Homeownership trends among the never married

In recent years, the population of unmarried single adults has grown globally especially in the developed world. In this paper, we explore homeownership among never married singles in the US from 2000 to 2013 using a sample from the Current Population Survey. In particular, we investigate potential differences in the relationship between several homeownership determinants for the never married in comparison to the married. We also test for heterogeneous effects across education levels and ethnicity in homeownership determinants for the never married. Our results show that age, gender and number of children affect the probability of homeownership differently for singles compared to those who are married. We also find that while on average there is a higher probability of homeownership from 2007 onwards for singles, there are significant gender, education and racial differences. In particular, our results show that among the never married, those with at least a college education reverse the gender gap in homeownership.     

Drivers of land abandonment in Southern Chile and implications for landscape planning

In recent decades, land abandonment has become one of the most important trends in land use and cover change. This study identifies drivers of land abandonment in Southern Chile through a spatially explicit statistical model, based on an economic theoretical framework. A logistic regression was used to model the probability of land abandonment, observed as the land cover change from agricultural land to arboreous shrubland or native forest between 1985 and 2007. We relied on census information and land cover data from 2472 farms, which represented 86.6% of the farm population in the study area. Land cover data came from two Landsat scenes taken in 1985 (TM) and 2007 (ETM+). The results from the spatial analysis show that agricultural land covered 30 061 ha in 1985. By 2007, only 16 518 ha had persisted in agriculture, while the rest had been covered by natural vegetation. Soil quality was a significant benefit-related geophysical driver of land abandonment. Important cost related socioeconomic drivers were the distances to secondary roads, aquaculture production centers, and national parks, and the existence of farm subsidies. Significant farm structural variables were the amount of bovine heads and farm's livestock carrying capacity. Variables such as age, education, and place of residence of the farmer were not statistically significant. These results may serve as an important planning tool for land managers, by helping identify areas and farming systems with high probability of land abandonment, and furthermore explore the outcomes of some potential scenarios on the future probability of the process.     

State transition probability for the Markov Model dealing with on/off cooling schedule in dwellings

We gathered field measurement data on five familial and three single dwellings during summer 2000 by deploying numerous handy type hygrothermal meters with self-recording functions to measure room air, globe and outdoor air temperatures. These measurements led to conclusions on the probability of turning on an air conditioning system versus indoor globe temperature and the ongoing probability of air conditioning versus outdoor temperature. This analysis was transformed into state transition probability functions, i.e. shifting from the off to on state and from the on to off state. Identifying these state transition probability functions is an important first step in applying the Markov Model to on/off state analysis for air conditioning systems, which is one of the significant approaches for dealing with the stochastic thermal load for HVAC system. The obtained state transition probability functions should help immeasurably in determining effective schedules for air conditioning operation from inhabitant occupancy schedules.     

Aging and Disability: Implications for the Housing Industry and Housing Policy in the United States

Problem: The elderly population of the United States is large and growing rapidly. Since disability rates increase with age, population aging will bring substantial increases in the number of disabled persons and have a significant impact on the nation's housing needs.

Purpose: We demonstrate the impact of population growth and aging on the projected number of households with at least one disabled resident and estimate the probability that a newly built single-family detached unit will have at least one disabled resident during its expected lifetime.

Methods: We calculate disability rates using two alternative measures of disability and construct projections of the number of households with at least one disabled resident. We develop and apply a technique for estimating the probability that a newly built single-family detached unit will house at least one disabled resident using data on the average lifespan of those units, the average length of residence for households occupying those units, and the projected proportion of households with at least one disabled resident.

Results and conclusions: Under our medium assumptions, we project that 21% of households will have at least one disabled resident in 2050 using our first disability measure (physical limitation) and 7% using our second (self-care limitation). We estimate that there is a 60% probability that a newly built single-family detached unit will house at least one disabled resident during its expected lifetime using our first measure, and a 25% probability using our second measure. When disabled visitors are accounted for, the probabilities rise to 91% and 53%, respectively. Given the desire of most people to live independently for as long as possible, these numbers reflect a large and growing need for housing units with features that make them accessible to disabled persons.

Takeaway for practice: The lack of accessible housing provides an opportunity for homebuilders to develop and market products that meet the needs of an aging population. In light of concerns about the civil rights of people with disabilities and the high public cost of nursing home care, housing accessibility is a critical issue for planners and policymakers as well. We believe planners should broaden their vision of the built environment to include the accessibility of the housing stock.

