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Cycling and walking: Explaining the spatial distribution of healthy modes of transportation in the United States
Transportation Research Part D 13 (2008) 462–470
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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Cycling and walking: Explaining the spatial distribution of healthy modes of transportation in the United States Sammy Zahran a,*, Samuel D. Brody b, Praveen Maghelal c, Andrew Prelog d, Michael Lacy e a
Department of Sociology, Colorado State University, B235 Clark Building, Fort Collins, CO 80523-1874, United States Environmental Planning and Sustainability Research Unit, Department of Landscape Architecture and Urban Planning, Texas A and M University, College Station, TX 77843-3137, United States c Department of Urban and Regional Planning, Florida Atlantic University, Fort Lauderdale, Florida 33301, United States d Department of Sociology, Colorado State University, Fort Collins, CO 80523-1874, United States e Department of Sociology, Colorado State University, B235 Clark Building, Fort Collins, CO 80523-1874, United States b
a r t i c l e Keywords: Physical activity Walking Biking Spatial distribution
i n f o
a b s t r a c t This study analyzes the spatial distribution of healthy modes of transportation (cycling and walking commuting) at the county scale. Geographic information systems, negative binomial, and zero-inflated negative binomial regression techniques are used to test four types of geographic covariates: built, natural, socioeconomic, and civic environments. Descriptive, GIS, and regression results indicate that the expected count of cycling and walking commuters in a county increases significantly with unit changes in population density, natural amenities, education and wealth, and estimates of local civic concern. Expected counts decrease significantly by unit changes in pollution and the average distance traveled to work by a typical commuter. Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction Despite the growing scholarly interest on walking and cycling as viable transportation alternatives, little empirical research has been conducted on the determinants of non-motorized transportation choice at large spatial scales, controlling for multiple characteristics of the natural, built, and social environment. Most studies investigating cycling and walking transport behaviors have been done at the neighborhood scale for limited spatial universes, and with limited sets of predictors. According to Saelens et al. (2003): ‘‘To date, transportation and urban planning research has been conducted in only a small number cities ... [with] rural areas remaining largely understudied”. Moreover, a report by the Institute of Medicine of the National Academies (2005) maintains that more research is needed on the ‘‘effect of the built environment on physical activity at scales larger than neighborhoods”. The study addresses these gaps, investigating walking and biking commuting behavior at a breadth and scale not addressed by previous researchers. Specifically, we investigate the spatial determinants of biking and walking as modes of transportation for every county in the continental US The study isolates the effects of multiple built, natural, socioeconomic and civic environment variables that increase or decrease walking and biking transport behaviors, providing an evidentiary basis for spatially targeted policy recommendations to enhance the use of walking and cycling as primary means of transportation to and from the workplace.
* Corresponding author. Tel.: +1 970 491 1877. E-mail address: [email protected] (S. Zahran). 1361-9209/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.trd.2008.08.001
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2. Research design and data Two transportation-related physical activities are statistically modeled: cycling and walking. Our first dependent variable, cycling commuters, is measured as the number of workers age 16 and older that use a bicycle as their primary means of transportation to and from their place of employment. The second dependent variable, walking commuters, is similarly measured as the number of workers age 16 and older that walk to work as their primary means of transportation. Both cycling and walking data are from the US Census Bureau’s Summary File 3 item P30, where a representative sample of American workers (in roughly 50 million households) were asked to indicate their primary means of transportation to and from work. We use three characteristics of the built environment to predict the count of cyclers and walkers in a county: population density, hazardous air pollution (HAP) emissions per capita and journey-to-work time. Population density is measured as the population in a county divided by the county area in square miles. Generally, more densely populated areas are characterized by higher directness or ease of travel between two points (Saelens et al., 2003). All things held equal, we expect to find higher counts of bikers and walkers in more densely populated areas. HAP emissions per capita is measured as the amount (in pounds) of HAPs emitted in a county area divided by population, as inventoried by the Environmental Protection Agency’s Air Data County Emissions Report in 1999. Localities with higher HAP emissions per capita are characterized by higher automobile density and industrial activities that may deter healthier modes of transportation like cycling and walking (Litman, 2003; Pollard, 2003). Finally, mean length of journey-to-work is measured as the aggregate travel time to work in minutes for workers 16 years of age and older, divided by the number of workers 16 years and older (excluding persons that work at home). Journey-to-work data are from the US Census Bureau’s Summary File 3 item P33. Average journey time variables are typically used to estimate local connectivity (directness of travel), proximity (straight-line distance between trip origins and destinations), and patterns of residential sprawl (Saelens et al., 2003), all of which should be inversely related to observed counts of bikers and walkers. We measure two natural environment variables that may increase the expected count of bikers and walkers in a county: natural amenities scale, and proximity to national forests and parks. The natural amenities scale measures environmental characteristics of a county area that enhance quality of life and residential desirability. The scale combines six standardized measures of climate, typography, and water bodies that reflect natural qualities most people prefer. These include a warm winter, winter sun, summer temperature, low summer humidity, topographic variation, and access to water bodies. The scale ranges from 11.7 in Ventura, California to 6.4 in Red Lake, Minnesota. Amenity scale data are from the Economic Research Service of US Department of Agriculture (1999). Our proximity to national forests and parks variable is measured as the number of US national forests and parks intersecting a county boundary as inventoried by the US Department of Agriculture, Forest Service. Consistent with Brownson et al. (2001) Nankervis (1999a, b), and Merrill et al. (2005), it is hypothesized that transportation-related physical activities like cycling and walking are more likely to occur in localities with higher natural amenities and recreational opportunities that are conducive to healthy lifestyles and human powered transportation. Three socioeconomic predictors are estimated, including: median home value, percent college educated, and percent Hispanic. All three measures are derived from the US Census Bureau. Median home value estimates how much a property (house and lot) would sell for in the local market. Median value calculations are rounded to the nearest hundred dollars. As suggested by previous research, we expect to find higher counts of bikers and walkers in areas of higher wealth. Wealthier localities have a higher tax base for construction of bike lanes and pedestrian friendly land uses, and host a greater percentage of persons with budget flexibilities to pursue healthier lifestyles. Percent college educated is measured as the number of persons in a county 25 years of age or over with a bachelor’s, master’s, professional, or doctorate degree, divided by the number of persons 25 years or older in a county. Consistent with previous literature, we anticipate higher counts of bikers and walkers in more highly educated localities (Berrigan and Troiano, 2002; Brownson et al., 2001). Lastly, percent Hispanic is measured as the number of persons identifying themselves as Mexican, Puerto Rican, Cuban, or other Hispanic residing in a county area, divided by the number of persons residing in a county. As suggested by Cervero and Duncan (2003) and National Highway Traffic Safety Administration and the Bureau of Transportation Statistics (2002), percent Hispanic should be positively associated with the count of persons that walk or bike for transportation purposes. Four civic variables are examined. Percent green party is measured as the number of votes cast for Ralph Nader in the 2000 Presidential election in a county, divided by the number of votes cast in a county. Environmental nonprofits is measured as the number non-profit organizations of tax-exempt status with $25,000 dollars in gross receipts required to file Form 990 with the IRS with an environmental focus (as defined in the NCCS taxonomy) in a county. It is assume that areas with higher percentages of green party voters and environmental nonprofits have a civic culture that is more amenable to the promotion of healthy, more environmentally friendly lifestyles and modes of transportation (Brownson et al., 2001). The bike organization variable is calculated as the number of bike advocacy organizations and clubs in county, as inventoried by the League of American Bicyclists. Similarly, the walk organization variable is measured by the number of groups that promote pedestrian travel in a county area. Insofar as advocacy organizations anchor and promote behaviors and lifestyles of interest, we expect to find higher counts of bikers and walkers in areas with higher organizational activity.
