scs 2014-15 update_20aug_final.pdf
TRANSCRIPT
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
1/59
Stafford County Public
Schools
2014-15 UpdateStudent Membership
Forecast
O P E R A T I O N S R E S E A R C H A N D E D U C AT I O N L A B O R AT O R Y
I N S T I T U T E F O R T R A N S P O R TAT I O N R E S E A R C H A N D E D U C A T I O N
C E N T E N N I A L C A M P U S @ N O R T H C A R O L I N A S T AT E U N I V E R S I T Y
A U G U S T 1 4 , 2 0 1 4
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
2/59
Integrated Planning for
School And Community
2014-15 Update and Forecast
Data-driven and policy-based model forforecasting school membership and
determining the optimal locations for
new schools and attendance zones.
Land Use Studies
Membership Forecasting
Out-of-Capacity Analysis
School Site Optimization
Attendance Boundary Optimization
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
3/59
Part of the Institute for Transportation Research and
Education (ITRE) at the NC State University, Centennial
Campus
Specializing in the applications of decision science for
school districts dealing with the politically sensitive andcomplex issues of student reassignment and new school
planning
Over 20 years of experience working with school districts
in NC, SC, and VA
Providing school planning solutions that are driven by
data and supported by policy
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
4/59
Alamance-Burlington School System02, 03, 06, 07, 08, 09, 10,
11, 12, 13
Asheboro City Schools04, 05, 06, 07
Berkeley County Schools (SC)09, 10, 11, 12
Bladen County Schools04
Buncombe County Schools98, 99
Brunswick County Schools03, 04
Cabarrus County Schools12
Carteret County Schools09
Chapel Hill-Carrboro Schools95, 96, 97, 98, 99, 00, 01, 02, 05,
06, 07, 12
Chatham County Schools03, 05, 06, 07, 08, 09, 10, 11, 12, 13
Craven County Schools96, 97, 98, 99, 00, 01, 02, 04, 05, 06, 07,08, 12
Cumberland County Schools08, 09
Cleveland County Schools08
Currituck County Schools09
Duplin County Schools09
Durham Public Schools08, 09, 10, 11, 12
Edgecombe County Public Schools09
Elizabeth City-Pasquotank County Schools07
Franklin County Schools08, 11, 12
Iredell-Statesville Schools98, 99, 00, 01, 02, 03, 04
Johnston County Schools94, 95, 96, 97, 98, 99, 00, 01, 02, 03,
04, 05, 06, 07, 08, 09, 10, 11, 12, 13
Jones County Schools09
Gaston County Schools98, 99, 00, 01, 02, 03, 04
Granville County Schools02, 03, 04, 05, 06, 07, 08, 09, 10
Guilford County Schools94, 95, 96, 97, 98, 09, 10, 11, 13, 14
Harnett County Schools98, 99, 00, 01, 02, 03, 06, 07, 08, 09, 10,
11, 12, 13
Haywood County Schools99
Hoke County Schools99, 08, 09, 11, 12
Lee County Schools08, 09
Lenoir County School09
Moore County Schools04, 06, 07, 08, 12, 13
Mooresville Graded Schools99, 00, 01, 04
Nash-Rocky Mount Schools04, 05, 06, 07, 08, 09, 10, 11, 12
New Hanover County Schools95, 96, 97, 98, 99, 00
Onslow County Schools03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13
Orange County Schools95, 09, 10, 11, 13
Pamlico County Schools09 Pender County Schools13
Randolph County Schools05, 06, 07, 08, 09
Richmond County Schools00, 08
Robeson County Schools08
Rock Hill Schools (SC)02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13
Rowan County Schools09
Pitt County Schools90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 00, 01,
02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13
Stafford County Public Schools (VA)12
Stanly County Schools12
Stokes County Schools05, 06, 08
Tupelo Public Schools (MS)07
Union County Schools99, 00, 01, 02, 03, 04, 05, 06, 07
Vance County Schools09
Wayne County Schools95
Wake County Public School System97, 04, 05, 06, 07, 08, 09, 10,
11, 12, 13, 14
OPERATIONS RESEARCH AND EDUCATION L ABORATORY
I N S TI TUTE FOR TRAN S PORTATI ON RES EARC H AN D ED U C ATI ON
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
5/59
Todays Presentation
Perspective
Land Use Update
Forecast Models Cohort Ratio Model
A P U Models
Out of Capacity Tables
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
6/59
Perspectives
Population
2000 92,446
2010 128,961
2013 136,788
2000 to 2005
Very high growth rate
Source: U S Census
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
7/59
Predicting Growth In Stafford County
Predictions in 2009 by Virginia Employment
Commission:
135806 2010
176710 2020
218722 2030
US Census Data
128961 2010136788 2013
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
8/59
Predicting Growth In Stafford County
Predictions in 2009 by Virginia Employment
Commission:
135806 2010
176710 2020
218722 2030
US Census Data
128961 2010136788 2013
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
9/59
Predicting Growth In Stafford County
80000
100000
120000
140000
160000
180000
2000 2005 2010 2015 2020
U S Census Data
VEC Projected Pop
2020 176710
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
10/59
Predicting Growth In Stafford County
80000
100000
120000
140000
160000
180000
2000 2005 2010 2015 2020
VEC Projected Pop
2020 176710162,000 ?
