Accuracy Of Statistics Research Articles

Has Third-Party Monitoring Improved Water Pollution Data Quality? Evidence from National Surface Water Assessment Sections in China

In China, the central government assesses local governments based on data monitored and reported by local agencies, and the accuracy of local statistics has been controversial. In order to further guarantee the authenticity and reliability of surface water monitoring data, the central government will gradually withdraw the local monitoring powers of the national surface water assessment section and implement third-party monitoring to achieve “national assessment and national monitoring.” This paper is based on the time-point water data of important national water quality automatic monitoring stations from 2015 to 2020, using the McCrary (2008) density test to infer possible data manipulation phenomena, and analyze whether third-party monitoring has improved the accuracy of China’s environmental data. The results of the study show that between 2015 and 2020, the observed 81 monitoring sites had varying degrees of data discontinuity. The discontinuity of the data after third-party monitoring was reduced in dissolved oxygen (DO) measurement, an important indicator in the assessment, implying that third-party monitoring has improved the quality of water environment data and the accuracy of the data. The research in this article provides a reference for third-party participation in environmental governance and proves that the participation of these organizations can reduce data manipulation behaviors of local governments and ensure the effectiveness of environmental data.

The role of migrants in the Malaysian seaweed value-chain

With increasing global demand for seaweed and derivative products including carrageenan, Malaysia has identified seaweed cultivation as a key sector in its Tenth Malaysian Plan, with the aim of increasing seaweed production and bringing economic benefits to coastal communities. After several years of growth however, since 2013 seaweed production has declined. Malaysia’s research and development has developed a strong understanding of the biological and macroeconomic aspects of seaweed cultivation, but research has focused less on socio-economic aspects of the carrageenan value chain, including its labour force. Previous studies have provided insights into the contribution of migrants to seaweed production in Malaysia. To further understand the constraints faced by seaweed farmers’ and examine why seaweed production is currently declining, this study uses an intersectional lens to document farmers’ experiences based on their citizenship status and the impacts their status has on seaweed production. It relies on a mixed-methods analysis of key informant interviews, questionnaire surveys and document analysis, as well as on the conceptual framework of VCA-New Institutional Environment-Structure (NIES). Findings suggest that migrants have a significant role in upstream activities, contributing substantially to seaweed cultivation, yet, they face a number of legal, financial and institutional constraints that restrict their productivity and participation in marketing activities. To recognise and support migrant seaweed farmers would not only improve the accuracy of official statistics, it would also support policymakers to strengthen the seaweed industry through adequate regulations and tailored incentives.

PEPATAC: an optimized pipeline for ATAC-seq data analysis with serial alignments.

As chromatin accessibility data from ATAC-seq experiments continues to expand, there is continuing need for standardized analysis pipelines. Here, we present PEPATAC, an ATAC-seq pipeline that is easily applied to ATAC-seq projects of any size, from one-off experiments to large-scale sequencing projects. PEPATAC leverages unique features of ATAC-seq data to optimize for speed and accuracy, and it provides several unique analytical approaches. Output includes convenient quality control plots, summary statistics, and a variety of generally useful data formats to set the groundwork for subsequent project-specific data analysis. Downstream analysis is simplified by a standard definition format, modularity of components, and metadata APIs in R and Python. It is restartable, fault-tolerant, and can be run on local hardware, using any cluster resource manager, or in provided Linux containers. We also demonstrate the advantage of aligning to the mitochondrial genome serially, which improves the accuracy of alignment statistics and quality control metrics. PEPATAC is a robust and portable first step for any ATAC-seq project. BSD2-licensed code and documentation are available at https://pepatac.databio.org.

Two-stage sampling exponential and chain ratio type estimators in the presence of measurement error

Currently one of the most important topics that are discussed in survey methodology is the accuracy of statistics or survey errors that may occur in the parameters estimation process and measurement error is one of the most important non-sampling error. In the present study general class of exponential and chain ratio type estimators are considered under two stage sampling for the estimation of population mean by using two auxiliary variables in the presence of measurement error. The bias and mean square error of these estimator has been obtained. Moreover, two artificial populations are generated to check the properties of proposed estimator among other estimator. The relative efficiency is also computed and the results revealed that proposed estimators are more efficient than the ratio estimator and by increasing sample size efficiency of suggested estimators are improved.

