Gis application to the projects

Disasters, 2003, 27(2): 127–140
© Overseas Development Institute, 2003.
Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148,
USA.
The Application of Geographic Information
Systems and Global Positioning Systems in
Humanitarian Emergencies: Lessons Learned,
Programme Implications and Future Research
Reinhard Kaiser Paul B. Spiegel
Centers for Disease Control CDC
and Prevention (CDC)
Alden K. Henderson Michael L. Gerber
CDC CDC
Geographic information systems (GIS), global positioning systems and remote sensing
have been increasingly used in public health settings since the 1990s, but application
of these methods in humanitarian emergencies has been less documented. Recent
areas of application of GIS methods in humanitarian emergencies include hazard,
vulnerability, and risk assessments; rapid assessment and survey methods; disease
distribution and outbreak investigations; planning and implementation of health
information systems; data and programme integration; and programme monitoring
and evaluation. The main use of GIS in these areas is to provide maps for decision-
making and advocacy, which allow overlaying types of information that may not
normally be linked. GIS is also used to improve data collection in the field (for
example, for rapid health assessments or mortality surveys). Development of GIS
methods requires further research. Although GIS methods may save resources and
reduce error, initial investment in equipment and capacity building may be substantial.
Especially in humanitarian emergencies, equipment and methodologies must be
practical and appropriate for field use. Add-on software to process GIS data needs to
be developed and modified. As equipment becomes more user-friendly and costs
decrease, GIS will become more of a routine tool for humanitarian aid organisations in
humanitarian emergencies, and new and innovative uses will evolve.
Keywords: geographic information, global positioning systems, remote sensing,
satellite imagery, humanitarian emergencies.
Introduction
The last decades have seen the introduction of geographic information systems (GIS) in
diverse public health settings. GIS methods include global positioning systems (GPS)
and remote sensing. Long used as planning and data collection tools by military and
civilian institutions, these technologies are now widely applied to epidemiology and
public health in developed countries and, to a lesser extent, in underdeveloped
countries. Most international health applications of GIS methods have been in

R. Kaiser, P.B. Spiegel, A.K. Henderson and M.L. Gerber128
outbreak investigations and epidemiologic studies of infectious diseases. These uses
have been well documented in the literature and are gaining acceptance as valuable
epidemiologic tools (Vine et al., 1997; Moore and Carpenter, 1999; Robinson, 2000).
Applications of GIS methods in humanitarian emergencies (HE) are less well
documented. Use of GIS methods for resource allocation, planning and logistics has
become a standard component in major HE (in Kosovo, Sierra Leone and Afghanistan).
Recent work by the International Emergency and Refugee Health Branch (IERHB) at
the Division of Emergency and Environmental Health Services, Centers for Disease
Control and Prevention (CDC), and by other organisations have used GIS methods in
diverse ways to complement and enhance epidemiologic methods in HE, such as
conducting rapid health assessments or estimating mortality.
In this report, we document current uses of GIS methods in HE, and discuss
lessons learned, programme implications and future research needs.
Background
The most famous example linking the science of epidemiology with the use of
geographic information occurred almost 150 years ago, when John Snow investigated
London’s cholera outbreak in 1854 (Snow, 1855). Today, GIS and satellite data are
used to examine the relation between cholera epidemics and the changing patterns of
warm-water ocean currents (Colwell, 1996). A Medline search in January 2003
showed the first public health article using GIS published in 1986, coinciding with the
time GIS became available on personal computers; 94 per cent of the 255 articles in the
search were published in 1995 and later.
GIS and GPS are powerful computerised tools to examine the spatial location
component of epidemiology and link it with person and time information. GIS has
been defined as a computer system that stores and links non-geographic attributes or
geographically referenced data with graphic map features to allow for a wide range of
information processing and display operations, as well as map production, analysis and
modelling (Tim, 1995). GPS is a constellation of earth-orbiting US-government
satellites that transmit information to hand-held or built-in receivers that can calculate a
location with a potential precision of one square metre (longitude, latitude and
elevation) (CDC, 1993). Remote sensing or satellite imagery refers to the data
obtained by sensors on earth-orbiting satellites. Remote sensing data can be used in
GIS to show animal and vector habitats; surface areas of water, deserts and forests; and
human activity (settlements, roads, land usage) (CDC, 1993).
These technologies are now widely used in the developed world for
epidemiology and public health (Vine et al., 1997; Moore and Carpenter, 1999;
Robinson, 2000). Functions of GIS encompass mapping of the location and
distribution of disease in either endemic settings or outbreak investigations
(Wartenberg, 2001). GIS has added new ways of presenting information collected with
traditional epidemiologic methods. Simple thematic maps can be used for decision-
making and advocacy. GIS allows overlaying of maps to link information from various
diseases or health sectors that may not usually be linked and can help with
interpretation of data. A potential example is overlaying of information on food
distribution, acute malnutrition, micronutrient deficiencies and a measles outbreak.
GIS maps and satellite imagery have advantages over conventional maps because more
information can be updated frequently and distributed rapidly through electronic

