Visualizing OSM Data using R

# Visualizing OSM Data using R
## OSM India Virtual Mappy Hours
### Anirudh Govind
### 2020-12-05

---

## Structure

**Focus on the process** of making a map using OSM data in R.

- Thematic Mapping: A brief introduction

- Conceptual ideas for working with R

- Making a map in R using OSM Data

- Open for discussion

]

---

## Who I am (and not)

- **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

]

![](images/map_challenge_themes_2020.jpg)

Initiated by Topi Tjukanov [@tjukanov](https://twitter.com/tjukanov) in 2019.

]

---

## Who I am (and not)

- **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

]

![](images/map_challenge_themes_2020_osm.jpg)

Initiated by Topi Tjukanov [@tjukanov](https://twitter.com/tjukanov) in 2019.

]

---
class: inverse, center, middle
name: slide4

## Thematic Mapping

---

## Thematic Mapping

**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.

]

---

## Examples

![](images/Day1.png)

]

![](images/Day2.png)

]

---

## Examples

![](images/Day5.png)

]

![](images/Day9.png)

]

---

## Conceptual Ideas for working with R

---

## How do we make a map?

**Steps in making a map**

1. Obtain data

2. Ensure this data is relevant & properly structured

3. Put the map together with appropriate representation

]

---

## 1. Obtain Data

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)

]

In R, we can get OpenStreetMap data using the {[`osmdata`](https://docs.ropensci.org/osmdata/)} package!

]

---

## 1. Obtain Data

{[`osmdata`](https://docs.ropensci.org/osmdata/)} package!

```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
```

]

```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
```

]

---

## 2. Tidy Data

Data is often messy!

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.

]

![](images/tidyData.png)

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.

]

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.

]

---

## 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> "à²…à²°à²µà²¿à²‚à²¦ à²µà³ƒà²¤à³\215à²¤", ...
## $ 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...
```

---

## 2. Tidy Data

The {[`osmdata`](https://docs.ropensci.org/osmdata/)} package gives us tidy data!

![](images/tidyTraffic.png)

---

## 2. Tidy Data

We can further clean up the data by keeping only the columns we need; `osm_id` and the `geometry`

```r
# Select specific columns

trafficSignals %>% 
  select(osm_id, geometry)
```

]

![](images/tidierTraffic.png)

]

---

## How do we make a map?

**Steps in making a map**

1. Obtain data

2. Ensure this data is relevant & properly structured

3. Put the map together with appropriate representation

]

**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?

---

## 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)!

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

]

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

]

---

## 3. Present Data

A layered approach with the Grammar of Graphics!

```r
# Plot Bangalore ward map using borders

tm_shape(bangaloreWards) +
  tm_borders()
```

]

]

---

## 3. Present Data

A layered approach with the Grammar of Graphics!

```r
# Fill with different colours

tm_shape(bangaloreWards) +
  tm_borders() +
  tm_fill(col = "#E5E5E5")
```

]

]

---

## 3. Present Data

A layered approach with the Grammar of Graphics!

```r
# Add traffic signals

tm_shape(bangaloreWards) +
  tm_borders() +
  tm_fill(col = "#E5E5E5") +
  tm_shape(trafficSignals) +
  tm_dots(size = 0.1,
          col = "#FFB703")
```

]

]

---

## 3. Present Data

A layered approach with the Grammar of Graphics!

```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)
```

]

]

---

## How do we make a map?

**Steps in making a map**

1. Obtain data

2. Ensure this data is relevant & properly structured

3. Put the map together with appropriate representation

]

**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?

[See another example!](#slide23)

]

[Summarize!](#slide32)

]
 
---

## Let's make a map!

---

## Bangalore's Parks

Let's recreate this map showing Bangalore's Parks!

]

![](images/Day7.png)

]

---

## Bangalore's Parks

```r
# Load Bangalore ward data and transform it

bangaloreWardBoundary <- read_sf(here::here("data/bangaloreWardBoundary.shp"))

bangaloreWardBoundary <- bangaloreWardBoundary %>% 
 st_transform(3857)
```

]

```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
```

]

---

## Bangalore's Parks

```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)
```

]

```r
# Calculate areas of parks

bangaloreParks <- bangaloreParks %>% 
 mutate(area = st_area(.)) %>%
 mutate(area = as.numeric(area)) %>% 
 mutate(area = round(area, 2))
```

]

---

## Bangalore's Parks

```r
# Plot Bangalore ward map using borders

tm_shape(bangaloreWardBoundary) +
  tm_borders()
```

]

]

---

## Bangalore's Parks

```r
# Plot Bangalore's Parks

tm_shape(bangaloreWardBoundary) +
  tm_borders() +
  tm_shape(bangaloreParks) +
  tm_fill()
```

]

]

---

## Bangalore's Parks

```r
# Add colours and lineweights

tm_shape(bangaloreWardBoundary) +
  tm_borders(col = "#a7c957",
             lwd = 3) +
  tm_shape(bangaloreParks) +
  tm_fill(col = "#a7c957")
```

]

]

---

## Bangalore's Parks

```r
# Add background colour and margins

]

]

---

## Bangalore's Parks

```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"))
```

]

]

---

## Examples

![](images/Day6.png)

]

![](images/Day8-2.png)

]

---

## Examples

![](images/Day4.png)

]

![](images/Day3-1.png)

]

---

## Summary

What we've seen today:

- A brief introduction to thematic mapping

- Core concepts for working with R

- Getting data from OSM with the {[`osmdata`](https://docs.ropensci.org/osmdata/)} package
  
  - Tidy data with the {[`tidyr`](https://tidyr.tidyverse.org/) and [`dplyr`](https://dplyr.tidyverse.org/)} packages which are part of the {[`tidyverse`](https://www.tidyverse.org/)}
  
  - Making maps based on the [`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 
  
- Put this into practice with an example

- Examples of maps made with OSM data

]

---

# Thanks!

Code used to create all the maps can be found on  [github](https://github.com/anirudhgovind/30DayMapChallenge)!

Find me on twitter at [_anirudhgovind](https://twitter.com/_anirudhgovind)

Slides created using the R package {[`xaringan`](https://slides.yihui.org/xaringan/#1)}]