Big Data and Economics
Spatial analysis in R
Kyle Coombs (adapted from Grant McDermott)
Bates College | ECON/DCS 368Note: This lecture is adapted from work by Grant McDermott on vector-based spatial analysis – check it for stuff like this. We will not cover raster-based spatial analysis, although this is an equally important subject. Here is a link to it a lecture on it by Grant McDermott. There are further resources below. Rasters are used to make satellite images, relief maps, etc.
Working through interactively
I heavily suggest that you work through this lecture interactively, you’ll get the most out of it if you’re able to run the code chunks yourself. There are two ways to do this:
GitHub Codespaces: This is the easiest way to get started. Just click on the green “Code” button on the top right of the repository and select “Open with Codespaces”. This will open an RStudio environment in your browser, with all the necessary packages pre-installed.
Fork the repository and clone it: If you have not yet forked these materials, you can do so by clicking the “Fork” button on the top right of the repository. Once you have your own fork, you can clone it to your local machine, then navigate within it.
Once you have access to these notes and your R environment setup,
navigate to the directory lectures/08-spatial-analysis and
open the 08-spatial.Rmd file. You can then start running
the code chunks.
As a caveat, everything here is self-contained. Any installations are included in the code chunks, so you can run everything from start to finish with copy-and-paste. But it will be a bit slow and tedious.
Requirements
External libraries (requirements vary by OS)
We’re going to be doing all our spatial analysis and plotting today in R. Behind the scenes, R provides bindings to powerful open-source GIS libraries. These include the Geospatial Data Abstraction Library (GDAL) and Interface to Geometry Engine Open Source (GEOS) API suite, as well as access to projection and transformation operations from the PROJ library. You needn’t worry about all this, but for the fact that you may need to install some of these external libraries first. The requirements vary by OS:
- Linux: Requirements vary by distribution. See here.
- Mac: You should be fine to proceed directly to the R packages installation below. An unlikely exception is if you’ve configured R to install packages from source; in which case see here.
- Windows: Same as Mac, you should be good to go unless you’re installing from source. In which case, see here.
R packages
- New: sf, lwgeom, maps, mapdata, spData, tigris, leaflet, mapview, tmap, tmaptools, nngeo
- Already used: tidyverse, data.table, hrbrthemes, tidycensus
Truth be told, you only need a handful of the above libraries to do 95% of the spatial work that you’re likely to encounter. But R’s spatial ecosystem and support is extremely rich, so I’ll try to walk through a number of specific use-cases in this lecture. Run the following code chunk to install (if necessary) and load everything.
## Load and install the packages that we'll be using today
if (!require("pacman")) install.packages("pacman")
pacman::p_load(sf, tidyverse, data.table, hrbrthemes, lwgeom, rnaturalearth, maps, mapdata, spData, tigris, tidycensus, leaflet, mapview, tmap, tmaptools,nngeo)
## My preferred ggplot2 plotting theme (optional)
theme_set(theme_minimal())Census API key
Finally, we’ll be accessing some data from the US Census Bureau
through the tidycensus
package. This will require a Census API key, which you can request
here. Once
that’s done, you can set it using the
tidycensus::census_api_key() function. I recommend using
the “install = TRUE” option to save your key for future usage. See the
function’s help file for more information.
Also tell the tigris package to automatically cache its results to
save on repeated downloading. I recommend adding this line to your
~/.Rprofile file so that caching is automatically enabled for future
sessions. A quick way to do that is with the
usethis::edit_r_profile() function.
Data Downloads
Today we’ll be mapping the Opportunity Atlas datasets at the county level. Specifically, we’re going to map the average income percentile of children born to parents at different percentiles.
You can manually download or automate the download using
download.file. download.file has a default
“timeout” of 60 seconds, which means it will stop trying to download the
file after 60 seconds. If you’re downloading a large file, you may need
to increase the timeout time. (I’ve set it to 600 seconds below.)
options(timeout=600) # 600 seconds to download the file -- default is 60
download.file("https://www2.census.gov/ces/opportunity/county_outcomes.zip", "data/county_outcomes.zip")
options(timeout=60) # Reset the timeout to the defaultOn your problem set, you will be using the Opportunity Atlas datasets at the Census tract level. It takes much longer to download and automating it may require your to increase the timeout even more. Alternatively, you can download manually.
There a couple ways to open up this file. We’ll go with a two-step
approach of unzipping the file and then reading it in.1 The code block below
shows you how to unzip then read with readr::read_csv.
unzip("data/county_outcomes.zip", exdir = "data")
op_atlas <- read_csv("data/county_outcomes.csv")
file.remove("data/county_outcomes.csv") # Remove the CSVThat read-in took a minute, huh? That’s because the file is pretty big. Let’s shrink it down to just the necessary variables and rows to save space. Today we’re only mapping the mean household income percentile rank of children born to parents at the 25th and 75th percentile for a few race groups in Maine. So let’s select and filter.
If you checkout the codebook,
you can see that variables have the form
[outcome]_[race]_[gender]_p[pctile]. We want the outcome of
average household income for children born to parents at the 25th
percentile and 75th percentile. Per the codebook:
kfr: Average household income percentile rank of childrenp25: Raised at the 25th percentilepooled: Pooled across all races/gendersstate: State fips codecounty: County fips code
Also, a quick Google search confirms that Maine’s FIPS code is 23.
Quick comprehension test:
- How do I select a variable for mean percentile rank of white people born in the 25th percentile?
- Is the variable kfr_p25 a valid variable?
Armed with that knowledge, we can carefully select the groups we want and rename to simplify the variable names.
op_atlas <- select(op_atlas, czname, state, county,
kfr_p25 = kfr_pooled_pooled_p25,
kfr_p75 = kfr_pooled_pooled_p75) %>%
filter(state==23) # Maine's fips code is 23!
write_csv(op_atlas, "data/county_outcomes_p25_p75.csv") # Save the fileBy shrinking down the file, we’ve made it much easier to work with. This is a good practice in general then you only have to get the big data open once.
The last thing we’ll need to do is to create a 5-digit FIPS code for each county by combining the state and county codes. This is a common practice in the US for uniquely identifying counties.
We’ll use this to join the Opportunity Atlas data with
Maine county shapefiles on the variable, GEOID, which is
the FIPS code as a character variable. Below I create a character
variable called GEOID that contains the 5-digit FIPS
code.
Introduction: CRS and map projections
Student presentation time.
If you’re reading this after the fact, I recommend these two helpful resources. The very short version is that spatial data, like all coordinate-based systems, only make sense relative to some fixed point. That fixed point is what the Coordinate Reference Systems, or CRS, is trying to set. In R, we can define the CRS in one of two ways:
EPSG code (e.g.
