Data Quality Inspection Research Articles

Design and Implementation of Quality Inspection System for Image on National Platform for Geospatial Information Services in China

Abstract. This article is based on the data update characteristics of the online service for National Platform for Geospatial Information Services (TIANDITU) imagery data, and it designs and implements a full-process imagery data quality inspection system specifically for imagery tile data. The development and application of this system have made the quality inspection and update process of TIANDITU imagery data more scientific and efficient, significantly enhancing the efficiency of the quality inspection and update process for TIANDITU imagery data. The article provides a detailed introduction to the system’s architecture, design, implementation, characteristics and application. It also proposes optimizations to be made in algorithm optimization, user interface, and system stability. This article provides new ideas for quality inspection and publishing based on image tile data.

Implementing mobile eye tracking in psychological research: A practical guide

Eye tracking provides direct, temporally and spatially sensitive measures of eye gaze. It can capture visual attention patterns from infancy through adulthood. However, commonly used screen-based eye tracking (SET) paradigms are limited in their depiction of how individuals process information as they interact with the environment in “real life”. Mobile eye tracking (MET) records participant-perspective gaze in the context of active behavior. Recent technological developments in MET hardware enable researchers to capture egocentric vision as early as infancy and across the lifespan. However, challenges remain in MET data collection, processing, and analysis. The present paper aims to provide an introduction and practical guide to starting researchers in the field to facilitate the use of MET in psychological research with a wide range of age groups. First, we provide a general introduction to MET. Next, we briefly review MET studies in adults and children that provide new insights into attention and its roles in cognitive and socioemotional functioning. We then discuss technical issues relating to MET data collection and provide guidelines for data quality inspection, gaze annotations, data visualization, and statistical analyses. Lastly, we conclude by discussing the future directions of MET implementation. Open-source programs for MET data quality inspection, data visualization, and analysis are shared publicly.

A multilevel stratified spatial sampling approach based on terrain knowledge for the quality assessment of OpenStreetMap dataset in Hong Kong

AbstractWith the development of Volunteered Geographical Information (VGI) data, the OpenStreetMap has high research value in terms of project activity, social influence, urban development, application scope, and historical richness and the number of buildings or roads is increasing every day. However, how to evaluate the quality of a large amount OpenStreetMaps efficiently and accurately is still not fully understood. This article presents the development of an approach regarding multilevel stratified spatial sampling based on slope knowledge and official 1:1000 thematic maps as the reference dataset for OpenStreetMap data quality inspection of Hong Kong. This multilevel stratified spatial sampling plan is as follows: (1) The terrain characteristics of Hong Kong are fully considered by dividing grids into quality estimate strata based on the slope information; (2) Spatial sampling for the selection of grids or objects is used; (3) A more reliable sampling subset is made, regarding the representation of the entire OpenStreetMap dataset of Hong Kong. This sampling plan displays a 10% higher sampling accuracy, but without increasing the sample size, particularly as regards building completeness inspection compared with simple random sampling and systematic random sampling. This research promotes further applications of the Open‐Street‐Map dataset, thus enabling us to have a better understanding of the OpenStreetMap data quality in urban areas.

GTQC: Automated Genotyping Array Quality Control and Report.

Genotyping array is the most economical approach for conducting large-scale genome-wide genetic association studies. Thorough quality control is key to generating high integrity genotyping data and robust results. Quality control of genotyping array is generally a complicated process, as it requires intensive manual labor in implementing the established protocols and curating a comprehensive quality report. There is an urgent need to reduce manual intervention via an automated quality control process. Based on previously established protocols and strategies, we developed an R package GTQC (GenoTyping Quality Control) to automate a majority of the quality control steps for general array genotyping data. GTQC covers a comprehensive spectrum of genotype data quality metrics and produces a detailed HTML report comprising tables and figures. Here, we describe the concepts underpinning GTQC and demonstrate its effectiveness using a real genotyping dataset. R package GTQC streamlines a majority of the quality control steps and produces a detailed HTML report on a plethora of quality control metrics, thus enabling a swift and rigorous data quality inspection prior to downstream GWAS and related analyses. By significantly cutting down on the time on genotyping quality control procedures, GTQC ensures maximum utilization of available resources and minimizes waste and inefficient allocation of manual efforts. GTQC tool can be accessed at https://github.com/slzhao/GTQC.

RESEARCH ON EVALUATION METHOD OF REAL ESTATE REGISTRATION DATA QUALITY

Abstract. In recent years, China has promulgated the "Civil Code of the People's Republic of China", "Implementation Rules of the Provisional Regulations on Real Estate Registration" and other laws and regulations, which have protected citizens' rights and obligations in real estate from the legal system. It shows that the quality of real estate registration data is very important. At present, there is no set of standards for evaluating the quality of real estate registration data. This article sorts out the production process of real estate registration data and focuses on the four stages of production: digitization results, field surveys and surveying and mapping results, group building results, integration and association. As a result, the main points of real estate registration data quality control were put forward, and a quality evaluation model was developed. Taking Beijing's real estate registration historical archives integrated data quality inspection as an application case, it shows that the quality evaluation model has been successfully applied to actual projects, ensuring the quality of Beijing real estate registration data. It also provides a reference for the next step in China's quality control of the unified registration of natural resources confirmation.

DATA QUALITY CHECK AND VISUAL ANALYSIS OF CORS STATION BASED ON ANUBIS SOFTWARE

Abstract. Aiming at the observation data of CORS multi-mode data quality and inspection in CORS station, based on the observation data of a CORS station in Guilin, Guangxi, the paper used TEQC and Anubis software to check the data quality from data utilization, cycle slip ratio and multipath. The results show that the three quality inspection indicators of Anubis software are better than TEQC software, which proves that Anubis' data quality inspection results are effective and reliable. On this basis, using Anubis drawing tools to perform quality inspection and visual analysis from satellite constellation, signal band and observation data itself, it shows that Anubis' visual content is more elaborate than TEQC, and users can more intuitively understand and control the data quality of CORS station.

