From 199dd82129e6356db673a0d9a7898d17f828c05a Mon Sep 17 00:00:00 2001 From: temospena Date: Tue, 24 Aug 2021 00:38:23 +0100 Subject: [PATCH 1/7] reorganize README and add some functions. --- README.Rmd | 235 ++++++++++----- README.md | 334 +++++++++++++++------- man/figures/README-dem-lisbon-1.png | Bin 78526 -> 47796 bytes man/figures/README-linestringmap-1.png | Bin 227612 -> 101219 bytes man/figures/README-plot_slope-1.png | Bin 24761 -> 6081 bytes man/figures/README-plotauto-1.png | Bin 25934 -> 6270 bytes man/figures/README-route-1.png | Bin 155308 -> 154725 bytes man/figures/README-slope-vis-1.png | Bin 84827 -> 49139 bytes man/figures/README-unnamed-chunk-11-1.png | Bin 0 -> 243050 bytes 9 files changed, 405 insertions(+), 164 deletions(-) create mode 100644 man/figures/README-unnamed-chunk-11-1.png diff --git a/README.Rmd b/README.Rmd index b62c184..ca6a613 100644 --- a/README.Rmd +++ b/README.Rmd @@ -13,7 +13,7 @@ knitr::opts_chunk$set( ) ``` -# slopes +# slopes package [![R-CMD-check](https://github.com/itsleeds/slopes/workflows/R-CMD-check/badge.svg)](https://github.com/itsleeds/slopes/actions) @@ -21,15 +21,27 @@ knitr::opts_chunk$set( [![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/420_status.svg)](https://github.com/ropensci/software-review/issues/420) -The **slopes** R package calculates the slope (longitudinal steepness, also known as gradient) of linear features such as roads and rivers, based on two main inputs: +The **slopes** R package calculates the slope (longitudinal steepness, also known as gradient) of roads, rivers and other linear (simple) features, based on two main inputs: -- [vector](https://geocompr.robinlovelace.net/spatial-class.html#vector-data) linestring geometries defined by classes in the [`sf` package](https://r-spatial.github.io/sf/) -- [raster](https://geocompr.robinlovelace.net/spatial-class.html#raster-data) objects with pixel values reporting average height, commonly known as digital elevation model (DEM) datasets, defined by classes in the [`raster`](https://cran.r-project.org/package=raster) or more recent [`terra`](https://rspatial.org/terra) packages +- [vector](https://geocompr.robinlovelace.net/spatial-class.html#vector-data) linestring geometries defined by classes in the [`sf`](https://r-spatial.github.io/sf/) package +- [raster](https://geocompr.robinlovelace.net/spatial-class.html#raster-data) objects with pixel values reporting average height, commonly known as digital elevation model (**DEM**) datasets, defined by classes in the [`raster`](https://cran.r-project.org/package=raster) or more recent [`terra`](https://rspatial.org/terra) packages -This README covers installation and basic usage. -For more information about slopes and how to use the package to calculate them, see the [slopes vignette](https://itsleeds.github.io/slopes/). +This README covers installation and basic usage. For more information about slopes and how to use the package to calculate them, see the [get started](https://itsleeds.github.io/slopes/) and the [introducion to slopes](https://itsleeds.github.io/intro-to-slopes/) vignette. -## Installation + +## How it works + +The package takes two main types of input data for slope calculation: +- vector geographic objects representing **linear features**, and +- **elevation values** from a digital elevation model representing a continuous terrain surface or which can be downloaded using functionality in the package + +The package can be used with two sources of elevation data: +- openly available elevation data via an interface to the [ceramic package](https://github.com/hypertidy/ceramic), enabling estimation of hilliness for routes anywhere worldwide even when local elevation data is lacking. The package takes geographic lines objects and returns elevation data per vertex (providing the output as a 3D point geometry in the sf package by default) and per line feature (providing average gradient by default). +- an elevation model, available on your machine. + +## Getting started + +### Installation @@ -44,9 +56,9 @@ Install the development version from [GitHub](https://github.com/) with: remotes::install_github("itsleeds/slopes") ``` -### Installation for DEM downloads +#### Installation for DEM downloads -If you do not already have DEM data and want to make use of the package's ability to download them using the `ceramic` package, install the package with suggested dependencies as follows: +If you do not already have DEM data and want to make use of the package's ability to download them using the `ceramic` package, install the package with suggested dependencies, as follows: ```{r, eval=FALSE} # install.packages("remotes") @@ -57,60 +69,59 @@ Furthermore, you will need to add a MapBox API key to be able to get DEM dataset ```{r, eval=FALSE} usethis::edit_r_environ() -# Type in (register on the mapbox website): -MAPBOX_API_KEY=xxxxx +MAPBOX_API_KEY=xxxxx # replace XXX with your api key ``` -## Basic example +## Basic examples -Load the package in the usual way: +Load the package in the usual way. We will also load the `sf` library: -```{r} +```{r message=FALSE, warning=FALSE} library(slopes) -``` - -We will also load the `sf` library: - -```{r} library(sf) ``` -The minimum input data requirement for using the package is an `sf` object containing LINESTRING geometries. -You can check your input dataset is suitable with the functions `class()` from base R and `st_geometry_type()` from the `sf` package, as demonstrated below on the object `lisbon_road_segment` that is contained within the package: +The minimum input data requirement for using the package is an `sf` object containing LINESTRING geometries. -```{r} -class(lisbon_road_segment) -st_geometry_type(lisbon_road_segment) -``` +### Sinlge road segment + no DEM -Don't worry if you don't yet have your linear features in this class: you can read-in data from a wide range of formats into an `sf` object. -You can also create `sf` objects from a matrix of coordinates, as illustrated below (don't worry about the details for now, you can read up on how all this works in the `sf` package [documentation](https://r-spatial.github.io/sf/articles/sf1.html)): - -```{r, eval=FALSE, echo=FALSE} -m = st_coordinates(sf::st_transform(lisbon_road_segment, 4326)) -s = seq(from = 1, to = nrow(m), length.out = 4) -round(m[s, 1:2], 5) -dput(round(m[s, 1], 4)) -dput(round(m[s, 2], 4)) -``` +You can check your input dataset is suitable with the functions `class()` from base R and `st_geometry_type()` from the `sf` package, as demonstrated below on the example object `lisbon_road_segment` that is contained within the package: ```{r} -m = cbind( - c(-9.1333, -9.134, -9.13), - c(38.714, 38.712, 38.710) -) -sf_linestring = sf::st_sf( - data.frame(id = 1), - geometry = st_sfc(st_linestring(m)), - crs = 4326 -) +sf_linestring = lisbon_road_segment class(sf_linestring) st_geometry_type(sf_linestring) ``` +Don't worry if you don't yet have your linear features in this class: you can read-in data from a wide range of formats into an `sf` object. + + + + + + + + + + + + + + + + + + + + + + + + + A quick way of testing if your object can have slopes calculated for it is to plot it in an interactive map and to check that underneath the object there is indeed terrain that will give the linestrings gradient: -```{r linestringmap} +```{r linestringmap, message=FALSE, warning=FALSE} library(tmap) tmap_mode("view") tm_shape(sf_linestring) + @@ -118,44 +129,122 @@ tm_shape(sf_linestring) + tm_basemap(leaflet::providers$Esri.WorldTopoMap) ``` -Imagine you want to calculate the gradient of the imaginary route shown above, starting at the Castelo de São Jorge and descending towards the coast. - +Imagine you want to calculate the gradient of the route shown above. You can do this as a two step process as follows. -Step 1: add elevations to each coordinate in the linestring (requires a MapBox API key): +**Step 1**: add elevations to each coordinate in the linestring (requires a [MapBox API](https://account.mapbox.com/access-tokens/) key): ```{r} sf_linestring_xyz = elevation_add(sf_linestring) ``` -You can check the elevations added to the new `sf_linestring_xyz` object by printing its coordinates, as follows (note the new Z column that goes from above 90 m above sea level to only 24 m in a short distance): +You can check the elevations added to the new `sf_linestring_xyz` object by printing its coordinates, as follows (note the new Z column that goes from above 87 m above sea level to only 79 m in a short distance). ```{r} st_coordinates(sf_linestring_xyz) ``` -Step 2: calculate the average slope of the linestring +You can use the `z_` functions to extract such values: + +```{r} +z_value(sf_linestring_xyz) # returns all the elevation values between xy coordinates + +z_mean(sf_linestring_xyz) # elevation mean value +z_min(sf_linestring_xyz) # elevation min value +z_max(sf_linestring_xyz) # elevation max value +z_start(sf_linestring_xyz) # first z +z_end(sf_linestring_xyz) # last z +``` + + +**Step 2**: calculate the average slope of the linestring ```{r} slope_xyz(sf_linestring_xyz) ``` -The result, just over 0.1, tells us that it's quite a steep slope, a 10% gradient *on average*. +The result, just over 0.2, tells us that it's quite a steep slope: a 21% gradient *on average*. + +### Route + available DEM + +Using the slopes package we can estimate the gradient of individual road segments. When these segments are combined into routes, we then need a means of assessing the hilliness of the entire route. A range of indices can be used to represent route hilliness. The choice of which index is most appropriate may be context dependent (see the [introducion to slopes](https://itsleeds.github.io/intro-to-slopes/) vignette). + +Again, let us use the same function with a entire route, `lisbon_route`, also available in the package: + +```{r message=FALSE, warning=FALSE} +sf_route = lisbon_route +class(sf_route) +st_geometry_type(sf_route) + +tm_shape(sf_route) + + tm_lines(lwd = 3) + + tm_basemap(leaflet::providers$Esri.WorldTopoMap) +``` + +**Step 1**: add elevations to each coordinate in the route: + +```{r} +sf_route_xyz = elevation_add(sf_route) +``` + +**Step 2**: calculate the average slope of the route + +```{r} +slope_xyz(sf_route_xyz) +``` + +The result shows a 7.7% gradient *on average*. -If you already have a DEM, you can calculate the slopes directly as follows, with `slope_raster()`: +Now, if you already have a DEM, you can calculate the slopes directly as follows, with `slope_raster()`: ```{r} class(dem_lisbon_raster) -sf_linestring_proj = st_transform(sf_linestring, st_crs(lisbon_road_segment)) -slope_raster(routes = sf_linestring_proj, dem = dem_lisbon_raster) +slope_raster(routes = sf_route, + dem = dem_lisbon_raster) +``` +The result shows a 7.8% gradient *on average*. +As you can see, the retrieved result from elevation information available in mapbox and in this Digital Elevation Model, is quite similar. (See more about these differences in [Verification of slopes](https://itsleeds.github.io/slopes/articles/verification.html).) + +### Route with xyz coordinates + +If your linestring object already has X, Y and Z coordinates (e.g. from a GPS device), you can use the `slope_` functions directly. + +```{r eval=FALSE, include=FALSE} +#not to use like this... it would ge good to have a gps example to demonstrate + +slope_vector(sf_route_xyz) +slope_distance(sf_route_xyz) +slope_distance_mean(sf_route_xyz) +slope_distance_weighted(sf_route_xyz) + +slope_vector(sf_linestring_xyz) +slope_distance(sf_linestring_xyz) +slope_distance_mean(sf_linestring_xyz) +slope_distance_weighted(sf_linestring_xyz) +``` +```{r} +# for a line xz +x = c(0, 2, 3, 4, 5, 9) +elevations = c(1, 2, 2, 4, 3, 1) / 10 +slope_vector(x, elevations) + + +# for a path xyz +xy = st_coordinates(sf_linestring) +dist = sequential_dist(xy, lonlat = FALSE) +elevations = elevation_extract(xy, dem_lisbon_raster) + +slope_distance(dist, elevations) +slope_distance_mean(dist, elevations) +slope_distance_weighted(dist, elevations) ``` -Likewise, if your linestring object already has X, Y and Z coordinates (e.g. from a GPS device), you can use the `slope_` functions directly. -In any case, to use the `slopes` package you need elevation points, either as a vector, a matrix or as a digital elevation model (DEM) encoded as a raster dataset. -## Calculating the gradient of roads +In any case, to use the `slopes` package you need **elevation points**, either as a vector, a matrix or as a digital elevation model (DEM) encoded as a raster dataset. -Typical use cases for the package are calculating the slopes of geographic objects representing roads or other linear features (not the imaginary line shown in the previous section). +## Calculating and plotting the gradient of roads + +Typical use cases for the package are calculating the slopes of geographic objects representing roads or other linear features. These two types of input data are represented in the code output and plot below. ```{r dem-lisbon} @@ -169,7 +258,7 @@ class(lisbon_road_segments) plot(sf::st_geometry(lisbon_road_segments), add = TRUE) ``` -Calculate the average gradient of each road segment as follows: +Calculate the average gradient of **each road segment** as follows: ```{r} lisbon_road_segments$slope = slope_raster(lisbon_road_segments, dem = dem_lisbon_raster) @@ -179,7 +268,8 @@ summary(lisbon_road_segments$slope) This created a new column, `slope` that represents the average, distance weighted slope associated with each road segment. The units represent the percentage incline, that is the change in elevation divided by distance. The summary of the result tells us that the average gradient of slopes in the example data is just over 5%. -This result is equivalent to that returned by ESRI's `Slope_3d()` in the [3D Analyst extension](https://desktop.arcgis.com/en/arcmap/10.3/tools/3d-analyst-toolbox/slope.htm), with a correlation between the ArcMap implementation and our implementation of more than 0.95 on our test dataset (we find higher correlations on larger datasets): + +This result is equivalent to that returned by ESRI's `Slope_3d()` in the [3D Analyst extension](https://desktop.arcgis.com/en/arcmap/10.3/tools/3d-analyst-toolbox/slope.htm), with a correlation between the ArcMap implementation and our implementation of more than 0.95 on our test dataset (we find higher correlations on larger datasets - see the [verification of slopes](https://itsleeds.github.io/slopes/articles/verification.html article): ```{r} cor( @@ -188,29 +278,28 @@ cor( ) ``` -We can now visualise the slopes calculated by the `slopes` package as follows: +We can now visualise the average slopes of each route calculated by the `slopes` package as follows: ```{r slope-vis} raster::plot(dem_lisbon_raster) plot(lisbon_road_segments["slope"], add = TRUE, lwd = 5) -# mapview::mapview(lisbon_road_segments["slope"], map.types = "Esri.WorldStreetMap") ``` -Imagine that we want to go from Santa Catarina to the East of the map to the Castelo de Sao Jorge to the West of the map: +Imagine that we want to go from East hilly area of the map (Santa Catarina) to the West hilly area of the map (Castelo de São Jorge): ```{r route} -library(tmap) -tmap_mode("view") +# library(tmap) +# tmap_mode("view") qtm(lisbon_route) ``` ```{r, echo=FALSE, eval=FALSE} # Removed because it's not rendering in RMarkdown +mapview::mapview(lisbon_road_segments["slope"], map.types = "Esri.WorldStreetMap") mapview::mapview(lisbon_route) ``` - -We can convert the `lisbon_route` object into a 3d linestring object with X, Y and Z coordinates, as follows: +We can convert the `lisbon_route` object into a 3d linestring object with X, Y and Z coordinates, using the elevation values stored in the DEM, as follows: ```{r, warning=FALSE, echo=FALSE, eval=FALSE} sln = stplanr::SpatialLinesNetwork(lisbon_road_segments) @@ -233,7 +322,7 @@ usethis::use_data(lisbon_route_3d, overwrite = TRUE) lisbon_route_xyz = elevation_add(lisbon_route, dem_lisbon_raster) ``` -We can now visualise the elevation profile of the route as follows: +We can now visualise the **elevation profile** of the route as follows: ```{r plot_slope} plot_slope(lisbon_route_xyz) @@ -255,6 +344,16 @@ lisbon_route_xyz_auto = elevation_add(lisbon_route) plot_slope(lisbon_route_xyz_auto) ``` +## See more in vignettes + +- [Get started](https://itsleeds.github.io/slopes/articles/slopes.html) +- [An introduction to slopes](https://itsleeds.github.io/slopes/articles/intro-to-slopes.html) +- [Reproducible example: gradients of a road network for a given city](https://itsleeds.github.io/slopes/articles/roadnetworkcycling.html) +- [Verification of slopes](https://itsleeds.github.io/slopes/articles/verification.html) +- [Benchmarking slopes calculation](https://itsleeds.github.io/slopes/articles/benchmark.html) + + + ## Code of Conduct Please note that the slopes project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms. diff --git a/README.md b/README.md index 3f2cb9f..1dfc829 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ -# slopes +# slopes package @@ -13,23 +13,43 @@ Review](https://badges.ropensci.org/420_status.svg)](https://github.com/ropensci The **slopes** R package calculates the slope (longitudinal steepness, -also known as gradient) of linear features such as roads and rivers, -based on two main inputs: +also known as gradient) of roads, rivers and other linear (simple) +features, based on two main inputs: - [vector](https://geocompr.robinlovelace.net/spatial-class.html#vector-data) - linestring geometries defined by classes in the [`sf` - package](https://r-spatial.github.io/sf/) + linestring geometries defined by classes in the + [`sf`](https://r-spatial.github.io/sf/) package - [raster](https://geocompr.robinlovelace.net/spatial-class.html#raster-data) objects with pixel values reporting average height, commonly known - as digital elevation model (DEM) datasets, defined by classes in the - [`raster`](https://cran.r-project.org/package=raster) or more recent - [`terra`](https://rspatial.org/terra) packages + as digital elevation model (**DEM**) datasets, defined by classes in + the [`raster`](https://cran.r-project.org/package=raster) or more + recent [`terra`](https://rspatial.org/terra) packages This README covers installation and basic usage. For more information -about slopes and how to use the package to calculate them, see the -[slopes vignette](https://itsleeds.github.io/slopes/). +about slopes and how to use the package to calculate them, see the [get +started](https://itsleeds.github.io/slopes/) and the [introducion to +slopes](https://itsleeds.github.io/intro-to-slopes/) vignette. -## Installation +## How it works + +The package takes two main types of input data for slope calculation: - +vector geographic objects representing **linear features**, and - +**elevation values** from a digital elevation model representing a +continuous terrain surface or which can be downloaded using +functionality in the package + +The package can be used with two sources of elevation data: - openly +available elevation data via an interface to the [ceramic +package](https://github.com/hypertidy/ceramic), enabling estimation of +hilliness for routes anywhere worldwide even when local elevation data +is lacking. The package takes geographic lines objects and returns +elevation data per vertex (providing the output as a 3D point geometry +in the sf package by default) and per line feature (providing average +gradient by default). - an elevation model, available on your machine. + +## Getting started + +### Installation @@ -43,11 +63,11 @@ Install the development version from [GitHub](https://github.com/) with: remotes::install_github("itsleeds/slopes") ``` -### Installation for DEM downloads +#### Installation for DEM downloads If you do not already have DEM data and want to make use of the package’s ability to download them using the `ceramic` package, install -the package with suggested dependencies as follows: +the package with suggested dependencies, as follows: ``` r # install.packages("remotes") @@ -61,62 +81,61 @@ steps: ``` r usethis::edit_r_environ() -# Type in (register on the mapbox website): -MAPBOX_API_KEY=xxxxx +MAPBOX_API_KEY=xxxxx # replace XXX with your api key ``` -## Basic example +## Basic examples -Load the package in the usual way: +Load the package in the usual way. We will also load the `sf` library: ``` r library(slopes) -``` - -We will also load the `sf` library: - -``` r library(sf) -#> Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1 ``` The minimum input data requirement for using