Estimation of unemployment rates in small areas of Portugal: a best linear unbiased prediction approach versus a hierarchical Bayes approach

The high level of unemployment is one of the major problems in most European countries nowadays. Hence, the demand for small area labor market statistics has rapidly increased over the past few years. The Labour Force Survey (LFS) conducted by the Portuguese Statistical Office is the main source of official statistics on the labour market at the macro level (e.g. NUTS2 and national level). However, the LFS was not designed to produce reliable statistics at the micro level (e.g. NUTS3, municipalities or further disaggregate level) due to small sample sizes. Consequently, traditional design-based estimators are not appropriate. A solution to this problem is to consider model-based estimators that "borrow information" from related areas or past samples by using auxiliary information. This paper reviews, under the model-based approach, Best Linear Unbiased Predictors and an estimator based on the posterior predictive distribution of a Hierarchical Bayesian model. The goal of this paper is to analyze the possibility to produce accurate unemployment rate statistics at micro level from the Portuguese LFS using these kinds of stimators. This paper discusses the advantages of using each approach and the viability of its implementation.

Denudation rates of small transient catchments controlled by former glaciation: The Hörnli nunatak in the northeastern Swiss Alpine Foreland

Extensive glaciers repeatedly occupied the northern Alpine Foreland during the Pleistocene and left a strongly glacially overprinted low slope landscape. Only few islands appeared as nunataks standing above the surface of the large piedmont glacier lobes. These nunatak areas kept their original shape, manifested in steep catchments with mean slopes up to 33 . Even though not glaciated, these catchments where significantly affected by base-level changes occurring as a consequence of phases of glacier advances and retreats. Both domains, the glacially eroded and non-eroded, are therefore prone to different mechanisms and time-scales of fluvial and colluvial re-adjustment. In this study we investigate these effects by exploring the spatial distribution and magnitude of denudation in the Hörnli region of the eastern Swiss Alpine Foreland in the present Interglacial. The area represents both domains in a relatively small area with largely uniform tectonic, lithologic and climatic conditions. The differences in Holocene andscape evolution are investigated using topographic analyses and catchment-averaged denudation rates derived from 10Be concentrations in fluvial quartz sand. We find that in formerly non-glaciated, fluvially dominated catchments close hillslope-channel coupling prevails and that these catchments yield high average denudation rates of 350 mm/ka. Glacially overprinted catchments yielded catchment-wide denudation rates an order of magnitude lower. These low denudation rates are hypothesized to be the consequence of both (i) a dominance of slow hillslope processes and (ii) admixture of high concentration, pre-LGM glacial sediment. This suggests that a) a careful field investigation must accompany the denudation rate studies and b) that the concept of area-weighted cosmogenic nuclide denudation rates must be considered in light of the predominant catchment processes.

Thorn Creek area assessment /

Examines a small area of southern Cook County and northeastern Will County in Illinois--a portion of which has been designated a state Resource Rich Area.

Can small MPAs protect local populations of a coastal flatfish, Solea senegalensis?

The benefits of protection of a small (4.3 km(2)) marine protected area (MPA) for Senegalese sole, Solea senegalensis, were investigated through experimental fishing trials and long-term (up to 293days) passive acoustic telemetry. A total of 106 trammel net sets were carried out between 2007 and 2011. Significant differences in abundance and biomass of sole between bottom types/depths (sandy bottoms between 12 and 20m deep vs muddy bottoms between 35 and 45m deep) were found, but no significant differences were attributable to the implementation of the no-take area. Passive acoustic telemetry revealed that most Senegalese sole spent a large part of their time between first and last detections (average residency index=69%) inside a relatively small area (average 95%=1.2km(2)), during which they preferred sandy bottoms, the most common habitat inside the MPA. Results also demonstrated that Senegalese sole do regular excursions beyond reserve boundaries, eventually emigrating from the MPA. The results suggest that small coastal MPAs providing adequate habitat may protect individuals of this species while allowing for moderate levels of adult spillover to neighbouring areas.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states—perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of “excess” zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to “excess” zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed—and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros

A modified dual-band microstrip monopole antenna

A modified microstrip-fed planar monopole antenna with open circuited coupled line is presented in this paper. The operational bandwidth of the proposed antenna covers the 2.4 GHz ISM band (2.42-2.48 GHz) and the 5 GHz WLAN band (5 GHz to 6 GHz). The radiating elements occupy a small area of 23×8 mm2. The Finite Difference Time Domain method is used to predict the input impedance of the antenna. The calculated return loss shows very good agreement with measured data. Reasonable antenna gain is observed across the operating band. The measured radiation patterns are similar to those of a simple monopole antenna.

