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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Lab05_Spatial-Data</title>
<meta charset="utf-8" />
<meta name="author" content="曾子軒 Dennis Tseng" />
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<textarea id="source">
class: center, middle, inverse, title-slide
# Lab05_Spatial-Data
## Spatial Data Manipulation and Visualization
### 曾子軒 Dennis Tseng
### 台大新聞所 NTU Journalism
### 2022/05/05
---
<style type="text/css">
.remark-slide-content {
padding: 1em 1em 1em 1em;
font-size: 28px;
}
.my-one-page-font {
padding: 1em 1em 1em 1em;
font-size: 20px;
/*xaringan::inf_mr()*/
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</style>
# 今日重點: Use R as GIS
- 認識地理資訊格式
- 認識地理資料結構 in R
- 操作地理資料
- 視覺化地理資料
- Extra: Geometry Operations
---
# 今日目標
<img src="photo/端_map_choropleth.png" width="45%" height="45%" /><img src="photo/viewsoftheworld_cartogram.png" width="45%" height="45%" />
.pull-left[Taiwan 2020 Presidential Choropleth by [端傳媒](https://theinitium.com/article/20200112-taiwan-election-data-ntu/)]
.pull-right[US 2016 Presidential Vote Share Map by [Views of the World](http://www.viewsoftheworld.net/wp-content/uploads/2016/11/USelection2016Cartogram.png)]
---
# 地理資訊格式
<img src="photo/Lab05_spatial_data.jpeg" width="40%" height="40%" /><img src="photo/Lab05_spatial_data_02.jpeg" width="40%" height="40%" />
.pull-left[[Geographic Information Systems and Science](https://andrewmaclachlan.github.io/CASA0005repo/index.html)]
.pull-right[[Intro to GIS and Spatial Analysis
](https://mgimond.github.io/Spatial/chp02-0.html)]
---
# 地理資訊格式
- Vector vs. Raster, 向量 vs. 網格
- The vector data model represents the world using points, lines and polygons.(點、線、多邊形)
- The raster data model divides the surface up into cells of constant size.(切成固定大小的格子)
- 來看一下[範例](https://dennistseng.shinyapps.io/taipeiapp/)
---
# Data: Taipei City Demography
- 台北市民政局：[台北市各里人口數](https://data.taipei/#/dataset/detail?id=a6394e3f-3514-4542-87bd-de4310a40db3)和[台北市里界圖](https://data.taipei/#/dataset/detail?id=6b17b31d-4e16-495e-95b1-9fd1f47c80d8)，前者的資料格式為 `csv` ，後者為 `shapefile`
- 內政部：[台北市人口統計_村里](https://segis.moi.gov.tw/STAT/Web/Platform/QueryInterface/STAT_QueryProductView.aspx?pid=E8C9BC9FA3B8955DB5ED36ECB95CC2B2&spid=2379CCC54C5DD0F4FEC8CE65233993C4)與和[台北市人口統計_最小統計區](https://segis.moi.gov.tw/STAT/Web/Platform/QueryInterface/STAT_QueryProductView.aspx?pid=D71B7495F95BC7C4E9AA7D13F5148AD1&spid=7ED8D58E129BC680)，資料格式則是 `csv` 和 `shapefile` 都有
---
class: inverse, center, middle
# 請大家去看/下載一下資料，[內政部](https://segis.moi.gov.tw/STAT/Web/Platform/QueryInterface/STAT_QueryInterface.aspx?Type=1)可以抓你喜歡的縣市，不用侷限在台北
---
# File Format Introduction
- `xlsx`/`ods`： 可以用 Excel/Google sheet 開啟的檔案格式，長相就是平常可以看到的試算表表格。政府單位常常以 `ods` 發布資料，台北市民政局的人口統計即是提供 `ods `
- `csv`： comma separated value, 用逗點分隔欄位，是非常常見的檔案格式。無論政府或私人單位都會以 `csv` 發布資料
- `tif`：儲存地理圖像的點陣圖格式，通常資料當中包含經緯度。衛星影像的原始格式即是 `tif`。
- `shapefile`：儲存地理圖資的檔案格式，由 `shp`, `shx`, `dbf`, `prj` 所組成，分別紀錄了地理參照資料、索引、屬性、投影。通常提供地理圖資時，會提供包含 `shp`, `shx`, `dbf`, `prj`, 等格式的壓縮檔案。
- `geojson`：基於 JSON 儲存地理資訊的檔案格式，內容包含空間範圍和屬性。
---
# File Format Introduction: Shapefile
- 最常見的地理資訊格式，由 ERSI 發明，當中包含數個檔案
- `.shp`: 儲存幾何特徵
- `.shx`: 儲存 `.shp` 當中特徵的 index
- `.dbf`: 儲存屬性資訊，像是幾何特徵的名字或變數
- `.prj`: 儲存座標系統資訊 (coordinate system information)
- 這個格式常見到有人創了 [twitter account](https://twitter.com/shapefiIe)
---
# File Format Introduction: GeoJSON
- 使用跟 JSON 類似的原始格式，但設計上有特別考慮在表現出地理特徵以外，也保留 non-spatial 的屬性
- 地理特徵包含 points, line strings, polygons, and multi-part collections of these types.