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
11/59
Predicting Growth In Stafford County
80000
100000
120000
140000
160000
180000
2000 2005 2010 2015 2020
VEC Projected Pop
2020 176710
155,000 ?
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
12/59
Housing Units
~1000
housing
units added
annually
Source: U S Census
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
13/59
Population
Housing Units
Membership
Year
County
Population
# Housing
Units
Membership
SCPS
Ratio:
M/# HU
Ratio:
M/Pop
2000 92446 31405 20000 0.64 0.22
2010 128961 41769 26500 0.63 0.21
2013 136788 44124 27000 0.61 0.20
OREd found the countys student generation factor (SGF a ratio of
students to existing housing units including single family & multi-family) to
be 0.61 in 2012 and 0.64 in 2014.
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
14/59
Year
County
Population
# Housing
Units M SCPS
Ratio:
M/# HU
Ratio:
M/Pop
2000 92446 31405 20000 0.64 0.22
2010 128961 41769 26500 0.63 0.21
2013 136788 44124 27000 0.61 0.20
2020 170000* 56700* 34000 0.60 0.20
Reaching the projected population of SC (VEC) by 2020 would
require a rate of growth would require 1800 housing startsannually from 2013 through 2020.
PopulationHousing Units -Membership
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
15/59
Year
County
Population
# Housing
Units M SCPS
Ratio:
M/# HU
Ratio:
M/Pop
2000 92446 31405 20000 0.64 0.22
2010 128961 41769 26500 0.63 0.21
2013 136788 44124 27000 0.61 0.20
2020 170000* 56700* 34000 0.60 0.20
A population of 170,000 persons in SC would suggest that SCPS
would have 34,000 students enrolled in 2020.
PopulationHousing Units -Membership
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
16/59
PopulationHousing Units -
Membership
Year
County
Population
# Housing
Units M SCPS
Ratio:
M/# HU
Ratio:
M/Pop
2000 92446 31405 20000 0.64 0.22
2010 128961 41769 26500 0.63 0.21
2013 136788 44124 27000 0.61 0.20
2020 160000* 53300* 32000 0.60 0.20
Adjusting the projected population of SC to 160,000 in 2020
would still require a rate of growth would require 1300 housing
starts annually from 2013 through 2020.
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
17/59
# Building Permits
Stafford County
Year # Building Permits
2000 1101
2001 14682002 1692
2003 1395
2004 1982
2005 1631
2006 860
2007 758
2008 416
2009 524
2010 546
2011 466
2012 640
2013 1004
2014
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
18/59
2000 to 2010 Census Data
Population by Age
2000 2010 %
0 to 4 years old 7172 8719 22%
5 to 17 years old 21997 28478 29%
18 to 64 years old 57803 83300 42%
65 years or older 5474 9464 73%
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
19/59
Conclusions
Projecting population or membership is
difficult during volatile periods
20142018 is likely to be a volatile period
Growth in Stafford County is not likely to mirror
the 20002005 growth rate
Demographics (in Stafford County and in the
US) are changing and these changes will impact
the number of school-aged childrenthe
number of school-aged children per household
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
20/59
Land UseStudy
July, 2014
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
21/59
Land Use Study
Data from
Stafford County Public
Schools
Stafford CountyPlanning
Stafford County GIS
Interviews with SCPand SCPS
Data
Student File: May 2014
data geocoded to identify
where each studentresides
GIS Files from SC GIS:
parcel data, structure
data, subdivision data
Subdivision Data from SCP
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
22/59
Land Use StudyActive Subdivisions
Brentsmill is an active subdivision, as defined by SC Planning, located in
APU 304. (OREd divided the county into 221 planning units that are, for the mostpart, homogeneous in terms of the type of residential development.)