the package is an `sf` -object containing LINESTRING geometries. You can check your input -dataset is suitable with the functions `class()` from base R and -`st_geometry_type()` from the `sf` package, as demonstrated below on the -object `lisbon_road_segment` that is contained within the package: +object containing LINESTRING geometries. + +### Sinlge road segment + no DEM + +You can check your input dataset is suitable with the functions +`class()` from base R and `st_geometry_type()` from the `sf` package, as +demonstrated below on the example object `lisbon_road_segment` that is +contained within the package: ``` r -class(lisbon_road_segment) +sf_linestring = lisbon_road_segment +class(sf_linestring) #> [1] "sf" "tbl_df" "tbl" "data.frame" -st_geometry_type(lisbon_road_segment) +st_geometry_type(sf_linestring) #> [1] LINESTRING #> 18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE ``` Don’t worry if you don’t yet have your linear features in this class: you can read-in data from a wide range of formats into an `sf` object. -You can also create `sf` objects from a matrix of coordinates, as -illustrated below (don’t worry about the details for now, you can read -up on how all this works in the `sf` package -[documentation](https://r-spatial.github.io/sf/articles/sf1.html)): -``` r -m = cbind( - c(-9.1333, -9.134, -9.13), - c(38.714, 38.712, 38.710) -) -sf_linestring = sf::st_sf( - data.frame(id = 1), - geometry = st_sfc(st_linestring(m)), - crs = 4326 -) -class(sf_linestring) -#> [1] "sf" "data.frame" -st_geometry_type(sf_linestring) -#> [1] LINESTRING -#> 18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE -``` + + + + + + + + + + + + + + + + + + + + + A quick way of testing if your object can have slopes calculated for it is to plot it in an interactive map and to check that underneath the @@ -125,7 +144,6 @@ object there is indeed terrain that will give the linestrings gradient: ``` r library(tmap) tmap_mode("view") -#> tmap mode set to interactive viewing tm_shape(sf_linestring) + tm_lines(lwd = 5) + tm_basemap(leaflet::providers$Esri.WorldTopoMap) @@ -133,46 +151,125 @@ tm_shape(sf_linestring) + -Imagine you want to calculate the gradient of the imaginary route shown -above, starting at the Castelo de São Jorge and descending towards the -coast. +Imagine you want to calculate the gradient of the route shown above. You +can do this as a two step process as follows. -You can do this as a two step process as follows. - -Step 1: add elevations to each coordinate in the linestring (requires a -MapBox API key): +**Step 1**: add elevations to each coordinate in the linestring +(requires a [MapBox API](https://account.mapbox.com/access-tokens/) +key): ``` r sf_linestring_xyz = elevation_add(sf_linestring) #> Loading required namespace: ceramic -#> Preparing to download: 16 tiles at zoom = 17 from +#> Preparing to download: 9 tiles at zoom = 18 from #> https://api.mapbox.com/v4/mapbox.terrain-rgb/ ``` You can check the elevations added to the new `sf_linestring_xyz` object by printing its coordinates, as follows (note the new Z column that goes -from above 90 m above sea level to only 24 m in a short distance): +from above 87 m above sea level to only 79 m in a short distance). ``` r st_coordinates(sf_linestring_xyz) -#> X Y Z L1 -#> [1,] -9.1333 38.714 91.4 1 -#> [2,] -9.1340 38.712 66.9 1 -#> [3,] -9.1300 38.710 24.0 1 +#> X Y Z L1 +#> [1,] -87064.34 -105506.3 87.80116 1 +#> [2,] -87065.47 -105514.3 87.15377 1 +#> [3,] -87066.60 -105522.3 79.30441 1 +#> [4,] -87067.73 -105530.3 85.72817 1 +#> [5,] -87068.86 -105538.2 85.90000 1 +#> [6,] -87069.99 -105546.2 85.06419 1 +#> [7,] -87075.24 -105548.4 83.11215 1 +#> [8,] -87080.48 -105550.5 81.09028 1 +#> [9,] -87080.06 -105560.1 80.41115 1 +#> [10,] -87079.65 -105569.6 79.72663 1 +#> [11,] -87079.23 -105579.2 78.76135 1 +#> [12,] -87078.81 -105588.8 77.93748 1 +#> [13,] -87078.39 -105598.3 77.00873 1 +#> [14,] -87069.73 -105601.7 79.73702 1 +#> [15,] -87068.93 -105608.4 79.20985 1 +#> [16,] -87068.14 -105615.1 78.59588 1 +#> [17,] -87067.34 -105621.7 78.05968 1 +#> [18,] -87062.16 -105625.7 79.19526 1 +#> [19,] -87056.99 -105629.6 79.92942 1 +``` + +You can use the `z_` functions to extract such values: + +``` r +z_value(sf_linestring_xyz) # returns all the elevation values between xy coordinates +#> [1] 87.80116 87.15377 79.30441 85.72817 85.90000 85.06419 83.11215 81.09028 +#> [9] 80.41115 79.72663 78.76135 77.93748 77.00873 79.73702 79.20985 78.59588 +#> [17] 78.05968 79.19526 79.92942 + +z_mean(sf_linestring_xyz) # elevation mean value +#> [1] 81.24877 +z_min(sf_linestring_xyz) # elevation min value +#> [1] 77.00873 +z_max(sf_linestring_xyz) # elevation max value +#> [1] 87.80116 +z_start(sf_linestring_xyz) # first z +#> [1] 87.80116 +z_end(sf_linestring_xyz) # last z +#> [1] 79.92942 ``` -Step 2: calculate the average slope of the linestring +**Step 2**: calculate the average slope of the linestring ``` r slope_xyz(sf_linestring_xyz) -#> 1 -#> 0.104923 +#> 1 +#> 0.21318 ``` -The result, just over 0.1, tells us that it’s quite a steep slope, a 10% +The result, just over 0.2, tells us that it’s quite a steep slope: a 21% gradient *on average*. -If you already have a DEM, you can calculate the slopes directly as +### Route + available DEM + +Using the slopes package we can estimate the gradient of individual road +segments. When these segments are combined into routes, we then need a +means of assessing the hilliness of the entire route. A range of indices +can be used to represent route hilliness. The choice of which index is +most appropriate may be context dependent (see the [introducion to +slopes](https://itsleeds.github.io/intro-to-slopes/) vignette). + +Again, let us use the same function with a entire route, `lisbon_route`, +also available in the package: + +``` r +sf_route = lisbon_route +class(sf_route) +#> [1] "sf" "tbl_df" "tbl" "data.frame" +st_geometry_type(sf_route) +#> [1] LINESTRING +#> 18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE + +tm_shape(sf_route) + + tm_lines(lwd = 3) + + tm_basemap(leaflet::providers$Esri.WorldTopoMap) +``` + + + +**Step 1**: add elevations to each coordinate in the route: + +``` r +sf_route_xyz = elevation_add(sf_route) +#> Preparing to download: 12 tiles at zoom = 15 from +#> https://api.mapbox.com/v4/mapbox.terrain-rgb/ +``` + +**Step 2**: calculate the average slope of the route + +``` r +slope_xyz(sf_route_xyz) +#> 1 +#> 0.07681812 +``` + +The result shows a 7.7% gradient *on average*. + +Now, if you already have a DEM, you can calculate the slopes directly as follows, with `slope_raster()`: ``` r @@ -180,24 +277,57 @@ class(dem_lisbon_raster) #> [1] "RasterLayer" #> attr(,"package") #> [1] "raster" -sf_linestring_proj = st_transform(sf_linestring, st_crs(lisbon_road_segment)) -slope_raster(routes = sf_linestring_proj, dem = dem_lisbon_raster) -#> 1 -#> 0.1218691 +slope_raster(routes = sf_route, + dem = dem_lisbon_raster) +#> 1 +#> 0.07817098 +``` + +The result shows a 7.8% gradient *on average*. As you can see, the +retrieved result from elevation information available in mapbox and in +this Digital Elevation Model, is quite similar. (See more about these +differences in [Verification of +slopes](https://itsleeds.github.io/slopes/articles/verification.html).) + +### Route with xyz coordinates + +If your linestring object already has X, Y and Z coordinates (e.g. from +a GPS device), you can use the `slope_` functions directly. + +``` r +# for a line xz +x = c(0, 2, 3, 4, 5, 9) +elevations = c(1, 2, 2, 4, 3, 1) / 10 +slope_vector(x, elevations) +#> [1] 0.05 0.00 0.20 -0.10 -0.05 + + +# for a path xyz +xy = st_coordinates(sf_linestring) +dist = sequential_dist(xy, lonlat = FALSE) +elevations = elevation_extract(xy, dem_lisbon_raster) + +slope_distance(dist, elevations) +#> [1] -0.047226259 -0.040883072 -0.025032918 -0.061124557 -0.017447060 +#> [6] -0.062426272 -0.123580541 0.033705378 0.004292243 -0.040360003 +#> [11] -0.151671893 -0.182367906 0.409246854 -0.034463974 -0.098406640 +#> [16] -0.161798173 0.076261379 0.100654228 +slope_distance_mean(dist, elevations) +#> [1] 0.09283052 +slope_distance_weighted(dist, elevations) +#> [1] 0.09501323 ``` -Likewise, if your linestring object already has X, Y and Z coordinates -(e.g. from a GPS device), you can use the `slope_` functions directly. -In any case, to use the `slopes` package you need elevation points, +In any case, to use the `slopes` package you need **elevation points**, either as a vector, a matrix or as a digital elevation model (DEM) encoded as a raster dataset. -## Calculating the gradient of roads +## Calculating and plotting the gradient of roads Typical use cases for the package are calculating the slopes of -geographic objects representing roads or other linear features (not the -imaginary line shown in the previous section). These two types of input -data are represented in the code output and plot below. +geographic objects representing roads or other linear features. These +two types of input data are represented in the code output and plot +below. ``` r # A raster dataset included in the package: @@ -218,7 +348,7 @@ plot(sf::st_geometry(lisbon_road_segments), add = TRUE) -Calculate the average gradient of each road segment as follows: +Calculate the average gradient of **each road segment** as follows: ``` r lisbon_road_segments$slope = slope_raster(lisbon_road_segments, dem = dem_lisbon_raster) @@ -231,12 +361,15 @@ This created a new column, `slope` that represents the average, distance weighted slope associated with each road segment. The units represent the percentage incline, that is the change in elevation divided by distance. The summary of the result tells us that the average gradient -of slopes in the example data is just over 5%. This result is equivalent -to that returned by ESRI’s `Slope_3d()` in the [3D Analyst +of slopes in the example data is just over 5%. + +This result is equivalent to that returned by ESRI’s `Slope_3d()` in the +[3D Analyst extension](https://desktop.arcgis.com/en/arcmap/10.3/tools/3d-analyst-toolbox/slope.htm), with a correlation between the ArcMap implementation and our implementation of more than 0.95 on our test dataset (we find higher -correlations on larger datasets): +correlations on larger datasets - see the [verification of +slopes](https://itsleeds.github.io/slopes/articles/verification.html%20article): ``` r cor( @@ -246,8 +379,8 @@ cor( #> [1] 0.9770436 ``` -We can now visualise the slopes calculated by the `slopes` package as -follows: +We can now visualise the average slopes of each route calculated by the +`slopes` package as follows: ``` r raster::plot(dem_lisbon_raster) @@ -256,30 +389,26 @@ plot(lisbon_road_segments["slope"], add = TRUE, lwd = 5) -``` r -# mapview::mapview(lisbon_road_segments["slope"], map.types = "Esri.WorldStreetMap") -``` - -Imagine that we want to go from Santa Catarina to the East of the map to -the Castelo de Sao Jorge to the West of the map: +Imagine that we want to go from East hilly area of the map (Santa +Catarina) to the West hilly area of the map (Castelo de São Jorge): ``` r -library(tmap) -tmap_mode("view") -#> tmap mode set to interactive viewing +# library(tmap) +# tmap_mode("view") qtm(lisbon_route) ``` We can convert the `lisbon_route` object into a 3d linestring object -with X, Y and Z coordinates, as follows: +with X, Y and Z coordinates, using the elevation values stored in the +DEM, as follows: ``` r lisbon_route_xyz = elevation_add(lisbon_route, dem_lisbon_raster) ``` -We can now visualise the elevation profile of the route as follows: +We can now visualise the **elevation profile** of the route as follows: ``` r plot_slope(lisbon_route_xyz) @@ -311,6 +440,19 @@ plot_slope(lisbon_route_xyz_auto) +## See more in vignettes + +- [Get + started](https://itsleeds.github.io/slopes/articles/slopes.html) +- [An introduction to + slopes](https://itsleeds.github.io/slopes/articles/intro-to-slopes.html) +- [Reproducible example: gradients of a road network for a given + city](https://itsleeds.github.io/slopes/articles/roadnetworkcycling.html) +- [Verification of + slopes](https://itsleeds.github.io/slopes/articles/verification.html) +- [Benchmarking slopes + calculation](https://itsleeds.github.io/slopes/articles/benchmark.html) + ## Code of Conduct Please note that the slopes project is released with a [Contributor Code diff --git a/man/figures/README-dem-lisbon-1.png b/man/figures/README-dem-lisbon-1.png index 5e343bc94172ac1406cb3e054590a07bba299efb..d0d9be1b23bc119b0e95ad2e25b68d9812d982f8 100644 GIT binary patch literal 47796 zcmeFY^;aBC)GgeD22apnfrJ3T-JK8!!QCymyW0SPgkZtlgS+eC!QCB#>mY;cojmXN zuJ!#5cikUmO|6=qs_N6HPVaMS?@&c~DJ*mnbN~Rbq(4h20{}u60D#tCAi#Srp`VT6 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zD%Cw&${Xd1z7bKZ$KhilEsbu0B9Tb7$K^bSagN25Op4N9Biq+*1fC(4m!6^$1d$=q zA-5IoN#pYJ0GgqO+peX9pTiF|A( zlc7*304nC|l3GJ3(y3u+>EBC#lTgKnG0nTt32|mpxP&kY8C+h@IfDmtyt|uY$BxCF z@}lmT6y#$H1eHwf(gPx0^`7TZR#rwXn5gCh literal 0 HcmV?d00001 From e2122e1115458a364c5e5b2b4770d50d529456c2 Mon Sep 17 00:00:00 2001 From: temospena Date: Tue, 24 Aug 2021 01:02:28 +0100 Subject: [PATCH 2/7] move "slopes" vignette to "intro to slopes", and add information. --- vignettes/intro-to-slopes.Rmd | 115 +++++++++++++++++++++++++++++ vignettes/slopes.Rmd | 135 +++++++++++++++++----------------- 2 files changed, 181 insertions(+), 69 deletions(-) create mode 100644 vignettes/intro-to-slopes.Rmd diff --git a/vignettes/intro-to-slopes.Rmd b/vignettes/intro-to-slopes.Rmd new file mode 100644 index 0000000..d04bda9 --- /dev/null +++ b/vignettes/intro-to-slopes.Rmd @@ -0,0 +1,115 @@ +--- +title: "An introduction to slopes" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{An introduction to slopes} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +bibliography: slope-references.bib +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +```{r setup} +library(slopes) +``` + +## Introduction + +Although there are several ways to name "slope", such as "steepness", "hilliness", "inclination", "aspect", "gradient", "declivity", the referred `slopes` in this package can be defined as the "longitudinal gradient" of linear geographic entities, as defined in the context of rivers by[@cohen_global_2018]. + +The package was initially developed to research road slopes but has also been used to calculate and visualise slopes of rivers and trajectories representing movement on roads, using an open dataset published by [@ariza-lopez_dataset_2019]. + +\ + +A growing number of people working with geospatial data and require accurate estimates of gradient. + +- Transport planning practitioners require accurate estimates of roadway gradient for estimating energy consumption, safety and mode shift potential in hilly cities (such as Lisbon, the case study city used in the examples in the documentation). +- Vehicle routing models that are sensitive to going up or down steep hills (e.g. bicycles, trains, and large trucks), such as active travel planning, logistics, and emergency services. +- Natural hazard researchers and risk assessors require estimates of linear gradient to inform safety and mitigation plans associated with project on hilly terrain. +- We believe that aquatic ecologists, flooding researchers and others could benefit from estimates of river gradient to support modelling of storm hydrographs, although we do not have experience in these domains +- Other domains, such as: agriculture, geology, and infrastructure constructions. + +An example of the demand for data provided by the package is a map showing gradients across Sao Paulo (Brazil, see image below) that has received more than 300 'likes' on Twitter and generated conversations: https://twitter.com/DanielGuth/status/1347270685161304069 + +![](https://camo.githubusercontent.com/30a3b814dd72aef5b51db635f2ab6e1b6b6c57b856d239822788967a4932d655/68747470733a2f2f7062732e7477696d672e636f6d2f6d656469612f45724a32647238574d414948774d6e3f666f726d61743d6a7067266e616d653d6c61726765){ width=50% } + + +## Calculating slopes + +The most common slope calculation method is defined by the vertical difference of the final and start point or line height (z1 and z0) divided by the horizontal length that separates them. + +$$ +s = \Delta z/l +$$ + +Depending on the purpose of application, it might me relevant to understand how hilliness is estimated. + +![Traffic sign](https://sinalnorte.com/wp-content/uploads/2018/02/A3b.jpg){ width=10% } + +### Measures of route hilliness + +There are many ways to measure hilliness, mean distance weighted hilliness being perhaps the most common. +These measures, and their implementation (or current lack thereof) in the package is summarised below. + +- **Mean distance weighted gradient**. Perhaps the simplest and most widely applicable measure is the mean gradient of the route. This should be weighted by the distance of each segment. Implemented by default in the `slope_raster()` function. + +- **Max gradient**. For activities like cycling, where steep hills have a disproportionate impact, it may be useful to consider the maximum gradient. Not yet implemented. + + + +- **Xth percentile gradient**. Since the maximum gradient gives no information about the rest of the route segments, other measures such as the 75th percentile gradient could be more informative. Not yet implemented. + +- **Inverted harmonic mean**. If we use the following formula we will get an index that (like the arithmetic mean) makes use of the full dataset, but that is weighted towards the higher gradient segments. Whether this index, the formula of which is shown below, is helpful, remains to be tested. Not yet implemented. + +$$ +H(x) = 1 - distance.weighted.harmonic.mean(1-x) +$$ + + +### Segments in a route: Cumulative slope + +The length of a segment in a route is also a relevant factor to have in consideration. If it is ok to bike through a segment of 8% with xx length, it is not so ok to bike in four segments in a row like that one (8% for 4xx length), as illustrated bellow. + +```{r cumulative-slopes, fig.cap="Illustration of the importance of slope length. 4 segments with an 8% gradient is not the same as a single segment with a gradient of 8%.", out.width="40%", echo=FALSE} +knitr::include_graphics("SLOPES-commulative-slope-1.png") +``` + + +This is accounted for in slope calculation methods that take the distance-weighted mean of slopes. + +```{r} +x = c(0, 2, 3, 4, 5, 9) +y = c(0, 0, 0, 0, 0, 9) +z = c(1, 2, 2, 4, 3, 1) / 10 +m = cbind(x, y, z) +d = sequential_dist(m = m, lonlat = FALSE) + +slopes::slope_distance_weighted(d = d, elevations = z) +slopes::slope_distance_mean(d = d, elevations = z) +``` + +The slope estimate that results from the distance-weighted mean is lower than the simple mean. +This is common: steep slopes tend to be short! + +A graphical representation of the scenario demonstrated above is shown in Figure \@ref(fig:weighted), that shows the relatively long and flat final segment reduces the slope by half. + +```{r weighted, fig.cap="Illustration of example data that demonstrates distance-weighted mean gradient, used by default in the slopes package."