A Pitch for School Geo-Demography
Richard Lycan Population Research Center
Portland State University, Oregon
Association of Pacific Coast Geographers
Olympia, Washington, October, 2012
Purpose of paper
 Provide a snapshot of the field of school demography as
practiced by those of a geographical persuasion.
 Briefly outline the major approaches used in school
enrollment forecasting.
 Show examples of how contract research in this area leads
to interesting research issues.
 Describe the enterprise and how it can fit into a university
setting.
Who we are
 The Population Research Center (PRC) is a community service organization in the
College of Urban and Public Affairs at Portland State University.
 PRC has some legislatively mandated functions, city and county estimates, and
liaison with the U.S. Bureau of the Census.
 PRC has a staff of 6 full time professionals and support staff and employs graduate
student assistants, mainly from Urban Studies and Planning and Geography. About
half our income is from contract research.




Most is for public agencies and non-profits.
School demography is our largest source of contract income and employs
about 1.5 FTE staff. Charles Rynerson does our school demography work.
We have provided demographic services for school districts for nearly 40
years.
For the last 20 years the school support has made use of GIS providing
geographically detailed forecasts.
The types of services to school districts
 The most commonly requested projects are enrollment forecasts for
school districts, or districts and their attendance areas.
 Other services for school districts include:

Attendance area boundary changes and boundary change
scenarios

School board member district reapportionment

Estimates of student generation from new housing

Field surveys to determine household composition
 The studies typically include a written report and a presentation to a
school board committee and/or the general public.
 We have provided service to most of the mid to large school districts in
Oregon and have a continuing agreement with a few.
Where do school districts
get demographic
services?

In house staff – Many large school districts have a staff, usually affiliated with facilities
planning, that provide demographic services. The staff have skills in demography, GIS,
planning, and database management. In a small district sometimes an assistant
superintendent who deals with facilities does the forecasting.

Turnkey Software – There are several firms that sell software tools to provide
enrollment forecasts. Often they are an appendage to school bus routing software.
Examples are Edulog in Montana and Davis Demographics in California.

Private Consultants – There are several consulting firms that provide demographic
services to school districts nationally. Examples are Gobalet and Lapkoff in California
and McKibben Demographics in South Carolina, as well as some who work at a more
local level. A&E firms sometimes provide forecasts as part of a capital plan.

Universities – Several university population research centers provide this type of
service. Examples include our center in Oregon and the Applied Demography Lab at
the University of Wisconsin.
The data environment

Student record is central to building forecasts.


We need historical data in order to calibrate enrollment forecasts.
The student record data is carefully geo-coded to the students’
residential locations.

Birth records also are geo-coded to mother’s residence and identify
potential enrollees some five years later.

Tax-lot data from the assessor’s office provides the information about the
housing units and neighborhoods where students live. We link birth records
and student records to their tax-lot.

We make use of data from the decennial census and the American
Community Survey.

Our forecasts are informed by advice from various other organizations such
as:

Local planning departments

The regional economists with the Oregon Employment Department
Enrollment forecasts

Long versus mid-range enrollment forecasts



Long range forecasts - typically required for facilities planning and usually are for about
ten years, but may extend as far as 15 or 20 years into the future.
Mid range forecasts – typically required for budget planning, staffing, transportation
planning, and use of existing facilities usually include extent five years into the future.
Three types of forecasting models typically used




