ANLY 512 - Problem Set 5
Spatial Visualization
Feng Ye
2019-06-16
Objectives
Using the spatial visualization techniques, explore this data set on Pennsylvania hospitals (http://www.arcgis.com/home/item.html?id=eccee5dfe01e4c4283c9be0cfc596882). Create a series of 5 maps that highlight spatial differences in hospital service coverage for the state of PA.
To help you in getting the data imported into R, I have included the code below:
To import the data I use the foreign package, if you do not have it than be sure to install it prior to testing the code.
library(foreign)
dat <- read.dbf("/Users/juliaye/Downloads/pennsylv (2)/pennsylv.dbf") # change this to connect to your versionThe dataset contains a number of variables about each hospital, many of them are clear and straight forward.
names(dat)## [1] "acc_trauma" "air_amb" "als" "arc_street" "arc_zone"
## [6] "bas_ls" "bassinets" "bb_id" "bc_beds" "bc_sus_bed"
## [11] "beds_sus" "birthing_r" "bone_marro" "burn_car" "burn_care"
## [16] "card_beds" "card_surge" "card_sus_b" "cardiac" "cardiac_ca"
## [21] "cardio_reh" "chemo" "city" "clin_lab" "clin_psych"
## [26] "county" "countyname" "ct_scan" "cty_key" "cystoscopi"
## [31] "deliv_rms" "dental" "detox_alc_" "diag_radio" "diag_xray"
## [36] "doh_hosp" "doh_phone" "emer_dept" "endoscopie" "fac_id"
## [41] "facility" "flu_old" "fred_con_1" "fred_conta" "fred_email"
## [46] "fred_fax" "fred_hosp" "fred_pager" "fred_phone" "gamma_knif"
## [51] "gen_outpat" "gene_counc" "heart_tran" "helipad" "hemodial_c"
## [56] "hemodial_m" "hosp_id" "hospice" "hyper_cham" "icu"
## [61] "icu_beds" "icu_sus_be" "inpat_flu_" "inpat_pneu" "kidney_tra"
## [66] "labor_rms" "lic_beds" "lic_dent" "lic_dos" "lic_mds"
## [71] "lic_pod" "linear_acc" "lithotrips" "liver_tran" "loc_method"
## [76] "ltc" "mcd" "mcd_key" "mcd_name" "medical"
## [81] "mob_ccu" "mob_icu" "mri" "ms1" "neo2_beds"
## [86] "neo2_sus_b" "neo3_beds" "neo3_sus_b" "neuro_surg" "neurology"
## [91] "obs_gyn" "occ_ther" "optometry" "organ_bank" "ped_trauma"
## [96] "pediatric" "pet" "pharmacy" "phys_med" "phys_ther"
## [101] "podiatry" "providerid" "psych" "psych_inpa" "reg_trauma"
## [106] "resp_ther" "so_flu_65u" "social_wor" "speech_pat" "street"
## [111] "surgical" "surgical_s" "thera_radi" "typ_org" "typ_serv"
## [116] "ultrasound" "x" "y" "zip"Now create 5 maps, including descriptions, that highlight the spatial distribution of hosptical services in the state of PA. Upload these maps as a document to rpubs.com and submit that link the Moodle assignment.