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Map 1. Geography of bike commuters (%) at the county scale, 2000.
3. Statistical procedures The count of cycling commuters (y) has more zero observations than predicted by a Poisson process, which assumes that the conditional variance of the distribution of commuters is equal to the expected value (Long, 1997; Long and Freese, 2006). For cycling data, Vuong model selection and Bayesian Information Criterion tests indicate significant over-dispersion, strongly favoring a zero-inflated negative binomial (ZINB) regression approach to analyzing the data. ZINB regression simultaneously estimates logistic regression and negative binomial models. The logistic analysis models the probability of y falling into the zero group (i.e., zero bike commuters in a county), while the negative binomial models the expectation of y conditional on a non-zero value – or an expected count of the number of bike commuters in a county. The distribution of walk commuters, by contrast, could be analyzed with a standard negative binomial regression procedure. To screen independent variables for multicollinearity we analyzed zero-order correlations and variance inflation factors (VIF). No two variables in regression models are unacceptably correlated, and all VIF scores are well below acceptable standards (mean VIF = 1.59). We also examined response variables for spatial autocorrelation. 4. Results on bike commuting We begin by ranking the top 25 counties in the US by the percentage of workers that bike to work as their primary means of transportation. Yolo County, California has the greatest percentage of bike commuters (7.5%). Nested in Yolo County is the city of Davis, recognized by the League of American Bicyclists as the most bike-friendly community in the country, with a robust cycling infrastructure, and a vast network of bike lanes, paths, and grade-separated bicycle crossings. A disproportionate number of counties in the top 25 are located in Colorado, with Hinsdale County having the highest percentage of bike commuters (2.79%). On natural environment, built environment, and socioeconomic predictors, data show that counties in Colorado scored significantly higher than average on natural amenities (4.08 vs. 0.029, t = 14.55, p < 0.01) educational attainment (25.89 vs.16.31, t = 6.11, p < 0.01), and Green Party voting (5.46 vs. 1.86, t = 7.79, p < 0.01), with residents spending significantly less time journeying to work (20.28 vs. 22.49 min, t = 3.029, p < 0.01). In the next phase, the percentage of bike commuters for the contiguous US is mapped at the county scale. Map 1 divides the percent distribution of county bike commuters into four intervals, with dark green indicating counties with a percentage of bike commuters at least 1.5 standard deviations above the national average, and red indicating an observed percentage at least 0.50 standard deviations below the national average Counties with lower percentages of bike commuters concentrate in the southern and Appalachian states of Alabama, Mississippi, Georgia, Tennessee, Kentucky, North Carolina and West Virginia1. 1 According to Centers for Disease Control data (.), these states are also among those with the higher prevalence of overweight persons in the country, with 25 to 30+ percent of adult population defined as clinically obese (Body–Mass Index P 30).
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Conversely, counties with high percentages of bike commuters concentrate in the geographic west, stretching from the Pacific Northwest down through California, over to Arizona and New Mexico, up through Colorado, and encircling lower commuter counties in portions of Wyoming, Utah, and Idaho. These Sunbelt states have higher natural amenities scores, topographical variation, and temperate climate conducive to cycling. Next, we model the count distribution of bike commuters at the county scale. As previously stated, because our dependent variable is a non-negative integer exhibiting significant over-dispersion with a disproportionate number of zero counts, we analyze the data using a zero-inflated negative binomial (ZINB) regression model. The independent variables are loaded incrementally, beginning with a baseline model that simply predicts the count of bike commuters by the number of workers in a county area, ending with a fully saturated model of built environment, natural environment, socioeconomic, and civic
Table 1 Zero-Inflated negative binomial regression models predicting bike commuter count Model 1
Model 2
Model 3
Model 4
4.78*** (0.11)
4.88*** (0.11)
2.91*** (0.15)
2.82*** (0.15)
2.63e05*** (1.10e06)
1.99e05*** (1.14e06)
1.46e05*** (9.80e07)
1.38e05*** (9.80e07)
4.62e04 (2.8e04) 3.35e04*** (1.0e04) 0.08*** (0.01)
1.85e03*** (3.21e04) 4.48e04*** (1.01e04) 0.09*** (0.05)
8.09e04*** (2.75e04) 4.57e04*** (1.01e-04) 0.07*** (0.01)
1.29e03*** (2.88e04) 4.14e04*** (9.81e05) 0.06*** (0.01)
0.12*** (0.01) 0.15*** (0.04)
0.03*** (0.01) 0.05 (0.04)
0.02** (0.01) 0.08** (0.04)
0.03*** (4.09e03) 1.34e05*** (1.02e06) 0.01*** (2.20e03)
0.02*** (4.23e03) 1.12e05*** (1.05e06) 0.01*** (2.21e03)
Constant
Baseline variable Total workers Built environment variables Population density HAP emissions per capita Mean journey to work (in minutes) Natural environment variables Natural amenities scale National parks and forests Socioeconomic variables Percent college educated Median home value Percent hispanic Civic variables Bike organizations Environmental non-profits Percent green party Inflated model Constant Total workers Population density Mean journey to work (in minutes)
0.08*** (0.03) 9.56e03 (9.01e02) 0.07*** (0.01) 5.47*** (0.50) 2.74e04*** (2.70e05) 0.01*** (2.00e03) 0.12*** (0.01)
2.52*** (0.26) 2.61e04*** (2.46e05) 0.01*** (1.92e03) 0.12*** (0.01) .0014638 (0.03)
1.44*** (0.47) 2.28e04*** (2.23e05) 0.01*** (1.87e03) 0.10*** (0.01) 0.03 (0.27) 0.06*** (0.02)
1.55*** (0.40) 2.12e04*** (2.14e05) 0.01*** (1.88e03) 0.10*** (0.01) 0.04 (0.03) 0.02 (0.02) 0.29*** (0.06)
0.33*** (0.03) 1.38 (0.04)
0.22*** (0.03) 1.25 (0.04)
0.02 (0.03) 1.02 (0.03)
0.01 (0.03) 0.99 (0.03)
Natural amenities scale Percent college educated Percent green party /lnalpha Alpha
Standard errors are in parentheses. Null test of coefficient equal to zero. p < 10. p < 05. *** p < 01. *
**
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Table 2 ZINB regression coefficients expressed as percent change in expected count for bike commuters for every unit and standard deviation increase in predictors Model 1 Unit D
rD
Model 2 Unit D
rD
Model 3 Unit D
rD
Model 4 Unit D
rD
Baseline variable Total workers
0.0
408.2
0.0
241.8
0.0
147.3
0.0
135.2
Built environment variables Population density HAP emissions per capita Mean journey to work
0.0 0.0 8.1
6.8 8.7 37.4
0.2 0.0 8.5
30.3 11.5 39.1
0.1 0.0 6.3
12.3 11.7 30.6
0.1 0.0 5.8
20..3 10.5 28.4
12.4 15.6
30.3 11.1
2.7 4.9
6.2 3.5
2.3 8.7
5.2 4.4
2.9 0.0 0.7
22.5 71.7 8.5
2.4 0.0 0.7
18.7 57.8 9.4
8.6 1.0 7.4
6.9 0.2 14.3
0.0 1.1 10.4 4.2 2.1 25.4
100.0 378.2 74.4 9.7 14.3 42.3
