Geographical Correlation Research Articles

Unsupervised geographically discriminative feature learning for landmark tagging

Recently, a large number of geo-tagged landmark images have been uploaded through various social media services. Usually, these geo-tagged images are annotated by users with GPS and tags related to the landmarks where they are taken. Landmark tagging aims to automatically annotate an image with the tags to describe the landmark where the image is taken. It has been observed that the images and tags show strong correlation with the geographical locations. The widely used assumption by many existing tagging methods is that images are independently and identically distributed is not effective to capture the geographical correlation. In this paper, we study the novel problem of utilizing the geographical correlation among images and landmarks for better tagging landmark images. In particular, we propose an unsupervised feature learning approach to learn the geographically discriminative features across geographical locations, by integrating latent space learning and geographically structural analysis (LSGSA) into a joint model. A latent space learning model is proposed to effectively fuse the heterogeneous features of visual content and tags. Meanwhile, the geographical structure analysis and group sparsity are applied to learn the geographically discriminative features. Then, a geo-guided sparse reconstruction method is proposed to tag images by utilizing the discriminative information of features, in which the landmark-specific tags are boosted by a weighting method. Experiments on the real-world datasets demonstrate the superiority of our approach.

Stability and Performance of Coalitions of Prosumers Through Diversification in the Smart Grid

Achieving a successful energetic transition through a smarter and greener electricity grid is a major goal for the 21st century. It is assumed that such smart grids will be characterized by bidirectional electricity flows coupled with the use of small renewable generators and a proper efficient information system. All these bricks might enable end users to take part in the grid stability by injecting power, or by shaping their consumption against financial compensation. In this paper, we propose an algorithm that forms coalitions of agents, called prosumers, that both produce and consume. It is designed to be used by aggregators that aim at selling the aggregated surplus of production of the prosumers they control. We rely on real weather data sampled across stations of a given territory in order to simulate realistic production and consumption patterns for each prosumer. This approach enables us to capture geographical correlations among the agents while preserving the diversity due to different behaviors. As aggregators are bound to the grid operator by a contract, they seek to maximize their offer while minimizing their risk. The proposed graph based algorithm takes the underlying correlation structure of the agents into account and outputs coalitions with both high productivity and low variability. We show then that the resulting diversified coalitions are able to generate higher benefits on a constrained energy market, and are more resilient to random failures of the agents.

Long-term differential effects of transport infrastructure investment in rural areas

European and national transport policies identify territorial cohesion as their main goal. However, the lack of attention to intra-regional relationships has complicated the understanding of the diverse repercussions of these policies on the economic and social equity of rural areas over time. Long-term assessments of transport effects in these less studied areas, which cover more than two thirds of the European territory, are essential to identify common problems associated with transport investments and to detect potential strategies to confront their future challenges. This paper, analyses the effects of transport policy development in the Spanish Northwest Area over the last 20 years, where about 95% of municipalities are considered rural. The assessment focuses on the spatial effects of transport development, using a Geographical Information System (GIS), and inequality and correlation analyses of accessibility, demographic and economic indicators. Results show that while the accessibility gap between urban and rural areas was reinforced by the construction of high-capacity infrastructures during the first half of the study period, the completion of the network during the second decade balanced this situation, enabling a more equitable access to basic services, goods and opportunities. However, these improvements have not been enough to reverse the initial lagging inertia of the rural areas and to reduce their socioeconomic disadvantages against urban ones.

Open data and stormwater systems in Los Angeles: applications for equitable green infrastructure

ABSTRACTUrban stormwater systems traditionally used “grey” infrastructure to manage runoff. Contemporary designs now incorporate “green” infrastructure, which offers additional potential benefits such as urban amenities and health. Understanding how green and grey infrastructure investments are distributed across urban areas is important for new goals of promoting environmental justice in planning. In California, for instance, public investments increasingly require a percentage of funds to be spent in disadvantaged communities. Recent advancements in the availability of high-detail geographic data in cities can support prioritising investments to fulfil these multiple benefits. This paper analyses the distribution of stormwater infrastructure in Los Angeles (LA) County in relation to design criteria, urban structure and sociodemographic information. It demonstrates an approach for identifying projects that simultaneously address engineering needs and promote equity. Statistical analysis of high-detail sewer locations reveals geographic correlations with key local design parameters, urban characteristics and sociodemographic indicators. Watershed areas in LA County were identified that support multi-benefit projects, meeting dual criteria for infrastructure improvements and disadvantaged community status. As stormwater systems are increasingly designed for multi-benefit outcomes, new design frameworks can emphasise both performance and social equity.

