scholarly journals A Comparative Analysis of the Gini Index

2021 ◽  
pp. 1-19
Author(s):  
Romina Mahinpei
Keyword(s):  
Difference Method ◽  
Gini Index ◽  
Original Method ◽  
Mean Difference ◽  
Geometric Method ◽  
Algebraic Methods ◽  
Data Sets ◽  
Income Distributions ◽  
Geometric Methods ◽  
Statistics Canada

Around the world, the Gini index is used to represent income inequality and is compared between regions. Proposed by Corrado Gini in 1912, the index summarizes the income disparity of an area into a single value that falls between zero and one [1]. There are numerous methods for evaluating the Gini index [2]. Considering its global use, it is essential for these different approaches to provide consistent results for a region. This paper compares the Gini indices obtained using three of the earliest developed methods. These methods include Gini’s original method, the relative mean difference method, and the geometric method. The geometric method, specifically, can be applied either algebraically or geometrically. In this report these three approaches were applied to the 2017 Canadian income distribution from Statistics Canada. To ensure a fair analysis, the methods were also applied to the Canadian income distributions from 1999 and 2010, with their calculations being summarized in Appendices A and B respectively.From the investigation, it was discovered that Gini’s original method and the relative mean difference method, (collectively referred to as the algebraic methods), provided identical results for all three data sets. However, the geometric methods, referring to the Trapezoid Rule and Logger Pro technology, provided values that differed from one another and the algebraic methods. This highlights the importance of acknowledging the method used to derive the Gini Index to ensure consistency and to allow a valid interpretation. The results of this paper also suggest that the algebraic methods should be preferred over the geometric methods when dealing with discrete data to ensure consistent results.

scholarly journals Validation of Scolioscan Air-Portable Radiation-Free Three-Dimensional Ultrasound Imaging Assessment System for Scoliosis

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2858
Author(s):  
Kelly Ka-Lee Lai ◽  
Timothy Tin-Yan Lee ◽  
Michael Ka-Shing Lee ◽  
Joseph Chi-Ho Hui ◽  
Yong-Ping Zheng
Keyword(s):  
Ultrasound Imaging ◽  
Imaging System ◽  
Outcome Measurement ◽  
Three Dimensional ◽  
3D Ultrasound ◽  
Mean Difference ◽  
Assessment System ◽  
Absolute Difference ◽  
Data Sets ◽  
Low Profile

To diagnose scoliosis, the standing radiograph with Cobb’s method is the gold standard for clinical practice. Recently, three-dimensional (3D) ultrasound imaging, which is radiation-free and inexpensive, has been demonstrated to be reliable for the assessment of scoliosis and validated by several groups. A portable 3D ultrasound system for scoliosis assessment is very much demanded, as it can further extend its potential applications for scoliosis screening, diagnosis, monitoring, treatment outcome measurement, and progress prediction. The aim of this study was to investigate the reliability of a newly developed portable 3D ultrasound imaging system, Scolioscan Air, for scoliosis assessment using coronal images it generated. The system was comprised of a handheld probe and tablet PC linking with a USB cable, and the probe further included a palm-sized ultrasound module together with a low-profile optical spatial sensor. A plastic phantom with three different angle structures built-in was used to evaluate the accuracy of measurement by positioning in 10 different orientations. Then, 19 volunteers with scoliosis (13F and 6M; Age: 13.6 ± 3.2 years) with different severity of scoliosis were assessed. Each subject underwent scanning by a commercially available 3D ultrasound imaging system, Scolioscan, and the portable 3D ultrasound imaging system, with the same posture on the same date. The spinal process angles (SPA) were measured in the coronal images formed by both systems and compared with each other. The angle phantom measurement showed the measured angles well agreed with the designed values, 59.7 ± 2.9 vs. 60 degrees, 40.8 ± 1.9 vs. 40 degrees, and 20.9 ± 2.1 vs. 20 degrees. For the subject tests, results demonstrated that there was a very good agreement between the angles obtained by the two systems, with a strong correlation (R2 = 0.78) for the 29 curves measured. The absolute difference between the two data sets was 2.9 ± 1.8 degrees. In addition, there was a small mean difference of 1.2 degrees, and the differences were symmetrically distributed around the mean difference according to the Bland–Altman test. Scolioscan Air was sufficiently comparable to Scolioscan in scoliosis assessment, overcoming the space limitation of Scolioscan and thus providing wider applications. Further studies involving a larger number of subjects are worthwhile to demonstrate its potential clinical values for the management of scoliosis.

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

scholarly journals Longitudinal child data: What can be gained by linking administrative data and cohort data?

