scholarly journals The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use

2019 ◽  
Vol 11 (3) ◽  
pp. 1385-1409 ◽  
Author(s):  
Stefan Leyk ◽  
Andrea E. Gaughan ◽  
Susana B. Adamo ◽  
Alex de Sherbinin ◽  
Deborah Balk ◽  
...  
Keyword(s):  
Large Scale ◽  
Population Data ◽  
Disaster Risk ◽  
Spatial Allocation ◽  
Quality Aspects ◽  
Data Products ◽  
Methodological Approaches ◽  
Different Characteristics ◽  
Population Counts ◽  
Relative Quality

Abstract. Population data represent an essential component in studies focusing on human–nature interrelationships, disaster risk assessment and environmental health. Several recent efforts have produced global- and continental-extent gridded population data which are becoming increasingly popular among various research communities. However, these data products, which are of very different characteristics and based on different modeling assumptions, have never been systematically reviewed and compared, which may impede their appropriate use. This article fills this gap and presents, compares and discusses a set of large-scale (global and continental) gridded datasets representing population counts or densities. It focuses on data properties, methodological approaches and relative quality aspects that are important to fully understand the characteristics of the data with regard to the intended uses. Written by the data producers and members of the user community, through the lens of the “fitness for use” concept, the aim of this paper is to provide potential data users with the knowledge base needed to make informed decisions about the appropriateness of the data products available in relation to the target application and for critical analysis.

scholarly journals Allocating people to pixels: A review of large-scale gridded population data products and their fitness for use

2019 ◽  
Author(s):  
Stefan Leyk ◽  
Andrea E. Gaughan ◽  
Susana B. Adamo ◽  
Alex de Sherbinin ◽  
Deborah Balk ◽  
...  
Keyword(s):  
Critical Analysis ◽  
Large Scale ◽  
Population Data ◽  
Disaster Risk ◽  
Quality Aspects ◽  
Data Products ◽  
Methodological Approaches ◽  
Different Characteristics ◽  
Population Counts ◽  
Relative Quality

Abstract. Population data represent an essential component in studies focusing on human-nature interrelationships, disaster risk assessment and environmental health. Several recent efforts have produced global and continental-extent gridded population data which are becoming increasingly popular among various research communities. However, these data products, which are of very different characteristics and based on different modeling assumptions, have never been systematically reviewed and compared which may impede their appropriate use. This article fills this gap and presents, compares and discusses a set of large-scale (global and continental) gridded datasets representing population counts or densities. It focuses on data properties, methodological approaches and relative quality aspects that are important to fully understand the characteristics of the data with regard to the intended uses. Written by the data producers and members of the user community, through the lens of the “fitness for use” concept, the aim of this paper is to provide potential data users with the knowledge base needed to make informed decisions about the appropriateness of the data products available in relation to the target application and for critical analysis.

The Impact of Quantitative Easing on Emerging Markets – Literature Review

e-Finanse ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 67-76
Author(s):  
Piotr Bartkiewicz
Keyword(s):  
Emerging Markets ◽  
Large Scale ◽  
Empirical Literature ◽  
Quantitative Easing ◽  
Methodological Choices ◽  
The Subject ◽  
Methodological Approaches ◽  
The Impact ◽  
Sufficient Precision

AbstractThe article presents the results of the review of the empirical literature regarding the impact of quantitative easing (QE) on emerging markets (EMs). The subject is of interest to policymakers and researchers due to the increasingly larger role of EMs in the world economy and the large-scale capital flows occurring after 2009. The review is conducted in a systematic manner and takes into consideration different methodological choices, samples and measurement issues. The paper puts the summarized results in the context of transmission channels identified in the literature. There are few distinct methodological approaches present in the literature. While there is a consensus regarding the direction of the impact of QE on EMs, its size and durability have not yet been assessed with sufficient precision. In addition, there are clear gaps in the empirical findings, not least related to relative underrepresentation of the CEE region (in particular, Poland).