The grade-progression model – driven by student enrollment trends for short term
forecasts and for smaller geographies, such as elementary school attendance areas.
The cohort cohort-component model – driven by births, deaths, and migration and used
for larger geographies, such as school districts, and longer term forecasts.
The housing based model – driven by housing types and numbers and neighborhood
turnover.
We use combinations of the three types.
Three types of enrollment
forecasting models utilized
component
 Grade
progression
 Housing
based
Students per Home
 Cohort-
GPR 2008/2007Chart 1
Enrollment
TTSD
Students
per Single
Home,06/05
Fall 2010
Births05/04
HS | MS | ES
01/00 02/01
03/02 Family
04/03
07/06 08/07 09/08 10/09 11/10 12/11
0.30
KG 1.10 1.00 1.01 0.89
1146|1144|1136
0.98 0.99 1.02 0.94 0.97 1.07 0.99 1.03
2000-2009
1146|1144|1137
1.11 1.02 Built
0.96
1.00
1.02
1.01
1.01
1.04
1.02 1.04 0.95 0.98
Population
01
0.25
1990-1999
1146|1144|1138
1.06 1.11 Built
0.94
1.57 1.00 1.00 1.30 1.00 1.33 0.63 0.89 0.75
1146|1144|1140
1.01 0.92 Built
0.93
1.04
00-04
before
1990 0.93 1.02 0.90 0.80
…. 0.92 1.01 0.87 0.97
1146|1145|1138
1.16
1.08
1.07
1.04
1.00
0.93
0.91
0.93
1.01 0.85 0.98 0.91
0.20
05-091.10 1.14 1.21 1.04
11 1.17 1.05 0.93 0.97
1146|1145|1139
1.12 1.14 1.09 1.10
1146|1145|4364
0.86 1.00 0.82 0.95
0.95 1.33 0.90 1.11
10-141.07 1.37 1.04 1.10
12 1.19 1.00 0.93 0.76
0.15
1301|1145|1138
1.75 1.35 0.69 1.21 0.80 0.90 1.08 0.58
1301|1145|1369
1.14 0.94 0.89 0.92
15-171.08 0.96 1.07 1.24 0.88 0.88 0.95 0.95
1301|1145|4364
1.02
1.10
1.18
1.15
1.11 1.04 0.98 0.98 1.07 1.00 0.98 1.00
0.10
……….
Migration
1301|1300|1135
1.04 1.12 0.98 1.00 0.96 0.94 0.96 1.00
0.94 0.91 1.08 0.94
1301|1300|1142
1.18 1.07 0.98 1.09
1.09
1.01
0.98
0.96
1.05 0.95 1.02 0.98
15-44
0.05
1301|1300|1143
1.02 0.96 1.01 0.91 1.01 1.01 1.02 1.00 0.93 1.04 0.92 1.02
1301|1300|1369
1.12 1.00 0.91 0.97
0.96 1.03 0.98 1.21 1.02 1.12 0.94 1.02
……..
0.00
District
1.07 1.03 1.00 1.02 1.02 1.03 1.01 1.00 1.03 0.99 0.97 0.97
K-5
 Combined
models used
for most of
our forecasts
6-8
75-79
80-84
Grade Level
85+
Deaths
9-12
The Cohort Concept
 Many demographic
models based on
tracking a cohort over
time.
Lexis Diagram
 Can be viewed at the
Cohort Over Time
x+5
level of the individual
Death of
person
x+4
x+3
 Or for groups of
common origin
Age
x+2
x+1
Age
x
x+5
Child born
x+4
Child born
x+3
Birth of
person
x+2
x+1
x
t
t+1
t+2
t+3
t+4
t+5
Time
t
t+1
t+2
Time
t+3
t+4
t+5
The 1994 Cohort View
Enrollment History by Grade and Year
KG
01
02
03
04
05
06
07
08
09
10
11
12
Other
1990
980
1,220