From SCP, there were 188 approved lots in Brentsmill on which 185
single-family dwellings have been built. (July 2014/SCP)
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
23/59
Land Use Data
GIS data (from March, 2014) shows Brentsmill Subdivision; the parcels and
the structures (purple having been constructed within the last 18 months).
GIS data shows 119 K-12 students living in the 181 structures producing a
student generation factor (SGF) of 0.66.
Further analysis indicates that dwelling units have been constructed on about
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
24/59
Further analysis indicates that dwelling units have been constructed on about
50 lots since 2/11/13. That indicates that this subdivision will have a potential
impact on 2014-15 numbers even though there are now only a few vacant lots left.
OREd calculations indicate that about 10 new K-12 students will enter SCPS
from this subdivision in 2014-15.
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
25/59
Leeland Station (sections 1-7) is in APU 124 with section 8 in APU 113.
The subdivision is approved for a total of 772 residential lots of which 448 have
single family dwelling units built on them as of July of 2014. There are 324 lots
that have either not been developed or not been built upon. GIS data shows
399 students producing a SGF of 0.891.
d
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
26/59
SCP data in July of 2014 showed 537
approved lots in LS (west of Leeland
Rd), 203 approved lots in sections 5&7
(east of Leeland Rd), and 32 approved
lots in section 8.
Section 6
Section 6
APU 124
Section 8
Sections 5&7
Land Use Data
SCP data showed 389 of 537 approved lots west
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
27/59
SCP data showed 389 of 537 approved lots west
of Leeland Rd, and 70 of 203 approved lots in
Sect 5&7 developed (Single Family Dwellings).
Note that many dwellings were built on lots
within the past 18 months (purple)
Sections 5 & 7
SCP data showed 389 of 537 approved lots
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
28/59
The forecast model uses 50 lots
impacting 2014-15 producing ~50 newstudents. The remaining ~280 lots (not
Section 8) spread out from 15-16 to 18-19
producing about 60 new K-12 students
each year. (Pace / Build-out)
The 32 lots in Section 8 appear in 17-18
through 19-20.
SCP data showed 389 of 537 approved lots
west of Leeland Rd, and 70 of 203 approved
lots in Sect 5&7 developed (Single Family
Dwellings). Note that many lots were built
within the past 18 months (purple)
Sections 5&7
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
29/59
Results of the Land Use StudyResidential Growth
Largely SFD
New Dwelling Units #
735 impacting 2014-15
869 impacting 2015-16
1066 impacting 2016-17
852 impacting 2017-18
828 impacting 2018-19
Student Growth
Number of students generated
by residential growth*
2014-15 426
2015-16 512
2016-17 613
2017-18 506
2018-19 464# The number of new dwelling units represents the result after dialogue with SCPS and SCP/GIS
and OREd; qualifying subdivisions, pace of development, and type of development.
* New residential growth does not always mean new students. Students occupying new
dwelling units may come from in-migration or from other dwelling units in Stafford County.
These calculations come from the product of the # of dwelling units and the SGF.
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
30/59
Results of the Land Use StudyResidential Growth
Largely SFD
New Dwelling Units
735 impacting 2014-15
869 impacting 2015-16
1066 impacting 2016-17
852 impacting 2017-18
828 impacting 2018-19
Student Growth
Number of students generated
by residential growth
2014-15 426
2015-16 512
2016-17 613
2017-18 506
2018-19 464Information gathered and analyzed in 2014 cannot accurately portray the potential for new
development past the next few years. New developments are being considered by the County
now that will impact numbers past 2018. Other developments will occur that OREd nor the
County know anything about. Hence, land use data should only be considered relevant over
the next few years.