Natural environment variables Natural amenities scale National parks and forests Socioeconomic variables Percent college educated Median home value Percent hispanic Civic variables Bike organizations Environmental non-profits Percent green party Inflated model Total workers Population density Mean journey to work Natural amenities scale Percent college educated Percent green party Nonzero observations Zero observations Vuong z Pr > z Log-likelihood Full model: Cragg and Uhler’s R2: AIC AIC*n BIC BIC0 N
0.0 1.3 12.6
100.0 534.2 93.6
2323 651 12.72 0.00 12474.98 0.59 8.40 24969.97 1244.90 2612.69 2974
0.0 1.3 12.4 0.1
100.0 505.4 92.0 0.3
2323 651 12.57 0.00 12365.89 0.62 8.58 24757.79 1050.71 2806.88 2974
0.0 1.3 10.4 3.5 5.8
100.0 526.6 73.4 8.1 35.0 2323 651 13.27 0.00 12122.736 0.68 8.16 24279.47 596.38 3261.21 2974
2248 644 13.69 0.00 11682.28 0.69 8.09 23406.56 483.539 3201.08 2871
variables (Tables 1 and 2). For ease of interpretation, Table 2 expresses model coefficients as percent changes in the expected count of bike commuters for unit and standard deviation changes in independent predictors and fit statistics for the models The full model attained a pseudo-R2 value of explains 68.5%. We concentrate interpretation on fully saturated model 4. We begin with built environment variables. For every additional minute of average journey time, the expected count of bike commuters decreased by 5.8% (where p < 0.01). Since journey time is closely connected to directness of travel, proximity, and patterns of residential settlement, the significant effect of journey time on cycling behavior reinforces existing literature on the importance of the built environment in determining healthy modes of transport. Similarly, the expected count of bike commuters decreases with observed emissions of hazardous air pollutants (b = 0.0004, p < 0.01). A standard deviation shift of 267 lbs of HAP emissions per person reduces the expected count by 10.5%. Finally, as population density levels increase so too does the level of bike commuting (b = 0.001, p < 0.01). Results in Tables 1 and 2 also show that the natural environment operates on cycling behavior predictably. Locales with higher natural amenities and opportunities for outdoor recreation are home to higher counts of bike commuters. Specifically, areas with warm winter seasons, temperate summers, low summer humidity, and topographic variation have higher counts of workers that cycle to work as their primary means of transportation (b = 0.02, p < 0.05). All things held equal, the presence of a national park or forest increases the expected count of bike commuters by 8.7% (where p < 0.05). However, this effect is small relative to that of the built environment. It takes only two minutes of time added to work journeys to erase the location advantages of national park or forest proximity. Small changes in the compactness of urban form can statistically eliminate natural environment characteristics favorable to healthy modes of transportation. All socioeconomic measures behave as expected. Rates of bike commuting increase with the percentage of college educated residents (b = 0.02, p < 0.01), median home values (b = 1.12e05, p < 0.01), and the percentage of Hispanic population (0.01, p < 0.01). More specifically, a unit increase in the percentage of residents with a college education increases the expected count of bike commuters by 2.4%. In other words, increasing the level of human capital in a locality significantly increases the likelihood that residents will choose to bike to work as their primary means of transportation. Civic capital
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Map 2. Geography of walk commuters (%) at the county scale, 2000.
variables also predict the level of bike commuting in a county. In localities with higher percentages of Green Party voters (b = 00.07, p < 0.01) observe higher counts of bike commuters are observed - a 1% increase in support for Ralph Nader and the Green Party platform was associated with a 7.4% increase in the expected bike commuting count. Finally, results show that the presence of a bike club or bike advocacy organization, as inventoried by the League of American Bicyclists, increases the count of bike commuters by 8.6%. Results relating to the civic composition variables suggest that bike commuting is partially explained by political and organizational dynamics, adjusting for measures of built, natural, and socioeconomic environment. 5. Results on walk commuting The same analytic sequence is used to analyze variation in walk commuting. We begin by ranking the Top 25 counties in the US on the percentage of workers 16 + that walk to work as their primary means of transportation. With a few exceptions, the top 25 counties on walk commuting have relatively small labor forces – in fact, 19 of the Top 25 counties have less than 10,000 employed workers. Prairie County, Montana leads all counties with 30% of its labor force walking to work as a primary means of transportation. All of Montana’s 56 counties are in the top half of the distribution on walk commuting, with four counties joining Prairie in the Top 25. The higher concentration of walk commuters in Montana may be attributable to natural and built environment characteristics conducive to walking. Montanans (on average) spend 16 min in work journeys compared to 22.55 min for the rest of the country, and are blessed with a natural environment favorable to walk commuting, scoring above the nation in both the count of national parks and forests (0.25 per county vs 0.19) and socially desirable natural amenities (1.29 vs 0.03, as measured by the natural amenities scale). Map 2 displays the spatial distribution of walk commuting in the US. As with bike commuting, the percent distribution of walk commuters is divided into four standard deviation intervals. Counties with a percentage of walk commuters at least 1.5 standard deviations above the national average are colored in dark green, and counties with a percentage of walk commuters 0.50 standard deviations below the national average are colored in red. Lower percentages of walk commuters are located in the Southeastern and Midwestern portions of the country. Higher values are observed in upstate New York, Vermont, and counties bordering Canada in Minnesota, North Dakota, and Montana. Somewhat surprising, localities in these high walk commuter states score lower than average on measures of population density and urbanization, variables commonly assumed to increase the walkability of localities.2
2 This counter-intuitive result may be explained as a problem of spatial scale. In general, the population density of a county is an adequate proxy for the compactness of spatial form. However, in Montana, counties are much larger than average in size (square miles), with populations concentrated in neighborhoods that constitute a small percentage of total county land area. In other words, the density of livable space is significantly higher than the observed density of the county. Prairie County, for example has a population of 1199 people, distributed in 511 block groups – 2.34 persons per block. The variance around the mean is 21.79 persons, implying that local population is distributed unevenly in Prairie County, with some block groups home to a disproportionate number of residents. In fact, about half of the population of Prairie County resides in 7.79% of 511 block groups.
