The Data Sources Behind School Place Planning

School place PSS are typically configured to provide intelligence on a single local authority area. The key inputs of an effective system are the data sources, providing: geographical context; details on the local authority’s schools and pupils; and a historical perspective on local demographic trends. In combination with details of forthcoming school changes and the anticipated pupil impact of planned new housing, these data sources enable planners to assess future school place requirements (Figure Below).

Figure 4 Key data sources for school place planning

The PSS must be underpinned by an appropriate base geography, formed from contiguous geographical units (‘zones’) within the local authority boundary. The choice of zones will depend on the reporting requirements of the council and the availability of other input data at this spatial scale. Examples include wards and school catchments (geographical areas from which resident children are eligible to attend individual schools). The inclusion of a ‘buffer’ surrounding the local authority area, to account for pupils that attend the council’s schools but live outside its administrative boundary, enables school place planners to report on daily cross-border pupil inflows. The extent of the buffer will reflect the council’s reporting requirements, data availability and the home-to-school distance that pupils travel. The buffer area may optionally be disaggregated into smaller geographical units, such as wards.

A list of school planning areas may be included in the system, each comprising a ‘cluster’ of the council’s primary or secondary schools. The inclusion of primary and secondary school planning areas enables system outputs to be generated in a form that is compatible with the DfE’s annual School CAPacity (SCAP) survey: a statutory data collection that gathers information on the supply of/demand for school places within each local authority area.

Fundamental to the PSS is a list of state-funded schools, including each establishment’s name, unique reference number and location (zone and planning area), together with details of any recent or anticipated changes (closures, mergers, openings, et cetera). The provision of the school list enables forecasts to be generated for individual establishments, whilst the provision of details on recent/planned school changes facilitates appropriate system configuration.

Details of the pupils attending each school are sourced from the school census, a statutory DfE data collection conducted by local authorities in October, January and May of each academic year (beginning in September and ending in July). To enable the derivation of appropriate forecasting assumptions, a PSS will typically include a three-year history of school census data, providing a count of pupils by year group, school and residential zone. The choice of school census data will determine the ‘base year’ of the forecasts (e.g., a system calibrated with school census data relating to October 2017, 2016 and 2015 will have a base academic year of 2017-18). School census data relating to the base year provide a ‘starting point’ for the pupil forecasts. The three-year history of data enables the derivation of appropriate school intake and migration assumptions.

Additional information on each school’s planned admissions number (PAN) and capacity may also be included in the PSS, providing a benchmark against which the forecasts can be compared. The provision of PAN data relating to the base year, base year-1 and base year-2 also enables temporary intake expansions (or reductions) to be identified and ‘removed’ for the purposes of system calibration.

A three-year history of pre-school population data is typically required for each zone, providing a count of resident children by single year of age (0+ to 4+) at the beginning of the base year, base year -1 and base year -2. These data may be sourced from General Practitioner (GP) registration statistics, or from mid-year population estimates (MYEs) published by the Office for National Statistics (ONS). In a PSS, data relating to the base year provides a ‘pool’ of resident children aged 4+ at the start of the first four forecast years. The three-year history of data enables the derivation of zone-specific assumptions on pre-school migration and school intake.

To enable the production of longer-term pupil forecasts, estimated births (children aged 0+ at the beginning of each, or at least the first forecast year) are required for each zone. Each year of estimated births included in the PSS will extend the pupil forecasts by one year (up to a specified forecast horizon).

The final key inputs to an effective school place PSS are a trajectory of planned housing growth for each zone, with accompanying primary and secondary pupil product ratios (PPRs). The housing growth trajectory, sourced from the council’s own datasets, may optionally comprise different housing ‘types’ (relating to size, tenure, et cetera). PPRs (also known as ‘pupil yield factors’) quantify the impact of housing growth on resident primary and secondary pupil numbers, by housing type and zone. Each local authority will have its own method for deriving appropriate PPR assumptions.

In a highly regulated sector such as education, where the allocation of public funds is subject to the highest degree of scrutiny, the quality and consistency of data used to inform planning decisions is critical. Robust decision making on the future need for school places requires all key datasets to be well managed, quality controlled and suitably configured for use within an appropriate forecasting model.

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