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
31/59
Data Student Numbers
Membership Forecast Models
CSR (Cohort Survival Ratio) Forecast
APU Forecast
Cohort Survival Ratio
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
32/59
Cohort Survival Ratio
System-Wide Forecast
Cohort Survival Ratios
are used to predict how
cohorts of students will
advance through the K-12system by grade.
CSR values greater than 1 suggestin-migration into the district.
Cohort Survival Ratio (CSR): Comparison of student counts
by consecutive grade for consecutive years.
Cohort Survival Ratio
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
33/59
Cohort Survival Ratio
System-Wide Forecast
Cohort Survival Ratios
are widely used as an
acceptable model for
system-wide forecasts.
Example:
The month-1 ADM for Grade 8 in 2012-13 was 2113. Themonth-1 ADM for Grade 9 in 2013-14 was 2254;
2254/2113 = 1.067, the CSR circled above.
This ratio is used to predict the number of 9thgraders in
2014-15: (# 8thgraders in 2013-14 x 1.067)
Cohort Survival Ratio
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
34/59
Cohort Survival Ratio
System-Wide Forecast
Each CSR contains
historical in-migration as
a portion of each ratio.
1.067 =
# of 8th
graders last year +# of 9th
graders who are new tothe system# of 8thgraders who moved out of the system
# of 8thgraders last year
Historical DataNew Student
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
35/59
Cohort Survival Ratio
System-Wide Forecast
Historical DataBirths Membership(11-15-13)
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
36/59
Forecast based on unadjusted Cohort Survival Ratios:Without any adjustments, the CSR forecast is fairly flat:
0.30% annual growth.
The COHORT model suggests 27060students in 2014-15 and27297students in 2018-19.
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
37/59
COHORT MODELforecast using
cohort survival ratios based onhistorical data
Area Planning Unit (APU) MODELforecast using smaller areas of the
County that are impacted by land-use data. Grade-by-grade cohortsare moved forward year-by-year
using cohort survival ratios.
Area Planning Unit (APU)
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
38/59
Area Planning Unit (APU)
Forecast
Geocoded student data is translated spatially to APU
Cohorts: students grouped by grade and by APU
APU Cohorts are moved by grade from year to year using
historically-based optimal cohort survival ratios Students from new development are added to APU
Cohorts by grade annually using the SGF for that APU
and the number of new dwelling units projected for that
APU each year.
Geocoded student data was obtained in the spring of 2014 meaning the number of K-12
students at that point in the 2013-14 school year was different from the ADM data collected
for month-1. In addition, the district grants a significant number of transfers meaning that all
students dont attend the school to which they would be assigned by attendance zone.
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
39/59
APU Forecast
ExampleAPU 124 includes most of Leeland Station, an active
subdivision with several phases remaining. There were 55
construction starts in 2011-12, 36in 2012-13 and 32from
9/13 through 5/14 (from SCPS Construction Start worksheet)
That leaves about 240 lots on which dwellings may be built.
OREd, in conjunction with SCPS and SC Planning and GIS,agreed on the pace of development as shown below.
Year 2014-15 2015-16 2016-17 2017-18 2018-19
# Dwellings 50 60 60 60 60
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
40/59
APU Forecast
These new dwellings are translated into new students using
the appropriate SGF. The growth in each cohort is largely a
factor of these new lots producing new students.
OREd K G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12
2013-14 24 30 27 23 28 33 22 41 50 31 35 35 27
2014-15 29 29 34 32 27 32 38 26 46 57 34 38 38
2015-16 34 35 35 40 37 33 38 44 32 52 64 40 44
Year 2014-15 2015-16 2016-17 2017-18 2018-19# Dwellings 50 60 60 60 60
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
41/59
APU Forecast Results
2008-09 2013-14 2018-19
25500
26000
26500
27000
27500
28000
28500
29000
29500
0 2 4 6 8 10 12
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
42/59
Forecast Comparison
2008-09 2013-14 2018-19
25500
26000
26500
27000
27500
28000
28500
29000
29500
0 2 4 6 8 10 12
Impact of adding
students from new
development into the
system
Cohort Survival Model
Unadjusted
APU Model
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
43/59
Cone of Uncertainty
2008-09 2013-14 2018-19
25500
26000
26500
27000
27500
28000
28500
29000
29500
0 2 4 6 8 10 12
Cohort Survival Model
Unadjusted
APU Model
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
44/59
Data Student Numbers
What are the advantages/disadvantages of
these different forecast models?