The Application of GIS and GPS in Humanitarian Emergencies 129
devices including the Internet (Foresman, 1999). Data collection (for example, during
rapid assessments or surveys) can be faster, more accurate and may require fewer
resources once equipment has been purchased (Melnick and Fleming, 1999). GIS
methods may increase accuracy (through improving random sample selection) and
precision (time-saving methods may allow increasing sample size) (Roper and Mays,
1999). Other applications of GIS may not only improve data presentation, but enhance
epidemiologic methods; those include time series modelling (diffusion of disease,
changes in vector habitat, seasonality of cyclical patterns in incidence) and spatial
correlation to examine risk factors for disease, accessibility modelling for health
service planning (O’Dwyer and Burton, 1998).
GIS methods in international health have been used in the research, outbreak
investigation and control of vector-borne diseases (Kitron, 1994; Dale et al., 1997) with
the main focus on malaria (Hay et al., 2000), as well as tuberculosis (Porter, 1999;
Tanser and Wilkinson, 1999), helminthic infections, including schistosomiasis
(Brooker and Michael, 2000; Brooker et al., 2000), leptospirosis (Barcellos and
Sabroza, 2001), gonorrhea (Becker et al., 1998) and hepatitis C (Mujeeb et al., 2000).
Other applications in international settings included prevention of diarrhoeal disease
(Njemanze et al., 1999), access to primary health-care (Perry and Gesler, 2000),
mapping of population movements for public health management (Stern, 1998) and
health inequalities research (Szwarcwald et al., 2000; Mitchell et al., 2002). GIS
mapping and remotely sensed data also are being used to assess and model catastrophic
events such as earthquakes, toxic runoff, and toxic plumes (Croner et al., 1996). For
example, GIS mapping of earthquake-related deaths and hospital admissions from the
1994 earthquake in Northridge, California, showed that factors such as age and activity
of people during the earthquake may be as important as seismic features in predicting
injury from earthquakes (Peek-Asa et al., 2000).
Spatial statistics for public health application is now a rapidly evolving field.
Methods include calculation of crude, adjusted and smoothed rates; tests for spatial
randomness; and regression analysis to examine associations between geographic
distribution of exposure and disease (Kulldorff, 1999). Statistical models may include
distance-oriented measures (for example, distance to the nearest clinic or water source)
or geospatial smoothing techniques similar to those used in time-series modelling. An
example is inverse density-weighted smoothing, where more weight is given to closer
samples and less to those that are farther away, resulting in reduced variability of
estimates (Isaaks and Srivastava, 1989). Statistical techniques also are available to
analyse spatio-temporal data (Kulldorff, 1999) (for example, to examine distribution of
vector habitats and malaria over time (Dale et al., 1997)).
Uses of GIS in humanitarian emergencies
We define a humanitarian emergency as a situation affecting large civilian populations
and usually involving a combination of conflict, food insecurities and population
displacement resulting in significant excess mortality or morbidity. We have identified
different uses of GIS that support and enhance epidemiologic methods in humanitarian
emergencies. Hazard, vulnerability and risk assessments are used as early warning
systems to prevent or mitigate emergencies resulting from famine. GIS can be a
component of rapid assessments to identify the magnitude of and resources needed for
an emergency. GIS methods can also be used in site planning and to enhance survey