The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

Abstract. The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB >250 Mg ha−1, where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at https://doi.org/10.1594/PANGAEA.894711 (Santoro, 2018).

How Does Target-Based Performance Evaluation Affect the Accuracy of Energy-Saving Data: Evidence From China

Energy intensity measured by energy consumption per unit of GDP has always been the main assessment indicator for the design of energy-saving policies, but its accuracy is highly dependent on the reliability of GDP data. This paper finds that the indicator accuracy is improving after the central government has included energy intensity into the performance appraisal system for local officials. This means that the energy-saving target-based performance evaluation has restrained the data misrepresentation behavior of local officials. Further mechanism analysis shows that the pressure of energy saving restricts the development of the industrial sector, which weakens the ability of local governments to manipulate GDP data, thus improving the accuracy of energy intensity statistics. These findings provide some insightful references for China’s future green development and policy design.

Accuraatheid van de door de Vlaamse overheid gepubliceerde statistiek van de doodsoorzaken: een forensische benadering

A study into the accuracy of the published cause of death statistics by the government of Flanders, Belgium The cause of death (COD) stated on death certificates is the key component to compose COD statistics in Belgium. The aim of the study was to ascertain whether CODs of homicides as confirmed by autopsy by the Department of Forensic Sciences (DoFS) at the University of Leuven were reported as such in government statistics. Out of 455 autopsies performed by the DoFS between 01/01/2010 and 31/12/2013, 64 medicolegal homicides were retained, which in turn were cross-referenced with a provided list of all deaths in the same period by the government. Applying the ICD-10 (International Classification of Diseases, 10th revision) methodology to the study population, 97% of the attained codes fell under the section ‘Assault’. This concerned only 78% when coded by the government. The remaining were located under the less specific section ‘Event of undetermined intent’. One homicide was coded by the government as a death by natural causes and one death was not accounted for. Specific training for physicians on correctly filling out death certificates, a more user-friendly certificate and systematic feedback of autopsy findings to government agencies are proposed in order to increase the accuracy of COD statistics.

An Improved Boundary-Aware U-Net for Ore Image Semantic Segmentation.

Particle size is the most important index to reflect the crushing quality of ores, and the accuracy of particle size statistics directly affects the subsequent operation of mines. Accurate ore image segmentation is an important prerequisite to ensure the reliability of particle size statistics. However, given the diversity of the size and shape of ores, the influence of dust and light, the complex texture and shadows on the ore surface, and especially the adhesion between ores, it is difficult to segment ore images accurately, and under-segmentation can be a serious problem. The construction of a large, labeled dataset for complex and unclear conveyor belt ore images is also difficult. In response to these challenges, we propose a novel, multi-task learning network based on U-Net for ore image segmentation. To solve the problem of limited available training datasets and to improve the feature extraction ability of the model, an improved encoder based on Resnet18 is proposed. Different from the original U-Net, our model decoder includes a boundary subnetwork for boundary detection and a mask subnetwork for mask segmentation, and information of the two subnetworks is fused in a boundary mask fusion block (BMFB). The experimental results showed that the pixel accuracy, Intersection over Union (IOU) for the ore mask (IOU_M), IOU for the ore boundary (IOU_B), and error of the average statistical ore particle size (ASE) rate of our proposed model on the testing dataset were 92.07%, 86.95%, 52.32%, and 20.38%, respectively. Compared to the benchmark U-Net, the improvements were 0.65%, 1.01%, 5.78%, and 12.11% (down), respectively.

