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Spatial Analysis of Engineering and IT Occupation Clusters

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

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Title: Spatial Analysis of Engineering and IT Occupation Clusters


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
  • ONET, 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- Wards
    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
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

Spatial Analysis of Engineering and IT
Occupation Clusters
11
Top Five Counties
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. Marys MD
5 Arlington, VA Roane, TN
Spatial Analysis of Engineering and IT
Occupation Clusters
12
  • Mean Center and Standard Deviational Ellipses
    are used on the specialized counties with LQ gt
    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)
Occupation Cluster Location Quotient (Year) Specialized Counties Nearest Neighbor Ratio (R) clustered lt 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)




Spatial Analysis of Engineering and IT
Occupation Clusters
14
  • Morans 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,
    15321-327
  • 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_at_purdue.edu Indraneel
Kumar, Purdue Center for Regional Development,
ikumar_at_purdue.edu Matthew Baller, Purdue Center
for Regional Development, mballer_at_purdue.edu R
achel Justis, Indiana Business Research Center,
rmjustis_at_indiana.edu
Spatial Analysis of Engineering and IT
Occupation Clusters
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