Map #1

A choropleth map with the sp package. Include a title and use RColorBrewer to create your own palette. Don’t forget to add a title to your map! (2)

# load required packages
library(ggmap)
library(rgdal)
library(RColorBrewer)
library(tmap)
library(tidyverse)
library(spdplyr)
# load spatial data abilities
getClass('Spatial')

## Class "Spatial" [package "sp"]
## 
## Slots:
##                               
## Name:         bbox proj4string
## Class:      matrix         CRS
## 
## Known Subclasses: 
## Class "SpatialPoints", directly
## Class "SpatialMultiPoints", directly
## Class "SpatialGrid", directly
## Class "SpatialLines", directly
## Class "SpatialPolygons", directly
## Class "SpatialPointsDataFrame", by class "SpatialPoints", distance 2
## Class "SpatialPixels", by class "SpatialPoints", distance 2
## Class "SpatialMultiPointsDataFrame", by class "SpatialMultiPoints", distance 2
## Class "SpatialGridDataFrame", by class "SpatialGrid", distance 2
## Class "SpatialLinesDataFrame", by class "SpatialLines", distance 2
## Class "SpatialPixelsDataFrame", by class "SpatialPoints", distance 3
## Class "SpatialPolygonsDataFrame", by class "SpatialPolygons", distance 2

# load census data (polygons)
tracts <- readOGR("C:/Users/Maggie/Documents/GEOG4950/census/CensusTracts2010.shp")

## OGR data source with driver: ESRI Shapefile 
## Source: "C:/Users/Maggie/Documents/GEOG4950/census/CensusTracts2010.shp", layer: "CensusTracts2010"
## with 588 features
## It has 31 fields
## Integer64 fields read as strings:  P0010011 P0010012 P0010013

data <- tracts@data
head(data)

##   STATEFP10 COUNTYFP10 TRACTCE10     GEOID10  NAME10 FUNCSTAT10
## 0        49        003    960601 49003960601 9606.01          S
## 1        49        003    960602 49003960602 9606.02          S
## 2        49        003    960702 49003960702 9607.02          S
## 3        49        003    960400 49003960400    9604          S
## 4        49        003    960300 49003960300    9603          S
## 5        49        003    960200 49003960200    9602          S
##    INTPTLAT10   INTPTLON10 LOGRECNO  AREALAND  AREAWATR POP100 HU100
## 0 +41.5159999 -112.0089180  0000009   2185421         0   3720  1395
## 1 +41.5292534 -111.9843889  0000010  31111836         0   2429   793
## 2 +41.4966363 -112.0069151  0000012   3141945         0   2281   861
## 3 +41.5599748 -112.2854779  0000007 657894066 101858980   4872  1632
## 4 +41.7027473 -112.1516054  0000006  35869732         0   8186  2748
## 5 +41.7070478 -112.0582397  0000005 259632270    547505   6901  2303
##   P0020001 P0020002 P0020003 P0020004 P0020005 P0020006 P0020007 P0020008
## 0     3720      396     3324     3262     3188       17       27       15
## 1     2429      109     2320     2276     2245        3       16        8
## 2     2281      292     1989     1947     1887       11       28       18
## 3     4872      289     4583     4531     4437       20       13       51
## 4     8186      927     7259     7158     6959       22       34      120
## 5     6901      406     6495     6416     6289       16       18       74
##   P0020009 P0020010 MTFCC P0010011 P0010012 P0010013 P0010014 SqMiles
## 0        6        9 G5020       11       30       17        0       0
## 1        4        0 G5020        9       23       12        0       0
## 2        3        0 G5020        2       24       16        0       0
## 3        2        8 G5020        2       26       26        0       0
## 4       14        9 G5020        8       43       45        0       0
## 5       18        1 G5020        9       34       27        0       0
##   SHAPE_Leng SHAPE_Area
## 0   6709.768    2184057
## 1  31613.347   31092117
## 2   7088.986    3139977
## 3 147127.817  759360502
## 4  43217.387   35849141
## 5 123915.036  260023433

