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<!DOCTYPE html>
<html>
<head>
<title>Introduction to GIS in R</title>
<meta charset="utf-8">
<meta name="author" content="Angela Li @CivicAngela Center for Spatial Data Science, UChicago Slides available at http://bit.ly/harris-gis" />
<meta name="date" content="2018-05-18" />
<link href="libs/remark-css/default.css" rel="stylesheet" />
<link href="libs/remark-css/default-fonts.css" rel="stylesheet" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# Introduction to GIS in R
## 🌎 🗺<br/>with sf and tmap
### Angela Li <br> <span class="citation">@CivicAngela</span> <br> Center for Spatial Data Science, UChicago <br> Slides available at <a href="http://bit.ly/harris-gis" class="uri">http://bit.ly/harris-gis</a>
### 2018-05-18
---
class: center, middle, inverse
# Want to try out the code in this presentation?
## Data and code are located at my Github repo [here](https://github.com/angela-li/gis-r).
---
# Three parts to this talk:
## 1. Why spatial data in R?
## 2. A quick tutorial
## 3. Examples and resources
---
class: inverse, center, middle
# Why spatial data in R?
---
class: inverse
# I had to make a map for a class
---
# Here's how you do it with a traditional GIS
---
# This is fine, until
--
👎 You want to remake your map with a slightly different set of data and have to redo everything
--
👎 You want to make a bunch of maps quickly
--
👎 You forgot what buttons you even clicked to make the map
--
👎👎👎 **The GIS software crashes!**
---
class: inverse, center, middle
# Enter R
## (and geographic data science)
---
# A map from my thesis
---
# And the code used to produce it
```r
library(tidyverse)
library(sf)
library(tmap)
sales <- read_csv("output/sales-tidy.csv")
tracts <- st_read("data/orig/shapefiles/detroit_tracts.shp")
tracts <- rename(tracts, tract = GEOID)
sales <- sales %>%
right_join(tracts, ., by = "tract")
med_sales_map <- tm_shape(sales, unit = "mi") +
tm_fill("med_price", palette = "Blues", breaks = quantile(a$med_price), title = "Median Sales Price") +
tm_facets("after_hhf") +
tm_shape(tracts) +
tm_borders() +
tm_compass(fontsize = 0.6, color.dark = "dark grey") +
tm_scale_bar(color.dark = "dark grey")
save_tmap(med_sales_map, "doc/figs/med_sales_map.png")
```
---
class: center, middle
# Not much code = pretty good results
Thanks, `sf` and `tmap`!
---
class: inverse, center, middle
# A quick tutorial
---
# Getting started
Install the `sf` and `tmap` packages.
```r
install.packages("sf")
install.packages("tmap")
```
- `sf` stores spatial data as (tidyverse-friendly!) dataframes
- `sp` is the original way to store spatial data in R, but it doesn't use dataframes
- Many spatial statistics and mapping packages still rely on `sp`, so you'll probably encounter a `SpatialPolygonsDataFrame` at some point.
- No worries, you can convert from `sf` to `sp` and vice versa pretty easily
- `tmap` provides a quick way to make useful thematic maps and works directly with spatial objects
- There are a bunch of other packages you can use to make interactive maps (`mapview`, `leaflet`, `ggplot2`, `shiny`), which I won't go into today
---
# Get some data
- You're looking for "shapefiles" but data with XY coordinates works too
- Many packages have been developed to acquire spatial data:
- `spData`
- `tidycensus`
- `usaboundaries`
- `osmdata`
- etc.
- If you have address data, you can geocode (translate addresses to latitude and longitude) with the `opencage` package, which I won't discuss today
- Check out all of these spatial packages later!
---
# Let's download some data
## Support your [local open data portal](https://data.cityofchicago.org/)
---
---
---
---
# You've downloaded the data
### What the heck are all of these files??
In general:
- .shp is the actual shape ("feature geometry") of the data
- .dbf represents the attributes associated with each shape
- .prj tells you how 3-D coordinates are "projected" into a 2-D map
- .sbn, .sbx, .shx are indexes that make it easier to work quickly with spatial data
FYI: spatial data tends to be BIG (because you have to store all the info about how to make the shapes!)
