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

Spatial Analysis of Engineering and IT Occupation Clusters 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

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

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

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

Occupation Cluster Location Quotients and Percent Change in LQs, Spatial Analysis of Engineering and IT Occupation Clusters

Occupation Cluster Location Quotients and Percent Change in LQs, Spatial Analysis of Engineering and IT Occupation Clusters

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

Where is the data available? Spatial Analysis of Engineering and IT Occupation Clusters

ArcGIS 9.3 Spatial Statistics Toolbox Other Tools for Spatial Analysis GeoDa; GeoDa Center for GeoSpatial Analysis and Computation SAM; Spatial Analysis in Macroecology hors Location Quotient: LQ = R1= Regional employment in occupation cluster; R2= Total Regional employment; N1 = National employment in occupation cluster; N2= Total National employment

Engineering Occupation Cluster, LQ, 2007 IT Occupation Cluster, LQ, 2007 RankIT, LQ 07Eng, LQ 07 1King George, VA Butte, ID 2Fairfax, VAMartin, IN 3Santa Clara, CAKing George, VA 4Broomfield, CO St. Mary’s MD 5Arlington, VARoane, TN Top Five Counties Spatial Analysis of Engineering and IT Occupation Clusters Eng LQ, 2007 IT LQ, 2007

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

Occupation Cluster Location Quotient (Year) Specialized Counties Nearest Neighbor Ratio (R) [clustered < 1] Diagnostics Information Technology Z score (-9.40 sd); P-value (0.000) Information Technology Z score (-8.88 sd); P-value (0.000) Engineering Z score (-9.38 sd); p-value (0.000) Engineering Z score ( ); p-value (0.000) ArcGIS Spatial Statistics Toolbox (Analyzing Patterns, Average Nearest Neighbors) Spatial Analysis of Engineering and IT Occupation Clusters

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

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

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

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

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

Spatial Analysis of Engineering and IT Occupation Clusters Hot Spot Analysis

Conclusions Spatial Analysis of Engineering and IT Occupation Clusters 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

References Anselin, Luc; GeoDa i5, Spatial Analysis Laboratory, Department of Agricultural and Consumer Economics, University of Illinois, Urbana-Champaign, Urbana, IL 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: 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, Spatial Analysis of Engineering and IT Occupation Clusters

Contacts & Affiliations Christine Nolan, Purdue Center for Regional Development, Indraneel Kumar, Purdue Center for Regional Development, Matthew Baller, Purdue Center for Regional Development, Rachel Justis, Indiana Business Research Center, Spatial Analysis of Engineering and IT Occupation Clusters