Digital  footprints  in  the  cityscape:  1    Finding  networks  of  segregation  through  Big  Data    

Vinicius  M.  Netto  Maíra  Pinheiro  

João  Vitor  Meirelles  Henrique  Leite2  

 Abstract.  Segregation  has  been  one  of   the  most  persistent   features  of  cities  and  therefore  one  of   the  main  research  topics   in  social  studies.  From  a  tradition  that  can  be  traced  back  to  the  Chicago  School   in  the  early  20th   century,   social   segregation   has   been   seen   as   the   natural   consequence   of   the   social   division   of   space,  reducing   segregation   territorial   segregation   and   taking   the   space   as   a   substitute   for   social   distance.   We  propose  a  change  in  the  focus  of  static  segregation  of  places  to  as  social  segregation  is  played  by  embodied  urban  trajectories.  We  analysed  trajectories  of  groups  of  social  actors  differentiated  by  income  levels  in  Rio  de  Janeiro,   Brazil.   Firstly,   we   used  metadata   from   Twitter   users  moving   around   the   city   to   derive   geographic  coordinates   and   timestamp   of   tweets,   and   identified   users’   origins   and   destinations.   Then   we   crossed  information  on   trajectories  with   socioeconomic  data   in  order   to   see  potential   social   networks   according   to  income,   assess   their   spatial   behaviour   and   potential   spaces   of   social   convergence   –   a   geography   of   the  segregative   /   integrative   potential   of   encounters.   This   approach   is   intended   to   recast   the   spatiality   of  segregation  potentially  active  in  the  circumstances  of  social  contact  in  the  city  rather  than  in  static  territories  and  patterns  of  residential  location.  Key  words:  segregation,  mobilities,  social  networks,  Big  Data.      1.  Introduction:  a  new  approach  to  urban  segregation  We  will  examine  in  this  paper  the  relationship  between  forms  of  social  segregation  in  the  city  and  the   mobilities   of   socially   different   actors.   We   suggest   that   the   usual,   purely   spatial   forms   of  segregation   cannot   explain   the   phenomenon   of   social   segregation.   Even   if   the   answer   to   the  question   of   the  ways  we   experience   social   segregation   still   implies   a   role   for   space,   we   hope   to  show   that   this   role   cannot   be   reduced   to   territorial   segregation.   We   will   argue   that,   since   our  societies  are  highly  mobile   interaction  systems,  we  need  to  see  urban  space  beyond   its  seemingly  static  condition.  Through  a  critique  of  usual  approaches  to  segregation  that  have  space  as  a  reason  and   an   explanation   for   social   distance,   we   will   emphasize   the   spatiality   of   our   daily   actions   and  urban  encounters  as  main  components  of  the  experience  and  the  phenomenon  of  segregation.   In  other   words,   in   contrast   to   a   literature   traditionally   focused   on   the   territorial   dimension,   our  approach  reformulates   the  spatiality  of  segregation,  showing  ways   in  which  segregation   is  shaped  by  the  segregative  /  integrative  potential  of  encounter.  This  shift  represents  a  change  of  focus,  from  the   vision   of   static   forms   of   segregation   inherent   in   places   –   where   social   distance   is   assumed,  rather   than   understood   in   all   its   material   manifestation   –   to   the   vision   of   social   segregation  reproduced  through  our  actions  and  trajectories  as  urban  actors.  This  reformulation  of  the  spatiality  of   segregation   should   put   the   body,   both   the   element   that   carries   the   signals   of   identities   and  personal  differences,  as  the  primordial  instance  where  segregation  is  socially  revealed  and  lived  by  the  actor.  In  doing  so,  we  intend  to  show  that  urban  space  maintains  a  key  role  in  segregation  –  in  

                                                                                                                         1  The  title  recalls  Frederico  de  Holanda’s  work  “Class  footprints  in  the  landscape”  (Holanda,  2000).  2  The  authors  are  with  Universidade  Federal  Fluminense  (UFF),  Escola  Nacional  de  Ciência  Estatística  (ENCE),  Universidade  de  São  Paulo  (USP)  and  Universidade  Federal  do  Rio  Grande  do  Sul  (UFRGS)  respectively.  Email:  v1n1netto@yahoo.co.uk    

 