Spatial transmission of H5N6 highly pathogenic avian influenza viruses among wild birds in Ibaraki Prefecture, Japan, 2016-2017.

From 29 November 2016 to 24 January 2017, sixty-three cases of H5N6 highly pathogenic avian influenza virus (HPAIV) infections were detected in wild birds in Ibaraki Prefecture, Japan. Here, we analyzed the genetic, temporal, and geographic correlations of these 63 HPAIVs to elucidate their dissemination throughout the prefecture. Full-genome sequence analysis of the Ibaraki isolates showed that 7 segments (PB2, PB1, PA, HA, NP, NA, NS) were derived from G1.1.9 strains while the M segment was from G1.1 strains; both groups of strains circulated in south China. Pathological studies revealed severe systemic infection in dead swans (the majority of dead birds and the only species necropsied), thus indicating high susceptibility to H5N6 HPAIVs. Coalescent phylogenetic analysis using the 7 G1.1.9-derived segments enabled detailed analysis of the short-term evolution of these highly homologous HPAIVs. This analysis revealed that the H5N6 HPAIVs isolated from wild birds in Ibaraki Prefecture were divided into 7 groups. Spatial analysis demonstrated that most of the cases concentrated around Senba Lake originated from a single source, and progeny viruses were transmitted to other locations after the infection expanded in mute swans. In contrast, within just a 5-km radius of the area in which cases were concentrated, three different intrusions of H5N6 HPAIVs were evident. Multi-segment analysis of short-term evolution showed that not only was the invading virus spread throughout Ibaraki Prefecture but also that, despite the small size of this region, multiple invasions had occurred during winter 2016-2017.

Flash Flood Risk Assessment for Kyushu Island, Japan

Using advanced geospatial analysis technologies, flash flood risk is assessed for the island of Kyushu, Japan. In this study, the flash flood risk is redefined in terms of the flash flood potential index (FFPI) and the flash flood residential hazard (FFRH). The island experiences rainy weather, especially in the summer (June–August), when catastrophic flash flood events have historically occurred. Studies of the surface hydrological properties of the island are very rare and localized; hence, geospatial techniques are most appropriate for the assessment process. The Soil Conservation Service rainfall-runoff model was used to estimate hydrological responses on the island. Four factors were included in the flash flood assessment. A multi-criteria analysis was carried out to map the FFPI and FFRH from the evaluation factors. The results show that the highest flash flood risk occurs in the northern parts of the island, where the soil displays relatively low infiltration rates and relatively high curve numbers, despite the comparatively low precipitation rates that occur there. The results indicate that soil hydrological properties are the main driving forces of flash floods, especially in regions with low precipitation rates. The results of this research are consistent with previous in situ measurements of runoff made at several sites on the island. The results also show a strong geographic correlation with historical flash flood events on the island. This research validates the use of geospatial analysis for large geographic regions where in situ measurements cannot be taken due to time or cost constraints. The results of this study provide decision makers with the information needed to select a management strategy to address possible future flash flood events that considers safety and water harvesting.

The knowledge economy city: Gentrification, studentification and youthification, and their connections to universities

We investigate the spatial relationships among three prominent facets of contemporary urbanism – gentrification, studentification, and youthification – in the context of Canadian post-secondary educational institutions (universities and colleges). We conduct the analysis in three major Canadian cities with substantial knowledge economy sectors using confidential Statistics Canada census files, which include information on individuals and their geographies, and the location of universities and colleges, by enrolment size. We document ‘spillover’ effects of expansions in student enrolment and the building of campuses by analysing the geographic correlations among universities and gentrification and youthification. Studentification and youthification are to some extent coincident but not entirely, whereas the connection to gentrification is more complex. Our work provides novel insight into the ways the three different facets of contemporary urbanism overlap and contribute to our understanding of how universities and colleges, as hallmarks of the knowledge economy, influence the social geography of cities.

