sat, 08-apr-2017, 11:30

Introduction

The Alaska Department of Transportation is working on updating their bicycling and pedestrian master plan for the state and their web site mentions Alaska as having high percentages of bicycle and pedestrian commuters relative to the rest of the country. I’m interested because I commute to work by bicycle (and occasionally ski or run) every day, either on the trails in the winter, or the roads in the summer. The company I work for (ABR) pays it’s employees $3.50 per day for using non-motorized means of transportation to get to work. I earned more than $700 last year as part of this program and ABR has paid it’s employees almost $40K since 2009 not to drive to work.

The Census Bureau keeps track of how people get to work in the American Community Survey, easily accessible from their web site. We’ll use this data to see if Alaska really does have higher than average rates of non-motorized commuters.

Data

The data comes from FactFinder. I chose ‘American Community Survey’ from the list of data sources near the bottom, searched for ‘bicycle’, chose ‘Commuting characteristics by sex’ (Table S0801), and added the ‘All States within United States and Puerto Rico’ as the Geography of interest. The site generates a zip file containing the data as a CSV file along with several other informational files. The code for extracting the data appears at the bottom of this post.

The data are percentages of workers 16 years and over and their means of transportation to work. Here’s a table showing the top 10 states ordered by the combination of bicycling and walking percentage.

  state total motorized carpool public_trans walk bicycle
1 District of Columbia 358,150 38.8 5.2 35.8 14.0 4.1
2 Alaska 363,075 80.5 12.6 1.5 7.9 1.1
3 Montana 484,043 84.9 10.4 0.8 5.6 1.6
4 New York 9,276,438 59.3 6.6 28.6 6.3 0.7
5 Vermont 320,350 85.1 8.2 1.3 5.8 0.8
6 Oregon 1,839,706 81.4 10.2 4.8 3.8 2.5
7 Massachusetts 3,450,540 77.6 7.4 10.6 5.0 0.8
8 Wyoming 289,163 87.3 10.0 2.2 4.6 0.6
9 Hawaii 704,914 80.9 13.5 7.0 4.1 0.9
10 Washington 3,370,945 82.2 9.8 6.2 3.7 1.0

Alaska has the second highest rates of walking and biking to work behind the District of Columbia. The table is an interesting combination of states with large urban centers (DC, New York, Oregon, Massachusetts) and those that are more rural (Alaska, Montana, Vermont, Wyoming).

Another way to rank the data is by combining all forms of transportation besides single-vehicle motorized transport (car pooling, public transportation, walking and bicycling).

  state total motorized carpool public_trans walk bicycle
1 District of Columbia 358,150 38.8 5.2 35.8 14.0 4.1
2 New York 9,276,438 59.3 6.6 28.6 6.3 0.7
3 Massachusetts 3,450,540 77.6 7.4 10.6 5.0 0.8
4 New Jersey 4,285,182 79.3 7.5 11.6 3.3 0.3
5 Alaska 363,075 80.5 12.6 1.5 7.9 1.1
6 Hawaii 704,914 80.9 13.5 7.0 4.1 0.9
7 Oregon 1,839,706 81.4 10.2 4.8 3.8 2.5
8 Illinois 6,094,828 81.5 7.9 9.3 3.0 0.7
9 Washington 3,370,945 82.2 9.8 6.2 3.7 1.0
10 Maryland 3,001,281 82.6 8.9 9.0 2.6 0.3

Here, the states with large urban centers come out higher because of the number of commuters using public transportation. Despite very low availability of public transportation, Alaska still winds up 5th on this list because of high rates of car pooling, in addition to walking and bicycling.

Map data

To look at regional patterns, we can make a map of the United States colored by non-motorized transportation percentage. This can be a little challenging because Alaska and Hawaii are so far from the rest of the country. What I’m doing here is loading the state data, transforming the data to a projection that’s appropriate for Alaska, and moving Alaska and Hawaii closer to the lower-48 for display. Again, the code appears at the bottom.

You can see that non-motorized transportation is very low throughout the deep south, and tends to be higher in the western half of the country, but the really high rates of bicycling and walking to work are isolated. High Vermont next to low New Hampshire, or Oregon and Montana split by Idaho.

Urban and rural, median age of the population

What explains the high rates of non-motorized commuting in Alaska and the other states at the top of the list? Urbanization is certainly one important factor explaining why the District of Columbia and states like New York, Oregon and Massachusetts have high rates of walking and bicycling. But what about Montana, Vermont, and Wyoming?

Age of the population might have an effect as well, as younger people are more likely to walk and bike to work than older people. Alaska has the second youngest population (33.3 years) in the U.S. and DC is third (33.8), but the other states in the top five (Utah, Texas, North Dakota) don’t have high non-motorized transportation.

So it’s more complicated that just these factors. California is a good example, with a combination of high urbanization (second, 95.0% urban), low median age (eighth, 36.2) and great weather year round, but is 19th for non-motorized commuting. Who walks in California, after all?

Conclusion

I hope DOT comes up with a progressive plan for improving opportunities for pedestrian and bicycle transportation in Alaska They’ve made some progress here in Fairbanks; building new paths for non-motorized traffic; but they also seem blind to the realities of actually using the roads and paths on a bicycle. The “bike path” near my house abruptly turns from asphalt to gravel a third of the way down Miller Hill, and the shoulders of the roads I commute on are filled with deep snow in winter, gravel in spring, and all manner of detritus year round. Many roads don’t have a useable shoulder at all.