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fact,   a   role   much   more   subtle,   penetrating   and   definitive   than   the   usual   reading   of   spatial  segregation  would  have;  a  new,  dynamic  aspect  of  the  experience  of  space  essential  to  explain  the  lasting  experience  of  segregation.    In  fact,  this  aim  involves  entering  an  almost  infinite  tangle  of  movements  and  interactions  in  cities.  This  paper  develops  a  method  to  capture  such  elusive  fabric,  making  an  eclectic  use  of  ideas,  from  Linton  Freeman's  view  (1978)  segregation  as  "restrictions  on  interaction"  to  references  to  the  time-­‐geography  of  Torsten  Hägerstrand  (1970).  This  method  is  illustrated  by  an  empirical  study  of  large-­‐scale  mapping  of  urban  trajectories  in  the  city  of  Rio  de  Janeiro.  Our  approach  is  intended  to  help  us  understand   the   elusive   forms  which   render   the  Other   invisible   in   our   daily   lives,   and   how   subtle  forms   of   social   distance   silently   penetrates   everyday   life,   transforming   social   differences   in  structural  distance,  and  the  Other  in  a  form  of  otherness  unknown  –  as  if  we  lived  in  different  social  worlds;  a  subtle  process  that  operates  in  the  final  analysis,  through  the  body.    This  reconsideration  of  segregation  seems  also  useful  if  we  take  into  account  the  complex  patterns  of  daily  mobility   in   contemporary   cities.   Since   the  work  of   the  Chicago  School  of  Park   (1916)   and  Burgess   (1928)   to   the  recent  approaches  to   the  contextual  characteristics  of  segregation,  mobility  has  been  mostly   represented  by  migration,  with   the  slow  temporality  of  production  of   residential  areas  and  location  as  an  expression  (as  in  Maloutas,  2004;  2007).  In  this  view,  the  temporality  and  spatiality  inherent  to  everyday  mobility,  such  as  the  ability  to  access  and  join  social  situations,  have  been  neglected.  Very  differently  from  that  vision,  we  want  to  explore  an  idea  of  mobility  closer  to  Georg   Simmel   (1997):   mobility   as   property   of   "a   world   in   flux,   whose   substantive   content   is  dissolved  in  motion"  (Frisby  in  Maloutas,  2004:  195).  In  fact,  there  are  highly  interdependent  forms  of  mobility,  including  displacement  of  people  for  work,  leisure  situations,  family  life,  migration  and  escape,  which   are   central   to   building   and  maintaining   complex   connections   in   a   network   society  (Urry,  2002:  1).  Hope  to  explore  this  approach  and  incorporate  connections  which  are  at  the  same  time  the  elusive  conditions  of  contact  between  the  actors,  and  what  Giddens  (1984)  considers  as  a  key   element   of   social   integration:   the   encounter.   First,   we   will   develop   these   ideas   in   order   to  achieve  a  clearer  understanding  of  the  relationship  between  mobility  and  the  circumstances  of  the  encounter.  Next,  we   list   the  producers  of  mobility  against  social  differences.  We  argue  that  social  differences  can  be  active  factors   in  mobility,  especially   in  strongly  unequal  societies.  We  will  build  on   these   relationships   in  order   to  break   free   from  the  spatial   reduction  of   segregation   to  a   social  division  of  space.    2.  Segregation  as  restrictions  on  interaction  One   of   the   most   powerful   views   of   segregation   is   Freeman’s   (1978:   413):   "All   restrictions   on  interaction,  whether  they  involve  physical  space  or  not,  are  forms  of  segregation  –  in  social  space."  Our   approach   is   strongly   related   to   Freeman's   view   of   the   restriction   on   interaction   in   order   to  understand   segregation   as   a   restriction   of   the   presence   of   the   other   in   our   actions   in   the   city,  immersed   in   the   delicate   fabric   of   encounters   and   interactions   that   keeps   local   social   systems  integrated.  Usual  approaches  seem  ill-­‐equipped  to  recognize  these  subtle  dimensions  of  segregation  active  in  urban  rhythms  of  the  encounter.  So  if  we  want  to  understand  the  integrative  /  segregative  potential  of  the  encounter,  we  must  turn  to  the  fabric  of  our  daily  actions  and  movement  beyond  

 

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segregated  areas.  This  means  the  possibility  of  finding  a  more  complex  spatiality  of  segregation  and  potentials   of   social   integration   perhaps   latent   in   places   that   allow   what   we   can   call   "social  convergence".  A  closer  view  of  urban  practices  in  different  social  groups  requires  a  concept  able  to  identify   how   actors   perform   their   actions   spatially   in   order   to   access   and   join   social   situations.  Importantly,  different  mobilities  could  be  associated  with  different  social  groups  and  different  forms  of  urban  experience.  We  will  argue  that  income  is  a  key  factor  and  may  have  effects  on  the  number  of  activities  in  which  they  are  able  to  engage.  The  location  of  activities  is  also  important,  and  here  approaches  to  spatial  segregation  still  have  much  to  say,  given  that  living  in  accessible  places  implies  we  are  closer  to  more  activities  and  can  perform  them  in  greater  numbers  and  more  efficiently.    Encounters  can  be  dispersed  in  the  streets  or  polarized  in  places  of  work,  leisure  and  consumption;  at   bus   stops,   subway   stations,   institutional   buildings   and   so   on.   If   we   understand   the   city   as   a  network,  we  can  see  activity  places  like  "attractors":3  a  substantial  part  of  social   life  unfolds  inside  buildings,  as  our  communication  and   the  possibility   to   relate  our  actions   to   the  actions  of  others.  We  can  participate  in  a  particular  activity  if  it  interests  us,  if  we  have  a  role  to  play  in  it,  if  we  afford  to  do  it,  and  if  we  are  unable  to  get  to  its  place  –  and  if  we  are  aware  that  it  exists  in  the  city  in  the  first   place.   All   these   factors   mean   that   at   least   some   activities   are   often   not   interesting   or   not  accessible  (socially  and  spatially)  to  everyone.  These  factors  also  have  an  impact  on  our  actions,  like  sparks  to  a  dense  network  of  daily  movements  from  residential  locations.  If  movement  could  leave  visible   traces   in   space,   such  appropriation  networks   could   reveal   the  potential   to  encounters  and  bodily  segregation  unfolding  in  the  city.    Mapping  these  actions  networks  and  movements  in  the  city  is  precisely  an  objective  of  this  paper.  In  fact,  the  idea  of  mapping  trajectories  is  far  from  new.  The  work  of  Hägerstrand  (1970)  was  the  first  systematic  attempt  to  capture  trajectories  and  restrictions  spatiotemporal  hanging  over  our  actions.  Although  Hägerstrand’s  approach  –  fashionable   in  the  early  1980s   in  human  geography  (e.g.  Pred,  1981)  –  has   lost   interest   to  most  since   then,   recent  empirical  approaches  have   taken   the  spirit  of  that  work,  making  use  of   technologies   capable  of   recording   the  movement  of   actors   and   identify  patterns  of  mobility.4  We  propose   to  add  new   layers   to   this   idea,  and  evaluate  how  the  everyday  appropriation   of   actors   shapes   encounters.   In   this   sense,   we   will   explore   mobility   patterns  potentially   related   to   different   social   groups.   Networks   of   movement   are   of   course   evanescent  features  of  our   effective  presence   in   space.   If  we   could   capture   at   least   some  of   them,  we   could  have  a  better   idea  of  how  socially  differentiated  groups  spatialize   their  actions.  These  "courses  of  action"  can  shape  the  possibilities  of  encounter  and  relationship  between  people  and  may  contain  the  potential  spaces  of  co-­‐presence  and  systematic  absence  of  socially  different  individuals.  In  other  words,   this  mapping   of   actions   can   allow   an   understanding   of   the   spatiality   of   the   presence   and  absence  as  active  features  of  social  segregation.    

