Poster presented at the Canadian Geomatics Conference. Calgary, Canada. 17 June 2010.

1. H A C K I N G T H E C Y B E R N E T I C G R I D!
Filling gaps in digital representations of marginalized populations in Mumbai using 'Social Geoviz'!
SPATIAL INTERFACE RESEARCH LAB
SIMON FRASER UNIVERSITY CANADA
INTRODUCTION!
This paper explores digital and theore1cal responses to the limited representa1ons of marginalized
real popula1ons in social geographic environments such as Google Earth. Geographers and ci1zens
have recently observed a revolu1on in social geographic compu1ng as a way to represent and
disseminate spa1al informa1on. The arrival and widespread adop1on of tools such as Google Earth
are part of an emerging shi> in the range of geographic informa1on tools making up the GeoWeb
in internet‐enabled countries.
O>en free to download and use, tools such as Google Earth have achieved incredible social uptake.
While they are not (yet) as spa1ally rigorous as conven1onal GIS tools, these ‘social
geovisualiza1on’ tools play an important role in the GeoWeb, and exert a powerful influence on
how people, communi1es and real geographic problems are represented. In future, we must
incorporate emerging social geovisualiza1on tools of the GeoWeb into our repertoire of geographic
methods to represent, communicate and disseminate informa1on about pressing real‐world
problems.
CASE STUDY: MUMBAI
Approximately 1 billion people worldwide currently live in slums. However, Much of Mumbai’s 13
million‐person popula1on lives in marginal slums (Baud et al. 2009). Reloca1on of individuals living
in slums has been promised by the state, but not executed (Bharucha 2009). There are thousands
of people living in unacceptable condi1ons, with lible or no access to clean water, electricity or
sanita1on. In addi1on to the largest slum in Asia (Dharavi), the slums in Mumbai fill the ‘nega1ve
spaces’ or gaps between infrastructure of mainstream society. Visually, we can see this
marginaliza1on in Google Earth satellite imagery. One such slum is Ambedkar Nagar ‐ situated on
the Southwest coastline of Mumbai wedged between the World Trade Center (WTC) buildings,
luxury apartments and hotels. To the untrained eye, slums are ad hoc, disorganized, ephemeral,
unsanitary, homogenous. However, informal seblements are much more complex than an outsider
would believe them to be. Structure and order do exist. Social classes and hierarchies are present,
where various groups are vital for the well being of their inhabitants. Slums o>en risk demoli1on,
because despite the community’s marginalized status, the land is valuable to developers.
PROBLEM STATEMENT!
GeoWeb interface technologies are far more than geographic informa1on tools. Increasingly used by
mainstream media networks, Google Earth and similar GeoWeb environments have become norma1ve
spaces that impose mappings of the world that are rapidly disseminated into society. By doing so, they are
steadily inscribing a limited view of the world into the minds of their viewers, restricted to whatever that
par1cular GeoWeb tool does (and does not) include in its representa1on of geographic space.
As ‘geovisualiza1on interfaces’, GeoWeb tools should help to reveal unknowns (MacEachren and Kraak,
2001). However, GeoWeb tools suppress, modify or omit representa1ons of informal seblements (slums,
shanty towns, townships, favelas). The nature of geographic data and visualiza1ons used in GeoWeb tools
effec1vely define these seblements, and inscribe limited representa1ons of them into society due to
widespread adop1on of GeoWeb tools. To explore these issues, we developed both digital and theore1cal
responses to the GeoWeb representa1on of a marginalized, impoverished community in Southern Mumbai,
India: Ambedkar Nagar.
Ambedkar Nagar slum is not indicated or documented at any scale in OpenStreetMap. Only
three main roads and the World Trade Center buildings are depicted.
SELECTED REFERENCES:
Baud, I.S.A., Pfeffer, K., Sridharan, N. and Navtej N. (2009). Matching deprivation mapping to urban governance in three Indian mega-cities. Habitat International 33: 365-77;
Bharucha, N. (2009, July 31). ‘Move to postpone Dharavi bid opening raises eyebrows’. The Times of India. Retrieved from
http://www.dharavi.org/F._Press/A.2009/A1.2009.07.31%3a_Postponing_Raises_Eyebrows
Dobson, J.E. (1983). Automated geography. The Professional Geographer 35(2):135-43.
Haraway, D. (1991). A cyborg manifesto. Simians, cyborgs, and women: The reinvention of nature, 149-81. New York: Routledge.
Kraak, M-J. 2005. Geovisualization and GIScience. Cartography and Geographic Information Science, 32 (2): 67-68.
MacEachren, A.M. 2001. Cartography and GIS: Extending collaborative tools to support virtual teams. Progress in Human Geography, 25 (3): 431-444.
Map Kibera. (2010). ‘Kibera’s first complete free and open map, November 2009. Retrieved from http://www.mapkibera.org/
Pickles, J. (1993). Discourse on method and the history of discipline: Reflections on Dobson’s 1983 ‘Automated Geography’. The Professional Geographer 45, 451-55.