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

scholarly journals Control method for the stable operation of distributed inverters following the introduction of large-scale renewable energy to a remote island grid

Clean Energy ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 196-207
Author(s):  
Shoichi Sato ◽  
Yasuhiro Noro
Keyword(s):  
Renewable Energy ◽  
Large Scale ◽  
Control Method ◽  
Synchronous Generator ◽  
Stable Operation ◽  
Battery Energy Storage Systems ◽  
Simulation Results ◽  
Battery Energy ◽  
Systems Simulation ◽  
Different Characteristics

Abstract The introduction of large-scale renewable energy requires a control system that can operate multiple distributed inverters in a stable way. This study proposes an inverter control method that uses information corresponding to the inertia of the synchronous generator to coordinate the operation of battery energy storage systems. Simulation results for a system with multiple inverters applying the control method are presented. Various faults such as line-to-line short circuits and three-phase line-to-ground faults were simulated. Two fault points with different characteristics were compared. The voltage, frequency and active power quickly returned to their steady-state values after the fault was eliminated. From the obtained simulation results, it was verified that our control method can be operated stably against various faults.

Selection and Substantiation of Methods for Evaluating Cosmetic Products for Deodorant and Antiperspirant Action

2021 ◽  
Vol 37 (2) ◽  
pp. 54-64
Author(s):  
D.V. Barabash ◽  
I.A. Butorova
Keyword(s):  
Large Scale ◽  
Gravimetric Method ◽  
Population Data ◽  
Diffusion Method ◽  
Disc Diffusion ◽  
Disc Diffusion Method ◽  
Large Scale Testing ◽  
Sweat Secretion ◽  
Microbiological Methods ◽  
Number Of Microorganisms

The possibility of using simple and available methods for analyzing deodorants/antiperspirants has been studied. The gravimetric method was shown to have acceptable metrological characteristics under repeatability conditions when evaluating antiperspirant activity. A decrease in the number of microorganisms (CFU) on the axilla skin was observed in a rinse test experiment 4 h and 8 h after the application of deodorants/antiperspirants. The microbial population data were inversely proportional to the antiperspirant activity values of the tested compositions. The sweat secretion reducing decreases the amount of nutrients required for microbial development, which makes it possible to use the rinse test to indirectly evaluate deodorant activity in research and development of personal care products. However, due to its laboriousness and the need for volunteers, the method cannot be recommended for large-scale testing. It was shown that the disc diffusion method (DDM) used to detect Staphylococcus aureus, Pseudomonas aeruginosa and Bacillus subtilis cannot be applied to the assessment of the intrinsic antimicrobial activity of the tested cosmetic compositions. This indicates the necessity of additional studies to select test microorganisms typical for the armpit area. In addition, DDM is useful if the deodorant effect of the composition is created by the addition of low-volatile antibacterial compounds. Therefore, microbiological methods have limited applications and are not suitable for widespread use. deodorant action; antiperspirant action, gravimetry, disc diffusion method, rinse test; deodorant; antiperspirant; cosmetic; efficiency; consumer properties, functional properties This work was supported by MUCTR (project no. K-2020-007).

Multilevel Regression and Poststratification Versus Survey Sample Weighting for Estimating Population Quantities in Large Population Health Studies

2020 ◽  
Vol 189 (7) ◽  
pp. 717-725 ◽  
Author(s):  
Marnie Downes ◽  
John B Carlin
Keyword(s):  
Population Health ◽  
Large Scale ◽  
Census Data ◽  
Large Population ◽  
Population Data ◽  
Superior Performance ◽  
Population Parameter ◽  
Multilevel Regression ◽  
Sample Weighting ◽  
Survey Weighting

