Exploration of regional poverty: Employing the information technology of the national scalable cluster project super computer for data extraction of the 1990 U.S. Census data

2.50
Hdl Handle:
http://hdl.handle.net/10755/148354
Type:
Presentation
Title:
Exploration of regional poverty: Employing the information technology of the national scalable cluster project super computer for data extraction of the 1990 U.S. Census data
Abstract:
Exploration of regional poverty: Employing the information technology of the national scalable cluster project super computer for data extraction of the 1990 U.S. Census data
Conference Sponsor:Sigma Theta Tau International
Conference Year:2001
Conference Date:November 10 - 14, 2001
Author:Vito, Kathleen, DNS/DNSc/DSN
P.I. Institution Name:Neumann College
Title:Assistant Professor
Objective: The purpose of this study was to test the 1987 thesis of Wilson in his book, The Truly Disadvantaged, that a trend exists in urban areas of increasing poverty concentration. Wilson used 1970 and 1980 census data of minority neighborhoods in mid-western and eastern cities to demonstrate his ideas that the key causes were macroeconomic. His work is controversial because he did not feature racism as the key underlying cause of poverty concentration. Whether one agrees with Wilson or not, there are many health policy and planning implications of increasing urban poverty. Design: The design of this study was exploratory. Sample: The sample of this study was the entire U.S. Census Data of 1990. Setting: This study was a collaborative effort between a National Scalable Cluster Project (NSCP) supercomputer of a large research university and faculty from three small liberal arts colleges in the mid-Atlantic area of the United States. The NSCP features a one and one half tera-byte super computer system that is designed to perform data extraction and data mining of very large data sets. Only a few computers of this magnitude exist and are available for academic research. Concepts: The faculty group and staff from NSCP formed a research group that worked together to examine poverty characteristics and trends utilizing the computing technology of the NSCP and the 1990 Census Data. Measures/Instruments: While a number of familiar quantitative measures (means, standard deviation, etc.) are used to analyze data, insights to aggregate data are customarily obtained primarily through some form of data visualization. When data sets become very large, as is increasingly the case with data processed, managed, and stored on computer systems, it becomes impossible for a human researcher to view, much less understand all of the information in detail. Data mining is a broad category of data intensive computing activities, which directly use machine computational power in categorizing data sets, finding trends, clusters, exceptional points, etc. In this approach, the user sets a number of conditions, which describe a range of manipulations and criteria to be applied. An automated process is invoked to scan through the data set and perform potentially exhaustive comparisons and aggregations. The results are presented to the user in a compact format. The super computing system sorted and retrieved data from the 1990 National Census. The research group was then able to study regional poverty issues using specific clusters of information from this very large database. Findings: After examination of the poverty clusters in the 1990 data, the research group found elements of change over time and trends within the clusters of more severe poverty levels than those reported by Wilson from the 1970 and1980 census data. Clusters specific to the urban areas under study featured higher rates of unemployment, single-parent families; households headed up by grandparents and health related difficulties. Conclusions: The data clusters provide strong evidence of the need for targeted as well as universal policy and planning. Implications: One of the most important implications for nursing is that this collaborative project developed the ability to hone in on small areas (census tracts) to better understand the effect of the neighborhood in a way that did not require labor-intensive conventional field work and statistical analysis. The community assessment and comparative analysis that can be done using census data with super computing technology is extremely beneficial to health care providers and health planners. The availability of large, inclusive data sets and the technology to link and analyze them brings us a new view of population sampling and statistical power.
Repository Posting Date:
26-Oct-2011
Date of Publication:
10-Nov-2001
Sponsors:
Sigma Theta Tau International

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleExploration of regional poverty: Employing the information technology of the national scalable cluster project super computer for data extraction of the 1990 U.S. Census dataen_GB
dc.identifier.urihttp://hdl.handle.net/10755/148354-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Exploration of regional poverty: Employing the information technology of the national scalable cluster project super computer for data extraction of the 1990 U.S. Census data</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Sigma Theta Tau International</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2001</td></tr><tr class="item-conference-date"><td class="label">Conference Date:</td><td class="value">November 10 - 14, 2001</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Vito, Kathleen, DNS/DNSc/DSN</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">Neumann College</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Assistant Professor</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">drkvito@yahoo.com</td></tr><tr><td colspan="2" class="item-abstract">Objective: The purpose of this study was to test the 1987 thesis of Wilson in his book, The Truly Disadvantaged, that a trend exists in urban areas of increasing poverty concentration. Wilson used 1970 and 1980 census data of minority neighborhoods in mid-western and eastern cities to demonstrate his ideas that the key causes were macroeconomic. His work is controversial because he did not feature racism as the key underlying cause of poverty concentration. Whether one agrees with Wilson or not, there are many health policy and planning implications of increasing urban poverty. Design: The design of this study was exploratory. Sample: The sample of this study was the entire U.S. Census Data of 1990. Setting: This study was a collaborative effort between a National Scalable Cluster Project (NSCP) supercomputer of a large research university and faculty from three small liberal arts colleges in the mid-Atlantic area of the United States. The NSCP features a one and one half tera-byte super computer system that is designed to perform data extraction and data mining of very large data sets. Only a few computers of this magnitude exist and are available for academic research. Concepts: The faculty group and staff from NSCP formed a research group that worked together to examine poverty characteristics and trends utilizing the computing technology of the NSCP and the 1990 Census Data. Measures/Instruments: While a number of familiar quantitative measures (means, standard deviation, etc.) are used to analyze data, insights to aggregate data are customarily obtained primarily through some form of data visualization. When data sets become very large, as is increasingly the case with data processed, managed, and stored on computer systems, it becomes impossible for a human researcher to view, much less understand all of the information in detail. Data mining is a broad category of data intensive computing activities, which directly use machine computational power in categorizing data sets, finding trends, clusters, exceptional points, etc. In this approach, the user sets a number of conditions, which describe a range of manipulations and criteria to be applied. An automated process is invoked to scan through the data set and perform potentially exhaustive comparisons and aggregations. The results are presented to the user in a compact format. The super computing system sorted and retrieved data from the 1990 National Census. The research group was then able to study regional poverty issues using specific clusters of information from this very large database. Findings: After examination of the poverty clusters in the 1990 data, the research group found elements of change over time and trends within the clusters of more severe poverty levels than those reported by Wilson from the 1970 and1980 census data. Clusters specific to the urban areas under study featured higher rates of unemployment, single-parent families; households headed up by grandparents and health related difficulties. Conclusions: The data clusters provide strong evidence of the need for targeted as well as universal policy and planning. Implications: One of the most important implications for nursing is that this collaborative project developed the ability to hone in on small areas (census tracts) to better understand the effect of the neighborhood in a way that did not require labor-intensive conventional field work and statistical analysis. The community assessment and comparative analysis that can be done using census data with super computing technology is extremely beneficial to health care providers and health planners. The availability of large, inclusive data sets and the technology to link and analyze them brings us a new view of population sampling and statistical power.</td></tr></table>en_GB
dc.date.available2011-10-26T09:43:57Z-
dc.date.issued2001-11-10en_GB
dc.date.accessioned2011-10-26T09:43:57Z-
dc.description.sponsorshipSigma Theta Tau Internationalen_GB
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