Large Survey Dataset Research Articles

Inconsistencies in Simple Thermal Model Results for Near-Earth Asteroids between Infrared Telescope Facility SpeX and NEOWISE Data

Understanding the properties of near-Earth asteroids (NEAs) is key for many aspects of planetary science, particularly planetary defense. Our current knowledge of NEA sizes and regolith properties is heavily dependent on simple thermal models. These models are often used to analyze data from missions such as NEOWISE because they are well suited to deal with large volumes of data. However, simple model results based on NEOWISE data may be inconsistent with results based on other types of observation in some cases. In this work, we seek to better understand these potential inconsistencies, as well as the situations for which they are most prevalent. We do this by comparing simple model results based on Infrared Telescope Facility SpeX data to similar results based on NEOWISE data. This is carried out for six NEAs that represent a range of spectral types, shapes, and rotation states. We find that models based on SpeX and NEOWISE data for these six objects are inconsistent in some cases, even though the SpeX results are consistent with other methods and observations. We find that objects observed at fainter magnitudes and objects with more primitive compositions are more likely to produce inconsistent fits. These results highlight the importance of better understanding the limitations of simple models as applied to large survey data sets like NEOWISE. This is particularly true as we move into an era where our understanding of the NEA population will be dominated by future large surveys such as NEO Surveyor.

Psychological and social wellbeing associated with regional population change in Finland

AbstractRegional depopulation is an increasingly common demographic issue in many countries, as population growth becomes concentrated in small number of urban centres. Depopulation has adverse effects on the region's socioeconomic development, and often leads to worse living circumstances, such as poor availability of services. Population growth, by contrast, has many socioeconomic benefits but may also introduce unwanted consequences, such as urban alienation. However, the associations of regional population change with inhabitants' psychological and social wellbeing have not been examined as extensively as the associations with socioeconomic outcomes. With a large survey dataset from Finland (n = 99,034 participants from 299 municipalities), we examined whether regional, municipality‐level population decline and growth over the preceding 10 years were associated with psychological and social wellbeing of the inhabitants of the municipalities. We were specifically interested in whether growth and decline were both related to these outcomes, or whether the associations were more pronounced for either decline or growth. Overall negative population change was related to lower social wellbeing (e.g., lower social support) and that these associations were more pronounced for the adverse associations of population decline than for the beneficial associations of population growth (e.g., less frequent social contact and higher risk of loneliness). Population change was not associated with inhabitants' depressive symptoms. These findings suggest that regional population decline can be particularly relevant for worsening social wellbeing of the inhabitants.

Exploring a link between faculty intrapreneurship, student entrepreneurship and ecosystem dynamism

Purpose This study aims to understand the impact of student entrepreneurship and university support on faculty intrapreneurship. The authors also analyze the role of the university’s dynamic and ordinary capabilities and the environmental dynamism in which the university is embedded. Design/methodology/approach With a large survey data set involving 680 professors and 2,230 students from 70 Brazilian universities, the authors use a multimethod approach with partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Findings The PLS-SEM results demonstrate that student entrepreneurship indirectly influences faculty intrapreneurship through the interaction of students with faculty and entrepreneurs, in addition to proving the intense influence of university support on faculty intrapreneurship, especially in a slow-growth environment. Additionally, the authors confirmed the moderating effect of universities’ dynamic and ordinary capabilities on student interaction and university support, respectively, and some exciting differences considering the ecosystem dynamism. The fsQCA results deepened the differences between environments, presenting different configurations between the antecedents that lead to high levels of faculty intrapreneurship in fast and slow-growth environments. Originality/value The study makes a unique and significant contribution to the literature on faculty intrapreneurship by examining the cross-interactions between individual, organizational and environmental levels about the promotion of faculty intrapreneurship. From a practical point of view, it is possible to identify more effective, innovative and systematic ways to encourage faculty intrapreneurship in a developing country. The findings help open up the black box of faculty intrapreneurship.