CSR Forecast
APU Forecast
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
45/59
Cohort Survival Ratio Forecast
During stable times, the Cohort Survival Ratiosprovide a dependable system-wide forecast.
Historical net-migration provides a reasonable
expectation for a forecast. System-wide forecasts are affected less by anomalies
found in APUs.
Student numbers by grade and by year dont provide
information on which to make good decisionsregarding shifting attendance lines.
A CSR may not include the total impact of newdevelopment
Forecast Comparison
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
46/59
APU Forecast
Smaller areas (individual APUs) are volatile: year-
by-year cohorts may increase and decrease
substantially without explainable cause.
By combining student numbers with planning data
on smaller segments of the district, the forecast
can identify areas of significant growth/decline.
APU forecast enable planners to shift attendance
lines based on reliable information and then see
what the forecast predicts because of those shifts.
Forecast Comparison
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
47/59
The predicted growth in the very large subdivisions now
underway begins to dwarf all other planned/forecastedgrowth in the system in the 2019-22 time period.
This makes it difficult to add enough students in fast-growing APUs simply because there arent enoughadditional students forecasted for the entire system.
There will be new subdivisions begun in this samewindow (2015 through 2022) that will alter growthpatterns and projections.
Forecast Limitations
F t R lt
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
48/59
During unstable times (times of significant growth or
declinewhen trends are broken), the APU forecastshould guide adjustments to the Cohort Forecast.
(using planning data at the subdivision-level)
Forecast Results
23000
23500
24000
24500
25000
25500
26000
26500
27000
27500
2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14
Forecast Results
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
49/59
Forecast Results
23000
23500
24000
24500
25000
25500
26000
26500
27000
27500
2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14
The recent economic rebound in Stafford County
bucks the trend of the past 4 years. However, there
are indications that this rebound may be short-lived;or, at the least, be in the midst of a hiccup!
I f d CSR F
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
50/59
Informed CSR Forecast
2008-09 2013-14 2018-19
Cohort Survival Model
Unadjusted
APU Model
25500
26000
26500
27000
27500
28000
28500
29000
29500
0 2 4 6 8 10 12
I f d CSR F
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
51/59
Informed CSR Forecast
11206 11332 1135711429 11501
1160011844 11863
12269 12367 1237912441 12487 12514
12618 12776
6254 6309 6323 6308 6294 63546458 6573 6554
6675 67877120
7313 7398 7441 7424
8671 87908859 8841
9130 9055 9074 90999307
9609 9725 97229972
1020710383
10879
0
2000
4000
6000
8000
10000
12000
14000
2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 2023-24
K to 5 6 to 8 9 to12
Elementary
Middle
High
Projected Growth Rates
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
52/59
Projected Growth Rates
The informed COHORT model projects
a system wide growth of 1.4% overthe next 10 years.
From 2013-14 to 2018-19, the growth
by level is
779 Elementary
433 Middle
670 High
1882 K-12
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
53/59
OutofCapacity Tables
OREd SGFSC SGF
Color-coded forecast at the school-level
Out Of Capacity TablesDesign Capacities
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
54/59
Out Of Capacity Tables
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
55/59
OREd was asked to create a second modelbased on what the County uses for a Student
Generation Factor when considering the impact
of new development. When the Countys SGF(generally a higher number than the OREd SGF)
is used for new development, more students
are added to the system because of new
development.
Out Of Capacity TablesDesign Capacities
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
56/59
Out Of Capacity Tables
Projected Growth Rates
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
57/59
Projected Growth RatesUsing the OREdSGF in
the APU model, the
COHORT model projects a
system wide growth of
1.4% over the next 10
years. From 2013-14 to2018-19, the growth by
level is
779 Elementary433 Middle
670 High
1882 K-12
Using theSC SGF in
the APU model, the
COHORT model projects
a system wide growth of
3.22% over the next 10
years. From 2013-14 to2018-19, the growth by
level is
1195 Elementary842 Middle
1129 High
3166 K-12
Out Of Capacity
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
58/59
OutOfCapacity
Tables
Provide an indication of where pressure points are regarding capacity.
Re-alignments to existing attendance zones, adjusted for significant
growth by locality, will alter this projection.
Changes to out-of-district ratios will alter this projection.
-
8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf
59/59