- [Processing of GeoJSON data in R](https://cran.r-project.org/web/packages/geojsonR/vignettes/the_geojsonR_package.html)
<img src="photo/R_example_geojson.png" width="55%" height="55%" />
---
# Data Structure: Simple Feature
- sf, simple feature
- 可以想像成幾何圖形加上 data frame 的組合
- data frame 的內容就是區域的屬性(attribute)，像是人口
- 幾何圖形有很多種 e.g. point, line string, polygon
- 不是簡介的介紹: [Simple Features for R](http://r-spatial.github.io/sf/articles/sf1.html); photo [source](https://geocompr.robinlovelace.net/spatial-class.html#intro-sf)
<img src="photo/Lab05_sf_structure.png" width="30%" height="30%" />
---
# Data Structure: Simple Feature
- 讀取原始資料
- 區分一下: file format & data sturcture
- import shapefile/GeoJSON 到 R 裏面變成 sf
- `st_read()`, `dsn`(data source name) and `layer`(layer name)
- 雖然 `shp` 設計得很好，但還是盡量完整把其他檔案也讀進來
```r
library(tidyverse)
library(sf)
library(cartogram)
library(maps)
```
---
# Data Importing
```r
sf_tpe <-
st_read(dsn = "data/Lab05/109年12月行政區人口統計_村里_臺北市_SHP/", layer = "109年12月行政區人口統計_村里", quiet = T) %>%
mutate(across(where(is.character), ~iconv(., from = "BIG5", to = "UTF8"))) %>%
rename_with(~str_to_lower(.), everything()) %>%
mutate(across(where(is.double), ~if_else(is.na(.),as.double(0),.))) %>%
st_set_crs(3826) %>% st_transform(4326) %>% filter(str_detect(county, "臺北市")) %>%
mutate(village = if_else(str_detect(village,"糖"),"糖廍里",village))
```
---
# Data Importing
```r
sf_tpe
```
```
## Simple feature collection with 456 features and 12 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 121.4571 ymin: 24.9605 xmax: 121.6659 ymax: 25.21018