---
# Load the R package, and the data
```r
# Load package
library(sf)
```
```
## Linking to GEOS 3.6.1, GDAL 2.1.3, proj.4 4.9.3
```
```r
# Read in shapefile
chi <- st_read("data/Neighborhoods_2012b.shp")
```
```
## Reading layer `Neighborhoods_2012b' from data source `/Users/angela/Desktop/R-Projects/Teaching/gis-r/data/Neighborhoods_2012b.shp' using driver `ESRI Shapefile'
## Simple feature collection with 98 features and 4 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 1091131 ymin: 1813892 xmax: 1205199 ymax: 1951669
## epsg (SRID): NA
## proj4string: +proj=tmerc +lat_0=36.66666666666666 +lon_0=-88.33333333333333 +k=0.9999749999999999 +x_0=300000 +y_0=0 +datum=NAD83 +units=us-ft +no_defs
```
---
# Take a look at the data
```r
head(chi)
```
```
## Simple feature collection with 6 features and 4 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 1145372 ymin: 1871188 xmax: 1182449 ymax: 1918816
## epsg (SRID): NA
## proj4string: +proj=tmerc +lat_0=36.66666666666666 +lon_0=-88.33333333333333 +k=0.9999749999999999 +x_0=300000 +y_0=0 +datum=NAD83 +units=us-ft +no_defs
## PRI_NEIGH SEC_NEIGH SHAPE_AREA SHAPE_LEN
## 1 Grand Boulevard BRONZEVILLE 48492503 28196.837
## 2 Printers Row PRINTERS ROW 2162138 6864.247
## 3 United Center UNITED CENTER 32520513 23101.364
## 4 Sheffield & DePaul SHEFFIELD & DEPAUL 10482592 13227.050
## 5 Humboldt Park HUMBOLDT PARK 125010426 46126.751
## 6 Garfield Park GARFIELD PARK 89976070 44460.919
## geometry
## 1 MULTIPOLYGON (((1182322 187...
## 2 MULTIPOLYGON (((1176453 189...
## 3 MULTIPOLYGON (((1165664 190...
## 4 MULTIPOLYGON (((1167948 191...
## 5 MULTIPOLYGON (((1145647 190...
## 6 MULTIPOLYGON (((1157952 190...
```
--
# `sf` objects are `data.frame`s!
```r
class(chi)
```
```
## [1] "sf" "data.frame"
```
---
# Make your first map (1)
```r
# Map it using base R: just shape outlines
plot(st_geometry(chi))
```
<!-- -->
---
# Make your first map (2)
```r
# This maps all the attributes
plot(chi)
```
<!-- -->
---
class: inverse, middle
# Get more interesting data
Lots of great cleaned datasets at my research center's website to play with.
<center> https://geodacenter.github.io/data-and-lab/ </center>
---
---
---
# Make a second map (1)
```r
chi2 <- st_read("data/ComArea_ACS14_f.shp")
```
```
## Reading layer `ComArea_ACS14_f' from data source `/Users/angela/Desktop/R-Projects/Teaching/gis-r/data/ComArea_ACS14_f.shp' using driver `ESRI Shapefile'
## Simple feature collection with 77 features and 86 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -87.94011 ymin: 41.64454 xmax: -87.52414 ymax: 42.02304
## epsg (SRID): 4326
## proj4string: +proj=longlat +ellps=WGS84 +no_defs
```
For reference:
- `geometry type` describes the basic structure of the spatial data. You could have points, polygons, lines, and more.
- `bbox` gives the bounding box for the data, and can be used to crop other layers when you make a map.
- `epsg (SRID)` is a special code that indicates what projection is being used. When in doubt, `4326` is a good one.
- `proj4string` refers to the same thing as the EPSG code. If the string starts with `+proj=longlat`, that means your data is **unprojected**.
---
# Make a second map (2)
Let's make a choropleth map of population density by neighborhood!