} +plot(x, z, ylim = c(-0.5, 0.5), type = "l") +(gxy = slope_matrix(m, lonlat = FALSE)) +abline(h = 0, lty = 2) +points(x[-length(x)], gxy, col = "blue") +title("Distance elevation profile", + sub = "Points show calculated gradients of subsequent lines") +``` + + + + +# References + diff --git a/vignettes/slopes.Rmd b/vignettes/slopes.Rmd index 123d6e1..ff1e3ec 100644 --- a/vignettes/slopes.Rmd +++ b/vignettes/slopes.Rmd @@ -1,8 +1,8 @@ --- -title: "An introduction to the slopes package" +title: "Get started" output: bookdown::html_vignette2 vignette: > - %\VignetteIndexEntry{An introduction to the slopes package} + %\VignetteIndexEntry{Get started} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} bibliography: slope-references.bib @@ -15,103 +15,100 @@ knitr::opts_chunk$set( ) ``` -# Introduction +## Installation +(again) -Slopes are important for many purposes, including flood risk, agriculture, geology, and infrastructure constructions. The information about slopes is very relevant for vehicle routing modes that are sensitive to going up or down steep hills (e.g. bicycles, trains, and large trucks). -![Traffic sign](https://sinalnorte.com/wp-content/uploads/2018/02/A3b.jpg){ width=10% } +## Functions -Although there are several ways to name "slope", such as "steepness", "hilliness", "inclination", "aspect", "gradient", "declivity", the referred `slopes` in this package can be defined as the "longitudinal gradient" [@cohen_global_2018]. +### Elevation -# Calculating slopes +elevation_add() Take a linestring and add a third dimension (z) to its coordinates +elevation_get() Get elevation data from hosted maptile services +elevation_extract() Extract elevations from coordinates -The most common slope calculation method is defined by the vertical difference of the final and start point or line height (z1 and z0) divided by the horizontal length that separates them. +z_value() retrieves elevation values for each z (as vector of sequential vertices) +z_start() retrieves the elevation value of the first linestring vertice +z_end() retrieves the elevation value of the last linestring vertice +z_mean() retrieves the elevation mean value +z_max() retrieves the elevation max value +z_min() retrieves the elevation min value -$$ -s = \Delta z/l -$$ +### Distance -Depending on the purpose of application, it might me relevant to understand how hilliness is estimated. +sequential_dist() Calculate the sequential distances between sequential coordinate pairs -# Implementation in the slopes package +### Slope -Using the `slopes` package we can estimate the gradient of individual road segments. When these segments are combined into routes, we then need a means of assessing the hilliness of the entire route. A range of indices can be used to represent route hilliness. The choice of which index is most appropriate may be context dependent. +slope_vector() calculates the slopes associated with consecutive elements in one dimensional distance and associated elevations. +slope_distance() calculates the slopes associated with consecutive distances and elevations. +slope_distance_mean() calculates the mean average slopes associated with consecutive distances and elevations. +slope_distance_weighted() calculates the slopes associated with consecutive distances and elevations, with the mean value associated with each set of distance/elevation vectors weighted in proportion to the distance between each elevation measurement, so longer sections have proportionally more influence on the resulting gradient estimate. -To help research slopes using open source software for reproducibility, we have developed the slopes package. -After you have installed it, load it as follows: +slope_raster() Calculate slopes of linestrings based on local raster map +slope_matrix() Calculate the gradient of line segments from a 3D matrix of coordinates +slope_matrix_weighted() Calculate the weighted gradient of line segments from a 3D matrix of coordinates -```{r setup} -library(slopes) -``` - - - - -## Segments in a route: Cumulative slope - -The lenght of a segment in a route is also a relevant factor to have in consideration. -If it is ok to bike through a segment of 8% with $x$ lenght, it is not so ok to bike in four segments in a row like that one (8% for 4$x$ length), as illustrated in Figure \@ref(fig:cumulative-slopes). - -```{r cumulative-slopes, fig.cap="Illustration of the importance of slope length. 4 segments with an 8% gradient is not the same as a single segment with a gradient of 8%.", out.width="40%", echo=FALSE} -knitr::include_graphics("SLOPES-commulative-slope-1.png") -``` - -This is accounted for in slope calculation methods that take the distance-weighted mean of slopes. +slope_xyz() Calculates the slope associated with linestrings that have xyz coordinates -```{r} -x = c(0, 2, 3, 4, 5, 9) -y = c(0, 0, 0, 0, 0, 9) -z = c(1, 2, 2, 4, 3, 1) / 10 -m = cbind(x, y, z) -d = sequential_dist(m = m, lonlat = FALSE) -slopes::slope_distance_weighted(d = d, e = z) -slopes::slope_distance_mean(d = d, e = z) -``` - -The slope estimate that results from the distance-weighted mean is lower than the simple mean. -This is common: steep slopes tend to be short. -A graphical representation of the scenario demonstrated above is shown in Figure \@ref(fig:weighted), that shows the relatively long and flat final segment reduces the slope by half. - -```{r weighted, fig.cap="Illustration of example data that demonstrates distance-weighted mean gradient, used by default in the slopes package."} -plot(x, z, ylim = c(-0.5, 0.5), type = "l") -(gxy = slope_matrix(m, lonlat = FALSE)) -abline(h = 0, lty = 2) -points(x[-length(x)], gxy, col = "blue") -title("Distance elevation profile", - sub = "Points show calculated gradients of subsequent lines") -``` +### Plot -## Measures of route hilliness +plot_dz() Plot a digital elevation profile based on xyz data +plot_slope() Plots the slope profile associated with a linestring with base R graphics -There are many ways to measure hilliness, mean distance weighted hilliness being perhaps the most common. -These measures, and their implementation (or current lack thereof) in the package is summarised below. +## Datasets -- Mean distance weighted gradient. Perhaps the simplest and most widely applicable measure is the mean gradient of the route. This should be weighted by the distance of each segment. Implemented by default in the `slope_raster()` function. +The key input datasets are: + linestrings representing roads + single linestring +https://itsleeds.github.io/slopes/reference/lisbon_road_segment.html +https://itsleeds.github.io/slopes/reference/cyclestreets_route.html +lisbon_route +road network +https://itsleeds.github.io/slopes/reference/lisbon_road_segments.html +https://itsleeds.github.io/slopes/reference/magnolia_xy.html -- Max gradient. For activities like cycling, where steep hills have a disproportionate impact, it may be useful to consider the maximum gradient. Not yet implemented. +digital elevation model +https://itsleeds.github.io/slopes/reference/dem_lisbon_raster.html - -- Xth percentile gradient. Since the maximum gradient gives no information about the rest of the route segments, other measures such as the 75th percentile gradient could be more informative. Not yet implemented. - -- Inverted harmonic mean. If we use the following formula we will get an index that (like the arithmetic mean) makes use of the full dataset, but that is weighted towards the higher gradient segments. Whether this index, the formula of which is shown below, is helpful, remains to be tested. Not yet implemented. - -$$ -H(x) = 1 - distance.weighted.harmonic.mean(1-x) -$$ +## Examples +One example for a single linestring ```{r, eval=FALSE, echo=FALSE} start_coords = stplanr::geo_code("bramley park") end_coods = stplanr::geo_code("university of leeds") rj = cyclestreets::journey(from = start_coords, to = end_coods) mapview::mapview(rj) + rj_3d = elevation_add(rj) plot_slope(rj_3d) plot(cumsum(rj$distances), cumsum(rj$elevation_change)) ``` +Adding the Z dimension + +lisbon_route +lisbon_route_slopes = slope_3d(routes = lisbon_route, dem = slopes::dem_lisbon_raster) +lisbon_route_slopes + + +from local raster: +routes = lisbon_road_segments +dem = dem_lisbon_raster +routes$slope = slope_raster(routes, dem) + +get from mapbox + get from local raster + +### Find slopes when you don't have a DEM + +usethis::edit_r_environ() +MAPBOX_API_KEY=xxxxx + plot viz +plot_slope(lisbon_route_slopes) -# References +One example for small network +see vignettes From 7787254a2f853b2cc6db24f7c3546541671a14bb Mon Sep 17 00:00:00 2001 From: temospena Date: Tue, 24 Aug 2021 01:24:54 +0100 Subject: [PATCH 3/7] add information to get started vignette --- vignettes/slopes.Rmd | 397 +++++++++++++++++++++++++++++++++++++------ 1 file changed, 346 insertions(+), 51 deletions(-) diff --git a/vignettes/slopes.Rmd b/vignettes/slopes.Rmd index ff1e3ec..a4b57bf 100644 --- a/vignettes/slopes.Rmd +++ b/vignettes/slopes.Rmd @@ -16,67 +16,373 @@ knitr::opts_chunk$set( ``` ## Installation -(again) + + + + + + + +Install the development version from [GitHub](https://github.com/) with: + +```{r, eval=FALSE} +# install.packages("remotes") +remotes::install_github("itsleeds/slopes") +``` + +#### Installation for DEM downloads + +If you do not already have DEM data and want to make use of the package's ability to download them using the `ceramic` package, install the package with suggested dependencies, as follows: + +```{r, eval=FALSE} +# install.packages("remotes") +remotes::install_github("itsleeds/slopes", dependencies = "Suggests") +``` + +Furthermore, you will need to add a MapBox API key to be able to get DEM datasets, by signing up and registering for a key at https://account.mapbox.com/access-tokens/ and then following these steps: + +```{r, eval=FALSE} +usethis::edit_r_environ() +MAPBOX_API_KEY=xxxxx # replace XXX with your api key +``` + ## Functions ### Elevation -elevation_add() Take a linestring and add a third dimension (z) to its coordinates -elevation_get() Get elevation data from hosted maptile services -elevation_extract() Extract elevations from coordinates +- `elevation_add()` Take a linestring and add a third dimension (z) to its coordinates +- `elevation_get()` Get elevation data from hosted maptile services +- `elevation_extract()` Extract elevations from coordinates -z_value() retrieves elevation values for each z (as vector of sequential vertices) -z_start() retrieves the elevation value of the first linestring vertice -z_end() retrieves the elevation value of the last linestring vertice -z_mean() retrieves the elevation mean value -z_max() retrieves the elevation max value -z_min() retrieves the elevation min value +- `z_value()` retrieves elevation values for each z (as vector of sequential vertices) +- `z_start()` retrieves the elevation value of the first linestring vertice +- `z_end()` retrieves the elevation value of the last linestring vertice +- `z_mean()` retrieves the elevation mean value +- `z_max()` retrieves the elevation max value +- `z_min()` retrieves the elevation min value ### Distance -sequential_dist() Calculate the sequential distances between sequential coordinate pairs +- `sequential_dist()` Calculate the sequential distances between sequential coordinate pairs ### Slope -slope_vector() calculates the slopes associated with consecutive elements in one dimensional distance and associated elevations. -slope_distance() calculates the slopes associated with consecutive distances and elevations. -slope_distance_mean() calculates the mean average slopes associated with consecutive distances and elevations. -slope_distance_weighted() calculates the slopes associated with consecutive distances and elevations, with the mean value associated with each set of