## Geodetic CRS: WGS 84
## First 10 features:
## county town village county_id town_id v_id village_id h_cnt p_cnt
## 1 臺北市 文山區 樟新里 63000 63000080 63000080-031 031 2199 5697
## 2 臺北市 文山區 老泉里 63000 63000080 63000080-037 037 362 923
## 3 臺北市 文山區 樟腳里 63000 63000080 63000080-032 032 2108 5674
## 4 臺北市 文山區 樟文里 63000 63000080 63000080-041 041 2374 6311
## 5 臺北市 文山區 樟樹里 63000 63000080 63000080-043 043 1825 4898
## 6 臺北市 文山區 順興里 63000 63000080 63000080-029 029 2902 7642
## 7 臺北市 文山區 指南里 63000 63000080 63000080-036 036 1766 4108
## 8 臺北市 文山區 樟林里 63000 63000080 63000080-030 030 3247 8424
## 9 臺北市 文山區 忠順里 63000 63000080 63000080-038 038 1781 4213
## 10 臺北市 文山區 試院里 63000 63000080 63000080-023 023 2808 6901
## m_cnt f_cnt info_time geometry
## 1 2682 3015 109Y12M MULTIPOLYGON (((121.5595 24...
## 2 485 438 109Y12M MULTIPOLYGON (((121.5715 24...
## 3 2735 2939 109Y12M MULTIPOLYGON (((121.5691 24...
## 4 3048 3263 109Y12M MULTIPOLYGON (((121.5599 24...
## 5 2324 2574 109Y12M MULTIPOLYGON (((121.5625 24...
## 6 3674 3968 109Y12M MULTIPOLYGON (((121.5678 24...
## 7 2099 2009 109Y12M MULTIPOLYGON (((121.5799 24...
## 8 4103 4321 109Y12M MULTIPOLYGON (((121.5585 24...
## 9 1986 2227 109Y12M MULTIPOLYGON (((121.5683 24...
## 10 3276 3625 109Y12M MULTIPOLYGON (((121.5507 24...
```
---
# Simple Feature 的組成
- 資料結構 - Simple feature collection
- 幾何組成 - geometry type
- 維度 - dimension
- 地理區域 - bbox
- 大地座標系統 - geographic CRS
---
# 馬上來畫一張圖
```r
sf_tpe %>% ggplot() + geom_sf()
```
<!-- -->
---
# 上個顏色
```r
sf_tpe %>% ggplot(aes(fill = p_cnt)) + geom_sf(color = NA) +
scale_fill_gradient(low = "white", high = "purple")
```
<!-- -->
```r
# 260 萬
```
---
# 上個顏色
```r
sf_tpe %>% mutate(p_density = p_cnt/(as.double(st_area(.))/1000000)) %>%
ggplot(aes(fill = p_density)) + geom_sf(color = NA) + scale_fill_gradient(low = "white", high = "purple")
```
<!-- -->
```r
# 272 平方公里
```
---
# 重要概念: 投影
- 投影系統 = 告訴你怎麼在地圖上呈現資料的數學公式
- 大地座標系統(geographic coordiate reference systems)
- 視資料為球體，舉例: a minute type of resolution，代表把地球切成 360 度，每度有 60 分，每分有 60 秒
- 經緯: Arc-seconds of latitude (緯線) 幾乎保持固定，但 arc-seconds of longitude (經線) 越往極點會越小
- 投影座標系統(projected coordinate reference systems)
- 視資料為二維平面，所以長寬、角度、面積都是固定的
---
# 重要概念: 投影
<img src="photo/Lab05_CRS.png" width="40%" height="40%" /><img src="photo/Lab05_CRS_02.png" width="40%" height="40%" />
.pull-left[geographic CRS, (0,0)是零度經線跟零度緯線的交叉]
.pull-right[projected CRS, (0,0)是地圖上左下角]
---
# 重要概念: 投影
- 考慮到上述兩種投影系統的差別，分析或視覺化的時候，因為尺度(scale)通常都是以國家、地區為單位，需要從立體投影到平面
- 大部分國家/地區有自己的投影座標系統，也就是把中心(0,0)從地球中心移到國家/地區的中心
- 區分: 以角度為單位(angular)，譬如 degrees, latitude and longitude，或者資料是全球尺度，則使用 GCRS，以線性為單位；譬如英尺、公尺，或者資料是地方尺度，則使用 PCRS
---
# 重要概念: 投影
- 從立體到平面
- 地球是立體的橢圓形，用參考橢球體(Ellipsoid)代表它
- 會有一個跟參考橢球體相符的大地基準面，用來作為 CRS 的依據
<img src="photo/Lab05_CRS_03.jpg" width="40%" height="40%" />
---
# 重要概念: 投影
- 每個區域都有適合的參考橢球體和大地基準面