```r
# Check what variables we have
names(chi2)
```
```
## [1] "ComAreaID" "community" "TRACTCnt" "shape_area" "shape_len"
## [6] "Pop2012" "Pop2014" "PopChng" "PopM" "PopMP"
## [11] "PopF" "PopFP" "Under5" "Under5P" "Under18"
## [16] "Under18P" "Over18" "Over18P" "Over21" "Over21P"
## [21] "Over65" "Over65P" "Wht14" "Wht14P" "Blk14"
## [26] "Blk14P" "AI14" "AI14P" "AS14" "AS14P"
## [31] "NHP14" "NHP14P" "Oth14" "Oth14P" "Hisp14"
## [36] "Hisp14P" "Property_C" "PropCrRt" "Violent_C" "VlntCrRt"
## [41] "PerCInc14" "PPop14" "Pov14" "field_37" "ChldPov14"
## [46] "NoHS14" "HSGrad14" "SmClg14" "ClgGrad14" "LaborFrc"
## [51] "Unemp14" "Pov50" "Pov50P" "Pov125" "Pov125P"
## [56] "Pov150" "Pov150P" "Pov185" "Pov185P" "Pov200"
## [61] "Pov200P" "COIave" "HISave" "SESave" "Hlitave"
## [66] "BirthRate" "FertRate" "LoBirthR" "PrenScrn" "PretBrth"
## [71] "TeenBirth" "Assault" "BrstCancr" "CancerAll" "Colorect"
## [76] "DiabetM" "FirearmM" "InfntMR" "LungCancer" "ProstateC"
## [81] "Stroke" "ChlBLLS" "ChlLeadP" "GonorrF" "GonorrM"
## [86] "Tuberc" "geometry"
```
---
# Make a second map (3)
Since we have population and area, we're going to calculate population density using the `dplyr` package.
```r
# Calculate population density
library(dplyr)
```
```
##
## Attaching package: 'dplyr'
```
```
## The following objects are masked from 'package:stats':
##
## filter, lag
```
```
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
```
```r
chi2 <- mutate(chi2, Pop2014 = Pop2014/shape_area)
```
```
## Warning: package 'bindrcpp' was built under R version 3.4.4
```
---
```r
# Map population density by neighborhood
plot(chi2["Pop2014"])
```
<!-- -->
---
# Use `tmap` to make a prettier map
```r
library(tmap)
tm_shape(chi2) +
tm_fill("Pop2014", palette = "Purples",
title = "Population by Neighborhood, 2014")
```
---
```
## Warning: package 'tmap' was built under R version 3.4.4
```
<!-- -->
---
class: inverse, center, middle
# Let's do some spatial analysis!
---
# How are grocery stores and population related?
Time to add a point layer with locations of grocery stores.
```r
groceries <- st_read("data/groceries.shp")
```
```
## Reading layer `groceries' from data source `/Users/angela/Desktop/R-Projects/Teaching/gis-r/data/groceries.shp' using driver `ESRI Shapefile'
## Simple feature collection with 149 features and 14 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 1124188 ymin: 1826196 xmax: 1201803 ymax: 1950151
## epsg (SRID): NA
## proj4string: +proj=tmerc +lat_0=36.66666666666666 +lon_0=-88.33333333333333 +k=0.9999749999999999 +x_0=300000.0000000001 +y_0=0 +ellps=GRS80 +units=us-ft +no_defs
```
Note that this is a `POINT` object, and that it has a projection: `+proj=tmerc` (Transverse Mercator). If we want to plot this in the same map as the neighborhood boundaries, we will need to make sure both files have the **same projection**.
⭐ This is a key source of frustration when working with spatial data. If some layers aren't showing up when you make a map, check that they all have the same projection! ⭐
---
# Project the neighborhood data
You generally project the data that has the `+proj=longlat` string, because it is initially **unprojected**.
```r
# Get the CRS (coordinate reference system) of the groceries point data
groceries_crs <- st_crs(groceries)
# Project the neighborhood boundaries
chi2 <- st_transform(chi2, groceries_crs)
```
---
# Plot population and grocery stores
```r
# Plot both
tm_shape(chi2) +
tm_borders() +
tm_fill("Pop2014", palette = "Purples",
title = "Grocery Stores and Population Density, 2014") +
tm_shape(groceries) +
tm_dots(title = "Groceries", size = 0.1, col = "black")