ACKNOWLEDGEMENTS: Ana Brandusescu will graduate with a double major BA in Environmental Geography and Economics
from SFU in June 2010. She has been working on this 3D social geovisualiza1on project independently with Dr. Nick Hedley
since Fall 2009. She has also just started working for a separate GEOIDE Project. Dr. Nick Hedley is a GEOIDE Researcher, and is
currently funded by GEOIDE Project 24.
1986 photograph “No jobs in
villages, no shelter in ci1es, where
do we go?”
Comparing GeoWeb representaCon of the
Kibera slum in Nairobi, Kenya.
We should be cau1ous not to assume that not all
marginalized communi1es are so poorly represented in
the GeoWeb. The Map Kibera ini1a1ve demonstrates
how informal marginalized communi1es could be
represented. On the upper right, we have a map of the
Kibera slum in Nairobi, created using OpenStreetMap by
the MapKibera ini1a1ve. The makeup of a community ‐‐
schools, academies, water points, vegetable gardens are
all represented. In contrast, to the lower right, is the
present day descrip1on of the Kibera slum in Google
Maps.
Extent of exis1ng 3D models in Google Earth in Ambedkar Nagar area
IMPLEMENTING GOOGLE EARTH’s FIRST 3D SLUM AS A PROOF-OF-CONCEPT
Our mo1va1on for this research was to explore how slums (and by extension, the people who live in them) are currently (mis)represented in mainstream ‘social geovisualiza1on’ plaforms of the GeoWeb. Close
inspec1on of 2D GeoWeb representa1ons of Ambedkar Nagar at different scales revealed inconsistencies and serious homogeniza1on occurring in the representa1on of these informal communi1es. The fact that
these misrepresenta1ons are implemented using popular, high‐impact GeoWeb tools, in turn acts to inscribe and reinforce (limited) mispercep1ons of slums by society. Also, a review revealed that no 3D
visualiza1ons of slums exist in Google Earth or in its 3D Warehouse. This suggests that in addi1on to being marginalized in the real world, these communi1es are now also being marginalized and misrepresented in
major social geovisualiza1on environments of the GeoWeb.
As one part of a larger mul1‐plaform plan to beber represent slums via the GeoWeb, we have developed and implemented the first 3D Google Earth visualiza1on of Ambedkar Nagar zopadpa. (slum) in Mumbai,
India. More than just building a 3D mashup, we considered the design and representa1on of a slum as a ‘social geovisualiza1on’ – with broader theore1cal implica1ons. These include how social geovisualiza1on
might be used to mi1gate virtual marginaliza1on of real ci1zens, and as a way to change social geographic compu1ng environments from ‘cyberne1c grids of control’, to ‘cyberne1c networks of transparency’, and
mul1‐scalar human representa1on. We intend for this Google Earth mashup to become a ‘receptacle’ for volunteered geographic informa1on (VGI), enabling structured, mul1modal representa1on.
A Google SketchUp 3D model of Ambedkar Nagar was built in its en1rety. We decided that the whole slum had to be constructed in order to be able to view the landscape as a whole, filled with dilapidated, small
dwellings. We believe that being able to view and move through a scene filled with these structures will have a powerful impact on the viewer in showing the current state of marginaliza1on and depriva1on.
LIMITED DIGITAL REPRESENTATIONS OF MARGINALIZED POPULATIONS IN THE GEOWEB!
Ana BRANDUSESCU!
Nick HEDLEY!
Spatial Interface Research Lab, Dept. of Geography, SFU!
Contact: anab@sfu.ca, hedley@sfu.ca!
GEOWEB INVENTORIES AND BEYOND: A SOCIAL THEORETICAL APPROACH TO THIS PROBLEM!
Our research aims to do more than just fill a gap in digital inventories of spa1al objects accessible in the GeoWeb. If we were only to contribute new collec1ons of 2D and 3D assets, we would be reinforcing a
reduc1onist approach to the representa1on of complex human systems in geographic space – such as Dobson’s ‘Automated Geography’ (Dobson 1983). Haraway’s (1991) Cyborg Manifesto, and Pickles’ (1993)
subsequent integra1on of it into a cri1cal approach to GIScience help us to iden1fy a more progressive theore1cal basis for the development of beber GeoWeb representa1ons of (in this case) marginalized
communi1es and their inhabitants. Haraway ques1oned the impacts of ‘new automated prac1ces’ on individual iden1ty, sugges1ng that they impose “cyberne1c grids of control” on people and the structuring of
communi1es. She argued that more sophis1cated representa1ons of society than those of simple “automated and applied geography” were needed (Haraway, 1991; Pickles 1993). Clearly, much representa1on in the
GeoWeb is of an ‘automated geographic’ nature. Volunteered and mul1‐modal geographic informa1on will help to improve the situa1on, and we hope our efforts contribute to this end.