Abstract Multilevel regression and poststratification (MRP) is a model-based approach for estimating a population parameter of interest, generally from large-scale surveys. It has been shown to be effective in highly selected samples, which is particularly relevant to investigators of large-scale population health and epidemiologic surveys facing increasing difficulties in recruiting representative samples of participants. We aimed to further examine the accuracy and precision of MRP in a context where census data provided reasonable proxies for true population quantities of interest. We considered 2 outcomes from the baseline wave of the Ten to Men study (Australia, 2013–2014) and obtained relevant population data from the 2011 Australian Census. MRP was found to achieve generally superior performance relative to conventional survey weighting methods for the population as a whole and for population subsets of varying sizes. MRP resulted in less variability among estimates across population subsets relative to sample weighting, and there was some evidence of small gains in precision when using MRP, particularly for smaller population subsets. These findings offer further support for MRP as a promising analytical approach for addressing participation bias in the estimation of population descriptive quantities from large-scale health surveys and cohort studies.

scholarly journals Towards a Region-Specific Impact Assessment of Water Degradation In Water Footprinting

2019 ◽  
Author(s):  
Natalia Finogenova ◽  
Markus Berger ◽  
Lennart Schelter ◽  
Rike Becker ◽  
Tim Aus der Beek ◽  
...  
Keyword(s):  
Life Cycle ◽  
Human Health ◽  
Water Footprint ◽  
Population Data ◽  
Health Impacts ◽  
Human Toxicity ◽  
Local Climate ◽  
Water Footprinting ◽  
Quality Aspects ◽  
Human Health Impacts

Water footprint evaluates impacts associated with the water use along a product’s life cycle. In order to quantify impacts resulting from water pollution in a comprehensive manner, impact categories, such as human toxicity, were developed in the context of Life Cycle Assessment (LCA). Nevertheless, methods addressing human health impacts often have a low spatial resolution and, thus, are not able to model impacts on a local scale. To address this issue, we develop a region-specific model for the human toxicity impacts for the cotton-textile industry in Punjab, Pakistan. We analysed local cause-effect chains and created a region “Punjab” in the USEtox model using local climate, landscape, and population data. Finally, we calculated human health impacts for the emissions of pesticides from the cotton cultivation and heavy metals from the textile production. The results were compared to that obtained for the region India+ (where Pakistan belongs) provided by USEtox. The overall result obtained for Punjab is higher than that for India+. In Punjab, the dominant pathway is ingestion via drinking water, which contributes to two-thirds of the total impacts. Nevertheless, the USEtox model does not reflect the local cause-effect chains completely due to absence of the groundwater compartment. Since groundwater is the main source for drinking in Punjab, a more detailed analysis of the fate of and exposure to the pollutants is needed. This study demonstrates that a region-specific assessment of the water quality aspects is essential to provide a more robust evaluation of the human health impacts within water footprinting.

scholarly journals Definição automática de áreas censitárias na freguesia de Algés

Finisterra ◽  
2012 ◽  
Vol 38 (75) ◽  
Author(s):  
Nelson Mileu
Keyword(s):  
Large Scale ◽  
Population Data ◽  
Population Census ◽  
Master Plan ◽  
Reference Number ◽  
Thiessen Polygon ◽  
Socio Economic Variables ◽  
Census Area ◽  
Statistical Area ◽  
Stable Unit

AUTOMATIC STATISTICAL AREA DEFINITION IN ALGÉS PARISH. Portugal, along with many other countries, conducts a population census every 10 years. This way of counting the population offers a detailed photography of the country where many socio-economic variables are captured. In this study, it is assumed that census will be the way of population data collection and therefore the discussion deals with the issue of geographical aggregation of individuals and households enumerated. In this context, a methodological proposal for an automatic census area creation in the parish of Algés is described. The minimum geographic unit for completing the aggregations is the building since it was the smallest and stable unit for this purpose. The building layer was obtained in the municipal GIS of Oeiras and is based on large scale cartography (1:1000). Mainly, because the building layer is not contiguous, the study starts with the construction of a Thiessen polygon layer. This layer is drawn with the buildings layer centroids and having the master plan classes as barriers. The Thiessen polygon layer associated with the building attributes assumed a key role in this process and considering the range of possible data aggregation alternatives the zone boundaries are drawn concerning with some principles of consistence and homogeneity. To achieve a measurement of homogeneity for the statistical areas the variables used were the number of households, floors and type of building (residential, commercial, both). The automatic statistical area definition deals with the aggregation of the basic geographic units (Thiessen polygon layer), seeking for new optimized areas, considering the characteristics of the building and having a reference number of households around 100. To demonstrate the methodology used for the automatic statistical area definition, three simulation results are presented in this article.

