Lin, Xiongbin; Maclachlan, Ian; Ren, Ting; Sun, Feiyang. (2019). Quantifying Economic Effects of Transportation Investment Considering Spatiotemporal Heterogeneity in China: A Spatial Panel Data Model Perspective. The Annals Of Regional Science, 63(3), 437 – 459.
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Abstract
Transportation investment plays a significant role in promoting economic development. However, in what scenario and to what extent transportation investment can stimulate economic growth still remains debatable. For developing countries undergoing rapid urbanization, answering these questions is necessary for evaluating proposals and determining investment plans, especially considering the heterogeneity of spatiotemporal conditions. Current literature lacks systematical research to consider the impacts of panel data and spatial correlation issue in examining the economic effects of transportation investment. To fill this gap, this study collects provincial panel data in China from 1997 to 2015 to evaluate multi-level temporal and spatial effects of transportation investment on economic growth by using spatial panel data analysis. Results show that transportation investment leads to significant and positive effects on growth and spatial concentration of economic activities, but these results vary significantly depending on the temporal and spatial characteristics of each province. The economic impacts of transportation investment are quite positive even considering the time lag effects. This study suggests that both central and local governments should carefully evaluate the multifaceted economic effects of transportation investment, such as a balanced transportation investment and economic development between growing and lagging regions, and considering the spatiotemporal heterogeneity of the economic environment.
Keywords
High-speed Rail; Infrastructure Investment; Causal Relationship; Empirical-analysis; Growth; Impact; Productivity; Efficiency; Spillover; Agglomeration; C33; R40; R58; Spatial Analysis; Time Lag; Urbanization; Transportation; Heterogeneity; Economic Growth; Economic Models; Economic Impact; Data Analysis; Spatial Data; Panel Data; Economic Development; Developing Countries--ldcs; Investments; Economic Analysis; Investment; Local Government; China
Shang-hsien Hsieh; Ken-yu Lin; Nai-wen Chi; Hsien-tang Lin. (2015). Domain Knowledge-Based Information Retrieval for Engineering Technical Documents. Ontology In The AEC Industry. A Decade Of Research And Development In Architecture, Engineering And Construction, chapter 1.
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Abstract
Technical documents with complicated structures are often produced in architecture/engineering/construction (AEC) projects and research. Information retrieval (IR) techniques provide a possible solution for managing the ever-growing volume and contexts of the knowledge embedded in these technical documents. However, applying a general-purpose search engine to a domain-specific technical document collection often produces unsatisfactory results. To address this problem, we research the development of a novel IR system based on passage retrieval techniques. The system employs domain knowledge to assist passage partitioning and supports an interactive concept-based expanded IR for technical documents in an engineering field. The engineering domain selected in this case is earthquake engineering, although the technologies developed and employed by the system should be generally applicable to many other engineering domains that use technical documents with similar characteristics. We carry out the research in a three-step process. In the first step, since the final output of this research is an IR system, as a prerequisite, we created a reference collection which includes 111 earthquake engineering technical documents from Taiwan's National Center for Research on Earthquake Engineering. With this collection, the effectiveness of the IR system can be further evaluated onceit is developed. In the second step, the research focuses on creating a base domain ontology using an earthquake-engineering handbook to represent the domain knowledge and to support the target IR system with the knowledge. In step three, the research focuses on the semantic querying and retrieval mechanisms and develops the OntoPassage approach to help with the mechanisms. The OntoPassage approach partitions a document into smaller passages, each with around 300 terms, according to the main concepts in the document. This approach is then used to implement the target domain knowledge-based IR system that allows users to interact with the system and perform concept-based query expansions. The results show that the proposed domain knowledge-based IR system can achieve not only an effective IR but also inform search engine users with a clear knowledge representation.
Keywords
Architecture; Construction; Engineering; Knowledge Based Systems; Ontologies (artificial Intelligence); Query Processing; Search Engines; Knowledge Representation; Concept-based Query Expansions; Base Domain Ontology; Earthquake Engineering; General-purpose Search Engine; Aec Projects; Architecture/engineering/construction Projects; Complicated Structures; Technical Documents; Domain Knowledge-based Information Retrieval
Lindell, Michael K.; House, Donald H.; Gestring, Jordan; Wu, Hao-Che. (2019). A Tutorial on Dynasearch: A Web-Based System for Collecting Process-Tracing Data in Dynamic Decision Tasks. Behavior Research Methods, 51(6), 2646 – 2660.
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Abstract
This tutorial describes DynaSearch, a Web-based system that supports process-tracing experiments on coupled-system dynamic decision-making tasks. A major need in these tasks is to examine the process by which decision makers search over a succession of situation reports for the information they need in order to make response decisions. DynaSearch provides researchers with the ability to construct and administer Web-based experiments containing both between- and within-subjects factors. Information search pages record participants' acquisition of verbal, numeric, and graphic information. Questionnaire pages query participants' recall of information, inferences from that information, and decisions about appropriate response actions. Experimenters can access this information in an online viewer to verify satisfactory task completion and can download the data in comma-separated text files that can be imported into statistical analysis packages.
Keywords
Downloading; Text Files; Tasks; Access To Information; Statistics; Dynamic Decision Making; Process Tracing; Web-based Experiments; Information Search; Human-behavior; Eye-tracking; Choice; Expectations; Strategies; Mousetrap; Software; Time
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
Moudon, Anne Vernez; Huang, Ruizhu; Stewart, Orion T.; Cohen-Cline, Hannah; Noonan, Carolyn; Hurvitz, Philip M.; Duncan, Glen E. (2019). Probabilistic Walking Models Using Built Environment and Sociodemographic Predictors. Population Health Metrics, 17(1).
