A traveler visiting Rio, Manila or Caracas does not need a report to learn that these cities are unequal; she can see it directly from the taxicab window. This is because in most cities inequality is conspicuous, but also, because cities express different forms of inequality that are evident to casual observers. Cities are highly heterogeneous and often unequal with respect to the income of their residents, but also with respect to the cleanliness of their neighborhoods, the beauty of their architecture, and the liveliness of their streets, among many other evaluative dimensions. Until now, however, our ability to understand the effect of a city's built environment on social and economic outcomes has been limited by the lack of quantitative data on urban perception. Here, we build on the intuition that inequality is partly conspicuous to create quantitative measure of a city's contrasts. Using thousands of geo-tagged images, we measure the perception of safety, class and uniqueness; in the cities of Boston and New York in the United States, and Linz and Salzburg in Austria, finding that the range of perceptions elicited by the images of New York and Boston is larger than the range of perceptions elicited by images from Linz and Salzburg. We interpret this as evidence that the cityscapes of Boston and New York are more contrasting, or unequal, than those of Linz and Salzburg. Finally, we validate our measures by exploring the connection between them and homicides, finding a significant correlation between the perceptions of safety and class and the number of homicides in a NYC zip code, after controlling for the effects of income, population, area and age. Our results show that online images can be used to create reproducible quantitative measures of urban perception and characterize the inequality of different cities.

/pdf/the-collaborative-image-of-the-city-mapping-the-inequality-2w3f08ossq.pdf

The Collaborative Image of The City: Mapping the Inequality of Urban Perception

An exploration of social networking site use, multitasking, and academic performance among United States and European university students

Based upon the self-regulated learning theory, this study examined the relationships between academic achievement and three key self-regulatory constructs - prior domain knowledge, self-efficacy, and the use of learning strategies - in two flipped undergraduate math courses. Structural equation modeling was employed as the primary method to analyze the relationships in both the pre-class and in-class learning environments of the flipped courses. The results of the study showed that students' self-efficacy in learning math and the use of help seeking strategies were all significantly positively related with academic achievement in both pre- and in-class learning environments. In addition, students' self-efficacy in collaborative learning had a positive impact on their use of help seeking strategies during in-class learning. The theoretical and instructional implications are discussed.

The role of self-regulated learning in students' success in flipped undergraduate math courses

http://www.gisclimat.fr/sites/default/files/Tisseuil_publi_JournalofHydrology_2010.pdf

Statistical downscaling of river flows

Objectives To identify characteristics of general practices associated with emergency hospital admission rates, and determine whether levels of performance and patient reports of access are associated with admission rates.

Design A cross-sectional study.

Setting Two primary care trusts (Leicester City and Leicestershire County and Rutland) in the East Midlands of England.

Participants 145 general practices.

Methods Hospital admission data were used to calculate the rate of emergency admissions from 145 practices, for two consecutive years (2006/7 and 2007/8). Practice characteristics (size, distance from principal hospital, quality and outcomes framework performance data, patient reports of access to their practices) and patient characteristics (deprivation, ethnicity, gender and age), were used as predictors in a two-level hierarchical model, developed with data for 2007/8, and evaluated against data for 2006/7.

Results Practice characteristics (shorter distance from hospital, smaller list size) and patient characteristics (higher proportion of older people, white ethnicity, increasing deprivation, female gender) were associated with higher admission rates. There was no association with quality and outcomes framework domains (clinical or organisation), but there was an association between patients reporting being able to see a particular general practitioner (GP) and admission rates. As the proportion of patients able to consult a particular GP increased, emergency admission rates declined.

Conclusions The patient characteristics of deprivation, age, ethnicity and gender are important predictors of admission rates. Larger practices and greater distance from a hospital have lower admission rates. Being able to consult a particular GP, an aspect of continuity, is associated with lower emergency admission rates.

https://emj.bmj.com/content/emermed/28/7/558.full.pdf

Characteristics of general practices associated with emergency admission rates to hospital: a cross-sectional study

