1. 1
USING
GIS
TO
FACE
PROBLEMS
RELATED
TO
SPATIAL
AND
SOCIAL
INEQUALITY
-­‐
CASE
STUDY:
CAPACITY
ISSUES
OF
PRE-­‐SCHOOLS
IN
GHENT,
BELGIUM
FRANSEN
Koos,
VERRECAS
Niels
University
College
Ghent,
Faculty
of
Applied
Engineering
Sciences,
Belgium
Abstract
The
growing
popularity
of
the
urban
fabric
as
qualitative
living
environment
has
apparent
effects
on
all
Flemish
regional
cities.
Social
and
spatial
inequality
is
perceptible
in
many
city
functionalities,
manifested
amongst
others
in
the
scholar
system.
Pupils
of
primary
schools
(in
Flanders
children
from
2.5
to
12
years)
living
in
the
proximity
of
a
suitable
school
are
forced
to
attend
schools
at
a
greater
distance
because
the
capacity
of
nearby
schools
is
exceeded.
The
research
at
hand
aims
to
provide
an
automated
and
adaptable
tool
for
local
authorities
to
visualise
and
analyse
the
current
school
constellation
and
support
policy
decisions
concerning
capacity
extensions
of
existing
schools,
implantation
of
new
schools
or
suppression
of
non-­‐
essential
school
locations.
In
the
general
applicable
model,
GIS
and
network
analysis
were
used
to
determine
the
catchment
area
for
each
school.
Furthermore,
the
model
was
used
to
produce
a
coverage
map
based
on
the
theoretical
catchment
areas
for
the
current
demography,
which
was
then
compared
to
the
actual
situation,
thus
pinpointing
and
identifying
problem
areas
for
which
appropriate
measures
have
to
be
taken.
Finally
the
model
was
used
to
predict
the
impact
of
future
demographic
evolutions
on
the
current
school
constellation,
analyse
modifications
on
the
datasets
and
determine
the
validity
of
certain
decision
policies.
As
so,
the
model
was
proven
to
be
adaptable
to
other
input
datasets.
The
model
was
validated
for
pre-­‐schools
in
the
city
of
Ghent,
Flemish
Region,
Belgium
and
proved
to
be
a
valuable
tool
to
support
local
policy
in
education.
Keywords:
GIS,
pre-­‐school,
education,
accessibility,
catchment
area,
location-­‐allocation,
network
analysis,
prediction
models,
spatial
inequality
1 Introduction
The
growing
migration
to
the
city
since
the
beginning
of
the
21st
century
leads
to
an
increase
of
the
population
in
the
city
centres
and
the
outer
city
rims
[1].
These
dynamics
strain
the
public
facilities
which
are
not
calculated
for
these
recent
evolutions.
An
example
can
be
found
in
the
capacity
of
schools,
which
in
a
lot
of
the
major
cities
in
Western
Europe
is
not
a
fit
for
the
increase
of
the
number
of
children
in
the
urban
agglomerations.
In
Flanders
(Belgium)
the