                                                                                                                         3  We  devise  a  form  of  analysing  the  city  drawing  on  configurational  studies,  namely  Kruger  (1979),  Hillier  and  Hanson  (1984)  and  Krafta  (1994).  4A  method  developed  by  Gonzales  et  al  (2008)  uses  an  extensive  data  base  recorded  through  mobile  phone  communication  in  American  cities  to  map  spatial  paths,  showing  that  actors  have  a  remarkable  tendency  to  recursivity.  

 

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We   would   like   to   explore   a   particular   definition   of   "social   network"   that   will   be   useful   for  understanding  how  the  spatiality  of  the  encounter  shapes  the  experience  of  social  segregation  as  a  dynamic   formation   of   (and   distance   between)   social   groups.   We   shall   not   use   the   concept   as   a  mathematically  identifiable  arrangement  of  personal  ties,  as  in  Social  Network  Analysis  approaches  in  quantitative   sociology,  active   since   the  1950s.5  We  will  use  a  definition  of   social  network  as  an  open  set  of   relationships  changing  over   time  –  one   taking   into  account   the  social  positions  of   the  actors   and   the   circumstances   of   time-­‐space   where   groups   are   formed.   This   intentionally   flexible  definition  of  networks  is  intended  to  include  the  probability  of  encounter  a  key  sociological  factor  to  understanding   how   increasing   or   decreasing   connections   and   settings   are   definitive   features   for  actors  generating  social   relations.  Graphically,  we  do  not  represent  actors  by  points  as   in  classical  social   networks   analysis.   Instead,   we   choose   invert   this   representation,   seeing   the   actors   as  “lifelines”   (as   in  Hägerstrand)   converging   to  positions   in   space-­‐time,   in  homological   relations  with  real  spatial  or  urban  paths.  This  inversion  seeks  to  make  the  spatiality  of  the  encounter  and  the  role  of  space  in  the  production  of  social  networks  more  intuitive  (Figure  1).    

   

Figure  1  –  Principles  of  homology  between  social  and  spatial  networks  operating  in  time.    3.  Social  differences  and  different  mobilities  What  is  the  chance  to  meet  someone  from  a  different  social  group?  We  shall  first  deploy  a  number  of  assumptions  apparently   reasonable   from  a  material   standpoint,  and   then  examine   them   in   the  light  of  previous  empirical  work.      ! First,   we   are   aware   that   the   formation   of   social   networks   in   cities   seems   to   depend  

substantially  on  circumstances  of  co-­‐presence.    ! Secondly,   cities   are   historically   produced   and   spatially   structured   so   as   to   make   social  

situations   in  principle  accessible   to  potential  participants.   In   fact,   city-­‐making  processes  are  consistently   related   to   patterns   of   location   and   accessibility,   as   many   studies   in   spatial  

                                                                                                                         5  See  graph  theoretical  approaches  in  Gravonetter  (1973),  Freeman  (1978;  2006),  Scott  (1991),  Wasserman  and  Faust  (1994)  and  Marques  (2012).  

 

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economics   (Hansen,   1959;   Glaeser,   2010)   and   urban   studies   (from   Lynch,   1960   to   Hillier,  2012)  have  systematically  revealed.6    

! Third,   activity   places   tend   to   increase   the   potential   for   convergence   of   actors   who   share  similar  interests  and  mobilities.7    

! Fourth,  courses  of  action  able  to   include  more  activity  places  also   increase  the  potential   for  social  contact.  The  more  broad  and  complex  the  pattern  of  appropriation  of  space  in  relation  to   the   number   of   houses   and   streets   that  make  up   cities,   larger  would   be   the   potential   to  enhance  our  personal  social  networks.  