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Table 3 Negative binomial regression models predicting walk commuter count Model 1
Model 2
Model 3
Model 4
6.71*** (0.07)
6.70*** (0.07)
5.55*** (0.09)
5.50*** (0.09)
1.83e05*** (7.50e07)
1.68e05*** (8.00e07)
1.34e05*** (7.20e07)
1.27e05*** (7.17e07)
1.35e03*** (2.00e04) 3.69e04*** (4.40e05) 0.06*** (2.80e03)
1.71e03*** (2.20e04) 3.83e04*** (4.30e05) 0.06*** (2.80e03)
9.64e04*** (2.00e04) 3.65e04*** (4.00e05) 0.05*** (3.10e03)
1.56e03*** (2.05e04) 3.32e04*** (4.11e05) 0.04*** (3.07e03)
0.01* (0.01) 0.15*** (0.03)
0.03*** (0.01) 0.10*** (0.03)
0.03*** (0.01) 0.12*** (0.03)
0.03*** (3.00e03) 7.84e06*** (6.80e07) 3.66e04 (1.20e03)
0.01*** (3.08e03) 5.27e06*** (6.88e07) 4.51e04 (1.24e03)
0.60 (0.02) 0.55 (0.01)
0.01 0.13 0.02 (0.07) 0.13*** (0.01) 0.64 (0.03) 0.53 (0.03)
Constant
Baseline variable Total workers Built environment variables Population density HAP emissions per capita Mean journey to work Natural environment variables Natural amenities scale National parks and forests Socioeconomic variables Percent college educated Median home value Percent hispanic Civic variables Walk organizations Environmental non-profits Percent green party /lnalpha Alpha
0.44 (0.02) 0.65 (0.02)
0.45 (0.02) 0.64 (0.02)
Standard errors are in parentheses. Null test of coefficient equal to zero p < .10. ** p < .05. *** p < .01. *
Next, the count distribution of walk commuters is modeled at the county scale. We concentrate our analysis on the fully saturated model 4 of built environment, natural environment, socioeconomic, and civic capital variables (see Tables 3 and 4). Table 4 reports model coefficients as percent changes in the expected count of walk commuters for unit and standard deviation changes in independent predictors. Beginning with built environment variables, results show that the expected count of walk commuters in a county increases significantly with population density (b = 0.002, p < 0.01). As predicted, hazardous air pollutant and journey-to-work measures are inversely related to the count of walk commuters. Specifically, the addition of 1 minute to the average time required for work travel decreases the expected count of walk commuters by 4.2%, where p < 0.01. Similarly, a standard deviation increase in HAP emissions per capita (267 lbs per person) reduces the forecasted count of walkers by 8.5%. In counties with high urban congestion, the majority (or plurality) of HAP emissions come from on-road mobile sources from licensed motor vehicles. These variables appear to reinforce each other – as vehicles miles traveled increase, hazardous air pollutants increase, aggravating the willingness of workers to walk to their place of employment. With regard to natural environment variables, results show that proximity to national parks and forests increases the expected count of walk commuters (b = 0.12, p < 0.01). Somewhat puzzling, the natural amenities scale is negatively associated with the count of walk commuters (b = 0.03, p < 0.01). In Model 3, the coefficient on natural amenities behaves as expected - it is positive (b = 0.013) and statistically significant, where p < .10. With the inclusion of socioeconomic variables (in Model 4) of median home value, percent college educated, and percent Hispanic population, the amenities scale measure flips statistical sign. This finding reinforces the point that a favorable climate or natural environment alone is insufficient to increase the expected count of walkers in county. Important socioeconomic and civic capital characteristics of a locality can meaningfully transform the value of a locality’s natural capital abundance. Socioeconomic and civic capital variables behave as hypothesized. Results indicate that a percent increase in the number of college educated persons in a county increases the expected count of walk commuters by 1.4%. The wealth of a locality (as measured by median home value) is positively associated with the count of walk commuters. For example, a $10,000
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Table 4 Negative Binomial Regression Coefficients Expressed as Percent Change in Expected Count for Walk Commuters for Every Unit and Standard Deviation Increase in Predictors Model 1 Unit D
rD
Model 2 Unit D
rD
Model 3 Unit D
rD
Model 4 Unit D
rD
Baseline variable Total workers
0.0
210.9
0.0
182.2
0.0
129.5
0.0
120.5
Built environment variables Population density HAP emissions per capita Mean journey to work
0.1 0.0 5.6
21.3 9.5 27.5
0.2 0.0 5.7
27.8 9.9 27.7
0.1 0.0 4.7
14.8 9.4 23.4
0.2 0.0 4.2
25.0 8.5 21.2
1.3 16.4
2.9 11.6
3.2 10.6
7.2 7.5
3.2 12.6
7.1 6.3
2.5 0.0 0.0
19.8 37.2 0.4
1.4 0.0 0.0
10.8 23.9 0.5
0.9 1.5 13.7
0.1 0.3 27.3
Natural environment variables Natural amenities scale National parks and forests Socioeconomic variables Percent college educated Median home value Percent Hispanic Civic variables Walk Organizations Environmental non-profits Percent green party LR chi2 Prob > chi2 Log-likelihood Full Model: Cragg and Uhler0 s R2: AIC AIC*n BIC BIC0 N
3314.73 0.00 20688.56 0.67 13.92 41389.11 17640.05 3282.74 2974
3364.05 0.00 20663.89 0.68 13.90 41343.79 18293.53 3316.07 2974
3866.45 0.00 20412.70 0.73 13.74 40847.39 17128.31 3794.47 2974
3889.760 0.00 19726.91 0.74 13.65 39481.82 16517.02 3794.12 2878
increase in median home value boosts the expected count of walk commuters by 5.6%, where p < 0.01. Walk commuting is also partially explained by the spatial distribution of Green Party voters. For every unit increase the percent of persons voting for Ralph Nader, we observe a 13.7% increase in the expected count of walk commuters. The causal direction of the relationship between Green politics and walk commuting is unknowable (given the cross-sectional research design). However, our results clearly show that the spatial distribution in walk commuting is strongly associated with green politics, adjusting for features of the natural and constructed environment. Overall, the full model had by Cragg and Uhler’s pseudo-R2 of about 74%. 6. Conclusion The study shows that cycling and walking transport behaviors depend on the built, natural, socioeconomic, and civic environment of a locality. Statistical relationships observed provide useful information to local transport and health planners aiming to encourage non-motorized commuting behaviors. First, local natural environmental characteristics influence the spatial distribution of healthy modes of transportation. Temperate summers, low humidity, and topographic variation are natural capital characteristics that increase expected counts of persons that bicycle or walk to work. Second, while the natural environment acts as an important foundation for soft modes of transportation like walking and cycling, features of the built environment such as local connectivity (directness of travel), proximity (straight-line distance between trip origins and destinations), and patterns of residential sprawl are equally important predictors of cycling and walking to work. As vehicle miles traveled increase, populations are exposed to higher amounts of on-road hazardous air pollutants (Pollard, 2003). Our results show that lower density settlements, longer trip journeys, and higher air pollution levels, significantly reduce expected counts of cycling and walking commuters. Third, local socioeconomic and civic conditions influence observed levels of walk and bike commuting. Localities with high human capital, wealth, and organizational infrastructure have significantly higher counts of walkers and bikers. More specifically, the presence of cycling and pedestrian organizations can increase the odds of walking and biking. Such organizations provide education, outreach, and group events that encourage physical activity. Acknowledgements Portions of the data collected for this research was supported under Award No. NA03OAR4310164 by the National Oceanic and Atmospheric Administration (NOAA), US Department of Commerce. The statements, findings, conclusions,
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and recommendations are those of the authors and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration or the Department of Commerce. References Berrigan, D., Troiano, R.P., 2002. The association between urban form and physical activity in US adults. American Journal of Preventative Medicine 23, 74– 79. Brownson, R.C., Baker, E.A., Housemann, R.A., Brennan, L.K., Bacak, S.J., 2001. Environmental and policy determinants of physical activity in the United States. American Journal of Public Health 91, 1995–2003. Cervero, R., Duncan, M., 2003. Walking, bicycling, and urban landscapes: evidence from the San Francisco Bay area. American Journal of Public Health 9, 1478–1483. Institute of Medicine of the National Academies, 2005. Does the Built Environment Influence Physical Activity? Examining the Evidence. Transportation Research Board, Washington DC. Litman, T., 2003. Integrating public health objectives in transportation decision-making. American Journal of Health Promotion 18, 103–108. Long, J.S., 1997. Regression Models for Categorical and Limited Dependent Variables. Sage Publications, Thousand Oaks. Long, J.S., Freese, J., 2006. Regression Models for Categorical Dependent Variables Using Stata, second ed. Stata Press, College Station. Merrill, R.M., Shields, E.C., White, G.L., Drunce, D., 2005. Climate conditions and physical activity in the united states. American Journal of Health and Behavior 29, 371–381. Nankervis, M., 1999a. The effect of weather and climate on bicycle commuting. Transportation Research Part A: Policy and Practice 33, 417–431. Nankervis, M., 1999b. Effects of weather and climate on urban bicycle commuters’ decision to ride. A pilot survey. Road Transportation Research 8, 85–97. Pollard, T., 2003. Policy prescriptions for healthier communities. American Journal of Health Promotion 18, 109–113. Saelens, B.E., Sallis, J.F., Frank, L.D., 2003. Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures. Annals of Behavioral Medicine 25, 80–91.