2. 2
capacity
issues
of
schools
are,
amongst
others,
expressed
by
the
periodical
returning
phenomenon
of
parents
camping
in
front
of
the
school
gates
during
the
enrolment
periods
in
order
to
be
sure
to
get
hold
of
place
for
their
children.
Another
symptom
of
the
school
capacity
problems
is
that
children
have
to
travel
over
greater
distances
because
there
is
not
enough
place
in
the
schools
in
their
neighbourhood.
Although
a
vast
amount
of
research
has
already
been
done
concerning
the
accessibility
of
schools
and
their
service
area
[2],
[3],
[4],
[5],
[6],
solutions
concerning
the
capacity
of
schools
which
are
directly
applicable
to
the
educational
system
are
still
lacking.
This
is
especially
the
case
for
elementary
schools
in
Flanders
(Belgium).
The
research
described
in
this
paper
offers
a
ready
to
use
tool
for
local
governments
and
school
communities
to
help
them
adapt
their
policy
to
demographic
and
spatial
evolutions
and
face
today’s
and
tomorrow’s
challenges.
2 Methodology
The
research
at
hand
presents
a
method
for
locating
areas
or
schools
with
accessibility
and/or
capacity
issues
by
using
a
set
of
indicators
determined
through
the
use
of
a
GIS
(Geographical
Information
System),
thus
allowing
efficient
budget
allocations
for
capacity
extensions
of
existing
schools,
implantation
of
new
schools
or
suppression
of
non-­‐essential
school
locations.
Two
sets
of
eleven
indicators
were
determined,
the
first
set
applies
to
the
level
of
statistical
or
spatial
areas
while
the
second
set
describes
the
schools.
Both
sets
were
then
used
as
input
for
a
choice-­‐driven
model.
This
automated
GIS
model
contains
a
set
of
tools
and
is
based
upon
the
closest
network
path
calculated
with
Esri
ArcGIS
10.1
Network
Analyst.
The
model
allows
the
assessment
of
the
present
situation
and
the
prediction
of
future
evolutions.
The
datasets
needed
as
input
were
[7],
[9]:
• a
geospatial
dataset
containing
the
borders
of
the
statistical
areas,
• a
geospatial
dataset
containing
the
address
and
the
age
of
the
inhabitants
of
all
statistical
areas,
• a
geospatial
dataset
containing
for
each
school
the
name,
the
address
and
the
educational
system
of
the
school
and
for
each
age
group
of
the
school
the
capacity,
the
actual
number
of
pupils
and
the
number
of
pupil
rejections,
• a
table
containing
the
relationship
between
the
statistical
area
of
the
pupil’s
domicile
and
the
statistical
area
of
the
school
he
or
she
attends,
• a
spatial
network
dataset
of
all
the
roads.

3. 3
The
indicators
were
generated
with
(automated)
sub-­‐models.
Each
indicator
can
also
be
used
outside
the
choice-­‐driven
model,
as
an
independent
analysis
or
in
combination
with
other
indicators.
The
indicators
on
the
level
of
statistical
areas
can
be
used
to
determine
in
which
areas
an
under-­‐
or
overcapacity
exists.
The
set
of
indicators
on
the
school
level
can
be
used
for
decisions
on
budget
allocation
within
a
school
community1.
Apart
from
the
basic
input
data
sets,
some
sub-­‐models
for
the
calculation
indicators
also
need
the
theoretical
catchment
area
of
the
school.
The
theoretical
catchment
area
is
the
area
for
which
the
maximal
capacity
of
each
school
is
reached
and
is
calculated
by
allocating
inhabitants
of
a
certain
age
category
to
the
school
based
upon
the
minimal
network
distance.
Overlaps
of
these
catchment
areas
result
in
a
theoretical
overcapacity
whereas
areas
that
are
not
covered,
indicate
a
theoretical
shortage
in
capacity.
The
theoretical
catchment
areas
of
the
schools
are
also
generated
from
the
basic
input
data
sets
using
an
automated
model.
The
indicators
for
the
statistical
areas
are
[7],
[9]:
• the
absolute
number
of
a
certain
age
category
in
the
statistical
area
and
the
percentage
of
inhabitants
of
a
certain
age
category
relative
to
the
total
number
of
inhabitants
of
the
statistical
area,
• the
number
of
schools
in
the
statistical
area,
• the
percentage
of
inhabitants
of
a
certain
age
category
that
attend
a
school
in
their
own
statistical
area
relative
to
the
total
number
of
inhabitants
of
that
age
category
in
the
statistical
area,
• the
percentage
of
inhabitants
of
a
certain
age
category
that
attend
a
school
in
an
adjacent
statistical
area
relative
to
the
total
number
of
inhabitants
of
that
age
category
in
the
statistical
area,
• the
percentage
of
inhabitants
of
a
certain
age
category
that
attend
a
school
in
a
statistical
area
which
is
not
their
own
or
an
adjacent
statistical
area,
relative
to
the
total
number
of
inhabitants
of
that
age
category
in
the
statistical
area,
• the
percentage
of
inhabitants
of
a
certain
age
category
that
attend
a
school
located
in
the
same
statistical
area
of
their
domicile,
relative
to
the
total
number
of
inhabitants
of
that
age
category
that
attend
a
school
in
that
statistical
area,
• the
percentage
of
inhabitants
of
a
certain
age
category
that
attend
a
school
in
a
certain
statistical
area,
but
live
in
an
adjacent
statistical
area,
relative
to
the
total
number
of
inhabitants
of
that
age
category
that
attend
a
school
in
that
statistical
area,
1
A
school
community
consists
of
more
than
one
school
settlement
on
different
locations.

