Spatial Database and GIS for CBMS, Experience from Palawan, Philippines 21/10/03
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example in symbolizing data, scaling of maps and others. Also, NRDB and the GIS software
used in Palawan can easily ‘communicate’ with each other through import and export of
spatial data layers, called shape files.
There are also some fundamental differences. A GIS contains sophisticated and highly
accurate processes for data capture. In GIS applications, data is assembled and arranged for
an individual project file so there is no common store for all available data as in the case of a
spatial database. In addition to vector data (polygons), a GIS handles raster (grid) formats,
which provides extensive analytical options.
The capacity for complex spatial analysis involving
statistical tests, modeling
and simulation is possibly
the largest difference
between a GIS and a spatial
database. While there are
basically no limits, GIS
analysis of CBMS data in
Palawan concentrates on
some standard methods. This is to keep the analytical process and the result understandable
and transparent for involved people.
Example: Mapping locations with high poverty density
The following example presents data from a complete enumeration on HH income (indicating
poverty incidence) in the Municipality of Narra, Palawan for the year 2002.
The map has been
generated with NRDB. It
gives a distribution of
poverty incidence at the
Barangay (village) level. The
dark green color represents
Barangays with a relatively
high proportion of
households with income
greater than the Poverty
Spatial Database and GIS for CBMS, Experience from Palawan, Philippines 21/10/03
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incidence (above the municipal average of 38.77%). The dark red color, on the other hand,
represents locations where the proportion of households with income above the poverty line is
low by comparison and where poverty incidence is high (below the provincial average of
21.49%). The map highlights that the urban Barangay ‘Poblacion Narra’ has the lowest
proportional poverty incidence, but is directly surrounded by three barangays where the
incidence is extremely high by comparison.
GIS analysis, using
Arc View GIS,
provides results that
are quite different.
The map below
shows the density of
poor households
(unit per sqkm). The
colors in the map
represent the
locations of poverty
not by
administrative unit
but by its actual
location. The darker the color, the higher is the absolute poverty density. Spatial interpretation
in this map derives its data from the count of poor households at the Purok level, the next
lower settlement level of the Barangay. The analysis assumes that the households within the
almost 100 Purok are clustered within about 2km of the physical Purok center. Interestingly, it
shows that the highest density of poor households (above 120 per sqkm) is found in
Poblacion Narra, the Barangay with the lowest
poverty incidence and its neighboring village
Panacan, where the incidence (expressed as
percentage) is also lower than in most other
Barangays in Narra. This result is less surprising,
when taking into consideration that both
Barangays have the highest absolute number of
households and among the highest density of
settlements (Purok sites).
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Example: Identifying distance to Primary and elementary schools
Spatial analysis helps to identify settlement locations, in this case again the Purok in the
Municipality of Narra, and their distance to facilities, such as schools hospital etc. In this way,
well served or underserved settlements can be visualized.
The example above shows the
location of primary/elementary
schools (blue dots). The
concentric rings represent a
distance unit of 1.5 km. For
example, the area marked by
a yellow ring is between 3 and
4.5 km away from the nearest
school. Analysis provides an
easy way to isolate
settlements that are within this
distance or even further away from the nearest school. In some instances, the school in the
neighboring Barangay is much closer that that located within the village.
Example: Identifying service gaps and finding locations for additional facilities
Showing distance of settlements from the schools in Narra is a simple and straightforward
means to identify which individual settlement are beyond a certain accessibility level, let’s say
3 km. But what, if in case of scarce resources, a more strategic interpretation is required that
asks: where do we have to locate a new school that has the highest impact on bringing
settlements closer to school?
In this case, the problem may be approached the other way around, based on the idea is to
map out different density levels of settlements and then correlate these with the location of
school facilities. The highest impact
of a new school building would be in
a location of highest settlement
density where currently no school is
present.
The map shows the settlement
density from 0 -0.1 per sqkm to 1 per
sqkm. The red polygons show
Spatial Database and GIS for CBMS, Experience from Palawan, Philippines 21/10/03
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location with a high density of settlements per area unit.
An overlay with the spatial
representation of school distance
data helps to identify those areas
which have high settlement
density and fall within or outside
certain distance levels
Spatial Database and GIS for CBMS, Experience from Palawan, Philippines 21/10/03
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Lessons Both tools, NRDB and GIS, have proven highly supportive to CBMS implementation in
Palawan. NRDB provides an effective means for storing and accessing CBMS information.
The GIS adds spatially relevant information to CBMS and provides analytical options.
Looking at CBMS ‘spatially’ with NRDB and GIS has substantially encouraged the use of
CBMS by the various stakeholders. More than this, this attractiveness has generated
additional resources towards maintenance and development of the CBMS database.
However, experience in Palawan shows that while setting up a spatial data base for CBMS
can be done quickly and easily, building it further takes time. Particularly keeping NRDB
updated, accurate and generating spatial data has turned out a tedious process.
Since the NRDB in Palawan is shared with a number of agencies, accuracy and credibility of data are crucial. Barangay officials, for example, will reject the system and its data if the
administrative boundaries drawn by the NRDB map do not conform to their own records.
Similarly, a municipal mayor will not subscribe to if CBMS data stored in the NRDB appears
unrealistic in his or her view.
Finally, the use of GIS sometimes encourages too complex and technical interpretation of
CBMS results by ‘experts’ in the planning office. This may run counter to the participatory
principles of CBMS. And it bears the risk that local communities will be confronted with
feedback on their development situation which they find difficult to comprehend and learn
from. Therefore, the use of GIS for CBMS data interpretation needs to consider the potential
conflict of bringing together local and expert information systems.
The use of NRDB and GIS for CBMS in Palawan can be seen as part of the attempt to
convert data, described as the unrefined and undifferentiated facts collected by CBMS
surveys, into information, which is understood as refined and organized data. Such
information then needs to be turned into knowledge as the internalized information.
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Thus, it is only one step to, for example, to find out that the coastal fishing communities in a
given municipality are poorer than their farming neighbors further uphill and to identify the
likely causes. Another and equally challenging work is to communicate such information. The
more a development community at the village, communal and provincial level has internalized
this knowledge, the more likely it will reach a consensus on what to do about it, and the more
likely it is that development activities and interventions will effectively address those issues
identified by CBMS. Both NRDB and GIS can promote this goal. But they are by no means
sufficient. And their application has to be adapted to the local conditions and to the ways and
principles along which CBMS is implemented in a particular case.
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