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<title>Project: Dealing with Point Data</title>
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<a href="pg_Tut1_about.html">1. About R, R-Studio & R-Studio Cloud</a>
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<a href="pg_Tut2_startup.html">2. Getting started</a>
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<a href="pg_Tut6_input_output.html">6. Reading in/saving data</a>
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<a href="pg_Tut7_wrangle.html">7. Data wrangling</a>
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<a href="364Data_TutorialDownload.html">Downloading Secondary datasets</a>
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<h1 class="title toc-ignore">Project: Dealing with Point Data</h1>
<h3 class="subtitle"><h4 style="font-style:normal">
GEOG-364 - Spatial Analysis
</h4></h3>
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<div id="required-libraries" class="section level2">
<h2>Required libraries</h2>
</div>
<div id="cropping-datazooming-in" class="section level2">
<h2>Cropping data/zooming in</h2>
<div id="required-libraries-1" class="section level3">
<h3>Required libraries</h3>
<pre class="r"><code>library(sp)
library(sf)
library(elevatr)
library(USAboundaries)
library(raster)
library(tidycensus)
library(rnaturalearth)
library(tmap)
library(exactextractr)
library(units)
library(tigris)
library(rmapshaper)
library(FedData)</code></pre>
</div>
<div id="filtering-point-data-sf" class="section level3">
<h3>Filtering point data sf</h3>
<p>I am basing my examples on Iowan farmers market locations</p>
<pre class="r"><code>plot(st_geometry(IA_farmersmarket.sf),col=rgb(0,0,0,.5),pch=16)
plot(st_geometry(border.county.IA.sf),border="red",add=TRUE)</code></pre>
<p><img src="364Data_TutorialWrangle_files/figure-html/unnamed-chunk-4-1.png" /><!-- --></p>
<p>Make sure your data is in sf format! Now there are a few options. You could filter to one of the columns and crop that way. For my data I have these columns</p>
<pre class="r"><code>names(IA_farmersmarket.sf)</code></pre>
<pre><code>## [1] "FID" "City" "County" "Latitude" "Location"
## [6] "Longitude" "Market_Name" "Open_Dates" "Open_Hours" "State"
## [11] "Weekday" "geometry"</code></pre>
<p>So for example</p>
<pre class="r"><code># THE COMMA AT THE END IS IMPORTANT!
IA_farmersmarket.MONDAY.sf <- IA_farmersmarket.sf[which(IA_farmersmarket.sf$Weekday %in% "Monday"), ]
#or
central_lat <-mean(IA_farmersmarket.sf$Latitude,na.rm=TRUE)
IA_farmersmarket.SOUTH.sf <- IA_farmersmarket.sf[which(IA_farmersmarket.sf$Latitude < central_lat), ]
#or
IA_farmersmarket.UIOWA.sf <- IA_farmersmarket.sf[which(IA_farmersmarket.sf$County %in% c("LINN","JOHNSON")), ]
map1 <- qtm(st_geometry(border.county.IA.sf),fill="white")+
tm_shape(IA_farmersmarket.sf)+tm_dots(size=.1,col="black")
map2 <- qtm(st_geometry(border.county.IA.sf),fill="white")+
tm_shape(IA_farmersmarket.MONDAY.sf)+tm_dots(size=.1,col="red")
map3 <- qtm(st_geometry(border.county.IA.sf),fill="white")+
tm_shape(IA_farmersmarket.SOUTH.sf)+tm_dots(size=.1,col="red")
map4 <- qtm(st_geometry(border.county.IA.sf),fill="white")+
tm_shape(IA_farmersmarket.UIOWA.sf)+tm_dots(size=.1,col="red")
tmap_arrange(map1,map2,map3,map4)</code></pre>
<p><img src="364Data_TutorialWrangle_files/figure-html/unnamed-chunk-6-1.png" /><!-- --></p>
<pre class="r"><code>rm(map1);rm(map2);rm(map3);rm(map4)</code></pre>
</div>
<div id="cropping-to-a-lonlat-box" class="section level3">
<h3>Cropping to a lon/lat box</h3>
<p>Ocean folks, this one is for you!</p>
<pre class="r"><code># Make sure your sf data is in lon/lat
IA_farmersmarket.sf <- st_transform(IA_farmersmarket.sf,4326)
# Turn it into sp data
IA_farmersmarket.sp <- as(IA_farmersmarket.sf,"Spatial")