Code

library(tidyverse)  # data import, manipulation
library(knitr)      # pretty tables
library(rpostgis)   # PostGIS support
library(rgdal)      # geographic transformation
library(maptools)   # geographic transformation
library(viridis)    # color blind color palette

# Read the heading
heading <- read_csv('ACS_15_1YR_S0801.csv', n_max = 1) %>% names()

# Read the data
s0801 <- read_csv('ACS_15_1YR_S0801.csv', col_names = FALSE, skip = 2)

names(s0801) <- heading

# Extract only the columns we need, add state postal codes
commute <- s0801 %>%
    transmute(state = `GEO.display-label`,
              total = HC01_EST_VC01,
              motorized = HC01_EST_VC03,
              carpool = HC01_EST_VC05,
              public_trans = HC01_EST_VC10,
              walk = HC01_EST_VC11,
              bicycle = HC01_EST_VC12) %>%
    filter(state != 'Puerto Rico') %>%
    mutate(state_postal = c('AL', 'AK', 'AZ', 'AR', 'CA', 'CO', 'CT',
                            'DE', 'DC', 'FL', 'GA', 'HI', 'ID', 'IL',
                            'IN', 'IA', 'KS', 'KY', 'LA', 'ME', 'MD',
                            'MA', 'MI', 'MN', 'MS', 'MO', 'MT', 'NE',
                            'NV', 'NH', 'NJ', 'NM', 'NY', 'NC', 'ND',
                            'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD',
                            'TN', 'TX', 'UT', 'VT', 'VA', 'WA', 'WV',
                            'WI', 'WY'))

# Print top ten tables
kable(commute %>% select(-state_postal) %>%
      arrange(desc(walk + bicycle)) %>% head(n = 10),
  format.args = list(big.mark = ","),
  row.names = TRUE)

kable(commute %>% select(-state_postal) %>%
      arrange(motorized) %>% head(n = 10),
  format.args = list(big.mark = ","),
  row.names = TRUE)

# Connect to the database with the state layer
layers <- src_postgres(host = "localhost",
                             dbname = "layers")

states <- pgGetGeom(layers$con, c("public", "states"),
                    geom = "wkb_geometry", gid = "ogc_fid")

# Transform to srid 3338 (Alaska Albers)
states_3338 <- spTransform(states, CRS("+proj=aea +lat_1=55 +lat_2=65 +lat_0=50
                                       +lon_0=-154 +x_0=0 +y_ 0=0 +ellps=GRS80
                                       +towgs84=0,0,0,0,0,0,0 +units=m
                                       +no_defs"))

# Convert to a data frame suitable for ggplot, move AK and HI
ggstates <- fortify(states_3338, region = "state") %>%
    filter(id != 'PR') %>%
    inner_join(commute, by = (c("id" = "state_postal"))) %>%
    mutate(lat = ifelse(state == 'Hawaii', lat + 2300000, lat),
           long = ifelse(state == 'Hawaii', long + 2000000, long),
           lat = ifelse(state == 'Alaska', lat + 1000000, lat),
           long = ifelse(state == 'Alaska', long + 2000000, long))

# Plot it
p <- ggplot() + geom_polygon(data = ggstates, colour = "black",
                        aes(x = long, y = lat, group = group,
                            fill = bicycle + walk)) +
    coord_fixed(ratio = 1) +
    scale_fill_viridis(name = "Non-motorized\n commuters (%)",
                       option = "plasma",
                       limits = c(0, 9), breaks = seq(0, 9, 3)) +
    theme_void() +
    theme(legend.position = c(0.9, 0.2))

width <- 16
height <- 9
resize <- 0.75

svg("non_motorized_commute_map.svg", width = width*resize, height = height*resize)
print(p)
dev.off()
pdf("non_motorized_commute_map.pdf", width = width*resize, height = height*resize)
print(p)
dev.off()

# Urban and rural percentages by state
heading <- read_csv('../urban_rural/DEC_10_SF1_P2.csv', n_max = 1) %>% names()

dec10 <- read_csv('../urban_rural/DEC_10_SF1_P2.csv', col_names = FALSE, skip = 2)

names(dec10) <- heading

urban_rural <- dec10 %>%
    transmute(state = `GEO.display-label`,
              total = D001,
              urban = D002,
              rural = D005) %>%
    filter(state != 'Puerto Rico') %>%
    mutate(urban_percentage = urban / total * 100)

# Median age by state
heading <- read_csv('../age_sex/ACS_15_1YR_S0101.csv', n_max = 1) %>% names()

s0101 <- read_csv('../age_sex/ACS_15_1YR_S0101.csv', col_names = FALSE, skip = 2)

names(s0101) <- heading

age <- s0101 %>%
    transmute(state = `GEO.display-label`,
              median_age = HC01_EST_VC35) %>%
    filter(state != 'Puerto Rico')

# Do urban percentage and median age explain anything about
# non-motorized transit?
census_data <- commute %>%
    inner_join(urban_rural, by = "state") %>%
    inner_join(age, by = "state") %>%
    select(state, state_postal, walk, bicycle, urban_percentage, median_age) %>%
    mutate(non_motorized = walk + bicycle)

u <- ggplot(data = census_data,
       aes(x = urban_percentage, y = non_motorized)) +
    geom_text(aes(label = state_postal))

svg("urban.svg", width = width*resize, height = height*resize)
print(u)
dev.off()

a <- ggplot(data = census_data,
       aes(x = median_age, y = non_motorized)) +
    geom_text(aes(label = state_postal))

svg("age.svg", width = width*resize, height = height*resize)
print(a)
dev.off()

# Not significant:
model = lm(data = census_data,
           non_motorized ~ urban_percentage + median_age)

# Only significant because of DC (a clear outlier)
model = lm(data = census_data,
           non_motorized ~ urban_percentage * median_age)
tags: bicycling  walking  census data  R 
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