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Stafford County Public Schools 2014-15 Update Student Membership Forecast OPERATIONS RESEARCH AND EDUCATION LABORATORY INSTITUTE FOR TRANSPORTATION RESEARCH.

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Presentation on theme: "Stafford County Public Schools 2014-15 Update Student Membership Forecast OPERATIONS RESEARCH AND EDUCATION LABORATORY INSTITUTE FOR TRANSPORTATION RESEARCH."— Presentation transcript:

1 Stafford County Public Schools 2014-15 Update Student Membership Forecast OPERATIONS RESEARCH AND EDUCATION LABORATORY INSTITUTE FOR TRANSPORTATION RESEARCH AND EDUCATION CENTENNIAL CAMPUS @ NORTH CAROLINA STATE UNIVERSITY AUGUST 14, 2014

2 Integrated Planning for School And Community 2014-15 Update and Forecast Data-driven and policy-based model for forecasting 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

3  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 and complex 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

4 Alamance-Burlington School System – 02, 03, 06, 07, 08, 09, 10, 11, 12, 13 Asheboro City Schools – 04, 05, 06, 07 Berkeley County Schools (SC) – 09, 10, 11, 12 Bladen County Schools – 04 Buncombe County Schools – 98, 99 Brunswick County Schools – 03, 04 Cabarrus County Schools – 12 Carteret County Schools – 09 Chapel Hill-Carrboro Schools – 95, 96, 97, 98, 99, 00, 01, 02, 05, 06, 07, 12 Chatham County Schools – 03, 05, 06, 07, 08, 09, 10, 11, 12, 13 Craven County Schools – 96, 97, 98, 99, 00, 01, 02, 04, 05, 06, 07, 08, 12 Cumberland County Schools – 08, 09 Cleveland County Schools – 08 Currituck County Schools – 09 Duplin County Schools – 09 Durham Public Schools – 08, 09, 10, 11, 12 Edgecombe County Public Schools – 09 Elizabeth City-Pasquotank County Schools – 07 Franklin County Schools – 08, 11, 12 Iredell-Statesville Schools – 98, 99, 00, 01, 02, 03, 04 Johnston County Schools – 94, 95, 96, 97, 98, 99, 00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13 Jones County Schools – 09 Gaston County Schools – 98, 99, 00, 01, 02, 03, 04 Granville County Schools – 02, 03, 04, 05, 06, 07, 08, 09, 10 Guilford County Schools – 94, 95, 96, 97, 98, 09, 10, 11, 13, 14 Harnett County Schools – 98, 99, 00, 01, 02, 03, 06, 07, 08, 09, 10, 11, 12, 13 Haywood County Schools – 99 Hoke County Schools – 99, 08, 09, 11, 12 Lee County Schools – 08, 09 Lenoir County School – 09 Moore County Schools – 04, 06, 07, 08, 12, 13 Mooresville Graded Schools – 99, 00, 01, 04 Nash-Rocky Mount Schools – 04, 05, 06, 07, 08, 09, 10, 11, 12 New Hanover County Schools – 95, 96, 97, 98, 99, 00 Onslow County Schools – 03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13 Orange County Schools – 95, 09, 10, 11, 13 Pamlico County Schools – 09 Pender County Schools – 13 Randolph County Schools – 05, 06, 07, 08, 09 Richmond County Schools – 00, 08 Robeson County Schools – 08 Rock Hill Schools (SC) – 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13 Rowan County Schools – 09 Pitt County Schools – 90, 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 Schools – 12 Stokes County Schools – 05, 06, 08 Tupelo Public Schools (MS) – 07 Union County Schools – 99, 00, 01, 02, 03, 04, 05, 06, 07 Vance County Schools – 09 Wayne County Schools – 95 Wake County Public School System – 97, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13, 14 OPERATIONS RESEARCH AND EDUCATION LABORATORY INSTITUTE FOR TRANSPORTATION RESEARCH AND EDUCATION

5 Today’s Presentation Perspective Land Use Update Forecast Models – Cohort Ratio Model – A P U Models Out of Capacity Tables