- 基準點(Datum)分為區域性的的(local)和全球的(global)，用來讓人從地球表面對應到笛卡兒座標系（就常見的 XY 啦XD）
- global 最常用: WGS84，以地球質心為中心
- local 最常用 TWD97，舊版是用 TWD67
- 如果你是地理狂可以看[
大地座標系統漫談](https://www.sunriver.com.tw/grid_tm2.htm)跟[座標系統介紹](http://140.121.160.124/GEO/%E5%BA%A7%E6%A8%99%E7%B3%BB%E7%B5%B1.pdf)
---
# 重要概念: 投影
- proj4string
- 可以用來快速看 CRS 屬於哪種
- a compact way of identifying a coordinate reference system
- 組成包含 projection, datum, units, ellps, etc.，用 + 號分隔
```r
st_crs(sf_tpe)$proj4string
```
```
## [1] "+proj=longlat +datum=WGS84 +no_defs"
```
---
# 重要概念: 投影
- EPSG
- 投影法有對應的代號稱為 EPSG(歐洲石油探勘組織)，他們制定了空間參考識別系統(SRID)
- 可以記兩個重要的: WGS84 = 4326, TWD97 = 3826
```r
st_crs(sf_tpe)
```
```
## Coordinate Reference System:
## User input: EPSG:4326
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## USAGE[
## SCOPE["Horizontal component of 3D system."],
## AREA["World."],
## BBOX[-90,-180,90,180]],
## ID["EPSG",4326]]
```
---
# 重要概念: 投影
- 用得到投影的情境
- 研究區域，想轉換座標(changing projections)
- 原始資料就缺投影方法
- 解方
- 修改 EPSG code 或是改掉 `proj4string` 的內容
- 加上 EPSG code 或是加上 `proj4string` 的內容
- function
- `st_crs()` 取用
- `st_transform()` 變換
- `st_set_crs()` 設定
---
# 變換投影看看
```r
# sf_tpe %>% st_crs_set(4326)
sf_tpe %>% st_transform(3826) %>% st_crs()
```
```
## Coordinate Reference System:
## User input: EPSG:3826
## wkt:
## PROJCRS["TWD97 / TM2 zone 121",
## BASEGEOGCRS["TWD97",
## DATUM["Taiwan Datum 1997",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",3824]],
## CONVERSION["Taiwan 2-degree TM zone 121",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",0,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",121,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",0.9999,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",250000,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",0,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["easting (X)",east,
## ORDER[1],
## LENGTHUNIT["metre",1]],
## AXIS["northing (Y)",north,
## ORDER[2],
## LENGTHUNIT["metre",1]],
## USAGE[
## SCOPE["Engineering survey, topographic mapping."],
## AREA["Taiwan, Republic of China - between 120°E and 122°E, onshore and offshore - Taiwan Island."],
## BBOX[20.41,119.99,26.72,122.06]],
## ID["EPSG",3826]]
```
---
class: inverse, center, middle
# 投影講完惹！！再來下載一下整個台灣的[地理圖資](https://data.gov.tw/dataset/7442)
---
# 操作 sf
- 可以用 `st_()` 開頭
- 引入圖資 `st_read()`, `st_as_sf()`
- 座標系統 `st_crs()`, `st_set_crs()`, `st_transform()`
- 處理圖資 `st_crop()`, `st_bbox()`, `st_simplify()`
- 或者也可以用 `dplyr` 的動詞，`rename()`、`filter()`、`mutate()`、`select()`、`group_by()` 配 `summarize()`
---
# 操作 sf
```r
sf_taiwan <-
st_read(dsn = "data/Lab05/直轄市縣市界線/", layer = "COUNTY_MOI_1090820", quiet = T) %>%
rename_with(~str_to_lower(.), everything()) %>% st_transform(3826)
sf_taiwan
```
```
## Simple feature collection with 22 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -478698 ymin: 1154437 xmax: 606875.6 ymax: 2919551