methods, investigate disease outbreaks, establish health information systems, integrate
data and programmes and monitor and evaluate programmes. Both rapid assessment
and survey methods may use households as sampling units and be similar in outcomes
studied as well as GIS methods used.
The initial equipment and capacity building to use GPS/GIS technology in
emergency humanitarian activities can range from using the available computer and
printer, obtaining shareware from the internet and reallocating personnel to collect data
as the need arises, to acquiring state-of-the-art computers, printers and GIS
instruments, purchasing the most recent versions of sophisticated GIS software,
obtaining training to use these resources and having dedicated personnel to run the
system and collect data. Equipment, capacity building and personnel (assuming one
full salary) may currently cost up to US$100,000. At the low end, a user with some
knowledge of surveying and navigation could use a basic computer, a GIS instrument
that obtains latitude and longitude of features and GIS shareware software for less than
$2,000. Through our experience in working in HE, most organisations have a
computer with sufficient power to run GIS software that allows entry of latitude and
longitude of features and draws and prints maps, but may not have a GPS unit.
However, GIS/GPS have become more common over the years and the hardware and
software prices have dropped. It is hoped that more organisations will purchase and
use this equipment and software. We are currently developing shareware software that
will enable people to use GIS/GPS to enumerate the HE-affected population and, at a
later stage of software development, conduct rapid assessments of vaccine coverage,
malnutrition, health conditions and sanitation.
Use of GIS in HE encompasses work under extreme field conditions. Our
experience with several different types of GPS instruments is that they worked with
minimal performance loss under high temperatures (above 40°C for several hours),
through sandstorms and torrential rain, and after being dropped or bounced around in
cars and in backpacks. Data were not lost during these conditions or even when the
batteries expired.
GIS has now become essential to the planning for, response to, and monitoring
of HE for many organisations active in HE. Many United Nations (UN) organisations
now have specific GIS units: the WHO Health Map unit (WHO, 2002), the UN High
Commissioner for Refugees’ (UNHCR) geographic information and mapping unit
(UNHCR, 2002) and the World Food Program’s vulnerability assessment mapping unit
(WFP, 2002). The UN Office for the Coordination of Humanitarian Affairs (OCHA)
also provides mapping services in conflict areas (OCHA, 2002a). Recent information
systems in emergencies have been designed as inter-agency projects using GIS such as
the Sierra Leone Information System (UNHCR/OCHA, 2002), the Afghanistan
Information Management Service (AIMS) (UNAMA, 2002) and the Humanitarian
Information Centers (HIC) established by OCHA in several countries surrounding Iraq
(OCHA, 2003). Donor organisations are increasingly adding GIS capacity to their
programmes. In the US, both the State Department and the US Agency for
International Development (USAID) have added GIS capacity to their HE programmes
(one example is in Guinea (USAID, 2003)) and are increasingly providing funds to
build such capacity in NGOs and other implementing partners. Similar initiatives are
taking place in Europe (for example, European Commission Humanitarian Aid Office
(ECHO, 2003), UK Department for International Development (DFID, 2003), Swedish
International Development Cooperation Agency (SIDA, 2003)) and other donor
organisations active in HE. As the importance of GIS increasingly becomes

recognised, the way it is used in HE will quickly evolve. Below are documented the
following current uses of GIS in HE.
Hazard, vulnerability and risk assessments
Hazard assessment has been used on a continental scale in Africa to identify and
predict drought through the analysis of long-term trend in vegetation patterns and its
relation with the El Niño/Southern Oscillation (ENSO) weather pattern (Eastman et al.,
1997). GIS has also been a component of vulnerability assessments in many different
African countries such as the recent severe famine in southern Africa. Food security
mapping examines the degree to which a population or region is susceptible to a range
of hazards that threaten food supplies or for which food assistance is required. For
example, major parts of Africa are affected by a severe food crisis that was estimated in
December 2002 to affect 38 million people (WFP, 2002). The crisis is aggravated by
the HIV/AIDS epidemic that has a direct impact on food security and nutrition for
individuals, households and communities (OCHA, 2002b). Vulnerability mapping and
GIS risk assessment methods have been used to design and develop an integrated
computer system for famine early warning (Marsh et al., 1994). USAID is funding a
famine early warning system that is based mainly on two types of satellite imagery:
normalised difference vegetation index and meteorological satellite rainfall estimation
(FEWS, 2003). The normalised difference vegetation index provides an indication of
changes in vegetation in response to biophysical conditions, including plant type,
weather and soil, whose main use is to compare the current state of vegetation with
previous time periods to detect anomalous conditions (for example, the same time in an
average year or a reference year). Meteorological satellite rainfall estimation imagery
uses satellite data; global telecommunication systems (GTS) gauge rain reports; model
analyses of wind, topography and relative humidity; and an algorithm that takes into
account geography and seasonality. The result is a spatial estimate of rainfall. The
main use of these images is to compare the current state of rainfall with previous time
periods to detect anomalous conditions (FEWS, 2003). These tools assist decision-
makers to determine food security and predict how many people will need food aid to
prevent or mitigate famine emergencies.
Risk assessment makes predictions on the morbidity or mortality by
examining the interaction between a hazard and the vulnerability of a population. For
example, the number of people at risk for malnutrition and malnutrition-related death
expected from the estimated distribution of drought severity factors and individual
factors of vulnerability (for example, socio-economic factors, loss of food access) was
mapped during the Ethiopian famine in 1984 (Eastman et al., 1997). This project could
have benefited from GIS-based mapping techniques (for an example of GIS use in the
current context of Ethiopian drought and food insecurity see also section on ‘Data and
programme integration’).
Rapid assessments
In the acute phase of HE, rapid assessments help to determine the magnitude and
severity of the situation, the amount of resources needed, as well as the need and
feasibility of an intervention (MSF, 1997). GIS methods can be used to map an area
for site planning, including information about environmental conditions; transport

