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A System for Household Enumeration and Re-identification in Densely Populated Slums to Facilitate Community Research, Education, and Advocacy

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A System for Household Enumeration and Re-identification in Densely Populated Slums to Facilitate Community Research, Education, and Advocacy

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We devised and implemented an innovative Location-Based Household Coding System (LBHCS) appropriate to a densely populated informal settlement in Mumbai, India. LBHCS codes were designed to double as unique household identifiers and as walking directions; when an entire community is enumerated, LBHCS codes can be used to identify the number of households located per road (or lane) segment. LBHCS was used in community-wide biometric, mental health, diarrheal disease, and water poverty studies. It also facilitated targeted health interventions by a research team of youth from Mumbai, including intensive door-to-door education of residents, targeted follow-up meetings, and a full census. In addition, LBHCS permitted rapid and low-cost preparation of GIS mapping of all households in the slum, and spatial summation and spatial analysis of survey data. LBHCS was an effective, easy-to-use, affordable approach to household enumeration and re-identification in a densely populated informal settlement where alternative satellite imagery and GPS technologies could not be used.
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A System for Household Enumeration and Re-
identification in Densely Populated Slums to Facilitate
Community Research, Education, and Advocacy
Dana R. Thomson
1
*, Shrutika Shitole
2
, Tejal Shitole
2
, Kiran Sawant
2
, Ramnath Subbaraman
2,3
,
David E. Bloom
4
, Anita Patil-Deshmukh
2
1Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America, 2Partners for Urban Knowledge, Action,
and Research, Mumbai, India, 3Division of Infectious Diseases, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America, 4Department of Global
Health and Population, Harvard School of Public Health, Boston Massachusetts, United States of America
Abstract
Background:
We devised and implemented an innovative Location-Based Household Coding System (LBHCS) appropriate
to a densely populated informal settlement in Mumbai, India.
Methods and Findings:
LBHCS codes were designed to double as unique household identifiers and as walking directions;
when an entire community is enumerated, LBHCS codes can be used to identify the number of households located per road
(or lane) segment. LBHCS was used in community-wide biometric, mental health, diarrheal disease, and water poverty
studies. It also facilitated targeted health interventions by a research team of youth from Mumbai, including intensive door-
to-door education of residents, targeted follow-up meetings, and a full census. In addition, LBHCS permitted rapid and low-
cost preparation of GIS mapping of all households in the slum, and spatial summation and spatial analysis of survey data.
Conclusion:
LBHCS was an effective, easy-to-use, affordable approach to household enumeration and re-identification in a
densely populated informal settlement where alternative satellite imagery and GPS technologies could not be used.
Citation: Thomson DR, Shitole S, Shitole T, Sawant K, Subbaraman R, et al. (2014) A System for Household Enumeration and Re-identification in Densely
Populated Slums to Facilitate Community Research, Education, and Advocacy. PLoS ONE 9(4): e93925. doi:10.1371/journal.pone.0093925
Editor: Thomas Eisele, Tulane University School of Public Health and Tropical Medicine, United States of America
Received December 21, 2013; Accepted March 11, 2014; Published April 10, 2014
Copyright: ß2014 Thomson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This project was funded by the Rockefeller Foundation. RS’s contribution was funded by the Fogarty International Clinical Research Scholars and
Fellows Program at Vanderbilt University (R24 TW007988) and more recently by a Harvard University T32 Post-Doctoral Fellowship (NIAID AI 007433). Financial
support was also provided by The Weatherhead Center for International Affairs at Harvard University. No funding bodies had any role in the study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: dana_thomson@hms.harvard.edu
Background
Conducting research and outreach in slum communities is
challenging for several reasons, including that the absence of a
street address system makes it difficult to uniquely identify and
follow up with households. Alternative household enumeration
methods, including use of GPS units and satellite imagery, do not
work in densely populated slums. With one in six people living in
slums worldwide, including one out of every three city dwellers [1],
household enumeration and identification poses an enormous
challenge for researchers, community organizers, and advocates
working with the urban poor. Slum environments are also
challenging because they change rapidly. Residents move
frequently, and structures are rebuilt often due to poor construc-
tion, natural disaster, change in tenant configurations, local
politics, and forced eviction [2]. In these dynamic settings,
enumeration of the community’s residents, physical structures,
and boundaries aids in recognition of the community’s existence
which can serve as a tool to lobby officials for pro-poor housing
policies and participatory slum redevelopment [2]. Adaptive, easy-
to-implement housing enumeration methods that can be used by
slum dwellers and researchers alike in high-density settings are
sorely needed.