Contents lists available at ScienceDirect
Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Cycling and walking: Explaining the spatial distribution of healthy modes of transportation in the United States Sammy Zahran a,*, Samuel D. Brody b, Praveen Maghelal c, Andrew Prelog d, Michael Lacy e a
Department of Sociology, Colorado State University, B235 Clark Building, Fort Collins, CO 80523-1874, United States Environmental Planning and Sustainability Research Unit, Department of Landscape Architecture and Urban Planning, Texas A and M University, College Station, TX 77843-3137, United States c Department of Urban and Regional Planning, Florida Atlantic University, Fort Lauderdale, Florida 33301, United States d Department of Sociology, Colorado State University, Fort Collins, CO 80523-1874, United States e Department of Sociology, Colorado State University, B235 Clark Building, Fort Collins, CO 80523-1874, United States b
a r t i c l e Keywords: Physical activity Walking Biking Spatial distribution
i n f o
a b s t r a c t This study analyzes the spatial distribution of healthy modes of transportation (cycling and walking commuting) at the county scale. Geographic information systems, negative binomial, and zero-inflated negative binomial regression techniques are used to test four types of geographic covariates: built, natural, socioeconomic, and civic environments. Descriptive, GIS, and regression results indicate that the expected count of cycling and walking commuters in a county increases significantly with unit changes in population density, natural amenities, education and wealth, and estimates of local civic concern. Expected counts decrease significantly by unit changes in pollution and the average distance traveled to work by a typical commuter. Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction Despite the growing scholarly interest on walking and cycling as viable transportation alternatives, little empirical research has been conducted on the determinants of non-motorized transportation choice at large spatial scales, controlling for multiple characteristics of the natural, built, and social environment. Most studies investigating cycling and walking transport behaviors have been done at the neighborhood scale for limited spatial universes, and with limited sets of predictors. According to Saelens et al. (2003): ‘‘To date, transportation and urban planning research has been conducted in only a small number cities ... [with] rural areas remaining largely understudied”. Moreover, a report by the Institute of Medicine of the National Academies (2005) maintains that more research is needed on the ‘‘effect of the built environment on physical activity at scales larger than neighborhoods”. The study addresses these gaps, investigating walking and biking commuting behavior at a breadth and scale not addressed by previous researchers. Specifically, we investigate the spatial determinants of biking and walking as modes of transportation for every county in the continental US The study isolates the effects of multiple built, natural, socioeconomic and civic environment variables that increase or decrease walking and biking transport behaviors, providing an evidentiary basis for spatially targeted policy recommendations to enhance the use of walking and cycling as primary means of transportation to and from the workplace.
* Corresponding author. Tel.: +1 970 491 1877. E-mail address: [email protected] (S. Zahran). 1361-9209/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.trd.2008.08.001
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2. Research design and data Two transportation-related physical activities are statistically modeled: cycling and walking. Our first dependent variable, cycling commuters, is measured as the number of workers age 16 and older that use a bicycle as their primary means of transportation to and from their place of employment. The second dependent variable, walking commuters, is similarly measured as the number of workers age 16 and older that walk to work as their primary means of transportation. Both cycling and walking data are from the US Census Bureau’s Summary File 3 item P30, where a representative sample of American workers (in roughly 50 million households) were asked to indicate their primary means of transportation to and from work. We use three characteristics of the built environment to predict the count of cyclers and walkers in a county: population density, hazardous air pollution (HAP) emissions per capita and journey-to-work time. Population density is measured as the population in a county divided by the county area in square miles. Generally, more densely populated areas are characterized by higher directness or ease of travel between two points (Saelens et al., 2003). All things held equal, we expect to find higher counts of bikers and walkers in more densely populated areas. HAP emissions per capita is measured as the amount (in pounds) of HAPs emitted in a county area divided by population, as inventoried by the Environmental Protection Agency’s Air Data County Emissions Report in 1999. Localities with higher HAP emissions per capita are characterized by higher automobile density and industrial activities that may deter healthier modes of transportation like cycling and walking (Litman, 2003; Pollard, 2003). Finally, mean length of journey-to-work is measured as the aggregate travel time to work in minutes for workers 16 years of age and older, divided by the number of workers 16 years and older (excluding persons that work at home). Journey-to-work data are from the US Census Bureau’s Summary File 3 item P33. Average journey time variables are typically used to estimate local connectivity (directness of travel), proximity (straight-line distance between trip origins and destinations), and patterns of residential sprawl (Saelens et al., 2003), all of which should be inversely related to observed counts of bikers and walkers. We measure two natural environment variables that may increase the expected count of bikers and walkers in a county: natural amenities scale, and proximity to national forests and parks. The natural amenities scale measures environmental characteristics of a county area that enhance quality of life and residential desirability. The scale combines six standardized measures of climate, typography, and water bodies that reflect natural qualities most people prefer. These include a warm winter, winter sun, summer temperature, low summer humidity, topographic variation, and access to water bodies. The scale ranges from 11.7 in Ventura, California to 6.4 in Red Lake, Minnesota. Amenity scale data are from the Economic Research Service of US Department of Agriculture (1999). Our proximity to national forests and parks variable is measured as the number of US national forests and parks intersecting a county boundary as inventoried by the US Department of Agriculture, Forest Service. Consistent with Brownson et al. (2001) Nankervis (1999a, b), and Merrill et al. (2005), it is hypothesized that transportation-related physical activities like cycling and walking are more likely to occur in localities with higher natural amenities and recreational opportunities that are conducive to healthy lifestyles and human powered transportation. Three socioeconomic predictors are estimated, including: median home value, percent college educated, and percent Hispanic. All three measures are derived from the US Census Bureau. Median home value estimates how much a property (house and lot) would sell for in the local market. Median value calculations are rounded to the nearest hundred dollars. As suggested by previous research, we expect to find higher counts of bikers and walkers in areas of higher wealth. Wealthier localities have a higher tax base for construction of bike lanes and pedestrian friendly land uses, and host a greater percentage of persons with budget flexibilities to pursue healthier lifestyles. Percent college educated is measured as the number of persons in a county 25 years of age or over with a bachelor’s, master’s, professional, or doctorate degree, divided by the number of persons 25 years or older in a county. Consistent with previous literature, we anticipate higher counts of bikers and walkers in more highly educated localities (Berrigan and Troiano, 2002; Brownson et al., 2001). Lastly, percent Hispanic is measured as the number of persons identifying themselves as Mexican, Puerto Rican, Cuban, or other Hispanic residing in a county area, divided by the number of persons residing in a county. As suggested by Cervero and Duncan (2003) and National Highway Traffic Safety Administration and the Bureau of Transportation Statistics (2002), percent Hispanic should be positively associated with the count of persons that walk or bike for transportation purposes. Four civic variables are examined. Percent green party is measured as the number of votes cast for Ralph Nader in the 2000 Presidential election in a county, divided by the number of votes cast in a county. Environmental nonprofits is measured as the number non-profit organizations of tax-exempt status with $25,000 dollars in gross receipts required to file Form 990 with the IRS with an environmental focus (as defined in the NCCS taxonomy) in a county. It is assume that areas with higher percentages of green party voters and environmental nonprofits have a civic culture that is more amenable to the promotion of healthy, more environmentally friendly lifestyles and modes of transportation (Brownson et al., 2001). The bike organization variable is calculated as the number of bike advocacy organizations and clubs in county, as inventoried by the League of American Bicyclists. Similarly, the walk organization variable is measured by the number of groups that promote pedestrian travel in a county area. Insofar as advocacy organizations anchor and promote behaviors and lifestyles of interest, we expect to find higher counts of bikers and walkers in areas with higher organizational activity.