3857), orPROJ string (e.g.
"+proj=merc").
We’ll see examples of both implementations in in this lecture. For the moment, however, just know that they are equally valid ways of specifying CRS in R (albeit with with different strengths and weaknesses). You can search for many different CRS definitions here.
Aside: There are some important updates happening in the world of CRS and geospatial software, which will percolate through to the R spatial ecosystem. Thanks to the hard work of various R package developers, these behind-the-scenes changes are unlikely to affect the way that you interact with spatial data in R. But they are worth understanding if you plan to make geospatial work a core component of your research. More here.
Similarly, whenever we try to plot (some part of) the earth on a map, we’re effectively trying to project a 3-D object onto a 2-D surface. This will necessarily create some kind of distortion. Different types of map projections limit distortions for some parts of the world at the expense of others. For example, consider how badly the standard (but infamous) Mercator projection distorts the high latitudes in a global map (source):
Bottom line: You should always aim to choose a projection that best represents your specific area of study. I’ll also show you how you can “re-orient” your projection to a specific latitude and longitude using the PROJ syntax. But first I’m obliged to share this XKCD summary. (Do yourself a favour and click on the link.)
Simple Features and the sf package
R has long provided excellent support for spatial analysis and plotting (primarily through the sp, rgdal, rgeos, and raster packages). However, until recently, the complex structure of spatial data necessitated a set of equally complex spatial objects in R. I won’t go into details, but a spatial object (say, a SpatialPolygonsDataFrame) was typically comprised of several “layers” — much like a list — with each layer containing a variety of “slots”. While this approach did (and still does) work perfectly well, the convoluted structure provided some barriers to entry for newcomers. It also made it very difficult to incorporate spatial data into the tidyverse ecosystem that we’re familiar with. Luckily, all this has changed thanks to the advent of the sf package (link).
The “sf” stands for simple features, which is a simple (ahem) standard for representing the spatial geometries of real-world objects on a computer.2 These objects — i.e. “features” — could include a tree, a building, a country’s border, or the entire globe. The point is that they are characterised by a common set of rules, defining everything from how they are stored on our computer to which geometrical operations can be applied them. Of greater importance for our purposes, however, is the fact that sf represents these features in R as data frames. This means that all of our data wrangling skills from previous lectures can be applied to spatial data; say nothing of the specialized spatial functions that we’ll cover next.
Somewhat confusingly, most of the functions in the
sf package start with the prefix st_. This
stands for spatial and temporal and a basic
command of this package is easy enough once you remember that you’re
probably looking for st_SOMETHING().3
Reading in spatial data
Vector-based spatial data is often stored in a shapefile, which is a file format for storing the geometric location and attribute information of geographic features. Rather than explain a shapefile, let’s just read one in and see what it looks like. Where can we get shapefiles? Why from the Census!
The Census Bureau maintains a database of shapefiles for all kinds of
geographic entities, from states to counties to census tracts. You can
download these shapefiles from the TIGER
(Topologically Integrated Geographic Encoding and Referencing) website.
Alternatively, you can use the tidycensus (or
tigris explained below) package to download them for
you by setting geometry=TRUE as an argument in functions
like tidycensus::get_acs().
Let’s demonstrate by reading in a shapefile for Maine. As you might
have guessed, we’re going to use the st_read() command and
sf package will handle all the heavy lifting behind the
scenes. Shapefile are a “geospatial vector data format” that is widely
used in GIS software that typically have the file ending
.shp.4 Basically, it contains the shape of each
feature (e.g. county) represented as coordinates, along with a bunch of
other information about the feature (e.g. county name, population,
etc.).
Simple Features as data frames
Let’s print out the me object that we just created and
take a look at its structure.
## Simple feature collection with 16 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -71.08433 ymin: 42.97776 xmax: -66.9499 ymax: 47.45969
## Geodetic CRS: NAD83
## First 10 features:
## GEOID NAME variable estimate moe
## 1 23001 Androscoggin County, Maine B01001_001 107882 NA
## 2 23003 Aroostook County, Maine B01001_001 72412 NA
## 3 23005 Cumberland County, Maine B01001_001 279994 NA
## 4 23007 Franklin County, Maine B01001_001 30657 NA
## 5 23009 Hancock County, Maine B01001_001 54309 NA
## 6 23011 Kennebec County, Maine B01001_001 121925 NA
## 7 23013 Knox County, Maine B01001_001 40111 NA
## 8 23015 Lincoln County, Maine B01001_001 34719 NA
## 9 23017 Oxford County, Maine B01001_001 57867 NA
## 10 23019 Penobscot County, Maine B01001_001 152934 NA
## geometry
## 1 MULTIPOLYGON (((-70.16011 4...
## 2 MULTIPOLYGON (((-68.5918 47...
## 3 MULTIPOLYGON (((-70.10624 4...
## 4 MULTIPOLYGON (((-70.83471 4...
## 5 MULTIPOLYGON (((-68.53108 4...
## 6 MULTIPOLYGON (((-69.74428 4...
## 7 MULTIPOLYGON (((-69.11864 4...
## 8 MULTIPOLYGON (((-69.31302 4...
## 9 MULTIPOLYGON (((-71.01489 4...
## 10 MULTIPOLYGON (((-68.03364 4...Now we can see the explicit data frame structure. The object has the
familiar tibble-style output that we’re used to (e.g. it only prints the
first 10 rows of the data). However, it also has some additional
information in the header, like a description of the geometry type
(“MULTIPOLYGON”) and CRS (e.g. EPSG ID 4267). One thing I want to note
in particular is the geometry column right at the end of
the data frame. This geometry column is how sf package
achieves much of its magic: It stores the geometries of each row element
in its own list column.5 Since all we really care about are the key
feature attributes — county name, FIPS code, population size, etc. — we
can focus on those instead of getting bogged down by hundreds (or
thousands or even millions) of coordinate points. In turn, this all
means that our favourite tidyverse operations and
syntax (including the pipe operator %>%) can be applied
to spatial data. Let’s review some examples, starting with plotting.