Various methods have been used for slum enumeration,
including hand-drawn maps on paper or in a geographic
information system (GIS) [3], mapping with GPS units [4],
enumerating from satellite imagery [3], or some combination of
the three [5]. Advanced technologies, such as spatially encoded
video [6], have also been used to capture community health risks.
Each enumeration method poses challenges. Drawing maps by
hand is time-intensive and requires many enumerators. Use of
GPS units and spatially encoded video require a technical team to
collect, clean, and maintain spatial data, and technically trained
workers or volunteers may not be readily available [7]. Members
of our team who worked with organizations in other slum
settlements found that locating a household with a GPS unit and a
printed map (a common way to identify households coded by GPS)
requires 20–30 minutes in the field per household to ground-truth
the GPS reading and converse with residents in nearby homes.
Furthermore, use of GPS is problematic in densely populated
slums because current GPS units have a spatial error of up to 33
feet (10 meters), and building cover prevents satellite connection
PLOS ONE | www.plosone.org 1 April 2014 | Volume 9 | Issue 4 | e93925
[7,8]. High building density is especially problematic in a city such
as Mumbai, where over half of the population lives in slums on six
percent of its land [9], making Mumbai’s slums some of the
highest-density settlements on earth. For example, Dharavi, one of
Mumbai’s largest slums, is estimated to have a population density
greater than 400,000 people per square kilometer, which is nearly
ten times denser than daytime Manhattan [10]. Enumeration from
recently collected satellite imagery can be very efficient and
accurate [11], though in very densely populated slums like
Dharavi, structures are stacked several stories high and share
walls so that the roofs belonging to different dwellings are
indistinguishable.
In 2008, researchers from Partners for Urban Knowledge,
Action and Research (PUKAR); Harvard School of Public Health
(HSPH); and New York University (NYU) formed a collaboration
to perform a series of health studies, and health and education
campaigns in Kaula Bandar, a slum of approximately 14,000
people in Mumbai, India. The slum is located on a former
shipping pier one-tenth of a square kilometer in size with one road
passable by motor vehicle down the center, and a labyrinth of
pedestrian-only lanes on either side, most of which are covered by
buildings. Many of the lanes are so narrow that individuals can
only pass through in a single-file configuration, and larger
individuals may need to turn sideways to navigate some of the
narrow spaces (Figure 1). Approximately half of the land space in
Kaula Bandar is residential - the rest is industrial or open space -
which means that residential density is approximately 280,000
people per square kilometer (see Figure 2). Dwellings in Kaula
Bandar range in size from 20 to 90 square feet (2 to 8 square
meters), and are home to 5 people on average.
Kaula Bandar is a ‘‘non-notified’’ slum, which means it is not
recognized by local and state governments. Non-notified status
greatly limits residents’ ability to pressure officials for basic water
and sanitation infrastructure, schools, or health services [12]. As a
result, all water, sanitation, and power infrastructure is improvised
by residents except for a few public toilet facilities. PUKAR is
unique in that the organization is staffed by a small national
research team that hires and trains a cadre of what Arjun
Appadurai calls ‘‘Barefoot Researchers’’ from the community to
collect data and perform community education and outreach [13].
Barefoot Researchers are often young (ages 18–23), and many
have limited formal education. Engagement and training of
Barefoot Researchers was conducted in the spirit of what
Kretzmann and McKnight call Community Asset Mapping; that
is, taking inventory of the capacities, skills, and assets of low-
income people and their communities to reinforce community
pride and internally generated development, rather than docu-
ment needs and deficiencies which reinforces dependency on
outsiders [14]. In this way, research, education, advocacy, and
community building in Kaula Bandar are integrated and informed
by one another.
