ORANGE COUNTY PUBLIC SCHOOLS

The GeoPlanning Services team has enjoyed a long history of involvement with the Orange County Public Schools (OCPS). Our initial project with OCPS was to provide population and pupil forecasting to support long range real estate acquisition planning. That project, undertaken while the team members were employed by Canin Associates of Orlando, required developing and applying a land use forecasting model based on GIS analysis of the likelihood for development of each vacant parcel in the county.

The land use model was developed and calibrated using two known data points, in the case of this project 1990 and 2000. Parcel development between 1990 and 2000 was “rolled back” to vacant and the model was run for forecast the location of development driven by population changes over the decade. Once a statistically valid result was achieved through model tuning, the model was applied to 2005, 2010, 2015, and 2020 based on BEBR mid-level population forecasts for the county.

The model considered the unique attributes of each parcel including environmental constraints, access to transportation, access to utilities, access to non-residential land uses (commercial, office, and industrial), proximity to water features, proximity to recent residential development, and location in DRI areas. Based on these attributes each parcel was ranked for its development potential.

The maximum density of development on a parcel was determined based on environmental constraints and historical development patterns in the same region of the county. Regions were defined by unique zoning, future land use, and municipal designations. Single family and multi-family dwelling unit potential was assigned to each parcel. Additionally the likely square footage of non-residential development was assigned to parcels with that potential development direction.

Potential persons in each unit was derived from 2000 census data on population per dwelling unit. The square footage for non-residential uses was modeled as dependent on growth in residential population.

Students were address matched to the parcel and the student generation rates for each type of residential development were determined.

The ultimate result of the project was delivery of an ArcGIS based tool to forecast student enrollment by sub-county geographies (generally school attendance zone) on demand.