STAT3622 Data Visualization (Lecture 8)

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# STAT3622 Data Visualization (Lecture 8)
## Geographical Data Visualization
### <br>Dr. Aijun Zhang<br> The University of Hong Kong
### 31 March 2020

---

# What's covered in this lecture?

- Display Maps in R

- World/Countries
  
  - Hong Kong SAR
  
  - R:leaflet Package
  
- Data Visualization on Maps

- Case Study: HK Home Prices

---
class: center, middle

# 1. Display Maps in R

---
# Map Visualization

- Geographical/spatial data visualization

- Spatial data structure

- Static and interactive maps

- R packages: maps, sp, ggplot2, plotly, leaflet …

---
# World Map in R

```r
library(maps)
map('world', fill=TRUE, border='black',  col='gray',   bg='white')
```

---
# World Map by R:ggplot2

```r
library(ggplot2)
worldmap = map_data('world')
ggplot(worldmap, aes(long, lat, group=group)) + geom_polygon(fill='yellow', colour="green") +
  theme_bw()
```

---
# Map Data Structure

```r
str(worldmap)
```

```
## 'data.frame':	99338 obs. of  6 variables:
##  $ long     : num  -69.9 -69.9 -69.9 -70 -70.1 ...
##  $ lat      : num  12.5 12.4 12.4 12.5 12.5 ...
##  $ group    : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ order    : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ region   : chr  "Aruba" "Aruba" "Aruba" "Aruba" ...
##  $ subregion: chr  NA NA NA NA ...
```

```r
head(worldmap)
```

```
##        long      lat group order region subregion
## 1 -69.89912 12.45200     1     1  Aruba      <NA>
## 2 -69.89571 12.42300     1     2  Aruba      <NA>
## 3 -69.94219 12.43853     1     3  Aruba      <NA>
## 4 -70.00415 12.50049     1     4  Aruba      <NA>
## 5 -70.06612 12.54697     1     5  Aruba      <NA>
## 6 -70.05088 12.59707     1     6  Aruba      <NA>
```

---
# Country Maps: US

```r
thismap = map_data("state") 
ggplot(thismap, aes(long, lat, group=group, fill=region)) +  geom_polygon(show.legend = F) 
```

---
# Country Maps: China

```r
library(mapdata) # map data for more countries
thismap = map("china", plot=F)
ggplot(thismap, aes(long, lat, group=group)) +  geom_path() + ylim(15,55)
```

- A tutorial in Chinese: https://cosx.org/2009/07/drawing-china-map-using-r/

---
# Hong Kong Map Data
-  Download map database (.rds) source: [http://gadm.org/country](http://gadm.org/country)

```r
library(sp)
hkmap = readRDS("HKG_adm1.rds") 
class(hkmap)
```

```
## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"
```

- ggplot2::fortify converts "sp" object to data.frame

```r
library(ggplot2)
hkmapdf = fortify(hkmap) 
head(hkmapdf,3)
```

```
##       long      lat order  hole piece id group
## 1 114.1670 22.28153     1 FALSE     1  1   1.1
## 2 114.1671 22.28086     2 FALSE     1  1   1.1
## 3 114.1672 22.28038     3 FALSE     1  1   1.1
```

---
# Hong Kong SAR Map

```r
ggplot(hkmapdf, aes(long, lat, group=group, fill=id)) + geom_polygon(show.legend = F) 
```

---
# Extra data from Wikipedia

- HK population by districts: https://en.wikipedia.org/wiki/Districts_of_Hong_Kong 
- Copy and paste datatable to Excel plus some manual editing

```r
wikidata = read.csv("HK18Districts.csv"); 
library(knitr); kable(wikidata, format="html")
```

