Detection and prediction of urban archetypes at the pedestrian scale: computational toolsets, morphological metrics, and machine learning methods

Granular, dense, and mixed-use urban morphologies are hallmarks of walkable and vibrant streets. However, urban systems are notoriously complex and planned urban development, which grapples with varied interdependent and oft conflicting criteria, may — despite best intentions — yield aberrant morphologies fundamentally at odds with the needs of pedestrians and the resiliency of neighbourhoods. This work addresses the measurement, detection, and prediction of pedestrian-friendly urban archetypes by developing techniques for high-resolution urban analytics at the pedestrian scale. A spatial-analytic computational toolset, the cityseer-api Python package, is created to assess localised centrality, land-use, and statistical metrics using contextually sensitive workflows applied directly over the street network. cityseer-api subsequently facilitates a review of mixed-use and street network centrality methods to improve their utility concerning granular urban analysis. Unsupervised machine learning methods are applied to recover ‘signatures’ — urban archetypes — using Principal Component Analysis, Variational Autoencoders, and clustering methods from a high-resolution multi-variable and multi-scalar dataset consisting of centralities, land-uses, and population densities for Greater London. Supervised deep-learning methods applied to a similar dataset developed for 931 towns and cities in Great Britain demonstrate how, with the aid of domain knowledge, machine-learning classifiers can learn to discriminate between ‘artificial’ and ‘historical’ urban archetypes. These methods use complex systems thinking as a departure point and illustrate how high-resolution spatial-analytic quantitative methods can be combined with machine learning to extrapolate benchmarks in keeping with more qualitatively framed urban morphological conceptions. Such tools may aid urban design professionals in better anticipating the outcomes of varied design scenarios as part of iterative and scalable workflows. These techniques may likewise provide robust and demonstrable feedback as part of planning review and approvals processes

Mapping healthcare IT

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 56-58).In this thesis I have developed a map of Healthcare Information Technology applications used in the United States for care delivery, healthcare enterprise management, clinical support, research and patient engagement. No attempt has previously been made to develop such a taxonomy for use by healthcare policy makers and on-the-spot decision makers. Using my own fifteen years of experience in HIT, along with an extensive set of literature reviews, interviews and on-site research I assembled lists of applications and organized them into categories based on primary workflows. Seven categories of HIT systems emerged, which are Practice Tools, Advisory Tools, Financial Tools, Remote Healthcare Tools, Clinical Research Tools, Health 2.0 Tools and Enterprise Clinical Analytics, each of which have different operational characteristics and user communities. The results of this pilot study demonstrate that a map is possible. The draft map presented here will allow researchers and investors to focus on developing the next generation of HIT tools, including software platforms that orchestrate a variety of healthcare transactions, and will support policy makers as they consider the impact of Federal funding for HIT deployment and adoption. Further studies will refine the map, adding an additional level of detail below the seven categories established here, thus supporting tactical decision making at the hospital and medical practice level.by William Charles Richards Crawford.S.M

Global Talentship: Toward a Decision Science Connecting Talent to Global Strategic Success

It is widely accepted that global competitive advantage frequently requires managing such complex situations that traditional organization and job structures are simply insufficient. Increasingly, in order to create a flexible and integrated set of decisions that balance local flexibility with global efficiency, organizations must rely on more social, informal and matrix-based shared visions among managers and employees. Research on global strategic advantage, global organizational structures, and even shared mindsets has suggested that dimensions of culture, product and function provide a valuable organizing framework. However, typical decisions about organization structure, HRM practices and talent often remain framed at such a high level as to preclude their solution. We maintain that there is often no logical answer to such questions as, “Should the sales force be local or global?” or “Should product authority rest with the countries or the corporate center?” However, we propose that embedding business processes or value chains within a Culture and Product matrix provides the necessary analytic detail to reveal otherwise elusive solutions. Moreover, by linking this global process matrix to a model that bridges strategy and talent, it is possible to identify global “pivotal talent pools,” and to target organizational and human resource investments toward those talent areas that have the greatest impact on strategic advantage. We demonstrate the Value-Chain, Culture and Product (VCCP) matrix using several examples, and discuss future research and practical implications, particularly for leadership and leadership development