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Selective Area van der Waals Epitaxy (SAVWE) of III-Nitride device has been proposed recently by our group as an enabling solution for h-BN-based device transfer. By using a patterned dielectric mask with openings slightly larger than device sizes, pick-and-place of discrete LEDs onto flexible substrates was achieved. A more detailed study is needed to understand the effect of this selective area growth on material quality, device performance and device transfer. Here we present a study performed on two types of LEDs (those grown on h-BN on patterned and unpatterned sapphire) from the epitaxial growth to device performance and thermal dissipation measurements before and after transfer. Millimeter-size LEDs were transferred to aluminum tape and to silicon substrates by van der Waals liquid capillary bonding. It is shown that patterned samples lead to a better material quality as well as improved electrical and optical device performances. In addition, patterned structures allowed for a much better transfer yield to silicon substrates than unpatterned structures. We demonstrate that SAVWE, combined with either transfer processes to soft or rigid substrates, offers an efficient, robust and low-cost heterogenous integration capability of large-size devices to silicon for photonic and electronic applications. [ABSTRACT FROM AUTHOR]

We report the numerical study of n-GaN/i-InGaN/p-GaN solar cells on Ga-face substrates with thin GaN interlayers present in the intrinsic InGaN region. These interlayers have recently been shown to significantly increase the crystal quality of thick InGaN layers $$(>\!\!\!120\,\text{ nm})$$. We find that tunneling is efficient in n-i-p structures having interlayers $$\le \! 1.5\,\text{ nm}$$ thick if polarization charges are sufficiently screened. If left unscreened, the large polarization charges naturally formed at the heterointerfaces degrades n-i-p performance, at a given interlayer thickness, because polarization charges increase the distance that carriers must tunnel. Simulations identify favorable parameter ranges. [ABSTRACT FROM AUTHOR]

Controlled Meteorological (CMET) balloons are small airborne platforms that use reversible lift-gas compression to regulate altitude. These balloons have approximately the same payload mass as standard weather balloons but can float for many days, change altitude on command, and transmit meteorological and system data in near-real time via satellite. Since 2004, more than 50 CMET balloons have been flown in nearly all of the earth's major climate zones, from the Amazon to Antarctica. This paper describes one notable flight in 2011 in which a CMET balloon performed continuous soundings in the Arctic marine boundary layer off the coast of Svalbard. It is likely that this is the first time such a feat has been accomplished by a free balloon or any other flight platform. The series 18 consecutive profiles show the time evolution of the boundary layer as it is advected northward over a 10-h period. The paper focuses on the balloon design, control algorithm, and in-flight performance. Analysis of the unique atmospheric dataset will be the subject of a subsequent publication. [ABSTRACT FROM AUTHOR]

This study revisits a spatial regression approach for small-area population forecasting that considers not only direct drivers of local area population growth but also neighbour growth and neighbour characteristics. Previous research suggested that the approach does not outperform extrapolation projections, the currently most-often-used small-area population forecasting technique. We argue the reason is that population growth is affected by its influential factors differently in urban, suburban, and rural areas. Therefore, we hypothesize that the spatial regression forecasting approach can perform better in one type of area at a time, where the influential factors' effects on population growth can be estimated more accurately. This study is focused on census tracts of the city of Milwaukee, USA, to test the performance of the spatial regression approach in an urban setting. The analyses reveal mixed results and do not suggest that the spatial regression approach unambiguously outperforms extrapolation projections. [ABSTRACT FROM AUTHOR]

Scholars in many social science disciplines have taken note of the re-emerging interest in issues concerning social processes embedded within a spatial context. While some argue that this awakening is refreshing and new and, in fact, long overdue, I demonstrate that spatially focused demographic theories and research agendas clearly predate contemporary interest in these topics. I assert that recent methodological advancements have merely encouraged and brought refinement to the expanding body of spatially oriented population research—research strongly rooted in demographic tradition and practice. Indeed, I make the claim that, until roughly the mid-20th century, virtually all demography in the United States (and elsewhere, but not specifically examined here) was spatial demography. Then, shortly after mid-century, a paradigm shift occurred, and the scientific study of population quickly came to be dominated by attention to the individual as the agent of demographic action. Traditional spatial demography—macro-demography—gave way to micro-demography, and, I argue, most demographers simply abandoned the data and approach of spatial demography. In closing the paper I include a brief discussion of the recent awakening that has come to spatial demographers from developments in other disciplines, principally geography, regional science, and spatial econometrics. [ABSTRACT FROM AUTHOR]

We apply methods of exploratory spatial data analysis (ESDA) and spatial regression analysis to examine intercounty variation in child poverty rates in the US. Such spatial analyses are important because regression models that exclude explicit specification of spatial effects, when they exist, can lead to inaccurate inferences about predictor variables. Using county-level data for 1990, we re-examine earlier published results [Friedman and Lichter (Popul Res Policy Rev 17:91–109, 1998)]. We find that formal tests for spatial autocorrelation among county child poverty rates confirm and quantify what is obvious from simple maps of such rates: the risk of a child living in poverty is not (spatially) a randomly distributed risk at the county level. Explicit acknowledgment of spatial effects in an explanatory regression model improves considerably the earlier published regression results, which did not take account of spatial autocorrelation. These improvements include: (1) the shifting of “wrong sign” parameters in the direction originally hypothesized by the authors, (2) a reduction of residual squared error, and (3) the elimination of any substantive residual spatial autocorrelation. While not without its own problems and some remaining ambiguities, this reanalysis is a convincing demonstration of the need for demographers and other social scientists to examine spatial autocorrelation in their data and to explicitly correct for spatial externalities, if indicated, when performing multiple regression analyses on variables that are spatially referenced. Substantively, the analysis improves the estimates of the joint effects of place-influences and family-influences on child poverty. [ABSTRACT FROM AUTHOR]