The accuracy of crime statistics: assessing the impact of police data bias on geographic crime analysis

ObjectivesPolice-recorded crimes are used by police forces to document community differences in crime and design spatially targeted strategies. Nevertheless, crimes known to police are affected by selection biases driven by underreporting. This paper presents a simulation study to analyze if crime statistics aggregated at small spatial scales are affected by larger bias than maps produced for larger geographies.MethodsBased on parameters obtained from the UK Census, we simulate a synthetic population consistent with the characteristics of Manchester. Then, based on parameters derived from the Crime Survey for England and Wales, we simulate crimes suffered by individuals, and their likelihood to be known to police. This allows comparing the difference between all crimes and police-recorded incidents at different scales.ResultsMeasures of dispersion of the relative difference between all crimes and police-recorded crimes are larger when incidents are aggregated to small geographies. The percentage of crimes unknown to police varies widely across small areas, underestimating crime in certain places while overestimating it in others.ConclusionsMicro-level crime analysis is affected by a larger risk of bias than crimes aggregated at larger scales. These results raise awareness about an important shortcoming of micro-level mapping, and further efforts are needed to improve crime estimates.

Multi-class fish stock statistics technology based on object classification and tracking algorithm

The development of intensive aquaculture has increased the need for video-based underwater monitoring technology to generate statistics on multi-class fish. However, the complex marine environment, e.g., light fluctuations, shape deformations, similar appearance of fish, and occlusions, makes this a challenging task. Therefore, there are relatively few studies in this field. This paper proposes a real-time multi-class fish stock statistics method (RMCF). The accuracy of fish stock statistics has reached 95.6% over the previous best approach. The proposed method uses YOLOv4 as a backbone network and a parallel two-branch structure based on deep learning to perform real-time detection and tracking of fish in a real marine ranch environment. The two-branch structure contains detection and tracking branches, where the detection branch detects fish species and improves tracking accuracy and online tracking time. The tracking branch tracks the fish and making a number statistics. Finally, we combine the detection and tracking branches to generate multi-class fish stock statistics. Here, the detection branch helps the tracking branch realize multi-class tracking. With the tracking results, we further analyze the changing trends of different fish over time. Compared to state-of-the-art video tracking and detection methods, the experiment results demonstrate the proposed method provides better fish detection and tracking performance in a complex real-world marine environment.

Partisanship and Survey Refusal

Survey refusal in the Current Population Survey (CPS) has tripled over the last decade. This rise coincides with the emergence of rhetoric, largely from the political right, questioning the accuracy and integrity of government statistics. We examine how support for the Tea Party and the Republican party have affected CPS refusal rates and whether households are more likely to participate in the survey when their preferred political party holds the White House. Using state and metro vote shares or an individual-level model based on the longitudinal structure of the CPS, we find no evidence that Republican or Tea Party supporters drive the long-term upward trend in refusals. We do find evidence of a political cycle in response rates. Refusal rates since 2015 exhibit polarization, with the fastest growth in refusals among those least likely to support Trump and the Tea Party. Evidence from an analysis which generates exogenous variation in Tea Party support using rain on the day of the first Tea Party rally indicates that exposure to anti-survey rhetoric decreases refusal rates, consistent with the findings from our other analyses.

Assessing the foundations for predicting tourist arrivals in Egypt

The problem of research is that there is a lack of accuracy and consistency of tourism statistics, which may negatively affect the prediction of tourism movement; especially in a crisis. Accordingly, this research paper is mainly aiming at highlighting the basics adopted by the official tourism authorities in preparing tourism statistics. In an attempt to achieve the aforementioned aim, a qualitative research methodology was adopted by employing two phases of data collection. In the first phase, data on the issue under investigation were collected from various secondary sources. In the second phase, rich qualitative data were gathered through distributing questionnaires to a sample of Egyptian tourist statistics experts from March to June 2019. The study ended up with a number of results and recommendations.

