The Experts below are selected from a list of 270 Experts worldwide ranked by ideXlab platform
Karen A. Woodrow - One of the best experts on this subject based on the ideXlab platform.
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estimation of population coverage in the 1990 united states census based on Demographic Analysis
Journal of the American Statistical Association, 1993Co-Authors: Gregory J Robinson, B Ahmed, Prithwis Das Gupta, Karen A. WoodrowAbstract:Abstract This article presents estimates of net coverage of the national population in the 1990 census, based on the method of Demographic Analysis. The general techniques of Demographic Analysis as an analytic tool for coverage measurement are discussed, including use of the Demographic accounting equation, data components, and strengths and limitations of the method. Patterns of coverage displayed by the 1990 estimates are described, along with similarities or differences from comparable Demographic estimates for previous censuses. The estimated undercount in the 1990 census was 4.7 million, or 1.85%. The undercount of males (2.8%) was higher than for females (.9%), and the undercount of Blacks (5.7%) exceeded the undercount of Non-Blacks (1.3%). Black adult males were estimated to have the highest rate of undercounting of all groups. Race-sex-age patterns of net coverage in the 1990 census were broadly similar to patterns in the 1980 and 1970 censuses. A final section presents the results of the first ...
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Estimation of population coverage in the 1990 United States census based on Demographic Analysis.
Journal of the American Statistical Association, 1993Co-Authors: J G Robinson, B Ahmed, P Das Gupta, Karen A. WoodrowAbstract:"This article presents estimates of net coverage of the national population in the 1990 [U.S.] census, based on the method of Demographic Analysis. The general techniques of Demographic Analysis as an analytic tool for coverage measurement are discussed, including use of the Demographic accounting equation, data components, and strengths and limitations of the method. Patterns of coverage displayed by the 1990 estimates are described, along with similarities or differences from comparable Demographic estimates for previous censuses....A final section presents the results of the first statistical assessment of the uncertainty in the Demographic coverage estimates for 1990." Comments by Clifford C. Clogg and Christine L. Himes (pp. 1,072-4) and Jeffrey S. Passel (pp. 1,074-7) and a rejoinder by the authors (pp. 1,077-9) are included.
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Estimating coverage of the 1990 United States census: Demographic Analysis.
1991Co-Authors: Robinson Jg, Ahmed B, Das Gupta P, Karen A. WoodrowAbstract:The purpose of the Demographic Analysis evaluation program for 1990 is twofold: (1) to evaluate the completeness of coverage of population in the 1990 [U.S.] census based on Demographic Analysis and (2) to develop a statistically-based assessment of the accuracy of those Demographic estimates of net coverage. This paper reports the results of the first set of Demographic estimates of coverage for 1990 and the assessment of the accuracy of the estimates. (EXCERPT)
Gregory J Robinson - One of the best experts on this subject based on the ideXlab platform.
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comparing the u s decennial census coverage estimates for children from Demographic Analysis and coverage measurement surveys
Population Research and Policy Review, 2016Co-Authors: William P Ohare, Gregory J Robinson, Kirsten West, Thomas MuleAbstract:Abstract Following every U.S. decennial census since 1960, the U.S. Census Bureau has evaluated the completeness of coverage using two different methods. Demographic Analysis (DA) compares the census counts to a set of independent population estimates to infer coverage differences by age, sex, and race. The survey-based approach (also called dual system estimation or DSE) provides coverage estimates based on matching data from a post-enumeration survey to census records. This paper reviews the fundamentals of the two methodological approaches and then initially examines the results of these two methods for the 2010 decennial census in terms of consistency and inconsistency for age groups. The authors find that the two methods produce relatively consistent results for all age groups, except for young children. Consequently, the paper focuses on the results for children. Results of the 1990, 2000, and 2010 decennial censuses are shown for the overall population in this age group and by Demographic detail (age, race, and Hispanic origin). Among children, the DA and DSE results are most inconsistent for the population aged 0–4 and most consistent for ages 10–17. Results also show that DA and DSE are more consistent for Black than non-Black populations. The authors discuss possible explanations for the differences in the two methods for young children and conclude that the DSE approach may underestimate the net undercount of young children due to correlation bias.
