Identifying opportunity places for urban regeneration through LBSNs

The use of location based social networks—LBSNs—for diagnosing phenomena in contemporary cities is evolving at a fast pace. However, methodological frameworks for informing urban regeneration at a fine-grain neighborhood scale through LBSNs is still by and large an unchartered territory, which this research seeks to address. This research bridges the knowledge gap by proposing a method to identify urban opportunity spaces for urban regeneration that involves pre-processing, analyzing and interpreting single and overlapped LBSN data. A two-fold perspective—people-based and place-based—is adopted. Data from four LBSNs—Foursquare, Twitter, Google Places and Airbnb—represent the people-based approach as it offers an insight into individual preferences, use and activities. The place-based approach is provided by an illustrative case study. Local unexpected nuances were gathered by the interlinking of data from different LBSNs, and opportunity places for urban regeneration have been recognized, as well as potential itineraries to boost urban liveliness and connectivity at both intra and inter- neighborhood scales. Findings show that overlapping data from various LBSNs enriches the analysis that would previously have relied on a single source.This work was supported by the Council of Education, Research, Culture and Sports – Generalitat Valenciana (Spain). Project: Valencian Community cities analyzed through Location-Based Social Networks and Web Services Data. Ref. no. AICO/2017/018

NASA university program management information system, FY 1985

The University Program Report provides current information and related statistics for approximately 4200 grants/contracts/cooperative agreements active during the reporting period. NASA Field Centers and certain Headquarters Program Offices provide funds for those research and development activities in universities which contribute to the mission needs of that particular NASA element. This annual report is one means of documenting the NASA-University relationship, frequently denoted, collectively, as NASA's University Program

Economics from the Top Down: Does Hierarchy Unify Economic Theory

What is the unit of analysis in economics? The prevailing orthodoxy in mainstream economic theory is that the individual is the ultimate unit of analysis. The implicit goal of mainstream economics is to root macro-level social structure in the micro-level actions of individuals. But there is a simple problem with this approach: our knowledge of human behavior is hopelessly inadequate for the task at hand. Faced with real-world complexities, economists are forced to make bold (and seldom tested) assumptions about human behavior in order to make models tractable. The result is theory that has little to do with the real world. This dissertation investigates an alternative approach to economics that I call economics from the top down. This approach begins with the following question: what happens when we take the analytical focus off of individuals and put it into social hierarchy? The effect of this analytical shift is that we are forced to deal with the realities of concentrated power. The focus on hierarchy leads to some surprising discoveries. First, I find evidence that hierarchical organization has a biophysical basis. I show that institution size (firms and governments) is strongly correlated with rates of energy consumption, and that the growth of institutions can be interpreted as the growth of social hierarchy. Second, I find that hierarchy plays an important role in shaping income and income distribution. I find that income scales strongly with hierarchical power (defined as the number of subordinates under ones control), and that hierarchical power affects income more strongly than any other factor measured. Lastly, using an empirically informed model of the hierarchical structure of US firms, I find that hierarchy plays a dominant role in shaping the income distribution tail. These results hint that hierarchy can be used to unify the study of economic growth (understood in biophysical terms) and income distribution. I conclude by making the first prediction of how the concentration of hierarchical power should relate to the growth of energy consumption. This prediction sheds new light on the origin of inequality. While this top down approach to economics is in its infancy, the results are encouraging. Focusing on hierarchy gives fresh insight into many of the important questions facing society insight that cannot be obtained by focusing on individuals

Geometric Collision Avoidance for Heterogeneous Crowd Simulation

Simulation of human crowds can create plausible human trajectories, predict likely flows of pedestrians, and has application in areas such as games, movies, safety planning, and virtual environments. This dissertation presents new crowd simulation methods based on geometric techniques. I will show how geometric optimization techniques can be used to efficiently compute collision-avoidance constraints, and use these constraints to generate human-like trajectories in simulated environments. This process of reacting to the nearby environment is known as local navigation and it forms the basis for many crowd simulation techniques, including those described in this dissertation. Given the importance of local navigation computations, I devote much of this dissertation to the derivation, analysis, and implementation of new local navigation techniques. I discuss how to efficiently exploit parallelization features available on modern processors, and show how an efficient parallel implementation allows simulations of hundreds of thousands of agents in real time on many-core processors and tens of thousands of agents on multi-core CPUs. I analyze the macroscopic flows which arise from these geometric collision avoidance techniques and compare them to flows seen in real human crowds, both qualitatively (in terms of flow patterns) and quantitatively (in terms of flow rates). Building on the basis of these strong local navigation models, I further develop many important extensions to the simulation framework. Firstly, I develop a model for global navigation which allows for more complex scenarios by accounting for long-term planning around large obstacles or emergent congestion. Secondly, I demonstrate methods for using data-driven approaches to improve crowd simulations. These include using real-world data to automatically tune parameters, and using perceptual user study data to introduce behavioral variation. Finally, looking beyond geometric avoidance based crowd simulation methods, I discuss methods for objectively evaluating different crowd simulation strategies using statistical measures. Specifically, I focus on the problem of quantifying how closely a simulation approach matches real-world data. I propose a similarity metric that can be applied to a wide variety of simulation approaches and datasets. Taken together, the methods presented in this dissertation enable simulations of large, complex humans crowds with a level of realism and efficiency not previously possible.Doctor of Philosoph

A FRAMEWORK FOR SOFTWARE RELIABILITY MANAGEMENT BASED ON THE SOFTWARE DEVELOPMENT PROFILE MODEL

Recent empirical studies of software have shown a strong correlation between change history of files and their fault-proneness. Statistical data analysis techniques, such as regression analysis, have been applied to validate this finding. While these regression-based models show a correlation between selected software attributes and defect-proneness, in most cases, they are inadequate in terms of demonstrating causality. For this reason, we introduce the Software Development Profile Model (SDPM) as a causal model for identifying defect-prone software artifacts based on their change history and software development activities. The SDPM is based on the assumption that human error during software development is the sole cause for defects leading to software failures. The SDPM assumes that when a software construct is touched, it has a chance to become defective. Software development activities such as inspection, testing, and rework further affect the remaining number of software defects. Under this assumption, the SDPM estimates the defect content of software artifacts based on software change history and software development activities. SDPM is an improvement over existing defect estimation models because it not only uses evidence from current project to estimate defect content, it also allows software managers to manage software projects quantitatively by making risk informed decisions early in software development life cycle. We apply the SDPM in several real life software development projects, showing how it is used and analyzing its accuracy in predicting defect-prone files and compare the results with the Poisson regression model