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> District </th>
   <th style="text-align:right;"> Population </th>
   <th style="text-align:right;"> Area </th>
   <th style="text-align:right;"> Density </th>
   <th style="text-align:left;"> Region </th>
   <th style="text-align:left;"> Code </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Central and Western </td>
   <td style="text-align:right;"> 244600 </td>
   <td style="text-align:right;"> 12.44 </td>
   <td style="text-align:right;"> 19983.92 </td>
   <td style="text-align:left;"> HK </td>
   <td style="text-align:left;"> CW </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Eastern </td>
   <td style="text-align:right;"> 574500 </td>
   <td style="text-align:right;"> 18.56 </td>
   <td style="text-align:right;"> 31217.67 </td>
   <td style="text-align:left;"> HK </td>
   <td style="text-align:left;"> EA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Southern </td>
   <td style="text-align:right;"> 269200 </td>
   <td style="text-align:right;"> 38.85 </td>
   <td style="text-align:right;"> 6962.68 </td>
   <td style="text-align:left;"> HK </td>
   <td style="text-align:left;"> SO </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Wan Chai </td>
   <td style="text-align:right;"> 150900 </td>
   <td style="text-align:right;"> 9.83 </td>
   <td style="text-align:right;"> 15300.10 </td>
   <td style="text-align:left;"> HK </td>
   <td style="text-align:left;"> WC </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Sham Shui Po </td>
   <td style="text-align:right;"> 390600 </td>
   <td style="text-align:right;"> 9.35 </td>
   <td style="text-align:right;"> 41529.41 </td>
   <td style="text-align:left;"> KL </td>
   <td style="text-align:left;"> SS </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Kowloon City </td>
   <td style="text-align:right;"> 405400 </td>
   <td style="text-align:right;"> 10.02 </td>
   <td style="text-align:right;"> 40194.70 </td>
   <td style="text-align:left;"> KL </td>
   <td style="text-align:left;"> KC </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Kwun Tong </td>
   <td style="text-align:right;"> 641100 </td>
   <td style="text-align:right;"> 11.27 </td>
   <td style="text-align:right;"> 56779.05 </td>
   <td style="text-align:left;"> KL </td>
   <td style="text-align:left;"> KU </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Wong Tai Sin </td>
   <td style="text-align:right;"> 426200 </td>
   <td style="text-align:right;"> 9.30 </td>
   <td style="text-align:right;"> 45645.16 </td>
   <td style="text-align:left;"> KL </td>
   <td style="text-align:left;"> WT </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Yau Tsim Mong </td>
   <td style="text-align:right;"> 318100 </td>
   <td style="text-align:right;"> 6.99 </td>
   <td style="text-align:right;"> 44864.09 </td>
   <td style="text-align:left;"> KL </td>
   <td style="text-align:left;"> YT </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Islands </td>
   <td style="text-align:right;"> 146900 </td>
   <td style="text-align:right;"> 175.12 </td>
   <td style="text-align:right;"> 825.14 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:left;"> IS </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Kwai Tsing </td>
   <td style="text-align:right;"> 507100 </td>
   <td style="text-align:right;"> 23.34 </td>
   <td style="text-align:right;"> 21503.86 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:left;"> KI </td>
  </tr>
  <tr>
   <td style="text-align:left;"> North </td>
   <td style="text-align:right;"> 310800 </td>
   <td style="text-align:right;"> 136.61 </td>
   <td style="text-align:right;"> 2220.19 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:left;"> NO </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Sai Kung </td>
   <td style="text-align:right;"> 448600 </td>
   <td style="text-align:right;"> 129.65 </td>
   <td style="text-align:right;"> 3460.08 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:left;"> SK </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Sha Tin </td>
   <td style="text-align:right;"> 648200 </td>
   <td style="text-align:right;"> 68.71 </td>
   <td style="text-align:right;"> 9433.85 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:left;"> ST </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Tai Po </td>
   <td style="text-align:right;"> 307100 </td>
   <td style="text-align:right;"> 136.15 </td>
   <td style="text-align:right;"> 2220.35 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:left;"> TP </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Tsuen Wan </td>
   <td style="text-align:right;"> 303600 </td>
   <td style="text-align:right;"> 61.71 </td>
   <td style="text-align:right;"> 4887.38 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:left;"> TW </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Tuen Mun </td>
   <td style="text-align:right;"> 495900 </td>
   <td style="text-align:right;"> 82.89 </td>
   <td style="text-align:right;"> 5889.38 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:left;"> TM </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Yuen Long </td>
   <td style="text-align:right;"> 607200 </td>
   <td style="text-align:right;"> 138.46 </td>
   <td style="text-align:right;"> 4297.99 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:left;"> YL </td>
  </tr>
</tbody>
</table>