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estimation of population coverage in the 1990 united states census based on Demographic Analysis
Journal of the American Statistical Association, 1993Co-Authors: Gregory J Robinson, B Ahmed, Prithwis Das Gupta, Karen A. WoodrowAbstract:Abstract This article presents estimates of net coverage of the national population in the 1990 census, based on the method of Demographic Analysis. The general techniques of Demographic Analysis as an analytic tool for coverage measurement are discussed, including use of the Demographic accounting equation, data components, and strengths and limitations of the method. Patterns of coverage displayed by the 1990 estimates are described, along with similarities or differences from comparable Demographic estimates for previous censuses. The estimated undercount in the 1990 census was 4.7 million, or 1.85%. The undercount of males (2.8%) was higher than for females (.9%), and the undercount of Blacks (5.7%) exceeded the undercount of Non-Blacks (1.3%). Black adult males were estimated to have the highest rate of undercounting of all groups. Race-sex-age patterns of net coverage in the 1990 census were broadly similar to patterns in the 1980 and 1970 censuses. A final section presents the results of the first ...
Fred Paccaud - One of the best experts on this subject based on the ideXlab platform.
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nonagenarians and centenarians in switzerland 1860 2001 a Demographic Analysis
Journal of Epidemiology and Community Health, 2005Co-Authors: Jeanmarie Robine, Fred PaccaudAbstract:Study objective: To explore the rapid rise of the extremely old population, showing the magnitude of the increase and indentifying Demographic mechanisms underlying this increase. Design: Demographic Analysis using census data, yearly population estimates, and mortality statistics. Setting: Switzerland 1860–2001. Main results: Indicators suggest a strong increase in the number of nonagenarians and centenarians in Switzerland as compared with other countries. The increase is mostly attributable to the decline in mortality after age 80. This decline started in the 1950s. Conclusion: Nonagenarians and centenarians constitute a new population, which became sizeable after 1950 in Switzerland. There is a need to monitor this population with appropriate Demographic and epidemiological indicators.
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Nonagenarians and centenarians in Switzerland, 1860–2001: a Demographic Analysis
Journal of Epidemiology and Community Health, 2005Co-Authors: Jeanmarie Robine, Fred PaccaudAbstract:Study objective: To explore the rapid rise of the extremely old population, showing the magnitude of the increase and indentifying Demographic mechanisms underlying this increase. Design: Demographic Analysis using census data, yearly population estimates, and mortality statistics. Setting: Switzerland 1860–2001. Main results: Indicators suggest a strong increase in the number of nonagenarians and centenarians in Switzerland as compared with other countries. The increase is mostly attributable to the decline in mortality after age 80. This decline started in the 1950s. Conclusion: Nonagenarians and centenarians constitute a new population, which became sizeable after 1950 in Switzerland. There is a need to monitor this population with appropriate Demographic and epidemiological indicators.
Le Hoang Son - One of the best experts on this subject based on the ideXlab platform.
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A novel kernel fuzzy clustering algorithm for Geo-Demographic Analysis
Information Sciences, 2015Co-Authors: Le Hoang SonAbstract:We proposed a novel kernel-based fuzzy clustering for Geo-Demographic Analysis.It relied on Gaussian kernel function, updated elements and standardized weights.The algorithm was both theoretical and experimental validated.Its clustering quality was better than those of other relevant algorithms.Some properties of solutions were examined. Geo-Demographic Analysis (GDA) is a major concentration of various interdisciplinary researches and has been used in many decision-making processes regarding the provision and distribution of products and services in society. Machine learning methods namely Principal Component Analysis, Self-Organizing Map, K-Means, fuzzy clustering and fuzzy geographically weighted clustering were proposed to enhance the quality of GDA. Among them, the state-of-the-art method - Modified Intuitionistic Possibilistic Fuzzy Geographically Weighted Clustering (MIPFGWC) has some drawbacks such as: (i) using the Euclidean similarity measure often results in high error rate and sensitivity to noises and outliers; (ii) updating the membership matrix by the Spatial Interaction - Modification Model (SIM2) model leads to new centers not being "geographically aware". In this paper, we present a novel fuzzy clustering algorithm named as Kernel Fuzzy Geographically Clustering (KFGC) that utilizes both the kernel similarity function and the new update mechanism of the SIM2 model to remedy the disadvantages of MIPFGWC. Some supported properties and theorems of KFGC are also examined in the paper. Specifically, the differences between solutions of KFGC and those of MIPFGWC and of some variants of KFGC are theoretically validated. Lastly, experimental Analysis is performed to compare the performance of KFGC with those of the relevant algorithms in terms of clustering quality.