# check data class of var of interest and make mappable as 'numeric'
class(data$SHAPE_Area)

## [1] "numeric"

tracts$AREAWATR <- as.numeric(tracts$AREAWATR)
# create color palette of choice
pal <- brewer.pal(n=9,name='Blues')
spplot(tracts, # plot census tracts
       "AREAWATR", # `AREAWATR` as choropleth sybmoblized variable
       col.regions=pal, # specify color palette
       cuts=8, # specify # breaks (n-1 from pal)
       col='transparent', 
       main="Water Resources in Utah (square-meters)") # title

Map #2

A map with two layers (point, polygon, or polyline) and a basemap using ggplot and ggmap. Include a title and be sure to fix the coordinate ratio. (4)

# load trails (polylines) and cities (points) data
trails <- readOGR("C:/Users/Maggie/Documents/GEOG4950/HistoricTrails/HistoricTrails.shp")

## OGR data source with driver: ESRI Shapefile 
## Source: "C:/Users/Maggie/Documents/GEOG4950/HistoricTrails/HistoricTrails.shp", layer: "HistoricTrails"
## with 3415 features
## It has 9 fields

cities <- readOGR("C:/Users/Maggie/Documents/GEOG4950/CitiesTownsLocations/CitiesTownsLocations.shp")

## OGR data source with driver: ESRI Shapefile 
## Source: "C:/Users/Maggie/Documents/GEOG4950/CitiesTownsLocations/CitiesTownsLocations.shp", layer: "CitiesTownsLocations"
## with 460 features
## It has 7 fields
## Integer64 fields read as strings:  POPULATION

# transform to ensure matching coordinate systems
crs <- CRS("+init=epsg:4269") 
cities <- spTransform(cities, crs)
trails <- spTransform(trails, crs)
# filter out desired trails and save as new object
unique(trails$TrailName)

## [1] California Trail           Mormon Pioneer Trail      
## [3] Old Spanish Trail          Pony Express Trail        
## [5] Stansbury Route            Dominguiez-Escalante Route
## [7] Fremont Route              Hole-In-The-Rock Trail    
## 8 Levels: California Trail Dominguiez-Escalante Route ... Stansbury Route

routes <- trails %>%
    filter(TrailName %in% c("Stansbury Route","Fremont Route"))
# convert target data to df's & merge with shp's for proper mapping
cities_df <- data.frame(cities)
routes_df <- fortify(routes)
routes@data$id <- rownames(routes@data)
routes_df <- merge(routes_df, routes@data, by="id")
# specify basemap
base_map <- get_map(location = "utah", zoom = 6, maptype = "roadmap", source = "google", crop = T) 
ggmap(base_map) + # baselayer 
  ggtitle("Major Foot Routes Centered in Utah\nStansbury (N) and Fremont (S)") + # map title
  # simplify background and specify title elements
  theme(plot.title=element_text(color="black", face="bold", size=14),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        axis.ticks=element_blank(),
        axis.title=element_blank(),
        axis.text=element_blank()) +  
  # plot cities using small black points
  geom_point(data=cities_df, 
             mapping=aes(x=coords.x1,y=coords.x2),
             color='black', pch=20) +
  # plot trails as red medium-thick lines
  geom_path(data=routes_df,
            mapping=aes(x=long,y=lat,group=group),color='red',lwd=2) +
  # fix coordinates
  coord_fixed()

Map #3

A map with two layers using tmap. Add a title, compass, and scale bar. (4)

# plot census data
tm_shape(tracts) +
  # choropleth with AREAWATR as symbolized variable
  tm_fill(col="AREAWATR",style="jenks",
          alpha=0.4, #transparency of layer
          title="Water Area (sq-m)", #legend title
          palette=pal) + #color palette
  tm_borders(alpha=0.3) + #transparency of polygon borders
  tm_compass(position=c("right","top")) + #add compass
  tm_scale_bar() + #add scale bar
  tm_shape(cities) + #draw cities
  tm_dots(col='brown',size=0.08) + #specify city points' elements
  tm_style_gray() + #specify background style
  tm_legend(bg.color='gray', #legend background
            bg.alpha=1, #background transparency
            frame="gray50", #frame color
            height=0.6) + #?
  #legend layout
  tm_layout(legend.outside=T,
            legend.position=c("left","top"))