Analyzing Student Geo-Demographics at Clark State Community

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Analyzing Student Geo-Demographics at
Clark State Community College
Aimée Bélanger-Haas, GISP
GEOG 596A
December 19th, 2012
Advisor: Stephen Matthews
Outline
• Background
• Goals and Objectives
• Proposed Methodology
• Anticipated Results
• Timeline
Clark State Community College
Funding
• Sources?
– Alumni society
– Fundraising
– Government funding (changing and reducing)
• A large part is generated via student tuition and
recruiting students
• Identifying “where” to recruit students from can be
an important financial strategy
Typical Clark State Student
Year
Total
Average
Enrollment Age
% Male
% Female % Full
Time
% Part
Time
2012
4,977
28.2
33.8
66.2
41.1
58.9
2011
5,139
28.5
32.4
67.6
43.6
56.4
2010
4,993
28.4
34.0
66.0
45.9
54.1
Average
5,036
28.36
33.4%
66.6%
43.5%
56.5%
Research Question
• Based on five years of registration data, what
are the demographic characteristics of a typical
CSCC student?
• Can other similar areas be identified to help with
marketing efforts?
• Other potential questions worth examining
include the characteristics of students based on
academic grade and major
Geodemographics
• Study of people according to where they live
• Loosely based on the assumption of “birds of a
feather flock together”
• Provides the capability to predict consumer behavior
based on a neighborhood classification
Education is like a Business
• The retail sector has fully embraced the use of
geodemographics to help increase business and
profits by better identifying potential customers
• This same methodology can be applied for Higher
Educational institutions
• Both have customers (students) with addresses that
can be geocoded that can help uncovering varying
themes through their geodemographic profile
Previous Research
• Studies have been accomplished at other higher
education institutions
• Most have been at 4-year universities who recruit
straight out of high school
• Many institutions do analysis but do not reveal their
methods
Methodology
Step 1: Acquire Student Data
• Get student information from Institutional Research
(IR)
Student Information
Address
Gender
Age
Ethnicity
Degree/Major
Grade Point Average
High School (if reported)
SAT score (if reported)
Step 2: Download Census 2010 data
• Tract level (n=355) data will be downloaded to
create the geodemographic segments
• American Community Survey (ACS) 5-year
estimates (2007-2011) data will be utilized for
demographic, social, economic and housing
characteristics
• SF1 data will be utilized for counts
Census Variables
Step 3: Create Geo-demographic
Segments
• Segments will be created based on the combination
of socioeconomic data
• Exploratory Spatial Data Analysis (ESDA) will be
conducted in OpenGeoDa and ArcGIS, variables will
be evaluated and paired down
• Census tracts will be grouped together based on
similarities
• Student dataset spatially joined to segments
Step 4: Analyze
• Identification of hot spots will be undertaken for
various sub-groupings
• I will use the R statistical package & the ArcGIS
spatial statistics toolset.
• I plan to explore the use of methods such as:
– On point data: Kernel Density Analysis (KDE), as well as
several functions such as Ripley's K, L, and the pair
correlation function (PCF).
– On area data: Spatial regression analysis to explicitly
model spatial relationships
Anticipated Results
• Students will be classified into different
geodemographic groups to help uncover areas that
match target demographics
• CSCC will gain a better understanding of its
student’s neighborhood socioeconomic
characteristics
• Areas surrounding CSCC will potentially be
identified and targeted marketing may occur in order
to help increase enrollment
Additional maps of use to the College
•
•
•
•
•
•
•
Enrollment per census tract
Educational attainment and median income per census tract
CPE students with total student population
CPE students versus total density per school district
CPE students with median family income
Drive time analysis
Enrollment as a percentage by census tract versus total
population college aged students (market penetration)
Timeline
Winter 2013:
– Present before IRB Board
– Geocode student datasets
– Download census data
Spring 2013
– Process data and create Geodemographic segments
– Analyze results
Summer 2013
– Present at ESRI Education User Conference
– Provide the CSCC with the maps and results
Acknowledgments
• Would like to acknowledge the following people:
– Advisor: Stephen Matthews
– Institutional Research: Cynthia Applin
– Marketing Director: Jennifer Diestch
References
Adnan, M., Longley, P., Singleton, A. and Brunsdon, C. (2010). Towards Real Time Geodemographics: Clustering Algorithm
Performance for Large Multidimentional Spatial Dabases. Transactions in GIS , 283-297.
Batey, P. (1999). Participation in higher education: A geodemocratic perspective on the potential for further expansion in student
numbers. Journal of Geographical Systems , 277-303.
Crosta, P., Leinbach, T. et al (2006). Using Census Data to Classify Community College Students by Socioeconomic Status and
Community College Characteristics. Community College Resource Center Research Tools , 1-12.
DesJardins, S. L. (2002). An Analytical Stragegy to Assist Institutional Recruitement and Marketing Efforts. Research in Higher
Education , 531-553.
Hanewicz, D. C. (2012, 02 28). Geographic Information Systems and the Political Process. Retrieved 2012 17-10 from
wpsa.research.pdx: http://wpsa.research.pdx.edu/meet/2012/hanewicz.pdf
Krestle, J. (2004). Geodemographic target clusters: A case study. Monday Report on Retailers , 2-4.
Lane, J. (2003). Studying Community Colleges and Their Students: Context and Research Issues. New Directions for
Institutional Research , 51-67.
Livinsgton, A. (2000, 07 16). Colleges search for applicants, with glitz and geodemographics. The Associated Press . New York.
Marble, D. (1997, 07). A Model for the Use of GIS Technology in College and University Admissions Planning. Retrieved 2012
йил 01-10 from ESRI User Conference Proceedings:
http://proceedings.esri.com/library/userconf/proc97/proc97/to250/pap218/p218.htm
Marble, D. and Herries, J (2001). A Model for the Use of GIS Technology in College and University Admissions Planning. ESRI
User Conference, San Diego California.
Marble, D. (1995, 07). Applying GIS Technology to the Freshman Admissions Process. Retrieved 2012 йил 01-10 from ESRI
User Conference Proceedings: http://proceedings.esri.com/library/userconf/proc95/to200/p182.html
Mora, V. (2003). Applications of GIS in Admissions and Targeting Recruiting Efforts. New Directions for Institutional Research ,
15-21.
Read, P., Higgs, G and Taylor, G. (2005). The potential and barriers to the use of geographical information systems for
marketing applications in higher education institutions. Marketing Intelligence & Planning , Volume 23, 30-42.
Questions
Please feel free to contact me at
[email protected]
Aimee Belanger-Haas
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