A pilot project of area sampling frame for maize statistics: Indonesia’s experience1

A critical issue in the context of food policy in Indonesia is the accuracy of crops statistics, particularly rice and maize. In 2018, Statistics Indonesia (BPS), in collaboration with the Indonesian Agency for Assessment and Application of Technology (BPPT), successfully implemented the area sampling frame (ASF) method to improve the accuracy of paddy harvested area estimation, which previously was estimated by conventional methods, mainly by the human eye (‘eye-estimate’). The achievement has encouraged BPS to replicate the method to estimate the harvested area of maize, for which there were indications it suffered from overestimation. In 2019, BPS initiated a pilot project on the implementation of the ASF for maize. One of the most challenging aspects in replicating the ASF method for maize is the frame construction. This issue arises due to insufficient spatial information on land that is specifically dedicated to maize cultivation. To address this challenge, BPS constructed the frame by making use of different sources of spatial information. This paper provides a comprehensive look at the development of the ASF for maize statistics. The discussion in this paper covers two main issues, namely the methodology applied and the business process of data collection.

Assessing the technical potential of ASEAN countries to achieve 100% renewable energy supply

Pathways to limit global temperature rise to 1.5 °C in line with the Paris Agreement long-term temperature goal and achievement of Sustainable Development Goals involve energy system transitions with strong uptake of renewables by 2050. Fossil fuels currently dominate the energy systems of the ASEAN countries, a region that has abundant untapped renewable resources. This paper expounds an approach of how the ASEAN region may substantially increase uptake of renewable energy. It provides an overview of the shortfalls in energy targets and investigates the technical potential of a selection of renewable energy technologies throughout the region.The findings demonstrate confidence that an energy system decarbonisation pathway for the ASEAN region is possible, however, current policies and action are lacking for achieving any level of decarbonisation by 2050. Further studies are necessary to refine the ASEAN region’s renewable resource technical potential as well as its commercial feasibility based on available technologies. Improvements are also necessary to build confidence in the accuracy of energy statistics to support such studies. Additionally, a review of the ASEAN region’s energy supply system would be required to inform such a feasibility study.

A Brief Analysis of The Defects and Countermeasures of EVM in Project Management

Through calculation and induction, demonstration by example, comparative analysis and other methods, this paper draws the following conclusion: the control of project progress and cost by EVM should be evaluated with a single project as the goal, and on the premise of the timely accuracy of phase cost statistics; In the evaluation of comprehensive project progress, it should be carried out in different levels, with the evaluation of key line work as the main and non-key line work as the auxiliary; The influence of safety, quality, environment and other factors on the cost should be fully considered when evaluating the project cost.

Apples, oranges and lemons: public sector debt statistics in the 21st century

In both academic research and policymaking, public sector debt and debt-to-GDP ratios are relied on for a multitude of important economic, political and socioeconomic decisions, especially as public sector balance sheets expand to an unprecedented size in the midst of the 2019–2020 COVID pandemic. The reliance on available data from reputable sources often overlooks the question of whether the denominator in this ratio is accurately measured or how well the denominator is understood by the audience interpreting it. Building on past work in international financial statistics, and making use of a unique and newly created dataset on media reporting of public sector debt, the purpose of this article is to examine the quality, accuracy, interpretation and overall meaningfulness of public sector financial statistics. The main findings suggest that i) most of the world’s governments still do not seem to feel sufficient pressure to voluntarily provide comprehensive financial statistics based on well-defined modern methodological frameworks and ii) high profile financial statistics, which are reported, have become increasingly numerous and complicated, making it difficult for non-experts to know which is most appropriate in the context of their analysis.

Is the role of forensic medicine in the covid-19 pandemic underestimated?

We believe that forensic medicine should play a significant role in the COVID-19 pandemic. Forensic pathologists should ask and answer various questions through autopsy cases during the COVID-19 period, thus providing a significant contribution to science. Some of the potential roles of forensic medicine in this issue include: determining the exact cause of death among the deceased who were SARS-CoV-2 positive, contribution to the accuracy of mortality statistics, understanding pathological mechanisms of COVID-19, tracking the presence of the virus over time, survival of the virus after death as well as dealing with medicolegal issues. A detailed multidisciplinary analysis of autopsy samples would undoubtedly help understand this new illness and its clinical management. Therefore, autopsies during the COVID-19 pandemic should not be an exception, but certainly a rule.