1,183
1,171
1,154
1,074
1,151
1,159
1,120
1,098
1,029
993
875
30
1991
1,011
1,223
1,235
1,203
1,242
1,207
1,148
1,212
1,173
1,132
1,066
987
929
31
1992
1,041
1,230
1,224
1,293
1,243
1,287
1,307
1,210
1,259
1,232
1,155
1,021
949
18
1993
1,144
1,250
1,253
1,297
1,352
1,290
1,336
1,327
1,239
1,295
1,233
1,099
953
13
1994
1,175
1,304
1,318
1,304
1,383
1,406
1,420
1,436
1,358
1,303
1,283
1,111
1,028
8
1995
1,186
1,325
1,343
1,357
1,360
1,401
1,409
1,473
1,453
1,436
1,312
1,180
1,039
8
1996
1,291
1,342
1,385
1,386
1,391
1,396
1,474
1,466
1,533
1,533
1,428
1,214
1,149
19
1997
1,195
1,449
1,391
1,428
1,442
1,457
1,431
1,525
1,504
1,587
1,505
1,311
1,138
15
1998
1,248
1,359
1,527
1,462
1,496
1,490
1,502
1,471
1,550
1,601
1,598
1,419
1,201
29
1999
1,245
1,393
1,392
1,561
1,488
1,562
1,552
1,587
1,550
1,735
1,600
1,531
1,350
35
2000
1,296
1,359
1,442
1,433
1,656
1,552
1,614
1,588
1,612
1,632
1,709
1,502
1,435
37
2001
1,378
1,434
1,424
1,515
1,541
1,722
1,687
1,647
1,679
1,735
1,644
1,668
1,399
35
2002
1,381
1,523
1,478
1,492
1,536
1,600
1,779
1,707
1,687
1,834
1,708
1,613
1,517
57
2003
1,504
1,530
1,567
1,566
1,561
1,592
1,667
1,800
1,759
1,815
1,815
1,657
1,470
65
2004
1,516
1,596
1,619
1,620
1,652
1,651
1,676
1,730
1,852
1,897
1,828
1,782
1,621
65
1994 KG Cohort
2,000
Enrolled
K-2 3,383 3,469 3,495 3,647 3,797 3,854 4,018 4,0351,8004,134 4,030 4,097
4,236 4,382 4,601 4,731
3-5 3,399 3,652 3,823 3,939 4,093 4,118 4,173 4,3271,6004,448 4,611 4,641
4,778 4,628 4,719 4,923
7-8 3,430 3,533 3,776 3,902 4,214 4,335 4,473 4,4601,4004,523 4,689 4,814
5,013 5,173 5,226 5,258
1,200
9-12 3,995 4,114 4,357 4,580 4,725 4,967 5,324 5,541 5,819 6,216 6,278
6,446 6,672 6,757 7,128
1,000
Other
30
31
18
13
8
8
19
15 800 29
35
37
35
57
39
65
Total 14,237 14,799 15,469 16,081 16,837 17,282 18,007 18,378 600
18,953 19,581 19,867 20,508 20,912 21,342 22,104
400
200
0
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
KG
01
02
03
04
05
06
07
08
09
10
Year, Grade Level
Grade Progression ratio for
Tigard School District
Grade Progression Ratio for District
Year2/Year1 01/00 02/01 03/02 04/03 05/04 06/05 07/06 08/07 09/08 10/09 11/10 12/11
2008/2007
1.07 1.03 1.00 1.02 1.02 1.03 1.01 1.00 1.03 0.99 0.97 0.97
2009/2008
1.05 1.01 1.00 0.96 1.00 0.98 0.99 1.01 1.01 1.00 0.96 0.98
2010/2009
1.07 1.00 1.01 1.02 1.04 1.01 1.01 0.99 1.04 1.01 1.02 1.04
1.08
1.06
1.04
1.02
1.00
0.98
0.96
2008/2007
0.94
2009/2008
0.92
2010/2009
0.90
01/00
02/01
03/02
04/03
05/04
06/05
07/06
08/07
09/08
10/09
11/10
12/11
The grade progression
ratio all can be
computed spatially
 The dots show the