‘Zopadpa.’
‘Slums’
Mumbai
Construc1on of wealthy residen1al buildings and businesses has rapidly increased over the past decade
such. DSK Durgamata towers overlook the Ambedkar Nagar slum – a stark contrast of great poverty found
right next to great wealth. However, only landmarks represen1ng status and power (Gateway of India, Taj
Mahal Hotel, and WTC buildings) have been added to Google Earth, whereas marginalized, informal
seblements (slums) have not.
OpenStreetMap
Below is a photograph of the Ambedkar Nagar slum in Mumbai. We explored the degree to which the spa1al and
human dimensions of this informal seblement are being represented and communicated to
Google Maps
No1ce that the area is 1tled ‘Slums’ when
the area was zoomed out more. Now it is
changed to ‘Zopad Pan’ meaning ‘slum’ in
Hindi. The same altera1on occurs, when
exploring our slum area in Google Earth.
Photo credits: An aerial view of Ambedkar Nagar, hbp://www.panoramio.com/photo/4355560; (1986
black and white): Jha, S. S. 1986. Structure of urban poverty: The case of Bombay slums; (all of the
photos with the excep1on of the World Trade Center by Flickr photojournalist nweum. hbp://
www.flickr.com/photos/nweum/2871797084/; Lloyd‐Jones, Alasdair. 2008. Ambedkar Nagar Slum
Ini1a1ve. hbp://chahana.org/Fish_%26 _Slum/Pages/Ambedkar_Nagar_Slum.html
SUMMARY & CONCLUSIONS!
The number of Google Earth users alone has been es1mated at 400 million (Jones, 2008). As an
accessible, user friendly interface, giving both experts and non experts the opportunity to explore
geometric, qualita1ve, conven1onal and volunteered informa1on, it exerts considerable influence
on what and how geographic objects, phenomena and rela1onships of all kinds.
The purpose of this work was to review the representa1onal limita1ons of GeoWeb tools to
communicate the characteris1c of slums as more than homogenous ‘problem areas’, and to
consider both the geoma1c and social theore1cal implica1ons of these limita1ons. We have been
using Google SketchUp and Google Earth to implement social geovisualiza1ons that aim to increase
the visibility of slums by society, using Mumbai as a case study.
We do not presume that the best way to represent poverty is a 3D visualiza1on. Instead we view
this as one part of a range of alterna1ve ways to communicate an understanding the dimensionality
of marginalized communi1es to society, via the GeoWeb.
(Le>) We find more evidence of inconsistent
GeoWeb representa1on of slums.
Completely zoomed‐in, clickable labels for
‘Zopad Pa.’ and ‘Dhobi Ghat’ appear. The
laber label leads us to a link for laundry
services. At the same 1me as the
mainstream GeoWeb has homogenized the
human dynamics/rela1onships of the slum,
we also see here its use by inhabitants to
communicate with the outside world.
Finally – evidence that the slum has a
pulse, and is more than a homogenous, ad
hoc se7lement. And evidence that GeoWeb
can represent people who live and work
inside the slum!
(Above) Scalar transi1ons in Google Maps
with satellite imagery show simplifica1on of
‘Slums’ as homogenous areas. At the same
1me however, a significant number of
‘mainstream’ human/urban objects appear
(such as restaurants, landmarks and
residences) demonstra1ng stark differences
in GeoWeb‐mediated representa1on.
In addi1on to showing the ‘macro’ structure of the slum, we also wanted to enable Google Earth users to go inside some of the structures, in order to beber understand how densely packed typical living quarters are.
The audience has the opportunity to explore one main street in detail, incorpora1ng a selec1on of living quarters. In Ambedkar Nagar, the homes are built very close to one another, and the street is narrow, with
many ropes and clothes hanging across from one roof to another, to give the audience a sense of the claustrophobic and chao1c senng (that can clearly be seen in the real photographs on this poster). The main
house provides a detailed, first‐person glimpse of a slum dwelling. Views of our detailed Ambedkar Nagar slum dwelling interior environment are shown below.
We were able control, building textures, ligh1ng and atmospheric abenua1on in an abempt to implement as representa1ve 3D mashup of the slum as possible. For the ‘main house’ built, a few objects from the 3D
Google Warehouse were manually taken apart and altered, in order to give them an aged look as found in the slum. For example, the stove inside the house was not originally clay and falling apart, and the mabress
was detached from the bed frame. Together, these macro and micro‐scale mashup details support mul1ple scales of experien1al learning by GeoWeb users.
Google Earth representa1on of Ambedkar Nagar,
before our 3D mashup was implemented
Google Earth representa1on of Ambedkar Nagar,
with our 3D mashup added to the view
Prototype of next version of our Ambedkar Nagar Google
Earth mashup, using building height extrac1on