Nearly half of Kaula Bandar’s adult residents have lived in the
slum for more than 20 years, and its population continues to grow
as new residents move in and young adults start their own families.
As a result, new lanes are formed by the construction of new
dwellings. Typically, a dwelling’s ground level is occupied by the
owner, while 2
nd
-level units are rented. A large portion of Kaula
Bandar’s residents move away seasonally for work or to care for
relatives in rural villages. The majority of dwellings in Kaula
Bandar have existed for years, though we estimate that the
Bombay Port Trust demolished at least 5% of homes in the last
five years targeting new construction in open spaces and on the
main road, and large fires in 2010 and 2013 destroyed
approximately 9% and 3% of dwellings, respectively. After these
fires, government support to rebuild was promised, though only a
few politically connected families received funds, and only after
months of delay. Most burned dwellings were rebuilt and
reoccupied by the same tenants using private resources, and those
households that started rebuilding first slightly increased the size
and quality of their dwellings.
In this densely populated community with regular changes to
housing and population, none of the typical enumeration methods
are feasible; GPS units cannot make satellite or cell phone network
contact from narrow, covered lanes, and dwelling rooftops are
indistinguishable in satellite imagery. While hand-mapping is an
option, it requires extensive human-power and time. In this paper,
we describe a method developed by the PUKAR research team to
code and find households efficiently, present how we extended the
coding system for mapping and spatial analysis, and discuss the
utility of this method for research and practice in other densely
populated or dynamic slum settings.
Methods
Location-based Household Coding
PUKAR’s three lead field researchers (authors SS, TS, and KS),
two of whom are from communities near Kaula Bandar, created a
simple, innovative Location-Based Household Coding System
(LBHCS) to uniquely identify households. This system allowed
them and each of the Barefoot Researchers to find their way to a
specific household in a matter of minutes. The PUKAR team
started with a simple hand-sketched map of Kaula Bandar that
included the main road and each of the community’s 25 main
lanes connecting the central road with the edge of the pier (see
Figure 1). At the time that the system was developed, each of the
three researchers led a team of Barefoot Researchers in a section of
the community. Main lanes were assigned codes like T4, S3, and
K7, indicating it was the 4
th
,3
rd
,or7
th
main lane in Tejal,
Shrutika, or Kiran’s section. Each sub-lane was coded with the
direction (left or right) from which it originated from the main lane
(from the perspective of where the pier connects to land), and the
order in which it appeared (1
st
,2
nd
,3
rd
sub-lane). Additional
branches of sub-lanes and smaller by-lanes were accordingly
coded, resulting in such codes as K9-LSL2-LBL1, which
represented the 9
th
main lane in Kiran’s section, 2
nd
left sub-
lane, 1
st
left by-lane. Special codes were created for homes near to
the sea (NS) and the road outside of Kaula Bandar (RO). Specific
home locations were indicating by adding a side of the lane and
house number to the lane code, as in K9-LSL2-LBL1-R3D, the
3
rd
house on the right, downstairs, on lane K9-LSL2-LBL1.
Second-story households were coded as U for upstairs level. In this
way, household codes doubled as detailed, easy-to-follow walking
directions and the basis for a relational database of all addresses at
a particular point in time.
For each study, PUKAR maintained a database of each
participant and their household code. Each time the participant
was visited in their home for data collection, medical follow up, or
educational outreach, the field researcher verified the accuracy of
the household code, and noted on paper any corrections resulting
from previous recording error or because the participant had
moved. Updated household codes were entered into in an excel
database by the database manager who tracked all old or incorrect
codes in a separate column.
GIS Mapping
As noted, Kaula Bandar is so densely populated that individual
households are indistinguishable in satellite imagery. And most
lanes, sub-lanes, and by-lanes are covered by housing so they do
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not receive GPS signals and they are not visible in satellite
imagery. PUKAR researchers (authors SS, TS, KS) with extensive
knowledge of the community mapped all lanes in a GIS with the
help of a GIS student (author DT) using detailed satellite imagery
and a few key GPS coordinates for reference. GPS coordinates
were taken at the beginning and end of each main lane, and the
satellite imagery provided additional cues about lane locations,
such as ‘‘seams’’ over lanes formed by rooftops.