The EDIBLES survey

Context. Small linear carbon chain radicals such as C2 and C3 act as both the building blocks and dissociation fragments of larger carbonaceous species. Their rotational excitation traces the temperature and density of local environments. However, these homo-nuclear di- and triatomic species are only accessible through their electronic and vibrational features because they lack a permanent dipole moment, and high signal-to-noise ratio data are necessary as the result of their generally low abundances in the interstellar medium (ISM). Aims. In order to improve our understanding of small carbonaceous species in the ISM, we carried out a sensitive survey of C2 and C3 using the ESO Diffuse Interstellar Bands Large Exploration Survey (EDIBLES) dataset. We also expanded our searches to C4, C5, and the 13C12C isotopologue in the most molecule-rich sightlines. Methods. We fitted synthetic spectra generated following a physical excitation model to the C2 (2-0) Phillips band to obtain the C2 column density (N) as well as the kinetic temperature (Tkin) and number density (n) of the host cloud. The C3 molecule was measured through its à − $ \tilde X$ (000-000) electronic origin band system. We simulated the excitation of this band with a double-temperature Boltzmann distribution. Results. We present the largest combined survey of C2 and C3 to date in which the individual transitions can be resolved. In total, we detected C2 in 51 velocity components along 40 sightlines, and C3 in 31 velocity components along 27 sightlines. Further analysis confirms the two molecules are detected in the same velocity components. We find a very good correlation between N(C2) and N(C3) with a Pearson correlation coefficient r = 0.93 and an average N(C2)/N(C3) ratio of 15.5± 1.4. A comparison with the behaviour of the C2 diffuse interstellar bands (DIBs) shows that there are no clear differences among sightlines with and without detections of C2 and C3. This is in direct contrast to the better-studied non-C2 DIBs, which have reduced strengths in molecule-rich environments, consistent with the idea that the C2 DIBs are indeed a distinguishable DIB family. We also identify, for the first time, the Q(2), Q(3), and Q(4) transitions of the 13C12C (2-0) Phillips band in the stacked average spectrum of molecule-rich sightlines, and estimate the isotopic ratio of carbon 12C/13C to be 79±8, consistent with literature results. At this stage it is not yet possible to identify these transitions in individual sightlines. Our search for the C4 and C5 optical bands was unsuccessful; even in stacked spectra no unambiguous identification could be made.

Predicting asthma using imbalanced data modeling techniques: Evidence from 2019 Michigan BRFSS data

Studies in the past have examined asthma prevalence and the associated risk factors in the United States using data from national surveys. However, the findings of these studies may not be relevant to specific states because of the different environmental and socioeconomic factors that vary across regions. The 2019 Behavioral Risk Factor Surveillance System (BRFSS) showed that Michigan had higher asthma prevalence rates than the national average. In this regard, we employ various modern machine learning techniques to predict asthma and identify risk factors associated with asthma among Michigan adults using the 2019 BRFSS data. After data cleaning, a sample of 10,337 individuals was selected for analysis, out of which 1,118 individuals (10.8%) reported having asthma during the survey period. Typical machine learning techniques often perform poorly due to imbalanced data issues. To address this challenge, we employed two synthetic data generation techniques, namely the Random Over-Sampling Examples (ROSE) and Synthetic Minority Over-Sampling Technique (SMOTE) and compared their performances. The overall performance of machine learning algorithms was improved using both methods, with ROSE performing better than SMOTE. Among the ROSE-adjusted models, we found that logistic regression, partial least squares, gradient boosting, LASSO, and elastic net had comparable performance, with sensitivity at around 50% and area under the curve (AUC) at around 63%. Due to ease of interpretability, logistic regression is chosen for further exploration of risk factors. Presence of chronic obstructive pulmonary disease, lower income, female sex, financial barrier to see a doctor due to cost, taken flu shot/spray in the past 12 months, 18–24 age group, Black, non-Hispanic group, and presence of diabetes are identified as asthma risk factors. This study demonstrates the potentiality of machine learning coupled with imbalanced data modeling approaches for predicting asthma from a large survey dataset. We conclude that the findings could guide early screening of at-risk asthma patients and designing appropriate interventions to improve care practices.