```
---
<!-- -->
---
class: inverse, center, middle
# You can also use dplyr to perform analysis!
---
# Which neighborhoods in Chicago have the most grocery stores?
```r
library(dplyr)
chi2 %>%
st_join(groceries, .) %>%
group_by(community) %>%
tally() %>%
arrange(desc(n))
```
```
## Simple feature collection with 55 features and 2 fields
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: 1124188 ymin: 1826196 xmax: 1201803 ymax: 1950151
## epsg (SRID): NA
## proj4string: +proj=tmerc +lat_0=36.66666666666666 +lon_0=-88.33333333333333 +k=0.9999749999999999 +x_0=300000.0000000001 +y_0=0 +ellps=GRS80 +units=us-ft +no_defs
## # A tibble: 55 x 3
## community n geometry
## <fct> <int> <MULTIPOINT [US_survey_foot]>
## 1 NEAR NORTH SIDE 12 (1169391 1910436, 1171916 1908252, 1174283 19056…
## 2 LOGAN SQUARE 8 (1150226 1915696, 1151916 1915701, 1153076 19184…
## 3 LAKE VIEW 7 (1165054 1922112, 1165147 1919657, 1166327 19242…
## 4 LOWER WEST SIDE 7 (1160692 1889319, 1162087 1889703, 1162758 18876…
## 5 NEAR WEST SIDE 7 (1165889 1894599, 1168996 1898801, 1171065 18998…
## 6 BELMONT CRAGIN 6 (1132379 1917230, 1133303 1916051, 1134857 19206…
## 7 PORTAGE PARK 5 (1138161 1931033, 1139403 1928722, 1143627 19270…
## 8 SOUTH LAWNDALE 5 (1150164 1886133, 1150463 1886334, 1154568 18865…
## 9 MCKINLEY PARK 4 (1161716 1880466, 1163330 1881780, 1163933 18828…
## 10 MORGAN PARK 4 (1162303 1832900, 1162826 1830936, 1167502 18268…
## # ... with 45 more rows
```
---
# More advanced spatial analysis involves buffers, distance, intersections, etc.
Code from my thesis:
```r
get_point_counts_in_buffer <- function(points_to_buffer,
points_to_intersect,
buffer_size = 500) {
number_points_within_buffer <- points_to_buffer %>%
st_buffer(buffer_size) %>%
st_contains(points_to_intersect) %>%
map_dbl(length) %>%
tibble(pts_in_buffer = .)
return(number_points_within_buffer)
}
```
---
# And what it did:
---
class: inverse, middle, center
# Now a few resources for learning more...
---
# A few other key packages to check out
- `tidycensus`: all census and ACS data from past several years, delivered directly into R
- `tigris`: TIGERline shapefiles (boundaries, etc)
- `osmdata`: OpenStreetMap data, delivered directly into R
- `spData`: sample spatial datasets to play around with in all sorts of formats
- `raster`: many functions for working with raster data
- `opencage`: geocoding
- `shiny`: the workhorse for R interactive webapps
---
# Resources to get you started
- [Geocomputation with R](https://geocompr.robinlovelace.net/), an open-source spatial analysis textbook currently under development, and the best resource for this new `sf` stuff, by Robin Lovelace, Jakub Nowosad, and Jannes Muenchow
--
- A great [Datacamp intro course](https://www.datacamp.com/courses/spatial-analysis-in-r-with-sf-and-raster) on sf by Zev Ross (the first part is free)
--
- [sf documentation](https://r-spatial.github.io/sf/) by Edzer Pebesma
--
- Jakub Nowosad's [slides on GIS with R](https://cdn.rawgit.com/Nowosad/gis_with_r_how_to_start/aea08f46/gis_with_r_start.html), off of which these slides are informed
--
- I tweet about GIS in R sometimes (read: a lot): [@CivicAngela](https://twitter.com/CivicAngela)
---
class: center, middle
# Thanks!
**Want to try out the code in this presentation? Data and code are located at my Github repo [here](https://github.com/angela-li/gis-r).**
Slides created via the R package [xaringan](https://github.com/yihui/xaringan) by [Yihui Xie](https://twitter.com/xieyihui?lang=en). If you're interested in learning how to make slide decks in R, check out Alison's [awesome guide here](https://github.com/rladies/resources/blob/master/xaringan-slides/how_to_use.md). It's focused on using the R-Ladies theme, but you can generalize!
Slides available at <font style="text-transform: lowercase;"><http://bit.ly/harris-gis></font> <br>
Contact me at @[CivicAngela](https://twitter.com/CivicAngela), ali6@uchicago.edu with feedback and any additional comments!
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