After the lane network was mapped, it was straightforward to
map all households based on a household census with LBHCS. A
household census using the coding system indicated the total
number of households located on each side of each lane segment.
The GIS student generated a latitude/longitude coordinate for
each ground-level dwelling (upstairs dwellings were assigned the
same coordinate as downstairs dwellings), which resulted in a map
of all addresses. Although the coordinates do not identify dwelling
centroids (geographic center of a shape), the resulting spatial data
can be thought of as a household entrance, similar to an address
geocoding service such as GoogleMaps. The household locations
are sufficiently accurate for property identification (for example,
locating a claim to rebuild after a fire), spatial analysis (for
example, hot spot analysis), calculation of distance variables (for
example, distance from home to nearest water tap), and
establishing the relative location of neighbors.
Several additional spatial datasets were generated from the
satellite imagery by a PUKAR researcher (author KS) and the GIS
student (author DT) that were not dependent on LBHCS, but
were essential for community organizing and advocacy (Table 1).
These spatial datasets included information on basic infrastructure
such as toilets, trash bins, trash piles, and water distribution sites,
as well as community resources such as temples, religious pre-
schools, and informal health clinics and pharmacies.
Resources
Our tool kit included a 25 square kilometer, 0.5 meter
resolution GeoEye-1 satellite image purchased with an academic
license for US $230 from a commercial provider (eMap), and a
walking census of households using LBHCS. In 2010 and 2012,
PUKAR researchers (authors TS, SS, KS) collected unique
household codes for all households in the community in just four
days, a database manager entered these data within two weeks,
and a PUKAR researcher (author KS) and a GIS graduate student
(author DT) mapped thousands of features and their attributes
Figure 1. Kaula Bandar, Mumbai, India: A densely populated slum.
doi:10.1371/journal.pone.0093925.g001
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directly in a GIS within three weeks. The success of this project
hinged on a small team with strong local place knowledge,
database management skills, and GIS skills. We used a computer
with Excel (a spreadsheet program) and ArcGIS (a geographic
information system), which at the time of this writing cost US
$1500 for a single ArcGIS license plus a few hundred dollars for a
computer with the MS Office Suite. Free spreadsheet and GIS
programs such as Apache OpenOffice and QGIS were also
Figure 2. Example community maps.
doi:10.1371/journal.pone.0093925.g002
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available. We borrowed one GPSMAP 76CSx GPS device to
collect the geographic coordinates of key landmarks, including the
start and end of covered lanes.
LBHCS can be used alone for data collection and outreach by
minimally trained staff at low cost. If using the household coding
system alone, it is important to have at least one team member
who is familiar with data entry and data cleaning, and to have a
computer. If advocacy or quantitative research are key activities,
we recommend integrating the household coding system with
mapping activities as we present here. See Figure 3 for a summary
of this toolkit.
Maintenance of the Data
LBHCS can be used in one of two ways. Users can either carry
out a household census at one point in time and update it
periodically, or users can regenerate household censuses period-
ically creating new unique household codes in each census that are
not linked to previous censuses. The ideal approach will depend on
the community and the reason for using a household coding
system. In slums in which structures are knocked down or replaced
regularly, it may be advantageous to regenerate household codes
periodically so they remain useful as walking directions and
capture an accurate snapshot of the community’s residents and
layout. In slums with a stable set of structures, it may make sense to
maintain and update a longitudinal database of household codes
and locations.
