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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Visualizing OSM Data using R</title>
<meta charset="utf-8" />
<meta name="author" content="Anirudh Govind" />
<meta name="date" content="2020-12-05" />
<script src="libs/header-attrs-2.5/header-attrs.js"></script>
<link href="libs/remark-css-0.0.1/default.css" rel="stylesheet" />
<link rel="stylesheet" href="custom.css" type="text/css" />
<link rel="stylesheet" href="custom-fonts.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# Visualizing OSM Data using R
## OSM India Virtual Mappy Hours
### Anirudh Govind
### 2020-12-05
---
class: inverse
name: slide1
## Structure
**Focus on the process** of making a map using OSM data in R.
--
.pull-left[
- Thematic Mapping: A brief introduction
- Conceptual ideas for working with R
- Making a map in R using OSM Data
- Open for discussion
]
---
class: inverse
name: slide2
## Who I am (and not)
.pull-left[
- **Not a Developer**
- Not about the code/ coding better
- **Not a cartographer**
- Not about making maps/ making maps better
- **Architect & Planner**
- Maps to understand specific aspects of cities/ urban areas (Bangalore)
- Thematic maps
- OpenStreetMap Data
]
--
.pull-right[
Initiated by Topi Tjukanov [@tjukanov](https://twitter.com/tjukanov) in 2019.
]
---
class: inverse
name: slide3
## Who I am (and not)
.pull-left[
- **Not a Developer**
- Not about the code/ coding better
- **Not a cartographer**
- Not about maps in general/ making maps better
- **Architect & Planner**
- Maps to understand specific aspects of cities/ urban areas (Bangalore)
- Thematic maps
- OpenStreetMap Data
]
.pull-right[
Initiated by Topi Tjukanov [@tjukanov](https://twitter.com/tjukanov) in 2019.
]
---
class: inverse, center, middle
name: slide4
## Thematic Mapping
---
class: inverse
name: slide5
## Thematic Mapping
.pull-left[
**Thematic maps** show geographical patterns for a particular subject.
- Primary focus is to **communicate information about that subject**. Everything else is secondary.
- Not meant to be used for geographical location (navigation).
- Language, population, temperature, etc.
]
---
class: inverse
name: slide6
## Examples
.pull-left[
]
--
.pull-right[
]
---
class: inverse
name: slide7
## Examples
.pull-left[
]
--
.pull-right[
]
---
class: inverse, center, middle
name: slide8
## Conceptual Ideas for working with R
---
class: inverse
name: slide9
## How do we make a map?
.pull-left[
**Steps in making a map**
1. Obtain data
2. Ensure this data is relevant & properly structured
3. Put the map together with appropriate representation
]
---
class: inverse
name: slide10
## 1. Obtain Data
--
.pull-left[
OSM uses [`tags`](https://wiki.openstreetmap.org/wiki/Tags) to describe features in maps.
[`Tags`](https://wiki.openstreetmap.org/wiki/Tags) have two parts: a [`key`](https://wiki.openstreetmap.org/wiki/Category:Keys) and a corresponding [`value`](https://wiki.openstreetmap.org/wiki/Map_features)
- `highway=residential`
- `natural=water`
Various methods are available for downloading data.
- OSM website
- Data extracts
- APIs (overpass)
]
--
.pull-right[
In R, we can get OpenStreetMap data using the {[`osmdata`](https://docs.ropensci.org/osmdata/)} package!
]
.footnote[Packages are collections of functions and the necessary code to run them.]
---
class: inverse
name: slide11
## 1. Obtain Data
{[`osmdata`](https://docs.ropensci.org/osmdata/)} package!