distance/elevation vectors weighted in proportion to the distance between each elevation measurement, so longer sections have proportionally more influence on the resulting gradient estimate. +- `slope_vector()` calculates the slopes associated with consecutive elements in one dimensional distance and associated elevations. +- `slope_distance()` calculates the slopes associated with consecutive distances and elevations. +- `slope_distance_mean()` calculates the mean average slopes associated with consecutive distances and elevations. +- `slope_distance_weighted()` calculates the slopes associated with consecutive distances and elevations, with the mean value associated with each set of distance/elevation vectors weighted in proportion to the distance between each elevation measurement, so longer sections have proportionally more influence on the resulting gradient estimate. -slope_raster() Calculate slopes of linestrings based on local raster map -slope_matrix() Calculate the gradient of line segments from a 3D matrix of coordinates -slope_matrix_weighted() Calculate the weighted gradient of line segments from a 3D matrix of coordinates +- `slope_raster()` Calculate slopes of linestrings based on local raster map +- `slope_matrix()` Calculate the gradient of line segments from a 3D matrix of coordinates +- `slope_matrix_weighted()` Calculate the weighted gradient of line segments from a 3D matrix of coordinates -slope_xyz() Calculates the slope associated with linestrings that have xyz coordinates +- `slope_xyz()` Calculates the slope associated with linestrings that have xyz coordinates ### Plot -plot_dz() Plot a digital elevation profile based on xyz data -plot_slope() Plots the slope profile associated with a linestring with base R graphics +- `plot_dz()` Plot a digital elevation profile based on xyz data +- `plot_slope()` Plots the slope profile associated with a linestring with base R graphics + +## Package datasets -## Datasets +The `slopes` package comes with some datasets to play with: -The key input datasets are: - linestrings representing roads - single linestring -https://itsleeds.github.io/slopes/reference/lisbon_road_segment.html -https://itsleeds.github.io/slopes/reference/cyclestreets_route.html -lisbon_route -road network -https://itsleeds.github.io/slopes/reference/lisbon_road_segments.html -https://itsleeds.github.io/slopes/reference/magnolia_xy.html +**Linestrings:** -digital elevation model -https://itsleeds.github.io/slopes/reference/dem_lisbon_raster.html +- `lisbon_road_segment`: a single road segment in Lisbon (XY) +- `lisbon_route`: a route with some variation in elevation in Lisbon (XY) +- `cyclestreets_route`: a bike route in Leeds (XY) +**Road network:** + +- `lisbon_road_segments`: a sample of road segments in downtown Lisbon +- `magnolia_xy`: a sample of road segments in center Seattle, in the Magnolia neighborhood + +**Digital elevation model (DEM):** + +- `dem_lisbon_raster` a DEM of downtown Lisbo (EPSG:3763) ## Examples -One example for a single linestring +Load the package in the usual way. We will also load the `sf` library: + +```{r message=FALSE, warning=FALSE} +library(slopes) +library(sf) +``` + +The minimum input data requirement for using the package is an `sf` object containing LINESTRING geometries. + +You can also create `sf` objects from a matrix of coordinates, as illustrated below (don't worry about the details for now, you can read up on how all this works in the `sf` package [documentation](https://r-spatial.github.io/sf/articles/sf1.html)): ```{r, eval=FALSE, echo=FALSE} +m = st_coordinates(sf::st_transform(lisbon_road_segment, 4326)) +s = seq(from = 1, to = nrow(m), length.out = 4) +round(m[s, 1:2], 5) +dput(round(m[s, 1], 4)) +dput(round(m[s, 2], 4)) +``` + +```{r} +m = cbind( + c(-9.1333, -9.134, -9.13), + c(38.714, 38.712, 38.710) +) +sf_linestring = sf::st_sf( + data.frame(id = 1), + geometry = st_sfc(st_linestring(m)), + crs = 4326 +) +class(sf_linestring) +st_geometry_type(sf_linestring) +``` + +> maybe remove this? or add step 1 and step 2 again. + +### Sinlge road segment + no DEM + +You can check your input dataset is suitable with the functions `class()` from base R and `st_geometry_type()` from the `sf` package, as demonstrated below on the example object `lisbon_road_segment` that is contained within the package: + +```{r} +sf_linestring = lisbon_road_segment +class(sf_linestring) +st_geometry_type(sf_linestring) +``` + + +A quick way of testing if your object can have slopes calculated for it is to plot it in an interactive map and to check that underneath the object there is indeed terrain that will give the linestrings gradient: + +```{r linestringmap, message=FALSE, warning=FALSE} +library(tmap) +tmap_mode("view") +tm_shape(sf_linestring) + + tm_lines(lwd = 5) + + tm_basemap(leaflet::providers$Esri.WorldTopoMap) +``` + +Imagine you want to calculate the gradient of the route shown above. +You can do this as a two step process as follows. + +**Step 1**: add elevations to each coordinate in the linestring (requires a [MapBox API](https://account.mapbox.com/access-tokens/) key): + +```{r} +sf_linestring_xyz = elevation_add(sf_linestring) +``` + +You can check the elevations added to the new `sf_linestring_xyz` object by printing its coordinates, as follows (note the new Z column that goes from above 87 m above sea level to only 79 m in a short distance). + +```{r} +st_coordinates(sf_linestring_xyz) +``` + +You can use the `z_` functions to extract such values: + +```{r} +z_value(sf_linestring_xyz) # returns all the elevation values between xy coordinates + +z_mean(sf_linestring_xyz) # elevation mean value +z_min(sf_linestring_xyz) # elevation min value +z_max(sf_linestring_xyz) # elevation max value +z_start(sf_linestring_xyz) # first z +z_end(sf_linestring_xyz) # last z +``` + + +**Step 2**: calculate the average slope of the linestring + +```{r} +slope_xyz(sf_linestring_xyz) +``` + +The result, just over 0.2, tells us that it's quite a steep slope: a 21% gradient *on average*. + +### Route + available DEM + +Using the slopes package we can estimate the gradient of individual road segments. When these segments are combined into routes, we then need a means of assessing the hilliness of the entire route. A range of indices can be used to represent route hilliness. The choice of which index is most appropriate may be context dependent (see the [introducion to slopes](https://itsleeds.github.io/intro-to-slopes/) vignette). + +Again, let us use the same function with a entire route, `lisbon_route`, also available in the package: + +```{r message=FALSE, warning=FALSE} +sf_route = lisbon_route +class(sf_route) +st_geometry_type(sf_route) + +tm_shape(sf_route) + + tm_lines(lwd = 3) + + tm_basemap(leaflet::providers$Esri.WorldTopoMap) +``` + +**Step 1**: add elevations to each coordinate in the route: + +```{r} +sf_route_xyz = elevation_add(sf_route) +``` + +**Step 2**: calculate the average slope of the route + +```{r} +slope_xyz(sf_route_xyz) +``` + +The result shows a 7.7% gradient *on average*. + +Now, if you already have a DEM, you can calculate the slopes directly as follows, with `slope_raster()`: + +```{r} +class(dem_lisbon_raster) +slope_raster(routes = sf_route, + dem = dem_lisbon_raster) +``` +The result shows a 7.8% gradient *on average*. +As you can see, the retrieved result from elevation information available in mapbox and in this Digital Elevation Model, is quite similar. (See more about these differences in [Verification of slopes](https://itsleeds.github.io/slopes/articles/verification.html).) + +### Route with xyz coordinates + +If your linestring object already has X, Y and Z coordinates (e.g. from a GPS device), you can use the `slope_` functions directly. + +```{r eval=FALSE, include=FALSE} +#not to use like this... it would ge good to have a gps example to demonstrate + +slope_vector(sf_route_xyz) +slope_distance(sf_route_xyz) +slope_distance_mean(sf_route_xyz) +slope_distance_weighted(sf_route_xyz) + +slope_vector(sf_linestring_xyz) +slope_distance(sf_linestring_xyz) +slope_distance_mean(sf_linestring_xyz) +slope_distance_weighted(sf_linestring_xyz) +``` +```{r} +# for a line xz +x = c(0, 2, 3, 4, 5, 9) +elevations = c(1, 2, 2, 4, 3, 1) / 10 +slope_vector(x, elevations) + + +# for a path xyz +xy = st_coordinates(sf_linestring) +dist = sequential_dist(xy, lonlat = FALSE) +elevations = elevation_extract(xy, dem_lisbon_raster) + +slope_distance(dist, elevations) +slope_distance_mean(dist, elevations) +slope_distance_weighted(dist, elevations) +``` + + +In any case, to use the `slopes` package you need **elevation points**, either as a vector, a matrix or as a digital elevation model (DEM) encoded as a raster dataset. + +## Calculating and plotting the gradient of roads + +Typical use cases for the package are calculating the slopes of geographic objects representing roads or other linear features. +These two types of input data are represented in the code output and plot below. + +```{r dem-lisbon} +# A raster dataset included in the package: +class(dem_lisbon_raster) # digital elevation model +summary(raster::values(dem_lisbon_raster)) # heights range from 0 to ~100m +raster::plot(dem_lisbon_raster) + +# A vector dataset included in the package: +class(lisbon_road_segments) +plot(sf::st_geometry(lisbon_road_segments), add = TRUE) +``` + +Calculate the average gradient of **each road segment** as follows: + +```{r} +lisbon_road_segments$slope = slope_raster(lisbon_road_segments, dem = dem_lisbon_raster) +summary(lisbon_road_segments$slope) +``` + +This created a new column, `slope` that represents the average, distance weighted slope associated with each road segment. +The units represent the percentage incline, that is the change in elevation divided by distance. +The summary of the result tells us that the average gradient of slopes in the example data is just over 5%. + +This result is equivalent to that returned by ESRI's `Slope_3d()` in the [3D Analyst extension](https://desktop.arcgis.com/en/arcmap/10.3/tools/3d-analyst-toolbox/slope.htm), with a correlation between the ArcMap implementation and our implementation of more than 0.95 on our test dataset (we find higher correlations on larger datasets - see the [verification of slopes](https://itsleeds.github.io/slopes/articles/verification.html