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Spatial Analysis of Engineering and IT Occupation Clusters

Spatial Analysis of Engineering and IT Occupation Clusters. Indiana GIS Conference, 2010 Tuesday, February 23 rd , 2010. Introduction. Knowledge-based Occupation Clusters across the counties O*NET, Occupational Information Network

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Spatial Analysis of Engineering and IT Occupation Clusters

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  1. Spatial Analysis of Engineering and IT Occupation Clusters Indiana GIS Conference, 2010 Tuesday, February 23rd, 2010

  2. Introduction • Knowledge-based Occupation Clusters across the counties • O*NET, Occupational Information Network • SOC, Standard Occupational Classification, 900+ occupations • Skills, Knowledge, and Education requirements of Occupations are given • Knowledge levels (Mathematics, Physics) by Occupations are given • Occupations by 33 Knowledge Variables • Statistical Cluster Analysis- Ward’s Agglomerative Hierarchical Clustering • Intuitive adjustments to cluster outputs • 15 Occupation Clusters are identified Spatial Analysis of Engineering and IT Occupation Clusters

  3. Occupation Clusters Defined in this Study • Agribusiness and Food Technology • Arts, Entertainment, Publishing and Broadcasting • Building, Landscape and Construction Design • Engineering and Related Sciences • Health Care and Medical Science (Aggregate) • Health Care and Medical Science (Medical Practitioners and Scientists) • Health Care and Medical Science (Medical Technicians) • Health Care and Medical Science (Therapy, Counseling, Nursing and Rehabilitation ) • Information Technology • Legal and Financial Services, and Real Estate • Managerial, Sales, Marketing and HR • Mathematics, Statistics, Data and Accounting • Natural Sciences and Environmental Management • Personal Services • Postsecondary Education and Knowledge Creation • Primary/Secondary and Vocational Education, Remediation & Social Services • Public Safety and Domestic Security • Skilled Production Workers: Technicians, Operators, Trades, Installers & Repairers Spatial Analysis of Engineering and IT Occupation Clusters

  4. Four mapping categories • Infrastructure & amenities • Dot density • Percent change of Location Quotients • Percent employment per total county workforce Spatial Analysis of Engineering and IT Occupation Clusters

  5. Occupation Cluster Employment Distribution by U.S. County, 2007 Spatial Analysis of Engineering and IT Occupation Clusters

  6. Occupation Cluster Location Quotients and Percent Change in LQs, 2001-2007 Spatial Analysis of Engineering and IT Occupation Clusters

  7. Occupation Cluster Location Quotients and Percent Change in LQs, 2001-2007 Spatial Analysis of Engineering and IT Occupation Clusters

  8. Economic Growth Region 11, Occupation Clusters, 2007 Economic Growth Region 6, Occupation Clusters, 2007 Spatial Analysis of Engineering and IT Occupation Clusters

  9. Where is the data available? http://www.statsamerica.org/innovation/ Spatial Analysis of Engineering and IT Occupation Clusters

  10. LQ = ArcGIS 9.3 Spatial Statistics Toolbox Other Tools for Spatial Analysis • GeoDa; GeoDa Center for GeoSpatial Analysis and Computation • http://geodacenter.asu.edu/ • SAM; Spatial Analysis in Macroecology • http://www.ecoevol.ufg.br/sam/#Authors Location Quotient: R1= Regional employment in occupation cluster; R2= Total Regional employment; N1 = National employment in occupation cluster; N2= Total National employment Spatial Analysis of Engineering and IT Occupation Clusters

  11. Top Five Counties Engineering Occupation Cluster, LQ, 2007 Eng LQ, 2007 IT Occupation Cluster, LQ, 2007 IT LQ, 2007 Spatial Analysis of Engineering and IT Occupation Clusters

  12. Mean Center and Standard Deviational Ellipses are used on the specialized counties with LQ >= 1.2, 2001 and 2007 • Compare changes in the distribution of geographical units, specialized counties • Distribution patterns are apparent by different Census regions, West, Midwest, Northeast, and South Spatial Analysis of Engineering and IT Occupation Clusters

  13. ArcGIS Spatial Statistics Toolbox (Analyzing Patterns, Average Nearest Neighbors) Spatial Analysis of Engineering and IT Occupation Clusters

  14. Moran’s I Spatial Autocorrelation • 3,000 + geographies, Queen Contiguity, 1st order • ArcGIS, first create the spatial weight matrix • Check the Z-value and P-value IT, LQ 2007 • Exploratory Spatial Data Analysis • Source: GeoDa Spatial Analysis of Engineering and IT Occupation Clusters

  15. ArcGIS 9.3 Spatial Statistics Toolbox Mapping Clusters • Spatial Weight Matrix is based on contiguity (edges and corners) Spatial Analysis of Engineering and IT Occupation Clusters

  16. Local Indicators of Spatial Association (LISA) Engineering Occupation Cluster, 2007; Cluster & Outlier Analysis, ArcGIS Spatial Analysis of Engineering and IT Occupation Clusters

  17. Local Indicators of Spatial Association (LISA) Engineering Occupation Cluster, 2007; Univariate LISA, GeoDa Spatial Analysis of Engineering and IT Occupation Clusters

  18. Local Indicators of Spatial Association (LISA) IT Occupation Cluster, 2007; Cluster & Outlier Analysis, ArcGIS Spatial Analysis of Engineering and IT Occupation Clusters

  19. Local Indicators of Spatial Association (LISA) IT Occupation Cluster, 2007; Univariate LISA, GeoDa Spatial Analysis of Engineering and IT Occupation Clusters

  20. Hot Spot Analysis Spatial Analysis of Engineering and IT Occupation Clusters

  21. Conclusions • LISA indicators including the Hot Spot analysis is one way of locating the spatial clusters • Useful tool for regional planning, can inform regional policies, programs, and projects • Useful tool for Cluster Based Economic Development strategies (CBED) • Different software might have different results • Cross-check the results, identify the broader patterns • Local knowledge is important Spatial Analysis of Engineering and IT Occupation Clusters

  22. References • Anselin, Luc; GeoDa 0.9.5-i5, Spatial Analysis Laboratory, Department of Agricultural and Consumer Economics, University of Illinois, Urbana-Champaign, Urbana, IL 61801 • Spatial Statistics for Commercial Applications, ESRI White Paper, 2005 • Gong, Jianxin; Clarifying the Standard Deviational Ellipse, Geographical Analysis, Vol. 34, No. 2, The Ohio State University • Rangel, T.F.L.V.B, Diniz-Filho, J.A.F and Bini, L.M. (2006) Towards an Integrated Computational Tool for Spatial Analysis in Macroecology and Biogeography. Global Ecology and Biogeography, 15:321-327 • PCRD, IBRC, RUPRI, SDG, and EMSI; Crossing the Next Regional Frontier: Information and Analytics Linking Regional Competitiveness to Investment in a Knowledge-Based Economy, 2009, www.statsamerica.org/innovation Spatial Analysis of Engineering and IT Occupation Clusters

  23. Contacts & Affiliations Christine Nolan, Purdue Center for Regional Development, cenolan@purdue.edu Indraneel Kumar, Purdue Center for Regional Development, ikumar@purdue.edu Matthew Baller, Purdue Center for Regional Development, mballer@purdue.edu Rachel Justis, Indiana Business Research Center, rmjustis@indiana.edu Spatial Analysis of Engineering and IT Occupation Clusters

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