library(ggplot2)
library(ggmap)## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.## Please cite ggmap if you use it! See citation("ggmap") for details.bone_marro <- subset(dat, bone_marro=="Y")
bone_marro_Philly<-subset(bone_marro, county == "Philadelphia")
qmplot(x,y, data = bone_marro_Philly, legend = "none", color = I('red'), mapcolor = "color", extent = "panel", darken = 0.1, main = "Hospitals with Bone Marro Service in Philadelphia", xlab = "Longitude", ylab = "Latitude", maptype = 'terrain', size = I(1), zoom = 13)## Source : http://tile.stamen.com/terrain/13/2384/3101.png## Source : http://tile.stamen.com/terrain/13/2385/3101.png## Source : http://tile.stamen.com/terrain/13/2386/3101.png## Source : http://tile.stamen.com/terrain/13/2384/3102.png## Source : http://tile.stamen.com/terrain/13/2385/3102.png## Source : http://tile.stamen.com/terrain/13/2386/3102.png
pediatricdata <- subset(dat, pediatric=="Y")
qmplot(x, y, data = pediatricdata, legend = "none", colour= I('black'), mapcolor = "color", extent = "panel",darken = 0.1, main = "Pediatrics in PA", col.main ="Orange", xlab = "Longitude", ylab = "Latitude",col = "skyblue" , maptype = 'terrain', size = I(1), zoom = 7)## Source : http://tile.stamen.com/terrain/7/35/47.png## Source : http://tile.stamen.com/terrain/7/36/47.png## Source : http://tile.stamen.com/terrain/7/37/47.png## Source : http://tile.stamen.com/terrain/7/35/48.png## Source : http://tile.stamen.com/terrain/7/36/48.png## Source : http://tile.stamen.com/terrain/7/37/48.png## Warning: Ignoring unknown parameters: col.main
philly <- subset(dat, county == 'Philadelphia')
qmplot(x, y, data = philly, legend = "none", colour= I('red'), mapcolor = "color", extent = "panel",darken = 0.1, main = "Number of lic beds in Philadelphia", col.main ="Orange", xlab = "Longitude", ylab = "Latitude",col = "red" , maptype = 'terrain', size = lic_beds, zoom = 12)## Source : http://tile.stamen.com/terrain/12/1191/1549.png## Source : http://tile.stamen.com/terrain/12/1192/1549.png## Source : http://tile.stamen.com/terrain/12/1193/1549.png## Source : http://tile.stamen.com/terrain/12/1194/1549.png## Source : http://tile.stamen.com/terrain/12/1195/1549.png## Source : http://tile.stamen.com/terrain/12/1191/1550.png## Source : http://tile.stamen.com/terrain/12/1192/1550.png## Source : http://tile.stamen.com/terrain/12/1193/1550.png## Source : http://tile.stamen.com/terrain/12/1194/1550.png## Source : http://tile.stamen.com/terrain/12/1195/1550.png## Source : http://tile.stamen.com/terrain/12/1191/1551.png## Source : http://tile.stamen.com/terrain/12/1192/1551.png## Source : http://tile.stamen.com/terrain/12/1193/1551.png## Source : http://tile.stamen.com/terrain/12/1194/1551.png## Source : http://tile.stamen.com/terrain/12/1195/1551.png## Warning: Ignoring unknown parameters: col.main## Warning: Removed 10 rows containing missing values (geom_point).
qmplot(x, y, data = dat, legend = "none", colour= typ_serv, mapcolor = "color", extent = "panel",darken = 0.1, main = "Types of Hospital in PA", col.main ="Orange", xlab = "Longitude", ylab = "Latitude",col =typ_serv, maptype = 'toner-lite', size = I(1), zoom = 8)## Warning: Duplicated aesthetics after name standardisation: colour## Source : http://tile.stamen.com/terrain/8/70/94.png## Source : http://tile.stamen.com/terrain/8/71/94.png## Source : http://tile.stamen.com/terrain/8/72/94.png## Source : http://tile.stamen.com/terrain/8/73/94.png## Source : http://tile.stamen.com/terrain/8/74/94.png## Source : http://tile.stamen.com/terrain/8/70/95.png## Source : http://tile.stamen.com/terrain/8/71/95.png## Source : http://tile.stamen.com/terrain/8/72/95.png## Source : http://tile.stamen.com/terrain/8/73/95.png## Source : http://tile.stamen.com/terrain/8/74/95.png## Source : http://tile.stamen.com/terrain/8/70/96.png## Source : http://tile.stamen.com/terrain/8/71/96.png## Source : http://tile.stamen.com/terrain/8/72/96.png## Source : http://tile.stamen.com/terrain/8/73/96.png## Source : http://tile.stamen.com/terrain/8/74/96.png## Source : http://tile.stamen.com/terrain/8/70/97.png## Source : http://tile.stamen.com/terrain/8/71/97.png## Source : http://tile.stamen.com/terrain/8/72/97.png## Source : http://tile.stamen.com/terrain/8/73/97.png## Source : http://tile.stamen.com/terrain/8/74/97.png## Warning: Ignoring unknown parameters: col.main
qmplot(x, y, data = dat)+stat_density2d(aes(x, y, colour = kidney_tra), data = dat, bins = 5, main = "Hospitals in PA with Kidney Transplant")## Using zoom = 8...## Warning: Ignoring unknown parameters: main