! Fifth,  income  plays  a  role  in  this  process.  People  with  smaller  budgets  face  further  restriction  in  mobility  and   less  diversity   in   their  activities   -­‐  which   in   turn   leads   to   less  diverse   forms  of  appropriation   of   space.   As   we   shall   see,   limitations   in   mobility   enhance   localism   –   the  dependency  on  proximity  to  do  generate  personal  social  networks.  In  these  cases,  the  density  of  encounters  tends  to  increase  especially  around  home,  and  actors  tend  to  use  places  in  the  neighbourhood  to  create  and  maintain  relationships.    

! Furthermore,  there  is  a  range  of  activities  not  dependent  on  proximity  to  the  residence,  such  as  those  around  the  work,  which  can  increase  the  length  and  complexity  of  urban  trajectories  of   lower   income   actors.   Public   transport   and   the   progressive   increase   of   private   vehicle  ownership   in   developing   countries   also   allow   a   broader   and  more   complex  mobility   in   the  city.   In   fact,   a   number   of   empirical   studies   have   consistently   shown   that   higher   levels   of  income  allow  less  dependence  on  spatial  proximity.8  

 Our  hypothesis  is  that  restrictions  in  patterns  of  mobility  and  appropriation  of  space  would  lead  to  increases   in   the   density   of   encounters   between   similar   social   actors.   In   turn,   this   spatial   trend  toward   both   higher   levels   of   homophily   and   different   degrees   of   connectivity   in   personal   social  networks,   both   generated   by   differences   in   income,   lifestyles   and   mobility,   may   have   strong  implications  for  social  performance.    A  number  of  studies  support  the  idea  of  a  substantial  change  in  the  dependency  on  proximity  in  the  formation  of  personal  social  networks  according  to  class  and  income  –  in  Brazil,  country  of  our  case  study,  and  other  countries  in  the  world.  For  example,  a  recent  study  by  Marques  (2012)  in  Sao  Paulo  analyses   the   profiles   of   sociability   of   actors   in   poverty   and   its   role   in   the   formation   of   social  networks,   and   reveals   differences   between   personal   network   structures   of   actors   from   different  social   classes.  Middle   class  networks   tend   to  be   less  dependent  on   the  proximity,   as   if   they  were  personal  deterritorialized  communities  (Wellman  in  Marques,  2012)  –  a  very  different  pattern  than  actors   in  poverty.  A   similar   relationship  of   income  and  network   formation   is   found   in   analyses  of  cases   in  California  USA,  and   Israel   (Fischer  and  Shavit,  1995),  France   (Grosseti,  2007),  Finland  and  Russia   (Lonkila,   2010)   and   Chinese   (Lee   et   al.,   2005),   among   others.   Their   findings   suggest   that  personal   networks   vary   according   to   the   class   more   than   in   relation   to   cultural   and   regional  

                                                                                                                         6  In  a  long  tradition  stemming  from  Alfred  Weber  (1909)  and  Hansen  (1959),  approaches  in  spatial  economics  have  been  able  to  identify  actors’  preferences  and  location  patterns  amidst  the  apparent  randomness  of  location.  7  The  effects  of  linear  paths  over  the  density  of  encounters  have  been  recently  theorized  by  Bettencourt  (2013).  8  See  Holanda  (2000)  and  Marques  (2012).  Empirical  data  on  transport  expenses  in  Brazil  show  that  higher  income  groups  not  only  spend  more  than  low-­‐income  groups,  they  spend  more  than  proportionally  (POF,  2009).  

 

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contexts.   The   inverse   relationship   between   the   dependence   on   proximity   and   income   also   finds  support  in  Briggs  (2003;  2005).    These  approaches  to  segregation,  however,  still  tend  to  view  the  spatiality  networks  limited  to  the  location  of  residential  areas.  We  have  to  clarify  how  the  formation  of  social  networks  is  effectively  performed   both   spatially   and   temporally,   involving   circumstances   of   co-­‐presence   and   absence.  Could  mobility  –  not  proximity  –  be  the  key   factor   in  networking?   In   turn,  approaches  to  mobility  that  make  use  of  geographic   information  derived   from  digital  data   (say,   the  data  usage  of  mobile  phones)   are   still   restricted   to   capture   spatial   patterns   of   behaviour   (e.g.,   Gonzales,   Hidalgo   and  Barabasi,   2008)   –  with  no   connection  with   the   social   conditions  of   spatial   behaviour,   such   as   the  influence  of  income  and  class.  We  propose  that  a  detailed  analysis  of  the  structure  of  routines  and  urban  trajectories  could  clarify  causal  factors  in  the  formation  of  segregated  networks  and  what  we  might  call,  reminding  Young  (1990),  the  “invisibility  of  the  Other.”    4.  Networks  of  segregation  Now  let's  analyse  the  temporal  formation  of  personal  social  networks  in  an  urban  context.  It  seems  quite   reasonable   to   say   that   increases   in   our   access   to   different   social   situations   could   lead   to  expansions  in  our  personal  networks.  It  also  seems  reasonable  to  say  that  this  increase  depends  on  increased  mobility.  We  know  that  broader  and  diverse  personal  networks  allow  more  opportunities  for   social   and   economic   activity   (Marques,   2012).   We   arrive   now   at   another   key   point   of   our  argument.  If  social  networks  are  to  really  offer  more  opportunities  for  activity,  networks  must  first  be  formed.  However,  social  networking  in  cities  is  a  spatial  capability.  In  other  words,  mobility  and  urban   trajectories   create   encounters   that   generate   and   expand   social   networks,   creating   new  contacts  and  opportunities  for  activity.    If  these  implications  are  correct,  mobility  must  be  considered  as  a  key  factor  in  overcoming  localism  and   in   the   diversification   of   sociability.   Income   certainly   keeps   its   central   role,   as   it   supports  mobility,  but  we  have  to  consider  that  increasing  mobility  is  directly  related  to  an  increased  capacity  to  form  personal  networks  and  perform  in  them.  On  the  other  hand,  a   low  mobility  tends  to   limit  interactions,  as  we  can  see  in  the  case  of  poor  actors.  Mobility  and  income  are  associated  in  a  circle  that   leads   to   increases   or   decreases   in   the   potential   to   create,   maintain   and   expand   personal  networks.  But  how  does  this  happen?  If  networking  depends  on  situations  of  encounter,  we  need  to  understand   how  mobility  matters   in   the   structure   of   urban   encounters,   as   the   instance   in  which  social  segregation  operates  in  everyday  life.    We  have   seen   that  mobility  may  have   a  direct   influence  on   interaction  potentials.  We  also   know  that   low-­‐income   actors,   despite   having   more   limited   mobility,   are   not   static   within   socially  homogeneous   areas.   Differences   in   levels   of   localism   and   sociability   in   their   personal   networks  suggest  variations  in  their  spatial  reach  and,  by  extension,  in  their  condition  of  presence  in  places  in  the   city.   But   how   (and   where)   does   the   potential   of   encounter   between   the   socially   different  materialize?  In  order  to  answer  this  question,  we  need  to  examine  the  effects  of  different  mobilities  in  the  formation  of  personal  networks  within  and  between  social  groups  in  order  to  understand  the  opportunities   of   encounter.   We   suggest   that   if   social   networks   are   formed   so   that   actors   can  