location of students
enrolled in Tigard Schools
in Fall 2010.
The color shades show
the ratio of the Fall 2010
enrollment to the same
cohort one year earlier.
Green shaded areas
show that the cohort
declined
Blue shaded areas show
growth in the cohort.
The cohort component
model





Enrollment is determined
as a share of the forecast
school age population.
The school age
population is determined
by earlier births
and net migration of
families with school age
children.
The model is recursive,
the forecast for each time
period building on that 5
or ten years previous.
Provides age detail, but
best used for larger
geographies.
Births
Population
00-04
05-09
Enrollment
10-14
KG
15-17
01
……….
….
15-44
11
……..
12
75-79
80-84
85+
Deaths
Migration
Data that inform the
cohort-component model
 Fertility rates – by age
 Net migration rates by
age and sex, perhaps
the most difficult to
estimate
 Public school capture
rates – the share of the
school age population
enrolled.
1.0
100
90
0.8
80
In-migration of young households
70
0.6
Percent
race and Hispanic
status. Shift from births
from younger to older
mothers.
Net Migration Rate 1990-2000
2000Portland
Capture
Rates
- ACS & Student Records
Public
Schools
Net Migrants 0.4
as share
of population 0.2
60
50
2000 - Student Records
2000 - NCES/Census
`
40
30
20
0.0
10
00- 05- 10- 15- 20- 250
04 09 05 14 0619 07
24 29
08
30- 35- 40- 45- 50- 5534
59
09 3910 44 1149 1254 13
-0.2
6064
14
65- 70- 75- 80- 85+
69
15 7416 79 1784 18
19
83.8 86.0 84.7 82.6 80.5 69.6 38.3
2000 - Student Records 35.9 80.2 83.7 83.2 82.7 87.3 84.8 86.2
Out-migration
of older households
Out-migration of households with
81.9 80.5 82.7 83.2 84.0 85.5 85.8 85.2
84.9 85.1
84.5 82.6 78.3 64.5 46.1
2000 - NCES/Census
and empty
nesters
school aged children
-0.4
Age
5 Year Age Group
Capture rate for Portland Public
Schools

Computed from grid
mapping of student record
and census block data.

In 2000. Lower values tend
to be in high income areas
where more students are
enrolled in private schools.

In 2010. High values tend to
be in low income and
minority areas where most
students are enrolled in the
public school system.

The change from 2000 to
2010.
The housing based model



We look first at birth mothers who’s
children will appear in school in
about 5 years.
Chart 1
TTSD Students per Single Family Home, Fall 2010
0.30
From work for Portland Public
Schools we show what type of
housing is most common for highly
educated age 30+ mothers. OMA is
a gentrifying neighborhood and
Remainder is a lower income area
with large Hispanic population.
And for less educated mothers
under age 30.
From work for Tigard School district
we show the loading of students
into single family housing by age of
the structure. Note how the loading
of KG-5 students declines as the
housing ages.
Built 2000-2009
Built 1990-1999
0.25
Built before 1990
Students per Home

0.20
0.15
0.10
0.05
0.00
K-5
6-8
Grade Level
9-12
Enrollment forecasting leads
raises interesting issues
worth of further exploration
 Example One – The Hillsboro School District in suburban
Portland has a large and increasing Hispanic population as well
as the families of employees of Intel. It has experienced rapid
population, and school enrollment, growth but recently the
enrollment growth peaked.
 Example Two – In some areas of older but substantial housing
in Portland enrollment has been declining and schools have
been closed. However in 2006 some of the schools in these
neighborhoods began to show enrollment growth in kindergarten
and the lower grades. A gentrification process was at work
involving older, highly educated, and affluent mothers.
Example One: An enrollment forecast for
Hillsboro School District focused on
student generation rates

Hillsboro SD has a large
Hispanic population many
of whom reside in multifamily housing.

Student generation rates
are relatively high in
Hispanic neighborhoods
but also in new suburban
areas.

Most areas of newly built
housing show high
student generation ratios.

Areas where seniors outmigrate result in turnover
and in-migration of
younger households.

As new housing ages the
students advance in age
and eventually leave
home.
Example Two: Draws from a forecast for
Portland Public Schools

School enrollment for the Portland District
reflects the economic and housing epochs of
the past 150 years, recently declining after the
echo of the post WWII baby boom passed
through.

However, in just since about 2005 the
District’s enrollment has shown a modest
uptick.
The setting for the Portland
School District

The Portland District is largely
built out and surrounded by
growing suburban areas.