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Leanne Findlay ◽  
Elizabeth Beasley ◽  
Jungwee Park ◽  
Dafna Kohen ◽  
Yann Algan ◽  
...  
Keyword(s):  
Administrative Data ◽  
Well Being ◽  
Evidence Base ◽  
Kindergarten Children ◽  
Data Sets ◽  
Original Cohort ◽  
Financial Outcomes ◽  
Probabilistic Linkage ◽  
Statistics Canada ◽  
Relationship Of

IntroductionLinked administrative data sets are an emerging tool for studying the health and well-being of the population. Previous papers have described methods for linking Canadian data, although few have specifically focused on children, nor have they described linkage between tax outcomes and a cohort of children who are particularly at risk for poor financial outcomes. Objective and methodsThis paper describes a probabilistic linkage performed by Statistics Canada linking the Montreal Longitudinal Experimental Study (MLES) and the Quebec Longitudinal Study of Kindergarten Children (QLSKC) survey cohorts and administrative tax data from 1992 through 2012. ResultsThe number of valid cases in the original cohort file with valid tax records was approximately 84\%. Rates of false positives, false negatives, sensitivity, and specificity of the linkage were all acceptable. Using the linked file, the relationship of childhood behavioural indicators and adult income can be investigated in future studies. ConclusionsInnovative methods for creating longitudinal datasets on children will assist in examining long-term outcomes associated with early childhood risk and protective factors as well as an evidence base for interventions that promote child well-being and positive outcomes.

scholarly journals Population networks from DNA sequences: methodological developments

2017 ◽  
Author(s):  
B. Schaeffer ◽  
V. Nicolas ◽  
F. Austerlitz ◽  
C. Larédo
Keyword(s):  
Graphical Models ◽  
Dna Sequences ◽  
Genetic Material ◽  
Simulated Data ◽  
Original Method ◽  
Population History ◽  
Data Sets ◽  
Key Factor ◽  
History Of ◽  
Simulated Data Sets

AbstractSeveral classes of methods have been proposed for inferring the history of populations from genetic polymorphism data. As connectivity is a key factor to explain the structure of populations, several graph-based methods have been developed to this aim, using population genetics data. Here we propose an original method based on graphical models that uses DNA sequences to provide relationships between populations. We tested our method on various simulated data sets, describing typical demographic scenarios, for different parameters values. We found that our method behaved noticeably well for realistic demographic evolutionary processes and recovered suitably the migration processes. Our method provides thus a complementary tool for investigating population history based on genetic material.

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (7) ◽  
pp. 2961-2980 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate-related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002–April 2012) and TANSO-FTS on-board GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System. We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between SCIAMACHY and GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT–SCIAMACHY (linear correlation coefficient r=0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT–TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY–TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (± 2 h, 10° x 10° around TCCON sites), i.e. the observed air masses are not exactly identical but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by also using data from other missions (e.g. OCO-2, GOSAT-2, CarbonSat) in the future.

Decision tree classification: Ranking journals using IGIDI

2019 ◽  
Vol 46 (3) ◽  
pp. 325-339
Author(s):  
Muhammad Shaheen ◽  
Tanveer Zafar ◽  
Sajid Ali Khan
Keyword(s):  
Decision Tree ◽  
Gini Index ◽  
Information Gain ◽  
Diversity Index ◽  
Selection Method ◽  
Attribute Selection ◽  
Data Sets ◽  
Average Value ◽  
Decision Tree Classification ◽  
Selection Measures

Selection of an attribute for placement of the decision tree at an appropriate position (e.g. root of the tree) is an important decision. Many attribute selection measures such as Information Gain, Gini Index and Entropy have been developed for this purpose. The suitability of an attribute generally depends on the diversity of its values, relevance and dependency. Different attribute selection measures have different criteria for measuring the suitability of an attribute. Diversity Index is a classical statistical measure for determining the diversity of values, and according to our knowledge, it has never been used as an attribute selection method. In this article, we propose a novel attribute selection method for decision tree classification. In the proposed scheme, the average of Information Gain, Gini Index and Diversity Index are taken into account for assigning a weight to the attributes. The attribute with the highest average value is selected for the classification. We have empirically tested our proposed algorithm for classification of different data sets of scientific journals and conferences. We have developed a web-based application named JC-Rank that makes use of our proposed algorithm. We have also compared the results of our proposed technique with some existing decision tree classification algorithms.

scholarly journals Combining feminist intersectional and community-engaged research commitments: Adaptations for scoping reviews and secondary analyses of national data sets

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Leah Levac ◽  
Ann B Denis
Keyword(s):  
Scoping Review ◽  
Public Services ◽  
Research Collaboration ◽  
Secondary Data ◽  
Lessons Learned ◽  
Data Sets ◽  
Community Engaged Research ◽  
Scoping Reviews ◽  
Secondary Analyses ◽  
Statistics Canada

