BIG DATA AND BIG CITIES: THE PROMISES AND LIMITATIONS OF IMPROVED MEASURES OF URBAN LIFE

New, “big data” sources allow measurement of city characteristics and outcome variables at higher collection frequencies and more granular geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big urban data has the most value for the study of cities when it allows measurement of the previously opaque, or when it can be coupled with exogenous shocks to people or place. We describe a number of new urban data sources and illustrate how they can be used to improve the study and function of cities. We first show how Google Street View images can be used to predict income in New York City, suggesting that similar imagery data can be used to map wealth and poverty in previously unmeasured areas of the developing world. We then discuss how survey techniques can be improved to better measure willingness to pay for urban amenities. Finally, we explain how Internet data is being used to improve the quality of city services. (JEL R1, C8, C18)     

Influence measure for the L1 regression

Because outliers and leverage observations unduly affect the least squares regression, the identification of influential observations is considered an important and integrai part of the analysis. However, very few techniques have been developed for the residual analysis and diagnostics for the minimum sum of absolute errors, L₁ regression. Although the L₁ regression is more resistant to the outliers than the least squares regression, it appears that outliers (leverage) in the predictor variables may affect it. In this paper, our objective is to develop an influence measure for the L₁ regression based on the likelihood displacement function. We illustrate the proposed influence measure with examples.     

Statistics in Latin America

Statistics is a relatively new discipline in Latin America. At present only sparse information is available about the state of the statistical profession in Latin America. We present and summarize the available information about the job market; training, education, and research; professional societies and their activities; and so forth. We also share our hopes, concerns, and suggestions for the future.     

A scope-of-practice survey leading to the development of standards of practice for health promotion in higher education

To review and analyze the scope of practice of health promotion services and draft standards of quality indicators for higher education communities, the American College Health Association (ACHA) initiated a Task Force on Health Promotion in Higher Education in May 1996. Members of the task force developed a National Survey on Health Promotion and Education in Institutions of Higher Education and mailed the survey to a stratified random sample of 600 ACHA member institutions, as well as to 97 key "best-practice health promotion leaders". The larger sample produced a 75.3% response rate, and 90.7% of the key informants returned usable surveys. The authors report selected findings from both groups that chronicle the state of health promotion practice in higher education at the close of the 20th century. The task force used the findings to establish a data-driven framework for the Year 2001 Standards of Practice for Health Promotion in Higher Education.     

On tail-ordering and comparison of failure rates

In this paper, we have studied some implications between tail-ordering (also known as dispersive ordering) and failure rate ordering (also called TP₂ ordering) of two probability distribution functions. Based on independent random samples from these distributions, a class of distribution-free tests has been proposed for testing the null hypothesis that the two life distributions are identical against the alternative that one failure rate is uniformly smaller than the other. The tests have good efficiencies as compared to their competitors.     

A response to M. Stone: 'How not to measure the efficiency of public services (and how one might)'

Summary.  This is a response to Stone's criticisms of the Spottiswoode report to the UK Treasury which was responding to the Treasury's request for improved methods to evaluate the efficiency and productivity of the 43 police districts in England and Wales. The Spottiswoode report recommended uses of data envelopment analysis (DEA) and stochastic frontier analysis (SFA), which Stone critiqued en route to proposing an alternative approach. Here we note some of the most serious errors in his criticism and inaccurate portrayals of DEA and SFA. Most of our attention is devoted to DEA, and to Stone's recommended alternative approach without much attention to SFA, partly because of his abbreviated discussion of the latter. In our response we attempt to be constructive as well as critical by showing how Stone's proposed approach can be joined to DEA to expand his proposal beyond limitations in his formulations.     

The effects of correlation among observations on the consistency property of sample variance

It is well known that even when the sample observations are correlated and not normal the sample variance, S² converges in probability to E(S²). But the required sample size for S² to be a consistent estimator of E(S²) is an open question. Some light is shed on this question in this paper. In particular the relation between the rate of convergence and the correlation property of the observations is explored. It is shown that the retardation to the rate of convergence is not appreciable if the correlation is moderate but it can be severe for extreme correlations.     

Partial least squares estimator for single-index models

The partial least squares (PLS) approach first constructs new explanatory variables, known as factors (or components), which are linear combinations of available predictor variables. A small subset of these factors is then chosen and retained for prediction. We study the performance of PLS in estimating single-index models, especially when the predictor variables exhibit high collinearity. We show that PLS estimates are consistent up to a constant of proportionality. We present three simulation studies that compare the performance of PLS in estimating single-index models with that of sliced inverse regression (SIR). In the first two studies, we find that PLS performs better than SIR when collinearity exists. In the third study, we learn that PLS performs well even when there are multiple dependent variables, the link function is non-linear and the shape of the functional form is not known.     

On vendor part delivery dates in a stochastic assembly system

This paper describes the development of a model for the determination of optimal mean part delivery dates in a stochastic assembly system for the objective of minimizing the expected cost of subassembly and part inventory. Parts are assembled at each station to a subassembly. The part delivery and processing times at assembly stations follow known probability distributions. An approximate solution technique based on the optimization of individual stations in isolation is developed. The approximation applies a correction factor, as a function of the variability in part delivery and processing time, cost parameters and number of stations, to the decisions from the single station solutions to compensate for interdependence between stations. Results indicate that this is an effective approach and yields good near-optimal solutions with very little computational effort. Insights regarding the effect of the type of distribution used, random processing times, variance of the distribution used and cost parameter values on part delivery dates are also reported.