Plotting and projection with ggplot2
Plotting sf objects is incredibly easy thanks to the
package’s integration with both base R plot() and
ggplot2. I’m going to focus on the latter here, but
feel free to experiment.6 The key geom to remember is
geom_sf(). For example:
# library(tidyverse) ## Already loaded
me_plot =
ggplot(me) +
geom_sf(aes(fill = estimate), alpha=0.8, col="white") +
scale_fill_viridis_c(name = "Population estimate") +
ggtitle("Counties of Maine")
me_plot
To reproject an sf object to a different CRS, we can
use sf::st_transform().
me %>%
st_transform(crs = "+proj=moll") %>% ## Reprojecting to a Mollweide CRS
head(2) ## Saving vertical space## Simple feature collection with 2 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -5752691 ymin: 5221221 xmax: -5348115 ymax: 5604910
## Projected CRS: +proj=moll
## GEOID NAME variable estimate moe
## 1 23001 Androscoggin County, Maine B01001_001 107882 NA
## 2 23003 Aroostook County, Maine B01001_001 72412 NA
## geometry
## 1 MULTIPOLYGON (((-5732558 52...
## 2 MULTIPOLYGON (((-5397957 55...Or, we can specify a common projection directly in the ggplot call
using coord_sf(). This is often the most convenient
approach when you are combining multiple sf data frames
in the same plot.7
Data wrangling with dplyr and tidyr
As I keep saying, the tidyverse approach to data wrangling carries
over very smoothly to sf objects. For example, the
standard dplyr verbs like filter(),
mutate() and select() all work.
Be sure to run the code in this block to split NAME into the COUNTY and STATE components.
me %>%
separate(NAME,c('COUNTY','STATE'),sep = ", ") %>%
filter(COUNTY %in% c("Sagadahoc County", "Androscoggin County", "Cumberland County")) %>%
mutate(estimate_div_1000 = estimate/1000) %>% # Estimate in 1000s
select(-variable,-moe) # Drop the variable and margin of error columns## Simple feature collection with 3 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -70.86658 ymin: 43.52726 xmax: -69.6888 ymax: 44.48722
## Geodetic CRS: NAD83
## GEOID COUNTY STATE estimate estimate_div_1000
## 1 23001 Androscoggin County Maine 107882 107.882
## 2 23005 Cumberland County Maine 279994 279.994
## 3 23023 Sagadahoc County Maine 35688 35.688
## geometry
## 1 MULTIPOLYGON (((-70.16011 4...
## 2 MULTIPOLYGON (((-70.10624 4...
## 3 MULTIPOLYGON (((-69.86599 4...You can also perform group_by() and
summarise() operations as per normal (see here for a nice example).
Furthermore, the dplyr family of join
functions also work, which can be especially handy when combining
different datasets by FIPS code or some other attribute.
However, this presumes that only one of the objects has a
specialized geometry column. In other words, it works when you
are joining an sf object with a normal data frame. In
cases where you want to join two sf objects based on
their geometries, there’s a specialized st_join()
function. I provide an example of this latter operation in the section
on geometric operations below.
Let’s try this by joining on Maine’s Opportunity Atlas county level data, which we read in and processed earlier.
ggplot(me_join) +
geom_sf(aes(fill = kfr_p25), alpha=0.8, col="white") +
scale_fill_viridis_c(name = "Average income percentile") +
ggtitle("Average income percentile for children raised at 25th percentile")
And, just to show that we’ve got the bases covered, you can also
implement your favourite tidyr verbs. For example, we
can tidyr::gather() the data to long format, which is
useful for facetted plotting.8 Here I demonstrate this by plotting the
average income percentile of children born to parents at the 25th
percentile and 75th percentile.
me_join %>%
select(GEOID, kfr_p25, kfr_p75, geometry) %>%
pivot_longer(cols=c(kfr_p25,kfr_p75), names_to='parent_ptile',names_prefix = 'kfr_p',values_to='kfr') %>%
mutate(kfr=kfr*100, # put in percent terms
parent_ptile=paste0('Raised at ',parent_ptile,'th percentile')) %>% # Make the parent_ptile variable more descriptive
ggplot() +
geom_sf(aes(fill = kfr), alpha=0.8, col="white") +
scale_fill_viridis_c(name = "Average income percentile rank") +
facet_wrap(~parent_ptile, ncol = 2) +
labs(title = "Mean income percentile of children born to parents at the 25th percentile") +
theme(legend.position="bottom")
On your problem set, you’ll have to do something similar at the Census tract level and by race, so take note.
Specialized geometric operations
Alongside all the tidyverse functionality, the sf package comes with a full suite of geometrical operations. You should take a look at at the third sf vignette or the Geocomputation with R book to get a complete overview.
There are two types of operations: unary and binary shown below. These categories are helpful to keep in mind when you’re trying to find a function to do something new, but you can still get pretty far without memorizing them.
Here are a few examples to get you started:
Unary operations
So-called unary operations are applied to a single object.
For instance, you can unite sub-elements of an sf
object (e.g. counties) into larger elements (e.g. states) using
sf::st_union():
Or, you can get the st_area(),
st_centroid(), st_boundary(),
st_buffer(), etc. of an object using the appropriate
command. For example:
## Units: [m^2]
## [1] 1284898459 17640149313 2469968814 4505380911 4615153654And:
me_centroid = st_centroid(me)
ggplot(me) +
geom_sf(fill = "black", alpha = 0.8, col = "white") +
geom_sf(data = me_centroid, col = "red") + ## Notice how easy it is to combine different sf objects
labs(
title = "Counties of Maine",
subtitle = "Centroids in red"
)
Binary operations
Another set of so-called binary operations can be applied to
multiple objects. So, we can get things like the distance between two
spatial objects using sf::st_distance(). In the below
example, I’m going to get the distance from Androscoggin county to York
county, as well as itself. The latter is just a silly addition to show
that we can easily make multiple pairwise comparisons, even when the
distance from one element to another is zero.
andro_york = me %>% filter(COUNTY %in% c("Androscoggin County", "York County"))
andro = me %>% filter(COUNTY %in% c("Androscoggin County"))
ay_dist = st_distance(andro_york, andro)
## We can use the `units` package (already installed as sf dependency) to convert to kilometres
ay_dist = ay_dist %>% units::set_units(km) %>% round()
ggplot(me) +
geom_sf(fill = "black", alpha = 0.8, col = "white") +
geom_sf(data = andro_york, aes(fill = COUNTY), col = "white") +
labs(
title = "Calculating distances",
subtitle = paste0("The distance between Androscoggin and Cumberland is ", ay_dist[2], " km")
) +
theme(legend.title = element_blank())
Binary logical operations
A sub-genre of binary geometric operations falls into the category of logic rules — typically characterising the way that geometries relate in space. (Do they overlap, are they nearby, etc.)
For example, we can calculate the intersection of different spatial
objects using sf::st_intersection(). For this next example,
I’m going to use the primary roads spatial object from the
tigris package. Don’t worry too much about the process
used for loading these datasets; I’ll cover that in more depth shortly.
