BACKGROUND: Underground miners exposed to high levels of radon have an excess risk of lung cancer. Residential exposure to radon is at much lower levels, and the risk of lung cancer with residential exposure is less clear. We conducted a systematic analysis of pooled data from all North American residential radon studies. METHODS: The pooling project included original data from 7 North American case-control studies, all of which used long-term alpha-track detectors to assess residential radon concentrations. A total of 3662 cases and 4966 controls were retained for the analysis. We used conditional likelihood regression to estimate the excess risk of lung cancer. RESULTS: Odds ratios (ORs) for lung cancer increased with residential radon concentration. The estimated OR after exposure to radon at a concentration of 100 Bq/m3 in the exposure time window 5 to 30 years before the index date was 1.11 (95% confidence interval = 1.00-1.28). This estimate is compatible with the estimate of 1.12 (1.02-1.25) predicted by downward extrapolation of the miner data. There was no evidence of heterogeneity of radon effects across studies. There was no apparent heterogeneity in the association by sex, educational level, type of respondent (proxy or self), or cigarette smoking, although there was some evidence of a decreasing radon-associated lung cancer risk with age. Analyses restricted to subsets of the data with presumed more accurate radon dosimetry resulted in increased estimates of risk. CONCLUSIONS: These results provide direct evidence of an association between residential radon and lung cancer risk, a finding predicted using miner data and consistent with results from animal and in vitro studies.
The stress-threshold model (Wolpert, 1965; Brown and Moore, 1970) assumes that people do not consider moving unless they experience residential stress. This paper develops a similar model of residential mobility in which residential satisfaction acts as an intervening variable between individual and residence variables and mobility. The model is tested with data from a panel study of Rhode Island residents. The results indicate that residential satisfaction at the first interview is related to the wish to move and to mobility in the year following the interview. Individual and residence characteristics such as age of head duration of residence, home ownership, and room crowding are shown to affect mobility through their effect on residential satisfaction.
Abstract While the impact of urban form on transportation energy use has been studied extensively, its impact on residential energy use has not. This article presents a conceptual framework linking urban form to residential energy use via three causal pathways: electric transmission and distribution losses, energy requirements of different housing stocks, and space heating and cooling requirements associated with urban heat islands. Two of the three can be analyzed with available national data. After we control for other influences, residents of sprawling counties are more likely to live in single‐family detached houses than otherwise comparable residents of compact counties and also more likely to live in big houses. Both lead to higher residential energy use. Because of the urban heat island effect, residents of sprawling counties across the nation on average pay a small residential energy penalty relative to residents of compact counties. Implications for urban planning are explored.
Ecological analysis is a promising approach to the study of urban social stratification, for differences in the residential distributions of occupations groups are found to parallel the differences among them in socio-economic status and recruitment. The occupation groups at the extremes of the socioeconomic scale are the most segregated. Residential concentration in low-rent areas and residential centralization are inversely related to socioeconomic status. Inconsistencies in the ranking of occupation groups according to residential patterns occur at points where there is evidence of status disequilibrium.
As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.
While researchers are increasingly re-conceptualizing international migration, far less attention has been devoted to re-thinking short-distance residential mobility and immobility. In this paper we harness the life course approach to propose a new conceptual framework for residential mobility research. We contend that residential mobility and immobility should be re-conceptualized as relational practices that link lives through time and space while connecting people to structural conditions. Re-thinking and re-assessing residential mobility by exploiting new developments in longitudinal analysis will allow geographers to understand, critique and address pressing societal challenges.
Residential satisfaction is not only an important component of individuals' quality of life but also determines the way they respond to residential environment. An understanding of the factors that facilitate a satisfied or dissatisfied response can play a critical part in making successful housing policies. This study reinvestigates the effects of housing, neighborhood, and household characteristics on individuals' satisfaction with both dwelling and neighborhood, in order to reconcile the inconsistencies in the previous research. The empirical analysis uses data drawn from the American Housing Survey (AHS) and ordered logit models (OLM). OLM is more appropriate than the widely‐used regression technique in such analysis due to the ordinal nature of the dependent variables representing satisfaction. The results show that residential satisfaction is a complex construct, affected by a variety of environmental and socio‐demographic variables. While the actual effects of the variables by and large confirm earlier findings in the literature, significant differences between the results from the OLM and regression models were found. This indicates that regression models should be used with caution and their results accepted with a grain of salt.