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Map 1. Geography of bike commuters (%) at the county scale, 2000.
3. Statistical procedures The count of cycling commuters (y) has more zero observations than predicted by a Poisson process, which assumes that the conditional variance of the distribution of commuters is equal to the expected value (Long, 1997; Long and Freese, 2006). For cycling data, Vuong model selection and Bayesian Information Criterion tests indicate significant over-dispersion, strongly favoring a zero-inflated negative binomial (ZINB) regression approach to analyzing the data. ZINB regression simultaneously estimates logistic regression and negative binomial models. The logistic analysis models the probability of y falling into the zero group (i.e., zero bike commuters in a county), while the negative binomial models the expectation of y conditional on a non-zero value – or an expected count of the number of bike commuters in a county. The distribution of walk commuters, by contrast, could be analyzed with a standard negative binomial regression procedure. To screen independent variables for multicollinearity we analyzed zero-order correlations and variance inflation factors (VIF). No two variables in regression models are unacceptably correlated, and all VIF scores are well below acceptable standards (mean VIF = 1.59). We also examined response variables for spatial autocorrelation. 4. Results on bike commuting We begin by ranking the top 25 counties in the US by the percentage of workers that bike to work as their primary means of transportation. Yolo County, California has the greatest percentage of bike commuters (7.5%). Nested in Yolo County is the city of Davis, recognized by the League of American Bicyclists as the most bike-friendly community in the country, with a robust cycling infrastructure, and a vast network of bike lanes, paths, and grade-separated bicycle crossings. A disproportionate number of counties in the top 25 are located in Colorado, with Hinsdale County having the highest percentage of bike commuters (2.79%). On natural environment, built environment, and socioeconomic predictors, data show that counties in Colorado scored significantly higher than average on natural amenities (4.08 vs. 0.029, t = 14.55, p < 0.01) educational attainment (25.89 vs.16.31, t = 6.11, p < 0.01), and Green Party voting (5.46 vs. 1.86, t = 7.79, p < 0.01), with residents spending significantly less time journeying to work (20.28 vs. 22.49 min, t = 3.029, p < 0.01). In the next phase, the percentage of bike commuters for the contiguous US is mapped at the county scale. Map 1 divides the percent distribution of county bike commuters into four intervals, with dark green indicating counties with a percentage of bike commuters at least 1.5 standard deviations above the national average, and red indicating an observed percentage at least 0.50 standard deviations below the national average Counties with lower percentages of bike commuters concentrate in the southern and Appalachian states of Alabama, Mississippi, Georgia, Tennessee, Kentucky, North Carolina and West Virginia1. 1 According to Centers for Disease Control data (
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Conversely, counties with high percentages of bike commuters concentrate in the geographic west, stretching from the Pacific Northwest down through California, over to Arizona and New Mexico, up through Colorado, and encircling lower commuter counties in portions of Wyoming, Utah, and Idaho. These Sunbelt states have higher natural amenities scores, topographical variation, and temperate climate conducive to cycling. Next, we model the count distribution of bike commuters at the county scale. As previously stated, because our dependent variable is a non-negative integer exhibiting significant over-dispersion with a disproportionate number of zero counts, we analyze the data using a zero-inflated negative binomial (ZINB) regression model. The independent variables are loaded incrementally, beginning with a baseline model that simply predicts the count of bike commuters by the number of workers in a county area, ending with a fully saturated model of built environment, natural environment, socioeconomic, and civic
Table 1 Zero-Inflated negative binomial regression models predicting bike commuter count Model 1
Model 2
Model 3
Model 4
4.78*** (0.11)
4.88*** (0.11)
2.91*** (0.15)
2.82*** (0.15)
2.63e05*** (1.10e06)
1.99e05*** (1.14e06)
1.46e05*** (9.80e07)
1.38e05*** (9.80e07)
4.62e04 (2.8e04) 3.35e04*** (1.0e04) 0.08*** (0.01)
1.85e03*** (3.21e04) 4.48e04*** (1.01e04) 0.09*** (0.05)
8.09e04*** (2.75e04) 4.57e04*** (1.01e-04) 0.07*** (0.01)
1.29e03*** (2.88e04) 4.14e04*** (9.81e05) 0.06*** (0.01)
0.12*** (0.01) 0.15*** (0.04)
0.03*** (0.01) 0.05 (0.04)
0.02** (0.01) 0.08** (0.04)
0.03*** (4.09e03) 1.34e05*** (1.02e06) 0.01*** (2.20e03)
0.02*** (4.23e03) 1.12e05*** (1.05e06) 0.01*** (2.21e03)
Constant
Baseline variable Total workers Built environment variables Population density HAP emissions per capita Mean journey to work (in minutes) Natural environment variables Natural amenities scale National parks and forests Socioeconomic variables Percent college educated Median home value Percent hispanic Civic variables Bike organizations Environmental non-profits Percent green party Inflated model Constant Total workers Population density Mean journey to work (in minutes)
0.08*** (0.03) 9.56e03 (9.01e02) 0.07*** (0.01) 5.47*** (0.50) 2.74e04*** (2.70e05) 0.01*** (2.00e03) 0.12*** (0.01)
2.52*** (0.26) 2.61e04*** (2.46e05) 0.01*** (1.92e03) 0.12*** (0.01) .0014638 (0.03)
1.44*** (0.47) 2.28e04*** (2.23e05) 0.01*** (1.87e03) 0.10*** (0.01) 0.03 (0.27) 0.06*** (0.02)
1.55*** (0.40) 2.12e04*** (2.14e05) 0.01*** (1.88e03) 0.10*** (0.01) 0.04 (0.03) 0.02 (0.02) 0.29*** (0.06)
0.33*** (0.03) 1.38 (0.04)
0.22*** (0.03) 1.25 (0.04)
0.02 (0.03) 1.02 (0.03)
0.01 (0.03) 0.99 (0.03)
Natural amenities scale Percent college educated Percent green party /lnalpha Alpha
Standard errors are in parentheses. Null test of coefficient equal to zero. p < 10. p < 05. *** p < 01. *
**
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Table 2 ZINB regression coefficients expressed as percent change in expected count for bike commuters for every unit and standard deviation increase in predictors Model 1 Unit D
rD
Model 2 Unit D
rD
Model 3 Unit D
rD
Model 4 Unit D
rD
Baseline variable Total workers
0.0
408.2
0.0
241.8
0.0
147.3
0.0
135.2
Built environment variables Population density HAP emissions per capita Mean journey to work
0.0 0.0 8.1
6.8 8.7 37.4
0.2 0.0 8.5
30.3 11.5 39.1
0.1 0.0 6.3
12.3 11.7 30.6
0.1 0.0 5.8
20..3 10.5 28.4
12.4 15.6
30.3 11.1
2.7 4.9
6.2 3.5
2.3 8.7
5.2 4.4
2.9 0.0 0.7
22.5 71.7 8.5
2.4 0.0 0.7
18.7 57.8 9.4
8.6 1.0 7.4
6.9 0.2 14.3
0.0 1.1 10.4 4.2 2.1 25.4
100.0 378.2 74.4 9.7 14.3 42.3
Natural environment variables Natural amenities scale National parks and forests Socioeconomic variables Percent college educated Median home value Percent hispanic Civic variables Bike organizations Environmental non-profits Percent green party Inflated model Total workers Population density Mean journey to work Natural amenities scale Percent college educated Percent green party Nonzero observations Zero observations Vuong z Pr > z Log-likelihood Full model: Cragg and Uhler’s R2: AIC AIC*n BIC BIC0 N