Where Do Local Experts Go? Evaluating User Geo-Topical Similarity for Top-N Place Recommendation

Recent years have witnessed the ever-growing popularity of location-based social network (LBSN) services. Top-N place recommendation, which aims at retrieving N most appealing places for a target user, has thus gained increasing importance. Yet existing solutions to this problem either provide non-personalized recommendations by selecting nearby popular places, or resort to collaborative filtering (CF) by treating each place as an independent item, overlooking the geographical and semantic correlations among places. In this paper, we propose GoTo, a collaborative recommender that provides top-N personalized place recommendation in LBSNs. Compared with existing methods, GoTo achieves its effectiveness by exploiting the wisdom of the so-called local experts, namely those who are geographically close and have similar preferences with regard to a certain user. At the core of GoTo lies a novel user similarity measure called geo-topical similarity, which combines geographical and semantic correlations among places for discovering local experts. In specific, the geo-topical similarity uses Gaussian mixtures to model users’ real-life geographical patterns, and extracts users’ topical preferences from the attached tags of historically visited places. Extensive experiments on real LBSN datasets show that compared with baseline methods, GoTo can improve the performance of top-N place recommendation by up to 50% in terms of accuracy.

The Standard of Living and Revolutions in Russia, 1700–1917 by Boris Mironov

Reviewed by: The Standard of Living and Revolutions in Russia, 1700–1917 by Boris Mironov Anna Nemirovskaya (bio) Boris Mironov, The Standard of Living and Revolutions in Russia, 1700–1917 ed. Gregory L. Freeze (Abingdon, UK: Routledge, 2012). 668 pp., ills. Index. ISBN: 978-0-415-60854-1. Click for larger view View full resolution In this fundamental study, renowned Russian historian Boris Mironov examines the historical dynamics of the Russian Empire through the prism of evolving populations' living standards. In the preface, he promises to challenge conventional historiographic views on the dynamics and weighty factors of Russia's social and economic development. Specifically, the book is positioned as the first comprehensive study of the "anthropometric" history of imperial Russia, based on extensive archival evidence from 1700 to 1917. This voluminous 668-page study consists of ten chapters and statistical appendixes that occupy 30 percent of the book. Logically starting with general methodological chapters, the author consistently proceeds to a detailed analysis of specific indicators of the standard of living and conclusions about Russia's modernization and its population's well-being. Chapter 1 compares views on living standards in imperial Russia in domestic and international historiography and discusses their limitations, arguing the need for the historical anthropometric approach. Chapter 2 explains the basics of historical anthropometrics, its methodology, indicators, and restrictions. Chapter 3 presents the sources of anthropometric data, their representativeness, and the principles of organization in the author's database. Chapters 4 and 5 deal with the biological status of the population, studied mainly based on the statistics of army recruits. Chapter 6 considers regional differences in height distribution and includes statistical analysis of geographic correlations of height, mortality, [End Page 436] and marriage. Chapter 7 reconstructs living standards through various indicators of consumption, health, and biological status. Chapter 8 examines the dynamics of wages and prices in imperial Russia, attempting to construct indexes of consumer prices and nominal and real wages. Chapter 9 presents the contemporaneous assessments of the population's standard of living. Chapter 10 generalizes the results of analysis of statistics and narrative sources in preceding chapters. It summarizes the author's views of modernization in the Russian Empire, the well-being of its population, domestic policies, and the effectiveness of the state reforms. Mironov's fundamental study transcends the research agenda and approaches of modern mainstream historiographies of imperial Russia. He offers a truly multifaceted survey of the living conditions over the period of two centuries to prove that there were no objective reasons for revolution. Mironov consistently refutes two ideas: the concept of Russia's Sonderweg and the theory that systematic crises of the imperial regime prompted revolutions. His analysis of anthropometric indicators, environmental conditions, vital statistics, consumption patterns, wages, and prices support the book's favorable assessment of the empire's development. Published six years ago, this book has already been widely discussed by scholars, so in this review I will try to avoid repeating the praise and criticism of previous reviewers. The critiques have mainly concerned certain distortions in the data analysis and misinterpretation of the primary sources. Quantitative researchers of historical dynamics have also challenged certain methodological aspects of Mironov's use of statistics.1 To be fair, tackling a multifactor problem situated at the intersection of several disciplines of social sciences and humanities makes criticism of the author's method or data analysis inevitable. The methodologically provocative nature of the study under review, however, underscores the need to follow with particular care the conventions of data analysis as the main foundation of the author's conclusions. The application of the statistical approach to the massive amount of data collected at different times and [End Page 437] using various criteria requires additional clarification of the methods of quantitative analysis employed in this book. In many cases, Mironov's argument is based on a simple comparison of data in the form of arranged statistical series in tables. Especially when the data are attributed to different time periods, it would be logical to see a graph with regression analysis or at least a correlation showing the relationship between these indicators instead of raw data. This was clearly needed in Table 2.2...