scholarly journals Upscaled diurnal cycles of land-atmosphere fluxes: a new global half-hourly data product

2018 ◽  
Author(s):  
Paul Bodesheim ◽  
Martin Jung ◽  
Fabian Gans ◽  
Miguel D. Mahecha ◽  
Markus Reichstein
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Gross Primary Production ◽  
Net Ecosystem Exchange ◽  
Predictor Variables ◽  
Energy Fluxes ◽  
Diurnal Cycles ◽  
Monthly Average ◽  
Hourly Data ◽  
Data Products

Abstract. Interactions between the biosphere and the atmosphere can be well characterized by fluxes between the two. In particular, carbon and energy fluxes play a major role for understanding biogeochemical processes on ecosystem level or global scale. However, the fluxes can only be measured at individual sites by eddy covariance towers and an upscaling of these local observations is required to analyze global patterns. Previous work focused on upscaling monthly, eight-day, or daily average values and global maps for each flux have been provided accordingly. In this paper, we raise the upscaling of carbon and energy fluxes between land and atmosphere to the next level by increasing the temporal resolution to subdaily scales. We provide continuous half-hourly fluxes for the period from 2001 to 2014 at 0.5◦ spatial resolution, which allows for analyzing diurnal cycles globally. The dataset contains four fluxes: gross primary production (GPP), net ecosystem exchange (NEE), latent heat (LE), and sensible heat (H). We propose two prediction approaches for the diurnal cycles based on large-scale regression models and compare them in extensive cross-validation experiments using different sets of predictor variables. We analyze the results for a set of FLUXNET tower sites showing the suitability of our approaches for this upscaling task. Finally, we have selected one approach to calculate the global half- hourly data products based on predictor variables from remote sensing and meteorology at daily resolution as well as half-hourly potential radiation. In addition, we provide a derived product that only contains monthly average diurnal cycles, which is a lightweight version in terms of data storage that still enables to study the important characteristics of diurnal courses globally. We recommend to primarily use these monthly average diurnal cycles, because they are less affected by the impacts of day-to-day variation, observation noise, and short- term fluctuations on subdaily scales compared to the plain half-hourly flux products. The global half-hourly data products are available at https://doi.org/10.17871/BACI.224.

scholarly journals Natural Leaders: Some interlocutors elicit greater convergence across conversations and across characteristics

2020 ◽  
Author(s):  
Uriel Cohen Priva ◽  
Chelsea Sanker
Keyword(s):  
Individual Differences ◽  
Large Scale ◽  
Social Aspects ◽  
Large Collection ◽  
Human Communication ◽  
Large Scale Study ◽  
Different Characteristics

Are there natural followers in human communication, speakers who consistently converge more than others? Similarly, are there natural leaders, speakers with whom others converge more? Are such tendencies consistent across different linguistic characteristics? We use the Switchboard Corpus, a large collection of telephone conversations, to perform a large-scale study of convergence of speakers in multiple conversations with different interlocutors, across six linguistic characteristics. Having data from each speaker in several conversations makes it possible to investigate whether there are individual differences in likelihood to converge, among speakers (more or less likely to converge), and among interlocutors (more or less likely to elicit convergence). We only find evidence for individual differences by interlocutor, not by speaker; this shows that there are natural leaders, who elicit greater degrees of convergence than others, across different characteristics and different conversations. The absence of a similar finding for natural followers, speakers who converge more than others, suggests that the role of social aspects in mediating convergence is stronger than that of putative individual differences in propensity to converge, or that such propensities are characteristic-specific.

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