0.0 1.3 12.6
100.0 534.2 93.6
2323 651 12.72 0.00 12474.98 0.59 8.40 24969.97 1244.90 2612.69 2974
0.0 1.3 12.4 0.1
100.0 505.4 92.0 0.3
2323 651 12.57 0.00 12365.89 0.62 8.58 24757.79 1050.71 2806.88 2974
0.0 1.3 10.4 3.5 5.8
100.0 526.6 73.4 8.1 35.0 2323 651 13.27 0.00 12122.736 0.68 8.16 24279.47 596.38 3261.21 2974
2248 644 13.69 0.00 11682.28 0.69 8.09 23406.56 483.539 3201.08 2871
variables (Tables 1 and 2). For ease of interpretation, Table 2 expresses model coefficients as percent changes in the expected count of bike commuters for unit and standard deviation changes in independent predictors and fit statistics for the models The full model attained a pseudo-R2 value of explains 68.5%. We concentrate interpretation on fully saturated model 4. We begin with built environment variables. For every additional minute of average journey time, the expected count of bike commuters decreased by 5.8% (where p < 0.01). Since journey time is closely connected to directness of travel, proximity, and patterns of residential settlement, the significant effect of journey time on cycling behavior reinforces existing literature on the importance of the built environment in determining healthy modes of transport. Similarly, the expected count of bike commuters decreases with observed emissions of hazardous air pollutants (b = 0.0004, p < 0.01). A standard deviation shift of 267 lbs of HAP emissions per person reduces the expected count by 10.5%. Finally, as population density levels increase so too does the level of bike commuting (b = 0.001, p < 0.01). Results in Tables 1 and 2 also show that the natural environment operates on cycling behavior predictably. Locales with higher natural amenities and opportunities for outdoor recreation are home to higher counts of bike commuters. Specifically, areas with warm winter seasons, temperate summers, low summer humidity, and topographic variation have higher counts of workers that cycle to work as their primary means of transportation (b = 0.02, p < 0.05). All things held equal, the presence of a national park or forest increases the expected count of bike commuters by 8.7% (where p < 0.05). However, this effect is small relative to that of the built environment. It takes only two minutes of time added to work journeys to erase the location advantages of national park or forest proximity. Small changes in the compactness of urban form can statistically eliminate natural environment characteristics favorable to healthy modes of transportation. All socioeconomic measures behave as expected. Rates of bike commuting increase with the percentage of college educated residents (b = 0.02, p < 0.01), median home values (b = 1.12e05, p < 0.01), and the percentage of Hispanic population (0.01, p < 0.01). More specifically, a unit increase in the percentage of residents with a college education increases the expected count of bike commuters by 2.4%. In other words, increasing the level of human capital in a locality significantly increases the likelihood that residents will choose to bike to work as their primary means of transportation. Civic capital
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Map 2. Geography of walk commuters (%) at the county scale, 2000.
variables also predict the level of bike commuting in a county. In localities with higher percentages of Green Party voters (b = 00.07, p < 0.01) observe higher counts of bike commuters are observed - a 1% increase in support for Ralph Nader and the Green Party platform was associated with a 7.4% increase in the expected bike commuting count. Finally, results show that the presence of a bike club or bike advocacy organization, as inventoried by the League of American Bicyclists, increases the count of bike commuters by 8.6%. Results relating to the civic composition variables suggest that bike commuting is partially explained by political and organizational dynamics, adjusting for measures of built, natural, and socioeconomic environment. 5. Results on walk commuting The same analytic sequence is used to analyze variation in walk commuting. We begin by ranking the Top 25 counties in the US on the percentage of workers 16 + that walk to work as their primary means of transportation. With a few exceptions, the top 25 counties on walk commuting have relatively small labor forces – in fact, 19 of the Top 25 counties have less than 10,000 employed workers. Prairie County, Montana leads all counties with 30% of its labor force walking to work as a primary means of transportation. All of Montana’s 56 counties are in the top half of the distribution on walk commuting, with four counties joining Prairie in the Top 25. The higher concentration of walk commuters in Montana may be attributable to natural and built environment characteristics conducive to walking. Montanans (on average) spend 16 min in work journeys compared to 22.55 min for the rest of the country, and are blessed with a natural environment favorable to walk commuting, scoring above the nation in both the count of national parks and forests (0.25 per county vs 0.19) and socially desirable natural amenities (1.29 vs 0.03, as measured by the natural amenities scale). Map 2 displays the spatial distribution of walk commuting in the US. As with bike commuting, the percent distribution of walk commuters is divided into four standard deviation intervals. Counties with a percentage of walk commuters at least 1.5 standard deviations above the national average are colored in dark green, and counties with a percentage of walk commuters 0.50 standard deviations below the national average are colored in red. Lower percentages of walk commuters are located in the Southeastern and Midwestern portions of the country. Higher values are observed in upstate New York, Vermont, and counties bordering Canada in Minnesota, North Dakota, and Montana. Somewhat surprising, localities in these high walk commuter states score lower than average on measures of population density and urbanization, variables commonly assumed to increase the walkability of localities.2
2 This counter-intuitive result may be explained as a problem of spatial scale. In general, the population density of a county is an adequate proxy for the compactness of spatial form. However, in Montana, counties are much larger than average in size (square miles), with populations concentrated in neighborhoods that constitute a small percentage of total county land area. In other words, the density of livable space is significantly higher than the observed density of the county. Prairie County, for example has a population of 1199 people, distributed in 511 block groups – 2.34 persons per block. The variance around the mean is 21.79 persons, implying that local population is distributed unevenly in Prairie County, with some block groups home to a disproportionate number of residents. In fact, about half of the population of Prairie County resides in 7.79% of 511 block groups.