 

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perform  connections,  then  class  networks  (i.e.,  social  networks  operating  within  large-­‐scale  groups  with   common   economic   features   that   strongly   influence   their   actions   and   lifestyles)9  and   other  forms  of  social  grouping  are  shaped  by  probabilities  of  encounter  in  personal  networks.    As  we   hope   to   show   in   our   empirical   study,   space  matters   here.   Even   though  we   do   not   usually  think   about   it,   our   daily   trajectories   constitute   the   backbone   of   our   interactions   and   shape   the  elusive  structure  of  social   life   in  the  city.  The  distance  between  locations   in  a  city,  associated  with  different   mobilities,   impose   limitations   on   probabilities   of   encounter.   Differences   in   mobility,  income  and  lifestyle  bring   inequalities   in  the  capacity  to  participate   in  social  situations.  Actually,  a  counterfactual   perspective   is   able   to   reveal   the   extent   of   this   problem   in   the   experience   of  segregation.   Inequalities   and   incompatibilities   in   patterns   of   appropriation   are   forms   of  what  we  may   call   the   disjunction   of   encounters   –   a   displacement   of   presences,   a   way   of   disrupting   the  possibility   of   encounters   that   otherwise   could   happen.   The   disjunction   of   encounters   seems  especially   active   among   socially   different   people   –   it   is   the   social   and   material   conditions   that  prevent  them  to  become  co-­‐present  and  acknowledge  each  other’s  existence.  Simply  put,  there  is  a  much  greater  chance  of  social  networks  incorporate  actors  who  share  similar  mobilities.      These  descriptions  begin  to  portray  the  complex  fabric  of  sociality  in  the  city.  However,  how  can  we  understand  in  detail  such  volatile  spatiality  of  the  encounter?  How  can  we  see  the  fabric  of  personal  trajectories  that  intertwine,  only  to  be  separated  later  in  the  space-­‐time  of  our  urban  lives?    5.  Digital  footprints  in  the  cityscape:  the  methodological  use  of  Big  Data  It  seems  almost  impossible  to  see  the  spatiality  of  the  tremendously  complex  flows  of  convergences  and  divergences  of  our  actions  and  paths  in  the  city.  Our  own  previous  studies  remained  at  a  level  of  small  scale,  using  social  and  spatial  data  derived  from  interviews  (Netto  et  al  2010).  They  offered  delicate  images  of  segregation  in  social  class  networks  operating  in  the  city.  So  a  key  question  posed  to  us  is  how  can  we  expand  this  dynamic  approach  in  order  to  grasp  the  panorama  of  segregation  in  an  entire  city?    The  answer  is  that  we  can  track  and  record  the  movement  of  a  large  number  of  (socially  different)  actors  in  the  city  exploring  the  enormous  potential  of  so-­‐called  'Big  Data'  as  a  way  of  dealing  with  an  urban   environment.   In   that   spirit,  we   conducted   an   empirical   study   in   the   city   of   Rio   de   Janeiro.  Twitter  offers  particularly  attractive  terms,  as  it  makes  its  metadata  bank  public  through  a  principle  of   anonymity.   The   set   of   variables   provided   by   Twitter   API   includes   user   IDs   along   with   the  timestamp  and  geographic  coordinates  for  each  tweet.  We  collected  metadata  from  tweets  posted  in  Rio  between  November   12th   (0:07:13   am)   and  14th   (2:36:45)   in   2014.  We  would   like   to   show  initial  results  from  an  ongoing  empirical  experiment  in  Rio.  We  recorded  posts  during  a  period  of  56  hours,  generating  a  database  of  14,960  users  and  the  positions  and  times  of  tweets  (Figure  2).      

