Global Report on Human Settlements 2007 - PoA-ISS

Mitigating the impacts of disasters
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The process of quantifying local knowledge through P-
GIS can further enhance the position of communities when
they negotiate with outside agencies. For example, when
assessments are conducted before and after external interventions,
the success or failure of plans can be made equally
transparent to local and external stakeholders. 21 P-GIS can
thus contribute to reaching consensus on the state of local
urban environments and on arriving at targets for land-use
planning that integrates risk reduction.
P-GIS has become established as a tool in risk management
for urban planning within richer cities; but scarcity of
human resources and technical capacity have meant that it
has received more limited application as a strategic tool in
cities at risk from disaster in middle- and low-income
countries. P-GIS is an important opportunity not to be
missed as it provides a mechanism for generating basic data
on hazard, vulnerability and loss when centralized data is not
available, which is predominantly the case in low-income
urban communities at risk. This said, the comprehensive use
of P-GIS is likely to prove costly to implement and maintain,
and may not be achievable in many poorer cities over the
short and medium term.
The methodology used in GIS is to construct individual
maps for specific social or environmental variables, such
as income class, housing quality or altitude. Individual maps
can then be layered on top of one another to identify risk as
a result of different combinations of vulnerability and hazard
variables. In this way, GIS is useful for identifying sites of
special concern (which may vary over time in response to
economic and environmental cycles) or areas of potential
land-use or social conflict.
For long-term analysis of trends in vulnerability and
impacts, regular and consistent data collection systems are
Box 12.5 Using geographic information systems (GIS) for
risk mapping
The Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) recommends the following
multi-method approach to risk mapping using geographic information systems (GIS).
First, use existing records to identify the areas potentially affected by a hazard of interest.
Supplement these with analysis of aerial and satellite images and surveys among the
population affected. In the case of very extensively flood-prone areas (e.g. Mozambique), hazardprone
areas might be delineated using satellite images (Landsat Thematic Mapper). Second,
collected data is entered manually or using GIS on topographical maps to a scale of 1:20,000 to
1:100,000 or larger. Depending upon the hazard type, more or less intensive use is made of
satellite and aerial images as a data source, together with GIS as an analytical instrument. To
determine hazard frequency, it is necessary to study aerial images from as long a time span as
possible, mostly on a scale of 1:15,000 to 1:30,000 (for small-scale flooding), and possibly
supplemented with National Aeronautics and Space Administration Agency (NASA) and/or
landscape photos.
Source: Kohler et al, 2004
required. There are few examples of such systematic data
collection systems operating among marginalized and at-risk
populations in urban settlements. Box 12.6 presents the
experience of the Monitoring, Mapping and Analysis of
Disaster Incidents in South Africa (MANDISA) database
operating in squatter settlements in Cape Town (South
Africa), with a focus on fire hazard loss data collection.
Cost–benefit analysis
Cost-benefit analysis allows a comparison to be made
between the costs and benefits of an investment decision.
Benefits are defined as anything that improves human well-
Box 12.6 Monitoring, Mapping and Analysis of Disaster Incidents in South Africa (MANDISA):
An urban fire inventory for small disasters in Cape Town, South Africa
In 1999, the Disaster Mitigation for Sustainable Livelihoods
Programme at the University of Cape Town developed the
Monitoring, Mapping and Analysis of Disaster Incidents in South
Africa (MANDISA). This initiative was developed in collaboration
with a range of local partners and aims to monitor incidents of
urban fire, many of which often fall under the radar of disaster
managers.
MANDISA covers the Cape Metropolitan Area and has
data from 1990 onwards. Data is collected from multiple sources
(e.g. fires services, social services, the Red Cross and newspapers),
and is managed and presented in text as well as geographic information
system (GIS) formats, allowing for both spatial and
temporal analysis of many different disaster types, with differing
impacts and scales.
Overall, the number of fires, most including one or, at most,
a small number of dwellings, has increased rapidly from 1250 events
per year between 1990 and 1999, to 3667 events per year between
2000 and 2002 (i.e. a doubling of the ten-year reported pattern in
three years). This evidence has prompted local government officials
to consider risk more seriously in their development planning. An
analysis of the distribution of fire incidents between planned and
unplanned settlements (1990–1999) in Guguletu, a township, shows
that fires in the informal housing sector constituted 86 per cent,
with only 11 per cent occurring in formal housing areas.
MANDISA is the first African-generated disaster events
database that has allowed the geo-referencing of these very small
and local events. This capability has provided a database to support
legal reform for disaster management.
Key challenges revealed by MANDISA are as follows:
• Determining the accurate trigger, particularly of an informal
dwelling fire, is highly problematic. Since all of the data is
provided by emergency and relief records, there is complete
reliance on the first responders to identify the trigger. In the
case of fires, the emergency and fire services are increasingly
unable to ascertain the trigger, and therefore report the
trigger unknown.
• The limited analytic capability of those involved in disaster
management, as well as those in development planning, has
delayed uptake, buy-in and use of the information generated.
Source: Disaster Mitigation for Sustainable Livelihoods Programme, University of Cape Town, South Africa, www.egs.uct.ac.za/dimp