There has been some infill and
replacement single family housing
in the District, but most of the
construction has been multifamily and does not generate
much student enrollment.

Only about 50 students come
from the 20,000 units in the
trendy Pearl District (A) but high
value new housing (such as at B)
has contributed some growth.

The recent up-tick in enrollment is
due more to turn-over than to new
construction.
B
A
An important factor in the turnaround
has been the larger share of births to
older mothers
 In 2001 the
number of births
in the District to
older (age 30+)
mothers

equaled those to
younger mothers
(under age 30)
 Total births
stayed about the
same
Proportion of Births
to Older Mothers
There was growth in the number of births to older
mothers across the District, but that on the east
side was mainly after 2000
Of particular interest was the “East Older
Mothers area (East OMA).
East
OMA
1990
East
OMA
1994
East
OMA
2002
East
OMA
1998
East
OMA
East
OMA
2006
2008
The four combinations of
mother’s age and education

Here is a map showing the births per
acre for older moms (age 30+) who
have a baccalaureate degree of higher
(42% of the District’s births from 20072009).

The counterpoint map shows mothers
under age 30 with less than
baccalaureate level of education. This
area include the areas with large
Hispanic populations (31%).

Mothers under age 30 with
baccalaureate level of education. This
may be a younger group of gentrifying
households, but perhaps many of them
are childless (10%).

The group of older mothers age 30 plus
with less than a baccalaureate
education (16%).

And finally, a combined map of older
more educated and younger less
educated mothers (combined 73% of
births).
Gentrification based on Percent College Educated
and Employed in MTP Occupations –
following Ley, 1996

From 1990 to 2000 the
average change in
proportion with college
degree and employed in
MTP occupations rose
around the edge of the East
OMA.

From 2000 to 2008 the
measure of gentrification
spread out beyond the East
OMA.

Ley depicts gentrifying
areas as being in the top
20% of tracts with respect
to change in the composite
education and MTP
employment index.
East
OMA
Conclusions

Yes, the shift in births from younger to older mothers and gentrification of some of Portland’s 100
year old housing did help turn around the decline in the district, and likely saved some schools
from closing.

Our school demography work has been successful with many districts returning for new or
updated forecasts. PRC has a reputation for unbiased and well thought out forecasts, although we
have not always been right.

We support about 1.5 researchers doing enrollment forecasts. The individual projects bring in from
$3,000 for a simple update to $60,000 for a metropolitan district.

A large part of the cost of a school forecast is in the database development. This provides
employment for students with GIS skills. Repeat business with districts lowers the cost for this part
of the work.

Should you take on this type of work?


Good – Learn about the social geography of communities, provide employment for students,
satisfaction with providing a valuable service, discover puzzles to be solved.
No so good – Need long term commitment, patience in working with school administrators
and committees, may be hard to manage time commitments if teaching.
Sources of information

The website for the Population Research Center.

Examples of forecasts:

http://www.pdx.edu/prc/school-enrollment

Papers and presentations on school demography:
http://www.pdx.edu/prc/publications

Books

Plane, David and Peter A. Rogerson. The Geographical Analysis of Population with
Applications to Planning and Business, John Wiley and Sons, 1993.

Siegal, Jacob S. and David A. Swanson. The Methods and Materials of Demography,
Second Edition. Elsevier Academic Press, 2004.

Conferences to learn, present

AAG, Population Specialty Group

Population Association of American, Applied Demography Specialty Group

Applied Demography biannual conference

ESRI Education Conference
Richard Lycan
– lycand@pdx.edu

Southern Demographic Association
Charles Rynerson – rynerson@pdx.edu
Fee Schedule

.016d(02) - Population Research Center – Fee Schedule




District-wide forecast by grade level $6,000 & up
Update district-wide school enrollment forecast by grade
level. $3,000 & up
District-wide forecast and individual schools or attendance
areas forecast $8,000 & up
Update district-wide school enrollment forecast and individual
schools or attendance area forecast. $4,000 & up