If new households are built between existing dwellings, the
function of the codes as walking directions decreases; therefore, we
recommend recoding households periodically (for example,
annually), keeping track of previous household codes and their
dates of use. However, for minor additions, users can add half
numbers (as in K9-LSL2-LBL1-R3.5D) or a letter for new
dwellings (as in K9-LSL2-LBL1-RND). Notably, in Kaula Bandar,
the pre-existing density of dwellings was very high (with nearly all
dwellings being immediately adjacent to other ones), so we rarely
ran into situations in which new dwellings could be built in
between others. Most new homes could only be built at the very
ends of existing lanes or in completely new lanes, which did not
interfere with the utility of the coding system. As such, the coding
system was very useful for longitudinal follow-up of homes in
Kaula Bandar; however, in less dense settlements, this may not be
the case.
Maintaining a longitudinal dataset of household IDs requires
substantially more data infrastructure and skills than simple cross-
sectional uses of the coding system, and may not be worthwhile for
advocacy, education, or research teams that operate on shorter
time scales. This observation applies to, for example, education
programs staffed by students, or researchers working on shorter
grant cycles. In those slums in which household IDs are not
maintained, the IDs may still facilitate follow-up for many months,
which is often the duration of an intervention, educational
campaign, or study.
Table 1. Spatial data layers and attributes collected.
#Spatial Data Layer [Vector type] Attributes
Base Layers
1 Health ‘‘Clinics’’ [point], clinic type, clinic name, degree and name of service provider, hours
2 Pharmacies [point], name
3 Industries [polygon], name, type
4 Land use [polygon], land use type, square meter
5 Lanes and Sub-lanes [line], name
6 Police Stations [point], name
7 Political Party Office [point], party name
8 Ration Stations [point]
9 Religions Places [point], denomination
10 Religious Pre-Schools [point], name, type, highest level, funding source
11 Theaters [point], type
12 Toilet outlets [point]
13 Trees [point]
14 Waste bins [point]
15 Waste piles [polygons]
16 Water distribution sites [point]
Advocacy & Research Layers
17 Defecation Areas [polygon]
18 Health Zones [polygon], name
19 High Tide Flood Areas [polygon]
20 Hospital [point], name, type
21 House Addresses [point], household id
22 Monsoon Season Flood Areas [polygon]
23 Proposed Water Distribution Sites [point]
doi:10.1371/journal.pone.0093925.t001
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Figure 3. Location-based household coding system toolkit and applications.
doi:10.1371/journal.pone.0093925.g003
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Ethics Statement
A number of studies were conducted in Kaula Bandar using
LBHCS including a biometric census, longitudinal water quality
study, cost of diarrheal illness study, and mental health study. The
biometric census included adults and children, while the other
three studies included only adult participants. Residents were
willing to participate in these studies largely because of PUKAR’s
community-based participatory research model, which involved
youth from the Kaula Bandar community in data collection. In
addition, findings were used to directly advocate for improved
outreach to residents by the local government.
In the biometrics study, signed consent, or thumbprint consent
for individuals who could not or did not know how to spell their
names, was obtained from all adults and guardians of minors.
During the study, the PUKAR team received feedback that many
participants were hesitant to sign or provide thumbprints, even if
they wanted to participate in the study, as these actions are
associated with government or police interactions. As a result,
verbal consent was obtained for all subsequent studies with the
signature of the researcher. For interviews that required audio
recording, the participant’s consent was also digitally recorded. All
study protocols, including the consent forms, received approval
from either the Institutional Review Board of the Harvard School
of Public Health (17740) or from the PUKAR Institutional Ethics
Committee (FWA00016911).
Results
A Household Coding System for Community Research
and Outreach
From March to December 2010, the PUKAR team engaged in
an extensive biometrics survey in Kaula Bandar, in which basic
demographic and health information, height, weight, blood
pressure, and a photo (holding a chalkboard with the individual’s
household ID) were collected from community residents at a
central research station that shifted to different parts of the
community on the main road. All households in Kaula Bandar
were coded by PUKAR researchers immediately prior to starting
the survey. Approximately 1200 new residents moved to Kaula
Bandar during the 10 months of the study. Every weekend,
Barefoot Researchers were provided with a list of household codes.