routes; habitable land; and availability of water, food and fuel. Additional information,
such as distance from the household to nearest health clinic, food distribution site,
drinking-water source or latrines, can be used to assess access of the population to
essential services. Decision-makers can use this information for planning and resource
allocation (such as water and food distribution sites and latrines) as well as for
determining whether certain minimum standards and indicators are being met. Remote
sensing maps can also be used to help identify water and fuel sources.
In the acute phase of HE, population size estimates are necessary to determine
the scope of the emergency and the resources needed and to provide reasonably
accurate population denominators for health indicators. In this early stage, systematic
random sampling of housing units is usually not feasible. A method called the
quadrant has been used in several refugee camps (Henderson and Spiegel, 1999; Brown
et al., 2001). It consists of mapping the boundary of the camp, laying a grid of square
blocks or quadrants over the camp, randomly selecting square blocks, counting the
population in all households (Brown et al., 2001) or a random subsample of households
in each randomly selected block (Henderson and Spiegel, 1999). The number of
people estimated per square block can then be extrapolated to the total population. GIS
and GPS can be used for mapping the area, creating the grid, and locating the selected
square blocks. Modern GIS software allows obtaining accurate geographic coordinates
of key roads, trails, landmarks, plots and non-residential areas to create a map that
includes inhabited space with precise outer limits of the area. However, population
estimation methods have not been standardised, and only a few examples have been
validated using camp registration data (Henderson and Spiegel, 1999; Brown et al.,
2001). Future research questions include the optimal number and size of square blocks,
addressing heterogeneity in population density between the square blocks, and methods
to deal with partial square blocks at the limits of the camp area. An alternative
approach to estimate population size may be distance-based methods such as the T-
square method (Espie, 2000), which has not yet been used, in HE. During population
estimations, a simple questionnaire can be added for a rapid assessment of the health
status of the population (for example, morbidity, mortality, nutrition or vaccination
status).
Mapping population movements is becoming a standard application of GIS in
the acute phase of HE as well as during repatriation and resettlement. Spontaneous
population movements may require reassessment of the situation in any phase of an
HE. Before population transfers are planned, GIS mapping can help ensure that a
secure location is chosen, sufficient water and sanitation facilities exist or can be
developed and adequate human and material resources and programmes are in place.
For example, in Guinea 2001 an increase in death rates among Sierra Leonean and
Liberian refugees was documented after most of them were relocated to new camps,
possibly because some refugees were not relocated to individual family shelters as
quickly as planned, causing overcrowding of temporary shelters and overburdening of
existing facilities (CDC, 2001; UNHCR, 2001).
Remote sensing maps and aerial views can also provide rapid information to
estimate population sizes and movements in HE. This may be particularly relevant in
areas that are not covered by humanitarian agencies — for example, because insecurity
does not allow access. In 1994 in Goma, Democratic Republic of Congo, the
population size was underestimated when aerial views alone were used, most likely
because displaced people without shelter were not included (NAS, 1997). Modern
high-resolution remote sensing using thermal imaging can provide additional
information that may improve population estimation (for example, fires for cooking

and heating). Geographic and environmental conditions such as dense tree cover,
smoke plumes and topographic features in the area may limit the use of GPS and
satellite imagery. Figures 1 and 2 show a satellite image and the corresponding map of
the refugee camps and environmental conditions in the Goma area in 1994.
Survey methods
Two sampling methods have applied GIS and GPS to survey mortality in the
Democratic Republic of Congo (IRC, 2000). The first method, a modification of
the Expanded Programme on Immunisations (EPI) methodology (Henderson and
Figure 1 Spot satellite image and corresponding map (see Figure 2) of the
refugee camps and environmental conditions in the Goma area bordering
Virunga National Park, Democratic Republic of Congo, 1995
Source: UNHCR Geneva, Copyright: CNES, Eurimage, UNHCR, I-mage Consult, ADG, ICCN,
Commission Europeenne, Project Virunga. Illustration courtesy of UNHCR, 2003.

