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https://github.com/ropensci/osmplotr

Data visualisation using OpenStreetMap objects
https://github.com/ropensci/osmplotr

data-visualisation highlighting-clusters openstreetmap osm overpass overpass-api peer-reviewed r r-package rstats

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Data visualisation using OpenStreetMap objects

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README 

        
---

title: "osmplotr, an R package for making maps with OpenStreetMap data"

keywords: "open street map, openstreetmap, OSM, map, visualisation, visualization"

output:

  rmarkdown::html_vignette:

    self_contained: no



  md_document:

    variant: markdown_github

---



```{r opts, echo = FALSE}

knitr::opts_chunk$set (

    collapse = TRUE,

    warning = TRUE,

    message = TRUE,

    width = 120,

    comment = "#>",

    fig.retina = 2,

    fig.path = "README-"

)

```



[![R build

status](https://github.com/ropensci/osmplotr/workflows/R-CMD-check/badge.svg)](https://github.com/ropensci/osmplotr/actions?query=workflow%3AR-CMD-check)

[![codecov](https://codecov.io/gh/ropensci/osmplotr/branch/master/graph/badge.svg)](https://codecov.io/gh/ropensci/osmplotr)

[![Project Status: Active](http://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/)



![](man/figures/map1.png)



[![CRAN Downloads](http://cranlogs.r-pkg.org/badges/grand-total/osmplotr?color=orange)](http://cran.r-project.org/package=osmplotr/)

[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/osmplotr)](http://cran.r-project.org/package=osmplotr/)

[![](https://badges.ropensci.org/27_status.svg)](https://github.com/ropensci/software-review/issues/27)



R package to produce visually impressive customisable images of OpenStreetMap

(OSM) data downloaded internally from the 

[overpass api](http://overpass-api.de/). The above map was produced directly

from `osmplotr` with no further modification. This `README` briefly demonstrates

the following functionality:



[1. Quick Introduction](#1 intro)



[2. Installation](#2 installation)



[3. A Simple Map](#3 simple map)



[4. Highlighting Selected Areas](#4 highlighting areas)



[5. Highlighting Clusters](#5 highlighting clusters)



[6. Highlighting Areas Bounded by Named Highways](#6 highlighting with highways)



[7. Data Surfaces](#7 data surfaces)



[8. Gallery](#8 gallery)



---------------



## 1. Quick Introduction



But first the easy steps to map making:

```{r, echo = FALSE, message = FALSE, eval = TRUE}

library (osmplotr)

```



1. Specify the bounding box for the desired region

    ```{r}

    bbox <- get_bbox (c (-0.15, 51.5, -0.10, 51.52))

    ```

2. Download the desired data---in this case, all building perimeters.

    ```{r, eval = FALSE}

    dat_B <- extract_osm_objects (key = "building", bbox = bbox)

    ```

3. Initiate an `osm_basemap` with desired background (`bg`) colour

    ```{r map1, eval = FALSE}

    map <- osm_basemap (bbox = bbox, bg = "gray20")

    ```

4. Overlay objects on plot in the desired colour.

    ```{r, eval = FALSE}

    map <- add_osm_objects (map, dat_B, col = "gray40")

    ```

5. Print the map to graphics device of choice

    ```{r, eval = FALSE}

    print_osm_map (map)

    ```



```{r london2, echo = FALSE, eval = FALSE}

library (osmdata)

bbox <- get_bbox (c (-0.15, 51.5, -0.10, 51.52))

q0 <- opq (bbox)

q1 <- add_osm_feature (q0, key = "building")

dat_B <- osmdata_sf (q1, quiet = FALSE)$osm_polygons

q1 <- add_osm_feature (q0, key = "highway")

dat_H <- osmdata_sf (q1, quiet = FALSE)$osm_lines

q1 <- add_osm_feature (q0, key = "leisure", value = "park")

dat_P <- osmdata_sf (q1, quiet = FALSE)$osm_polygons

q1 <- add_osm_feature (q0, key = "landuse", value = "grass")

dat_G <- osmdata_sf (q1, quiet = FALSE)$osm_polygons

london2 <- list (dat_B = dat_B, dat_H = dat_H, dat_P = dat_P, dat_G = dat_G)

save (london2, file = "london2.rda")

```



---------------



## 2. Installation



First install the package

```{r, eval = FALSE}

install.packages ("osmplotr")

```

or the development version

```{r, eval = FALSE}

devtools::install_github ("ropensci/osmplotr")

```

And then load it in the usual way

```{r, eval = FALSE}

library (osmplotr)

```



---------------



## 3. A Simple Map



Simple maps can be made by overlaying different kinds of OSM data in different

colours:

```{r, eval = FALSE}

dat_H <- extract_osm_objects (key = "highway", bbox = bbox)

dat_P <- extract_osm_objects (key = "park", bbox = bbox)

dat_G <- extract_osm_objects (key = "landuse", value = "grass", bbox = bbox)

