Transcript School pupil forecasting: Can GIS improve our methods and understanding?

BSPS Annual Conference
2006
School Pupil Forecasting – can
GIS improve our methods and
understanding?
Wendy Pontin
Norfolk County Council
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BSPS Annual Conference
2006
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Background
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Setting the scene
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Methodology
school catchment forecasts
school rolls forecasts
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GIS desk top survey of pupil yield from new housing
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Issues that have arisen
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Lessons learnt
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Background
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Residence based school catchment forecasts carried out
since the early 1990’s – increasingly using GIS
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2005 – Audit commission Report
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November 2005 – Seminar looking at best practise
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2005/06 – Pupil Forecasting Project
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Pilot study to develop methodology to:
 forecast the number of 0-19 year olds in Norfolk on
individual school rolls and on a resident school
catchment basis for each year group over a six year
forecast period
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Setting the scene
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Over 400 schools:
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School size
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Highs – 52
Primaries – 200
Infant / First – 112
Junior / middle – 70
42 schools of under 50 pupils
160 schools of under 100 pupils
High schools vary from 600-1,500 pupils
Numbers
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105,000 pupils Year R to Year 11
approx. 8,000 in any year cohort
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Methodology
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Brief non-technical description of the actual school
forecasting methodology
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Concentrate on the extensive use of GIS
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No maps!!!
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The power of GIS to process and analyse large data
sets – a means to an end!
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School catchment forecasts
the base data
Base year for forecasts Sept 06
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Individual pupil records Year R to Year 11 – with valid
postcodes
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Individual pupil records of pupils living in Norfolk attending
schools in Suffolk, Cambridgeshire and Lincolnshire – with
postcodes
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FHSA patient register data giving aggregated data to unit
postcode level of 0-4 year olds (as of 31 Aug 06)
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New house build data aggregated to smallest building block
level (geo-referenced) – permissions, allocations etc
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Historic completions data aggregated to smallest building
block level (geo-referenced)
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School catchment forecasts
assembling the base data
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All 2006 base data geo-referenced and taken into GIS
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Aggregated to:
 County
 Children’s Services Areas (5)
 Current High school catchment areas
 Current Primary school catchment areas (includes
infant, first, junior, middle)
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Records of 0-4s and school pupil records from Sept 03,
Sept 04 and Sept 05 re-aggregated to current school
catchment areas etc – already geo-referenced in GIS
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School catchment forecasts
into production!
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Top down approach
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Using cohort survival roll forward and taking account of
children generated by new house build
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Forecast for
 County
 Children living outside the County attending Norfolk
schools
 Children’s Services areas controlled to County
 High Schools controlled to Children’s Services Areas
 Primary, Infant and First controlled to High schools
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Produce by year group (0-15) forecasts for every school
catchment area
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Select appropriate year groups to produce school catchment
forecast for each school –robust forecasts!
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School roll forecasts
data preparation(1)
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Sept 2006 Patterns of ‘parental choice’ – or is it???
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Produce for each Year group a matrix of ‘parental preference
An example (Year 1 say)
School attended
A
B
School
catchment
Resident
Children
A
50
40
5
5
B
100
0
80
20
C
70
0
0
70
40
85
95
Total attending
C
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School roll forecasts
1st attempt
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Apply Sept 06 pattern of ‘parental preference’ to forecast
years for each school
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Roll forward pattern of parental preference (e.g. for Sept 07
forecast for Year2 roll forward pattern of parental preference
of Year1 - Sept 06 to Year2 – Sept 07 and apply to residence
based forecasts for Year2 – Sept 07)
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For entrance year group in any school assume the Sept 06
pattern of parental preference throughout the projection
period.
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This assures consistency with residence based forecasts does it reflect reality?
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What next?
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School roll forecasts
More data preparation!
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Look at past admission capacities for each school to actual
numbers on school roll in Sept 2006 to set capacity limits for
all year groups in Sept 06
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Project forward admission capacities for the six year forecast
period
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Assess historically which schools remain at full capacity
(oversubscribed) – assume that they will remain so for the
duration of the forecasting period
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For these schools, pupils who cannot get into one of them as
1st choice build up patterns of schools to which they do attend
(e.g. of pupils who cannot get into school A, 20% go to school
B, 30% to school B and 50% to school C)
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School roll forecasts
Finalised results
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For schools that are assumed to be oversubscribed (i.e. up to
full capacity) for the duration of the forecast period, adjust
results to either bring them up or down to capacity, taking
away from or adding the residual from other schools
according to the pre-determined patterns – manual
adjustment to oversubscribed schools but automatic
redistribution
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For all other schools where overcapacity is identified in any
year group, redistribute this to likely neighbouring school –
manual adjustment and redistribution
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Model will automatically adjust succeeding forecast years
after any manual adjustment – assumes that pupils stay in a
school once they have been allocated there’!
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Again consistency with residence based forecasts is
maintained.
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GIS desk top survey of school pupil yield from new
housing
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From districts Housing Land Availability studies (April 2005)
digitise boundaries of all recent permissions with 5 or more
completions
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Analyse geo-referenced records of Sept 05 school pupils and
younger children aged 0-4 who are resident within these
boundaries
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Analyse by
 Age
 Area
 Type of housing (for the future)
 Size of permission (i.e. total number of houses)
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Determine pupil ratios which can be used in the residence
based forecasts.
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Issues that have arisen
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Fundamental – accuracy of base data. This means
awareness of those administering this data of the importance
of accuracy and full coverage
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Good intelligence of reorganisation (present and future) and
of quality of data
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Using GIS opens up potential and possibilities but still
resource hungry (both in terms of computing capacity and
people) – processing and analysing very large data sets
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Need to get buy in from customers – managing expectations!
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Validation – key to success (resource hungry!!!)
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Lessons learnt
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Methodology proved – can be implemented
Methodology proved – seems to be delivering accurate
results (pilot results accuracy for first forecast year)
 Western Children’s Service Area within 1%
 High schools majority within 1%
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For the first time we have produced consistent forecasts
(resident and school rolls) fit for purpose for a variety of
service provision
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The real test is still to come - a full run for the whole County
to very tight time scales
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Wish us luck!!!
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