6 Perspectives Population 2000 92,446 2010128,961 2013136,788 2000 to 2005 Very high growth rate Source: U S Census

7 Predicting Growth In Stafford County Predictions in 2009 by Virginia Employment Commission: 1358062010 1767102020 2187222030 US Census Data 1289612010 1367882013

8 Predicting Growth In Stafford County 1767102020 Predictions in 2009 by Virginia Employment Commission: 1358062010 1767102020 2187222030 US Census Data 1289612010 1367882013

9 Predicting Growth In Stafford County VEC Projected Pop 2020 176710

10 Predicting Growth In Stafford County VEC Projected Pop 2020 176710 162,000 ?

11 Predicting Growth In Stafford County VEC Projected Pop 2020 176710 155,000 ?

12 Housing Units ~1000 housing units added annually Source: U S Census

13 Population – Housing Units – Membership Year County Population # Housing Units M Membership SCPS Ratio: M/# HU Ratio: M/Pop 20009244631405200000.640.22 201012896141769265000.630.21 201313678844124270000.610.20 OREd found the county’s 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.

14 Year County Population # Housing UnitsM SCPS Ratio: M/# HU Ratio: M/Pop 20009244631405200000.640.22 201012896141769265000.630.21 201313678844124270000.610.20 2020170000*56700*340000.600.20 Reaching the projected population of SC (VEC) by 2020 would require a rate of growth would require 1800 housing starts annually from 2013 through 2020. Population – Housing Units - Membership

15 Year County Population # Housing UnitsM SCPS Ratio: M/# HU Ratio: M/Pop 20009244631405200000.640.22 201012896141769265000.630.21 201313678844124270000.610.20 2020170000*56700*340000.600.20 A population of 170,000 persons in SC would suggest that SCPS would have 34,000 students enrolled in 2020. Population – Housing Units - Membership

16 Year County Population # Housing UnitsM SCPS Ratio: M/# HU Ratio: M/Pop 20009244631405200000.640.22 201012896141769265000.630.21 201313678844124270000.610.20 2020160000*53300*320000.600.20 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.

17 # Building Permits Stafford County Year# Building Permits 20001101 20011468 20021692 20031395 20041982 20051631 2006860 2007758 2008416 2009524 2010546 2011466 2012640 20131004 2014

18 2000 to 2010 Census Data Population by Age 20002010% ▲ 0 to 4 years old7172871922% 5 to 17 years old219972847829% 18 to 64 years old578038330042% 65 years or older5474946473%

19 Conclusions Projecting population or membership is difficult during volatile periods 2014 – 2018 is likely to be a volatile period Growth in Stafford County is not likely to mirror the 2000 – 2005 growth rate Demographics (in Stafford County and in the US) are changing and these changes will impact the number of school-aged children – the number of school-aged children per household

20 Land Use Study July, 2014

21 Land Use Study Data from Stafford County Public Schools Stafford County Planning Stafford County GIS Interviews with SCP and SCPS Data Student File: May 2014 data geocoded to identify where each student resides GIS Files from SC GIS: parcel data, structure data, subdivision data Subdivision Data from SCP

22 Land Use Study – “Active 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 most part, 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)

23 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). student generation factor (SGF) GIS data shows 119 K-12 students living in the 181 structures producing a student generation factor (SGF) of 0.66.

24 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.

25 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.

26 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 APU 124 Section 8 Sections 5&7 Land Use Data

27 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

28 (Pace / Build-out) The forecast model uses 50 lots impacting 2014-15 producing ~50 new students. 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

29 Results of the Land Use Study Residential 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* 2014-15426 2015-16512 2016-17613 2017-18506 2018-19464 # 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.

30 Results of the Land Use Study Residential 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 2014-15426 2015-16512 2016-17613 2017-18506 2018-19464 Information 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.

31 Data  Student Numbers Membership Forecast Models CSR (Cohort Survival Ratio) Forecast APU Forecast

32 Cohort Survival Ratio System-Wide Forecast Cohort Survival Ratios are used to predict how cohorts of students will advance through the K-12 system by grade. CSR values greater than 1 suggest in-migration into the district. Cohort Survival Ratio (CSR): Comparison of student counts by consecutive grade for consecutive years.