```

```{r, echo = FALSE, eval = FALSE}

load ("london2.rda")

dat_B <- london2$dat_B

dat_H <- london2$dat_H

dat_G <- london2$dat_G

dat_P <- london2$dat_P

```

```{r map2, eval = FALSE}

map <- osm_basemap (bbox = bbox, bg = "gray20")

map <- add_osm_objects (map, dat_B, col = "gray40")

map <- add_osm_objects (map, dat_H, col = "gray80")

map <- add_osm_objects (map, dat_P, col = "darkseagreen")

map <- add_osm_objects (map, dat_G, col = "darkseagreen1")

print_osm_map (map)

```

```{r map2-print, eval = FALSE, echo = FALSE}

print_osm_map (map, file = "map2.png", width = 600, units = "px", dpi = 72)

```






---------------



## 4. Highlighting Selected Areas



`osmplotr` is primarily intended as a data visualisation tool, particularly

through enabling selected regions to be highlighted. Regions can be defined

according to simple point boundaries:



```{r}

pts <- sp::SpatialPoints (cbind (

    c (-0.115, -0.13, -0.13, -0.115),

    c (51.505, 51.505, 51.515, 51.515)

))

```

OSM objects within the defined regions can then be highlighted with different

colour schemes. `cols` defines colours for each group (with only one here),

while `bg` defines the colour of the remaining, background area.

```{r map3, eval = FALSE}

map <- osm_basemap (bbox = bbox, bg = "gray20")

map <- add_osm_groups (map, dat_B, groups = pts, cols = "orange", bg = "gray40")

map <- add_osm_objects (map, london$dat_P, col = "darkseagreen1")

map <- add_osm_groups (

    map,

    london$dat_P,

    groups = pts,

    cols = "darkseagreen1",

    bg = "darkseagreen",

    boundary = 0

)

print_osm_map (map)

```

```{r map3-print, eval = FALSE, echo = FALSE}

print_osm_map (map, filename = "map3.png", width = 600, units = "px", dpi = 72)

```






Note the `border = 0` argument on the last call divides the park polygons

precisely along the border. The same map highlighted in dark-on-light:

```{r map4, eval = FALSE}

map <- osm_basemap (bbox = bbox, bg = "gray95")

map <- add_osm_groups (map, dat_B, groups = pts, cols = "gray40", bg = "gray85")

map <- add_osm_groups (map, dat_H, groups = pts, cols = "gray20", bg = "gray70")

print_osm_map (map)

```

```{r map4-print, eval = FALSE, echo = FALSE}

print_osm_map (map, filename = "map4.png", width = 600, units = "px", dpi = 72)

```






---------------



## 5. Highlighting Clusters



`add_osm_groups` also enables plotting an entire region as a group of

spatially distinct clusters of defined colours. Groups can be defined by simple

spatial points denoting their centres:



```{r, echo = TRUE}

set.seed (2)

ngroups <- 12

x <- bbox [1, 1] + runif (ngroups) * diff (bbox [1, ])

y <- bbox [2, 1] + runif (ngroups) * diff (bbox [2, ])

groups <- cbind (x, y)

groups <- apply (groups, 1, function (i) {

    sp::SpatialPoints (matrix (i, nrow = 1, ncol = 2))

})

```

Calling `add_osm_groups` with no `bg` argument forces all points lying outside

those defined groups to be allocated to the nearest groups, and thus produces an

inclusive grouping extending across an entire region.



```{r map5, eval = FALSE}

map <- osm_basemap (bbox = bbox, bg = "gray20")

map <- add_osm_groups (

    map,

    dat_B,

    groups = groups,

    cols = rainbow (length (groups)),

    border_width = 2

)

print_osm_map (map)

```

```{r map5-print, eval = FALSE, echo = FALSE}

print_osm_map (map, filename = "map5.png", width = 600, units = "px", dpi = 72)

```






---------------



## 6. Highlighting Areas Bounded by Named Highways



An alternative way of defining highlighted groups is by naming the highways

encircling desired regions.

```{r, eval = FALSE}

# These highways extend beyond the previous, smaller bbox

bbox_big <- get_bbox (c (-0.15, 51.5, -0.10, 51.52))

highways <- c (

    "Davies.St", "Berkeley.Sq", "Berkeley.St", "Piccadilly",

    "Regent.St", "Oxford.St"

)

highways1 <- connect_highways (highways = highways, bbox = bbox_big)

highways <- c ("Regent.St", "Oxford.St", "Shaftesbury")

highways2 <- connect_highways (highways = highways, bbox = bbox_big)

highways <- c (

    "Piccadilly", "Shaftesbury.Ave", "Charing.Cross.R",

    "Saint.Martin", "Trafalgar.Sq", "Cockspur.St",

    "Pall.Mall", "St.James"

)

highways3 <- connect_highways (highways = highways, bbox = bbox_big)

highways <- c (

    "Charing.Cross", "Duncannon.St", "Strand", "Aldwych",

    "Kingsway", "High.Holborn", "Shaftesbury.Ave"

)

highways4 <- connect_highways (highways = highways, bbox = bbox_big)

highways <- c (

    "Kingsway", "Holborn", "Farringdon.St", "Strand",

    "Fleet.St", "Aldwych"

)

highways5 <- connect_highways (highways = highways, bbox = bbox_big)

groups <- list (highways1, highways2, highways3, highways4, highways5)