4. 4
• the
percentage
of
inhabitants
of
a
certain
age
category
that
attend
a
school
in
a
certain
statistical
area
and
do
not
live
in
that
or
an
adjacent
statistical
area,
relative
to
the
total
number
of
inhabitants
of
that
age
category
that
attend
a
school
in
that
statistical
area,
• the
absolute
number
of
inhabitants
of
a
certain
age
category,
living
outside
the
theoretical
catchment
area
of
that
age
category
per
statistical
area
(Bu),
• the
multiplication
of
the
number
of
overlaps
minus
one
(O
–
1)
and
the
absolute
number
of
inhabitants
of
a
certain
age
category
domiciled
in
the
theoretical
catchment
area
of
that
age
category
per
statistical
area
(Bi),
• the
theoretical
overcapacity
or
shortage
of
the
statistical
area
as
result
of
the
operation:
R
=
Bi
x
(O
–
1)
-­‐
Bu
The
indicators
for
the
schools
are
[7],
[9]:
• the
school
capacity
of
a
certain
age
category,
• the
educational
network
to
which
the
school
belongs,
• the
actual
number
of
pupils
of
a
certain
age
category
per
school,
• the
percentage
of
pupils
of
a
certain
age
category
in
relation
to
the
school
capacity
per
school,
• the
number
of
refusals
of
a
certain
age
category
per
school,
• the
percentage
of
inhabitants
of
a
certain
age
category
that
attend
the
school
and
live
in
the
same
statistical
area
that
school
is
located
in,
relative
to
the
total
number
of
pupils
attending
that
school,
• the
percentage
of
inhabitants
of
a
certain
age
category
that
attend
the
school
and
live
in
a
statistical
area
adjacent
to
the
area
the
school
is
located
in,
relative
to
the
total
number
of
pupils
attending
that
school,
• the
percentage
of
inhabitants
of
a
certain
age
category
that
attend
the
school
and
live
outside
the
same
or
an
adjacent
statistical
area
that
school
is
located
in,
relative
to
the
total
number
of
pupils
attending
that
school,
• the
minimal
distance
of
the
theoretical
catchment
area
of
the
school,
• the
average
distance
of
the
theoretical
catchment
area
of
the
school,
• the
maximum
distance
of
the
theoretical
catchment
area
of
the
school.
All
the
models
were
created
using
Esri
Modelbuilder.

5. 5
3 Case
study:
The
city
of
Ghent
To
validate
the
models,
the
city
of
Ghent
was
used
as
test
case.
Geographically,
Ghent
is
characterized
by
a
historical
city
centre
encircled
by
an
area
of
19th
century
urban
expansion.
This
19th
century
belt
is
surrounded
by
a
peripheral
area
with
a
village-­‐like
structuring
[7].
Ghent
is
the
capital
of
East-­‐Flanders
and
is
the
city
that
attracts
the
largest
number
of
pupils
and
students
in
Belgium.
Ghent
counts
98
pre-­‐schools.
The
overall
capacity
shortage
for
pre-­‐schools
in
the
year
2012-­‐
2013
was
resolved
by
implementing
temporary
solutions
such
as
the
use
of
‘container
classes’
[8].
However,
these
ad
hoc
solutions
are
not
sufficient
to
face
the
global
capacity
problems
to
be
expected
in
the
years
to
come.
For
41
of
the
98
pre-­‐schools,
the
actual
service
area
was
computed
based
on
the
closest
network
path
between
the
home
of
each
pupil
and
the
school.
To
assess
the
usability
of
the
choice-­‐driven
model
on
the
level
of
the
school,
the
outcome
of
the
model
was
evaluated
in
detail
for
two
schools
[7],
[9].
4 Results
In
what
follows,
the
most
important
results
of
the
developed
sub-­‐models
will
be
discussed
as
well
as
the
outcome
for
both
choice-­‐driven
models
(statistical
area
and
school).
Finally,
changing
the
model’s
input,
thus
indicating
the
usability
of
the
model
for
predicting
future
developments,
proves
the
adaptability
of
the
model.
An
overview
of
the
complete
analysis
can
be
found
in
our
master’s
thesis
and
in
a
previously
published
article
[7],
[9].
The
specific
input
datasets
for
the
case
study
of
Ghent
are:
-­‐ spatial
dataset
with
the
borders
of
the
201
statistical
sectors2
in
Ghent,
-­‐ the
characteristics
of
the
entire
Ghent
population
(age,
address,
…),
-­‐ the
characteristics
of
all
pre-­‐schools
(location,
capacity
for
each
age
group,
actual
number
of
pupils
for
each
age
group,
…),
-­‐ a
table
featuring
the
allocation
of
each
child
to
the
school
it
attends,
-­‐ a
spatial
network
dataset
of
all
the
roads
of
Ghent.
The
age
for
children
going
to
pre-­‐schools
is
two
to
five
year.
2
A
statistical
sector
is
the
smallest
geographical
unit
available
in
Belgium.

