GIS hw 5
Rebecca Luttinen
2023-04-03
Perform the following analysis of these data: 1. Mean Center of the points - Make a map showing this. 2. Convex hull of the points - Make a map showing this. 3. Spatial join of the points to the ACS layer (in sa_tracts_2019.gpkg). Set the Symbology to Graduated, Pretty Breaks, 5 Classes, for the variable ppov. - Make a map showing this. 4. Nearest Neighbor Analysis of the bar layer. Report the Z-score
#download A to Z databases drinking venues information
GIS_drinking_venues <- read_csv("C:/Users/rlutt/OneDrive/Spring 2022/GIS drinking venues.csv")## New names:
## Rows: 23 Columns: 227
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (75): Source...1, Date, Obsolescence Date, Business Name, Legal Name, P... dbl
## (41): Physical Address Number, Physical ZIP, Physical ZIP 4, Location E... lgl
## (111): Physical Post Direction, Corporate Employee Size, Mailing Post Di...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `Source` -> `Source...1`
## • `Source` -> `Source...227`GIS_drinking_venues<-GIS_drinking_venues[c(6, 12:14)]
names(GIS_drinking_venues)<-c("street", "city", "st", "zip")
head(GIS_drinking_venues)## # A tibble: 6 × 4
## street city st zip
## <chr> <chr> <chr> <dbl>
## 1 108 King William San Antonio TX 78204
## 2 905 Nogalitos St San Antonio TX 78204
## 3 507 Ruiz St San Antonio TX 78207
## 4 514 W Commerce St San Antonio TX 78207
## 5 5721 W Commerce St San Antonio TX 78237
## 6 254 Hobart St San Antonio TX 78237#project data
results<-cxy_geocode(GIS_drinking_venues,
street = "street",
city = "city",
state ="st",
zip = "zip",
class="sf",
output = "simple")## 2 rows removed to create an sf object. These were addresses that the geocoder could not match.results.proj<-st_transform(results,
crs = 2278)mapview(results.proj)#calculate mean
mean_feature<-apply(st_coordinates(results.proj), MARGIN = 2, FUN = mean)
mean_feature<-data.frame(place="meanfeature", x=mean_feature[1], y= mean_feature[2])
mean_feature<-st_as_sf(mean_feature, coords = c("x", "y"), crs= 2278)
mapview(mean_feature, col.regions="pink")+mapview( results)#calculate median
median_feature<-apply(st_coordinates(results.proj), MARGIN = 2, FUN = median)
median_feature<-data.frame(place="medianfeature", x=median_feature[1], y= median_feature[2])
median_feature<-st_as_sf(median_feature, coords = c("x", "y"), crs= 2278)
mapview(median_feature, col.regions="yellow")+
mapview(mean_feature, col.regions="orange")+
mapview( results)#buffer points
wicbuff<- st_buffer(results.proj, dist = 2500)
mapview(wicbuff)+mapview(results.proj, col.regions="purple")#convex hull
chull <- st_convex_hull(st_union(results))
mapview(chull)+
mapview(results, col.regions = "blue")library(tidycensus)
#spatial join data with acs
#load census tract data
sa_acs<-get_acs(geography = "tract",
state="TX",
county = "Bexar",
year = 2019,
variables=c( "DP05_0001E", "DP03_0009P", "DP03_0062E", "DP03_0119PE",
"DP05_0001E","DP02_0009PE","DP02_0008PE","DP02_0040E","DP02_0038E",
"DP02_0066PE","DP02_0067PE","DP02_0080PE","DP02_0092PE",
"DP03_0005PE","DP03_0028PE","DP03_0062E","DP03_0099PE","DP03_0101PE",
"DP03_0119PE","DP04_0046PE","DP05_0072PE","DP05_0073PE",
"DP05_0066PE", "DP05_0072PE", "DP02_0113PE") ,
geometry = T, output = "wide")## Getting data from the 2015-2019 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.## Using the ACS Data Profile