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Abstract
BackgroundIndividual sociodemographic and home neighborhood built environment (BE) factors influence the probability of engaging in health-enhancing levels of walking or moderate-to-vigorous physical activity (MVPA). Methods are needed to parsimoniously model the associations.MethodsParticipants included 2392 adults drawn from a community-based twin registry living in the Seattle region. Objective BE measures from four domains (regional context, neighborhood composition, destinations, transportation) were taken for neighborhood sizes of 833 and 1666 road network meters from home. Hosmer and Lemeshow's methods served to fit logistic regression models of walking and MVPA outcomes using sociodemographic and BE predictors. Backward elimination identified variables included in final models, and comparison of receiver operating characteristic (ROC) curves determined model fit improvements.ResultsBuilt environment variables associated with physical activity were reduced from 86 to 5 or fewer. Sociodemographic and BE variables from all four BE domains were associated with activity outcomes but differed by activity type and neighborhood size. For the study population, ROC comparisons indicated that adding BE variables to a base model of sociodemographic factors did not improve the ability to predict walking or MVPA.ConclusionsUsing sociodemographic and built environment factors, the proposed approach can guide the estimation of activity prediction models for different activity types, neighborhood sizes, and discrete BE characteristics. Variables associated with walking and MVPA are population and neighborhood BE-specific.
Keywords
Walking; Confidence Intervals; Research Funding; Transportation; Logistic Regression Analysis; Built Environment; Socioeconomic Factors; Predictive Validity; Receiver Operating Characteristic Curves; Data Analysis Software; Descriptive Statistics; Psychology; Washington (state); Active Travel; Home Neighborhood Domains; Physical Activity; Physical-activity; United-states; Life Stage; Adults; Attributes; Health; Associations; Destination; Pitfalls
Launching the Inspire Fund: An early step for CBE’s Office of Research “For a small college, CBE has a broad range of research paradigms, from history and arts, to social science and engineering.” — Carrie Sturts Dossick, Associate Dean of Research Upon taking on the role of Associate Dean of Research, Carrie Sturts Dossick, professor in the Department of Construction Management, undertook listening sessions to learn about the research needs of faculty, staff and students across the College of Built…
Julie Kriegh, researcher with the Carbon Leadership Forum and other CBE research centers, and owner of Kriegh Architecture Studios, collaborated with other CBE faculty and external partners to lead a UW CBE studio course in collaboration with Google that developed and delivered a design proposal for a sustainable data center. CBE collaborators included Hyun Woo “Chris” Lee, P.D. Koon Professorship in Construction Management; Jan Whittington, Associate Professor of the Department of Urban Design and Planning, and Director of the Urban…
Urban systems, system complexity, big data, artificial intelligence, smart cities, communities, and coupled human-built-environmental systems
Established in 1989 through two legislative programs, the WCRER compiles real estate sale transaction data, rental market statistics, and development metrics throughout the State of Washington. From this, the WCRER also develops affordable housing metrics for the state with data published in quarterly Washington State Housing Market Reports, a twice yearly Washington State Apartment Market Report, and the Washington Housing Market Data Toolkit. The WCRER also provides bespoke data driven research, educational outreach programs, and policy guidance to professional organizations consistent with its public service mandate.
The Washington Center for Real Estate Research (WCRER) was initially established by the Board of Regents at Washington State University (WSU) to provide a bridge between academic study and research on real estate topics and the professional real estate industries. It served that mission at WSU until merging with the Runstad Center at the beginning of 2012. WCRER works with faculty to ensure their rigorous research is accessible and easily usable by industry participants, the media and the general public, regardless of their statistical sophistication.
WCRER aims to provide credible research, value-added information, education services and project-oriented research to real estate licensees, real estate consumers, real estate service providers, institutional customers, public agencies, and communities in Washington state and the Pacific Northwest region.
The Washington Center for Real Estate Research is a key provider of real estate research and data across the State of Washington. The Center is primarily funded by the State, hence its central role in the provision of quality and robust data and market reports. Among its core activities are the Quarterly Washington State Housing Market Report and the semi-annual Apartment Market Survey for the State Department of Licensing.
The Center is active across a range of other research projects and works closely with stakeholders both across the University of Washington with the public and private sectors.
The Urban Form Lab (UFL) research aims to affect policy and to support approaches to the design and planning of more livable environments. The UFL specializes in geospatial analyses of the built environment using multiple micro-scale data in Geographic Information Systems (GIS). Current research includes the development of novel GIS routines for performing spatial inventories and analyses of the built environment, and of spatially explicit sampling techniques. Projects address such topics as land monitoring, neighborhood and street design, active transportation, non-motorized transportation safety, physical activity, and access to food environments.
Research at the UFL has been supported by the U.S. and Washington State Departments of Transportation, the Centers for Disease Control and Prevention, the Robert Wood Johnson Foundation, the National Institutes of Health, and local agencies.
The Urban Form Lab is directed by Anne Vernez Moudon, Dr es Sc, a leading researcher and educator in quantifying the properties of the built environment as related to health and transportation behaviors. Philip M. Hurvitz, PhD, a veteran of geographic information science and data processing, leads data management and GIS work.