With minimal training about the coding system, the Barefoot
Researchers were able to (1) independently identify each
household in the community, (2) perform a household census, (3)
recruit individuals to the main research station, and (4) perform
home-based follow-up with nearly 100 individuals who had
hypertension at the research station and whose blood pressure
was re-measured so the individual could be referred to the hospital
for appropriate evaluation, if needed. Moreover, each study
subject’s health information and photo were printed on a card,
and these health cards were returned to residents to provide them
with a basic health record. Using the coding system, Barefoot
Researchers returned roughly 96% of health cards to residents
within a week of initial data collection, as repeat identification of
homes was easily and quickly performed by any of the research
team members.
In 2011, PUKAR performed a study of water issues in the
community, in which a consistent set of 21 homes located
throughout the slum were followed longitudinally for a year to
assess water cost, quantity, and quality, using serial microbiological
testing of water samples [12]. On days when water testing was
performed, it was important to locate all homes very rapidly, as
water samples had to be transported in bulk to the laboratory
within a couple of hours of collection. Without the household
coding system, such rapid repeat identification of homes through-
out the community on the same day would not have been feasible.
The coding system also showed remarkable stability over a year of
follow-up, largely because the community is so dense that new
homes cannot be constructed between pre-existing homes.
In 2012, PUKAR engaged in a study of mental health in Kaula
Bandar, for which a random sample of households was required.
Since the previous coding of community households in 2010, a few
new lanes had been added to the community, and new households
had been added to the ends of lanes. As such, we decided to re-
code the entire community to ensure an accurate roster for
randomized sampling. With a team of ten people, all households in
the settlement were re-coded in less than four days. This new
registry allowed us to select a subset of households for the mental
health survey using a random number generator.
In 2011 and 2012, the PUKAR team engaged in extensive
door-to-door education of community residents on critical health
topics such as child immunizations and diarrheal disease. The
household coding system allowed Barefoot Researchers to record
homes that were missed in initial rounds of the educational efforts,
such that repeat efforts could be made to find the residents of these
households to make sure they received individualized education.
Maps for Advocacy and Outreach
The PUKAR research team worked with a GIS student to map
23 spatial layers of existing infrastructure and services, and
produce an atlas of maps that the team has since used extensively
for community outreach and advocacy (Figure 2). In October
2010, for example, PUKAR was invited to present its work to
Mumbai’s Municipal Health Commissioner as part of a consul-
tation of the city government with non-governmental organiza-
tions (NGOs). In preparation for this presentation, PUKAR
researchers mapped nearby government health centers to empha-
size the distance of Kaula Bandar from these resources. These
maps graphically highlighted the community’s isolation from
public health centers, and they were critical in motivating the
municipality of Mumbai to extend twice-monthly health camps to
Kaula Bandar for provision of immunizations and other basic
medical care. These efforts, along with door-to-door education
efforts by PUKAR, helped to improve the fraction of children who
are fully immunized with the basic set of vaccinations in Kaula
Bandar from 29% to 80% in just three years [Unpublished results
presented by PUKAR at the 2012 NIH Summit on the Science of
Eliminating Health Disparities].
After PUKAR completed a study on water poverty in Kaula
Bandar [15], PUKAR researchers presented their data to
Mumbai’s Municipal Commissioner for Water in late 2011,
including a map of Kaula Bandar’s limited water and sanitation
infrastructure. As a result of this dialogue, the Water Commis-
sioner took steps to facilitate extension of a new water supply to the
Kaula Bandar community. The development of a new water
supply is still in process, as fundamental water infrastructure needs
to be extended to the entire nearby area before the local water
supply in Kaula Bandar can be developed further. The Water
Commissioner and the local corporator (equivalent to an
alderman) suggested that once this main supply is in place,
approximately 60 new community water taps can be placed in
Kaula Bandar.
PUKAR researchers have concerns regarding equitable place-
ment of these water taps, as studies have shown that the distance of
a household from a water source may have a major impact on
health outcomes [16–18]. Given these concerns, PUKAR
researchers proposed a plan for community water tap placement
and mapped it (Figure 2). Tap placement on the map was decided
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based on the population density in each lane, as well as PUKAR
researchers’ knowledge about open spaces in the slum to facilitate
ease of access and mobility within the community. PUKAR
presented this plan to the Water Commissioner and local
corporator, and both officials agreed on this plan for tap
placement. We hope that this map remains a shared roadmap
for the community and government officials, and facilitates
government accountability for equitable water tap placement.