Figure 2 The corresponding map (see Figure 1) of the refugee camps and
environmental conditions in the Goma area
Sundaresan, 1982) requires that the approximate population size and location of sub-
areas (villages or clinic referral areas) or clusters are known. In the first stage, clusters
are selected proportional to population size. In the second stage, boundaries of clusters
are mapped and a grid of square blocks is laid over the map, random numbers are
assigned to square blocks, and a square block is randomly selected as the location
where all selected or a sample of households are surveyed. GIS and GPS are used for
mapping and to locate the randomly selected square blocks (IRC, 2000). Main
questions for future research are the number of clusters (a minimum of 30 clusters has
been recommended (Binkin et al., 1995)), the size of the grids and heterogeneity in the
outcome of interest between clusters. Design effect and intraclass correlation indicate
heterogeneity that can be anticipated on the basis of the nature of the outcome (for
example, conflict-related mortality may vary considerably between villages because of
the intensity of military activities and destruction) and experiences from previous
surveys (Katz and Zeger, 1994). Studies are needed comparing this method with the
standard EPI method in terms of quality of the outcome estimate, cost and degree of
difficulty.
The second method is applied when the overall population can be estimated
but population sizes of sub-areas are not known. A random starting location is chosen
for sampling along a north-south road. Additional sampling locations are selected at
predefined increments to the east and the west, called east-west transects. The process
of sampling along east-west transects is repeated at predefined intervals to the north

and south until the whole survey area is covered. This method oversamples rural areas
and undersamples areas with high population density. The radius of each sampling
point, defined as the distance between the first and the farthest household of the
sampling location, can be used as a weight to adjust for this sampling bias (Roberts and
Despines, 1999). GPS are used to locate sampling locations and to measure the radius
of the sampling point. Main questions for future research are the number of sampling
locations (which also can be called systematic random clusters), and addressing
heterogeneity in population density between sampling locations. The distance between
sampling locations depends on the size of the survey area and the number of sampling
locations. The number of households per sampling location depends on the sample
size, the number of sampling locations and the average number of household members
eligible for the survey. Validation of this methodology is warranted using standard
methods.
GIS and palm-pilot-like devices with a GPS unit allow questionnaires to be
read and answers recorded electronically, reducing the use of paper. Data can be
transferred directly to a computer for analysis. This represents significant time savings
and reduces one potential source of error when data are transferred from paper to
computer (Roper and Mays, 1999; Selanikio et al., 2002). Furthermore, while doing
the survey, key landmarks, such as latrines, water points and health clinics, can be
recorded, mapped and used in conjunction with data recorded from households to aid in
interpretation.
Disease distribution/outbreak investigation
GIS improves our ability to investigate infectious disease outbreaks in HE. Clustering
can give first hints toward causality within neighbourhoods or according to geographic
features. On a larger scale, case distribution maps can help to direct vaccination
campaigns.
Health Information Systems
After a multinational UN force had restored peace in East Timor in 1999, UNHCR
asked CDC to implement a health information system in camps for East Timorese
refugees in West Timor. Fourteen sentinel sites in four districts were selected for
mortality surveillance by size, location and accessibility. The selected sites represented
approximately 50,000 (36 per cent) of the 140,000 refugees. The UNHCR geographic
information and mapping unit provided maps of the refugee camps in West Timor for
the selection process. These maps can be further used to show local or regional trends
in mortality over time and relate them to environmental conditions or disease outbreaks
(Friedman and Spiegel, 2000).
Data and programme integration
CDC collected information about nutrition surveys among children under five years of
age conducted by various NGOs from September 1999 through June 2000 during the
Ethiopian famine. The surveys provided overlaying maps showing where nutrition and
health organisations had operated, food had been distributed and acute malnutrition

surveys had been conducted. The maps allowed decision-makers to modify
programmes and resource allocation by directing organisations and food delivery to
areas of greatest need, and conducting additional surveys where information was
lacking (Spiegel et al., 2000).
Monitoring and evaluation
GIS maps can be used to monitor and evaluate aid programmes in HE. Maps of
refugee populations in a camp, water distribution sites and latrines indicate whether or
not minimum standards in water supply and sanitation have been achieved (Sphere,
2000). Trends in malnutrition rates and locations of surveys, aid activities and food
distributions can show whether resources are adequately allocated or programmes need
adjustment according to changing needs of emergency-affected populations.
Lessons learned and future research
The documented uses of GIS in HE have a variety of programme implications. Many
of the documented applications need further methods development. To use GIS
methods effectively in HE, potential disadvantages and unanswered questions also have
to be addressed in future research. First, GIS/GPS equipment and software can be
expensive and require specific skills and training (Melnick and Fleming, 1999).
Limitations for developing countries or small organisations may be the costly nature of
resources required to generate useful GIS data. Hiring a GIS consultant may allow for
the costs associated with purchasing software and training an individual to perform GIS
work to be bypassed. Cost-effectiveness and time-saving studies are needed to
evaluate the use of GIS methods in HE. GIS users need to be trained in epidemiology
to understand that GIS is an enhancement rather than an alternative to traditional
epidemiologic methods (Vine et al., 1997). Users should be aware that data in HE may
be insufficient or inadequate. One of the main potential limitations of GIS is that of
‘masking’ the underlying problems of data with a cosmetic technology: everything
looks neat and tidy, while it is often not. In contrast to tables and graphs, maps do not
allow for cross-checking. Therefore, training must include appropriate presentation
and interpretation of data to make them comprehensible by decision-makers (Melnick
and Fleming, 1999) and to prevent over-emphasising or obscuring evidence (Scholten
and de Lepper, 1991). To examine causal relations between exposure and disease,
multivariate models are required that control for potential confounders and effect
modifiers (Melnick and Fleming, 1999). Maps based on those models can show the
predicted geographic variation of disease risk (Clarke et al., 1996). Second, equipment
and methodologies have to be practical, durable and appropriate for field use. GIS
methods for rapid assessments and surveys need further study, and add-on software
needs to be developed and modified to make this method easier to implement. Finally,
although geo-coded datasets and satellite imagery become increasingly available in the
public domain or commercially (Clarke et al., 1996), most GIS data in HE will still
require persons in the field to collect data and interpret it according to the
circumstances on the ground.

