Studies have previously found a significant relationship between student writing center usage and demographic factors including gender, GPA, and English-language proficiency (Salem, 2015). Additional research has been conducted on writing center outcomes and student conceptions and misconceptions of writing centers as academic resources. However, previous scholarship has attested to the need for continuous research into writing center usage patterns and the factors that affect them. This will allow centers to make the necessary changes and improvements to become more accessible and inclusive for the benefit of all students. The present research contributes to the ongoing discussion about why students choose to use or not use the writing center and how their identities and pre-existing ideas about the center inform this decision. Further, it addresses research gaps by surveying students in an honors college setting at a large public university and considering new decision-making factors such as race, mental health, and social stigma. By comparing students demographics and impressions of the Barrett Writing Center (BWC) on the ASU campus, the study draws conclusions about the significant gap between positive perception and usage, the influence of social anxiety and stigma amongst honors students, the successes and failures of tutoring for second language English speakers, and the benefit derived by students who attend multiple writing center sessions. Suggestions to improve the BWC and guide future research are offered based on these observations and significant trends in the data.

There is a need for indicators of transportation-land use system quality that are understandable to a wide range of stakeholders, and which can provide immediate feedback on the quality of interactively designed scenarios. Location-based accessibility indicators are promising candidates, but indicator values can vary strongly depending on time of day and transfer wait times. Capturing this variation increases complexity, slowing down calculations. We present new methods for rapid yet rigorous computation of accessibility metrics, allowing immediate feedback during early-stage transit planning, while being rigorous enough for final analyses. Our approach is statistical, characterizing the uncertainty and variability in accessibility metrics due to differences in departure time and headway-based scenario specification. The analysis is carried out on a detailed multi-modal network model including both public transportation and streets. Land use data are represented at high resolution. These methods have been implemented as open-source software running on commodity cloud infrastructure. Networks are constructed from standard open data sources, and scenarios are built in a map-based web interface. We conclude with a case study, describing how these methods were applied in a long-term transportation planning process for metropolitan Amsterdam.

Accessibility is increasingly used as a metric when evaluating changes to public transport systems. Transit travel times contain variation depending on when one departs relative to when a transit vehicle arrives, and how well transfers are coordinated given a particular timetable. In addition, there is necessarily uncertainty in the value of the accessibility metric during sketch planning processes, due to scenarios which are underspecified because detailed schedule information is not yet available. This article presents a method to extend the concept of "reliable" accessibility to transit to address the first issue, and create confidence intervals and hypothesis tests to address the second.