# Chapter 13 Basic Mapping

## 13.1 Outline of Today’s Workshop

We’ll be doing the following few things:

1. finishing up Spatial Data Handling

2. discussing finding open-source geospatial data,

3. then working from this Basic Mapping lab tutorial.

## 13.2 Spatial Data Handling, fin

There’s a long section in the tutorial about PDF scraping. If you’ve already done it, sorry! I just put together a data package that you can install to use with these tutorials.

Install the package with:

# install.packages("remotes")
remotes::install_github("spatialanalysis/geodaData")

Then, you can pull up the community area data with population attached with:

library(geodaData)
library(sf)
data("chicago_comm")

## 13.3 Finding Spatial Data

Based on a question from last week, I put together a list with some sources of spatial data.

Question

What type of spatial data are you interested in using for your research? Please write down the research question and the type of data you are thinking about using here!

Question

Where do you think you might be able to find that geospatial data? Take 5 minutes to do a search, discuss with a partner, and jot down your ideas here!

I’m moving to mapping to make sure we cover the necessary spatial concepts, as some of the exploratory data analysis you may have already encountered. We may go back to the multivariate analysis next week, if time.

Question

How many people feel comfortable with ggplot already? How many people can run a regression in R?

## 13.4 Basic Mapping

We’ll start where we left off last week on Basic Mapping. (We’re running a bit behind schedule, but thank you for asking good questions!)

To use the NYC boundary data included in the tutorial, run the following code:

library(geodaData)
library(sf)
head(nyc_sf)
## Simple feature collection with 6 features and 34 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 913037.2 ymin: 120117 xmax: 1025633 ymax: 230204.7
## epsg (SRID):    2263
## proj4string:    +proj=lcc +lat_1=41.03333333333333 +lat_2=40.66666666666666 +lat_0=40.16666666666666 +lon_0=-74 +x_0=300000.0000000001 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=us-ft +no_defs
##   bor_subb                 name code         subborough forhis06 forhis07
## 1      501          North Shore  501        North Shore  37.0657  34.0317
## 2      502           Mid-Island  502         Mid-Island  27.9822  18.1193
## 3      503          South Shore  503        South Shore  10.7019  12.1404
## 4      401              Astoria  401            Astoria  52.0961  53.9585
## 5      402 Sunnyside / Woodside  402 Sunnyside/Woodside  62.7242  69.3969
## 6      403      Jackson Heights  403    Jackson Heights  68.4834  68.5405
##   forhis08 forhis09 forwh06 forwh07 forwh08 forwh09 hhsiz1990 hhsiz00
## 1  27.3767  29.3091 13.2540 11.8768 11.1788 11.1459    2.7146  2.7338
## 2  24.0452  31.1566 20.0616 19.8575 22.4870 17.0371    2.8233  2.7176
## 3   9.6890  14.6638 10.3060 12.7699  9.3561 10.2830    3.0547  2.8497
## 4  54.6968  47.8050 38.3658 35.6551 32.1289 34.6578    2.4279  2.4995
## 5  67.0897  58.2963 37.0512 31.9057 32.3264 33.8794    2.4646  2.6287
## 6  66.5080  69.1580 34.3999 38.2428 38.1470 26.5347    2.8081  3.1650
##   hhsiz02 hhsiz05 hhsiz08 kids2000 kids2005 kids2006 kids2007 kids2008
## 1  2.7412  2.8010  2.6983  39.2995  43.3788  38.4022  41.5213  39.8390
## 2  2.5405  2.6228  2.5749  36.2234  35.7630  36.9081  37.6798  37.2447
## 3  2.6525  2.6121  2.6483  39.7362  42.5232  40.3577  40.3797  40.4820
## 4  2.3032  2.3227  2.2746  28.4592  27.2223  25.2556  24.8911  22.0364
## 5  2.5300  2.4993  2.4766  29.8808  28.6841  28.1440  26.3675  29.9032
## 6  2.9108  2.8599  2.8604  41.6335  40.2431  39.3135  37.4414  39.4144
##   kids2009 rent2002 rent2005 rent2008 rentpct02 rentpct05 rentpct08
## 1  40.3169      800      900     1000   21.1119   24.8073   28.5344
## 2  37.8176      650      800      950   32.3615   27.2584   27.9567
## 3  35.3880      750      775      800   23.0547   20.4146   18.1590
## 4  17.9996     1000     1100     1400   25.6022   26.7685   28.0467
## 5  26.2156     1000     1000     1400   18.8079   22.6752   21.3009
## 6  39.0377      910     1000     1100   34.0156   34.8050   27.1032
##   pubast90 pubast00  yrhom02  yrhom05  yrhom08
## 1 47.32913 6.005791 10.80507 12.12785 11.54743
## 2 35.18232 2.287034 15.24125 15.18311 14.68212
## 3 23.89404 1.350208 12.70425 12.97228 13.56149
## 4 80.53393 5.204510 12.83917 13.37751 12.54464
## 5 75.51687 2.974139 15.38766 12.51879 12.66691
## 6 66.64228 5.332569 12.64923 12.58035 11.96598
##                         geometry
## 1 MULTIPOLYGON (((962498.9 17...
## 2 MULTIPOLYGON (((928296.9 16...
## 3 MULTIPOLYGON (((932416.3 14...
## 4 MULTIPOLYGON (((1010873 226...
## 5 MULTIPOLYGON (((1011647 216...
## 6 MULTIPOLYGON (((1014790 220...

