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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  

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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.  

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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.    

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• 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.  

   

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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.  

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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)

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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)  

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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  

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  

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  

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  

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,   Geron   tijdschrift   over   ouder  

worden  &  samenleving,  The  Netherlands,  vol.  14/issue  3,  pp  33-­‐36,  2012.  

[2]  Pearce   J.  Techniques   for  defining  school  catchment  areas   for  comparison  with  census  data,  

Computers,  Environment  and  Urban  Systems,  United  Kingdoms,  pp  283-­‐303,  2000.  

[3]   Talen   E.   School,   community,   and   spatial   equity:   An   empirical   investigation   of   access   to  

elementary  schools  in  West  Virginia,  Annals  of  the  Association  of  American  Geographers,  United  

States  of  America,  vol.  91/issue  3,  pp  465-­‐486,  2001.  

[4]  Bejleri  I.,  Steiner  R.  L.,  Fischman  A.  &  Schmucker  J.  M.  Using  GIS  to  analyze  the  role  of  barriers  

and   facilitators   to   walking   in   children's   travel   to   school,   Urban   Design   International,   vol.  

16/issue  1,  pp  51-­‐62,  2011.  

[5]  Mulaku  G.  C.  &  Nyadimo  E.  GIS   in  Education  Planning:   the  Kenyan  School  Mapping  Project,  

Survey  Review,  vol.  43/issue  323,  pp  567-­‐578,  2011.  

[6]  Singleton  A.  D.,  Longley  P.  A.,  Allen  R.  &  O'Brien  O.  Estimating  secondary  school  catchment  

areas  and  the  spatial  equity  of  access,  Computers  Environment  and  Urban  Systems,  vol.  35/issue  

3,  pp  241-­‐249,  2011  

[7]  Deruyter,  G.,  Fransen,  K.,  Verrecas,  N.,  De  Maeyer,  Ph.,  (2013),  Evaluating  spatial  inequality  in  

preschools   in   Ghent,   Belgium,   13th     International   Multidisciplinary   Scientific   Geoconference    -­‐  

SGEM  2013,  Cartography  and  GIS,  16  -­‐  22  June  2013  

 [8]  Apostel  K.  Capaciteitsprobleem:  over  Vraag  en  Aanbod,  School  in  de  Stad,  Stad  in  de  School,  

ed.  ASP,  Belgium,  pp  96-­‐120,  2012.  

  13  

 [9]   Fransen,   K.,   Verrecas,  N.   (2013).   Evaluating   spatial   and   social   inequality   in   pre-­‐schools   in  

Ghent,   Belgium   -­‐   An   accessibility   and   service   area   analysis   using   GIS,   Master’s   thesis  

(unpublished),  University  College  Ghent,  Faculty  of  Applied  Engineering  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.