Public/private ownership and quality of care: Evidence from Danish nursing homes

The involvement of private for-profit (FP) and not-for-profit (NFP) providers in the otherwise public delivery of welfare services is gradually changing the Nordic welfare state towards a more market-oriented mode of service delivery. This article examines the relationship between ownership and quality of care in public and private FP and NFP nursing homes in Denmark. The analysis draws on original survey data and administrative registry data (quality inspection reports) for the full population of almost 1000 nursing homes in Denmark. Quality is measured in terms of structural quality, process quality and outcome quality. We find that public nursing homes have a higher structural quality (in terms of, for instance, staffing), while FP providers perform better in terms of process quality (e.g. in the form of individualised care). NFP providers perform well in terms of structural criteria such as employment of full-time staff and receive fewer critical comments in the inspection reports. However, the results depend to some extent upon the method of data collection, which underlines the benefits of using multiple data sources to examine the relationship between ownership and the quality of care.

STUDY ON CLASSIFICATION ACCURACY INSPECTION OF LAND COVER DATA AIDED BY AUTOMATIC IMAGE CHANGE DETECTION TECHNOLOGY

Abstract. The purpose of carrying out national geographic conditions monitoring is to obtain information of surface changes caused by human social and economic activities, so that the geographic information can be used to offer better services for the government, enterprise and public. Land cover data contains detailed geographic conditions information, thus has been listed as one of the important achievements in the national geographic conditions monitoring project. At present, the main issue of the production of the land cover data is about how to improve the classification accuracy. For the land cover data quality inspection and acceptance, classification accuracy is also an important check point. So far, the classification accuracy inspection is mainly based on human-computer interaction or manual inspection in the project, which are time consuming and laborious. By harnessing the automatic high-resolution remote sensing image change detection technology based on the ERDAS IMAGINE platform, this paper carried out the classification accuracy inspection test of land cover data in the project, and presented a corresponding technical route, which includes data pre-processing, change detection, result output and information extraction. The result of the quality inspection test shows the effectiveness of the technical route, which can meet the inspection needs for the two typical errors, that is, missing and incorrect update error, and effectively reduces the work intensity of human-computer interaction inspection for quality inspectors, and also provides a technical reference for the data production and quality control of the land cover data.

Estimating parameters for probabilistic linkage of privacy-preserved datasets

BackgroundProbabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. The linkage strategy and associated match probabilities are often estimated through investigations into data quality and manual inspection. However, as privacy-preserved datasets comprise encrypted data, such methods are not possible. In this paper, we present a method for estimating the probabilities and threshold values for probabilistic privacy-preserved record linkage using Bloom filters.MethodsOur method was tested through a simulation study using synthetic data, followed by an application using real-world administrative data. Synthetic datasets were generated with error rates from zero to 20% error. Our method was used to estimate parameters (probabilities and thresholds) for de-duplication linkages. Linkage quality was determined by F-measure. Each dataset was privacy-preserved using separate Bloom filters for each field. Match probabilities were estimated using the expectation-maximisation (EM) algorithm on the privacy-preserved data. Threshold cut-off values were determined by an extension to the EM algorithm allowing linkage quality to be estimated for each possible threshold. De-duplication linkages of each privacy-preserved dataset were performed using both estimated and calculated probabilities. Linkage quality using the F-measure at the estimated threshold values was also compared to the highest F-measure. Three large administrative datasets were used to demonstrate the applicability of the probability and threshold estimation technique on real-world data.ResultsLinkage of the synthetic datasets using the estimated probabilities produced an F-measure that was comparable to the F-measure using calculated probabilities, even with up to 20% error. Linkage of the administrative datasets using estimated probabilities produced an F-measure that was higher than the F-measure using calculated probabilities. Further, the threshold estimation yielded results for F-measure that were only slightly below the highest possible for those probabilities.ConclusionsThe method appears highly accurate across a spectrum of datasets with varying degrees of error. As there are few alternatives for parameter estimation, the approach is a major step towards providing a complete operational approach for probabilistic linkage of privacy-preserved datasets.

A general framework for spatial data inspection and assessment

The quality aspects of spatial data are very important in the decision-making process. However, the quality inspection of spatial data is still dependent on manual checking, and there is an urgent need to develop an automatic or semi-automatic generic system for spatial data quality inspection. In this paper, we present a general framework that automatically copes with spatial data quality inspection based on various spatial data quality standards and specifications. The framework involves all descriptions of given spatial data, a data quality model characterized by quality elements, scheme batch checking and spatial data quality assessment based on quality control and assessment procedures. It is implemented in Unified Modeling Language with four main sets of classes: data dictionary, quality model, scheme checking and quality assessment. Accordingly, we have designed four structured Extensible Markup Language files for the framework to organize and describe the data dictionary, quality model, scheme check and quality assessment. It is very easy for users to describe the data requirements using the data dictionary file, and to extend the quality elements or check rules using the quality model file. Users can design the specified checks and quality assessment schemes without coding by configuring the scheme check files and quality assessment scheme files. The framework also incorporates a checking tool capable of solving the difficulties inherent in the diversity of spatial data quality standards and specifications. The proposed framework and its implementation, as a quality inspection system, will facilitate automatic multiple spatial data quality inspection and acceptance. As a result, the quality of diversified spatial data can be ensured and improved, which is extremely important in the era of spatial big data.