From buildings to cities: techniques for the multi-scale analysis of urban form and function

The built environment is a significant factor in many urban processes, yet direct measures of built form are seldom used in geographical studies. Representation and analysis of urban form and function could provide new insights and improve the evidence base for research. So far progress has been slow due to limited data availability, computational demands, and a lack of methods to integrate built environment data with aggregate geographical analysis. Spatial data and computational improvements are overcoming some of these problems, but there remains a need for techniques to process and aggregate urban form data. Here we develop a Built Environment Model of urban function and dwelling type classifications for Greater London, based on detailed topographic and address-based data (sourced from Ordnance Survey MasterMap). The multi-scale approach allows the Built Environment Model to be viewed at fine-scales for local planning contexts, and at city-wide scales for aggregate geographical analysis, allowing an improved understanding of urban processes. This flexibility is illustrated in the two examples, that of urban function and residential type analysis, where both local-scale urban clustering and city-wide trends in density and agglomeration are shown. While we demonstrate the multi-scale Built Environment Model to be a viable approach, a number of accuracy issues are identified, including the limitations of 2D data, inaccuracies in commercial function data and problems with temporal attribution. These limitations currently restrict the more advanced applications of the Built Environment Model

A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics

The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics, namely the prediction of genetic interactions and protein functions, to demonstrate their efficacy on real-world datasets and draw useful conclusions about their behavior. These methods include simple aggregation, meta-learning, cluster-based meta-learning, and ensemble selection using heterogeneous classifiers trained on resampled data to improve the diversity of their predictions. We present a detailed analysis of these methods across 4 genomics datasets and find the best of these methods offer statistically significant improvements over the state of the art in their respective domains. In addition, we establish a novel connection between ensemble selection and meta-learning, demonstrating how both of these disparate methods establish a balance between ensemble diversity and performance.Comment: 10 pages, 3 figures, 8 tables, to appear in Proceedings of the 2013 International Conference on Data Minin

Regional Coalitions for Healthcare Improvement: Definition, Lessons, and Prospects

Outlines how regional quality coalitions can collaborate to help deliver evidence-based healthcare; improve care processes; and measure, report, and reward results. Includes guidelines for starting and running a coalition and summaries of NRHI coalitions

Inequality Adjustment Criteria for the Human Development Index

Our goal is to analyse the inequality aspects of Human Development Index and to propose a new aggregation function that can adjust it by considering inequality penalisation. We take into account inequality across dimensions and across individuals and three laws of inequality penalisation: decreasing, constant and increasing. At the beginning, we describe the features of standard Human Development Index and after we survey main analytical contributions regarding the inequality adjustment of Human Development Index. Successively, we decline the basic properties of the human development indices and also we present specific properties enjoyed by the aggregation function proposed: the Inequality Adjusted Exponential Mean (IAEM). This function is a specific case of the generalised mean. Three are the innovative aspects of IAEM function not enjoyed by the others ones. Firstly, the domain of IAEM function is unlimited. Secondly, IAEM function enjoys the property of incomplete compensability. Thirdly, with IAEM function it is possible to build three different rating and ranking classification according to the laws of penalisation. Finally, we apply the IAEM function to the database with 32 countries, developing and developed. According to the results, the Inequality Adjusted Human Development Index built by the IAEM function is significantly different from the standard Human Development Index built by the arithmetic mean, especially for the cases of decreasing and increasing penalisation. Moreover there is a negative correlation between the level of standard Human Development Index and the Penalisation Index, both in terms of rating and ranking.inequality, human development index, aggregation functions

A Law of Large Numbers for Weighted Majority

Consider an election between two candidates in which the voters' choices are random and independent and the probability of a voter choosing the first candidate is $p>1/2$. Condorcet's Jury Theorem which he derived from the weak law of large numbers asserts that if the number of voters tends to infinity then the probability that the first candidate will be elected tends to one. The notion of influence of a voter or its voting power is relevant for extensions of the weak law of large numbers for voting rules which are more general than simple majority. In this paper we point out two different ways to extend the classical notions of voting power and influences to arbitrary probability distributions. The extension relevant to us is the ``effect'' of a voter, which is a weighted version of the correlation between the voter's vote and the election's outcomes. We prove an extension of the weak law of large numbers to weighted majority games when all individual effects are small and show that this result does not apply to any voting rule which is not based on weighted majority