Lisbon that we will measure with
topographical instruments
OR with official cartography available from Lisbon Municipality open
data, scale 1:1000, with several marked points
Cross-check with the validation of Lisbon Municipality cartography
Broach, Joseph, Jennifer Dill, and John Gliebe. 2012. “Where Do Cyclists Ride? A Route Choice Model Developed with Revealed Preference GPS Data.” Transportation Research Part A: Policy and Practice 46 (10): 1730–40. https://doi.org/10.1016/j.tra.2012.07.005.
“Copernicus Land Monitoring Service - Reference Data: EU-DEM (Tech.).” 2017. Copernicus. European Environment Agency. https://land.copernicus.eu/user-corner/publications/eu-dem-flyer/at_download/file.
El-Ashmawy, Khalid L. A. 2016. “Investigation of the Accuracy of Google Earth Elevation Data.” Artificial Satellites 51 (3): 89–97. https://doi.org/https://doi.org/10.1515/arsa-2016-0008.
“EU-DEM V1.1.” 2019. Copernicus. https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1.
Harris, Matt. 2014. “Compare Google API Elevation to Known Resolution Digital Elevation Models (DEM).” RPubs. RStudio. https://rpubs.com/mharris/GoogleAPI.
Iseki, Hiroyuki, and Matthew Tingstrom. 2014. “A New Approach for Bikeshed Analysis with Consideration of Topography, Street Connectivity, and Energy Consumption.” Computers, Environment and Urban Systems 48: 166–77. https://doi.org/10.1016/j.compenvurbsys.2014.07.008.
Macias, Karina. 2016. “Alternative Methods for the Calculation of Pedestrian Catchment Areas for Public Transit.” Transportation Research Record 2540 (1): 138–44. https://doi.org/10.3141/2540-15.
Rodrı́guez, Daniel A., and Joonwon Joo. 2004. “The Relationship Between Non-Motorized Mode Choice and the Local Physical Environment.” Transportation Research Part D: Transport and Environment 9 (2): 151–73. https://doi.org/10.1016/j.trd.2003.11.001.
Scott, Chelsea. 2010. “LiDAR Beginning to Appear in Google Maps Terrain Layer.” OpenTopography. National Science Foundation. https://www.opentopography.org/blog/lidar-beginning-appear-google-maps-terrain-layer.
Tang, Jing, Petter Pilesjö, and Andreas Persson. 2013. “Estimating Slope from Raster Data – a Test of Eight Algorithms at Different Resolutions in Flat and Steep Terrain.” Geodesy and Cartography 39 (2): 41–52. https://doi.org/10.3846/20296991.2013.806702.
Tang, J, and P Pilesjö. 2011. “Estimating Slope from Raster Data: A Test of Eight Different Algorithms in Flat, Undulating and Steep Terrain.” River Basin Management VI. https://www.witpress.com/elibrary/wit-transactions-on-ecology-and-the-environment/146/22157.
Vale, David S., Miguel Saraiva, and Mauro Pereira. 2016. “Active Accessibility: A Review of Operational Measures of Walking and Cycling Accessibility.” Journal of Transport and Land Use 9 (1). https://doi.org/10.5198/jtlu.2015.593.
Wang, Yajie AND Henrickson, Yinsong AND Zou. 2017. “Google Earth Elevation Data Extraction and Accuracy Assessment for Transportation Applications.” PLOS ONE 12 (4): 1–17. https://doi.org/10.1371/journal.pone.0175756.