## Projected CRS: TWD97 / TM2 zone 121
## First 10 features:
## countyid countycode countyname countyeng
## 1 Z 09007 連江縣 Lienchiang County
## 2 G 10002 宜蘭縣 Yilan County
## 3 N 10007 彰化縣 Changhua County
## 4 M 10008 南投縣 Nantou County
## 5 P 10009 雲林縣 Yunlin County
## 6 C 10017 基隆市 Keelung City
## 7 A 63000 臺北市 Taipei City
## 8 F 65000 新北市 New Taipei City
## 9 B 66000 臺中市 Taichung City
## 10 D 67000 臺南市 Tainan City
## geometry
## 1 MULTIPOLYGON (((146282.4 28...
## 2 MULTIPOLYGON (((346997.1 27...
## 3 MULTIPOLYGON (((194797.1 26...
## 4 MULTIPOLYGON (((277507.6 26...
## 5 MULTIPOLYGON (((156164 2602...
## 6 MULTIPOLYGON (((321581.3 27...
## 7 MULTIPOLYGON (((307543.1 27...
## 8 MULTIPOLYGON (((304125.1 27...
## 9 MULTIPOLYGON (((283293.5 27...
## 10 MULTIPOLYGON (((192793.9 25...
```
---
# 操作 sf
```r
sf_taiwan_simplify <- sf_taiwan %>% st_transform(3826) %>%
st_simplify(dTolerance = 100) %>% st_transform(4326)
sf_taiwan_simplify %>% object.size()
```
```
## 216736 bytes
```
```r
sf_taiwan %>% object.size()
```
```
## 5471776 bytes
```
---
# 操作 sf
```r
sf_taiwan_simplify %>% st_bbox()
```
```
## xmin ymin xmax ymax
## 114.35968 10.37135 124.56115 26.38394
```
---
# 畫圖 - 4326
```r
sf_taiwan_simplify %>% ggplot() + geom_sf()
```
<!-- -->
---
# 畫圖 - 3826
```r
sf_taiwan_simplify %>% st_transform(3826) %>% ggplot() + geom_sf()
```
<!-- -->
---
# 畫圖 - 切台灣本島
- `filter()`
```r
sf_taiwan_simplify %>%
filter(! str_detect(countyname, "連江|金門|澎湖")) %>%
ggplot() + geom_sf()
```
<!-- -->
---
# 畫圖 - 切台灣本島
- `st_crop()`
```r
sf_taiwan_simplify %>%
st_crop(xmin = 120, xmax = 122, ymin = 22, ymax = 25.5) %>%
ggplot() + geom_sf()
```
<!-- -->
---
class: inverse, center, middle
# 再次回到面量圖！來畫 2020 總統大選得票
---
# Map: Choropleth
<ul>
<li>Definition
<ul>
<li>A choropleth map displays divided geographical areas or regions that are coloured in relation to a numeric variable.</li>
<li><a href="https://www.r-graph-gallery.com/choropleth-map.html">Choropleth Map by the R Graph Gallery</a></li>
</ul>
<li>Process
<ul>
<li>Get Taiwan 2020 Election Results Data</li>
<li>Decide the variable plotted, such as KMT vote per</li>
<li>Join Election with Taiwan sf object</li>
<li>Draw the map colored by the variable</li>
</ul>
</ul>
---
class: inverse, center, middle
# R time: Choropleth
---
# Map: Dot Distribution Map/Bubble Map
<ul>
<li>Definition
<ul>
<li>Choropleths aggregate individual data points into a single geographic region. In contrast, a dot distribution/density map uses a dot symbol to show the presence of a feature or a phenomenon.</li>
<li><a href="https://www.r-graph-gallery.com/bubble-map.html">Bubble map by the R Graph Gallery</a></li>
</ul>
<li>Process
<ul>
<li>Get Taiwan Cities Population Data</li>
<li>Decide the variable plotted, such as city population</li>
<li>Plot the base map and add city dots</li>
</ul>
</ul>
---
class: inverse, center, middle
# R time: Dot Distribution Map
---
# Map: Cartogram
<ul>
<li>Definition
<ul>
<li>A cartogram is a map in which the geometry of regions is distorted in order to convey the information of an alternate variable.</li>
<li><a href="https://www.r-graph-gallery.com/cartogram.html">Cartogram by the R Graph Gallery</a></li>
</ul>
<li>Process
<ul>
<li>Get Taiwan 2020 Election Results Data and population data</li>
<li>Decide the variable for distortion, such as population</li>
<li>Also decide the variable cared about, such as KMT vote per</li>
<li>Distort the sf object based on the variable</li>
<li>Plot the distorted map and colored by chosen variable</li>
</ul>
</ul>
---
class: inverse, center, middle
# R time: Cartogram
---