# My new region from https://www.geoplaner.com/
Crop.Region <- as(extent(-94,-92,41,42), "SpatialPolygons")
# Subset the polygons to my new region
IA_farmersmarket.BOX.sp <- crop(IA_farmersmarket.sp, Crop.Region, byid=TRUE)
# and convert back to sf and your other map projection
IA_farmersmarket.BOX.sf <- st_as_sf(IA_farmersmarket.BOX.sp)
IA_farmersmarket.sf <- st_transform(IA_farmersmarket.sf,3744)
IA_farmersmarket.BOX.sf <- st_transform(IA_farmersmarket.BOX.sf,3744)
# and plot
map1 <- qtm(st_geometry(border.county.IA.sf),fill="white")+
tm_shape(IA_farmersmarket.sf)+tm_dots(size=.1,col="black")
map2 <- qtm(st_geometry(border.county.IA.sf),fill="white")+
tm_shape(IA_farmersmarket.BOX.sf)+tm_dots(size=.1,col="red")
# tell R the bounding box
mybox <- st_bbox(IA_farmersmarket.BOX.sf)
map3 <- qtm(st_geometry(border.county.IA.sf),fill="white",bb=mybox)+
tm_shape(IA_farmersmarket.BOX.sf)+tm_dots(size=.1,col="red")
tmap_arrange(map2,map1,map3)</code></pre>
<p><img src="364Data_TutorialWrangle_files/figure-html/unnamed-chunk-7-1.png" /><!-- --></p>
<pre class="r"><code>rm(map1);rm(map2);rm(map3)</code></pre>
</div>
<div id="cropping-to-a-stateshapefile" class="section level3">
<h3>Cropping to a state/shapefile</h3>
<p>Sometimes we want to crop to a state/shapefile. For example imagine we want to crop to a city metropoliton area (see Lab 7)</p>
<pre class="r"><code># This could be any sf boundary you want to crop to.
# it doesn't have to be a city - here i look for Des Moines
pl <- places(state = "IA", cb = TRUE, year=2017)</code></pre>
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<pre class="r"><code>DesMoines.borders.sf <- dplyr::filter(pl, NAME == "Des Moines")
# make sure they are the same map projection
IA_farmersmarket.sf <- st_transform(IA_farmersmarket.sf,4326)
DesMoines.borders.sf <- st_transform(DesMoines.borders.sf,4326)
# and crop and save to a new thing
DesMoines.Markets.sf <- ms_clip(target = IA_farmersmarket.sf, clip = DesMoines.borders.sf, remove_slivers = TRUE)
# change the map projection back
IA_farmersmarket.sf <- st_transform(IA_farmersmarket.sf,3744)
DesMoines.borders.sf <- st_transform(DesMoines.borders.sf,3744)
DesMoines.Markets.sf <- st_transform(DesMoines.Markets.sf,3744)
# and plot
map1 <- qtm(st_geometry(border.county.IA.sf),fill="white")+
tm_shape(IA_farmersmarket.sf)+tm_dots(size=.05,col="black")+
tm_shape(DesMoines.borders.sf)+tm_borders()+
tm_shape(DesMoines.Markets.sf)+tm_dots(size=.05,col="red")
map2 <- qtm(st_geometry(DesMoines.borders.sf),fill="white")+
tm_shape(DesMoines.Markets.sf)+tm_dots(size=.1,col="red")
tmap_arrange(map1,map2)</code></pre>
<p><img src="364Data_TutorialWrangle_files/figure-html/unnamed-chunk-8-1.png" /><!-- --></p>
<pre class="r"><code>rm(map1);rm(map2)</code></pre>
</div>
</div>
<div id="adding-in-secondary-data" class="section level2">
<h2>Adding in secondary data</h2>
<p>IF IN DOUBT, TRANSFORM EVERYTHING INTO LONG/LAT MAP PROJECTION (4326) - and merge like that, <em>then</em> transform back to the one you want.</p>
<p>You will need the normal libraries at the top of this tutorial. I am going to extract some data to add to my Iowan Farm Market Locations:</p>
<div id="extracting-point-values-from-polygons" class="section level3">
<h3>Extracting point values from polygons</h3>
<p>This is very easy using the <code>st_join()</code> command from the <code>sf</code> package. To add my census data (or any sf polygon data) to my Iowa farmers market sf, I can simply do</p>
<pre class="r"><code>#--------------------------------------------------------------
# Transform all to lon/lat crs=4326 - just in case
#--------------------------------------------------------------
ACS_county.sf <- st_transform(ACS_county.sf,4326)
IA_farmersmarket.sf <- st_transform(IA_farmersmarket.sf,4326)
#--------------------------------------------------------------