Birth-order effects on risk taking are limited to the family environment.

Why is the empirical evidence for birth-order effects on human psychology so inconsistent? In contrast to the influential view that competitive dynamics among siblings permanently shape a person's personality, we find evidence that these effects are limited to the family environment. We tested this context-specific learning hypothesis in the domain of risk taking, using two large survey datasets from Germany (SOEP, n = 19,994) and the United States (NLSCYA, n = 29,627) to examine birth-order effects on risk-taking propensity across a wide age range. Specification-curve analyses of a sample of 49,621 observations showed that birth-order effects are prevalent in children aged 10-13 years, but that they decline with age and disappear by middle adulthood. The methodological approach shows the effect is robust. We thus replicate and extend previous work in which we showed no birth-order effects on adult risk taking. We conclude that family dynamics cause birth-order effects on risk taking but that these effects fade as siblings transition out of the home.

Job Satisfaction and the ‘Great Resignation’: An Exploratory Machine Learning Analysis

Labor market dynamics is shaped by various social, psychological and economic drivers. Studies have suggested that job quit and labor market turnover are associated with job satisfaction. This study examines the determinants of job satisfaction using a large survey dataset, namely the LISS Work and Schooling module on an extensive sample of persons from the Netherlands. To handle these big data, machine learning models based on binary recursive partitioning algorithms are employed. Particularly, sequential and randomized tree-based techniques are used for prediction and clustering purposes. In order to interpret the results, the study calculates the sizes and directions of the effects of model features using computations based on the concept of Shapley value in cooperative game theory. The findings suggest that satisfaction with the social atmosphere among colleagues, wage satisfaction, and feeling of being appreciated are major determinants of job satisfaction.

Gaps in education and marital dissolution: Evidence from India

AbstractIndia has had a low divorce rate only recently, but the divorce rate is rising following the international trend. The rising divorce rate has gained considerable attention among social scientists. The paper identifies gaps in spouses’ educational attainment as a potential cause of rising failed marriages in India. Using a nationally representative large survey dataset, namely the fourth round of the District Level Household Survey, we show that the likelihood of divorce and separation is higher among women who are comparatively more educated than their partners. Moreover, the likelihood increases as the positive education gap increases. The incidences of divorce are significant among those residing in rural areas, educated up to secondary and beyond, and unemployed despite being more educated than their husbands. Although the results show that women are gaining autonomy in deciding whether to move out of marriages, the government must provide free education for women, at least up to the secondary standard, as being more educated than their partner does not provide them the autonomy to dissolve their marriages unless they are educated at least up to secondary standard.

Should activism be treated as an indicator of attitudes towards political violence?

Multiple contemporary meta-analyses pointed out activism as one of the risk factors for engagement in political violence, which is in line with the early radicalization models. However, others pointed out that joining activist organizations could prevent individuals from conducting violent acts. This study attempted to empirically evaluate whether activism should be treated as a risk factor using a large international World Value Survey data set and controlling for support for family violence. The initial outcomes have confirmed that activists tend to hold more positive attitudes towards political violence than non-activists. However, this relationship lost practical significance when the support for family violence was included in the model, which was replicated across different political regimes and contexts. Although such results do not support the notion of activists exhibiting more positive attitudes towards political violence, they also do not imply that their attitudes towards political violence are more negative compared to non-activists. Therefore, without knowing more about the specific social issue and context in which it had developed, conclusions regarding one’s attitudes towards political violence based only on his or her history of activism seem to be negligibly more correct than random guessing.