--
.pull-left[
```r
# Build a query
query <- getbb("LOCATION") %>%
opq() %>%
add_osm_feature("KEY", "VALUE")
# Query the OSM database (overpass)
data <- osmdata_sf(query)
# Extract geometry
dataExtract <- data$osm_points OR
dataExtract <- data$osm_lines OR
dataExtract <- data$osm_polygons
```
]
--
.pull-right[
```r
# Get traffic lights or signals location data from OSM.
# Build a query
query <- getbb("Bangalore") %>%
opq() %>%
add_osm_feature("highway", "traffic_signals")
# Query the OSM database (overpass)
trafficSignalsData <- osmdata_sf(query)
# Extract geometry
trafficSignals <- trafficSignalsData$osm_points
```
]
---
class: inverse
name: slide12
## 2. Tidy Data
Data is often messy!
--
.pull-left[
R relies on the [`tidy data framework`](http://statseducation.com/Introduction-to-R/modules/tidy%20data/tidy-data/)
1. Every variable has its own column
2. Every observation has its own row
3. Every value has its own cell
The {[`tidyr`](https://tidyr.tidyverse.org/)} and {[`dplyr`](https://dplyr.tidyverse.org/)} packages provide a set of tools for wrangling data into this format.
]
.pull-right[
Hadley Wickham and Garrett Grolemund. 2017. [R for Data Science](https://r4ds.had.co.nz/): Import, Tidy, Transform, Visualize, and Model Data (1st. ed.). O'Reilly Media, Inc.
]
--
.footnote[
The {[`tidyr`](https://tidyr.tidyverse.org/)} and {[`dplyr`](https://dplyr.tidyverse.org/)} packages are part of the [`Tidyverse`](https://www.tidyverse.org/); a set of packages sharing the same design philosophies, grammar and data structures.
]
---
class: inverse
name: slide13
## 2. Tidy Data
```
## Rows: 633
## Columns: 21
## $ osm_id <chr> "17327106", "26529522", "26529553", "6095...
## $ name <chr> "Aurobindo Circle", NA, NA, "Prof Ashirva...
## $ alt_name <chr> NA, NA, NA, NA, "Vellara Junction", NA, N...
## $ barrier <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ bicycle <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ camera.mount <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ camera.type <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ crossing <chr> NA, NA, "traffic_signals", NA, NA, "traff...
## $ designation <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ highway <chr> "traffic_signals", "traffic_signals", "tr...
## $ junction <chr> NA, NA, NA, "yes", "yes", NA, NA, NA, NA,...
## $ man_made <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ name.kn <chr> "ಅರವಿಂದ ವೃತà³\215ತ", ...
## $ note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ source <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ surveillance <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ surveillance.type <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ surveillance.zone <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ traffic_signals <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ traffic_signals.direction <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ geometry <POINT [°]> POINT (77.58583 12.91716), POINT (7...
```
---
class: inverse
name: slide14
## 2. Tidy Data
The {[`osmdata`](https://docs.ropensci.org/osmdata/)} package gives us tidy data!
---
class: inverse
name: slide15
## 2. Tidy Data
We can further clean up the data by keeping only the columns we need; `osm_id` and the `geometry`
.pull-left[
```r
# Select specific columns
trafficSignals %>%
select(osm_id, geometry)
```
]
.pull-right[
]
---
class: inverse
name: slide16
## How do we make a map?
.pull-left[
**Steps in making a map**
1. Obtain data
2. Ensure this data is relevant & properly structured
3. Put the map together with appropriate representation
]
--
.pull-right[
**How we go about doing it**
1. OpenStreetMap - {[`osmdata`](https://docs.ropensci.org/osmdata/)}
2. Tidy it - {[`tidyr`](https://tidyr.tidyverse.org/)} & {[`dplyr`](https://dplyr.tidyverse.org/)} - which are part of the {[`Tidyverse`](https://www.tidyverse.org/)}
3. ?
]
--
## Questions?
---
class: inverse
name: slide17
## 3. Present Data
A layered approach with the [`Grammar of Graphics`](https://www.google.co.in/books/edition/The_Grammar_of_Graphics/NRyGnjeNKJIC?hl=en&gbpv=0)!