6. 6
Indicators
1. The
percentage
of
children
attending
a
school
in
their
own
statistical
area
(figure
1)
The
population
of
children
attending
a
school
in
their
own
statistical
area
is
highest
in
the
peripheral
areas
containing
one
or
more
schools,
indicating
a
high
degree
of
self-­‐sufficiency.
All
these
statistical
areas
can
be
marked
as
peripheral
village
centres
with
a
high
sense
of
community.
Before
the
fusion
of
1976
they
were
independent
villages.
In
the
area
just
outside
the
city
centre
some
statistical
areas
with
two
or
three
schools
also
have
a
high
degree
of
self-­‐sufficiency,
but
in
general
the
percentage
of
pupils
attending
a
school
in
their
own
statistical
area
is
low
in
this
area
[9].
2. The
percentage
of
children
that
attend
a
school
and
do
not
live
in
the
same
or
an
adjacent
statistical
area
according
to
the
statistical
area
of
the
school
(figure
2)
figure 1: The percentage of children attending a
school in their own statistical area (Ghent 2012-
2013)
figure 2: The percentage of children that attend a
school and do not live in the same or an adjacent
statistical area (Ghent 2012-2013)

7. 7
This
indicator
is
a
measure
for
the
supra-­‐local
attractiveness
of
the
schools
in
a
certain
statistical
area,
relative
to
the
capacity.
Low
percentages
can
therefore
indicate
local
capacity
issues.
The
highest
percentages
are
found
in
the
city
centre
and
in
the
environment
of
the
Gent-­‐Sint-­‐Pieters
railway
station,
south
from
the
city
centre.
This
is
in
accordance
with
the
city
centre’s
high
degree
of
facilities
and
emphasizes
the
import
nature
of
these
schools
and
their
local
overcapacity.
Moreover,
these
areas
are
well
served
by
public
transportation.
3. The
theoretical
overcapacity
or
shortage
based
upon
the
children
of
2
to
5
years
living
outside
and
inside
the
theoretical
catchment
areas
of
the
schools
(figure
3)
This
indicator
is
also
used
to
determine
local
capacity
issues,
be
it
now
on
a
theoretical
level.
North
of
the
city
centre,
the
apparent
local
shortage
is
problematic,
because
of
the
clustering
of
high
ratios
of
shortage
in
the
surroundings.
Other
theoretical
local
under
capacities
are
countered
by
neighbouring
theoretical
local
overcapacities.
The
centre
and
the
Gent-­‐Sint-­‐Pieters
railway
station
surroundings,
have
a
high
local
overcapacity,
which
confirms
the
existence
of
‘import’
schools
[7],
[8].
figure
3:
The
theoretical
overcapacity
or
shortage
(Ghent
2012-­‐2013)
figure
4:
Capacity
and
education
portal
of
the
school
(Ghent
2012-­‐2013)