Research support: None.     

利用累积损伤模型分析场地地震响应的动力可靠性

本文将地震作用由随机场筒化为随机过程，针对软土地基的动力特征，结合可靠度理论与随机振动理论，运用累积损伤模型对场地非确定的地震响应进行了动力可靠性分析。     

Clusters galore: insights about environmental clusters from probability theory.

The posterior probability of a causal explanation given that an environmental cancer cluster is statistically significant depends on the prior probability of an environmentally caused cluster, the sensitivity of the statistical test and its specificity. The prior probability is low, because it is rare to have enough carcinogen in the general environment to cause a relative risk of cancer high enough to achieve statistical significance in a small geographic area. The sensitivity and specificity are not great. The likelihood that a census tract escapes statistically significant elevations in all 80 types of cancer can be calculated. Many of the thousands of census tracts will, by chance alone, have at least one type of cancer whose elevation is statistically significant. Actual observation from a large cancer registry confirms this probabilistic prediction. Applying the principles of Bayes' Theorem would suggest that most statistically significant environmental cancer clusters are not due to environmental carcinogens. One would have to investigate hundreds of environmental cancer clusters to find one with a true environmental cause.     

Renewable energy potential and adoption of renewable portfolio standards

Thirty states have adopted renewable portfolio standards (RPSs) that set targets for renewable energy generation by mandating electric power utilities obtain a minimum percentage of their retail load from renewable sources. To date, a number of studies have consistently found that political and economic factors impact RPS adoption. Studies have also examined the impact of renewable energy potential in a state on the probability of RPS adoption, but results have largely been statistically weak and inconclusive. After controlling for political and economic factors, we estimate that a one standard deviation increase in wind potential is associated with an approximately 4.2 percentage point increase in the probability of having an RPS, and a one standard deviation increase in solar potential is associated with a 6.1 percentage point increase in the probability of having an RPS.     

Reliability of an engineering system under a strong earthquake with application to offshore platforms

This paper presents a method developed for the performance evaluation of an engineering system during a strong earthquake. The seismic reliability of the system is evaluated by considering the different possible failure modes, and seismic reliability of the components of the system. The method is applied to the oil production system at the Statfjord Field in the North Sea. The objective of the analysis is to document the reliability of oil production from the field in the event of an exceptionally large earthquake, and to investigate the effect of spatial variation of earthquake loading parameters on the computed system reliability. It is shown that, with a realistic model of the spatial variation of earthquake motion, there is very little correlation between the earthquake loading parameters at different platform locations. The main consequence of this lack of correlation is that the computed failure probability of the oil production system under the design earthquake is about one order of magnitude smaller than the probability of failure of individual platforms at the Statfjord Field.     

不确定型层次分析的Kolmogorov检验软件系统设计

不确定型层次分析的单一准则下排序主要研究:第一,如何由区间判断矩阵求解区间特征向量,以此作为单一准则下被比较元素的排序权向量;第二,排序向量序反转的概率估计问题。我们假定区间特征向量的分量w_i的概率分布服从正态分布,采用了Kolmogorov检验,并编制了通用程序,为解决不确定型层次分析问题提供了一个计算功能。     

Location choice in early adulthood: Millennials versus Baby Boomers

This paper contributes to the literature on the effect of individual‐level characteristics on urban location choice by examining whether young people aged 25–34 with a bachelor's degree or higher are more likely to live in central cities in 2011 than in 1990. When I control for individual‐level characteristics I find that the effect of education on the probability of living in a central city remains similar across generations. This is evidence that to the extent education plays a role in the larger population of high human capital 25–34 year olds in cities in 2011 it is due to a composition effect rather than cities becoming more attractive to educated people at the margin.     

Modelling residential location choice in an area with spatial barriers

A model is presented for residential location choice in rural areas with spatial barriers. We address the problem through comparative static analysis focusing on how residential location choices are affected by a new road link across the spatial barrier. We proceed through a probability theoretical approach: choose a family of utility functions representing every possible location, and equip this family with a probability measure. Then choose a representative within an equivalence class of utility functions, and represent the probability distribution by a parametrized family of distributions. Our analysis demonstrates that investments in new road links do not necessarily represent an adequate instrument for achieving ambitions in regional policy. We identify reasonable situations where a new road link could just as easily generate net migration from the area in which the investments are directed. In general, our analysis demonstrates how agglomeration and centralisation tendencies can be considerably affected by transportation infrastructure innovations. Received: June 1999/Accepted: June 2001 This paper benefited from comments by Bj?rn Sandvik and Lars-G?ran Mattsson.     

