The District of Columbia has made enormous strides towards bringing all public school facilities to a level of quality that supports great teaching and learning. Since 2008, the District has spent nearly $1.5 billion and completed work at 64 schools, encompassing 7.3 million square feet. This unprecedented investment in facilities was matched by a proliferation of high quality educational options throughout the city. As a result of these efforts, more families are choosing DC public education than at any point in the past 12 years.
Future progress in public education requires that the District continue to invest in high quality public education facilities. This Master Facilities Plan (MFP) will help to ensure that such investments are strategic and efficient and that we prioritize neighborhoods with the greatest need for capital investment. It is, however, only a starting point. The MFP will inform the District's Capital Improvement Plan, which includes detailed plans for individual schools.
A PROCESS INFORMED BY DATA AND STAKEHOLDER INPUT
The MFP brings together an unprecedented range of data sets to create a comprehensive fact base that policy makers can use to make strategic decisions about facilities allocation over the next five years. Data was collected for all District of Columbia Public School (DCPS) and public charter school facilities open during the 2011- 2012 and 2012-2013 school years, with the exception of alternative and special education facilities.
Data was collected to assess need in five key areas:
» Capacity and Utilization
» Population Forecast/Predicted Enrollment
» Facility Condition and Quality
» Neighborhood Characteristics (Density of children per acre and average travel distance)
» Modernization Equity
This fact base was then shared extensively with stakeholders and with a working group of District agency officials and DCPS and charter school leaders. The working group determined priorities for assessing data as well as guiding principles for development of the
plan. Based on these priorities and guiding principles,
a prioritization framework and a needs model were developed to assess need across all data sets for each neighborhood cluster.
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