ANLY 512 - Problem Set 5
Spatial Visualization
Jiani Wu
2019-08-10
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/jwu/Downloads/pennsylv.dbf")The 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 hospital 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.Philly<-subset(dat, city == "Philadelphia")
qmplot(x, y, data = Philly, color = "red", size = .1, darken = .5, extent = "panel", main = "Hospitals in Philly", xlab = "Longitude", ylab = "Latitude")## Using 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
register_google(key = "AIzaSyDhQYahgnjWHQCoJwdXp5pY4TthoCIhlvI")
medicalcenter = subset(dat, medical == "Y")
medicalcenterPA = subset(medicalcenter, city == "Philadelphia")
qmplot(x, y, data = medicalcenterPA, colour= I('black'), mapcolor = "color", extent = "panel", main = "Hospital in Philadelphia with Medical", xlab = "Longitude", ylab = "Latitude", source = 'osm', maptype = 'terrain', zoom = 12)
BC = subset(dat, burn_care=="Y")
BCPI = subset(BC, city == "Pittsburgh")
qmplot(x,y, data = BCPI, colour = I('blue'), size = I(4), zoom = 14, extent = "panel", source = "osm", maptype = "toner", main = "Hospitals in Pittsburgh with Burn Care", xlab = "Longitude", ylab = "Latitude")## Source : http://tile.stamen.com/terrain/14/4551/6175.png## Source : http://tile.stamen.com/terrain/14/4552/6175.png## Source : http://tile.stamen.com/terrain/14/4553/6175.png## Source : http://tile.stamen.com/terrain/14/4551/6176.png## Source : http://tile.stamen.com/terrain/14/4552/6176.png## Source : http://tile.stamen.com/terrain/14/4553/6176.png
dentaloffice = subset(dat, dental=="Y")
dentalofficePA = subset(dentaloffice, city == "Philadelphia")
qmplot(x,y, data = dentalofficePA, colour = I('yellow'), size = I(6), zoom = 13, extent = "panel", source = "osm", maptype = "toner", main = "Hospitals in Philadelphia with Dental Offices", xlab = "Longitude", ylab = "Latitude")## Source : http://tile.stamen.com/terrain/13/2384/3100.png## Source : http://tile.stamen.com/terrain/13/2385/3100.png## Source : http://tile.stamen.com/terrain/13/2386/3100.png## 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
optometry = subset(dat, optometry == "Y")
optometryPA = subset(optometry, city == "Philadelphia")
qmplot(x,y, data = optometryPA, colour = I('blue'), size = I(2), zoom = 13, extent = "panel", source = "osm", maptype = "toner", main = "Hospitals in Dauphin County with Optometry capability", xlab = "Longitude", ylab = "Latitude")