article): + +```{r} +cor( + lisbon_road_segments$slope, # slopes calculates by the slopes package + lisbon_road_segments$Avg_Slope # slopes calculated by ArcMap's 3D Analyst extension +) +``` + +We can now visualise the average slopes of each route calculated by the `slopes` package as follows: + +```{r slope-vis} +raster::plot(dem_lisbon_raster) +plot(lisbon_road_segments["slope"], add = TRUE, lwd = 5) +``` + +Imagine that we want to go from East hilly area of the map (Santa Catarina) to the West hilly area of the map (Castelo de São Jorge): + +```{r route} +# library(tmap) +# tmap_mode("view") +qtm(lisbon_route) +``` + +```{r, echo=FALSE, eval=FALSE} +# Removed because it's not rendering in RMarkdown +mapview::mapview(lisbon_road_segments["slope"], map.types = "Esri.WorldStreetMap") +mapview::mapview(lisbon_route) +``` + +We can convert the `lisbon_route` object into a 3d linestring object with X, Y and Z coordinates, using the elevation values stored in the DEM, as follows: + +```{r, warning=FALSE, echo=FALSE, eval=FALSE} +sln = stplanr::SpatialLinesNetwork(lisbon_road_segments) +points = sf::st_as_sf(crs = 4326, coords = c("X1", "X2"), data.frame(rbind( + stplanr::geo_code("Santa Catarina, Lisbon"), + stplanr::geo_code("Castelo, Lisbon") +))) +points_proj = sf::st_transform(points, sf::st_crs(lisbon_road_segments)) +coords = sf::st_coordinates(points_proj) +nodes = stplanr::find_network_nodes(sln, coords[, 1], coords[, 2]) +lisbon_route = stplanr::sum_network_routes(sln = sln, start = nodes[1], end = nodes[2]) +mapview::mapview(lisbon_route) + + mapview::mapview(lisbon_road_segments["slope"]) +lisbon_route_3d = elevation_add(lisbon_route, dem_lisbon_raster) +usethis::use_data(lisbon_route, overwrite = TRUE) +usethis::use_data(lisbon_route_3d, overwrite = TRUE) +``` + +```{r} +lisbon_route_xyz = elevation_add(lisbon_route, dem_lisbon_raster) +``` + +We can now visualise the **elevation profile** of the route as follows: + +```{r plot_slope} +plot_slope(lisbon_route_xyz) +``` + +If you do not have a raster dataset representing elevations, you can automatically download them as follows (a step that is automatically done in the function `elevation_add()` shown in the basic example above, results of the subsequent code chunk not shown): + +```{r, message=FALSE, warning=FALSE, eval=FALSE} +dem_mapbox = elevation_get(lisbon_route) +lisbon_road_proj = st_transform(lisbon_route, raster::crs(dem_mapbox)) +lisbon_route_xyz_mapbox = elevation_add(lisbon_road_proj, dem = dem_mapbox) +plot_slope(lisbon_route_xyz_mapbox) +``` + +As outlined in the basic example above this can be done more concisely, as: + +```{r plotauto} +lisbon_route_xyz_auto = elevation_add(lisbon_route) +plot_slope(lisbon_route_xyz_auto) +``` + + + + +A route in Leeds! + +```{r} start_coords = stplanr::geo_code("bramley park") end_coods = stplanr::geo_code("university of leeds") rj = cyclestreets::journey(from = start_coords, to = end_coods) @@ -87,28 +393,17 @@ plot_slope(rj_3d) plot(cumsum(rj$distances), cumsum(rj$elevation_change)) ``` -Adding the Z dimension - -lisbon_route -lisbon_route_slopes = slope_3d(routes = lisbon_route, dem = slopes::dem_lisbon_raster) -lisbon_route_slopes -from local raster: -routes = lisbon_road_segments -dem = dem_lisbon_raster -routes$slope = slope_raster(routes, dem) -get from mapbox + get from local raster -### Find slopes when you don't have a DEM -usethis::edit_r_environ() -MAPBOX_API_KEY=xxxxx +## See more in vignettes - plot viz -plot_slope(lisbon_route_slopes) +- [slopes package](https://itsleeds.github.io/slopes/index.html) +- [An introduction to slopes](https://itsleeds.github.io/slopes/articles/intro-to-slopes.html) +- [Reproducible example: gradients of a road network for a given city](https://itsleeds.github.io/slopes/articles/roadnetworkcycling.html) +- [Verification of slopes](https://itsleeds.github.io/slopes/articles/verification.html) +- [Benchmarking slopes calculation](https://itsleeds.github.io/slopes/articles/benchmark.html) -One example for small network -see vignettes From c424e9a7e389adcd8f3bb43cb30080ef8cac1836 Mon Sep 17 00:00:00 2001 From: temospena Date: Tue, 24 Aug 2021 01:40:30 +0100 Subject: [PATCH 4/7] remove bibliography in get started. --- vignettes/slopes.Rmd | 1 - 1 file changed, 1 deletion(-) diff --git a/vignettes/slopes.Rmd b/vignettes/slopes.Rmd index a4b57bf..149f638 100644 --- a/vignettes/slopes.Rmd +++ b/vignettes/slopes.Rmd @@ -5,7 +5,6 @@ vignette: > %\VignetteIndexEntry{Get started} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} -bibliography: slope-references.bib --- ```{r, include = FALSE} From 485b709a36efac59804b6324fa44910fc9c497f9 Mon Sep 17 00:00:00 2001 From: Robin Date: Tue, 24 Aug 2021 08:19:42 +0100 Subject: [PATCH 5/7] Suggest tmap --- DESCRIPTION | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 3516420..8e6509c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -47,6 +47,7 @@ Suggests: osmextract, stplanr, dplyr, - rgdal + rgdal, + tmap VignetteBuilder: knitr Config/testthat/edition: 3 From 84d0f29acd005388e58ad07bc69359813486404d Mon Sep 17 00:00:00 2001 From: temospena Date: Tue, 24 Aug 2021 13:49:37 +0100 Subject: [PATCH 6/7] replace cyclestreets get journey by a existing journey. #38 --- vignettes/slopes.Rmd | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/vignettes/slopes.Rmd b/vignettes/slopes.Rmd index 149f638..e4568a1 100644 --- a/vignettes/slopes.Rmd +++ b/vignettes/slopes.Rmd @@ -382,14 +382,9 @@ plot_slope(lisbon_route_xyz_auto) A route in Leeds! ```{r} -start_coords = stplanr::geo_code("bramley park") -end_coods = stplanr::geo_code("university of leeds") -rj = cyclestreets::journey(from = start_coords, to = end_coods) -mapview::mapview(rj) - -rj_3d = elevation_add(rj) -plot_slope(rj_3d) -plot(cumsum(rj$distances), cumsum(rj$elevation_change)) +cyclestreets_xyz = elevation_add(cyclestreets_route) +plot_slope(cyclestreets_xyz) +plot(cumsum(cyclestreets_xyz$distances), cumsum(cyclestreets_xyz$elevation_change)) ``` From a9021ab759e60df648b9ee7ffa4cd92bb7dc76da Mon Sep 17 00:00:00 2001 From: temospena Date: Tue, 24 Aug 2021 14:09:15 +0100 Subject: [PATCH 7/7] rename `lisbon_road_segments` to `lisbon_road_network` for a more intuitive name. #38 #32 --- R/data.R | 8 +++--- R/slopes.R | 6 ++-- README.Rmd | 22 +++++++-------- data-raw/dem_lisbon.R | 14 +++++----- data-raw/issues.Rmd | 14 +++++----- data-raw/slides.Rmd | 4 +-- data/lisbon_road_network.rda | Bin 0 -> 79815 bytes data/lisbon_road_segments.rda | Bin 79803 -> 0 bytes man/dem_lisbon_raster.Rd | 2 +- man/elevation_add.Rd | 2 +- man/elevation_extract.Rd | 2 +- ...oad_segments.Rd => lisbon_road_network.Rd} | 10 +++---- man/slope_raster.Rd | 2 +- tests/testthat/test-slopes.R | 4 +-- vignettes/benchmark.Rmd | 4 +-- vignettes/slopes.Rmd | 26 +++++++++--------- 16 files changed, 60 insertions(+), 60 deletions(-) create mode 100644 data/lisbon_road_network.rda delete mode 100644 data/lisbon_road_segments.rda rename man/{lisbon_road_segments.Rd => lisbon_road_network.Rd} (75%) diff --git a/R/data.R b/R/data.R index 01f28ae..6d78592 100644 --- a/R/data.R +++ b/R/data.R @@ -9,7 +9,7 @@ #' @examples #' names(dem_lisbon_raster) #' raster::plot(dem_lisbon_raster) -#' plot(lisbon_road_segments["Avg_Slope"], add = TRUE) +#' plot(lisbon_road_network["Avg_Slope"], add = TRUE) "dem_lisbon_raster" #' Road segments in Lisbon @@ -21,9 +21,9 @@ #' @source Produced by ESRI's #' [3D Analyst extension](https://pro.arcgis.com/en/pro-app/help/analysis/) #' @examples -#' names(lisbon_road_segments) -#' plot(lisbon_road_segments["Avg_Slope"]) -"lisbon_road_segments" +#' names(lisbon_road_network) +#' plot(lisbon_road_network["Avg_Slope"]) +"lisbon_road_network" #' A road segment in Lisbon, Portugal #' diff --git a/R/slopes.R b/R/slopes.R index c544302..4d044ff 100644 --- a/R/slopes.R +++ b/R/slopes.R @@ -148,7 +148,7 @@ slope_matrices = function(m_xyz_split, fun = slope_matrix_weighted, ...) { #' @importFrom methods is #' @export #' @examples -#' routes = lisbon_road_segments[1:3, ] +#' routes = lisbon_road_network[1:3, ] #' dem = dem_lisbon_raster #' (s = slope_raster(routes, dem)) #' cor(routes$Avg_Slope, s) @@ -209,7 +209,7 @@ slope_xyz = function(route_xyz, fun = slope_matrix_weighted, lonlat = TRUE) { #' @inheritParams slope_matrix #' @export #' @examples -#' m = sf::st_coordinates(lisbon_road_segments[1, ]) +#' m = sf::st_coordinates(lisbon_road_network[1, ]) #' dem = dem_lisbon_raster #' elevation_extract(m, dem) #' elevation_extract(m, dem, method = "simple") @@ -245,7 +245,7 @@ elevation_extract = function(m, #' @export #' @examples #' library(sf) -#' routes = lisbon_road_segments[204, ] +#' routes = lisbon_road_network[204, ] #' dem = dem_lisbon_raster #' (r3d = elevation_add(routes, dem)) #' sf::st_z_range(routes) diff --git a/README.Rmd b/README.Rmd index ca6a613..ce6c291 100644 --- a/README.Rmd +++ b/README.Rmd @@ -254,15 +254,15 @@ summary(raster::values(dem_lisbon_raster)) # heights range from 0 to ~100m raster::plot(dem_lisbon_raster) # A vector dataset included in the package: -class(lisbon_road_segments) -plot(sf::st_geometry(lisbon_road_segments), add = TRUE) +class(lisbon_road_network) +plot(sf::st_geometry(lisbon_road_network), add = TRUE) ``` Calculate the average gradient of **each road segment** as follows: ```{r} -lisbon_road_segments$slope = slope_raster(lisbon_road_segments, dem = dem_lisbon_raster) -summary(lisbon_road_segments$slope) +lisbon_road_network$slope = slope_raster(lisbon_road_network, dem = dem_lisbon_raster) +summary(lisbon_road_network$slope) ``` This created a new column, `slope` that represents the average, distance weighted slope associated with each road segment. @@ -273,8 +273,8 @@ This result is equivalent to that returned by ESRI's `Slope_3d()` in the [3D Ana ```{r} cor( - lisbon_road_segments$slope, # slopes calculates by the slopes package - lisbon_road_segments$Avg_Slope # slopes calculated by ArcMap's 3D Analyst extension + lisbon_road_network$slope, # slopes calculates by the slopes package + lisbon_road_network$Avg_Slope # slopes calculated by ArcMap's 3D Analyst extension ) ``` @@ -282,7 +282,7 @@ We can now visualise the average slopes of each route calculated by the `slopes` ```{r slope-vis} raster::plot(dem_lisbon_raster) -plot(lisbon_road_segments["slope"], add = TRUE, lwd = 