Extinction risk in cloud forest fragments under climate change and habitat loss

Aim: To quantify the consequences of major threats to biodiversity, such as climate and land-use change, it is important to use explicit measures of species persistence, such as extinction risk. The extinction risk of metapopulations can be approximated through simple models, providing a regional snapshot of the extinction probability of a species. We evaluated the extinction risk of three species under different climate change scenarios in three different regions of the Mexican cloud forest, a highly fragmented habitat that is particularly vulnerable to climate change. Location: Cloud forests in Mexico. Methods: Using Maxent, we estimated the potential distribution of cloud forest for three different time horizons (2030, 2050 and 2080) and their overlap with protected areas. Then, we calculated the extinction risk of three contrasting vertebrate species for two scenarios: (1) climate change only (all suitable areas of cloud forest through time) and (2) climate and land-use change (only suitable areas within a currently protected area), using an explicit patch-occupancy approximation model and calculating the joint probability of all populations becoming extinct when the number of remaining patches was less than five. Results: Our results show that the extent of environmentally suitable areas for cloud forest in Mexico will sharply decline in the next 70 years. We discovered that if all habitat outside protected areas is transformed, then only species with small area requirements are likely to persist. With habitat loss through climate change only, high dispersal rates are sufficient for persistence, but this requires protection of all remaining cloud forest areas. Main conclusions: Even if high dispersal rates mitigate the extinction risk of species due to climate change, the synergistic impacts of changing climate and land use further threaten the persistence of species with higher area requirements. Our approach for assessing the impacts of threats on biodiversity is particularly useful when there is little time or data for detailed population viability analyses. © 2013 John Wiley & Sons Ltd.

Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis

Background: Preventing risk factor exposure is vital to reduce the high burden from lung cancer. The leading risk factor for developing lung cancer is tobacco smoking. In Australia, despite apparent success in reducing smoking prevalence, there is limited information on small area patterns and small area temporal trends. We sought to estimate spatio-temporal patterns for lung cancer risk factors using routinely collected population-based cancer data. Methods: The analysis used a Bayesian shared component spatio-temporal model, with male and female lung cancer included separately. The shared component reflected exposure to lung cancer risk factors, and was modelled over 477 statistical local areas (SLAs) and 15 years in Queensland, Australia. Analyses were also run adjusting for area-level socioeconomic disadvantage, Indigenous population composition, or remoteness. Results: Strong spatial patterns were observed in the underlying risk factor exposure for both males (median Relative Risk (RR) across SLAs compared to the Queensland average ranged from 0.48-2.00) and females (median RR range across SLAs 0.53-1.80), with high exposure observed in many remote areas. Strong temporal trends were also observed. Males showed a decrease in the underlying risk across time, while females showed an increase followed by a decrease in the final two years. These patterns were largely consistent across each SLA. The high underlying risk estimates observed among disadvantaged, remote and indigenous areas decreased after adjustment, particularly among females. Conclusion: The modelled underlying exposure appeared to reflect previous smoking prevalence, with a lag period of around 30 years, consistent with the time taken to develop lung cancer. The consistent temporal trends in lung cancer risk factors across small areas support the hypothesis that past interventions have been equally effective across the state. However, this also means that spatial inequalities have remained unaddressed, highlighting the potential for future interventions, particularly among remote areas.

A hybrid of modified PSO and local search on a multi-robot search system

Particle swarm optimization (PSO), a new population based algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obstacles. One of these defects is premature convergence. This means that one of the properties of basic PSO is that when particles are spread in a search space, as time increases they tend to converge in a small area. This shortcoming is also evident on a multi-robot search system, particularly when there are big static obstacles in the search space that prevent the robots from finding the target easily; therefore, as time increases, based on this property they converge to a small area that may not contain the target and become entrapped in that area.Another shortcoming is that basic PSO cannot guarantee the global convergence of the algorithm. In other words, initially particles explore different areas, but in some cases they are not good at exploiting promising areas, which will increase the search time.This study proposes a method based on the particle swarm optimization (PSO) technique on a multi-robot system to find a target in a search space containing big static obstacles. This method is not only able to overcome the premature convergence problem but also establishes an efficient balance between exploration and exploitation and guarantees global convergence, reducing the search time by combining with a local search method, such as A-star.To validate the effectiveness and usefulness of algorithms,a simulation environment has been developed for conducting simulation-based experiments in different scenarios and for reporting experimental results. These experimental results have demonstrated that the proposed method is able to overcome the premature convergence problem and guarantee global convergence.

Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study

Background Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival. Multilevel models assume independent geographical areas, whereas spatial models explicitly incorporate geographical correlation, often via a conditional autoregressive prior. However the relative merits of these methods for large population-based studies have not been explored. Using a case-study approach, we report on the implications of using multilevel and spatial survival models to study geographical inequalities in all-cause survival. Methods Multilevel discrete-time and Bayesian spatial survival models were used to study geographical inequalities in all-cause survival for a population-based colorectal cancer cohort of 22,727 cases aged 20–84 years diagnosed during 1997–2007 from Queensland, Australia. Results Both approaches were viable on this large dataset, and produced similar estimates of the fixed effects. After adding area-level covariates, the between-area variability in survival using multilevel discrete-time models was no longer significant. Spatial inequalities in survival were also markedly reduced after adjusting for aggregated area-level covariates. Only the multilevel approach however, provided an estimation of the contribution of geographical variation to the total variation in survival between individual patients. Conclusions With little difference observed between the two approaches in the estimation of fixed effects, multilevel models should be favored if there is a clear hierarchical data structure and measuring the independent impact of individual- and area-level effects on survival differences is of primary interest. Bayesian spatial analyses may be preferred if spatial correlation between areas is important and if the priority is to assess small-area variations in survival and map spatial patterns. Both approaches can be readily fitted to geographically enabled survival data from international settings

Predicting health programme participation: A gravity‐based, hierarchical modelling approach

Statistical analyses of health program participation seek to address a number of objectives compatible with the evaluation of demand for current resources. In this spirit, a spatial hierarchical model is developed for disentangling patterns in participation at the small area level, as a function of population-based demand and additional variation. For the former, a constrained gravity model is proposed to quantify factors associated with spatial choice and account for competition effects, for programs delivered by multiple clinics. The implications of gravity model misspecification within a mixed effects framework are also explored. The proposed model is applied to participation data from a no-fee mammography program in Brisbane, Australia. Attention is paid to the interpretation of various model outputs and their relevance for public health policy.

Remote sensing of nitrogen and water stress in wheat

Nitrogen (N) is the largest agricultural input in many Australian cropping systems and applying the right amount of N in the right place at the right physiological stage is a significant challenge for wheat growers. Optimizing N uptake could reduce input costs and minimize potential off-site movement. Since N uptake is dependent on soil and plant water status, ideally, N should be applied only to areas within paddocks with sufficient plant available water. To quantify N and water stress, spectral and thermal crop stress detection methods were explored using hyperspectral, multispectral and thermal remote sensing data collected at a research field site in Victoria, Australia. Wheat was grown over two seasons with two levels of water inputs (rainfall/irrigation) and either four levels (in 2004; 0, 17, 39 and 163 kg/ha) or two levels (in 2005; 0 and 39 kg/ha N) of nitrogen. The Canopy Chlorophyll Content Index (CCCI) and modified Spectral Ratio planar index (mSRpi), two indices designed to measure canopy-level N, were calculated from canopy-level hyperspectral data in 2005. They accounted for 76% and 74% of the variability of crop N status, respectively, just prior to stem elongation (Zadoks 24). The Normalised Difference Red Edge (NDRE) index and CCCI, calculated from airborne multispectral imagery, accounted for 41% and 37% of variability in crop N status, respectively. Greater scatter in the airborne data was attributable to the difference in scale of the ground and aerial measurements (i.e., small area plant samples against whole-plot means from imagery). Nevertheless, the analysis demonstrated that canopy-level theory can be transferred to airborne data, which could ultimately be of more use to growers. Thermal imagery showed that mean plot temperatures of rainfed treatments were 2.7 °C warmer than irrigated treatments (P < 0.001) at full cover. For partially vegetated fields, the two-Dimensional Crop Water Stress Index (2D CWSI) was calculated using the Vegetation Index-Temperature (VIT) trapezoid method to reduce the contribution of soil background to image temperature. Results showed rainfed plots were consistently more stressed than irrigated plots. Future work is needed to improve the ability of the CCCI and VIT methods to detect N and water stress and apply both indices simultaneously at the paddock scale to test whether N can be targeted based on water status. Use of these technologies has significant potential for maximising the spatial and temporal efficiency of N applications for wheat growers. ‘Ground–breaking Stuff’- Proceedings of the 13th Australian Society of Agronomy Conference, 10-14 September 2006, Perth, Western Australia.

