How accessibility and transportation options affect neighborhood livability: evidence from the 2017 oregon livability survey

ABSTRACT

Promoting accessibility and expanding transportation options are at the center of many livability programs. Using the 2017 Oregon Livability Survey administered in three regions in Oregon, this paper studies the livability impacts of accessibility and transportation options in the broader context of residential preferences and choices. Findings show that residents valued accessibility and transportation options in both livability conceptualization and their residential choice process within a similar a hierarchy of preferences. Expanding accessibility and transportation options can enhance livability, but its effectiveness is limited if other situational factors critical to residents’ residential choice are not adequately addressed.

KEYWORDS:

• Livability
• accessibility
• transportation options
• housing choice
• residential preference

Acknowledgements

The Department of Land Conservation and Development provided initial funding to seed the project. A team of student researchers contributed to data collection through Community Planning Workshop: Sadie DiNatale, Tyler Green, Brandon Pike, Jenna Whitney, and Seth Thompson. Brook Eastman provided GIS support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. We chose these three regions and excluded Portland, the largest city in Oregon, for two reasons. We believe that these smaller urban regions likely exhibit livability contributions from neighborhood accessibility and transportation options that are distinctive from large urban regions such as Portland. Additionally, there is already a high volume of research on Portland and considerably less research on other regions in the state.

2. The potential bias in the sample representativeness is similar across the three regions. Pooling the cases from all three regions together does not affect the outcomes of the statistical analyses as the stratified analyses by region produced similar results. We only report the pooled data analyses in this paper.

3. Private automobile travel is often the default transportation choice for most Americans. This research focuses on the characteristics of the built environments that support transportation options beyond the private automobile transportation. The survey questionnaire defines transportation options as walking, biking, and using public transit for trips to various destinations. This definition is similar to the one adopted by the MPOs considered in the study except that we excluded car share and bike share from the option set since they are not directly connected the planning realm at the center of our discussions.

4. The term ‘land use mix’ was defined in the survey as having non-residential land uses in one’s neighborhood. Specifically, four types of land use mix were provided to the respondents: 1. ‘Only residential in my neighborhood’; 2. ‘Residential surrounding my house but a mix of residential, retail, and services further away is okay’; 3. ‘A mix of residential, retail, services in my neighborhood’; 4 ‘A mix of residential, retail and office in my neighborhood’. Given the low frequencies of the 3^rd and 4^th types of land use mix in the responses, we combined these two types of land use mix as one category, ‘land use mix throughout the neighborhood,’ reported in Table 3.

5. The survey questionnaire did not include some housing choice factors – housing affordability, crime level, housing characteristics, and school quality – in questions that ask respondents to rate the importance levels of environmental characteristics for an ideal livable neighborhood. Thus, not all factors displayed in Figure 3 have comparable data values.

6. We compared the outputs from GLM logistic regression models using unweighted and weighted data. The results were similar in terms of regression coefficients and model fit. The regression results reported in Table 6 are from models using unweighted observations.

Additional information

Funding

This work was supported by the National Institute for Transportation and Communities [NITC-RR-1050].