Effective Privacy-Preserving Collection of Health Data from a User’s Wearable Device

The popularity of wearable devices equipped with a variety of sensors that can measure users’ health status and monitor their lifestyle has been increasing. In fact, healthcare service providers have been utilizing these devices as a primary means to collect considerable health data from users. Although the health data collected via wearable devices are useful for providing healthcare services, the indiscriminate collection of an individual’s health data raises serious privacy concerns. This is because the health data measured and monitored by wearable devices contain sensitive information related to the wearer’s personal health and lifestyle. Therefore, we propose a method to aggregate health data obtained from users’ wearable devices in a privacy-preserving manner. The proposed method leverages local differential privacy, which is a de facto standard for privacy-preserving data processing and aggregation, to collect sensitive health data. In particular, to mitigate the error incurred by the perturbation mechanism of location differential privacy, the proposed scheme first samples a small number of salient data that best represents the original health data, after which the scheme collects the sampled salient data instead of the entire set of health data. Our experimental results show that the proposed sampling-based collection scheme achieves significant improvement in the estimated accuracy when compared with straightforward solutions. Furthermore, the experimental results verify that an effective tradeoff between the level of privacy protection and the accuracy of aggregate statistics can be achieved with the proposed approach.

Large eddy simulation of inertial particles dispersion in a turbulent gas-particle channel flow bounded by rough walls

The purpose of this paper is to understand the capability and consistency of large eddy simulation (LES) in Eulerian–Lagrangian studies aimed at predicting inertial particle dispersion in turbulent wall-bounded flows, in the absence of ad hoc closure models in the Lagrangian equations of particle motion. The degree of improvement granted by LES models is object of debate, in terms of both accurate prediction of particle accumulation and local particle segregation; therefore, we assessed the accuracy in the prediction of the particle velocity statistics by comparison against direct numerical simulation (DNS) of a finer computational mesh, under both one-way and two-way coupling regimes. We performed DNS and LES at friction Reynolds number $$\hbox {Re}_{\tau }=180$$ in smooth and rough channels, tracking particles with different inertia, with the aim to conduct a parametric study that examines the accuracy of particle statistics obtained from LES computations. The issue has been widely analysed in turbulent flow bounded by smooth walls, whereas the effect of rough boundaries on momentum coupled two-phase flows has been much less investigated until now. The action of the roughness of the wall is studied in terms of both turbulence modification and particle interaction with the wall surface due to particle rebounding off the solid boundary, without the introduction of a virtual rebound model. Results show that resolved LES adequately predicts particle-induced changes in both fluid and particle statistics in rough channels, at least for the range of parameters considered here.

The Prediction of Urban Growth Trends and Patterns using Spatio-temporal CA-MC Model in Seremban Basin

Urban growth, a dynamic and demographic phenomenon, refers to the increased spatial value of urban areas, such as cities and towns, due to social and economic forces. Nowadays, urban lands are rapidly increasing, replacing non-urban lands such as agricultural, forest, water, rural, and open lands. In this study, a CA-Markov model was utilized to predict the growth of urban lands and their spatial trends in Seremban, Malaysia. The performance of the CA-Markov model was also assessed. The Markov chain model was applied to produce the quantitative values of transition probabilities for urban and non-urban lands. Subsequently, the CA model was used to predict the dynamic spatial trends of land changes. The change in urban and non-urban land use from 1984 to 2010 was modeled using the CA-Markov model for calibration purposes and to compute optimal CA transition rules as well as to predict future urban growth. In the accuracy assessment process, the CA-Markov model was validated using a Kappa coefficient. The overall accuracy of the Kappa index statistics was 83%, which indicates the excellent performance of the model proposed in this study. Finally, based on the CA transition rules and the transition area matrix produced from the calibration process using the Markov Chain model, future urban growth in Seremban for 2020 and 2030 was simulated.