The U.S. Census Bureau designed the American Community Survey (ACS) to provide annual estimates of social and economic characteristics for states, counties, municipalities, census tracts, and block groups. Because of its April 1 reference date, in northern nonmetropolitan counties with substantial seasonal population fluctuations the decennial census provides a statistical representation of the demographic and social characteristics of the population at a time when the population is close to its annual minimum. The year-round monthly ACS sample survey has the potential to provide local communities with an unprecedented understanding of the average population characteristics over the course of a year. In the future, the ACS even has the potential for providing social and economic characteristics of the population by season. This paper examines four ACS pilot data collection counties, Oneida and Vilas Counties in northern Wisconsin, and Lake and Flathead Counties in northwest Montana. We hypothesize that the ACS will reflect a resident population over the course of the year that is different from the traditional April 1 decennial census population. While the ACS holds much promise, our research uncovered some sampling problems that are not yet fully resolved. In addition, our analysis was not able to examine ACS estimates for minor civil divisions (MCDs), which are functioning governmental units in many states. The fact that these MCDs often have very small populations, together with the fact that estimated standard errors at the much larger census tract level in these counties are disconcertingly large, raises (currently unanswerable) questions concerning the eventual statistical quality of ACS estimates for small MCDs. Consequently, the adequacy of the ACS as a replacement for the census long form may depend on the ability of the Census Bureau to effectively address the concerns presented in this analysis. [ABSTRACT FROM AUTHOR]

As fertility differences in the United States diminish, population redistribution trends are increasingly dependent on migration. This research used newly developed county-level age-specific net migration estimates for the 1990s, supplemented with longitudinal age-specific migration data spanning the prior 40 years, to ascertain whether there are clear longitudinal trends in age-specific net migration and to determine if there is spatial clustering in the migration patterns. The analysis confirmed the continuation into the 1990s of distinct net migration "signature patterns" for most types of counties, although there was temporal variation in the overall volume of migration across the five decades. A spatial autocorrelation analysis revealed large, geographically contiguous regions of net in-migration (in particular, Florida and the Southwest) and geographically contiguous regions of net out-migration (the Great Plains, in particular) that persisted over time. Yet the patterns of spatial concentration and fragmentation over time in these migration data demonstrate the relevance of this "neighborhood" approach to understanding spatiotemporal change in migration. [ABSTRACT FROM AUTHOR]

Lakeshore development in Vilas County, northern Wisconsin (USA) is heterogeneous, ranging from lakes that are surrounded by homes and commercial establishments to lakes that have no buildings on their shorelines. Development in this recreational area has increased, and since the 1960s over half of new homes have been built on the lakeshore. We examined building density around lakes in relationship to 11 variables, including in-lake, shoreline, and social characteristics. Buildings in many parts of northern Wisconsin tend to be concentrated around shorelines; in Vilas County 61% of all medium-sized buildings (our proxy for residential development) on private land were ≤100 m of a lake. The probability of development on a lake was largely related to lake surface area, with larger, more accessible lakes showing a higher probability of development. Building density along shorelines varied with travel cost, lake surface area, presence of wetlands, and extent of public land ownership. Building density was greater on larger, more accessible lakes that were surrounded by forest (as opposed to wetlands) and public lands. Gaining a more precise understanding of human settlement patterns can help direct planning and resource protection efforts to lakes most likely to experience future development. [ABSTRACT FROM AUTHOR]

This case study illustrates the extent to which internal migration data from decennialcensus sources, including published reports, Summary Tape Files, special migration tabulations, and Public Use Microdata Samples, as well as other migration data including decadal net migration estimates and Internal Revenue Service annual estimates can be combined to comprehensively evaluate the influence of migration on a particular county. The analysis of migration addresses public-policy concerns raised by the Dane County, Wisconsin, Department of Human Services regarding ``welfare magnetism'', the in-migration from out-of-state of poor, less-educated female householders with children receiving public assistance, and the county's contribution to Wisconsin's perceived ``brain drain'', the net loss through migration of more highly-educated individuals. While net migration is contributing to higher numbers and rates of persons living in the county with incomes at or below poverty, the in-migration of persons fitting the ``welfare magnet'' description is dwarfed by the in-migration of poor persons fitting the ``college student'' description. [ABSTRACT FROM AUTHOR]

The article focuses on the population data in social science data archives. The record of use by demographers of sample survey data is long and impressive. The early days of survey research in the U.S., especially the activities of the pioneering commercial polling firms beginning in the mid-1930s, produced hundreds of data sets, amounting to a tremendously rich potential resource for the scientific community at large. Local data archives, both general and specialized, exist at many U.S. universities and serve primarily on-campus scholars, the local community and/or state data needs. By the mid-1950s, a growing number of educators had expressed strong interest in bringing to their own campuses, for student training and faculty research, a survey data resource comparable to that which had been created on the Roper Centre at Williamstown, Victoria campus. More than 100 survey organizations around the world regularly contribute their data to the public domain through the Roper Center, and annually 400 to 500 new surveys are put through the Center's standardized accession procedures.

The article lists reviewers whose work appeared in the "Population Research and Policy Review."

The article discusses various reports published within the issue including one on the analysis of data for areal units and another on attributes of spatially referenced data violate some of the critical assumptions underlying statistical measures and models.