---
# Map Wikidata with HKmap$HASC_1

```r
hkmapmeta = data.frame(mapid=hkmap$ID_1, Code=hkmap$HASC_1)
hkmapmeta$Code = gsub('HK.', '', as.character(hkmapmeta$Code))
hkmapmeta = merge(wikidata, hkmapmeta, by="Code", sort=FALSE)
kable(hkmapmeta, format="html") 
```

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> Code </th>
   <th style="text-align:left;"> District </th>
   <th style="text-align:right;"> Population </th>
   <th style="text-align:right;"> Area </th>
   <th style="text-align:right;"> Density </th>
   <th style="text-align:left;"> Region </th>
   <th style="text-align:right;"> mapid </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> CW </td>
   <td style="text-align:left;"> Central and Western </td>
   <td style="text-align:right;"> 244600 </td>
   <td style="text-align:right;"> 12.44 </td>
   <td style="text-align:right;"> 19983.92 </td>
   <td style="text-align:left;"> HK </td>
   <td style="text-align:right;"> 1 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> EA </td>
   <td style="text-align:left;"> Eastern </td>
   <td style="text-align:right;"> 574500 </td>
   <td style="text-align:right;"> 18.56 </td>
   <td style="text-align:right;"> 31217.67 </td>
   <td style="text-align:left;"> HK </td>
   <td style="text-align:right;"> 2 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> SO </td>
   <td style="text-align:left;"> Southern </td>
   <td style="text-align:right;"> 269200 </td>
   <td style="text-align:right;"> 38.85 </td>
   <td style="text-align:right;"> 6962.68 </td>
   <td style="text-align:left;"> HK </td>
   <td style="text-align:right;"> 11 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> WC </td>
   <td style="text-align:left;"> Wan Chai </td>
   <td style="text-align:right;"> 150900 </td>
   <td style="text-align:right;"> 9.83 </td>
   <td style="text-align:right;"> 15300.10 </td>
   <td style="text-align:left;"> HK </td>
   <td style="text-align:right;"> 15 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> SS </td>
   <td style="text-align:left;"> Sham Shui Po </td>
   <td style="text-align:right;"> 390600 </td>
   <td style="text-align:right;"> 9.35 </td>
   <td style="text-align:right;"> 41529.41 </td>
   <td style="text-align:left;"> KL </td>
   <td style="text-align:right;"> 10 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> KC </td>
   <td style="text-align:left;"> Kowloon City </td>
   <td style="text-align:right;"> 405400 </td>
   <td style="text-align:right;"> 10.02 </td>
   <td style="text-align:right;"> 40194.70 </td>
   <td style="text-align:left;"> KL </td>
   <td style="text-align:right;"> 4 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> KU </td>
   <td style="text-align:left;"> Kwun Tong </td>
   <td style="text-align:right;"> 641100 </td>
   <td style="text-align:right;"> 11.27 </td>
   <td style="text-align:right;"> 56779.05 </td>
   <td style="text-align:left;"> KL </td>
   <td style="text-align:right;"> 6 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> WT </td>
   <td style="text-align:left;"> Wong Tai Sin </td>
   <td style="text-align:right;"> 426200 </td>
   <td style="text-align:right;"> 9.30 </td>
   <td style="text-align:right;"> 45645.16 </td>
   <td style="text-align:left;"> KL </td>
   <td style="text-align:right;"> 16 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> YT </td>
   <td style="text-align:left;"> Yau Tsim Mong </td>
   <td style="text-align:right;"> 318100 </td>
   <td style="text-align:right;"> 6.99 </td>
   <td style="text-align:right;"> 44864.09 </td>
   <td style="text-align:left;"> KL </td>
   <td style="text-align:right;"> 17 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> IS </td>
   <td style="text-align:left;"> Islands </td>
   <td style="text-align:right;"> 146900 </td>
   <td style="text-align:right;"> 175.12 </td>
   <td style="text-align:right;"> 825.14 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:right;"> 3 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> KI </td>
   <td style="text-align:left;"> Kwai Tsing </td>
   <td style="text-align:right;"> 507100 </td>
   <td style="text-align:right;"> 23.34 </td>
   <td style="text-align:right;"> 21503.86 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:right;"> 5 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> NO </td>
   <td style="text-align:left;"> North </td>
   <td style="text-align:right;"> 310800 </td>
   <td style="text-align:right;"> 136.61 </td>
   <td style="text-align:right;"> 2220.19 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:right;"> 7 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> SK </td>
   <td style="text-align:left;"> Sai Kung </td>
   <td style="text-align:right;"> 448600 </td>
   <td style="text-align:right;"> 129.65 </td>
   <td style="text-align:right;"> 3460.08 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:right;"> 8 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> ST </td>
   <td style="text-align:left;"> Sha Tin </td>
   <td style="text-align:right;"> 648200 </td>
   <td style="text-align:right;"> 68.71 </td>
   <td style="text-align:right;"> 9433.85 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:right;"> 9 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> TP </td>
   <td style="text-align:left;"> Tai Po </td>
   <td style="text-align:right;"> 307100 </td>
   <td style="text-align:right;"> 136.15 </td>
   <td style="text-align:right;"> 2220.35 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:right;"> 12 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> TW </td>
   <td style="text-align:left;"> Tsuen Wan </td>
   <td style="text-align:right;"> 303600 </td>
   <td style="text-align:right;"> 61.71 </td>
   <td style="text-align:right;"> 4887.38 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:right;"> 13 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> TM </td>
   <td style="text-align:left;"> Tuen Mun </td>
   <td style="text-align:right;"> 495900 </td>
   <td style="text-align:right;"> 82.89 </td>
   <td style="text-align:right;"> 5889.38 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:right;"> 14 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> YL </td>
   <td style="text-align:left;"> Yuen Long </td>
   <td style="text-align:right;"> 607200 </td>
   <td style="text-align:right;"> 138.46 </td>
   <td style="text-align:right;"> 4297.99 </td>
   <td style="text-align:left;"> NT </td>
   <td style="text-align:right;"> 18 </td>
  </tr>
</tbody>
</table>