33 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. The month-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 9 th graders in 2014-15: (# 8 th graders in 2013-14 x 1.067)

34 Cohort Survival Ratio System-Wide Forecast Each CSR contains historical in-migration as a portion of each ratio. 1.067 = # of 8 th graders last year + # of 9 th graders who are new to the system ― # of 8 th graders who moved out of the system # of 8 th graders last year

35 Cohort Survival Ratio System-Wide Forecast Historical Data New Births Student Membership (11-15-13)

36 Forecast based on unadjusted Cohort Survival Ratios: Without any adjustments, the CSR forecast is fairly flat: 0.30% annual growth. 27060 The COHORT model suggests 27060 students in 2014-15 and 27297 students in 2018-19.

37 COHORT MODEL COHORT MODEL – forecast using cohort survival ratios based on historical data Area Planning Unit (APU) MODEL Area Planning Unit (APU) MODEL – forecast using smaller areas of the County that are impacted by land- use data. Grade-by-grade cohorts are moved forward year-by-year using cohort survival ratios.

38 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 don’t attend the school to which they would be assigned by attendance zone.

39 APU Forecast Example APU 124 includes most of Leeland Station, an active subdivision with several phases remaining. There were 55 construction starts in 2011-12, 36 in 2012-13 and 32 from 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. Year2014-152015-162016-172017-182018-19 # Dwellings5060

40 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 KG1G2G3G4G5G6G7G8G9G10G11G12 2013-142430272328332241503135 27 2014-1529 34322732382646573438 2015-163435 40373338443252644044 Year2014-152015-162016-172017-182018-19 # Dwellings5060

41 APU Forecast Results 2008-09 2013-14 2018-19

42 Forecast Comparison 2008-09 2013-14 2018-19 Impact of adding students from new development into the system Cohort Survival Model Unadjusted APU Model

43 Cone of Uncertainty 2008-09 2013-14 2018-19 Cohort Survival Model Unadjusted APU Model

44 Data  Student Numbers What are the advantages/disadvantages of these different forecast models? CSR Forecast APU Forecast

45 Cohort Survival Ratio Forecast During stable times, the Cohort Survival Ratios provide 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 don’t provide information on which to make good decisions regarding shifting attendance lines. A CSR may not include the total impact of new development Forecast Comparison

46 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

47 The predicted growth in the very large subdivisions now underway begins to dwarf all other planned/forecasted growth in the system in the 2019-22 time period. This makes it difficult to add enough students in fast- growing APUs simply because there aren’t enough additional students forecasted for the entire system. There will be new subdivisions begun in this same window (2015 through 2022) that will alter growth patterns and projections. Forecast Limitations

48 APU forecast should guide adjustments to the Cohort Forecast During unstable times (times of significant growth or decline – when trends are broken), the APU forecast should guide adjustments to the Cohort Forecast. (using planning data at the subdivision-level) Forecast Results

49 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!

50 Informed CSR Forecast 2008-09 2013-14 2018-19 Cohort Survival Model Unadjusted APU Model

51 Informed CSR Forecast

52 Projected Growth Rates The informed COHORT model projects a system wide growth of 1.4% over the next 10 years. From 2013-14 to 2018-19, the growth by level is 779 Elementary 433 Middle 433 Middle 670 High 670 High 1882 K-12 1882 K-12

53 Out – of – Capacity Tables OREd SGF SC SGF Color-coded forecast at the school-level

54 Out – Of – Capacity Tables Design Capacities

55 OREd was asked to create a second model based on what the County uses for a Student Generation Factor when considering the impact of new development. When the County’s 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.

56 Out – Of – Capacity Tables Design Capacities

57 Projected Growth Rates Using the OREd SGF in the APU model, the COHORT model projects a system wide growth of 1.4% over the next 10 years. From 2013-14 to 2018-19, the growth by level is 779 Elementary 433 Middle 433 Middle 670 High 670 High 1882 K-12 1882 K-12 Using the SC SGF in the APU model, the COHORT model projects a system wide growth of 3.22% over the next 10 years. From 2013-14 to 2018-19, the growth by level is 1195Elementary 842 Middle 842 Middle 1129High 3166 K-12 1129High 3166 K-12

58 Out – Of – Capacity 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.

59


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