.pull-left[
Hadley Wickham (2010) [`A Layered Grammar of Graphics`](https://vita.had.co.nz/papers/layered-grammar.html), Journal of Computational and Graphical Statistics, 19:1, 3-28, DOI: 10.1198/jcgs.2009.07098
]
--
.pull-right[
We build maps or visualizations step-by-step rather than a trial and error approach.
1. Geometry or Shape; points, lines or polygons or a combination of them
2. Data;
3. Aesthetics; colour, size, transparency, etc
4. Composition; titles, sub-titles, footnotes, graphic scales, etc
]
---
class: inverse
name: slide18
## 3. Present Data
A layered approach with the Grammar of Graphics!
.pull-left[
```r
# Plot Bangalore ward map using borders
tm_shape(bangaloreWards) +
tm_borders()
```
]
--
.pull-right[
<img src="2020-OSMMappyHour_files/figure-html/unnamed-chunk-7-1.png" width="100%" height="100%" />
]
---
class: inverse
name: slide19
## 3. Present Data
A layered approach with the Grammar of Graphics!
.pull-left[
```r
# Fill with different colours
tm_shape(bangaloreWards) +
tm_borders() +
tm_fill(col = "#E5E5E5")
```
]
.pull-right[
<img src="2020-OSMMappyHour_files/figure-html/unnamed-chunk-9-1.png" width="100%" height="100%" />
]
---
class: inverse
name: slide20
## 3. Present Data
A layered approach with the Grammar of Graphics!
.pull-left[
```r
# Add traffic signals
tm_shape(bangaloreWards) +
tm_borders() +
tm_fill(col = "#E5E5E5") +
tm_shape(trafficSignals) +
tm_dots(size = 0.1,
col = "#FFB703")
```
]
.pull-right[
<img src="2020-OSMMappyHour_files/figure-html/unnamed-chunk-11-1.png" width="100%" height="100%" />
]
---
class: inverse
name: slide21
## 3. Present Data
A layered approach with the Grammar of Graphics!
.pull-left[
```r
# Add titles
tm_shape(bangaloreWards) +
tm_borders() +
tm_fill(col = "#E5E5E5") +
tm_shape(trafficSignals) +
tm_dots(size = 0.1,
col = "#FFB703") +
tm_layout(main.title = "Bangalore's Traffic Lights",
frame = F)
```
]
.pull-right[
<img src="2020-OSMMappyHour_files/figure-html/unnamed-chunk-13-1.png" width="100%" height="100%" />
]
---
class: inverse
name: slide22
## How do we make a map?
.pull-left[
**Steps in making a map**
1. Obtain data
2. Ensure this data is relevant & properly structured
3. Put the map together with appropriate representation
]
.pull-right[
**How we go about doing it**
1. OpenStreetMap - {[`osmdata`](https://docs.ropensci.org/osmdata/)}
2. Tidy it - {[`tidyr`](https://tidyr.tidyverse.org/)} & {[`dplyr`](https://dplyr.tidyverse.org/)} - which are part of the {[`Tidyverse`](https://www.tidyverse.org/)}
3. [`Grammar of Graphics`](https://vita.had.co.nz/papers/layered-grammar.html) - using the {[`tmap`](https://cran.r-project.org/web/packages/tmap/vignettes/tmap-getstarted.html)} package
]
--
## Questions?
--
.pull-left[
[See another example!](#slide23)
]
.pull-right[
[Summarize!](#slide32)
]
---
class: inverse, middle, center
name: slide23
## Let's make a map!
---
class: inverse
name: slide24
## Bangalore's Parks
.pull-left[
Let's recreate this map showing Bangalore's Parks!
