scs 2014-15 update_20aug_final.pdf

8/10/2019 SCS 2014-15 Update_20AUG_FINAL.pdf

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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


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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


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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


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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


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Todays Presentation

Perspective

Land Use Update

Forecast Models Cohort Ratio Model

A P U Models

Out of Capacity Tables


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Perspectives

Population

2000 92,446

2010 128,961

2013 136,788

2000 to 2005

Very high growth rate

Source: U S Census


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Predicting Growth In Stafford County

Predictions in 2009 by Virginia Employment

Commission:

135806 2010

176710 2020

218722 2030

US Census Data

128961 2010136788 2013


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Predictions in 2009 by Virginia Employment

Commission:

135806 2010

176710 2020

218722 2030

US Census Data

128961 2010136788 2013


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80000

100000

120000

140000

160000

180000

2000 2005 2010 2015 2020

U S Census Data

VEC Projected Pop

2020 176710


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80000

100000

120000

140000

160000

180000

2000 2005 2010 2015 2020

VEC Projected Pop

2020 176710162,000 ?


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80000

100000

120000

140000

160000

180000

2000 2005 2010 2015 2020

VEC Projected Pop

2020 176710

155,000 ?


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Housing Units

~1000

housing

units added

annually

Source: U S Census


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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.


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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


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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


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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.


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# 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


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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%


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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


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Land UseStudy

July, 2014


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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


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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)


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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


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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.


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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


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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


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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


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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


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Results of the Land Use StudyResidential Growth

Largely SFD

New Dwelling Units #

735 impacting 2014-15





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.


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Results of the Land Use StudyResidential Growth

Largely SFD

New Dwelling Units






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.


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Data Student Numbers

Membership Forecast Models

CSR (Cohort Survival Ratio) Forecast

APU Forecast

Cohort Survival Ratio


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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.



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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)



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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


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Historical DataBirths Membership(11-15-13)


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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.


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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)


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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.


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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


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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


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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


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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


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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


Unadjusted

APU Model


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Data Student Numbers

What are the advantages/disadvantages of

these different forecast models?

CSR Forecast

APU Forecast


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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


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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


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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


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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


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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


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Informed CSR Forecast

2008-09 2013-14 2018-19


Unadjusted

APU Model

25500

26000

26500

27000

27500

28000

28500

29000

29500

0 2 4 6 8 10 12

I f d CSR F


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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


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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


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OutofCapacity Tables

OREd SGFSC SGF

Color-coded forecast at the school-level

Out Of Capacity TablesDesign Capacities


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Out Of Capacity Tables


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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


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Out Of Capacity Tables



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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


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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.


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scs 2014-15 update_20aug_final.pdf

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