For the moment, just focus on the idea that we want to see which
counties are intersected by the river network.
First, I want to make sure the roads have the same projection as
maine using the st_crs() package.
road = tigris::primary_roads()
## Make sure they have the same projection
road = st_transform(road, crs = st_crs(me))Second, let me plot all the data to see what we’re working with.
ggplot() +
geom_sf(data = me, alpha = 0.8, fill = "white", col = "black") +
geom_sf(data = road, col = "red", lwd = 1) +
labs(
title = "States of the US",
subtitle = "Also showing the US road network"
)
Uh oh, it looks like we mapped all of the roads in the USA!
# Turn off spherical geometry see: https://r-spatial.org/r/2020/06/17/s2.html
road = st_transform(road, crs = st_crs(me))
me_intersected = st_intersection(road, me)
me_intersected## Simple feature collection with 84 features and 10 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: -70.76654 ymin: 43.09266 xmax: -67.78124 ymax: 46.14542
## Geodetic CRS: NAD83
## First 10 features:
## LINEARID FULLNAME RTTYP MTFCC GEOID COUNTY STATE
## 11240 1104470961382 I- 95 I S1100 23001 Androscoggin County Maine
## 11405 1106087854960 I- 95 I S1100 23001 Androscoggin County Maine
## 11407 1106087854909 I- 95 I S1100 23001 Androscoggin County Maine
## 11507 1105084078814 I- 95 I S1100 23001 Androscoggin County Maine
## 11520 1104471317791 I- 95 I S1100 23001 Androscoggin County Maine
## 14594 1103681344737 Maine Tpke M S1100 23001 Androscoggin County Maine
## 14595 1103681783061 Maine Tpke M S1100 23001 Androscoggin County Maine
## 14596 1103681783104 Maine Tpke M S1100 23001 Androscoggin County Maine
## 14597 1103681344751 Maine Tpke M S1100 23001 Androscoggin County Maine
## 11294 110469245634 I- 95 I S1100 23003 Aroostook County Maine
## variable estimate moe geometry
## 11240 B01001_001 107882 NA LINESTRING (-70.00712 44.12...
## 11405 B01001_001 107882 NA LINESTRING (-70.29294 44.02...
## 11407 B01001_001 107882 NA LINESTRING (-70.00602 44.12...
## 11507 B01001_001 107882 NA LINESTRING (-70.29294 44.02...
## 11520 B01001_001 107882 NA LINESTRING (-70.00602 44.12...
## 14594 B01001_001 107882 NA LINESTRING (-70.00712 44.12...
## 14595 B01001_001 107882 NA LINESTRING (-70.00602 44.12...
## 14596 B01001_001 107882 NA LINESTRING (-70.00602 44.12...
## 14597 B01001_001 107882 NA LINESTRING (-70.00712 44.12...
## 11294 B01001_001 72412 NA LINESTRING (-68.42664 45.85...Note that st_intersection() only preserves
exact points of overlap. As in, this is the exact path that the
road follow within these regions. We can see this more explicitly in map
form:
me_intersected %>%
ggplot() +
geom_sf(alpha = 0.8, aes(fill = FULLNAME, col = FULLNAME)) +
labs(
title = "Maine road system",
caption = "Colours depict each road"
) +
theme(legend.title = element_blank())
If we instead wanted to plot the intersected counties (i.e. keeping
their full geometries), we have a couple options. We could filter the
me object by matching its region IDs with the
me_intersected object. However, a more direct option is to
use the sf::st_join() function which matches objects based
on overlapping (i.e. intersecting) geometries9:
# Select only the road variables
st_join(me,road,left=FALSE) %>% # left=FALSE means we keep only the intersected geometries
ggplot() +
geom_sf(alpha = 0.8, fill = "black", col = "gray50") +
geom_sf(data = me_intersected, col = "#05E9FF", lwd = 1) +
labs(title = "Counties with primary roads only") 
Bordering counties
Did you note above that we used st_join to find the
intersected counties? Let’s do that to get bordering counties!
st_join(me,andro,left=FALSE) %>%
ggplot() +
geom_sf(alpha = 0.8, fill = "black", col = "gray50") +
labs(title = "Androscoggin County and its Neighbors")
But what if we wanted to leave out Androscoggin? We tell
st_join that we want to join using the
st_touches predicate to only show the counties that touch
Androscoggin.
st_join(me,andro,left=FALSE,join=st_touches) %>% # Note the new predicate!
ggplot() +
geom_sf(alpha = 0.8, fill = "black", col = "gray50") +
#geom_sf(data = andro, col = "red",fill='red', lwd = 1) + # Highlight androscoggin
labs(title = "Androscoggin County and its Neighbors") 
Wait, what’s going on? How did we drop Androscoggin?
st_touches is a predicate that returns TRUE if
the geometries have at least one point in common, but their interiors do
not intersect. In other words, it returns TRUE if the
geometries are adjacent. Let’s visualize that:
## Simple feature collection with 5 features and 12 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -71.08433 ymin: 43.52726 xmax: -69.37242 ymax: 45.66783
## Geodetic CRS: NAD83
## GEOID.x COUNTY.x STATE.x variable.x estimate.x moe.x GEOID.y
## 3 23005 Cumberland County Maine B01001_001 279994 NA 23001
## 4 23007 Franklin County Maine B01001_001 30657 NA 23001
## 6 23011 Kennebec County Maine B01001_001 121925 NA 23001
## 9 23017 Oxford County Maine B01001_001 57867 NA 23001
## 12 23023 Sagadahoc County Maine B01001_001 35688 NA 23001
## COUNTY.y STATE.y variable.y estimate.y moe.y
## 3 Androscoggin County Maine B01001_001 107882 NA
## 4 Androscoggin County Maine B01001_001 107882 NA
## 6 Androscoggin County Maine B01001_001 107882 NA
## 9 Androscoggin County Maine B01001_001 107882 NA
## 12 Androscoggin County Maine B01001_001 107882 NA
## geometry
## 3 MULTIPOLYGON (((-70.10624 4...
## 4 MULTIPOLYGON (((-70.83471 4...
## 6 MULTIPOLYGON (((-69.74428 4...
## 9 MULTIPOLYGON (((-71.01489 4...
## 12 MULTIPOLYGON (((-69.86599 4...Note the .x and the .y because the column
names overlapped. R uses .x and .y to
distinguish between the two objects’ columns.10
If you just wan to subset down, you can use st_filter()
to accomplish the same task. By default st_filter() returns
the intersection, but the argument .predicate can be used
to specify a different operation like st_touches like
join in st_join.
