1. Economic And Social Commission For Western Asia
GEOSPATIAL REPRESENTATION OF DATA
FOR THE VULNERABILITY ASSESSMENT
Issues for Discussion
Nanor Momjian
GIS Expert
WRS - SDPD
Sixth Expert Group Meeting
08/12/2014
Cairo, Egypt
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Table of Content
• Introduction
• Challenges/Issues
• Responses
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Introduction
Raster v/s vector
• RASTER: a matrix of cells (or pixels) organized into rows and columns (or
a grid) where each cell contains a value representing information data
(example interpolated maps, satellite imagery, scanned maps etc.)
• VECTOR: A coordinate-based that represents data as
geographic features as points, lines, and polygons.
Attributes are associated with each vector feature
• Different resolutions
• Climate models are in Raster format
• Final VA aggregation with raster files
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Challenges/Issues
Data Sources
• OPEN SOURCE: data being used for sake of transparency
• SOURCES:
•National statistical bureaus: one value per country, densely populated
areas are treated similar to dessert or rural areas
•UN/LAS
•Research centers/NGOs/Universities
• METHODOLOGY:
•Combining many datasets to produce one indicator
•Using Remote sensing to produce indicators
• ACCURACY:
•Data quality
•Consistent and Harmonized datasets
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Challenges/Issues
Geographic distribution of data
• NATIONAL LEVEL DATA V/S GRIDDED DATA
•GDP
•Population
• BORDERS:
•LAS recommendation regarding country boundaries maps
•UN-agreed international borders v/s disputed areas
• ARAB COUNTRIES V/S ARAB DOMAIN
•Irrigation
•Land use land cover
• DATA GAPS
•Hydrogeology
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EXAMPLE: LAND USE
LAND COVER
•Arab Domain (Glocover)
EXAMPLE: IRRIGATED
AREAS
•Arab Region only (FAO)
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Challenges/Issues
Indicators
• PRIMARY DATA V/S ADJUSTED DATA
•Redistributing national level data over spatially distributed data to
convert national-level data into geospatially distributed map
•Example: Migrants
• PRIMARY DATA V/S COMPOSITE DATA
•Composite indicators are indicators that include several sub-datasets to
produce one final indicator.
•The sub-datasets could overlap with other existing layers in the
Vulnerability Assessment map and have a multiplying affect on that
specific indicator.
•Example: Gender Inequality Index (health, education…)
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EXAMPLE: MIGRANTS
DISTRIBUTED BY THE
POPULATION DENSITY
•Arab Domain, gridded
(United Nations – Population
Division and CIESIN)
EXAMPLE: MIGRANTS
•National-level data (United
Nations – Population
Division)
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Challenges/Issues
Indicators
• PRIMARY DATA V/S ADJUSTED DATA
•Redistributing national level data over spatially distributed data to
convert national-level data into geospatially distributed map
•Example: Migrants
• PRIMARY DATA V/S COMPOSITE DATA
•Composite indicators are indicators that include several sub-datasets to
produce one final indicator.
•The sub-datasets could overlap with other existing layers in the
Vulnerability Assessment map and have a multiplying affect on that
specific indicator.
•Example: Gender Inequality Index (health, education…)
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Reproductive
health
Empowerment
Economic status
expressed as
labour market
participation
Gender inequality
index
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Responses
Data Sources (1/2)
• OPEN SOURCE: data being used for sake of transparency
•Any country should be able to download the available data and produce
or check the results
•Example:
•CIESIN population data - free
•LandScan population data– not free
• SOURCES:
•National statistical bureaus: used to quality check available data
•UN/LAS: used to produce percentages or conduct analysis to the data
•Example:
•Agriculture sector as percent of GDP
•Unemployed as percent of total population
•Research centers/NGOs/Universities: data produced by these entities
are usually geospatially distributed, which is very important for the
integrated vulnerability assessment
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Responses
Data Sources (2/2)
• METHODOLOGY:
•Combining many datasets to produce one indicator:
•Analyze the methodologies of producing the dataset and all the input
data
•Assure indicators are not repeated, make sure no repetition of
indicators occur.
•Data from Remote sensing:
•Cross-check with National or UN-data
• ACCURACY:
•Quality Check/peer review plan to be put in place based on:
•Experts’ opinion
•Existing literature
•National and UN/LAS data
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Responses
Geographic distribution of data
• NATIONAL LEVEL DATA V/S GRIDDED DATA
•Geospatial representation (grids) of data would be better to present data
• BORDERS:
•Using water bodies (Rivers and lakes)
•Capital cities for orientation
• ARAB COUNTRIES V/S ARAB DOMAIN
•Depending on the available datasets
•For the integrated vulnerability assessment will be for the Arab region
• DATA GAPS
•Example for the Hydrogeology data gaps were checked from satellite
images and filled with suitable data based on expert judgment
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Maps
EXAMPLE: POPULATION
DENSITY PER GRID
•Arab Domain, gridded
(CIESIN)
EXAMPLE: POPULATION
DENSITY PER COUNTRY
•National-level data
(UNStat/ESCWA)
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Maps
EXAMPLE: GDP PER
CAPITA PER GRID
•Arab Domain, gridded
(CIESIN)
EXAMPLE: GDP PER
CAPITA
•National-level data
(UNStat/ESCWA)
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Responses
Geographic distribution of data
• NATIONAL LEVEL DATA V/S GRIDDED DATA
•Geospatial representation (grids) of data would be better to present data
• BORDERS:
•Using water bodies (Rivers and lakes)
•Capital cities for orientation
• ARAB COUNTRIES V/S ARAB DOMAIN
•Depending on the available datasets
•For the integrated vulnerability assessment will be for the Arab region
• DATA GAPS
•Example for the Hydrogeology data gaps were checked from satellite
images and filled with suitable data based on expert judgment
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Integrated vulnerability assessment
results will be for the Arab region
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Responses
Geographic distribution of data
• NATIONAL LEVEL DATA V/S GRIDDED DATA
•Geospatial representation (grids) of data would be better to present data
• BORDERS:
•Using water bodies (Rivers and lakes)
•Capital cities for orientation
• ARAB COUNTRIES V/S ARAB DOMAIN
•Depending on the available datasets
•For the integrated vulnerability assessment will be for the Arab region
• DATA GAPS
•Example for the Hydrogeology data gaps were checked from satellite
images and filled with suitable data based on expert judgment
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According to satellite images, the “No Data”
areas on this map are Sand Dunes, thus given
the equivalent “Local sedimentary aquifer
type category”
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Responses
Indicators (1/2)
• PRIMARY DATA V/S ADJUSTED DATA
•Redistributing national level data over spatially distributed data should
be done only
•With result validation making sure the data is not distorted
•Or results cannot be misinterpreted
• PRIMARY DATA V/S COMPOSITE DATA
•For Composite indicators
•The sub-datasets could be used as separate indicators
•Composite indicators are used making sure no overlap exist with
other indicator layers
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Responses
Indicators (2/2)
REGIONAL KNOWLEDGE HUB (RKH)
V/S
INTEGRATED VULNERABILITY ASSESSMENT (VA)
•All indicators (sub-indicators in the case of composite indicators) will be
added into the regional knowledge hub as separate layers
•The indicators/layers will be presented both
•In map format (showing the information geospatially)
•In excel format (showing the attributes/data in tables)
•This will insure transparency of the integrated vulnerability
assessment process