Topography Controls the Abundance of Siberian Larch Forest

AbstractLarch trees are the dominant elements of coniferous forests in Eastern Siberia, and its abundance largely depends on local hydrological conditions, which are predominantly mediated by topographic properties at the 100 m scale. The present study evaluated how geographic heterogeneity of the larch forest is formed with a special focus on topography and its related environmental heterogeneity. A principal component analysis (PCA) was conducted for larch‐dominated areas to aggregate the geographic correlations among environmental variables, namely, topographic properties, flooding risk, and climatic variables. In the PCA, the first three principal components (PC1, PC2, and PC3) explained the 63.6% of geographical heterogeneity of environmental variables. PC1, PC2, and PC3 were tightly correlated with the environmental gradient along latitude, soil water holding capacity, and slope aspect, respectively. Employing these first three PCs as independent variables, a multiple regression equation was calculated to explain geographic heterogeneity of larch LAI. The multiple regression equation extracted trends that the larch LAI becomes higher for environmental conditions of (1) lower latitude, (2) higher soil water holding capacity until a certain threshold, and (3) south‐faced slope. The 2 × 2 contingency tables of inundation risks and presence of larch forest showed that larch forests avoid areas threatened by flooding. These results suggest that topographic properties, besides climatic environment, control the abundance of larch forest via both drought and flooding stresses in Eastern Siberia.

Genetic characterization of bovine respiratory syncytial virus strains isolated in Italy: evidence for the circulation of new divergent clades.

Bovine respiratory syncytial virus (BRSV) is circulating in cattle in Europe. Although vaccination helps control the disease, its prevalence within and among herds remains high. Previous genetic characterization studies revealed a strict geographic correlation between viral variants; on the other hand, they showed the emergence of new variants in northern Europe. Few studies have described BRSV distribution, and little is known about the genetic features of BRSV strains circulating in Italy. We studied sample-positive tests for BRSV, and sequenced the coding regions of the G and N proteins to determine the presence of divergent variants. Two different sets of sequences were found, including in samples from animals from vaccinated herds. The 2 groups of sequences correspond to 2 time periods and suggest an active role of herd immunity in preventing the spread of infection. Our findings that different strains of BRSV are circulating in Italy and that the virus is evolving rapidly highlight the importance of updating vaccination strategies.

The many faces of Peridinium cinctum (Peridiniaceae, Peridiniales): morphological and molecular variability in a common dinophyte

ABSTRACTPeridinium cinctum is a common freshwater dinophyte with a long history of research. Erich Lindemann was the first to assess intraspecific variability in this species focusing on plate pattern variation. Since then, this issue has been neglected but with the application of DNA sequence diagnostics, a combination of morphological and molecular characters may enable taxonomic delimitations. Our aim was to identify distinct morphotypes using plate pattern as the main characteristic and then compare them to the geographic occurrence of particular ribotypes (as inferred from sequences of the Internal Transcribed Spacer: ITS) in samples from Central Europe. Approximately 200 observations were carried out under the inverse light microscope for each of a total of 15 strains. We observed two main variations from the abundant plate pattern in P. cinctum, namely an unusual position of the 2a plate and the irregular shape of the 1a plate. In 88 (predominantly clonal) strains, we identified five different ribotypes (submitted as 71 new GenBank entries) which had no clear correlation to the defined morphotypes and/or spatial occurrences. In four cases, we detected two distinct ribotypes at the same locality. However, samples collected south of the Danube River presented a different predominant morphotype from the rest of the samples, thus implying a potential biogeographic signal as inferred from morphology. In general, there is morphological and molecular variability in P. cinctum, which is under-studied and which may uncover geographic or ecological correlations or even the existence of cryptic species.

Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach

AbstractFusing satellite observations and station measurements to estimate ground‐level PM2.5 is promising for monitoring PM2.5 pollution. A geo‐intelligent approach, which incorporates geographical correlation into an intelligent deep learning architecture, is developed to estimate PM2.5. Specifically, it considers geographical distance and spatiotemporally correlated PM2.5 in a deep belief network (denoted as Geoi‐DBN). Geoi‐DBN can capture the essential features associated with PM2.5 from latent factors. It was trained and tested with data from China in 2015. The results show that Geoi‐DBN performs significantly better than the traditional neural network. The out‐of‐sample cross‐validation R2 increases from 0.42 to 0.88, and RMSE decreases from 29.96 to 13.03 μg/m3. On the basis of the derived PM2.5 distribution, it is predicted that over 80% of the Chinese population live in areas with an annual mean PM2.5 of greater than 35 μg/m3. This study provides a new perspective for air pollution monitoring in large geographic regions.

West Nile Virus Lineage 2 in Horses and Other Animals with Neurologic Disease, South Africa, 2008-2015.

During 2008–2015 in South Africa, we conducted West Nile virus surveillance in 1,407 animals with neurologic disease and identified mostly lineage 2 cases in horses (7.4%, 79/1,069), livestock (1.5%, 2/132), and wildlife (0.5%, 1/206); 35% were fatal. Geographic correlation of horse cases with seropositive veterinarians suggests disease in horses can predict risk in humans.

Drawing New Boundaries: Finding the Origins of Dragons in Carboniferous Plant Fossils

Dragons thrive in gaps between and beyond spatial boundaries. Can science help explain their existence? Did humans’ investigation of natural phenomena create bits and pieces of dragon lore across cultures? The researchers used a transdisciplinary lens to reveal data unique among extant dragon origin explanations, including fossil evidence and descriptions of Carboniferous-Period plants, dragon folklore descriptions and locations and geographic correlations between the fossils and folklore. The hypothesis is that early humans came across these fossils, constructed meaning for them contextualized by current knowledge of the natural world and created or enhanced dragon lore narratives.

Geographic Variation in Morbidity and Mortality of Cerebrovascular Diseases in Korea during 2011-2015

Little is known about within-country variation in morbidity and mortality of cerebrovascular diseases (CVDs). Geographic differences in CVD morbidity and mortality have yet to be properly examined. This study examined geographic variation in morbidity and mortality of CVD, neighborhood factors for CVD morbidity and mortality, and the association between CVD morbidity and mortality across the 245 local districts in Korea during 2011-2015. District-level health care utilization and mortality data were obtained to estimate age-standardized CVD morbidity and mortality. The bivariate Pearson correlation was used to examine the linear relationship between district-level CVD morbidity and mortality Z-scores. Simple linear regression and multivariate analyses were conducted to investigate the associations of area characteristics with CVD morbidity, mortality, and discrepancies between morbidity and mortality. Substantial variation was found in CVD morbidity and mortality across the country, with 1074.9 excess CVD inpatients and 73.8 excess CVD deaths per 100,000 between the districts with the lowest and highest CVD morbidity and mortality, respectively. Higher rates of CVD admissions and deaths were clustered in the noncapital regions. A moderate geographic correlation between CVD morbidity and mortality was found (Pearson correlation coefficient = .62 for both genders). Neighborhood level indicators for socioeconomic disadvantages, undersupply of health care resources, and unhealthy behaviors were positively associated with CVD morbidity and mortality and the relative standing of CVD mortality vis-à-vis morbidity. Policy actions targeting life-course socioeconomic conditions, equitable distribution of health care resources, and behavioral risk factors may help reduce geographic differences in CVD morbidity and mortality in Korea.

Large-scale genomic analyses reveal the population structure and evolutionary trends of Streptococcus agalactiae strains in Brazilian fish farms

Streptococcus agalactiae is a major pathogen and a hindrance on tilapia farming worldwide. The aims of this work were to analyze the genomic evolution of Brazilian strains of S. agalactiae and to establish spatial and temporal relations between strains isolated from different outbreaks of streptococcosis. A total of 39 strains were obtained from outbreaks and their whole genomes were sequenced and annotated for comparative analysis of multilocus sequence typing, genomic similarity and whole genome multilocus sequence typing (wgMLST). The Brazilian strains presented two sequence types, including a newly described ST, and a non-typeable lineage. The use of wgMLST could differentiate each strain in a single clone and was used to establish temporal and geographical correlations among strains. Bayesian phylogenomic analysis suggests that the studied Brazilian population was co-introduced in the country with their host, approximately 60 years ago. Brazilian strains of S. agalactiae were shown to be heterogeneous in their genome sequences and were distributed in different regions of the country according to their genotype, which allowed the use of wgMLST analysis to track each outbreak event individually.