Conclusions
GIS methods provide improved ways of presenting traditional epidemiologic data as
well as new spatio-temporal information that has a variety of implications for
programmatic decision-making, monitoring and evaluation in HE. Main limitations
and future research needs are related to equipment and training development including
the appropriate presentation and interpretation of information. Future use of GIS in HE
will increase and become essential. As equipment becomes more user-friendly and
costs decrease, more and more humanitarian aid organisations will incorporate it into
their basic tools for programming, as well as monitoring and evaluation. New and
innovative uses for GIS in HE will also evolve.
Acknowledgements
We thank Dabo Brantly, Renata Caldwell, Carol Crawford, Thomas Handzel, Allan
Hightower, Mary Kay Larson and Sam Posner, all from CDC, for fruitful discussions
and inspirations that helped us in writing the paper. We also thank the WHO health
map unit and the UNHCR geographic information and mapping unit for providing GIS
maps. We also thank the UNHCR geographic information and mapping unit for
providing the satellite image and GIS map.
References
Barcellos, C. and P.C. Sabroza (2001) The Place Behind the Case: Leptospirosis Risks and
Associated Environmental Conditions in a Flood-related Outbreak in Rio de Janeiro.
Cadernos de Saude Publica 17 (Suppl): 59–67.
Becker, K.M., G.E. Glass, W. Brathwaite and J.M. Zenilman (1998) Geographic Epidemiology
of Gonorrhea in Baltimore, Maryland, Using a Geographic Information System. American
Journal of Epidemiology 147(7): 709–16.
Binkin, N., K. Sullivan, N. Staehling and P. Nieburg (1995) Rapid Nutrition Surveys: How Many
Clusters Are Enough? Disasters 16(2): 96–9.
Brooker, S. and E. Michael (2000) The Potential of Geographical Information Systems and
Remote Sensing in the Epidemiology and Control of Human Helminth Infections.
Advances in Parasitology 47: 245–88.
—— , M. Rowlands, L. Haller, L. Savioli and D.A. Bundy (2000) Towards an Atlas of Human
Helminth Infection in Sub-Saharan Africa: The Use of Geographical Information Systems
(GIS). Parasitology Today 16(7): 303–7.
Brown, V., G. Jacquier, D. Coulombier, S. Balandine, F. Belanger and D. Legros (2001) Rapid
Assessment of Population Size by Area Sampling in Disaster Situations. Disasters 25(2):
164–71.
CDC (1993) Satellite Surveillance: Geographic Information Technology Used to Prevent and
Control Infectious Diseases. CDC/NCID Focus 3, Atlanta.
—— (2001) Surveillance of Mortality During a Refugee Crisis – Guinea, January–May 2001.
MMWR 50: 1029–32.
Clarke, K.C., S.L. McLafferty and B.J. Tempalski (1996) On Epidemiology and Geographic
Information Systems: A Review and Discussion of Future Directions. Emerging Infectious
Diseases 2(2): 85–92.
Colwell, R.R. (1996) Global Climate and Infectious Disease: The Cholera Paradigm. Science
274(5295): 2025–31.

