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Advanced Student Population Projections Overview of Projection Factors

Advanced Student Population Projections Overview of Projection Factors

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Page 1: Advanced Student Population Projections Overview of Projection Factors

Advanced StudentPopulation Projections

Overviewof

Projection Factors

Page 2: Advanced Student Population Projections Overview of Projection Factors

Factors That Influence Enrollment

• Births in the District area (BIRTH FACTORS)

• New residential construction (TRACT PHASING and STUDENT YIELD FACTORS)

• Move in/out of families in existing housing (MOBILITY)

• Private school transitions (MOBILITY)

• Drop-outs (MOBILITY)

• Residential redevelopment (MOBILITY and TRACT PHASING and STUDENT YIELD FACTORS)

• Parcel splits (MOBILITY)

Page 3: Advanced Student Population Projections Overview of Projection Factors

Birth Data Sources:

• State’s Department of Public Health or Vital Statistics

• Counties

• Data usually available by zip code

• You should correlate data to rough District or attendance area boundaries – maybe not exact, but close enough

Future Kindergarten Classes Estimates from Birth Data

Page 4: Advanced Student Population Projections Overview of Projection Factors

Future Kindergarten Classes Estimates from Birth Data

Birth Data:• Assists in estimating future kindergarten class sizes• Most children are 5 years old entering kindergarten• Compare the number of births within the District

(or attendance areas) from five years ago with the most recent birth data to estimate future trends in kindergarten classes

• Future K class size usually corresponds to recent birth trends

EXAMPLE OF ABIRTH FACTORSPREADSHEET

Page 5: Advanced Student Population Projections Overview of Projection Factors

Residential Development Data

Maintain a Residential DevelopmentTract layer that contains certain fields

updated regularly.

Page 6: Advanced Student Population Projections Overview of Projection Factors

You can export the Development data in SchoolSite Projections……to generate a table that can be read into Excel…

Residential Development Data

SAMPLE

…and a Development Summary can be prepared.

Page 7: Advanced Student Population Projections Overview of Projection Factors

Student Yield Factors (SYF’s)

This example shows a listing forunits built within the last 5 years.

And also has the Student Yieldsbroken down by specific grade groupings and by housing type.

You have the ability to focused upon a specific type of housing such as ”affordable housing”in a specific area and produce a different rate than the newer apartments units being built.

NEW HOUSING UNITS MULTIPLIEDBY THE APPROPRIATE

STUDENT YIELD FACTORESTIMATES STUDENT

GENERATED FROM FUTURE RESIDENTIALCONSTRUCTION.

Page 8: Advanced Student Population Projections Overview of Projection Factors

Calculating Student Yield Factors

• Also referred to as Student Generation Rates (SGR’s)

• To Calculate these rates, two data sets are required: Assessor parcel information and geocoded students.

An example of geocoded parcel data andstudent points (simultaneous selection)

An example of a layer of individuallymapped Assessor Parcel polygons

Page 9: Advanced Student Population Projections Overview of Projection Factors

Calculating Student Yield Factors

EXERCISE #1

Go to the SYF_Study.mxd where you have been set-up to begin querying and calculating Student Yield Factors

Page 10: Advanced Student Population Projections Overview of Projection Factors

Calculating the Mobility Factors

ISSUES TO ADDRESS• Do I have student data at the study area level?• And if so, how many consecutive years do I have?• What boundary areas do I want to use as my

criteria?

DDP’s Ideal Situation:• 4 consecutive years of geocoded student data

(that would provide 3 years of change)

• Use boundaries that would break the District up into 3-5 attendance areas or regions

(to capture data specific to certain areas in the District)

Page 11: Advanced Student Population Projections Overview of Projection Factors

Calculating the Mobility Factors

The SchoolSite Projection Module will summarize your student data by

grade and by study area.

Individual grade counts

By Study Area

You can click on the export button and save the table as a DBF to open and

query in Excel.

Remember to keep track of what year/month the student data represents

when using multiple files.

Page 12: Advanced Student Population Projections Overview of Projection Factors

Calculating the Mobility Factors

Or you can scroll through the individual Study Area

reports to choose the appropriate study areas to use (large enough sample

in built-out areas) and then manually type the

“actual” grade grouping counts into the Excel

spreadsheet.

Ignore the projected figures and focus on the “actual” counts

Page 13: Advanced Student Population Projections Overview of Projection Factors

Less students from year to year = mobility less than 1.0More students from year to year = mobility more than 1.0

Ideally, you want touse the established,built-out Study Areas

with no new development(especially within the

last five years)

If there is not enoughhistorically geocoded student data, then the

next best analysis wouldbe by individual

annual student countsby school or District-wide

Calculating the Mobility Factors

Page 14: Advanced Student Population Projections Overview of Projection Factors

Calculating the Mobility Factors

Let’s calculate Mobility Factors using historically mapped student

data

EXERCISE #2

Exported annual student counts from SchoolSite (in DBF format)(SchoolSite will summarize all K-12 student counts by Study Area)

Open it up in Excel and begin inputting data into the provided template

Page 15: Advanced Student Population Projections Overview of Projection Factors

Creating Projection Factors in SchoolSite

QUESTIONS?