Peter Schmiedeskamp Archives

Whittington, Jan; Calo, Ryan; Simon, Mike; Jesse Woo; Meg Young; Schmiedeskamp, Peter. (2015). Push, Pull, and Spill: A Transdisciplinary Case Study in Municipal Open Government. Berkeley Technology Law Journal, 30(3), 1899 – 1966.

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Abstract

Municipal open data raises hopes and concerns. The activities of cities produce a wide array of data, data that is vastly enriched by ubiquitous computing. Municipal data is opened as it is pushed to, pulled by, and spilled to the public through online portals, requests for public records, and releases by cities and their vendors, contractors, and partners. By opening data, cities hope to raise public trust and prompt innovation. Municipal data, however, is often about the people who live, work, and travel in the city. By opening data, cities raise concern for privacy and social justice. This article presents the results of a broad empirical exploration of municipal data release in the City of Seattle. In this research, parties affected by municipal practices expressed their hopes and concerns for open data. City personnel from eight prominent departments described the reasoning, procedures, and controversies that have accompanied their release of data. All of the existing data from the online portal for the city were joined to assess the risk to privacy inherent in open data. Contracts with third parties involving sensitive or confidential data about residents of the city were examined for safeguards against the unauthorized release of data. Results suggest the need for more comprehensive measures to manage the risk latent in opening city data. Cities should maintain inventories of data assets, produce data management plans pertaining to the activities of departments, and develop governance structures to deal with issues as they arise--centrally and amongst the various departments--with ex ante and ex post protocols to govern the push, pull, and spill of data. In addition, cities should consider conditioned access to pushed data, conduct audits and training around public records requests, and develop standardized model contracts to protect against the spill of data by third parties. [ABSTRACT FROM AUTHOR]; Copyright of Berkeley Technology Law Journal is the property of University of California School of Law and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Keywords

Public Records; Open Data Movement; Acquisition Of Data; Ubiquitous Computing; Data Analysis; Social Justice

Schmiedeskamp, Peter; Zhao, Weiran. (2016). Estimating Daily Bicycle Counts in Seattle, Washington, from Seasonal and Weather Factors. Transportation Research Record, 2593, 94 – 102.

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Abstract

This paper examines the relationship between several seasonal and weather factors and bicycle ridership from 2 years of automated bicycle counts at a location in Seattle, Washington. The authors fitted a negative binomial model and then estimated quantities of interest using counterfactual simulation. The findings confirm the significance of season (+), temperature (+), precipitation (), as well as holidays (-), day of the week (+ for Monday through Saturday, relative to Sunday), and an overall trend (+). This paper improves on prior work by demonstrating the use of the negative binomial instead of a Poisson model, which is appropriate given the potential for overdispersion, as observed in these data. In addition to validating the significance of factors identified from the literature, this paper contributes methodologically through its intuitive visualization of effect sizes to nonstatistical audiences. The authors believe that the combination of model type and counterfactual simulation and visualization reflects a reasonable compromise between model complexity and interpretability. Results such as these can aid policy makers and planners in understanding bicycle travel demand elasticities and in guiding interventions aimed at increasing rates of bicycling. The methods presented are fully reproducible and invite adaptation to other locations.