Motives to restructure industries: Finnish evidence of cross-border and domestic mergers and acquisitions

Abstract.  We estimate a multilogit model for the probabilities that a firm will acquire or become a target in different M&A categories, which are defined according to the location of an acquiring firm with respect to a target. We find that certain firm characteristics of an acquiring firm regarded as indicative of having a high capability of monitoring a target – or internalising the potential synergies of M&A – increase the probability of distant M&As at the expense of close M&As. Factors which ease the monitoring of the target firm's value increase the probability that such a firm becomes a target located far from an acquiring firm.     

基于Normal需求的二级零售系统最优库存水平确定

考虑客户需求是随机的且服从Normal分布,由一个分销中心和N个零售商组成的两级分销库存系统.假设两级库存补充均采取周期订货策略,当产品缺货时,顾客愿意等待特别订货的概率为一常数.我们的目标是在满足给定的客户服务水平的条件下,寻求两级库存总费用的最小化.对库存费用的计算进行了精确地推导,并且得到了最优的库存水平.     

Determinants of fertility in urban and rural Kenya: estimates and a simulation of the impact of education policy

Fertility behavior and population growth and its impact on economics in Kenya are a concern because of recent trends. The aim of this research study was to examine the impact of multiple factors on fertility in urban and rural areas based on the 1984 Kenya contraceptive Prevalence Survey. Simulation models were used to project the number of births expected with the current educational program. The expectation was that increasing female education in rural areas would have an impact on birth probabilities. Policy implications are discussed in terms of increasing educational expenditures, particularly for rural women. The empirical model selected was based on theories on proximate determinants, demographic effects, and neoclassical models. A probit model was used to estimate whether or not a woman had a child in the year preceding the survey. Explanatory variables were economic (female employment and years of schooling), biological (mother's age and child mortality), and cultural (marital state, number of live births, and religion). The sample included 6405 women aged 15-49 years. Married urban women were 37% more likely than unmarried urban women to have given birth. Married rural women in monogamous marriages were 23% more likely to have given birth than their unmarried counterparts. The number in a marital union of wives did not affect the probability of birth in urban areas; the effect was quite small in rural areas. Probability of birth increased until age 25 in urban areas and to age 28 in rural areas. Previous live births had a small impact, and the inhibiting effect of an additional child is 2.7 times larger in urban than in rural areas. Only in urban areas did employment reduce the probability of a birth (by 7.4%). In rural areas, increases in education after the primary level were related to a decline in the probability of a birth (for a 26-year old woman there is a reduction in the probability of .18). The fertility rate of a woman completing secondary school was 3.5 and 7.8 for a woman completing primary school. There were no differences in urban Nairobi and Mombasa. The total fertility rate should decline by 25% between 1984 and 2014, and should fall further based on educational increases which affect family planning use, employment, and infant mortality. By 2014, the savings in education and related expenses for reduction in births of 5.8 million will be considerable.     

EVALUATION OF THE PROBABILITY OF FAILURE WITH UNCERTAIN DISTRIBUTION PARAMETERS

Formulations are presented for estimating probability of failure considering uncertainty of distribution parameters in time invariant and time variant reliability analyses. Based on the formulations the probability of failure can be calculated by recursively using the first order reliability method. Also, a more efficient approximate analysis procedure by using the point estimate method to estimate the probability of failure is given. In this analysis procedure, the point estimate method is used to discretize the uncertain distribution parameters in the time invariant reliability analysis, and to discretize the time-independent random variables and uncertain distribution parameters in the time variant reliability analysis. The probability of failure is then obtained by weighting the probability of failure conditioned at each of discrete points. The conditional probability of failure can be calculated by using first order reliability method, second order reliability method or any other convenient methods. The use of point estimate method to treat uncertain distribution parameters in calculating probability of failure is less computer time consuming than the one of recursively using FORM. Illustrative numerical examples of calculating probability of failure are presented.     