```

And then passing these lists of groups returned by `connect_highways` to

`add_osm_groups`, this time with some Wes Anderson flair.

```{r map 6, eval = FALSE}

map <- osm_basemap (bbox = bbox, bg = "gray20")

library (wesanderson)

cols <- wes_palette ("Darjeeling", 5)

map <- add_osm_groups (

    map,

    dat_B,

    groups = groups,

    boundary = 1,

    cols = cols,

    bg = "gray40",

    colmat = FALSE

)

map <- add_osm_groups (

    map,

    dat_H,

    groups = groups,

    boundary = 0,

    cols = cols,

    bg = "gray70",

    colmat = FALSE

)

print_osm_map (map)

```

```{r map6-print, eval = FALSE, echo = FALSE}

print_osm_map (map, filename = "map6.png", width = 600, units = "px", dpi = 72)

```






---------------



## 7. Data Surfaces



Finally, `osmplotr` contains a function `add_osm_surface` that spatially

interpolates a given set of spatial data points and colours OSM objects

according to a specified colour gradient. This is illustrated here with the

`volcano` data projected onto the `bbox`.

```{r}

x <- seq (bbox [1, 1], bbox [1, 2], length.out = dim (volcano) [1])

y <- seq (bbox [2, 1], bbox [2, 2], length.out = dim (volcano) [2])

xy <- cbind (rep (x, dim (volcano) [2]), rep (y, each = dim (volcano) [1]))

z <- as.numeric (volcano)

dat <- data.frame (x = xy [, 1], y = xy [, 2], z = z)

```

```{r map7, eval = FALSE}

map <- osm_basemap (bbox = bbox, bg = "gray20")

cols <- gray (0:50 / 50)

map <- add_osm_surface (map, dat_B, dat = dat, cols = cols)

# Darken cols by ~20%

map <- add_osm_surface (

    map,

    dat_H,

    dat = dat,

    cols = adjust_colours (cols, -0.2)

)

map <- add_colourbar (map, cols = cols, zlims = range (volcano))

map <- add_axes (map)

print_osm_map (map)

```

```{r map7-print, eval = FALSE, echo = FALSE}

print_osm_map (map, filename = "map7.png", width = 600, units = "px", dpi = 72)

```



```{r map1-print, eval = FALSE, echo = FALSE}

# This is map1 used as the title

# extrafont::loadfonts ()

lab_dat <- data.frame (

    x = mean (bbox [1, ]), y = mean (bbox [2, ]),

    lab = "osmplotr"

)

aes <- ggplot2::aes (x, y, label = lab)



bbox <- get_bbox (c (-0.15, 51.5, -0.10, 51.52))

map <- osm_basemap (bbox = bbox, bg = "gray20")

cols <- gray (0:50 / 50)

map <- add_osm_surface (map, dat_B, dat = dat, cols = cols)

map <- add_osm_surface (

    map,

    dat_H,

    dat = dat,

    cols = adjust_colours (cols, -0.2)

)



# map2 <- map + ggplot2::geom_text (dat = dat, mapping = aes, size = 60,

#                                  colour = "white",

#                                 family = "Lato Light", nudge_y = 0.0015)

map2 <- map + ggplot2::geom_text (

    dat = lab_dat, mapping = aes, size = 45,

    colour = "black",

    family = "Purisa", fontface = 2,

    nudge_y = 0.0005, nudge_x = 0.0005

)

map2 <- map2 + ggplot2::geom_text (

    dat = lab_dat, mapping = aes, size = 45,

    colour = "white", family = "Purisa",

    nudge_y = 0.001, fontface = 2

)

print_osm_map (map2,

    filename = "map1.png", width = 800, units = "px",

    dpi = 72

)

```






---------------



## 8. Gallery



Got a nice `osmplotr` map? Please contribute in one of the following ways:



1. Fork repo, add link to `README.md/.Rmd`, and send pull request; or



2. Open issue with details; or



3. Send email to address in

   [`DESCRIPTION`](https://github.com/ropensci/osmplotr/blob/master/DESCRIPTION).



---------------



See package vignettes 

([basic maps](https://docs.ropensci.org/osmplotr/articles/basic-maps.html) and

[data maps](https://docs.ropensci.org/osmplotr/articles/data-maps.html)) for a

lot more detail and further capabilities of `osmplotr`.  Please note that this

project is released with a [Contributor Code of Conduct](CODE_OF_CONDUCT.md). By

participating in this project you agree to abide by its terms.



--------------



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