Demographics, pollination syndrome and conservation status of Macrozamia platyrhachis (Zamiaceae), a geographically restricted Queensland cycad

To assess the International Union for Conservation of Nature (IUCN) status of Macrozamia platyrhachis F.M.Bailey, we surveyed this central Queensland cycad for its population abundance and health and its pollinator type and pollination syndrome (thermogenesis and volatile emissions). Plants are locally abundant within the 11 discrete populations surveyed, with an estimated population of 611 315 adult plants. Plants are highly restricted to a small area of occupancy, seed dispersal is nearly non-existent and extreme fires appear to have destroyed almost all seeds and seedlings and decimated the pollinators. Of known Macrozamia pollinators, only the thrips, Cycadothrips chadwicki Mound, were found on cones, and these were found in very low numbers. The pollination syndrome for this cycad appears to be unique, based on two cone traits. For one, thermogenesis peaks in early evening, a contrast with daytime peaks of other Cycadothrips-pollinated Macrozamia, but matches that of the Tranes weevil-pollinated Macrozamia machinii. In addition, cone volatiles include both previously unreported compounds as well as those reported exclusively on either Cycadothrips- or Tranes-pollinated species. Based on its small, fragmented area of occupancy, projected population declines and the unique pollination syndrome, we recommend that M. platyrhachis retain its current status as 'Endangered'. Habitat management plans should stipulate that controlled burns be avoided during cycad coning season and that wildfires be controlled to minimise damage to seedlings and pollinators.

Phytoplankton ecology of subarctic lakes in Finnish Lapland

Climate is warming and it is especially seen in arctic areas, where the warming trend is expected to be greatest. Arctic freshwater ecosystems, which are a very characteristic feature of the arctic landscape, are especially sensitive to climate change. They could be used as early warning systems, but more information about the ecosystem functioning and responses are needed for proper interpretation of the observations. Phytoplankton species and assemblages could be especially suitable for climate-related studies, since they have short generation times and react rapidly to changes in the environment. In addition, phytoplankton provides a good tool for lake classifications, since different species have different requirements and tolerance ranges for various environmental factors. The use of biological indicators is especially useful in arctic areas, were many of the chemical factors commonly fall under the detection limit and therefore do not provide much information about the environment. This work brings new information about species distribution and dynamics of arctic freshwater phytoplankton in relation to environmental factors. The phytoplankton of lakes in Finnish Lapland and other European high-altitude or high-latitude areas were compared. Most lakes were oligotrophic and dominated by flagellated species belonging to chrysophytes, cryptophytes and dinoflagellates. In Finnish Lapland cryptophytes were of less importance, whereas desmids had high species richness in many of the lakes. In Pan-European scale, geographical and catchment-related factors were explaining most of the differences in species distributions between different districts, whereas lake water chemistry (especially conductivity, SiO2 and pH) was most important regionally. Seasonal and interannual variation of phytoplankton was studied in subarctic Lake Saanajärvi. Characteristic phytoplankton species in this oligotrophic, dimictic lake belonged mainly to chrysophytes and diatoms. The maximum phytoplankton biomass in Lake Saanajärvi occurs during autumn, while spring biomass is very low. During years with heavy snow cover the lake suffers from pH drop caused by melt waters, but the effects of this acid pulse are restricted to surface layers and last for a relatively short period. In addition to some chemical parameters (mainly Ca and nutrients), length of the mixing cycle and physical factors such as lake water temperature and thermal stability of water column had major impact on phytoplankton dynamics. During a year with long and strong thermal stability, the phytoplankton community developed towards an equilibrium state, with heavy dominance of only a few taxa for a longer period of time. During a year with higher windiness and less thermal stability, the species composition was more diverse and species with different functional strategies were able to occur simultaneously. The results of this work indicate that although arctic lakes in general share many common features concerning their catchment and water chemistry, large differences in biological features can be found even in a relatively small area. Most likely the lakes with very different algal flora do not respond in a similar way to differences in the environmental factors, and more information about specific arctic lake types is needed. The results also show considerable year to year differences in phytoplankton species distribution and dynamics, and these changes are most likely linked to climatic factors.