TDCOSMO

Context. Time-delay cosmography uses strong gravitational lensing of a time-variable source to infer the Hubble constant. The measurement is independent from both traditional distance ladder and CMB measurements. An accurate measurement with this technique requires considering the effects of objects along the line of sight outside the primary lens, which is quantified by the external convergence (κext). In absence of such corrections, H0 will be biased towards higher values in overdense fields and lower values in underdense fields. Aims. We discuss the current state of the methods used to account for environment effects. We present a new software package built for this kind of analysis and others that can leverage large astronomical survey datasets. We apply these techniques to the SDSS J0924+0219 strong lens field. Methods. We infer the relative density of the SDSS J0924+0219 field by computing weighted number counts for all galaxies in the field, and comparing to weighted number counts computed for a large number of fields in a reference survey. We then compute weighted number counts in the Millennium Simulation and compare these results to infer the external convergence of the lens field. Results. Our results show the SDSS J0924+0219 field is a fairly typical line of sight, with median κext = −0.012 and standard deviation σκ = 0.028.

Coupling men‐to‐women: Promoting innovation in emerging markets

AbstractMotivated by calls to explore corporate outcomes of gender diversity‐related dependencies in a firm's upper echelons, we examine whether gender diversity in manager‐owner(s) teams shapes innovation. Using the context of emerging markets and a large surveydataset drawn from the Business Environment and Enterprise Performance Survey (BEEPS), we find robust evidence suggesting that gender‐diverse manager‐owner(s) teams are associated with higher odds of undertaking innovation. Specifically, women (men) owned firms managed by a male (female) top manager are associated with a higher likelihood of undertaking innovation relative to women (men) owned firms managed by women (men). Additionally, we find that female‐owned firms run by male (female) top managers are the most (least) innovative and male‐owned firms run by female (male) top managers are the second (third) innovative. Our findings indicate that heterophilic manager‐owner(s) teams have the potential to foster innovation in emerging markets. More importantly, our results suggest that initiatives promoting manager‐owner(s) team's gender diversity have the potential to overcome the social and structural barriers that impede innovation in emerging economies.

Leveraging Predictive Modelling from Multiple Sources of Big Data to Improve Sample Efficiency and Reduce Survey Nonresponse Error

Abstract Big data has been fruitfully leveraged as a supplement for survey data—and sometimes as its replacement—and in the best of worlds, as a “force multiplier” to improve survey analytics and insight. We detail a use case, the big data classifier (BDC), as a replacement to the more traditional methods of targeting households in survey sampling for given specific household and personal attributes. Much like geographic targeting and the use of commercial vendor flags, we detail the ability of BDCs to predict the likelihood that any given household is, for example, one that contains a child or someone who is Hispanic. We specifically build 15 BDCs with the combined data from a large nationally representative probability-based panel and a range of big data from public and private sources, and then assess the effectiveness of these BDCs to successfully predict their range of predicted attributes across three large survey datasets. For each BDC and each data application, we compare the relative effectiveness of the BDCs against historical sample targeting techniques of geographic clustering and vendor flags. Overall, BDCs offer a modest improvement in their ability to target subpopulations. We find classes of predictions that are consistently more effective, and others where the BDCs are on par with vendor flagging, though always superior to geographic clustering. We present some of the relative strengths and weaknesses of BDCs as a new method to identify and subsequently sample low incidence and other populations.

Population density, urbanisation and agricultural mechanisation in modern Ghana

AbstractFarmers' technological choices take place within farming systems that are shaped by population pressure, connectivity to urban markets and agro‐ecological conditions. The relationship between these drivers and agricultural technology use is ambiguous. On the one hand, population growth can increase the supply of labour, driving down wage rates and reducing the incentives for mechanisation. On the other hand, rural–urban movements of people can reduce rural labour supply while simultaneously driving up the demand for food and hence the demand for mechanisation. Past theories of agricultural mechanisation have explained the low adoption of agricultural machinery in land‐abundant cereal production systems of SSA in terms of these drivers. However, recent empirical observations find extensive adoption of mechanised ploughing technology by small, medium and large‐scale farmers in Ghana. Examining the Ghanaian experience can thus shed new light on theories of mechanisation. A large household survey dataset covering eight districts is combined with geo‐spatial data on population, urban proximity and agro‐ecological factors to consider whether the existing theories are able to explain farm‐level adoption decisions in this context. The analysis finds that a farmer's decision to use agricultural machinery is associated with lower population density and proximity to urban centres. In northern Ghana, these drivers of technological change are as important as farm household characteristics in understanding cross‐sectional patterns of machinery adoption.