Introduction to Statistical Concepts

Chapter 1. Assurance Technologies, Profits, and Managing Safety-Related Risks. 1.1 Introduction. 1.2 Cheaper, Better, and Faster Products. 1.3 What is System Assurance. 1.4 Key Management Responsibilities. 1.5 Is System Assurance a Process?. 1.6 System Assurance Programs. References. Further Reading. Chapter 2. Introduction to Statistical Concepts. 2.1 Probabilistic Designs. Construction of a histogram and the empirical distribution. 2.2 Computing reliability. Failure rate and hazard function. 2.3 Normal Distribution. 2.4 Log Normal Distribution. 2.5 Exponential Distribution. 2.6 Weibull Distribution. Plotting the data. 2.8 Discrete Distributions. 2.9 Topics for Student Projects and Theses. References. Further Reading. Chapter 3. Reliability Engineering and Safety-related Applications. 3.1 Reliability Principles. 3.2 Reliability in the Design Phase. 3.3 Reliability in the Manufacturing Phase. 3.4 Reliability in the Test Phase. 3.5 Reliability in the Use Phase. 3.6 Reliability and Safety Commonalities. 3.7 Topics foe Student Projects and Theses. References. Further Reading. Chapter 4. Maintainability Engineering and Safety-related Applications. 4.1 Maintainability Engineering Principles. 4.2 Maintainability during the Design Phase. 4.3 Maintainability in the Manufacturing Stage. 4.4 Maintainability in the Test Stage. 4.5 Maintainability in the Use Stage. 4.6 Maintainability and System Safety. 4.7 Topics for Student Projects and Theses. References. Further Reading. Chapter 5. System Safety Engineering. 5.1 System Safety Principles. 5.2 System Safety in Design. 5.3 System Safety in manufacturing. 5.4 System Safety in the Test Stage. 5.5 System Safety in the Use Stage. 5.6 Analyzing System Hazards and Risks. 5.7 Hazard Identification. 5.8 Topics for Student Projects and Theses. References. Further Reading. Chapter 6. Quality Assurance Engineering and Preventing Latent Safety Defects. 6.1 Quality Assurance Principles. 6.2 Quality Assurance in the Design Phase. 6.3.Evaluation of pilot run. 6.4 Quality Assurance in the Test Phase. 6.5 Quality Assurance in the Use Phase. 6.6 Topics for Student Projects and Theses. References. Further Reading. Chapter 7. Logistics Support Engineering and System Safety Considerations. 7.1 Logistics Support Principles. 7.2 Logistics Engineering during the Design Phase. documentation. 7.3 Logistics Engineering during the Manufacturing Phase. 7.4 Logistics Engineering during the Test Phase. 7.5 Logistics Engineering in the Use Phase. 7.6 Logistics Support Engineering and System Safety. 7.7 Topics for Student Projects and Theses. References. Further Reading. Chapter 8. Human Factors Engineering and System Safety Considerations. 8.1 Human Engineering Principles. 8.2 Human Factors in the Design Phase. 8.3 Human Factors in the Manufacturing Phase. 8.4 Human Factors in the Test Phase. 8.5 Human Factors in the Use Phase. 8.6 Additional considerations involving Human Factors and System Safety. 8.7 Real Time and Latent Errors. 8.8 Analyses in Support of Human Factors and System Safety. 8.9 Topics for Student Projects and Theses. References. Further Reading. Chapter 9. Software Performance Assurance. 9.1 Software Performance Principles. 9.2 Software Performance in the Design Phase. 9.3 Software Requirements during Coding and Integration. 9.4 Software Testing. 9.5 Software Performance in the Use Stage. 9.6 Topics for Student Projects and Theses. References. Chapter 10. System Effectiveness. 10.1 Introduction. 10.2 System Effectiveness Principles. 10.3 Implementing the Programs. 10.4 Managing by Life-Cycle Costs. 10.5 System Effectiveness Model. 10.6 Author?s Recommendation. 10.7 System Risk and Effects on System Effectiveness. 10.8 Topics for Student Projects and Theses. References. Further Reading. Additional Reading. Chapter 11. Managing Safety-Related Risks. 11.1 Establish the Appropriate Safety Program to Manage Risk. 11.2 Programs to Address Product, Process, and System Safety. 11.3 Resource Allocation and Coast Analysis in Safety Management. 11.4 Topics for Student Projects and Theses. 11.5 System Safety-related Literature. Chapter 12. Statistical Concepts, Loss Analysis, and Safety-Related Applications. 12.1 Use of Distributions and Statistical Applications Associated with Safety. 12.2 Statistical Analysis Techniques used within Safety Analysis. 12.3 Using Statistical Control in Decision-Making for Safety. 12.4 Behavior Sampling. 12.5 Calculating Hazardous Exposures to the Human System. 12.6 Topics for Student Projects and Theses. Further Reading. Chapter 13. Models, Concepts and Examples: Applying Scenario-Driven Hazard Analysis. 13.1 Adverse Sequences. 13.2 Designing Formats for Conducting Analysis and Reporting Results. 13.3 Documentation Reports. 13.4 Conceptual Models. 13.5 Lifecycle of a System Accident. 13.6 Operating and Support Hazard Analysis Example. 13.7 Topics for Student Projects and Theses. Additional Reading and Reference Sources. Chapter 14. Automation, Computer, and Software Complexities. 14.1 Complex Systems Analysis. 14.2 System Context. 14.3 Understanding the Adverse Sequence. 14.4 Additional Software Safety Analysis Techniques. 14.5 True Redundancy. 14.6 Complexities and Hazards within Computer Hardware. 14.7 Initiators, contributors, the Errors Associated with Software. 14.8 Other Specialized Techniques, Analysis Methods, and Tools for Evaluating Software and Computer Systems. 14.9 Existing Legacy Systems, Reusable Software, Commercial Off-the-Shelf Software (COTS) and Non-Development Items (NDI). 14.10 Topics for Student Projects and Theses. Additional References.

Assurance technologies : principles and practices : a product, process, and system safety perspective

http://www.opsalacarte.com/pdfs/Tech_Papers/System_Safety_Engineering_Presentation_by_Dev_Raheja,Opsalacarte.pdf

Dev G. Raheja

Papers

Introduction to Statistical Concepts

Assurance technologies : principles and practices : a product, process, and system safety perspective

System Safety Engineering

Reliability Engineering and Safety‐Related Applications