P.3.f.008 - The report on current state and effectiveness of occupational therapy with mentally disabled people in Korea

Little is known about within-country variation in morbidity and mortality of cerebrovascular diseases (CVDs). Geographic differences in CVD morbidity and mortality have yet to be properly examined. This study examined geographic variation in morbidity and mortality of CVD, neighborhood factors for CVD morbidity and mortality, and the association between CVD morbidity and mortality across the 245 local districts in Korea during 2011-2015.District-level health care utilization and mortality data were obtained to estimate age-standardized CVD morbidity and mortality. The bivariate Pearson correlation was used to examine the linear relationship between district-level CVD morbidity and mortality Z-scores. Simple linear regression and multivariate analyses were conducted to investigate the associations of area characteristics with CVD morbidity, mortality, and discrepancies between morbidity and mortality.Substantial variation was found in CVD morbidity and mortality across the country, with 1074.9 excess CVD inpatients and 73.8 excess CVD deaths per 100,000 between the districts with the lowest and highest CVD morbidity and mortality, respectively. Higher rates of CVD admissions and deaths were clustered in the noncapital regions. A moderate geographic correlation between CVD morbidity and mortality was found (Pearson correlation coefficient = .62 for both genders). Neighborhood level indicators for socioeconomic disadvantages, undersupply of health care resources, and unhealthy behaviors were positively associated with CVD morbidity and mortality and the relative standing of CVD mortality vis-à-vis morbidity.Policy actions targeting life-course socioeconomic conditions, equitable distribution of health care resources, and behavioral risk factors may help reduce geographic differences in CVD morbidity and mortality in Korea.

Low Dimensional Embedding of Climate Data for Radio Astronomical Site Testing in the Colombian Andes

We set out to evaluate the potential of the Colombian Andes for millimeter-wave astronomical observations. Previous studies for astronomical site testing in this region have suggested that nighttime humidity and cloud cover conditions make most sites unsuitable for professional visible-light observations. Millimeter observations can be done during the day, but require that the precipitable water vapor column above a site stays below ∼10 mm. Due to a lack of direct radiometric or radiosonde measurements, we present a method for correlating climate data from weather stations to sites with a low precipitable water vapor column. We use unsupervised learning techniques to low dimensionally embed climate data (precipitation, rain days, relative humidity, and sunshine duration) in order to group together stations with similar long-term climate behavior. The data were taken over a period of 30 years by 2046 weather stations across the Colombian territory. We find six regions with unusually dry, clear-sky conditions, ranging in elevations from 2200 to 3800 masl. We evaluate the suitability of each region using a quality index derived from a Bayesian probabilistic analysis of the station type and elevation distributions. Two of these regions show a high probability of having an exceptionally low precipitable water vapor column. We compared our results with global precipitable water vapor maps and find a plausible geographical correlation with regions with low water vapor columns (∼10 mm) at an accuracy of ∼20 km. Our methods can be applied to similar data sets taken in other countries as a first step toward astronomical site evaluation.

Efficient Uncertainty Quantification in Stochastic Economic Dispatch

Stochastic economic dispatch models address uncertainties in forecasts of renewable generation output by considering a finite number of realizations drawn from a stochastic process model, typically via Monte Carlo sampling. Accurate evaluations of expectations or higher order moments for quantities of interest, e.g., generating cost, can require a prohibitively large number of samples. We propose an alternative to Monte Carlo sampling based on polynomial chaos expansions. These representations enable efficient and accurate propagation of uncertainties in model parameters, using sparse quadrature methods. We also use Karhunen–Loeve expansions for efficient representation of uncertain renewable energy generation that follows geographical and temporal correlations derived from historical data at each wind farm. Considering expected production cost, we demonstrate that the proposed approach can yield several orders of magnitude reduction in computational cost for solving stochastic economic dispatch relative to Monte Carlo sampling, for a given target error threshold.