# Map: Parliament Plots
<ul>
<li>Definition
<ul>
<li>A visual representations of the composition of legislatures that display seats colour-coded by party.</li>
<li><a href="https://github.com/RobWHickman/ggparliament">ggparliament by RobWHickman</a></li>
<li><a href="https://erocoar.github.io/ggpol/">ggpol by Frederik Tiedemann</a></li>
</ul>
<li>Process
<ul>
<li>Get Taiwan 2020 Parliament Raw Data</li>
<li>Create x, y, and theta columns</li>
<li>Plot Parliament Composition</li>
</ul>
</ul>
---
class: inverse, center, middle
# R time: Parliament Plots
---
# Map: Hexmap
<ul>
<li>Definition
<ul>
<li>A a geospatial object where all regions of the map are represented as hexagons.</li>
<li><a href="https://pitchinteractiveinc.github.io/tilegrams/">tilegramps showcase with JS</a></li>
<li><a href="https://github.com/olihawkins/clhex">library(clhex)</a></li>
<li><a href="https://olihawkins.com/project/hexjson-editor/">Hexmap Editor</a></li>
</ul>
<li>Process
<ul>
<li>Create empty Taiwan Constituency hexjson with library(clhex)</li>
<li>Draw Taiwan Constituency with hexjson editor according to geographical posistion</li>
<li>Import edited hexjson into R and plot with geom_sf()</li>
<li>Export the .SVG for further use</li>
</ul>
</ul>
---
class: inverse, center, middle
# R time: Hexmap
---
# Map: Summary
<ul>
<li>Plots
<ul>
<li>背景地圖/Background Map</li>
<li>統計地圖/Choropleth</li>
<li>點示地圖/Dot Distribution Map; Bubble Map</li>
<li>示意地圖/Cartogram</li>
<li>國會席次圖/Parliament Plots</li>
<li>六邊型網格圖/Hexmap/Tilegram</li>
</ul>
---
# Some Map
<img src="photo/端_map_cartogram.png" width="45%" height="45%" /><img src="photo/viewsoftheworld_cartogram.png" width="45%" height="45%" />
.pull-left[Taiwan 2020 Presidential Choropleth by [端傳媒](https://theinitium.com/article/20200112-taiwan-election-data-ntu/)]
.pull-right[US 2016 Presidential Vote Share Map by [Views of the World](http://www.viewsoftheworld.net/wp-content/uploads/2016/11/USelection2016Cartogram.png)]
---
# Some Map
<img src="photo/NYT_map_bubble.png" width="45%" height="45%" /><img src="photo/端_map_hexbin.png" width="45%" height="45%" />
.pull-left[US 2012 Presidential Vote Lead Map by [The New York Times](https://www.nytimes.com/interactive/2016/11/01/upshot/many-ways-to-map-election-results.html)]
.pull-right[Taiwan 2020 Parliament Hexmap by [端傳媒](https://theinitium.com/article/20200112-taiwan-election-data-ntu-1/)]
---
# Some Map
<img src="photo/reddit_map_hex_swing.png" width="50%" height="50%" /><img src="photo/關鍵_plot_parliament.png" width="50%" height="50%" />
.pull-left[UK 2019 Parliament Swing Map by [TeHuia](https://www.reddit.com/r/MapPorn/comments/eah5j1/uk_2019_election_swing_map/)]
.pull-right[Taiwan 2020 Parliament Plot by [關鍵評論網](https://www.thenewslens.com/article/129934)]
---
# 今日重點: Use R as GIS
- 認識地理資訊格式
- 認識地理資料結構 in R
- 操作地理資料
- 視覺化地理資料
- Extra: Geometry Operations
---
# 之後有機會: 空間分析
- [Geometry Operations](https://geocompr.robinlovelace.net/geometric-operations.html#buffers)
- [可達性空間](https://medium.com/ivc-invisiblecities/passive-space-remake-4faea442d78a)
- [重疊區域切分](https://medium.com/geopainter/%E7%94%A8%E6%8D%B7%E9%81%8B%E8%B3%87%E6%96%99%E8%A7%80%E5%AF%9F%E4%B9%98%E5%AE%A2%E7%9A%84%E7%A7%BB%E5%8B%95%E8%BB%8C%E8%B7%A1-%E4%B8%8B-22d70ea76ddb)
- [空間層級變換](https://medium.com/ivc-invisiblecities/%E5%8F%B0%E5%8C%97%E4%BA%BA%E5%8F%A3%E5%88%86%E5%B8%83%E5%9C%B0%E5%9C%96%E7%A9%BA%E9%96%93%E8%B3%87%E6%96%99%E7%9A%84%E5%B0%BA%E5%BA%A6%E8%AE%8A%E6%8F%9B-7278e319c5fe)
- Spatial Analysis
- 熱點分析
- 空間自相關
- 熱區分析
---
class: inverse, center, middle
# Thanks
</textarea>
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