                                                                                                                         9  We  derive  this  notion  from  Giddens’  concept  of  class  (Giddens,  1993).  

 

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 Figure  2  -­‐  Total  number  of  tweets  in  Rio  de  Janeiro  November  12th  (12:07:13)  –  14th  (2:36:45).  

 We  could  find  a  clear  temporal  pattern  in  tweeting  behaviour.  There  is  a  steady  increase  during  the  day,  a  little  peak  around  midday,  raising  again  to  drop  immediately  around  midnight,  to  raise  again  in  the  early  morning,  when  the  cycle  begins  again  –  something  like  “the  social  pulse  of  the  city”  seen  through  the  prism  of  digital  information  (figure  3).    

 Figure  3  –  Twitter  city  pulse:  frequency  in  series  of  tweets  in  Rio  de  Janeiro.  

 This  metadata  allowed  us  to  generate  a  classification  of  users  according  to  their  posting  behaviour.  Filters   were   used   to   ignore   users   that   do   not   provide   sufficient   data   to   infer   their   residential  location,  say  excluding  40%  of  users  with  only  two  or  less  tweets  during  the  study  period.  Automatic  tweeters  posting  for  commercial  purposes  (bots),  identifiable  by  the  large  number  of  tweets  posted  from   the   same   position   in   space,   were   also   excluded.   Furthermore,   the   analysis   of   average  Euclidean  distance  between  tweet  spatial  positions  revealed  users  whose  movements  were  also  not  relevant  to  our  study.  A  statistical  analysis  via  quantile  classification  of  the  results  showed  that  the  threshold   between   high   frequencies   of   short   distances   and   the   exponential   distribution   of   long  distance  was  106  meters  per  tweet.  Values   lower  than  this  were  filtered,  reducing  the  data  set  to  

 

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78,825  tweets  posted  by  4,325  users.  After  these  filtering  procedures  in  the  initial  metadata  set,  we  selected  a  number  of  2,543  users  whose  movement  was  relevant  to  this  study.    We  also  identified  spatial  and  temporal  patterns  of  posts  in  order  to  deduct  the  place  of  residence  of   users.   Then   the   tweets   of   valid   users   were   placed   in   the   urban   network   via   a   model   able   to  connect   tweet  positions   through   the   logic  of   shortest  paths,  using  GIS   software.  The   result  was  a  network   of   thousands   of   paths   within   the   network   of   streets.   It   should   be   clear   that   the   link  between   tweet   positions   via   shortest   paths   should   not   be   taken   as   the   actual   route   covered   by  users   between   their   tweets.   Nevertheless,   there   are   enough   theoretical   work   and   empirical  evidence  (especially  in  the  fields  of  space  syntax,  urban  networks  and  way-­‐finding  studies)  to  offer  reliability  to  this  procedure  as  a  proxy  for  actual  trajectories.  Evidence  shows  that  humans  tend  to  choose  the  shortest  path  between  two  positions   in  space,   in  a  metric  sense  as  well  as  topological  and  angular  minimization  along  the  segments  that  compose  an  urban  path  (see  Hillier,  2012).    The   next   step   was   to   differentiate   users   using   the   criterion   of   income.   This   step   requires   the  crossing  of  residential   locations  and  paths   inferred  from  tweets  with  economic  data.  We  used  the  2010  Census  (Brazilian  Institute  of  Geography  and  Statistics,  IBGE),  which  provides  income  data  for  areas  in  the  city.  Then  we  associated  the  initial  positions  of  tweets  with  census  data,  allowing  us  to  assign  average  income  levels  for  users.  We  analysed  income  through  the  standard  deviation  of  the  average  income  per  capita  in  Rio  de  Janeiro,  which  suggested  ranges  between  R  $  750;  R  $  1,600;  $  2,500;  R  $  3,400,  R  $  6,200,  and  above.  These  values  were  identified  as  low,  lower-­‐middle,  middle,  middle-­‐high  and  high-­‐income  users  (differentiated  by  colours  in  figure  4).    

 Figure  4  –  Residential  pattern  and  tweet  positions  according  to  income  levels  (blue  to  red).  

 This  methodological  procedure  can  be  summarized  as  follows:    1.  Collecting  metadata  Identification  of  the  position  in  time  and  space  of  tweets  posted  on  the  city  Period:  12  Nov  (0:07:13)  -­‐  14  (02:36:45)  2014  Active  Twitter  users  in  the  period:  14,960  users    2.  Filtering  users  

 

