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The R environment for statistical computing and graphics (R Development Core Team, 2008) offers practitioners a rich set of statistical methods ranging from random number generation and optimization methods through regression, panel data, and time series methods, by way of illustration. The standard R distribution (base R) comes preloaded with a rich variety of functionality useful for applied econometricians. This functionality is enhanced by user-supplied packages made available via R servers that are mirrored around the world. Of interest in this chapter are methods for estimating nonparametric and semiparametric models. We summarize many of the facilities in R and consider some tools that might be of interest to those wishing to work with nonparametric methods who want to avoid resorting to programming in C or Fortran but need the speed of compiled code as opposed to interpreted code such as Gauss or Matlab by way of example. We encourage those working in the field to strongly consider implementing their methods in the R environment thereby making their work accessible to the widest possible audience via an open collaborative forum.

The library metadata of the twenty-first century is moving toward a linked data model. BIBFRAME, which stands for Bibliographic Framework Initiative, was launched in 2011 with the goal to make bibliographic descriptions sharable and interoperable on the web. Since its inception, BIBFRAME development has made remarkable progress. The focus of BIBFRAME discussions has now shifted from experimentation to implementation. The library community is collaborating with all stakeholders to build the infrastructure for BIBFRAME production in order to provide the environment where BIBFRAME data can be easily created, reused, and shared. This chapter addresses library community's BIBFRAME endeavors, with the focus on Library of Congress, Program for Cooperative Program, Linked Data for Production Phase 2, and OCLC. This chapter discusses BIBFRAME's major differences from the MARC standard with the hope of helping metadata practitioners get a general understanding of the future metadata activity. While the BIBFRAME landscape is beginning to take shape and its practical implications are beginning to develop, it is anticipated that MARC records will continue to be circulated for the foreseeable future. Upcoming multistandard metadata environments will bring new challenges to metadata practitioners, and this chapter addresses the required knowledge and skills for this transitional and multistandard metadata landscape. Finally, this chapter explores BIBFRAME's remaining challenges to realize the BIBFRAME production environment and asserts that BIBFRAME's ultimate goal is to deliver a value-added next-web search experience to our users.

Data fusion and the combination of multiple data sources have been part of travel survey processes for some time. In the current context, where technologies and information systems spread and become more and more diverse, the transportation community is getting more and more interested in the potential of data fusion processes to help gather more complete datasets and help give additional utility to available data sources. Research is looking for ways to enhance the available information by using both various data collection methods and data from various sources, surveys or observation systems. Survey response rates are decreasing over the world, and combining survey modes appears to be an interesting way to address this problem. Letting interviewees choose their survey mode allows increasing response rates, but survey mode could impact the data collected. This paper first discusses issues rising when combining survey modes within the same survey and presents a method to merge the data coming from different survey modes, in order to consolidate the database. Then, it defines and describes the data fusion process and discusses how it can be relevant for transportation analysis and modelling purposes. Benefiting from the availability of various datasets from the Greater Montréal Area and the Greater Lyon Area, some applications of data fusion are constructed and/or reproduced to illustrate and test some of the methods described in the literature.

We extend Vuong’s (1989) model-selection statistic to allow for complex survey samples. As a further extension, we use an M-estimation setting so that the tests apply to general estimation problems – such as linear and nonlinear least squares, Poisson regression and fractional response models, to name just a few – and not only to maximum likelihood settings. With stratified sampling, we show how the difference in objective functions should be weighted in order to obtain a suitable test statistic. Interestingly, the weights are needed in computing the model-selection statistic even in cases where stratification is appropriately exogenous, in which case the usual unweighted estimators for the parameters are consistent. With cluster samples and panel data, we show how to combine the weighted objective function with a cluster-robust variance estimator in order to expand the scope of the model-selection tests. A small simulation study shows that the weighted test is promising.