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Fuzzy geographically weighted clustering using artificial bee colony: An efficient geo-Demographic Analysis algorithm and applications to the Analysis of crime behavior in population
Applied Intelligence, 2015Co-Authors: Arie Wahyu Wijayanto, Ayu Purwarianti, Le Hoang SonAbstract:Geo-Demographic Analysis is an essential part of a geographical information system (GIS) for predicting people's behavior based on statistical models and their residential location. Fuzzy Geographically Weighted Clustering (FGWC) serves as one of the most efficient algorithms in geo-Demographic Analysis. Despite being an effective algorithm, FGWC is sensitive to initialize when the random selection of cluster centers makes the iterative process falling into the local optimal solution easily. Artificial Bee Colony (ABC), one of the most popular meta-heuristic algorithms, can be regarded as the tool to achieve global optimization solutions. This research aims to propose a novel geo-Demographic Analysis algorithm that integrates FGWC to the optimization scheme of ABC for improving geo-Demographic clustering accuracy. Experimental results on various datasets show that the clustering quality of the proposed algorithm called FGWC-ABC is better than those of other relevant methods. The proposed algorithm is also applied to a decision-making application for analyzing crime behavior problem in the population using the US communities and crime dataset. It provides fuzzy rules to determine the violent crime rate in terms of linguistic labels from socioeconomic variables. These results are significant to make predictions of further US violent crime rate and to facilitate appropriate decisions on prevention such the situations in the future.
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Enhancing clustering quality of geo-Demographic Analysis using context fuzzy clustering type-2 and particle swarm optimization
Applied Soft Computing, 2014Co-Authors: Le Hoang SonAbstract:Geo-Demographic Analysis, which is one of the most interesting inter-disciplinary research topics between Geographic Information Systems and Data Mining, plays a very important role in policies decision, population migration and services distribution. Among some soft computing methods used for this problem, clustering is the most popular one because it has many advantages in comparison with the rests such as the fast processing time, the quality of results and the used memory space. Nonetheless, the state-of-the-art clustering algorithm namely FGWC has low clustering quality since it was constructed on the basis of traditional fuzzy sets. In this paper, we will present a novel interval type-2 fuzzy clustering algorithm deployed in an extension of the traditional fuzzy sets namely Interval Type-2 Fuzzy Sets to enhance the clustering quality of FGWC. Some additional techniques such as the interval context variable, Particle Swarm Optimization and the parallel computing are attached to speed up the algorithm. The experimental evaluation through various case studies shows that the proposed method obtains better clustering quality than some best-known ones.
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Spatial interaction - modification model and applications to geo-Demographic Analysis
Knowledge Based Systems, 2013Co-Authors: Le Hoang Son, Bui Cong Cuong, Hoang Viet LongAbstract:In this paper, we introduce a novel model so-called Spatial Interaction - Modification Model (SIM^2), serving for the classification of spatially-referenced Demographic data. It is integrated with the main part of the best fuzzy clustering algorithm for geo-Demographic Analysis problem - IPFGWC to form the new method named as MIPFGWC. Theoretical and experimental analyses show that MIPFGWC achieves better clustering quality than IPFGWC and other available algorithms.
Thomas Mule - One of the best experts on this subject based on the ideXlab platform.
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comparing the u s decennial census coverage estimates for children from Demographic Analysis and coverage measurement surveys
Population Research and Policy Review, 2016Co-Authors: William P Ohare, Gregory J Robinson, Kirsten West, Thomas MuleAbstract:Abstract Following every U.S. decennial census since 1960, the U.S. Census Bureau has evaluated the completeness of coverage using two different methods. Demographic Analysis (DA) compares the census counts to a set of independent population estimates to infer coverage differences by age, sex, and race. The survey-based approach (also called dual system estimation or DSE) provides coverage estimates based on matching data from a post-enumeration survey to census records. This paper reviews the fundamentals of the two methodological approaches and then initially examines the results of these two methods for the 2010 decennial census in terms of consistency and inconsistency for age groups. The authors find that the two methods produce relatively consistent results for all age groups, except for young children. Consequently, the paper focuses on the results for children. Results of the 1990, 2000, and 2010 decennial censuses are shown for the overall population in this age group and by Demographic detail (age, race, and Hispanic origin). Among children, the DA and DSE results are most inconsistent for the population aged 0–4 and most consistent for ages 10–17. Results also show that DA and DSE are more consistent for Black than non-Black populations. The authors discuss possible explanations for the differences in the two methods for young children and conclude that the DSE approach may underestimate the net undercount of young children due to correlation bias.