]
.pull-right[
]
---
class: inverse
name: slide25
## Bangalore's Parks
.pull-left[
```r
# Load Bangalore ward data and transform it
bangaloreWardBoundary <- read_sf(here::here("data/bangaloreWardBoundary.shp"))
bangaloreWardBoundary <- bangaloreWardBoundary %>%
st_transform(3857)
```
]
--
.pull-right[
```r
# Get parks data from OSM
# Build a query
query <- getbb("Bangalore") %>%
opq() %>%
add_osm_feature("leisure", "park")
# Query the OSM database (overpass)
parksData <- osmdata_sf(query)
# Extract geometry
parks <- parksData$osm_polygons
```
]
---
class: inverse
name: slide26
## Bangalore's Parks
.pull-left[
```r
# Tidy the parks data and keep only necessary columns
parks <- parks %>%
select(osm_id, geometry)
# Transform
parks <- parks %>%
st_transform(3857)
# Keep only those parks which are within the boundary
bangaloreParks <- st_intersection(parks, bangaloreWardBoundary)
```
]
--
.pull-right[
```r
# Calculate areas of parks
bangaloreParks <- bangaloreParks %>%
mutate(area = st_area(.)) %>%
mutate(area = as.numeric(area)) %>%
mutate(area = round(area, 2))
```
]
---
class: inverse
name: slide27
## Bangalore's Parks
.pull-left[
```r
# Plot Bangalore ward map using borders
tm_shape(bangaloreWardBoundary) +
tm_borders()
```
]
--
.pull-right[
<img src="2020-OSMMappyHour_files/figure-html/unnamed-chunk-20-1.png" width="100%" height="100%" />
]
---
class: inverse
name: slide28
## Bangalore's Parks
.pull-left[
```r
# Plot Bangalore's Parks
tm_shape(bangaloreWardBoundary) +
tm_borders() +
tm_shape(bangaloreParks) +
tm_fill()
```
]
.pull-right[
<img src="2020-OSMMappyHour_files/figure-html/unnamed-chunk-22-1.png" width="100%" height="100%" />
]
---
class: inverse
name: slide29
## Bangalore's Parks
.pull-left[
```r
# Add colours and lineweights
tm_shape(bangaloreWardBoundary) +
tm_borders(col = "#a7c957",
lwd = 3) +
tm_shape(bangaloreParks) +
tm_fill(col = "#a7c957")
```
]
.pull-right[
<img src="2020-OSMMappyHour_files/figure-html/unnamed-chunk-24-1.png" width="100%" height="100%" />
]
---
class: inverse
name: slide30
## Bangalore's Parks
.pull-left[
```r
# Add background colour and margins
tm_shape(bangaloreWardBoundary) +
tm_borders(col = "#a7c957",
lwd = 3) +
tm_shape(bangaloreParks) +
tm_fill(col = "#a7c957") +
tm_layout(bg.color = "#56666b",
frame = F,
attr.outside = T,
outer.margins = 0,
asp = 0,
scale = 0.8)
```
]
.pull-right[
<img src="2020-OSMMappyHour_files/figure-html/unnamed-chunk-26-1.png" width="100%" height="100%" />
]
---
class: inverse
name: slide31
## Bangalore's Parks
.pull-left[
```r
# Add titles and sub-titles
tm_shape(bangaloreWardBoundary) +
tm_borders(col = "#a7c957",
lwd = 3) +
tm_shape(bangaloreParks) +
tm_fill(col = "#a7c957") +
tm_layout(bg.color = "#56666b",
frame = F,
attr.outside = T,
outer.margins = 0,
asp = 0,
scale = 0.8,
main.title = "Bangalore's Parks",
main.title.color = "#a7c957",
main.title.size = 1.75,
main.title.fontfamily = "Arial Narrow",
title = "Total area of parks in Bangalore = ~9.35km²",
title.color = "#a7c957",
title.size = 0.6,
title.position = c("right", "bottom"))
```
]
.pull-right[
<img src="2020-OSMMappyHour_files/figure-html/unnamed-chunk-28-1.png" width="100%" height="100%" />
]
---
class: inverse
name: slide32
## Examples
.pull-left[
]
--
.pull-right[
]
---
class: inverse
name: slide33
## Examples
.pull-left[
]
--
.pull-right[
]
---
class: inverse
name: slide34
## Summary
.pull-left[
What we've seen today:
- A brief introduction to thematic mapping