# MAKE SURE THE POINT DATA GOES FIRST
# I'm calling it something new so I dont accidentally overwrite
#--------------------------------------------------------------
IA_farmers_census.sf <- st_join(IA_farmersmarket.sf,ACS_county.sf)
#--------------------------------------------------------------
# Transform back
#--------------------------------------------------------------
ACS_county.sf <- st_transform(ACS_county.sf,3744)
IA_farmersmarket.sf <- st_transform(IA_farmersmarket.sf,3744)
IA_farmers_census.sf <- st_transform(IA_farmers_census.sf,3744)
#--------------------------------------------------------------
# Have a look at your new column names
#--------------------------------------------------------------
names(IA_farmers_census.sf)</code></pre>
<pre><code>## [1] "FID" "City" "County"
## [4] "Latitude" "Location" "Longitude"
## [7] "Market_Name" "Open_Dates" "Open_Hours"
## [10] "State" "Weekday" "geometry"
## [13] "GEOID" "NAME" "housevalueE"
## [16] "housevalueM" "total_popE" "total_popM"
## [19] "total_houseE" "total_houseM" "med.incomeE"
## [22] "med.incomeM" "County.areaE" "PopulationDensityE"
## [25] "Log_PopulationDensityE"</code></pre>
<pre class="r"><code>#--------------------------------------------------------------
# Make my weekdays plot in the right order
#--------------------------------------------------------------
IA_farmers_census.sf$Weekday <- as.factor(IA_farmers_census.sf$Weekday)
levels(IA_farmers_census.sf$Weekday) <- c("Saturday","Sunday","Monday","Tuesday","Wednesday","Thursday","Friday")
#--------------------------------------------------------------
# and plot
#--------------------------------------------------------------
tmap_mode("plot")
map_Week <- qtm(IA_farmers_census.sf,dots.col="Weekday",title="Market Day",
dots.size=.1,dots.palette="Spectral")+
tm_shape(border.county.IA.sf)+tm_polygons(alpha=0)+
tm_layout(legend.outside = TRUE,legend.outside.position = "bottom")
map_Popdens <- qtm(IA_farmers_census.sf,dots.col="med.incomeE",title="Median Income",
dots.size=.1,dots.palette="Blues")+
tm_shape(border.county.IA.sf)+tm_polygons(alpha=0)+
tm_layout(legend.outside = TRUE,legend.outside.position = "bottom")
tmap_arrange(map_Week,map_Popdens,ncol=2)</code></pre>
<p><img src="364Data_TutorialWrangle_files/figure-html/unnamed-chunk-10-1.png" /><!-- --></p>
<pre class="r"><code># clean up your workspace, optional but good practice unless you later need this map
rm(map_Week); rm(map_Popdens2)</code></pre>
</div>
<div id="extracting-point-values-from-rastersfields" class="section level3">
<h3>Extracting point values from rasters/fields</h3>
<p>A raster is a field like elevation or land cover.</p>
<p>Here the elevation data is numeric whilst the land cover is categorical.</p>
<div id="extracting-point-values-from-numeric-rasters" class="section level4">
<h4>Extracting point values from numeric rasters</h4>
<p>This is very easy using the <code>extract()</code> command from the <code>raster</code> package. To extract my elevation data</p>
<p>For point data, we choose simply the extract function. Remember to put results=FALSE</p>
<pre class="r"><code>#--------------------------------------------------------------
# Turn everything to the same map projection
#--------------------------------------------------------------
IA_farmers_census.sf <- st_transform(IA_farmers_census.sf,4326)
elevation.raster_IA <- raster::projectRaster(elevation.raster_IA,crs=4326)
#--------------------------------------------------------------
# add to point data
#--------------------------------------------------------------
IA_farmers_census.sf$Elevation <- raster::extract(elevation.raster_IA,IA_farmersmarket.sf)
#--------------------------------------------------------------
# Turn everything to the final map projection
#--------------------------------------------------------------
