Cities boomed and doomed

Posted on December 14, 2018 by R & Census in R bloggers | 0 Comments

[This article was first published on R & Census, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Population in the United States grew by 3.76% from 313.91 million in 2012 to 325.72 million in 2017. The growth, however, is not uniform geographically. One of the geographical areas of particular interest is the city. Some cities are shrinking in population while some others exploding. The purpose of this post is to find cities that decrease and increase most in population. In addition, we will show the population and its change of all cities in an interactive map.

We will use population data of cities published in the American Community Survey 5-year estimates. The numbers are 5-year rolling average, for example, the data published in 2017 are averaged over the 5 year period of 2013 – 2017. ACS 1-year estimates provide data of one particular year but unfortunately they only cover cities with population greater than 65000.

Read data from summary files

In ACS data, city is presented by summary level “place”. The summary level “place” also includes census designated places (CDPs), which strictly speaking are not cities. So we will select only those places that have “city” in their names. We will also keep cities with population greater than 5000 in the 2008-2012 or 2013-2017 ACS 5-year estimates. Let’s get the population data of cities using totalcensus package. The codes below read 2012 and 2017 ACS 5-year population data of all places, join the data, and calculate the percentage change in population from 2012 to 2017.

library(totalcensus)
library(data.table)
library(magrittr)
library(leaflet)

# read population data of 2007-2012 and 2013-2017 ACS 5-year 
p2012 <- read_acs5year(year = 2012, 
                     states = states_DC,
                     summary_level = "place", 
                     show_progress = FALSE) %>%
    .[, .(GEOID, p1 = population)]

p2017 <- read_acs5year(year = 2017, 
                     states = states_DC,
                     summary_level = "place", 
                     show_progress = FALSE) %>%
    .[, .(GEOID, NAME, lon, lat, p2 = population)]

# joint data, calculate change of pupulation for cities with population over 5000
popul <- p2012[p2017, on = .(GEOID)]  %>%
    # keep only those population greater than 1000, too many microcities
    .[p1 > 5000 | p2 > 5000] %>%
    # keep 48 states for better plotting
    .[lon > -130 & lon < -68] %>%
    # keep places that have "city" in their names
    .[NAME %like% "city"] %>%
    # calculate percentage change 
    .[, change := round(100 * (p2 - p1) / p1, 2)] %>%
    .[order(change)]

Top 10 doomed and boomed cities

Illinois has 3 cities in the top 10 list of doomed cities and Texas has 4 in the top 10 list of boomed cities. Surprisingly, Texas also has 2 cities in the list of top 10 doomed cities. The tables below give city names, population in 2012 and 2017, and the population change.

The doomed: top 10 cities with the most population loss

kable(popul[1:10, .(NAME, p2012 = p1,  p2017 = p2, "change(%)" = change)])

NAME	p2012	p2017	change(%)
Sumner city, Illinois	5073	3285	-35.25
San Elizario city, Texas	14207	9213	-35.15
Gatesville city, Texas	15779	12426	-21.25
Rio Communities city, New Mexico	5625	4593	-18.35
Sullivan city, Missouri	7830	6587	-15.87
Genoa city, Illinois	5693	4808	-15.55
Avenal city, California	15341	13119	-14.48
Susanville city, California	17384	15286	-12.07
Shelbyville city, Illinois	5267	4650	-11.71
Trinidad city, Colorado	9032	8054	-10.83

The boomed: top 10 cities with the most population gain

kable(popul[(.N-9):.N, .(NAME, p2012 = p1,  p2017 = p2, "change(%)" = change)])

NAME	p2012	p2017	change(%)
Selma city, Texas	5637	9217	63.51
Wilmore city, Kentucky	3736	6226	66.65
Clarkston city, Georgia	7612	12702	66.87
Buda city, Texas	7835	13253	69.15
Cibolo city, Texas	15541	26602	71.17
Kirkland city, Washington	49090	86772	76.76
Sanford city, Maine	10084	20920	107.46
Chamblee city, Georgia	13297	28433	113.83
Watford City city, North Dakota	2088	5441	160.58
Fulshear city, Texas	1365	6203	354.43

Interactive mapping

We divide the population change into 5 groups: obvious decline (< -2%), marginally change (-2% - 2%), average gain (2% - 6%), significant increase (6% - 10%), and extraordinary gain (> 10%).

popul <- popul[!is.na(change), group := "< -2%"] %>%
    .[change > -2, group := "-2% - 2%"] %>%
    .[change > 2, group := "2% - 6%"] %>%
    .[change > 6, group := "6% - 10%"] %>%
    .[change > 10, group := "> 10%"] %>%
    .[, group := factor(group, levels = c(
        "< -2%", "-2% - 2%", "2% - 6%", "6% - 10%", "> 10%"
    ))] %>%
    .[order(-p2)]

And here is the code to make interactive mapping using leaflet. Zoom in and click to view the details of each city.

pal <- colorFactor(
    palette = c("blue", "green", "cyan", "orange", "magenta"),
    domain = popul$group
)
leaflet(popul) %>% addTiles() %>%
    addCircles(lng = ~lon, lat = ~lat, weight = 1,
               radius = ~p2^0.5 * 30, 
               popup = paste0(popul$NAME, "<br>", 
                              "2017 population: ", popul$p2, "<br>",
                              "5-year population change: ", 
                              popul$change, "%"), 
               color = ~pal(group), fillOpacity = 0.5) %>%
    addLegend("bottomleft", pal = pal, values = ~group,
              title = "population change",
              opacity = 1)

To leave a comment for the author, please follow the link and comment on their blog: R & Census.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.