Table 3. Random-intercept factional logit regression results on tract-level percentage workers biking to work (BTW).
| Variables | Rural tracts | Urban tracts | t-test for differencea p-value | ||
|---|---|---|---|---|---|
|
| |||||
| Marginal effects | p-value | Marginal effects | p-value | ||
| Tract pop. density (1,000/sq mile) | 0.013 | 0.0331 | -0.004 | <.0001 | 0.1515 |
| Median housing age | 0.006 | <.0001 | 0.015 | <.0001 | <.0001 |
| Tract intersection density/sq mile | 0.000 | 0.9855 | 0.001 | <.0001 | 0.3411 |
| Tract % area green canopy | -0.004 | <.0001 | -0.005 | <.0001 | 0.8936 |
| Average distance to 7 closest parks | 0.001 | 0.1993 | -0.004 | 0.0118 | 0.0412 |
| EPA poor air quality status | 0.026 | 0.3963 | -0.204 | <.0001 | 0.9964 |
| Tract med. income (in $1,000) | -0.003 | 0.0005 | -0.005 | <.0001 | 0.3285 |
| Tract Gini coefficient (%) | 0.000 | 0.9037 | 0.001 | 0.2667 | <.0001 |
| Tract med. housing value (in $10,000) | 0.008 | <.0001 | 0.002 | <.0001 | 0.1578 |
| Tract % housing owner-occupied | -0.770 | <.0001 | -0.883 | <.0001 | 0.005 |
| County total crime/1,000 people | 0.001 | 0.0662 | 0.003 | 0.0002 | 0.0088 |
| Tract % 16+ commuting 1 hour+ | -0.007 | <.0001 | -0.009 | <.0001 | 0.2884 |
| Tract % living in college dorms | -0.003 | 0.0007 | -0.002 | 0.0029 | 0.127 |
| Tract % living in military quarters | -0.002 | 0.6729 | -0.008 | 0.0185 | 0.8277 |
| Tract median age | -0.008 | <.0001 | -0.005 | <.0001 | 0.0033 |
| Tract % Asians | 0.003 | 0.2665 | -0.004 | <.0001 | 0.2265 |
| Tract % Blacks | -0.003 | <.0001 | -0.002 | <.0001 | 0.0162 |
| Tact % Hispanics | -0.001 | 0.4612 | 0.002 | <.0001 | 0.0063 |
| Tract % foreign-born | -0.002 | 0.1831 | 0.001 | 0.3207 | 0.0946 |
| Tract % 25+ college educated | 0.010 | <.0001 | 0.014 | <.0001 | 0.0359 |
| R2 | 0.15 | 0.18 | |||
| R2 environmental variables only | 0.05 | 0.07 | |||
| R2 non-environmental variables only | 0.13 | 0.16 | |||
Note: The random-intercept fractional logit models were estimated with BTW prevalence specified between 0 and 1. However, the marginal effects were multiplied by 100 for ease of presentation. As an example, a marginal effect of -0.770 for tract percentage of owner-occupied housing should be interpreted as: On average, a one percent increase in tract percentage of owner-occupied housing was associated with a 0.770% decrease in prevalence of BTW in rural tracts.
a
t-tests evaluated the significance of the difference between rural tracts and urban tracts.