Faculty and Staff

Anthropology librarian Juliann Couture and Joyce Martin, curator of the Labriola National American Indian Data Center, looking at the Center's display of unique Hopi Kachina dolls. Four of the kachinas (Navan Kachina; Talavi Kachina; Flute Kachina; and Ahöla Kachina) were created by artist, carver, and former ASU employee Tony Dukepoo as a gift to the libraries in 1979. The kachina dolls are on display in the Labriola Center located on the 2nd floor of the Hayden Library on ASU's Tempe campus.

The Simon Ortiz and Labriola Center Lecture on Indigenous Land, Culture, and Community addresses topics and issues across disciplines in the arts, humanities, sciences, and politics. Underscoring Indigenous American experiences and perspectives, this Series seeks to create and celebrate knowledge that evolves from an Indigenous worldview that is inclusive and that is applicable to all walks of life.” Professor Simon Ortiz discussed the overall nature of the Series, especially emphasizing the global nature of Indigenous concerns. Joyce Martin and Matthew Harp elaborated on the contributions of the Labriola National American Indian Data Center and ASU Libraries to the Series.

The Labriola Center hosts an informal event in Hayden Library which facilitates close interaction between the featured speaker and audience members. The ASU Libraries records the evening lectures which take place at the Heard Museum and presents both an audio podcast and streaming video of each lecture on the ASU Library Channel webpage. This lecture series provides not only a chance for community discussion at the events themselves, but through the innovative use of technology the ASU Libraries enables additional forums for discussion in blogs and web pages which choose to link to the streaming videos.

The Future of Wastewater Sensing workshop is part of a collaboration between Arizona State University Center for Nanotechnology in Society in the School for the Future of Innovation in Society, the Biodesign Institute’s Center for Environmental Security, LC Nano, and the Nano-enabled Water Treatment (NEWT) Systems NSF Engineering Research Center. The Future of Wastewater Sensing workshop explores how technologies for studying, monitoring, and mining wastewater and sewage sludge might develop in the future, and what consequences may ensue for public health, law enforcement, private industry, regulations and society at large. The workshop pays particular attention to how wastewater sensing (and accompanying research, technologies, and applications) can be innovated, regulated, and used to maximize societal benefit and minimize the risk of adverse outcomes, when addressing critical social and environmental challenges.

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.

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.