1	
  
USING	
  GIS	
  TO	
  FACE	
  PROBLEMS	
  RELATED	
  TO	
  SPATIAL	
  AND	
  SOCIAL	
  INEQUALITY	
  	
  
-­‐	
  C...
  2	
  
capacity	
   issues	
   of	
   schools	
   are,	
   amongst	
   others,	
   expressed	
   by	
   the	
   periodica...
  3	
  
The	
  indicators	
  were	
  generated	
  with	
  (automated)	
  sub-­‐models.	
  Each	
  indicator	
  can	
  also...
  4	
  
• the	
  percentage	
  of	
  inhabitants	
  of	
  a	
  certain	
  age	
  category	
  that	
  attend	
  a	
  school...
  5	
  
3 Case	
  study:	
  The	
  city	
  of	
  Ghent	
  
To	
  validate	
  the	
  models,	
  the	
  city	
  of	
  Ghent	...
  6	
  
Indicators	
  
	
  
1. The	
  percentage	
  of	
  children	
  attending	
  a	
  school	
  in	
  their	
  own	
  st...
  7	
  
This	
  indicator	
  is	
  a	
  measure	
  for	
  the	
  supra-­‐local	
  attractiveness	
  of	
  the	
  schools	
...
  8	
  
4. Capacity	
  and	
  educational	
  portal3	
  of	
  the	
  school	
  (figure	
  4)	
  
The	
  concentration	
  o...
  9	
  
6. The	
   percentage	
   of	
   children	
   that	
   attend	
   the	
   school	
   and	
   live	
   outside	
   ...
  10	
  
The	
  adaptability	
  of	
  the	
  model	
  for	
  evaluating	
  future	
  developments	
  
	
  
	
  
	
  
	
  
...
  11	
  
The	
  spread	
  of	
  the	
  school	
  catchment	
  areas	
  diminishes	
  for	
  most	
  areas,	
  with	
  some...
  12	
  
The	
   general	
   applicability	
   of	
   the	
   models	
   indicate	
   that	
   they	
   are	
   adaptable	...
  13	
  
	
  [9]	
  Fransen,	
  K.,	
  Verrecas,	
  N.	
  (2013).	
  Evaluating	
  spatial	
  and	
  social	
  inequality	...
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Rapport using gis to face problems related to spatial and social inequality koos fransen & niels verrecas, university college ghent

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Rapport using gis to face problems related to spatial and social inequality koos fransen & niels verrecas, university college ghent

  1. 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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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. 13.   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.  

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