## Simple feature collection with 5 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -71.08433 ymin: 43.52726 xmax: -69.37242 ymax: 45.66783
## Geodetic CRS: NAD83
## GEOID COUNTY STATE variable estimate moe
## 1 23005 Cumberland County Maine B01001_001 279994 NA
## 2 23007 Franklin County Maine B01001_001 30657 NA
## 3 23011 Kennebec County Maine B01001_001 121925 NA
## 4 23017 Oxford County Maine B01001_001 57867 NA
## 5 23023 Sagadahoc County Maine B01001_001 35688 NA
## geometry
## 1 MULTIPOLYGON (((-70.10624 4...
## 2 MULTIPOLYGON (((-70.83471 4...
## 3 MULTIPOLYGON (((-69.74428 4...
## 4 MULTIPOLYGON (((-71.01489 4...
## 5 MULTIPOLYGON (((-69.86599 4...So what if we st_touches join Maine to itself? That
leaves you with a long dataset where each row is a pair of touching
counties.
## Simple feature collection with 66 features and 12 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -71.08433 ymin: 42.97776 xmax: -66.9499 ymax: 47.45969
## Geodetic CRS: NAD83
## First 10 features:
## GEOID.x COUNTY.x STATE.x variable.x estimate.x moe.x GEOID.y
## 1 23001 Androscoggin County Maine B01001_001 107882 NA 23005
## 1.1 23001 Androscoggin County Maine B01001_001 107882 NA 23007
## 1.2 23001 Androscoggin County Maine B01001_001 107882 NA 23011
## 1.3 23001 Androscoggin County Maine B01001_001 107882 NA 23017
## 1.4 23001 Androscoggin County Maine B01001_001 107882 NA 23023
## 2 23003 Aroostook County Maine B01001_001 72412 NA 23019
## 2.1 23003 Aroostook County Maine B01001_001 72412 NA 23021
## 2.2 23003 Aroostook County Maine B01001_001 72412 NA 23025
## 2.3 23003 Aroostook County Maine B01001_001 72412 NA 23029
## 3 23005 Cumberland County Maine B01001_001 279994 NA 23001
## COUNTY.y STATE.y variable.y estimate.y moe.y
## 1 Cumberland County Maine B01001_001 279994 NA
## 1.1 Franklin County Maine B01001_001 30657 NA
## 1.2 Kennebec County Maine B01001_001 121925 NA
## 1.3 Oxford County Maine B01001_001 57867 NA
## 1.4 Sagadahoc County Maine B01001_001 35688 NA
## 2 Penobscot County Maine B01001_001 152934 NA
## 2.1 Piscataquis County Maine B01001_001 17555 NA
## 2.2 Somerset County Maine B01001_001 52261 NA
## 2.3 Washington County Maine B01001_001 33154 NA
## 3 Androscoggin County Maine B01001_001 107882 NA
## geometry
## 1 MULTIPOLYGON (((-70.16011 4...
## 1.1 MULTIPOLYGON (((-70.16011 4...
## 1.2 MULTIPOLYGON (((-70.16011 4...
## 1.3 MULTIPOLYGON (((-70.16011 4...
## 1.4 MULTIPOLYGON (((-70.16011 4...
## 2 MULTIPOLYGON (((-68.5918 47...
## 2.1 MULTIPOLYGON (((-68.5918 47...
## 2.2 MULTIPOLYGON (((-68.5918 47...
## 2.3 MULTIPOLYGON (((-68.5918 47...
## 3 MULTIPOLYGON (((-70.10624 4...Comprehension check
Why would you want know the neighboring counties of a given county?
Nearest neighbors
Sometimes you’ll want to know more than neighbors, you’ll want to know the nearest \(k\) neighbors, where \(k\) is some arbitrary number. Consider this plot:
This is plots the spatial correlation of income mobility with poverty for neighboring census tracts. The spatial correlation is highest within group, but then decays.
It is possible to do this using the sf package using
the st_nearest_feature() function to get the nearest
feature and then repeating to get the next most nearest and so on. A for
loop or some purrr magic (covered later) and you’re off
to the races. But it turns out, there’s a package that makes this
easier: nngeo and its functions st_nn for
“nearest neighbors.”
st_nn(andro, # The object to find the nearest neighbors for
me, # The object to find the nearest neighbors in
k=3) # The number of nearest neighbors## lines or polygons##
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## [1] 1 3 4st_join(me, me,
join = st_nn, # Use st_nn
k = 5,# The number of nearest neighbors
maxdist=100000) # The max distance in meters to look for neighbors## lines or polygons##
|
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|======================================================================| 100%## Simple feature collection with 80 features and 12 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -71.08433 ymin: 42.97776 xmax: -66.9499 ymax: 47.45969
## Geodetic CRS: NAD83
## First 10 features:
## GEOID.x COUNTY.x STATE.x variable.x estimate.x moe.x GEOID.y
## 1 23001 Androscoggin County Maine B01001_001 107882 NA 23001
## 1.1 23001 Androscoggin County Maine B01001_001 107882 NA 23005
## 1.2 23001 Androscoggin County Maine B01001_001 107882 NA 23007
## 1.3 23001 Androscoggin County Maine B01001_001 107882 NA 23011
## 1.4 23001 Androscoggin County Maine B01001_001 107882 NA 23017
## 2 23003 Aroostook County Maine B01001_001 72412 NA 23003
## 2.1 23003 Aroostook County Maine B01001_001 72412 NA 23019
## 2.2 23003 Aroostook County Maine B01001_001 72412 NA 23021
## 2.3 23003 Aroostook County Maine B01001_001 72412 NA 23025
## 2.4 23003 Aroostook County Maine B01001_001 72412 NA 23029
## COUNTY.y STATE.y variable.y estimate.y moe.y
## 1 Androscoggin County Maine B01001_001 107882 NA
## 1.1 Cumberland County Maine B01001_001 279994 NA
## 1.2 Franklin County Maine B01001_001 30657 NA
## 1.3 Kennebec County Maine B01001_001 121925 NA
## 1.4 Oxford County Maine B01001_001 57867 NA
## 2 Aroostook County Maine B01001_001 72412 NA
## 2.1 Penobscot County Maine B01001_001 152934 NA
## 2.2 Piscataquis County Maine B01001_001 17555 NA
## 2.3 Somerset County Maine B01001_001 52261 NA
## 2.4 Washington County Maine B01001_001 33154 NA
## geometry
## 1 MULTIPOLYGON (((-70.16011 4...
## 1.1 MULTIPOLYGON (((-70.16011 4...
## 1.2 MULTIPOLYGON (((-70.16011 4...
## 1.3 MULTIPOLYGON (((-70.16011 4...
## 1.4 MULTIPOLYGON (((-70.16011 4...
## 2 MULTIPOLYGON (((-68.5918 47...
## 2.1 MULTIPOLYGON (((-68.5918 47...
## 2.2 MULTIPOLYGON (((-68.5918 47...
## 2.3 MULTIPOLYGON (((-68.5918 47...
## 2.4 MULTIPOLYGON (((-68.5918 47...This returns the five nearest neighbors to each county including itself.
That’s about as much sf functionality as I can show you for today. The remaining part of this lecture will cover some additional mapping considerations and some bonus spatial R “swag”. However, I’ll try to slip in a few more sf-specific operations along the way.