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Minimum  number  of  tweets  per  user  (3)  Eliminating  automatic  tweeters  Selected  users:  2,543  users    3.  Geographical  analysis  of  tweets  and  residential  location  of  users  Identification  of  the  location  of  the  first  tweet  in  the  morning  (first  in  the  sequence  of  tweets)  Confirmation  by  repeating  the  location  of  the  first  tweet  day.    4.  Generation  of  shortest  paths  between  tweet  positions  An   analytical   procedure   performed   through   GIS   software   allowed   us   to   find   the   shortest   paths   within   the  urban  fabric,  as  a  proxy  for  the  actual  paths  of  users.    5.  Crossing  census  data  (income)  with  the  inferred  location  of  users  Procedure  performed  through  GIS  software.    There   is  a  striking  difference  between  what  the  map  generated  from  census  data  shows  (figure  4,  left)   and   a   finer   scale   analysis,  when  we   look   into   the   actors   scale   (figure   4,   right).   Twitter   users  derived   locations   reveal  more   complexities   in   locational   pattern   than  procedures   based  on  mean  income   levels   for   areas   in   census  maps   suggest,  whereas   there   is   also   room   for   clear   patterns  of  aggregation.  Perhaps  ironically,  the  Twitter  users  map  seems  closer  to  the  real  city.      A  final  methodological  question  involves  the  representation  Twitter  in  relation  to  the  population  of  Rio  de  Janeiro  –  for  example,  how  do  we  have  all  social  classes  and  income  groups  that  make  up  the  city   proportions   in   the   use   of   Twitter?   First   we   compared   histograms   of   the   average   per   capita  income  in  Rio’s  population  and  Twitter  users.  Although  histograms  display  a  similar  trend,  there  are  differences   (figure   5,   left).   We   investigated   this   question   and   found   a   significant   correlation  between  population   (at   different   levels   of   income)   and   income   profile   of   Twitter   users   (figure   5,  right).  Linear  regression  (dark   line)  brings  an  adjusted  R  squared  of  0.67,  showing  that  the  income  distribution  of  users  has  a  fairly  reasonable  degree  of  similarity  with  the  income  distribution  of  the  population  in  general.      

   Figure  5  –  Histograms  of  average  per  capita  income  in  Rio’s  population  and  Twitter  users  (left);  and  regression  

between  users  (Y)  and  population  (X)  in  urban  districts;  colours  display  income  level  (right).    

Distribution of users x Populations in sectors

 

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Therefore,  the  use  of  Twitter  does  not  seem  to  be  associated  with  specific  income  levels  –  it  seems  like   a  pretty  democratic  medium   in  Rio.   This   empirical   observation   leads  us   to   rule  out   the  usual  hypothesis   of   a   digital   divide   in   the   city.   Other   graphic   information   clarifies   the   proportions   of  population  distributed  in  different  income  levels  in  Rio  and  among  Twitter  users  (figure  6).    

   

 Figure  6  –  Histograms  of  income  distribution  in  Rio’s  population  and  Twitter  users  (above);  and  the  

progressive  increase  in  income  in  Rio,  showing  a  great  difference  between  the  income  of  the  richer  and  the  other  groups,  and  among  the  Twitter  users  (grey  area,  below).  The  red  line  shows  the  mean  income  variation.    What   does   our   experiment   reveal   about   the   dynamic   segregation,   contained   in   Twitter   users'  trajectories   in  the  city?  Results  of  the  analysis  seem  to  grasp   interesting  traces  of  the  spatiality  of  potential   encounter   and   dynamic   segregation.   We   may   first   note   that   residential   segregation  following  a  major   factor:   there   is  a  wide  distribution  of  socially  differentiated  residential   locations  (figure   7).   Low-­‐income   and   lower-­‐middle   income   groups   show   considerable   overlap,   but   with  differences   in  depth   and  distance   from   the  CBD,   larger   for   low-­‐income  paths.  A  more   substantial  difference   appears   between   lower-­‐middle   and  middle-­‐income   users.   Trajectories   of  medium-­‐high  and  high-­‐income  users  confirm  this  trend.  The  overlapping  of  all  trajectories  in  the  last  map  in  figure  6  shows  the  dominant  income  groups  in  specific  spaces  in  the  street  network.  The  dynamic  nature  of   segregation  becomes  more   visible   in   this   representation.  On   the  other   hand,   there   is   a   strong  convergence  in  the  CBD  and  in  South  Rio  (actually  southeast),  where  users  of  all  classes  converge  in  an   interesting   intertwining   of   networks,   although   clearly   coexistent  with   a   dominant   presence   of  particular  groups.  Nevertheless,  the  closer  to  the  sea,  in  South  Rio,  the  most  present  the  networks  of  movement  of  higher  income  users.    

 

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 Figure  7  –  A  dynamic  picture  of  segregation:  blue  (low  income),  green  (lower-­‐middle),  yellow  (middle),  orange  

(middle-­‐upper)  and  red  (high  income)  groups.    We  also  quantified  the  length  of  streets  covered  by  users  in  their  trajectories  in  order  to  verify  the  potential   of   co-­‐presence   of   income   groups,   seeking   more   precision   in   the   description   of   spatial  convergences  and  divergences  between  socially  differentiated  users.  Most  overlapping  groups  are  low   and   lower-­‐middle   income   (1,996.10   Km).   Overlapping   between   the   poorer   and   the   richer   is  substantially   lower   (112.26  Km),  whereas  between   low  and  middle   income   is  102.81  Km.  We  can  also  assess  how  much  overlapping  every  single  income  group  has  with  other  groups  (table  1).    

Twitter  users  –  Income  Group   Sum  (Km)   %  Overlapped  paths  

Low  Income  Total  path:   13,695.65    -­‐    Overlapped:   2,300.22   17%  

Lower-­‐Middle  Income  Total  path:   22,605.81    -­‐    Overlapped:   2,974.04   13%  

Middle  Income   Total  path:   3,929.42    -­‐    Overlapped:   528.05   13%  

Middle-­‐Upper  Income  Total  path:   6,090.69    -­‐    Overlapped:   491.62   8%  

Upper  Income  Total  path:   5,784.36    -­‐    Overlapped:   381.32   7%  

Table  1  –  Level  of  overlapping  between  every  single  income  group  and  the  others.    The  overlap  of  the  five  income  groups  could  be  highlighted  in  spatial  representations.  The  lines  and  dots  in  red  are  the  most  likely  places  to  find  socially  different  actors  (figure  8).    