Spatial Data for Research and Advocacy
The research team at Harvard used the household coordinates
to reaggregate data from a biometric study to sub-community
health zones to perform spatial summaries and analyses of
exposures and outcomes. Health zone boundaries corresponded
to landmarks. The team distinguished households located near the
center or at the periphery of the pier because households on the
periphery were believed to have higher exposure to contaminated
water due to high tide and monsoon flooding. As noted, Kaula
Bandar has a small geographic footprint, covering less than one-
tenth of a square kilometer of land. We found little spatial
variability of health outcomes and exposures within the slum; all of
our exploratory spatial analyses produced null results. In larger
slums with greater variability in terrain, resources, or within-slum
socioeconomic disparities, use of the household coding system with
geolocated addresses would facilitate extensive spatial analysis
including spatial regression and cluster analysis (also called ‘‘hot
spot analysis’’).
Maintaining a Household Coding System Over Time
The coding system worked well with the fluid, malleable nature
of the community. Kaula Bandar is fluid in two ways. First, people
in the community migrate seasonally to rural areas and residents
who accrue resources migrate to ‘‘better’’ slums. They also move
because of demolition of their homes by the government and
eviction by owners of the dwelling. The dwellings also change
periodically. Homes disappear and reappear due to fire, and some
of the homes on the main road get periodically demolished. As
such, the coding system allowed us to re-enumerate the
community in four days and account for changes in living
structures due to expansion, fires, and demolition. By integrating
LBHCS with spatial data collection we were able to keep a
permanent record of former household locations and lane
configurations.
Discussion
Urban slums comprise one-sixth of the global population [1],
but can be difficult to work in or study because lack of an address
system limits follow-up with households over time. Household
enumeration systems are needed in slum communities that
comprise a substantial portion of the global population, particu-
larly in high-density slums in which GPS units, satellite imagery,
and other common enumeration tools do not work. We created an
innovative Location-Based Household Coding System that facil-
itated several community-based research projects and door-to-
door health interventions in a densely populated, non-notified
slum in Mumbai, India. The coding system doubled as walking
directions, which allowed rapid identification of individual in their
households by a research team of youth from Mumbai, many of
whom live in the slum community itself. For the cost of a few hours
of training and one computer for data management, LBHCS
allowed a household census and reliable household re-identifica-
tion for over one year. Re-enumeration of all households was
performed for a new study in just four days by ten people.
By combining a LBHCS with GIS, two people mapped several
thousand households in just a few days, which allowed spatial
analysis of detailed household survey data. Mapping at this scale
by other methods would not have been possible, or it would have
been prohibitively time-intensive and expensive. By generating a
few additional spatial datasets, PUKAR researchers produced base
maps of community infrastructure that added leverage in several
important meetings between Kaula Bandar residents and city
officials to extend city services.
Although Kaula Bandar is geographically small, these methods
should easily scale to slums with larger populations covering larger
geographic areas. The simplicity of the coding system means rapid
training for any sized research or outreach team, including teams
composed of members who are young or who have limited
technology skills. LBHCS is extremely effective for following the
same households over weeks and months because the codes double
as clear walking directions. These methods are particularly
amenable to high-density slums in which new development occurs
on the periphery of the community, and new dwellings are not
frequently added between existing dwellings. LBHCS is an
efficient, easy-to-learn, cost-effective, scalable approach to house-
hold enumeration and re-identification in densely populated
settings.
Acknowledgments
Preliminary versions of this manuscript were presented at the 10th
International Conference on Urban Health in Belo Horizonte, Brazil, and
at the 2012 Conference on the Science of Eliminating Health Disparities in
National Harbor, MD, USA.
Author Contributions
Conceived and designed the experiments: SS TS KS DRT. Wrote the
paper: DRT RS. Conducted research, education, and advocacy using
LBHCS: APD DEB RS SS TS KS DRT.
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