Spatial Analysis of Engineering and IT Occupation Clusters

Spatial Analysis of Engineering and IT Occupation Clusters Spatial Analysis of Engineering and IT Occupation Clusters

Indiana GIS Conference, 2010Indiana GIS Conference, 2010Tuesday, February 23Tuesday, February 23rdrd, 2010 , 2010

Spatial Analysis of Engineering and IT Occupation ClustersSpatial Analysis of Engineering and IT Occupation Clusters

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


Four mapping categoriesFour mapping categories

• Infrastructure & Infrastructure & amenitiesamenities

• Dot densityDot density

• Percent change of Percent change of Location QuotientsLocation Quotients

• Percent employment Percent employment per total county per total county

workforce workforce


Occupation Cluster Employment Distribution by U.S. County, 2007Occupation Cluster Employment Distribution by U.S. County, 2007


Occupation Cluster Location Quotients and Percent Change in LQs, 2001-2007Occupation Cluster Location Quotients and Percent Change in LQs, 2001-2007


Occupation Cluster Location Quotients and Percent Change in LQs, 2001-2007Occupation Cluster Location Quotients and Percent Change in LQs, 2001-2007


Economic Growth Region 6, Economic Growth Region 6, Occupation Clusters, 2007Occupation Clusters, 2007

Economic Growth Region 11, Occupation Clusters, 2007Economic Growth Region 11, Occupation Clusters, 2007


Where is the data available?Where is the data available?

http://www.statsamerica.org/innovation/



ArcGIS 9.3 Spatial Statistics ToolboxArcGIS 9.3 Spatial Statistics Toolbox Other Tools for Spatial AnalysisOther 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: Location Quotient:

LQ = 2/1

2/1

NN

RR

R1= Regional employment in occupation cluster; R2= Total Regional employment; N1 = National employment in occupation cluster; N2= Total National employment

Engineering Occupation Engineering Occupation Cluster, LQ, 2007Cluster, LQ, 2007

IT Occupation IT Occupation Cluster, LQ, 2007Cluster, LQ, 2007

Rank IT, LQ 07 Eng, LQ 07

1 King George, VA

Butte, ID

2 Fairfax, VA Martin, IN

3 Santa Clara, CA King George, VA

4 Broomfield, CO St. Mary’s MD

5 Arlington, VA Roane, TN

Top Five CountiesTop Five Counties


Eng LQ, 2007

IT LQ, 2007


• 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

2007 135 0.577 Z score (-9.40 sd); P-value (0.000)

Information Technology

2001 128 0.589 Z score (-8.88 sd); P-value (0.000)

Engineering 2007 240 0.684 Z score (-9.38 sd); p-value (0.000)

Engineering 2001 211 0.634 Z score (-10.16); p-value (0.000)

ArcGIS Spatial Statistics Toolbox (Analyzing Patterns, Average Nearest Neighbors)ArcGIS Spatial Statistics Toolbox (Analyzing Patterns, Average Nearest Neighbors)


Moran’s I Spatial AutocorrelationMoran’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 2007IT, LQ 2007

• Exploratory Spatial Data AnalysisSource: GeoDa


ArcGIS 9.3 Spatial Statistics ToolboxArcGIS 9.3 Spatial Statistics Toolbox

Mapping ClustersMapping Clusters

• Spatial Weight Matrix is based on contiguity (edges and corners)


Engineering Occupation Cluster, 2007; Cluster & Outlier Analysis, ArcGIS

Local Indicators of Spatial Association (LISA)Local Indicators of Spatial Association (LISA)


Engineering Occupation Cluster, 2007; Univariate LISA, GeoDa



IT Occupation Cluster, 2007; Cluster & Outlier Analysis, ArcGIS



IT Occupation Cluster, 2007; Univariate LISA, GeoDa



Hot Spot AnalysisHot Spot Analysis

ConclusionsConclusions


• 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

ReferencesReferences

• 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


Contacts & AffiliationsContacts & Affiliations

Christine Nolan, Purdue Center for Regional Development, [email protected]

Indraneel Kumar, Purdue Center for Regional Development, [email protected]

Matthew Baller, Purdue Center for Regional Development, [email protected]

Rachel Justis, Indiana Business Research Center, [email protected]


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Spatial Analysis of Engineering and IT Occupation Clusters