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Table 3 Negative binomial regression models predicting walk commuter count Model 1
Model 2
Model 3
Model 4
6.71*** (0.07)
6.70*** (0.07)
5.55*** (0.09)
5.50*** (0.09)
1.83e05*** (7.50e07)
1.68e05*** (8.00e07)
1.34e05*** (7.20e07)
1.27e05*** (7.17e07)
1.35e03*** (2.00e04) 3.69e04*** (4.40e05) 0.06*** (2.80e03)
1.71e03*** (2.20e04) 3.83e04*** (4.30e05) 0.06*** (2.80e03)
9.64e04*** (2.00e04) 3.65e04*** (4.00e05) 0.05*** (3.10e03)
1.56e03*** (2.05e04) 3.32e04*** (4.11e05) 0.04*** (3.07e03)
0.01* (0.01) 0.15*** (0.03)
0.03*** (0.01) 0.10*** (0.03)
0.03*** (0.01) 0.12*** (0.03)
0.03*** (3.00e03) 7.84e06*** (6.80e07) 3.66e04 (1.20e03)
0.01*** (3.08e03) 5.27e06*** (6.88e07) 4.51e04 (1.24e03)
0.60 (0.02) 0.55 (0.01)
0.01 0.13 0.02 (0.07) 0.13*** (0.01) 0.64 (0.03) 0.53 (0.03)
Constant
Baseline variable Total workers Built environment variables Population density HAP emissions per capita Mean journey to work Natural environment variables Natural amenities scale National parks and forests Socioeconomic variables Percent college educated Median home value Percent hispanic Civic variables Walk organizations Environmental non-profits Percent green party /lnalpha Alpha
0.44 (0.02) 0.65 (0.02)
0.45 (0.02) 0.64 (0.02)
Standard errors are in parentheses. Null test of coefficient equal to zero p < .10. ** p < .05. *** p < .01. *
Next, the count distribution of walk commuters is modeled at the county scale. We concentrate our analysis on the fully saturated model 4 of built environment, natural environment, socioeconomic, and civic capital variables (see Tables 3 and 4). Table 4 reports model coefficients as percent changes in the expected count of walk commuters for unit and standard deviation changes in independent predictors. Beginning with built environment variables, results show that the expected count of walk commuters in a county increases significantly with population density (b = 0.002, p < 0.01). As predicted, hazardous air pollutant and journey-to-work measures are inversely related to the count of walk commuters. Specifically, the addition of 1 minute to the average time required for work travel decreases the expected count of walk commuters by 4.2%, where p < 0.01. Similarly, a standard deviation increase in HAP emissions per capita (267 lbs per person) reduces the forecasted count of walkers by 8.5%. In counties with high urban congestion, the majority (or plurality) of HAP emissions come from on-road mobile sources from licensed motor vehicles. These variables appear to reinforce each other – as vehicles miles traveled increase, hazardous air pollutants increase, aggravating the willingness of workers to walk to their place of employment. With regard to natural environment variables, results show that proximity to national parks and forests increases the expected count of walk commuters (b = 0.12, p < 0.01). Somewhat puzzling, the natural amenities scale is negatively associated with the count of walk commuters (b = 0.03, p < 0.01). In Model 3, the coefficient on natural amenities behaves as expected - it is positive (b = 0.013) and statistically significant, where p < .10. With the inclusion of socioeconomic variables (in Model 4) of median home value, percent college educated, and percent Hispanic population, the amenities scale measure flips statistical sign. This finding reinforces the point that a favorable climate or natural environment alone is insufficient to increase the expected count of walkers in county. Important socioeconomic and civic capital characteristics of a locality can meaningfully transform the value of a locality’s natural capital abundance. Socioeconomic and civic capital variables behave as hypothesized. Results indicate that a percent increase in the number of college educated persons in a county increases the expected count of walk commuters by 1.4%. The wealth of a locality (as measured by median home value) is positively associated with the count of walk commuters. For example, a $10,000
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Table 4 Negative Binomial Regression Coefficients Expressed as Percent Change in Expected Count for Walk Commuters for Every Unit and Standard Deviation Increase in Predictors Model 1 Unit D
rD
Model 2 Unit D
rD
Model 3 Unit D
rD
Model 4 Unit D
rD
Baseline variable Total workers
0.0
210.9
0.0
182.2
0.0
129.5
0.0
120.5
Built environment variables Population density HAP emissions per capita Mean journey to work
0.1 0.0 5.6
21.3 9.5 27.5
0.2 0.0 5.7
27.8 9.9 27.7
0.1 0.0 4.7
14.8 9.4 23.4
0.2 0.0 4.2
25.0 8.5 21.2
1.3 16.4
2.9 11.6
3.2 10.6
7.2 7.5
3.2 12.6
7.1 6.3
2.5 0.0 0.0
19.8 37.2 0.4
1.4 0.0 0.0
10.8 23.9 0.5
0.9 1.5 13.7
0.1 0.3 27.3
Natural environment variables Natural amenities scale National parks and forests Socioeconomic variables Percent college educated Median home value Percent Hispanic Civic variables Walk Organizations Environmental non-profits Percent green party LR chi2 Prob > chi2 Log-likelihood Full Model: Cragg and Uhler0 s R2: AIC AIC*n BIC BIC0 N
3314.73 0.00 20688.56 0.67 13.92 41389.11 17640.05 3282.74 2974
3364.05 0.00 20663.89 0.68 13.90 41343.79 18293.53 3316.07 2974
3866.45 0.00 20412.70 0.73 13.74 40847.39 17128.31 3794.47 2974
3889.760 0.00 19726.91 0.74 13.65 39481.82 16517.02 3794.12 2878
increase in median home value boosts the expected count of walk commuters by 5.6%, where p < 0.01. Walk commuting is also partially explained by the spatial distribution of Green Party voters. For every unit increase the percent of persons voting for Ralph Nader, we observe a 13.7% increase in the expected count of walk commuters. The causal direction of the relationship between Green politics and walk commuting is unknowable (given the cross-sectional research design). However, our results clearly show that the spatial distribution in walk commuting is strongly associated with green politics, adjusting for features of the natural and constructed environment. Overall, the full model had by Cragg and Uhler’s pseudo-R2 of about 74%. 6. Conclusion The study shows that cycling and walking transport behaviors depend on the built, natural, socioeconomic, and civic environment of a locality. Statistical relationships observed provide useful information to local transport and health planners aiming to encourage non-motorized commuting behaviors. First, local natural environmental characteristics influence the spatial distribution of healthy modes of transportation. Temperate summers, low humidity, and topographic variation are natural capital characteristics that increase expected counts of persons that bicycle or walk to work. Second, while the natural environment acts as an important foundation for soft modes of transportation like walking and cycling, features of the built environment such as local connectivity (directness of travel), proximity (straight-line distance between trip origins and destinations), and patterns of residential sprawl are equally important predictors of cycling and walking to work. As vehicle miles traveled increase, populations are exposed to higher amounts of on-road hazardous air pollutants (Pollard, 2003). Our results show that lower density settlements, longer trip journeys, and higher air pollution levels, significantly reduce expected counts of cycling and walking commuters. Third, local socioeconomic and civic conditions influence observed levels of walk and bike commuting. Localities with high human capital, wealth, and organizational infrastructure have significantly higher counts of walkers and bikers. More specifically, the presence of cycling and pedestrian organizations can increase the odds of walking and biking. Such organizations provide education, outreach, and group events that encourage physical activity. Acknowledgements Portions of the data collected for this research was supported under Award No. NA03OAR4310164 by the National Oceanic and Atmospheric Administration (NOAA), US Department of Commerce. The statements, findings, conclusions,
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