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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.