ANLY 512 - Problem Set 5

Spatial Visualization

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("C:/Users/yg/Desktop/HU Master/ANLY 512 - Data Visualization/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.

Hospital with psychiatric services

library(ggmap)

## Loading required package: ggplot2

## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.

## Please cite ggmap if you use it! See citation("ggmap") for details.

psych <- subset(dat, psych == "Y")
qmplot(x, y, data = psych, legend = "none", color= I('black'), mapcolor = "color", extent = "panel",darken = 0.1, main = "Hospital with Psychiatric Services in PA", xlab = "Longitude", ylab = "Latitude", source = 'osm', maptype = 'terrain')

## Using zoom = 8...

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Hospitals with psychiatric service are spread over in the area but Phily as a big city has quite a few hospitals of that service.

Hospital with surgical services

surgical <- subset(dat, surgical == "Y")
qmplot(x, y, data = surgical, legend = "none", color= I('black'), mapcolor = "color", extent = "panel",darken = 0.1, main = "Hospitals with Surgical Service in PA", xlab = "Longitude", ylab = "Latitude", source = 'osm', maptype = 'terrain')

## Using zoom = 8...

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The second map shows almost the same pattern as the first map.

hospitals Provide flu and/or pneumococcal vaccine to elederly patients

flu_old <- subset(dat, flu_old == "Y")
qmplot(x, y, data = flu_old, legend = "none", color= I('black'), mapcolor = "color", extent = "panel",darken = 0.1, main = "Hospitals Provide flu and/or pneumococcal vaccine to elderly patients", xlab = "Longitude", ylab = "Latitude", source = 'osm', maptype = 'terrain')

## Using zoom = 8...

There are much more hopitals providing flu vaccine to elderly patients than hospitals providing surgical service.

Hospitals with helipad in PA

helipad <- subset(dat, helipad == "Y")
qmplot(x, y, data = helipad, legend = "none", color= I('black'), mapcolor = "color", extent = "panel",darken = 0.1, main = "Hospitals with HELIPAD in PA", xlab = "Longitude", ylab = "Latitude", source = 'osm', maptype = 'terrain')

## Using zoom = 8...

I was thinking hospitals with helipad are very few because they are probably very high end but it looks like everywhere.

hospital with Heart Transplant

heart_tran <- subset(dat, heart_tran == "Y")
qmplot(x, y, data = heart_tran, legend = "none", color= I('black'), mapcolor = "color", extent = "panel",darken = 0.1, main = "Hospitals with Heart Transplant in PA", xlab = "Longitude", ylab = "Latitude", source = 'osm', maptype = 'terrain')

## Using zoom = 10...

## 48 tiles needed, this may take a while (try a smaller zoom).

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Very few hospitals providing heart transplant in PA which is close to my assumption because heart transplant requires very high skills and is not in demand so no many hospitals can do that.