8. 8
4. Capacity
and
educational
portal3
of
the
school
(figure
4)
The
concentration
of
schools
with
the
highest
capacity
(more
than
120
pupils)
are
located
in
the
city
centre
and
some
peripheral
areas.
Although
the
school
density
is
higher
just
outside
the
city
centre,
the
capacities
are
mainly
lower.
Nearly
all
neighbourhoods
are
characterized
by
the
combination
of
one
school
subsidized
by
the
city
and
one
or
more
adjacent
bigger
schools
of
the
catholic
network
(portal).
5. The
number
of
refusals
(figure
5)
Most
schools
with
a
high
ratio
of
actual
pupils
in
relation
to
their
capacity,
also
have
a
high
number
of
refusals.
This
indicates
the
popularity
of
a
school,
especially
for
the
ones
in
the
centre
of
the
city.
In
the
area
just
outside
the
city
centre,
the
high
number
of
refusals
indicates
a
local
shortage
of
capacity.
3
The
following
educational
portals
are
possible
for
the
choice
in
primary
schools
in
Ghent:
Education
Secretariat
of
Cities
and
Municipalities
(OVSG),
Community
Education
(GO!),
the
free
Subsidized
Catholic
Education
(VSKO)
and
Small
Talk
Education
Providers
(OKO)
figure
5:
Amount
of
refusals
(Ghent
2012-­‐2013)
figure
6:
The
percentage
of
children
that
attend
the
school
and
live
outside
the
same
or
an
adjacent
statistical
area
(Ghent
2012-­‐2013)

9. 9
6. The
percentage
of
children
that
attend
the
school
and
live
outside
the
same
or
an
adjacent
statistical
area
in
accordance
to
that
school
(figure
6)
High
percentages
are
an
indicator
for
a
high
degree
of
supra-­‐local
attractiveness,
relative
to
the
capacity.
In
the
city
centre,
the
high
percentages
can
be
explained
by
the
popularity
of
these
schools,
while
in
the
environment
of
the
Gent-­‐Sint-­‐Pieters
railway
station,
the
high
degree
of
supra-­‐local
attractiveness
can
be
ascribed
to
local
overcapacity.
Low
percentages
can
also
indicate
local
capacity
issues,
especially
in
densely
populated
areas,
as
for
example
in
the
north
of
the
city
centre.
Choice-­‐driven
model
On
the
level
of
the
statistical
area,
the
model
was
applied
using
values
for
the
indicators
in
accordance
with
a
policy
aimed
at
statistical
areas
in
which
a
local
shortage
is
to
be
expected.
Four
statistical
sectors
were
selected
as
a
result
of
the
choice-­‐driven
model
(figure
7).
Afterwards,
the
statistical
sectors
were
arranged
by
increasing
theoretical
shortage
in
capacity,
thus
pinpointing
the
most
problematic
areas.
The
selected
areas
are
regions
in
which
locally
situated
capacity
issues
are
currently
imminent,
thus
validating
the
model
as
a
useful
query
tool
[9].
The
choice
driven
model
was
also
applied
on
the
level
of
the
schools,
but
this
time
in
accordance
with
a
policy
aimed
at
locating
schools
with
large
travel
distances
for
the
children
attending
these
schools.
Applying
the
model
resulted
in
the
selection
of
two
schools
(figure
7):
one
school
is
located
in
the
peripheral
area
and
the
other
in
the
city
centre.
Comparing
the
theoretical
to
the
actual
data
on
address
level,
indicates
that
both
schools
have
a
widely
spread
average
service
area.
Studying
the
actual
relation
between
the
location
of
the
school
and
pupils’
addresses
more
closely,
leads
to
conclude
that
the
school
located
in
the
city
center
attracts
a
lot
of
pupils
from
the
entire
urban
tissue
due
to
its
popularity,
while
the
school
in
the
peripheral
area
especially
attracts
pupils
from
areas
with
local
capacity
shortages.
figure
7:
Selection
of
the
choice-­‐driven
model
at
the
level
of
the
statistical
sector
and
the
level
of
the
school
(Ghent
2012-­‐2013)

10. 10
The
adaptability
of
the
model
for
evaluating
future
developments
By
changing
the
age
category
as
input
for
the
theoretical
models
(1
to
4
and
0
to
3
year
olds),
it
is
possible
to
make
predictions
concerning
over-­‐
and
under
capacity
for
the
near
future.
The
prediction
of
the
theoretical
overcapacity
or
shortage
for
the
next
two
years,
indicates
that
the
overall
overcapacity
in
the
city
centre
gradually
reduces
or
disappears,
especially
in
the
north
(figure
8).
figure
8:
Changes
in
the
theoretical
overcapacity
or
shortage
for
the
school
years
2013-­‐2014
and
2014-­‐2015
(Ghent)
figure
9:
Changes
in
the
school
catchment
areas
for
the
school
years
2013-­‐
2014
and
2014-­‐2015
(Ghent)