Spectator Interest in Attending Future Danish Men’s National Soccer Team Matches: A Study of Demand

Abstract: Existing research on spectator demand tends to focus primarily on elite club sport. This paper aims to expand on the literature by applying regression models to a large and unique set of survey data collected from Danish men’s national soccer team matches held from 2013 to 2017. The output from our models suggests that the number of matches attended is positively related to future demand, as are the results of the matches. Our results have implications for managers of national sports federations because they provide information on how spectators’ interest in and intention to attend matches involving national teams are related, which can assist them in improving spectator demand in the future.

Phosphorus storage and allocation in vegetation on the Tibetan Plateau

Phosphorus (P) is critical for vegetation growth and ecosystem productivity. P distribution and allocation patterns of plants in natural communities are associated with their resource allocation strategies. However, owing to the lack of systematic research, the spatial variations of vegetation P on the Tibetan Plateau (TP) remain unclear. Based on the 0.5° standardized spatial grid sampling method and survey data obtained from plant organs (leaf, branch, trunk, and root) in 2,040 plant communities, we analyzed the distribution of vegetation P contents (g kg−1) and densities (g P m−2) on the TP. The results indicate that the vegetation P contents and densities differed significantly among the plant organs on the TP. Active organs contained higher P contents but lower P densities. Radiation dominated vegetation P storage and allocation on the TP. Strong radiation had a negative effect on vegetation P accumulation and caused more P to be allocated to the underground parts. The TP vegetation stored approximately 3.64 Tg of P, of which approximately 1.68, 1.28, 0.68, and 0.01 Tg of P were stored in forests, shrublands, grasslands, and deserts, respectively. Based on a large survey dataset, we determined the spatial distributions of vegetation P and constructed a P density distribution map for TP vegetation, which can be used for ecosystem modeling.

Dynamic Model for Caragana korshinskii Shrub Aboveground Biomass Based on Theoretical and Allometric Growth Equations

As one of the ways to achieve carbon neutralization, shrub biomass plays an important role for natural resource management decision making in arid regions. To investigate biomass dynamic variations of Caragana korshinskii, a typical shrub found in the arid desert area of Ningxia, northwest China, we combined a nonlinear simultaneous (NLS) equation system with theoretical growth (TG) and allometric growth (AG) equations. On the basis of a large biomass survey dataset and analytical data of shrub stems, four methods (NOLS, NSUR, 2SLS, and 3SLS) of the NLS equations system were combined with the TG and AG equations. A model was subsequently established to predict the AGB growth of C. korshinskii. The absolute mean residual (AMR), root mean system error (RMSE), and adjusted determination coefficient (adj-R2) were used to evaluate the performance of the equations. Results revealed that the NSUR method of the NLS equations had better performance than other methods and the independent equations for BD and H growth and AGB. Additionally, the NSUR method exhibited extremely significant differences (p < 0.0001) when compared with the equations without heteroscedasticity on the basis of the likelihood ratio (LR) test, which used the power function (PF) as the variance function. The NSUR method of the NLS equations was an efficient method for predicting the dynamic growth of AGB by combining the TG and AG equations and could estimate the carbon storage for shrubs accurately, which was important for stand productivity and carbon sequestration capacity.