The debate over the role of the forces that create the patterns of residential separation has identified neighborhood preferences as one of the explanatory variables, but although we possess some empirical data on the nature of neighborhood racial preferences, the theoretical contributions have received only limited empirical evaluation. Among the theoretical statements, Schelling's model of the effects of small differences in preferences on residential patterns has provided a basic building block in our understanding of preferences, choices, and patterns. Several recent surveys of residential preferences provide the data with which to evaluate the underpinnings of the Schelling model. The preference/tolerance schedules that are derived from the data have a different functional form from that suggested by Schelling, but confirm the view that stable integrated equilibria are unlikely.
A model of electric residential end-use is proposed for establishing the load diagram of an area by a process of synthesis. The model follows a "bottom-up" approach, allowing construction of the relative load shape of the area, starting from knowledge of its most relevant socioeconomic and demographic characteristics, unitary energy consumption and the load profiles of individual household appliances. Several probability functions have been introduced in order to cover the close relationship existing between the demand of residential customers and the psychological and behavioral factors typical of the household; the model makes frequent use of the latter through a Monte Carlo extraction process. The model has been applied for the simulation of a residential area where field measurements of power demand had been made at 15-minute intervals and a combined mail survey had been conducted to investigate household energy usage. The paper reports the results of a comparison between recorded and predicted load profiles.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Racial residential segregation is a fundamental cause of racial disparities in health. The physical separation of the races by enforced residence in certain areas is an institutional mechanism of racism that was designed to protect whites from social interaction with blacks. Despite the absence of supportive legal statutes, the degree of residential segregation remains extremely high for most African Americans in the United States. The authors review evidence that suggests that segregation is a primary cause of racial differences in socioeconomic status (SES) by determining access to education and employment opportunities. SES in turn remains a fundamental cause of racial differences in health. Segregation also creates conditions inimical to health in the social and physical environment. The authors conclude that effective efforts to eliminate racial disparities in health must seriously confront segregation and its pervasive consequences.
Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimal</i> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">automatic</i> residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">electricity payment</i> and minimizing the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">waiting time</i> for the operation of each appliance in household in presence of a real-time pricing tariff <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">combined</i> with <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inclining block rates</i> . Our design requires minimum effort from the users and is based on simple <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">linear programming</i> computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">price prediction</i> capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weighted average price prediction</i> filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coefficients</i> for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">peak-to-average ratio</i> in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.
Previous models of urban residential segregation have virtually ignored the affects of tertiary, or small, residential–type streets, despite the intuition that they are where “neighborly” relations primarily occur. This article argues that racial similarity among neighborhoods emerges primarily from their relational connections via tertiary streets rather than as a result of geographic proximity. Analyzing tertiary streets can better predict racial composition than can spatial considerations. Segregated networks of neighborly relations emerge from segregated networks of residential streets. Racial populations are organized in space with respect to who is “down the street” rather than in terms of mere physical distance.
STUDY OBJECTIVES: To study the association between greenery filled public areas that are nearby a residence and easy to walk in and the longevity of senior citizens in a densely populated, developed megacity. DESIGN: Cohort study. METHODS: The authors analysed the five year survival of 3144 people born in 1903, 1908, 1913, or 1918 who consented to a follow up survey from the records of registered Tokyo citizens in relation to baseline residential environment characteristics in 1992. MAIN RESULTS: The survival of 2211 and the death of 897 (98.9% follow up) were confirmed. The probability of five year survival of the senior citizens studied increased in accordance with the space for taking a stroll near the residence (p<0.01), parks and tree lined streets near the residence (p<0.05), and their preference to continue to live in their current community (p<0.01). The principal component analysis from the baseline residential environment characteristics identified two environment related factors: the factor of walkable green streets and spaces near the residence and the factor of a positive attitude to a person's own community. After controlling the effects of the residents' age, sex, marital status, and socioeconomic status, the factor of walkable green streets and spaces near the residence showed significant predictive value for the survival of the urban senior citizens over the following five years (p<0.01). CONCLUSIONS: Living in areas with walkable green spaces positively influenced the longevity of urban senior citizens independent of their age, sex, marital status, baseline functional status, and socioeconomic status. Greenery filled public areas that are nearby and easy to walk in should be further emphasised in urban planning for the development and re-development of densely populated areas in a megacity. Close collaboration should be undertaken among the health, construction, civil engineering, planning, and other concerned sectors in the context of the healthy urban policy, so as to promote the health of senior citizens.
▪ Abstract The publication of American Apartheid ( Massey & Denton 1993 ) was influential in shifting public discourse back toward racial residential segregation as fundamental to persisting racial inequality. At the end of the twentieth century, the majority of blacks remained severely segregated from whites in major metropolitan areas. Due to the persistence of high-volume immigration, Hispanic and Asian segregation from whites has increased, although it is still best characterized as moderate. This review examines trends in the residential segregation of blacks, Hispanics, and Asians and recent research focused on understanding the causes of persisting segregation. This discussion is organized around two broad theoretical perspectives—spatial assimilation and place stratification. After detailing the consequences of segregation for affected groups, I identify gaps in our understanding and goals for future research.
Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighbourhoods. What is less well understood is the extent to which the observed patterns of travel behaviour can be attributed to the residential built environment (BE) itself, as opposed to attitude‐induced residential self‐selection. To date, most studies addressing this self‐selection issue fall into nine methodological categories: direct questioning, statistical control, instrumental variables, sample selection, propensity score, joint discrete choice models, structural equations models, mutually dependent discrete choice models and longitudinal designs. This paper reviews 38 empirical studies using these approaches. Virtually all of the studies reviewed found a statistically significant influence of the BE remaining after self‐selection was accounted for. However, the practical importance of that influence was seldom assessed. Although time and resource limitations are recognized, we recommend usage of longitudinal structural equations modelling with control groups, a design which is strong with respect to all causality requisites.
Research traditions across the social sciences have explored the drivers of individual behavior and proposed different models of decision making. Four diverse perspectives are reviewed here: conventional and behavioral economics, technology adoption theory and attitude-based decision making, social and environmental psychology, and sociology. The individual decision models in these traditions differ axiomatically. Some are founded on informed rationality or psychological variables, and others emphasize physical or contextual factors from individual to social scales. Each perspective suggests particular lessons for designing interventions to change behavior. Throughout the review, these lessons are applied to decisions affecting residential energy use. Examples are drawn from both intuitive and reasoning-based types of decision as well as from a range of decision contexts that include capital investments in weatherization and repetitive behaviors such as appliance use. Areas of difference and similarity between various theoretical approaches and their practical implications are highlighted. Conclusions are drawn on how to develop a more integrated approach to both behavioral research and intervention design in a residential energy context.
Residential load forecasting has been playing an increasingly important role in modern smart grids. Due to the variability of residents' activities, individual residential loads are usually too volatile to forecast accurately. A long short-term memory-based deep-learning forecasting framework with appliance consumption sequences is proposed to address such volatile problem. It is shown that the forecasting accuracy can be notably improved by including appliance measurements in the training data. The effectiveness of the proposed method is validated through extensive comparison studies on a real-world dataset.
With the growing strength of minority voices in recent decades has come much impassioned discussion of residential schools, the institutions where attendance by Native children was compulsory as recently as the 1960s. Former students have come forward in increasing numbers to describe the psychological and physical abuse they suffered in these schools, and many view the system as an experiment in cultural genocide. In this first comprehensive history of these institutions, J.R. Miller explores the motives of all three agents in the story. He looks at the separate experiences and agendas of the government officials who authorized the schools, the missionaries who taught in them, and the students who attended them. Starting with the foundations of residential schooling in seventeenth-century New France, Miller traces the modern version of the institution that was created in the 1880s, and, finally, describes the phasing-out of the schools in the 1960s. He looks at instruction, work and recreation, care and abuse, and the growing resistance to the system on the part of students and their families. Based on extensive interviews as well as archival research, Miller's history is particularly rich in Native accounts of the school system. This book is an absolute first in its comprehensive treatment of this subject. J.R. Miller has written a new chapter in the history of relations between indigenous and immigrant peoples in Canada. Co-winner of the 1996 Saskatchewan Book Award for nonfiction. Winner of the 1996 John Wesley Dafoe Foundation competition for Distinguished Writing by Canadians Named an 'Outstanding Book on the subject of human rights in North America' by the Gustavus Myer Center for the Study of Human Rights in North America.
The problem of translating the theory of economic choice behavior into concrete models suitable for analyzing housing location is discussed. The analysis is based on the premise that the classical, economically rational consumer will choose a residential location by weighing the attributes of each available alternative and by selecting the alternative that maximizes utility. The assumption of independence in the commonly used multinomial logit model of choice is relaxed to permit a structure of perceived similarities among alternatives. In this analysis, choice is described by a multinomial logit model for aggregates of similar alternatives. Also discussed are methods for controlling the size of data collection and estimation tasks by sampling alternatives from the full set of alternatives. /Author/
This paper conceives of residential segregation as a multidimensional phenomenon varying along five distinct axes of measurement: evenness, exposure, concentration, centralization, and clustering. Twenty indices of segregation are surveyed and related conceptually to one of the five dimensions. Using data from a large set of U.S. metropolitan areas, the indices are intercorrelated and factor analyzed. Orthogonal and oblique rotations produce pattern matrices consistent with the postulated dimensional structure. Based on the factor analyses and other information, one index was chosen to represent each of the five dimensions, and these selections were confirmed with a principal components analysis. The paper recommends adopting these indices as standard indicators in future studies of segregation.