11. 11
The
spread
of
the
school
catchment
areas
diminishes
for
most
areas,
with
some
exceptions.
By
applying
the
automated
models
for
the
near
future
in
relation
to
the
current
demographic
evolutions,
urgent
interventions
can
be
planned
more
easily
(figure
9).
Finally,
the
geospatial
dataset
containing
the
schools
was
altered,
in
order
to
validate
the
applicability
of
the
model
for
the
simulation
of
the
impact
of
future
interventions.
This
was
tested
by
adding
a
school
with
a
certain
capacity
to
the
dataset
and
running
the
different
theoretical
automated
models.
Adding
a
school
in
the
north
of
the
19th
century
belt,
characterized
by
a
cluster
of
high
degrees
of
under
capacity,
resulted
in
local
switch
to
theoretical
overcapacity
(figure
10).
5 Conclusion
Validation
of
the
outcome
of
the
automated
model
results
in
a
usable
tool
for
educational
decision
policies.
Not
only
the
selections
of
the
BOS-­‐models
(Beleidsondersteunend
Selectie-­‐
model
or
Policy
Supporting
Selection
Model),
but
also
the
individual
indicators
generate
a
valuable
output.
By
developing
the
models
on
two
levels
(statistical
sector
and
school),
local
decision-­‐making
is
supported,
both
for
interventions
regarding
a
particular
area
or
a
specific
school.
The
tool
is
already
approved
by
the
local
government
and
will
be
used
for
determining
the
location
of
a
new
school
or
budget
allocation
in
accordance
to
the
current
school
constellation.
figure
10:
Changes
in
the
theoretical
overcapacity
or
shortage
by
implantation
of
an
extra
school
(simulation
for
Ghent
2012-­‐2013)

12. 12
The
general
applicability
of
the
models
indicate
that
they
are
adaptable
for
use
in
analyzing
different
urban
dynamics.
The
models
are
transferable
to
other
policies,
aimed
at
different
stakeholders.
Therefor,
using
a
different
dataset
as
input
can
lead
to
an
analysis
of
other
urban
phenomena,
for
example
the
critical
shortage
of
kindergartens
or
the
allocation
of
homes
for
the
elderly.
A
further
elaboration
of
the
models
in
combination
with
a
detailed
survey
of
the
educational
system,
will
lead
to
a
more
thorough
study
of
the
gathered
outcomes.
Mainly
socio-­‐economic
aspects
that
play
a
critical
role
in
this
study
should
be
further
analysed.
Also,
the
impact
of
public
transport
on
the
accessibility
of
schools
should
be
taken
in
to
consideration.
6 References
[1]
Deboosere
P.
België
en
de
transitie
van
krimp
naar
groei,
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ouder
worden
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samenleving,
The
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14/issue
3,
pp
33-­‐36,
2012.
[2]
Pearce
J.
Techniques
for
defining
school
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comparison
with
census
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Computers,
Environment
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Urban
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United
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pp
283-­‐303,
2000.
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School,
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Virginia,
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of
American
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I.,
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R.
L.,
Fischman
A.
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Schmucker
J.
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A.
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P.
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R.
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and
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Deruyter,
G.,
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N.,
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Maeyer,
Ph.,
(2013),
Evaluating
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in
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in
Ghent,
Belgium,
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13. 13
[9]
Fransen,
K.,
Verrecas,
N.
(2013).
Evaluating
spatial
and
social
inequality
in
pre-­‐schools
in
Ghent,
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analysis
using
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Master’s
thesis
(unpublished),
University
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Ghent,
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Applied
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sciences.
7 Acknowledgements
We
would
like
to
thank
the
people
of
the
Department
Strategy
and
Coordination
–
Data
Analysis
and
GIS
–
City
of
Ghent,
for
their
valuable
and
insightful
comments
and
suggestions.