1. Advanced Student Population Projections Overview of Projection Factors

2. 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)

3. 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

4. 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 A BIRTH FACTOR SPREADSHEET Madison High School

5. Residential Development Data Maintain a Residential Development Tract layer that contains certain fields updated regularly.

6. You can export the Development data in SchoolSite Projections… Residential Development Data …and a Development Summary table can be prepared. …to an Excel spreadsheet… SAMPLE

7. Student Yield Factors (SYF’s) This example shows a listing for units built within the last 5 years. And also has the Student Yields broken 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 MULTIPLIED BY THE APPROPRIATE STUDENT YIELD FACTOR ESTIMATES STUDENT GENERATED FROM FUTURE RESIDENTIALCONSTRUCTION.

8. 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 and student points (simultaneous selection) An example of a layer of individually mapped Assessor Parcel polygons

9. 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

10. 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 at least 4-6 attendance areas or regions (to capture data specific to certain areas in the District) If there is not enough historically geocoded student data, then the next best analysis would be by individual annual student counts by school or District-wide

11. 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. (Using 3-4 years of mapped student data)

12. Calculating the Mobility Factors Copy the exported student counts found in the Excel table… …and paste (“special” – values only) into the Mobility Factor worksheet template. (Using 3-4 years of mapped student data)

13. Calculating the Mobility Factors What boundary areas do I want to use as my criteria and what are the steps I need to do to finish calculating Mobility Factors? This is critical for determining how you want to divide up your Mobility Factors. Try to pick your existing attendance areas as the criteria for breaking your District up into at least four distinct, logical areas. This could be elementary, middle or high school attendance boundaries, depending up how many of each your District has. If you decide to use elementary school boundaries, for example, then once the total number of students by grade, by study area are entered into the Mobility Factor worksheet, then the elementary schools that are assigned to each study area need to be manually entered. Then you can resort the cells by school name. Once all of the student counts are sorted by school, then you need to survey each individual study area and REMOVE those study areas (rows) that have had any new residential development over the past 5 years. You need to have only “built-out” or “established” neighborhoods to be included in any Mobility Factor analysis. (Using 3-4 years of mapped student data)

14. Less students from year to year = mobility less than 1.0 More students from year to year = mobility more than 1.0 Ideally, you want to use the established, built-out Study Areas with no new development (especially within the last five years) If there is not enough historically geocoded student data, then the next best analysis would be by individual annual student counts by school or District-wide Calculating the Mobility Factors (Using 3-4 years of mapped student data)

15. Calculating the Mobility Factors Let’s calculate Mobility Factors using historically mapped student data EXERCISE #2 Exported annual student counts from SchoolSite (SchoolSite will summarize all K-12 student counts by Study Area) Open it up in Excel and begin inputting data into the provided template (Using 3-4 years of mapped student data)

16. Creating Projection Factors in SchoolSite QUESTIONS?