---
# Merge HKMap DataFrame with Wikidata

```r
hkmapdf = merge(hkmapdf,hkmapmeta, by.x="id", by.y="mapid")
head(hkmapdf)
```

```
##   id     long      lat order  hole piece group Code            District Population  Area
## 1  1 114.1670 22.28153     1 FALSE     1   1.1   CW Central and Western     244600 12.44
## 2  1 114.1671 22.28086     2 FALSE     1   1.1   CW Central and Western     244600 12.44
## 3  1 114.1672 22.28038     3 FALSE     1   1.1   CW Central and Western     244600 12.44
## 4  1 114.1682 22.27885     4 FALSE     1   1.1   CW Central and Western     244600 12.44
## 5  1 114.1683 22.27865     5 FALSE     1   1.1   CW Central and Western     244600 12.44
## 6  1 114.1683 22.27623     6 FALSE     1   1.1   CW Central and Western     244600 12.44
##    Density Region
## 1 19983.92     HK
## 2 19983.92     HK
## 3 19983.92     HK
## 4 19983.92     HK
## 5 19983.92     HK
## 6 19983.92     HK
```

---
# Hong Kong Map: 3 Regions

```r
ggplot(hkmapdf, aes(long, lat, group=group, fill=Region)) + geom_polygon()
```

---
# Hong Kong Map: 18 Districts

```r
ggplot(hkmapdf, aes(long, lat, group=group, fill=Code)) + geom_polygon() +
  guides(fill=guide_legend(title='18 Districts', ncol = 2)) 
```

---
class: center, middle

# 2. Data Visualization on Maps

---
# HK Population by Districts

```r
ggplot(hkmapdf, aes(long, lat, group=group, fill=Population)) + geom_polygon() + 
  ggtitle("Population by Districts") 
```

---
# HK Population Density by Districts

```r
ggplot(hkmapdf, aes(long, lat, group=group, fill=Density)) + geom_polygon() +
  ggtitle("Population Density by Districts")  
```

---
# HK Population Density by Districts

- Customized color bar by manually setting `scale_fill_gradient`

```r
ggplot(hkmapdf, aes(long, lat, group=group, fill=Density)) + geom_polygon() +
  scale_fill_gradient(limits=range(hkmapdf$Density), low="white", high="red") +
  ggtitle("Population Density by Districts")  
```

---
class: center, middle

# 3. Case Study: HK Home Prices

<br />
Purpose of study: to demonstrate geographical/spatiotemporal data visualization.

Click the [HTML](Lecture8_HomePrice.html) file generated by R markdown.

---
class: center, middle

# Thank you!

Q&A or Email ajzhang@umich.edu。