The effects of the 2004 reduction in the price of alcohol on alcohol-related harm in Finland : A natural experiment based on register data

Changes in alcohol pricing have been documented as inversely associated with changes in consumption and alcohol-related problems. Evidence of the association between price changes and health problems is nevertheless patchy and is based to a large extent on cross-sectional state-level data, or time series of such cross-sectional analyses. Natural experimental studies have been called for. There was a substantial reduction in the price of alcohol in Finland in 2004 due to a reduction in alcohol taxes of one third, on average, and the abolition of duty-free allowances for travellers from the EU. These changes in the Finnish alcohol policy could be considered a natural experiment, which offered a good opportunity to study what happens with regard to alcohol-related problems when prices go down. The present study investigated the effects of this reduction in alcohol prices on (1) alcohol-related and all-cause mortality, and mortality due to cardiovascular diseases, (2) alcohol-related morbidity in terms of hospitalisation, (3) socioeconomic differentials in alcohol-related mortality, and (4) small-area differences in interpersonal violence in the Helsinki Metropolitan area. Differential trends in alcohol-related mortality prior to the price reduction were also analysed. A variety of population-based register data was used in the study. Time-series intervention analysis modelling was applied to monthly aggregations of deaths and hospitalisation for the period 1996-2006. These and other mortality analyses were carried out for men and women aged 15 years and over. Socioeconomic differentials in alcohol-related mortality were assessed on a before/after basis, mortality being followed up in 2001-2003 (before the price reduction) and 2004-2005 (after). Alcohol-related mortality was defined in all the studies on mortality on the basis of information on both underlying and contributory causes of death. Hospitalisation related to alcohol meant that there was a reference to alcohol in the primary diagnosis. Data on interpersonal violence was gathered from 86 administrative small-areas in the Helsinki Metropolitan area and was also assessed on a before/after basis followed up in 2002-2003 and 2004-2005. The statistical methods employed to analyse these data sets included time-series analysis, and Poisson and linear regression. The results of the study indicate that alcohol-related deaths increased substantially among men aged 40-69 years and among women aged 50-69 after the price reduction when trends and seasonal variation were taken into account. The increase was mainly attributable to chronic causes, particularly liver diseases. Mortality due to cardiovascular diseases and all-cause mortality, on the other hand, decreased considerably among the-over-69-year-olds. The increase in alcohol-related mortality in absolute terms among the 30-59-year-olds was largest among the unemployed and early-age pensioners, and those with a low level of education, social class or income. The relative differences in change between the education and social class subgroups were small. The employed and those under the age of 35 did not suffer from increased alcohol-related mortality in the two years following the price reduction. The gap between the age and education groups, which was substantial in the 1980s, thus further broadened. With regard to alcohol-related hospitalisation, there was an increase in both chronic and acute causes among men under the age of 70, and among women in the 50-69-year age group when trends and seasonal variation were taken into account. Alcohol dependence and other alcohol-related mental and behavioural disorders were the largest category in both the total number of chronic hospitalisation and in the increase. There was no increase in the rate of interpersonal violence in the Helsinki Metropolitan area, and even a decrease in domestic violence. There was a significant relationship between the measures of social disadvantage on the area level and interpersonal violence, although the differences in the effects of the price reduction between the different areas were small. The findings of the present study suggest that that a reduction in alcohol prices may lead to a substantial increase in alcohol-related mortality and morbidity. However, large population group differences were observed regarding responsiveness to the price changes. In particular, the less privileged, such as the unemployed, were most sensitive. In contrast, at least in the Finnish context, the younger generations and the employed do not appear to be adversely affected, and those in the older age groups may even benefit from cheaper alcohol in terms of decreased rates of CVD mortality. The results also suggest that reductions in alcohol prices do not necessarily affect interpersonal violence. The population group differences in the effects of the price changes on alcohol-related harm should be acknowledged, and therefore the policy actions should focus on the population subgroups that are primarily responsive to the price reduction.