Croner, C.M., J. Sperling and F.R. Broome (1996) Geographic Information Systems (GIS): New
Perspectives in Understanding Human Health and Environmental Relationships. Statistics
in Medicine 15(17–18): 1961–77.
Dale, P.E.R., S.A. Ritchie, B.M.Territo, C.D. Morris, A. Muhar and B.H. Kay (1997) An
Overview of Remote Sensing and GIS for Surveillance of Mosquito Vector Habitats and
Risk Assessment. Journal of Vector Ecology 23(1): 54–61.
DFID (2003) Available at: http://www.dfid.gov.uk/.
Eastman, J.R., S. Emani, S. Hulina, H. Jiang, A. Johnson and M. Ramachandran (1997)
Applications of Geographic Information Systems (GIS) Technology in Environmental Risk
Assessement and Management. Clark Labs/Clark University (in cooperation with UNEP),
Worcester.
ECHO (2003) Available at: http://europa.eu.int/comm/echo/index_en.htm
Espie, E. (2000) Memoire de DEA: Evaluation de trois methods d’estimation de population.
Universite V. Segalen, Bordeaux.
FEWS (2003) Famine Early Warning System. Available at: http://www.fews.net/resources/
gcontent/gcontent.cfm?submit=y&f=al&g=1000006&d=al&l=en.
Foresman, T.W. (1999) Spatial Analysis and Mapping on the Internet. J Public Health
Management Practice 5: 57–64.
Friedman, C. and P. Spiegel (2000) Mission report to West Timor by UNHCR/CDC Medical
Coordinators. January 24–2 March, Atlanta.
Hay, S.I., J.A. Omumbo, M.H. Craig and R.W. Snow (2000) Earth Observation, Geographic
Information Systems and Plasmodium Falciparum Malaria in Sub-Saharan Africa.
Advances in Parasitology 47: 173–215.
Henderson, A. and P. Spiegel (1999) Mission Report: Field Testing of a Method to Rapidly
Enumerate a Refugee Population. Centers for Disease Control and Prevention, Atlanta.
Henderson, R.H. and T. Sundaresan (1982) Cluster sampling to assess immunization coverage: a
review of experience with a simplified sampling method. Bulletin of the World Health
Organisation 60(2): 253–60.
IRC (2000) Mortality in Eastern DRC, Results from Five Mortality Surveys. International
Rescue Committee, New York,
Isaaks, E.H. and R.M. Srivastava (1989) An Introduction to Applied Geostatistics. Oxford
University Press, Oxford.
Katz, J. and S.L. Zeger (1994) Estimation of Design Effects in Cluster Surveys. Annals of
Epidemiology 4(4): 295–301.
Kitron, U. (1994) Landscape Ecology and Epidemiology of Vector-borne Diseases: Tools for
Spatial Analysis. Journal of Medical Entomology 35(4): 435–45.
Kulldorff, M. (1999) Geographic Information Systems (GIS) and Community Health: Some
Statistical Issues. J Public Health Management Practice 5(2): 100–06.
Marsh, S.E., C.F. Hutchinson, E.E. Pfirman, S.A. Des Rosiers and C. Van Der Harten (1994)
Development of a Computer Workstation for Famine Early Warning and Food Security.
Disasters 18(2): 117–29.
Melnick, A.L. and D.W. Fleming (1999) Modern Geographic Information Systems – Promise
and Pitfalls. J Public Health Management Practice 5: viii–x.
Mitchell, R., D. Dorling and M. Shaw (2002) Population Production and Modelling Mortality –
An Application of Geographic Information Systems in Health Inequalities Research. Health
and Place 8(1): 15–24.
Moore, D.A. and T.E. Carpenter (1999) Spatial Analytical Methods and Geographic Information
Systems: Use in Health Research and Epidemiology. Epidemiologic Reviews 21(2): 143–
61.
MSF (1997) Médecins Sans Frontières: Refugee Health. An Approach to Emergency Situations.
Macmillan Education, Basingstoke.
Mujeeb, S.A., S. Shahab and A.A. Hyder (2000) Geographical Display of Health Information:
Study of Hepatitis C Infection in Karachi, Pakistan. Public Health 114(5): 413–15.
NAS (1997) Workshop on the Demography of Forced Migration. Refugee Policy Group,
National Academy of Science, Washington.

