Homework 3 Lab 2
Joshua Reyna
2023-01-29
## Finding Variables
sa_acs<-get_acs(geography = "tract",
state="TX",
county = c("Bexar"),
year = 2019,
variables=c( "DP05_0001E",
"DP03_0119PE") ,
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##
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|======================================================================| 100%#create a county FIPS code - 5 digit
sa_acs$county<-substr(sa_acs$GEOID, 1, 5)
#rename variables and filter missing cases
sa_acs2<-sa_acs%>%
mutate(totpop= DP05_0001E, ppov=DP03_0119PE) %>%
# st_transform(crs = 102740)%>%
na.omit()##Finding Coordinates
st_crs(sa_acs2)## Coordinate Reference System:
## User input: NAD83
## wkt:
## GEOGCRS["NAD83",
## DATUM["North American Datum 1983",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["latitude",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["longitude",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4269]]library(tmap)
library(tmaptools)
tm_shape(sa_acs2)+
tm_polygons("ppov", title="% in Poverty",
palette="Blues",
style="quantile",
n=5 )+
tm_format("World",
main.title="San Antonio Poverty Estimates (2019) - Quintile Breaks",
main.title.position=c('center','top'),
main.title.size=1.5,
title="Author: Joshua A. Reyna, \nSource: ACS 2019",
legend.title.size=1.7,
legend.outside=T,
legend.text.size=1.2)+
tm_scale_bar(position = c("left","bottom"))+
tm_compass()
## using tsc
new_sa<-st_transform(sa_acs2, crs = 2278)
twtr<-new_sa%>%
filter(GEOID %in% c(48029181820, 48029110600))
tr_co<-st_centroid(twtr)## Warning in st_centroid.sf(twtr): st_centroid assumes attributes are constant
## over geometries of xhead(tr_co)## Simple feature collection with 2 features and 9 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 2090862 ymin: 13704280 xmax: 2125261 ymax: 13758300
## Projected CRS: NAD83 / Texas South Central (ftUS)
## GEOID NAME DP05_0001E DP05_0001M
## 1 48029181820 Census Tract 1818.20, Bexar County, Texas 8305 833
## 2 48029110600 Census Tract 1106, Bexar County, Texas 5293 423
## DP03_0119PE DP03_0119PM geometry county totpop ppov
## 1 15.2 9.1 POINT (2090862 13758297) 48029 8305 15.2
## 2 37.8 15.2 POINT (2125261 13704278) 48029 5293 37.8st_distance(tr_co)## Units: [US_survey_foot]
## 1 2
## 1 0.00 64041.12
## 2 64041.12 0.0064041.12/5280 #To get feet into miles## [1] 12.129#Reprojection NAD83
new_sa<-st_transform(sa_acs2, crs = 4269)
twtr<-new_sa%>%
filter(GEOID %in% c(48029181820, 48029110600))
tr_co<-st_centroid(twtr)## Warning in st_centroid.sf(twtr): st_centroid assumes attributes are constant
## over geometries of xhead(tr_co)## Simple feature collection with 2 features and 9 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -98.61502 ymin: 29.43017 xmax: -98.50751 ymax: 29.57909
## Geodetic CRS: NAD83
## GEOID NAME DP05_0001E DP05_0001M
## 1 48029181820 Census Tract 1818.20, Bexar County, Texas 8305 833
## 2 48029110600 Census Tract 1106, Bexar County, Texas 5293 423
## DP03_0119PE DP03_0119PM geometry county totpop ppov
## 1 15.2 9.1 POINT (-98.61502 29.57909) 48029 8305 15.2
## 2 37.8 15.2 POINT (-98.50751 29.43017) 48029 5293 37.8st_distance(tr_co)## Units: [m]
## [,1] [,2]
## [1,] 0.00 19555.86
## [2,] 19555.86 0.0019555.86/1609.344#To get feet into miles## [1] 12.15145# Project into TCA
new_sa<-st_transform(sa_acs2, crs = 3083)
twtr<-new_sa%>%
filter(GEOID %in% c(48029181820, 48029110600))
tr_co<-st_centroid(twtr)## Warning in st_centroid.sf(twtr): st_centroid assumes attributes are constant
## over geometries of xhead(tr_co)## Simple feature collection with 2 features and 9 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 1633963 ymin: 7257682 xmax: 1644584 ymax: 7274079
## Projected CRS: NAD83 / Texas Centric Albers Equal Area
## GEOID NAME DP05_0001E DP05_0001M
## 1 48029181820 Census Tract 1818.20, Bexar County, Texas 8305 833
## 2 48029110600 Census Tract 1106, Bexar County, Texas 5293 423
## DP03_0119PE DP03_0119PM geometry county totpop ppov
## 1 15.2 9.1 POINT (1633963 7274079) 48029 8305 15.2
## 2 37.8 15.2 POINT (1644584 7257682) 48029 5293 37.8st_distance(tr_co)## Units: [m]
## 1 2
## 1 0.00 19536.16
## 2 19536.16 0.0019536.16/1609.344 #To get feet into miles## [1] 12.13921##Repeat this process, but use the NAD83 layer instead. What is the distance between the two points? Is this distance interpretable?
#Using the NAd83 layer, the distance between utsa main and downtown campus appears to be over 19,555 meters. Which, at first is not interpretable but after converting meters to miles, the distance between the two points is 12.15 miles, which is far more interpretable.
##Reproject the layer into a new coordinate system, use NAD83 / Texas Centric Albers Equal Area.
#Re-measure the distance. How does it compare to the one you got using the Texas South Central projection?
#The Texas Centric Albers system similarly to the NAd83 layer, following conversion reveals the distance between the two points to be 12.13 miles, with the Texas South Central projection reveals about a 12.12 mile difference. Which seems to indicate more precise distance between the two points. All three projection styles required conversion to be more interpretable.
##In general, why is it important to have an accurate system of projection? How could your results be sensitive to this?
# The more accurate the system of projection, the more interpretable your results will be. For example while using the NAD83 layer produced results that were similar to the others, they were still translated in meters, and thus were also made for a different type of map build. In which case your resulting projections might both be off in terms of precise location, and difficult to interpret based on the unit of analysis produced.