Economic preferences and temperament traits among business leaders and paid employees

This paper explores individuals’ economic preferences and temperament traits among different categories of business leaders (i.e., managers, self-employed with employees, self-employed without employees) and paid employees. We assume that these quite stable preferences and traits play a role in predicting occupational choice toward leadership roles. We use a large individual-level survey dataset (n = 5890) from the Northern Finland Birth Cohort 1966 at age 46 with linkages to nationally registered data. We construct survey measures for three types of economic preferences: risk, time, and social preferences. We use Cloninger’s inventory to measure four main temperament traits: harm avoidance, reward dependence, novelty-seeking, and persistence. We show that business leaders, in general, have different economic preferences and temperament traits than paid employees. To become a manager, especially the temperament trait of novelty-seeking seems relevant; and to become self-employed, particularly the economic preference of risk-taking appears as important.

Groups and Protocluster Candidates in the CLAUDS and HSC-SSP Joint Deep Surveys

Using the extended halo-based group finder developed by Yang et al., which is able to deal with galaxies via spectroscopic and photometric redshifts simultaneously, we construct galaxy group and candidate protocluster catalogs in a wide redshift range (0 < z < 6) from the joint CFHT Large Area U-band Deep Survey and Hyper Suprime-Cam Subaru Strategic Program deep data set. Based on a selection of 5,607,052 galaxies with i-band magnitude m i < 26 and a sky coverage of 34.41 deg2, we identify a total of 2,232,134 groups, of which 402,947 groups have at least three member galaxies. We have visually checked and discussed the general properties of these richest groups at redshift z > 2.0. By checking the galaxy number distributions within a 5–7 h −1Mpc projected separation and a redshift difference Δz ≤ 0.1 around those richest groups at redshift z > 2, we identify lists of 761, 343, and 43 protocluster candidates in the redshift bins 2 ≤ z < 3, 3 ≤ z < 4, and z ≥ 4, respectively. In general, these catalogs of galaxy groups and protocluster candidates will provide useful environmental information in probing galaxy evolution along cosmic time.

Measuring re-identification risk using a synthetic estimator to enable data sharing.

BackgroundOne common way to share health data for secondary analysis while meeting increasingly strict privacy regulations is to de-identify it. To demonstrate that the risk of re-identification is acceptably low, re-identification risk metrics are used. There is a dearth of good risk estimators modeling the attack scenario where an adversary selects a record from the microdata sample and attempts to match it with individuals in the population.ObjectivesDevelop an accurate risk estimator for the sample-to-population attack.MethodsA type of estimator based on creating a synthetic variant of a population dataset was developed to estimate the re-identification risk for an adversary performing a sample-to-population attack. The accuracy of the estimator was evaluated through a simulation on four different datasets in terms of estimation error. Two estimators were considered, a Gaussian copula and a d-vine copula. They were compared against three other estimators proposed in the literature.ResultsTaking the average of the two copula estimates consistently had a median error below 0.05 across all sampling fractions and true risk values. This was significantly more accurate than existing methods. A sensitivity analysis of the estimator accuracy based on variation in input parameter accuracy provides further application guidance. The estimator was then used to assess re-identification risk and de-identify a large Ontario COVID-19 behavioral survey dataset.ConclusionsThe average of two copula estimators consistently provides the most accurate re-identification risk estimate and can serve as a good basis for managing privacy risks when data are de-identified and shared.

The Economic Impact of Climate Change on Wheat and Maize Yields in the North China Plain.

Climate change has significantly affected agricultural production. As one of China’s most important agricultural production regions, the North China Plain (NCP) is subject to climate change. This paper examines the influence of climate change on the wheat and maize yields at household and village levels, using the multilevel model based on a large panel survey dataset in the NCP. The results show that: (i) Extreme weather events (drought and flood) would significantly reduce the wheat and maize yields. So, the governments should establish and improve the emergency service system of disaster warning and encourage farmers to mitigate the adverse effects of disasters. (ii) Over the past three decades, the NCP has experienced climate change that affects its grain production. Therefore, it is imperative to build the farmers’ adaptive capacity to climate change. (iii) Spatial variations in crop yield are significantly influenced by the household characteristics and the heterogeneity of village economic conditions. Therefore, in addition to promoting household production, it is necessary to strengthen and promote China’s development of the rural collective economy, especially the construction of rural irrigation and drainage infrastructures.