BONUS 1: Where to get map data
As our first Maine examples demonstrate, you can easily import
external shapefiles, KML files, etc., into R. Just use the generic
sf::st_read() function on any of these formats and the
sf package will take care of the rest. However, we’ve
also seen with the France example that you might not even need an
external shapefile. Indeed, R provides access to a large number of base
maps — e.g. countries of the world, US states and counties, etc. —
through the maps, (higher resolution)
mapdata and spData packages, as well
as a whole ecosystem of more specialized GIS libraries.11 To convert these
maps into “sf-friendly” data frame format, we can use
the sf::st_as_sf() function as per the below examples.
Example 1: The World
# library(maps) ## Already loaded
world = st_as_sf(map("world", plot = FALSE, fill = TRUE))
world_map =
ggplot(world) +
geom_sf(fill = "grey80", col = "grey40", lwd = 0.3) +
labs(
title = "The world",
subtitle = paste("EPSG:", st_crs(world)$epsg)
)
world_map
All of the usual sf functions and transformations can then be applied. For example, we can reproject the above world map onto the Lambert Azimuthal Equal Area projection (and further orientate it at the South Pole) as follows.
world_map +
coord_sf(crs = "+proj=laea +y_0=0 +lon_0=155 +lat_0=-90") +
labs(subtitle = "Lambert Azimuthal Equal Area projection")
Several digressions on projection considerations
Winkel tripel projection
As we’ve already seen, most map projections work great “out of the box” with sf. One niggling and notable exception is the Winkel tripel projection. This is the preferred global map projection of National Geographic and requires a bit more work to get it to play nicely with sf and ggplot2 (as detailed in this thread). Here’s a quick example of how to do it:
# library(lwgeom) ## Already loaded
wintr_proj = "+proj=wintri +datum=WGS84 +no_defs +over"
world_wintri = lwgeom::st_transform_proj(world, crs = wintr_proj)
## Don't necessarily need a graticule, but if you do then define it manually:
gr =
st_graticule(lat = c(-89.9,seq(-80,80,20),89.9)) %>%
lwgeom::st_transform_proj(crs = wintr_proj)
ggplot(world_wintri) +
geom_sf(data = gr, color = "#cccccc", size = 0.15) + ## Manual graticule
geom_sf(fill = "grey80", col = "grey40", lwd = 0.3) +
coord_sf(datum = NA) +
theme_ipsum(grid = F) +
labs(title = "The world", subtitle = "Winkel tripel projection")
Equal Earth projection
The latest and greatest projection, however, is the “Equal Earth” projection. This
does work well out of the box, in part due to the
ne_countries dataset that comes bundled with the
rnaturalearth package (link). I’ll explain
that second part of the previous sentence in moment. But first let’s see
the Equal Earth projection in action.
# library(rnaturalearth) ## Already loaded
countries =
ne_countries(returnclass = "sf") %>%
st_transform(8857) ## Transform to equal earth projection
# st_transform("+proj=eqearth +wktext") ## PROJ string alternative
ggplot(countries) +
geom_sf(fill = "grey80", col = "grey40", lwd = 0.3) +
labs(title = "The world", subtitle = "Equal Earth projection")
Pacfic-centered maps and other polygon mishaps
As noted, the rnaturalearth::ne_countries spatial data
frame is important for correctly displaying the Equal Earth projection.
On the face of it, this looks pretty similar to our
maps::world spatial data frame from earlier. They both
contain polygons of all the countries in the world and appear to have
similar default projections. However, some underlying nuances in how
those polygons are constructed allows us avoid some undesirable visual
artefacts that arise when reprojecting to the Equal Earth projection.
Consider:
world %>%
st_transform(8857) %>% ## Transform to equal earth projection
ggplot() +
geom_sf(fill = "grey80", col = "grey40", lwd = 0.3) +
labs(title = "The... uh, world", subtitle = "Projection fail")
These types of visual artefacts are particularly common for Pacific-centered maps and, in that case, arise from polygons extending over the Greenwich prime meridian. It’s a suprisingly finicky problem to solve. Even the rnaturalearth doesn’t do a good job. Luckily, Nate Miller has you covered with an excellent guide to set you on the right track.
Example 2: A single country (i.e. Norway)
The maps and mapdata packages have detailed county- and province-level data for several individual nations. We’ve already seen this with France, but it includes the USA, New Zealand and several other nations. However, we can still use it to extract a specific country’s border using some intuitive syntax. For example, we could plot a base map of Norway as follows.
norway = st_as_sf(map("world", "norway", plot = FALSE, fill = TRUE))
## For a hi-resolution map (if you *really* want to see all the fjords):
# norway = st_as_sf(map("worldHires", "norway", plot = FALSE, fill = TRUE))
norway %>%
ggplot() +
geom_sf(fill="black", col=NA)
Hmmm. Looks okay, but I don’t really want to include non-mainland
territories like Svalbaard (to the north) and the Faroe Islands (to the
east). This gives me the chance to show off another handy function,
sf::st_crop(), which I’ll use to crop our
sf object to a specific extent (i.e. rectangle). While
I am at, we could also improve the projection. The Norwegian Mapping
Authority recommends the ETRS89 / UTM projection, for which we can
easily obtain the equivalent EPSG code (i.e. 25832) from this website.
norway %>%
st_crop(c(xmin=0, xmax=35, ymin=0, ymax=72)) %>%
st_transform(crs = 25832) %>%
ggplot() +
geom_sf(fill="black", col=NA)
There you go. A nice-looking map of Norway. Fairly appropriate that it resembles a gnarly black metal guitar.
Aside: I recommend detaching the maps package once you’re finished using it, since it avoids potential namespace conflicts with
purrr::map.
BONUS 2: More on US Census data with tidycensus and tigris
Note: Before continuing with this section, you will first need to request an API key from the Census.