 

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 Figure  8  –  Spatial  relations  between  income  groups:  overlapping  pairs  

R1  (low  income),  R2  (lower-­‐middle),  R3  (middle),  R4  (middle-­‐upper)  and  R5  (high  income).      Spatial  analysis   shows  quite  distinct  patterns  of   spatial   relations  of   copresence  between  actors  of  different   income   levels,   visible   in   the   extent   of   overlapping   of   spatialised   networks.   Pairing   the  poorer  and   the   richer   (R1xR5  and  R2xR5),  maps   show   that  world-­‐famous  South  Rio   (Copacabana,  Ipanema,  Leblon)  is  not  merely  a  segregated  area  marked  by  the  dominating  presence  of  the  richer.  It   is  the  main  area  for  mutual  visibility  and  copresence  between  the  poorer  and  the  richer.  Lower-­‐middle   and   upper   income   users   (R2xR5)   also   converge   in   São   Conrado,   Barra   and   Recreio   in  

 

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Southeast  Rio,   in   the  CBD,  and   in  Gloria,  Catete  and  Flamengo  to  southeast.   In   fact,   lower-­‐middle  income  users   (R2)  have  a  key  social   role  created  by  a  powerful   range  of  appropriation   in   the  city,  already  able  to  afford  a  higher  mobility,  overlapping  more  than  any  other  group  with  other  income  groups  above  or  below  their  position  in  the  income  grade.  Lower-­‐middle-­‐income  users  also  overlap  with  middle-­‐income  users  (R2xR3)  across  areas  like  Tijuca  (North  Rio  near  the  CBD)  and  Jacarapaguá  (between  North   and   Southwest   Rio,   near   the   ocean).  Nevertheless,   poorer   income  users   (R1xR2)  share  much  more  spaces  when  appropriating  the  city,  mostly  in  North  and  Northwest  Rio.  Spaces  of  social  convergence  are  to  be  found  in  denser,  busier  areas  like  the  CBD  and  South  Rio.  These  are  the  most  likely  spaces  to  find  alterities.    6.  Conclusion:  segregation  and  the  probability  of  interaction  Considering   that   this  experiment  consists  of  a  proxy  can  only   show  trends  of   segregation,  we  can  see  potential  encounters  even  within  the  trajectories  of  a   limited  number  of  users,  suggesting  the  city   also   as   a   place   of   coexistence.   The   study   suggests   that   the   probability   of   encounters   is  impregnated  with   spatiality,  as  much  as  different   spatialities  also   seem  to  contain  different   social  potentials  in  its  visible  structures.  These  material  properties  of  the  action  and  its  spaces  seem  active  in   the  passage   from  the   individual  action   to  co-­‐presence   as  a  key   factor   for   the  social  experience.  Urban  spatiality  seems  to  render  this  passage  in  a  rather  non-­‐mechanistic  way:  the  materialisation  of   social   life   involves   high   variability   in   the   arrangement   of   urban   trajectories.   The   relationship  between  inherent  mobility,  encounter  and  spatial  heterogeneity  opens  up  possibilities  for  constant  change   and   unpredictability   –   a   non-­‐deterministic   relationship   immersed   in   randomness.  Contingencies   come   into   play   as   unpredictable   changes   in   decisions   and   choices   and   trajectories,  and  in  the  city  itself,  where  new  activities  and  events  arise  all  the  time.    The   analysis   of   the   digital   footprints   of   class   networks   in   the   cityscape   seems   to   reveal   this  complexity   in  a  way  that  the  purely   territorial  analysis  of  segregation  could  not.   In   this  sense,  our  approach   is   intended   to   shed   light   on   the   complexity   of   segregation   now   captured   as   a   highly  dynamic  micro-­‐segregation  that  occurs  at  the  level  of  our  actions,  trajectories  and  their  spaces.  The  odds  of  finding  the  'other'  seem  distributed  according  to  the  spatial  and  temporal  frames  of  action  of  different  groups  within  a  city.  These  frames  seem  to  have  a  stronger  potential  for  cohesion  factor  in  networks  operating  within  specific  social  classes,  shaped  by  differences  in  income,  social  mobility  and  access  to  urban  places  and  events.  The  paths  of  Twitter  users,  mapped  in  space-­‐time,  suggest  greater   compatibility   between   certain   users   –   and,   by   extension,   a   greater   potential   for   social  interaction  generated  through  encounters.  Social  ties  in  personal  networks  are  formed  through  the  recursivity   of   encounters,   so   that   networks   can   arise   more   cohesively   within   social   classes   than  between   them.   Incompatibilities   between   patterns   of   appropriation   of   space   take   the   form   of  differences   in   choices   and   capabilities   to   access   activities,   the   elimination   of   certain   places   as  opportunities   of   social   coexistence,   and   structural   differences   in   the   materialisation   of   urban  trajectories  in  time.    References  Briggs,   X.   (2003)   “Bridging  networks,   social   capital   and   racial   segregation   in  America”.   Faculty   Research  Working   Paper  

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