Njemanze, P.C., J. Anozie, J.O. Ihenacho, M.J. Russell and A.B. Uwaeziozi (1999) Application
of Risk Analysis and Geographic Information System Technologies to the Prevention of
Diarrheal Diseases in Nigeria. Am J of Tropical Medicine and Hygiene 61(3): 356–60.
OCHA (2002) Background Note on the Humanitarian Crisis in Southern Africa and the
HIV/AIDS Pandemic, 1 October. Available at: http://www.reliefweb.int/ocha_ol/
—— (2002) UN Office for the Coordination of Humanitarian Affairs. Available at:
http://www.reliefweb.int/ocha_ol/
—— (2003) Humanitarian Information Center for Iraq. Available at:
http://www.agoodplacetostart.org/hic_flyer.php
O’Dwyer, L.A. and D.L. Burton (1998) Potential Meets Reality: GIS and Public Health
Research in Australia. Australian and New Zealand Journal of Public Health 22(7): 819–23.
Peek-Asa, C., M.R. Ramirez, K. Shoaf, H. Seligson and J.F. Kraus (2000) GIS Mapping of
Earthquake-related Deaths and Hospital Admissions from the 1994 Northridge, California,
Earthquake. Annals of Epidemiology 10(1): 5–13.
Perry, B. and W. Gesler (2000) Physical Access to Primary Health Care in Andean Bolivia.
Social Science and Medicine 50(9): 1177–88.
Porter, J.D. (1999) Geographical Information Systems (GIS) and the Tuberculosis DOTS
Strategy. Tropical Medicine and International Health 4(10): 631–3.
Roberts, L. and M. Despines (1999) Mortality in the Democratic Republic of the Congo. Lancet
353(9171): 2249–50.
Robinson, T.P. (2000) Spatial Statistics and Geographical Information Systems in Epidemiology
and Public Health. Advances in Parasitology 47: 81–128.
Roper, W.L. and G.P. Mays (1999). GIS and Public Health Policy: A New Frontier for
Improving Community Health. J Public Health Management Practice 5: vi–vii.
Scholten, H.J. and M.J. de Lepper (1991) The Benefits of the Application of Geographical
Information Systems in Public and Environmental Health. World Health Statistics
Quarterly – Rapport Trimestriel de Statistiques Sanitaires Mondiales 44(3): 160–70.
Selanikio, J.D., T.M. Kemmer, M. Bovill and K. Geisler (2002) Mobile Computing in the
Humanitarian Assistance Setting: An Introduction and some First Steps. Journal of
Medical Systems 26(2): 113–25.
SIDA (2003) Available at http://www.sida.se
Snow, J. (1855) On the Mode of Communication of Cholera. Churchill, London.
Sphere (2000) The Sphere Project. Humanitarian Charter and Minimum Standards in Disaster
Response. Oxford, Oxfam Publishing.
Spiegel, P., P. Salama, S. Maloney and A. Van der Veen (2000) Mission Report: Nutrition
Cluster Surveys in Ethiopia in 1999/2000. Centers for Disease Control and Prevention,
Atlanta.
Stern, A. (1998) International Population Movements and Public Health in the Mekong Region:
An Overview of some Issues Concerning Mapping. Southeast Asian Journal of Tropical
Medicine and Public Health 29(2): 201–12.
Szwarcwald, C.L., F.I. Bastos, C. Barcellos, M.F. Pina and M.A. Esteves (2000) Health
Conditions and Residential Concentration of Poverty: A Study in Rio de Janeiro, Brazil.
Journal of Epidemiology and Community Health 54(7): 530–6.
Tanser, F. and D. Wilkinson (1999) Spatial Implications of the Tuberculosis DOTS Strategy in
Rural South Africa: A Novel Application of Geographical Information System and Global
Positioning System Technologies. Tropical Medicine and International Health 4(10): 634–
8.
Tim, U.S. (1995) The Application of GIS in Environmental Health Sciences: Opportunities and
Limitations. Environmental Research 71(2): 75–88.
UNAMA (2002) Afghanistan Information Management Service (AIMS). Available at:
http://www.aims.org.pk/.
UNHCR (2001) UNHCR Global Report 2001 – Guinea. Available at: http://www.unhcr.ch.
—— (2002) Geographic Information and Mapping Unit (GIMU). Available at:
http://www.unhcr.ch/cgi-

bin/texis/vtx/home/++wwFqzvxsw8+mn6xFqzvxsw8+mn6hFqhT0NuItFqnp1xczFqn7uFP
AFqwDzmwwwwwwww1Fqn7uFP.
UNHCR/OCHA (2002) Sierra Leone Information System. Available at:
http://www2.reliefweb.int/sle/hic/sierra_leone_information_system.htm.
USAID (2003) Available at http://www.usaid.gov/gn/infotechnology/gis/.
Vine, M.F., D. Degnan and C. Hanchette (1997) Geographic Information Systems: Their Use in
Environmental Epidemiologic Research. Environmental Health Perspectives 105(6): 598–
605.
Wartenberg, D. (2001) Investigating Disease Clusters: Why, When and How? J Royal Statistical
Society 164(1): 13–32.
WFP (2002) Africa Hunger Alert Campaign Begins. Press Release, 16 December. Available at:
http://www.wfp.org/index.asp?section=2
—— (2002) Vulnerability Assessment Mapping. Available at:
http://www.wfp.org/index.asp?section=5
WHO (2002) Health Mapper. Available at: http://www.who.int/emc/healthmap/healthmap.html.
Address for correspondence: Reinhard Kaiser, International Emergency and
Refugee Health Branch, National Center for Environmental Health, Centers for Disease
Control and Prevention, Mail Stop F-48, 4770 Buford Highway NE, Atlanta, Georgia
30341, USA. E-mail: <<RKaiser@cdc.gov>>

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