System reliability analysis for mixed uncertain variables

Uncertainties in a physical system should be modeled accurately to obtain an accurate estimate of its safety. Based on the amount and type of information available, either probability theory or possibility theory can be used. In probability theory variation in the parameters is modeled using probability density functions and in possibility theory it is modeled using fuzzy membership functions. But when dealing with a combination of both probability distributions and fuzzy membership functions, the computational cost involved in estimating the bounds of reliability increases exponentially because one reliability analysis, which is a computationally expensive procedure, is performed at each possibility level. Moreover, the failure of structural systems is governed by multiple limit-state functions, all of which are to be taken into consideration for determining its safety. These limit-state functions are often correlated and the accuracy of the estimated system reliability is dependent on the ability to model the joint failure surface. To reduce the computational cost involved without loss of accuracy, high quality function approximations for each of the limit-states and the joint failure surface are developed in this paper. Numerical examples are presented to demonstrate the efficiency and accuracy of the proposed methodology.     

Novel decoupled framework for reliability-based design optimization of structures using a robust shifting technique

In a reliability-based design optimization (RBDO), computation of the failure probability (P_f) at all design points through the process may suitably be avoided at the early stages. Thus, to reduce extensive computations of RBDO, one could decouple the optimization and reliability analysis. The present work proposes a new methodology for such a decoupled approach that separates optimization and reliability analysis into two procedures which significantly improve the computational efficiency of the RBDO. This technique is based on the probabilistic sensitivity approach (PSA) on the shifted probability density function. Stochastic variables are separated into two groups of desired and non-desired variables. The three-phase procedure may be summarized as: Phase 1, apply deterministic design optimization based on mean values of random variables; Phase 2, move designs toward a reliable space using PSA and finding a primary reliable optimum point; Phase 3, applying an intelligent self-adaptive procedure based on cubic B-spline interpolation functions until the targeted failure probability is reached. An improved response surface method is used for computation of failure probability. The proposed RBDO approach could significantly reduce the number of analyses required to less than 10% of conventional methods. The computational efficacy of this approach is demonstrated by solving four benchmark truss design problems published in the structural optimization literature.     

Effect of response surface parameter variation on structural reliability estimates

In the reliability estimation of complex structures, response surface methodology has been suggested as a way to estimate the actual but implicit limit state function. Typically the response surface is constructed from a polynomial function and fitted to the implicit function at a number of points. The location of these points has been noted as being an issue but the effect of varying their location has had little attention in the literature. In the present paper some simple examples are used to indicate possible effects. It is noted that the probability can be both under- and over-estimated, depending on the choice of points, but that no clear guidance for point selection can be given in any one case. A particularly disturbing feature is that for some types of problems there can be instability in the probability estimate as the location of the points is changed. This is demonstrated through a previously well-discussed example.     

Modeling of fire spread through probabilistic networks

The postflashover fire spread from room to room is treated in a stochastic analysis beginning with the development of a probabilistic network, and followed by a method of solving the network for discrete probability distributions. The first step in this analysis is the construction of a graph representing a space in which the rooms are nodes, and the walls and other fire barriers are links between the nodes. The space network graph is then transformed into a probabilistic network by introducing one node for representing the preflashover state and another node for representing the postflashover state of each room with the link between them representing the probability of flashover and the time characteristic to flashover. Each link between a flashed-over room and an adjacent space has a probability of the barrier being breached and a characteristic time of fire resistance. The probabilistic network is then solved by creating an “equivalent network” which has multiple links between the nodes to represent the uncertainty intrinsic to fire spread. For instance, a door may be open or closed. This would be represented by two links, one with the probability of the door being open with a characteristic time of zero, and the other with the probability of the door being closed with the time associated with the fire resistance of the door. The analysis of the possible flow through equivalent networks is discussed and the probability of a source node connecting with the sink node as well as the expected shortest travel time are calculated. Finally a numerical example is solved in which the source node is the room of origin of a fire, and the sink node is a section of the corridor which is critical to the escape of the occupants in nearby rooms Two cases are developed, one with “5-minute” doors and the other with “20-minute” doors and automatic closures. A different fire scenario is shown to be represented by each path through the equivalent network, and the probability and characteristic time for each of these scenarios is also calculated. The consequence of the changing to a 20-minute door is presented in quantitative terms and the probability of the door being open is used as a sensitivity parameter.     

The modeling of fire spread in buildings by Bayesian network

Fire spread modeling is very important to fire safety engineering and to insurance industries involved in fire risk–cost analysis of buildings. In this paper, the Bayesian network is introduced. The directed acyclic graph of a fire spread model is presented. When the fire ignition location is known, the fire spread model based on the Bayesian network from the compartment of fire origin to another compartment can be built, and the probability of fire spread can be calculated by making use of the joint probability distribution of the Bayesian network. A specific application for an office building is presented for a case without sprinkler and one with sprinkler installed.