elevation.raster_IA <- raster::projectRaster(elevation.raster_IA,crs=3744)
IA_farmers_census.sf <- st_transform(IA_farmers_census.sf,3744)
#--------------------------------------------------------------
# And plot
# see here for more colours: https://www.r-graph-gallery.com/38-rcolorbrewers-palettes.html
#--------------------------------------------------------------
tmap_mode("plot")
map_elev <- tm_shape(elevation.raster_IA)+
tm_raster(title="Elevation",palette="PuRd")+
tm_shape(IA_farmers_census.sf)+
tm_dots(size=.1)+
tm_shape(border.county.IA.sf)+
tm_polygons(alpha=0)+
tm_layout(legend.outside = TRUE,legend.outside.position = "bottom")
map_elev2 <- qtm(IA_farmers_census.sf,dots.col="Elevation",title="Height Above Sea Level",
dots.size=.1,dots.palette="PuRd")+
tm_shape(border.county.IA.sf)+tm_polygons(alpha=0)+
tm_layout(legend.outside = TRUE,legend.outside.position = "bottom")
tmap_arrange(map_elev,map_elev2,ncol=2)</code></pre>
<p><img src="364Data_TutorialWrangle_files/figure-html/unnamed-chunk-12-1.png" /><!-- --></p>
<pre class="r"><code>rm(map_elev); rm(map_elev2)</code></pre>
</div>
<div id="extracting-point-values-from-categorical-rasters" class="section level4">
<h4>Extracting point values from categorical rasters</h4>
<p>This is exactly the same. I have an extra step just because I had a separate file with the category names,</p>
<pre class="r"><code>#--------------------------------------------------------------
# Turn everything to the same map projection
#--------------------------------------------------------------
IA_farmers_census.sf <- st_transform(IA_farmers_census.sf,crs(IA_Landcover))
#--------------------------------------------------------------
# add to point data
#--------------------------------------------------------------
IA_farmers_census.sf$LandcoverNumber <- as.factor(raster::extract(IA_Landcover,IA_farmers_census.sf))
#--------------------------------------------------------------
# for the landcover data specifically, this might be better
#--------------------------------------------------------------
key <- nlcd_colors()[,c("ID","Class")]
names(key) <- c("LandcoverNumber","LandcoverClass")
IA_farmers_census.sf <- merge(IA_farmers_census.sf ,key,by="LandcoverNumber",all.x=TRUE,all.y=FALSE)
IA_farmers_census.sf$LandcoverClass <- as.factor(IA_farmers_census.sf$LandcoverClass )
#--------------------------------------------------------------
# Turn everything to the final map projection
#--------------------------------------------------------------
IA_farmers_census.sf <- st_transform(IA_farmers_census.sf,3744)
#--------------------------------------------------------------
# And plot
# see here for more colours: https://www.r-graph-gallery.com/38-rcolorbrewers-palettes.html
#--------------------------------------------------------------
tmap_mode("plot")
map_lc1 <- tm_shape(IA_Landcover)+
tm_raster(title="Landcover")+
tm_shape(IA_farmers_census.sf)+
tm_dots(size=.1)+
tm_shape(border.county.IA.sf)+
tm_polygons(alpha=0)+
tm_layout(legend.outside = TRUE,legend.outside.position = "bottom")
map_lc2 <- qtm(IA_farmers_census.sf,dots.col="LandcoverNumber",title="Land cover code", dots.size=.1)+
tm_shape(border.county.IA.sf)+
tm_polygons(alpha=0)+
tm_layout(legend.outside = TRUE,legend.outside.position = "bottom")
map_lc3 <- qtm(IA_farmers_census.sf,dots.col="LandcoverClass",title="Land cover",dots.size=.1)+
tm_shape(border.county.IA.sf)+
tm_polygons(alpha=0)+
tm_layout(legend.outside = TRUE,legend.outside.position = "bottom")
tmap_arrange(map_lc1,map_lc2,map_lc3,ncol=3)</code></pre>
<p><img src="364Data_TutorialWrangle_files/figure-html/unnamed-chunk-13-1.png" /><!-- --></p>
<pre class="r"><code>rm(map_lc1); rm(map_lc1); rm(map_lc3)</code></pre>
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