Question

Take a few minutes and try to understand what the NYC data is about.

• How many observations and variables are there? What data is stored? (dim(), str(), head(), summary())
• What does the metadata tell you about this data? (?nyc_sf)
• What geometries are in this data? Can you make a quick map with plot()?
• What coordinate reference system is there? Is this data projected? (st_crs()) Can you Google the EPSG code and figure out what it means?

We’ll be working with the tmap R package today. Some resources I find useful for tmap include:

library(tmap)
tm_shape(nyc_sf) +
tm_polygons("rent2008")

Question

Try using tm_fill() or tm_borders() in place of tm_polygons(). Can you produce the same thing as tm_polygons() with these two functions?

There are multiple ways to do the same thing in R!

In this case, tm_polygons() is a superset of tm_fill() and tm_borders().

### 13.4.1 Adding a basemap

If you want a basemap for your map, change the tmap_mode to “view”.

tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(nyc_sf) +
tm_polygons("rent2008")

Want a prettier basemap? R can connect to other map servers. You can preview basemaps and find their names at this Leaflet Provider Demo. You can also pull up a list of all the providers with leaflet::providers.

tm_shape(nyc_sf) +
tm_polygons("rent2008") +
tm_basemap(server = "OpenStreetMap")

### 13.4.2 Customizing tmap function parameters

Each tm_ function has arguments you can set to other things. This allows you to change the color, the transparency, the outlines, the labels, and more. All this customization is very powerful!

Question

Can you look up the help documentation of tm_polygons with ?tm_polygons? Which parameter controls transparency? How would you change the title of the legend to something else?

I can’t teach you everything in a workshop, so the best thing I can leave you with is an ability to poke through R documentation to find what you need.

### 13.4.3 Adding a histogram

Certain functionalities are only available in plotting mode, like adding a histogram of the data:

tmap_mode("plot")
## tmap mode set to plotting
tm_shape(nyc_sf) +
tm_polygons("rent2008", legend.hist = TRUE)

### 13.4.4 Getting our map ready for production

In order to do our final tweaking, we need to do things to the map as a whole. In this case, we’ll use tm_layout(), as we’re not working on one specific aspect of the map.

Question

Look up tm_layout() in the R documentation. (Get ready to be overwhelmed by the options.) What other customizations exist?

We can add a title with title =.

tm_shape(nyc_sf) +
tm_polygons("rent2008", legend.hist = TRUE) +
tm_layout(title = "Rent 2008 NYC Sub-Boroughs")

A schematic to clarify how this works:

Question

Try some of the following changes:

• Add a compass and a scale bar
• Move the legend outside
• Move the title outside and center it (try main.title())

### 13.4.5 Using Examples

It’s sometimes easier to look for examples, and replicate that code than digging through the documentation.

Question

Can you try to replicate one of the map styles in the tmap examples (here’s the code used to create them), using the NYC data we just worked with? Take a few minutes and try it out!