Working with Census data has traditionally quite a pain. You need to register on the website, then download data from various years or geographies separately, merge these individual files, etc. Thankfully, this too has recently become much easier thanks to the Census API and — for R at least — the tidycensus (link) and tigris (link) packages from Kyle Walker (a UO alum). This next section will closely follow a tutorial on his website.
We start by loading the packages and setting our Census API key. Note that I’m not actually running the below chunk, since I expect you to fill in your own Census key. You only have to run this function once.
# library(tidycensus) ## Already loaded
# library(tigris) ## Already loaded
## Replace the below with your own census API key. We'll use the "install = TRUE"
## option to save the key for future use, so we only ever have to run this once.
census_api_key("YOUR_CENSUS_API_KEY_HERE", install = TRUE)
## Also tell the tigris package to automatically cache its results to save on
## repeated downloading. I recommend adding this line to your ~/.Rprofile file
## so that caching is automatically enabled for future sessions. A quick way to
## do that is with the `usethis::edit_r_profile()` function.
options(tigris_use_cache=TRUE)Let’s say that our goal is to provide a snapshot of Census rental
estimates across different cities in the Pacific Northwest. We start by
downloading tract-level rental data for Oregon and Washington using the
tidycensus::get_acs() function. Note that you’ll need to
look up the correct ID variable (in this case: “DP04_0134”).
rent =
tidycensus::get_acs(
geography = "tract", variables = "DP04_0134",
state = c("WA", "OR"), geometry = TRUE
)
rent## Simple feature collection with 2785 features and 5 fields (with 11 geometries empty)
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -124.7631 ymin: 41.99179 xmax: -116.4635 ymax: 49.00249
## Geodetic CRS: NAD83
## # A tibble: 2,785 × 6
## GEOID NAME variable estimate moe geometry
## <chr> <chr> <chr> <dbl> <dbl> <MULTIPOLYGON [°]>
## 1 53033010102 Census Tract 1… DP04_01… 2182 676 (((-122.291 47.57247, -1…
## 2 53053062901 Census Tract 6… DP04_01… 1296 72 (((-122.4834 47.21488, -…
## 3 53053072305 Census Tract 7… DP04_01… 1215 61 (((-122.5264 47.22459, -…
## 4 53067990100 Census Tract 9… DP04_01… NA NA (((-122.6984 47.10377, -…
## 5 53077001504 Census Tract 1… DP04_01… 766 264 (((-120.501 46.60201, -1…
## 6 53053062400 Census Tract 6… DP04_01… 1421 434 (((-122.442 47.22307, -1…
## 7 53063000700 Census Tract 7… DP04_01… 1168 87 (((-117.454 47.71541, -1…
## 8 53033005803 Census Tract 5… DP04_01… 1850 260 (((-122.393 47.64116, -1…
## 9 53053062802 Census Tract 6… DP04_01… 1189 229 (((-122.5088 47.19201, -…
## 10 53063012701 Census Tract 1… DP04_01… 1082 95 (((-117.2399 47.64983, -…
## # ℹ 2,775 more rowsThis returns an sf object, which we can plot directly.
rent %>%
ggplot() +
geom_sf(aes(fill = estimate, color = estimate)) +
coord_sf(crs = 26910) +
scale_fill_viridis_c(name = "Rent ($)", labels = scales::comma) +
scale_color_viridis_c(name = "Rent ($)", labels = scales::comma) +
labs(
title = "Rental rates across Oregon and Washington",
caption = "Data: US Census Bureau"
) 
Hmmm, looks like you want to avoid renting in Seattle if possible…
The above map provides rental information for pretty much all of the Pacific Northwest. Perhaps we’re not interested in such a broad swatch of geography. What if we’d rather get a sense of rents within some smaller and well-defined metropolitan areas? Well, we’d need some detailed geographic data for starters, say from the TIGER/Line shapefiles collection. The good news is that the tigris package has you covered here. For example, let’s say we want to narrow down our focus and compare rents across three Oregon metros: Portland (and surrounds), Corvallis, and Eugene.
or_metros =
tigris::core_based_statistical_areas(cb = TRUE) %>%
# filter(GEOID %in% c("21660", "18700", "38900")) %>% ## Could use GEOIDs directly if you know them
filter(grepl("Portland|Corvallis|Eugene", NAME)) %>%
filter(grepl("OR", NAME)) %>% ## Filter out Portland, ME
select(metro_name = NAME)Now we do a spatial join on our two data sets using the
sf::st_join() function.
## Simple feature collection with 682 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -124.1587 ymin: 43.4374 xmax: -121.5144 ymax: 46.38863
## Geodetic CRS: NAD83
## # A tibble: 682 × 7
## GEOID NAME variable estimate moe geometry metro_name
## * <chr> <chr> <chr> <dbl> <dbl> <MULTIPOLYGON [°]> <chr>
## 1 530110407… Cens… DP04_01… 1370 45 (((-122.5528 45.7037, -1… Portland-…
## 2 530110404… Cens… DP04_01… NA NA (((-122.641 45.72918, -1… Portland-…
## 3 530110423… Cens… DP04_01… 1016 112 (((-122.6871 45.64037, -… Portland-…
## 4 530110411… Cens… DP04_01… 1302 210 (((-122.5861 45.6788, -1… Portland-…
## 5 530110413… Cens… DP04_01… 1564 189 (((-122.5277 45.6284, -1… Portland-…
## 6 530110407… Cens… DP04_01… 1688 85 (((-122.5759 45.68595, -… Portland-…
## 7 530110404… Cens… DP04_01… 1764 54 (((-122.6614 45.75089, -… Portland-…
## 8 530110412… Cens… DP04_01… 1445 104 (((-122.5822 45.60845, -… Portland-…
## 9 530110407… Cens… DP04_01… 1346 114 (((-122.5525 45.67782, -… Portland-…
## 10 530110413… Cens… DP04_01… 1404 45 (((-122.5589 45.62102, -… Portland-…
## # ℹ 672 more rowsOne useful way to summarize this data and compare across metros is
with a histogram. Note that “regular” ggplot2 geoms and
functions play perfectly nicely with sf objects
(i.e. we aren’t limited to geom_sf()).
That’s a quick taste of working with tidycensus (and tigris). In truth, the package can do a lot more than I’ve shown you here. For example, you can also use it to download a variety of other Census microdata such as PUMS, which is much more detailed. See the tidycensus website for more information.
Aside: sf and data.table
sf objects are designed to integrate with a tidyverse workflow. They can also be made to work a data.table workflow too, but the integration is not as slick. This is a known issue and I’ll only just highlight a few very brief considerations.
You can convert an sf object into a data.table. But note that the key geometry column appears to lose its attributes.