## Using the ACS Data Profile##
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|======================================================================| 100%#rename variables and filter missing cases
sa_acs2<-sa_acs%>%
mutate(totpop= DP05_0001E, pwhite=DP05_0072PE,
pblack=DP05_0073PE , phisp=DP05_0066PE,
phsormore=DP02_0066PE,punemp=DP03_0009PE, medhhinc=DP03_0062E,
ppov=DP03_0119PE)%>%
dplyr::select(GEOID, totpop, pblack, pwhite, phisp, punemp, medhhinc, ppov)
sa_acs2<-st_transform(sa_acs2, crs = 2278)
sa_trol<-st_cast(sa_acs2, "MULTILINESTRING")
spjoin<-st_join(results.proj, sa_acs2)
head(spjoin)## Simple feature collection with 6 features and 12 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 2100089 ymin: 13694870 xmax: 2130647 ymax: 13705940
## Projected CRS: NAD83 / Texas South Central (ftUS)
## street city st zip GEOID totpop pblack pwhite
## 2 108 King William San Antonio TX 78204 48029192100 2299 0.7 42.4
## 23 905 Nogalitos St San Antonio TX 78204 48029150100 5588 0.8 83.1
## 12 507 Ruiz St San Antonio TX 78207 48029110600 5293 1.1 65.0
## 14 514 W Commerce St San Antonio TX 78207 48029110100 3160 0.5 52.0
## 15 5721 W Commerce St San Antonio TX 78237 48029171200 4065 4.6 89.1
## 9 254 Hobart St San Antonio TX 78237 48029171501 2609 0.0 77.5
## phisp punemp medhhinc ppov geometry
## 2 0.0 3.8 73194 1.2 POINT (2130647 13699644)
## 23 2.3 7.8 40302 14.5 POINT (2124919 13694869)
## 12 6.2 16.1 15250 37.8 POINT (2125077 13705944)
## 14 0.4 2.0 50865 24.7 POINT (2128147 13702613)
## 15 3.2 4.0 28284 22.3 POINT (2104639 13705051)
## 9 0.2 6.6 35194 23.6 POINT (2100089 13698413)#Set the Symbology to Graduated, Pretty Breaks, 5 Classes, for the variable ppov
library(tmap)
library(tmaptools)
map5 <- tm_shape(sa_acs2)+
tm_polygons()+
tm_shape(spjoin)+
tm_dots("ppov", title="% in Poverty",
palette="Greens",
style="pretty",
n=5,
size=0.3)+
tm_format("World",
main.title=" Poverty Estimates- (SA 2019) - Pretty Breaks",
main.title.position=c('center','top'),
main.title.size=1,
title="Author: R.Luttinen \nSource: ACS 2019",
legend.title.size=1.7,
legend.outside=T,
legend.text.size= 1)+
tm_scale_bar(position = c("left","bottom"))+
tm_compass()
map5
#Nearest Neighbor Analysis of the bar layer
library(spatstat)## Loading required package: spatstat.data## Loading required package: spatstat.geom## spatstat.geom 3.0-3## Loading required package: spatstat.random## spatstat.random 3.0-1## Loading required package: spatstat.explore## Loading required package: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:dplyr':
##
## collapse## spatstat.explore 3.0-5## Loading required package: spatstat.model## Loading required package: rpart## spatstat.model 3.0-2## Loading required package: spatstat.linnet## spatstat.linnet 3.0-3##
## spatstat 3.0-2
## For an introduction to spatstat, type 'beginner'library(maptools)## Loading required package: sp## Checking rgeos availability: TRUE
## Please note that 'maptools' will be retired during 2023,
## plan transition at your earliest convenience;
## some functionality will be moved to 'sp'.bar.pp<-as.ppp(as(results.proj, "Spatial"))
plot(nearest.neighbour(bar.pp))
I am unable to get the rest of the nearest neighborhood analysis code to run so I will interpret the NNI and z-score provided on blackboard. An NNI of 0.43 means the pattern of bars/drinking venues in San Antonio is clustered and a Z-score of -9.8 would mean the value is significant. An NNI less than one indicates a clustered pattern and a z score greater any absolute value greater than 1.96 indicates the result is significant.