Like pro-bono service of law and other professional practices, micro-credentialing has become the quasi-charity project of the rapidly commodifying higher education (HE) sector. Produced with minimum waste and expense, qualifications through micro-credentialing economises time spent and optimises efforts. As a result, these bite-size educational offerings are becoming abundant and attractive to both learners and providers. While learners learn on the job or in the job market, providers get an opportunity to package knowledge and skills. Among a plethora of courses with potential overlapping and mismatched competencies, however, a clear pathway to target a career through pastures of micro-credentialing is yet to emerge. If not, it may result in another system of skill poverty and thus alienate the essential autonomy of credential seekers. Autonomy or self-tailoring your own package of micro-credentials (MC) without a proper wayfinding system could be overwhelming, sometimes even counterproductive, if competency management is based on a one-size-fits-all model. Via a local case, this chapter reviews current MC available to capacitate a career using Construction Management (CM) as a point of departure. It aims to develop a wayfinding framework or user-friendly menu for those wanting to craft a career via a micro-credentialing ecosystem. Broadly, it investigates navigating various MC offerings equivalent to undergraduate qualifications needed to become a CM professional recognised by accreditation bodies. In the process, it systematically discourses multiple ontologies of micro-credentialing in links, lapses, overlaps, and intersections among competencies to untangle complexities and optimise opportunities. It was hoped that learners could target a career instead of matching a job profile in the micro-credentialing universe.

This paper tests the feasibility and empirical implications of a spatial econometric model with a full set of interaction effects and weight matrix defined as an equally weighted group interaction matrix applied to research productivity of individuals. We also elaborate two extensions of this model, namely with group fixed effects and with heteroskedasticity. In our setting, the model with a full set of interaction effects is overparameterised: only the SDM and SDEM specifications produce acceptable results. They imply comparable spillover effects, but by applying a Bayesian approach taken from LeSage (2014), we are able to show that the SDEM specification is more appropriate and thus that colleague interaction effects work through observed and unobserved exogenous characteristics common to researchers within a group.

New computer-assisted techniques for visualizing data are evolving in a number of areas in transportation. For example, in engineering, 3D visualization and microsimulation techniques are being applied for the identification and evaluation of geometric and operational solutions for improving visually impaired pedestrian access to roundabouts and channelized turn lanes. For planning, visualization is being used for corridor analysis. Data visualization is being used as a tool for improving decision-making within transit agencies, as well as a tool for understanding truck trip generation on highways. Many of these new techniques take advantage of archived intelligent transportation systems (ITS) data. Examples of other innovative data sources include global positioning systems (GPS), geographic information systems (GIS), computer-aided design (CAD), and a variety of visualization tools available for use with travel survey data. As these various techniques and software applications move forward, consideration needs to be given to how the “lessons learned” from these applications can facilitate the use of data visualization techniques for travel survey data analysis and decision-making.

This paper aims to investigate spillover effects of public capital stock in a production function model that accounts for spatial dependencies. In many settings, ignoring spatial dependency yields inefficient, biased and inconsistent estimates in cross country panels. Although there are a number of studies aiming to estimate the output elasticity of public capital stock, many of those fail to reach a consensus on refining the elasticity estimates. We argue that accounting for spillover effects of the public capital stock on the production efficiency and incorporating spatial dependences are crucial. For this purpose, we employ a spatial autoregressive stochastic frontier model based on a number of specifications of the spatial dependency structure. Using the data of 21 OECD countries from 1960 to 2001, we estimate a spatial autoregressive stochastic frontier model and derive the mean indirect marginal effects of public capital stock, which are interpreted as spillover effects. We found that spillover effects can be an important factor explaining variations in technical inefficiency across countries as well as in explaining the discrepancies among various levels of output elasticity of public capital stock in traditional production function approaches.

This chapter discusses the potential role of geographic information systems (GIS) for infection control within the hospital system. The chapter provides a brief overview of the role of GIS in public health and reviews current work applying these methods to the hospital setting. Finally, it outlines the potential opportunities and challenges for adapting GIS for use in the hospital setting for infection prevention. A targeted literature review is used to illustrate current use of GIS in the hospital setting. The discussion of complexity was compiled using the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework. Challenges and opportunities were then extracted from this exercise by the authors. There are multiple challenges to implementation of a Hospital GIS for infection prevention, mainly involving the domains of technology, organization, and adaptation. Use of a transdisciplinary approach can address many of these challenges. More research, specifically prospective, reproducible clinical trials, needs to be done to better assess the potential impact and effectiveness of a Hospital GIS in real-world settings. This chapter highlights a powerful but rarely used tool for infection prevention within the hospital. Given the importance of reducing hospital-acquired infection rates, it is vital to identify relevant methods from other fields that could be translated into the field of hospital epidemiology.