As Hankivsky & Cormier (2011) and Denis (2008) note, the theoretical evolution of intersectionality has outpaced its methodological development. While past work has contributed to our understanding of how to apply intersectionality in research (CRIAW-ICREF & DAWN-RAFH 2014; Morris & Bunjan 2007; Simpson 2009), gaps persist. Drawing on a four-year community-university research collaboration called ‘Changing public services: Women and intersectional analysis’, we explore the incorporation of feminist intersectional and community-engaged research commitments into secondary data analyses, specifically a scoping review and secondary analyses of two Statistics Canada data sets. We discuss our application of these commitments across all stages of designing and undertaking these analyses, in particular drawing into focus the importance of dialogue and deliberation throughout our process. Our application of feminist intersectional and community-engaged commitments – including prioritising community benefit and practising self-reflexivity – revealed gaps and silences in the data, in turn improving our understanding of differences in people’s experiences, our critiques of policies and our identification of new research questions. The lessons learned, we conclude, are valuable for scholars, whether or not community engagement is central to their scholarly commitment. Keywordsfeminist intersectionality, community-engaged research, scoping review, logistic regression, community-university partnerships, Canadian public services

Minor Concentration Ratios for the Bounds of the Gini Index

2004 ◽  
Author(s):  
Tomson Ogwang
Keyword(s):  
Gini Index ◽  
Concentration Ratio ◽  
The United States ◽  
Grouped Data ◽  
Lower And Upper Bounds ◽  
Income Distributions ◽  
The Lorenz Curve ◽  
Distribution Studies ◽  
Minor Concentration ◽  
Concentration Ratios

The minor concentration ratio is used to supplement the Gini index in income distribution studies. The appeal of the minor concentration ratio stems form the fact that it examines the relative position of the “poor”, an important focus group in the analysis of income distributions. In this note, minor concentration ratios associated with the lower and upper bounds of the Gini index are derived based on the observed points of the Lorenz curve. When the two minor concentration ratios are computed using grouped data for the United States, they turn out to be fairly close.

A New Selection Measure for Classification Using Decision Trees

2004 ◽  
Vol 03 (01) ◽  
pp. 1-7
Author(s):  
B. Chandra ◽  
Gaurav Saxena
Keyword(s):  
Decision Trees ◽  
Classification Accuracy ◽  
Gini Index ◽  
Real Life ◽  
Data Sets ◽  
Life Data ◽  
The Past ◽  
Real Life Data ◽  
Selection Measures ◽  
Balance Factor

The paper proposes a new selection measure for classification using decision trees for Data mining. Various algorithms have been proposed in the past for classification using decision trees viz. ID3, CART, SLIQ, etc. Selection measures like the Gain, Gain ratio, and Gini index have been proposed in these algorithms. However, none of the selection measures developed so far take into account the balancing of trees. This paper proposes a new selection measure which also takes into account the balancing of trees that will facilitate in improving the classification accuracy. The performance of the original SLIQ algorithm, C5 and the algorithm using the new selection measure (which takes into account the accuracy as well as the balance factor) was measured on the basis of the classification accuracy. Three real life data sets were chosen for this purpose.

scholarly journals Seafloor Density Contrast Derived From Gravity and Shipborne Depth Observations: A Case Study in a Local Area of Atlantic Ocean

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaoyun Wan ◽  
Weipeng Han ◽  
Jiangjun Ran ◽  
Wenjie Ma ◽  
Richard Fiifi Annan ◽  
...  
Keyword(s):  
Atlantic Ocean ◽  
Gravity Data ◽  
Gravity Anomalies ◽  
Local Area ◽  
Density Variation ◽  
Mean Difference ◽  
Density Contrast ◽  
Data Sets ◽  
Marine Gravity ◽  
Band Pass

Marine gravity data from altimetry satellites are often used to derive bathymetry; however, the seafloor density contrast must be known. Therefore, if the ocean water depths are known, the density contrast can be derived. This study experimented the total least squares algorithm to derive seafloor density contrast using satellite derived gravity and shipborne depth observations. Numerical tests are conducted in a local area of the Atlantic Ocean, i.e., 34°∼32°W, 3.5°∼4.5°N, and the derived results are compared with CRUST1.0 values. The results show that large differences exist if the gravity and shipborne depth data are used directly, with mean difference exceeding 0.4 g/cm3. However, with a band-pass filtering applied to the gravity and shipborne depths to ensure a high correlation between the two data sets, the differences between the derived results and those of CRUST1.0 are reduced largely and the mean difference is smaller than 0.12 g/cm3. Since the spatial resolution of CRUST1.0 is not high and in many ocean areas the shipborne depths and gravity anomalies are much denser, the method of this study can be an alternative method for providing seafloor density variation information.

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