## STATEFP10 COUNTYFP10 COUNTYNS10 GEOID10 NAME10 NAMELSAD10 LSAD10
## 1: 23 019 00581295 23019 Penobscot Penobscot County 06
## 2: 23 029 00581300 23029 Washington Washington County 06
## 3: 23 003 00581287 23003 Aroostook Aroostook County 06
## 4: 23 009 00581290 23009 Hancock Hancock County 06
## 5: 23 007 00581289 23007 Franklin Franklin County 06
## 6: 23 025 00581298 23025 Somerset Somerset County 06
## CLASSFP10 MTFCC10 CSAFP10 CBSAFP10 METDIVFP10 FUNCSTAT10 ALAND10
## 1: H1 G4020 <NA> 12620 <NA> A 8799125852
## 2: H1 G4020 <NA> <NA> <NA> A 6637257545
## 3: H1 G4020 <NA> <NA> <NA> A 17278664655
## 4: H1 G4020 <NA> <NA> <NA> A 4110034060
## 5: H1 G4020 <NA> <NA> <NA> A 4394196449
## 6: H1 G4020 <NA> <NA> <NA> A 10164156961
## AWATER10 INTPTLAT10 INTPTLON10 geometry COUNTYFP STATEFP
## 1: 413670635 +45.3906022 -068.6574869 <XY[1]> 019 23
## 2: 1800019787 +44.9670088 -067.6093542 <XY[1]> 029 23
## 3: 404653951 +46.7270567 -068.6494098 <XY[1]> 003 23
## 4: 1963321064 +44.5649063 -068.3707034 <XY[1]> 009 23
## 5: 121392907 +44.9730124 -070.4447268 <XY[1]> 007 23
## 6: 438038365 +45.5074824 -069.9760395 <XY[1]> 025 23The good news is that all of this information is still there. It’s just hidden from display.
## Geometry set for 16 features
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -71.08392 ymin: 42.91713 xmax: -66.88544 ymax: 47.45985
## Geodetic CRS: NAD83
## First 5 geometries:## MULTIPOLYGON (((-69.28127 44.80866, -69.28143 4...## MULTIPOLYGON (((-67.7543 45.66757, -67.75186 45...## MULTIPOLYGON (((-68.43227 46.03557, -68.43285 4...## MULTIPOLYGON (((-68.80096 44.46203, -68.80007 4...## MULTIPOLYGON (((-70.29383 45.1099, -70.29348 45...What’s the upshot? Well, basically it means that you have to refer to
this “geometry” column explicitly whenever you implement a spatial
operation. For example, here’s a repeat of the st_union()
operation that we saw earlier. Note that I explicitly refer to the
“geometry” column both for the st_union() operation (which,
moreover, takes place in the “j” data.table slot) and when assigning the
aesthetics for the ggplot() call.
me_dt[, .(geometry = st_union(geometry))] %>% ## Explicitly refer to 'geometry' col
ggplot(aes(geometry = geometry)) + ## And here again for the aes()
geom_sf(fill=NA, col="black") +
labs(title = "Outline of Maine",
subtitle = "This time brought to you by data.table") 
Of course, it’s also possible to efficiently convert between the two
classes — e.g. with as.data.table() and
st_as_sf() — depending on what a particular section of code
does (data wrangling or spatial operation). I find that often use this
approach in my own work.
Further reading
You could easily spend a whole semester (or degree!) on spatial analysis and, more broadly, geocomputation. I’ve simply tried to give you as much useful information as can reasonably be contained in one lecture. Here are some resources for further reading and study:
- The package websites that I’ve linked to throughout this tutorial are an obvious next port of call for delving deeper into their functionality: sf, leaflet, etc.
- The best overall resource right now may be Geocomputation with R, a superb new text by Robin Lovelace, Jakub Nowosad, and Jannes Muenchow. This is a “living”, open-source document, which is constantly updated by its authors and features a very modern approach to working with geographic data. Highly recommended.
- Similarly, the rockstar team behind sf, Edzer Pebesma and Roger Bivand, are busy writing their own book, Spatial Data Science. This project is currently less developed, but I expect it to become the key reference point in years to come. Imporantly, both of the above books cover raster-based spatial data.
- On the subject of raster data… If you’re in the market for shorter guides, Jamie Afflerbach has a great introduction to rasters here. At a slightly more advanced level, UO’s very own Ed Rubin has typically excellent tutorial here. Finally, the sf team is busy developing a new package called stars, which will provide equivalent functionality (among other things) for raster data. UPDATE: I ended up caving and wrote up a short set of bonus notes on rasters here.
- If you want more advice on drawing maps, including a bunch that we didn’t cover today (choropleths, state-bins, etc.), Kieran Healy’s Data Vizualisation book has you covered.
- Something else we didn’t really cover at all today was spatial statistics. This too could be subject to a degree-length treatment. However, for now I’ll simply point you to Spatio-Temporal Statistics with R, by Christopher Wikle and coauthors. (Another free book!) Finally, since it is likely the most interesting thing for economists working with spatial data, I’ll also add that Darin Christensen and Thiemo Fetzer have written a very fast R-implementation (via C++) of Conley standard errors. The GitHub repo is here. See their original blog post (and update) for more details.
If you’re curious about reading in the file directly from the zip file, check the Data Tips notes.↩︎
See the first of the excellent sf vignettes for more details.↩︎
I rather wish they’d gone with a
sf_prefix myself — or at least created aliases for it — but the package developers are apparently following standard naming conventions from PostGIS.↩︎For example, we could print out the coordinates needed to plot the first element in our data frame, Lincoln county, by typing
me$geometry[[1]]. In contrast, I invite you to see how complicated the structure of a traditional spatial object is by running, say,str(as(me, "Spatial")).↩︎Plotting sf objects with the base
plotfunction is generally faster. However, I feel that you give up a lot of control and intuition by moving away from the layered, “graphics of grammar” approach of ggplot2.↩︎Note that we used a PROJ string to define the CRS reprojection below. But we could easily use an EPSG code instead. For example, here’s the ME west plane projection, which we could use by setting
crs=26984.↩︎In case you’re wondering: the newer
tidyr::pivot_*functions do not yet work with sf objects.↩︎The function
st_filter()is more efficient, but has less functionality. So I am teaching youst_join.↩︎I know it is confusing to keep all these ideas in your head at once. With practice, you’ll get experience and greater familiarity with R’s syntax, which will make it easier to try new stuff!↩︎
The list of specialised maps packages is far too long for me to cover here. You can get marine regions, protected areas, nightlights, …, etc., etc.↩︎