5) +plot(lisbon_road_network["slope"], add = TRUE, lwd = 5) ``` Imagine that we want to go from East hilly area of the map (Santa Catarina) to the West hilly area of the map (Castelo de São Jorge): @@ -295,24 +295,24 @@ qtm(lisbon_route) ```{r, echo=FALSE, eval=FALSE} # Removed because it's not rendering in RMarkdown -mapview::mapview(lisbon_road_segments["slope"], map.types = "Esri.WorldStreetMap") +mapview::mapview(lisbon_road_network["slope"], map.types = "Esri.WorldStreetMap") mapview::mapview(lisbon_route) ``` We can convert the `lisbon_route` object into a 3d linestring object with X, Y and Z coordinates, using the elevation values stored in the DEM, as follows: ```{r, warning=FALSE, echo=FALSE, eval=FALSE} -sln = stplanr::SpatialLinesNetwork(lisbon_road_segments) +sln = stplanr::SpatialLinesNetwork(lisbon_road_network) points = sf::st_as_sf(crs = 4326, coords = c("X1", "X2"), data.frame(rbind( stplanr::geo_code("Santa Catarina, Lisbon"), stplanr::geo_code("Castelo, Lisbon") ))) -points_proj = sf::st_transform(points, sf::st_crs(lisbon_road_segments)) +points_proj = sf::st_transform(points, sf::st_crs(lisbon_road_network)) coords = sf::st_coordinates(points_proj) nodes = stplanr::find_network_nodes(sln, coords[, 1], coords[, 2]) lisbon_route = stplanr::sum_network_routes(sln = sln, start = nodes[1], end = nodes[2]) mapview::mapview(lisbon_route) + - mapview::mapview(lisbon_road_segments["slope"]) + mapview::mapview(lisbon_road_network["slope"]) lisbon_route_3d = elevation_add(lisbon_route, dem_lisbon_raster) usethis::use_data(lisbon_route, overwrite = TRUE) usethis::use_data(lisbon_route_3d, overwrite = TRUE) diff --git a/data-raw/dem_lisbon.R b/data-raw/dem_lisbon.R index 7114c0b..7d1c3d4 100644 --- a/data-raw/dem_lisbon.R +++ b/data-raw/dem_lisbon.R @@ -4,11 +4,11 @@ remotes::install_github("zonebuilders/zonebuilder") lisbon_centrepoint = tmaptools::geocode_OSM("lisbon", as.sf = TRUE) lisbon_1km = stplanr::geo_buffer(shp = lisbon_centrepoint, dist = 1000) mapview::mapview(lisbon_1km) -lisbon_road_segments_esri = sf::read_sf("~/wip/pctLisbon-data/Shapefiles/route_segments_gradient2_Zelevation.gpkg") -lisbon_1km_projected = sf::st_transform(lisbon_1km, sf::st_crs(lisbon_road_segments_esri)) -lisbon_road_segments = sf::st_intersection(lisbon_road_segments_esri, lisbon_1km_projected) -plot(lisbon_road_segments["Avg_Slope"]) -usethis::use_data(lisbon_road_segments) +lisbon_road_network_esri = sf::read_sf("~/wip/pctLisbon-data/Shapefiles/route_segments_gradient2_Zelevation.gpkg") +lisbon_1km_projected = sf::st_transform(lisbon_1km, sf::st_crs(lisbon_road_network_esri)) +lisbon_road_network = sf::st_intersection(lisbon_road_network_esri, lisbon_1km_projected) +plot(lisbon_road_network["Avg_Slope"]) +usethis::use_data(lisbon_road_network) download.file("https://github.com/geocompr/d/releases/download/1/r1.zip", "r1.zip") unzip("r1.zip")dem_lisbon_raster = raster::raster("r1") @@ -28,7 +28,7 @@ raster::writeRaster(dem_lisbon_raster, "inst/dem_lisbon_raster.asc", overwrite = file.size("inst/dem_lisbon_raster.asc") / 1e6 # large, so deleted # With terra -lisbon_road_segments_terra = terra::vect("~/wip/pctLisbon-data/Shapefiles/route_segments_gradient2_Zelevation.gpkg") +lisbon_road_network_terra = terra::vect("~/wip/pctLisbon-data/Shapefiles/route_segments_gradient2_Zelevation.gpkg") dem_lisbon_terra = terra::rast("r1/hdr.adf") -dem_lisbon_terra = terra::crop(dem_lisbon_terra, terra::ext(lisbon_road_segments_terra)) +dem_lisbon_terra = terra::crop(dem_lisbon_terra, terra::ext(lisbon_road_network_terra)) diff --git a/data-raw/issues.Rmd b/data-raw/issues.Rmd index b6cdb62..f35fabe 100644 --- a/data-raw/issues.Rmd +++ b/data-raw/issues.Rmd @@ -106,17 +106,17 @@ plot(network_ls["slopeNASA"], lwd = 5) ```{r} remotes::install_github("itsleeds/slopes") library(slopes) -class(lisbon_road_segments) +class(lisbon_road_network) raster::plot(dem_lisbon_raster) -plot(sf::st_geometry(lisbon_road_segments), add = TRUE) +plot(sf::st_geometry(lisbon_road_network), add = TRUE) demterra = terra::rast(dem_lisbon_raster) -plot(sf::st_geometry(lisbon_road_segments), add = TRUE) -lisbon_road_segments$sloperaster = slope_raster(lisbon_road_segments, e = dem_lisbon_raster) +plot(sf::st_geometry(lisbon_road_network), add = TRUE) +lisbon_road_network$sloperaster = slope_raster(lisbon_road_network, e = dem_lisbon_raster) library(terra) plot(demterra) -lisbon_road_segments$slopeterra = slope_raster(lisbon_road_segments, e = demterra, terra = T) -summary(lisbon_road_segments$sloperaster) -summary(lisbon_road_segments$slopeterra) +lisbon_road_network$slopeterra = slope_raster(lisbon_road_network, e = demterra, terra = T) +summary(lisbon_road_network$sloperaster) +summary(lisbon_road_network$slopeterra) # Looking at the code, this seems to be the culprit: diff --git a/data-raw/slides.Rmd b/data-raw/slides.Rmd index e0ff030..5da4a1b 100644 --- a/data-raw/slides.Rmd +++ b/data-raw/slides.Rmd @@ -178,7 +178,7 @@ The key input datasets are: linestrings representing roads/rivers/other, and... ```{r maps} -tm_shape(lisbon_road_segments) + tm_lines() +tm_shape(lisbon_road_network) + tm_lines() ``` ] @@ -360,7 +360,7 @@ See [`roadnetworkcycling`](https://itsleeds.github.io/slopes/articles/roadnetwor ## A smaller example using packaged data ```{r} -routes = lisbon_road_segments +routes = lisbon_road_network dem = dem_lisbon_raster routes$slope = slope_raster(routes, dem) plot(dem) diff --git a/data/lisbon_road_network.rda b/data/lisbon_road_network.rda new file mode 100644 index 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test_that("slope_* functions work", { r_xyz = lisbon_road_segment_3d expect_equal(slope_xyz(r_xyz), 0.0950132312274622, ignore_attr = TRUE) - r = lisbon_road_segments[204, ] + r = lisbon_road_network[204, ] r3d = elevation_add(r, e) expect_equal( sf::st_z_range(r3d$geom), diff --git a/vignettes/benchmark.Rmd b/vignettes/benchmark.Rmd index 7ee253f..6f578aa 100644 --- a/vignettes/benchmark.Rmd +++ b/vignettes/benchmark.Rmd @@ -38,7 +38,7 @@ A benchmark can reveal how many route gradients can be calculated per second: ```{r, results='hide'} e = dem_lisbon_raster -r = lisbon_road_segments +r = lisbon_road_network et = terra::rast(f) res = bench::mark(check = FALSE, slope_raster = slope_raster(r, e), @@ -65,7 +65,7 @@ To go faster, you can chose the `simple` method to gain some speed at the expens ```{r, results='hide'} e = dem_lisbon_raster -r = lisbon_road_segments +r = lisbon_road_network res = bench::mark(check = FALSE, bilinear1 = slope_raster(r, e), bilinear2 = slope_raster(r, et), diff --git a/vignettes/slopes.Rmd b/vignettes/slopes.Rmd index e4568a1..a57c650 100644 --- a/vignettes/slopes.Rmd +++ b/vignettes/slopes.Rmd @@ -95,12 +95,12 @@ The `slopes` package comes with some datasets to play with: **Road network:** -- `lisbon_road_segments`: a sample of road segments in downtown Lisbon +- `lisbon_road_network`: a sample of road segments in downtown Lisbon - `magnolia_xy`: a sample of road segments in center Seattle, in the Magnolia neighborhood **Digital elevation model (DEM):** -- `dem_lisbon_raster` a DEM of downtown Lisbo (EPSG:3763) +- `dem_lisbon_raster` a DEM of downtown Lisbon (EPSG:3763) ## Examples @@ -286,15 +286,15 @@ summary(raster::values(dem_lisbon_raster)) # heights range from 0 to ~100m raster::plot(dem_lisbon_raster) # A vector dataset included in the package: -class(lisbon_road_segments) -plot(sf::st_geometry(lisbon_road_segments), add = TRUE) +class(lisbon_road_network) +plot(sf::st_geometry(lisbon_road_network), add = TRUE) ``` Calculate the average gradient of **each road segment** as follows: ```{r} -lisbon_road_segments$slope = slope_raster(lisbon_road_segments, dem = dem_lisbon_raster) -summary(lisbon_road_segments$slope) +lisbon_road_network$slope = slope_raster(lisbon_road_network, dem = dem_lisbon_raster) +summary(lisbon_road_network$slope) ``` This created a new column, `slope` that represents the average, distance weighted slope associated with each road segment. @@ -305,8 +305,8 @@ This result is equivalent to that returned by ESRI's `Slope_3d()` in the [3D Ana ```{r} cor( - lisbon_road_segments$slope, # slopes calculates by the slopes package - lisbon_road_segments$Avg_Slope # slopes calculated by ArcMap's 3D Analyst extension + lisbon_road_network$slope, # slopes calculates by the slopes package + lisbon_road_network$Avg_Slope # slopes calculated by ArcMap's 3D Analyst extension ) ``` @@ -314,7 +314,7 @@ We can now visualise the average slopes of each route calculated by the `slopes` ```{r slope-vis} raster::plot(dem_lisbon_raster) -plot(lisbon_road_segments["slope"], add = TRUE, lwd = 5) +plot(lisbon_road_network["slope"], add = TRUE, lwd = 5) ``` Imagine that we want to go from East hilly area of the map (Santa Catarina) to the West hilly area of the map (Castelo de São Jorge): @@ -327,24 +327,24 @@ qtm(lisbon_route) ```{r, echo=FALSE, eval=FALSE} # Removed because it's not rendering in RMarkdown -mapview::mapview(lisbon_road_segments["slope"], map.types = "Esri.WorldStreetMap") +mapview::mapview(lisbon_road_network["slope"], map.types = "Esri.WorldStreetMap") mapview::mapview(lisbon_route) ``` We can convert the `lisbon_route` object into a 3d linestring object with X, Y and Z coordinates, using the elevation values stored in the DEM, as follows: ```{r, warning=FALSE, echo=FALSE, eval=FALSE} -sln = stplanr::SpatialLinesNetwork(lisbon_road_segments) +sln = stplanr::SpatialLinesNetwork(lisbon_road_network) points = sf::st_as_sf(crs = 4326, coords = c("X1", "X2"), data.frame(rbind( stplanr::geo_code("Santa Catarina, Lisbon"), stplanr::geo_code("Castelo, Lisbon") ))) -points_proj = sf::st_transform(points, sf::st_crs(lisbon_road_segments)) +points_proj = sf::st_transform(points, sf::st_crs(lisbon_road_network)) coords = sf::st_coordinates(points_proj) nodes = stplanr::find_network_nodes(sln, coords[, 1], coords[, 2]) lisbon_route = stplanr::sum_network_routes(sln = sln, start = nodes[1], end = nodes[2]) mapview::mapview(lisbon_route) + - mapview::mapview(lisbon_road_segments["slope"]) + mapview::mapview(lisbon_road_network["slope"]) lisbon_route_3d = elevation_add(lisbon_route, dem_lisbon_raster) usethis::use_data(lisbon_route, overwrite = TRUE) usethis::use_data(lisbon_route_3d, overwrite = TRUE)

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