Spatial data infrastructure in Kyrgyzstan

Spatial Data Infrastructure
in Kyrgyzstan.
Limitations, opportunities, and implications for climate
adaptation
Brendan McGill – Brendanmc6@gmail.com - October, 2016

Contents
 What is Spatial Data and Spatial Data Infrastructure?
 Importance of, and challenges of Spatial Data
 Research and findings; summary assessment of Kyrgyz SDI
 Opportunities, recommendations, next steps

About Me
 Brendan McGill - Intern, Unison Group. August – October 2016
 Student, MSc. Environmental Governance, Albert-Ludwigs
Universität Freiburg, Germany. 2015-2017.
 B.A. Environmental Studies and Planning, Sonoma State
University, California, USA. 2013.
 2 years experience as a GIS analyst for REC Consultants Inc.
 Civil Engineering & Environmental Analysis

Why research SDI?
 Something related to climate change.
 Climate change in Kyrgyzstan means...
Melting glaciers-- water availability, hydropower generation,
glacial lake outburst floods, soil destabilization
Shifting seasonal patterns, heat stress
Impacting food-insecure and impoverished rural communities
Impacting Kyrgyz economy: Agriculture = 18% of GDP and 48% of
labor force
Per-capita GHG emissions < 1/3rd of global average [1].

Why research SDI?
 Something related to climate change.
 Climate change in Kyrgyzstan means...
Melting glaciers-- water availability, hydropower generation,
glacial lake outburst floods, soil destabilization
Shifting seasonal patterns, heat stress
Impacting food-insecure and impoverished rural communities
Impacting Kyrgyz economy: Agriculture = 18% of GDP and 48% of
labor force
Per-capita GHG emissions < 1/3rd of global average.
Mitigation, adaptation & vulnerability

Why research SDI?
 Vulnerability mapping
There is none.
Little GIS mapping and data in general.
Questions: What kind of data exists? Where is it? Is it sufficient for
adaptation efforts? Are people using it?
These questions, and more, are all related to the concept of
Spatial Data Infrastructure.

Why research SDI?
 Vulnerability mapping
There is none.
Little GIS mapping and data in general.
Questions: What kind of data exists? Where is it? Is it sufficient for
adaptation efforts? Are people using it?
These questions, and more, are all related to the concept of
Spatial Data Infrastructure.
SDI Assessment

What Is Spatial Data Infrastructure?
“The technology, policies, criteria, standards and
people necessary to promote geospatial data sharing
throughout all levels of government, the private and
non-profit sectors, and academia” - United States
Federal Geographic Data Committee
National Spatial Data Infrastructure
Formal, national level framework

What Is Spatial Data?
 Represents a real-world location.
 Vector (point, line, polyline) or Raster
(image, coverage)
 Defined with coordinates based on a
Coordinate Reference System
(Latitude, Longitude) or (X,Y)

Many file types (.shp, .gdb,
GeoTIFF, Spatialite)
Organized by “layers”
Contains much more than just
location, shape and color;
‘Attributes’ (e.g. Road Name,
Length, ID, Category, etc.)

Raster formats
 ADRG – National Geospatial-Intelligence Agency (NGA)'s ARC Digitized Raster Graphics[2]
 Binary file
 Digital raster graphic (DRG) – digital scan of a paper USGS topographic map
 ECRG – National Geospatial-Intelligence Agency
 ECW – Enhanced Compressed Wavelet (from ERDAS).
 Esri grid – proprietary binary and metadataless
 GeoTIFF – TIFF variant enriched with GIS relevant metadata
 IMG – ERDAS IMAGINE image file format
 JPEG2000 – Open-source raster format. A compressed format, allows both lossy and lossless
compression.
 MrSID – Multi-Resolution Seamless Image Database (by Lizardtech). A compressed wavelet
format, allows both lossy and lossless compression.
 netCDF-CF – netCDF file format
Vector formats
 AutoCAD DXF – contour elevation plots in AutoCAD DXF format (by Autodesk)
 Cartesian coordinate system (XYZ) – simple point cloud
 Digital line graph (DLG) – a USGS format for vector data
 Esri TIN - proprietary binary format for triangulated irregular network data used by Esri
 Geography Markup Language (GML) – XML based open standard (by OpenGIS) for GIS
data exchange
 GeoJSON – a lightweight format based on JSON, used by many open source GIS packages
 GeoMedia – Intergraph's Microsoft Access based format for spatial vector storage
 ISFC – Intergraph's MicroStation based CAD solution attaching vector elements to a
relational Microsoft Access database
 Keyhole Markup Language (KML) – XML based open standard (by OpenGIS) for GIS data
exchange
 MapInfo TAB format – MapInfo's vector data format using TAB, DAT, ID and MAP files
 National Transfer Format (NTF) – National Transfer Format (mostly used
by the UK Ordnance Survey)
 Spatialite – is a spatial extension to SQLite, providing vector
geodatabase functionality. It is similar to PostGIS, Oracle Spatial, and
SQL Server with spatial extensions
 Shapefile – a popular vector data GIS format, developed by Esri
 Simple Features – Open Geospatial Consortium specification for vector
data
 SOSI – a spatial data format used for all public exchange of spatial data
in Norway
 Spatial Data File – Autodesk's high-performance geodatabase format,
native to MapGuide
 TIGER – Topologically Integrated Geographic Encoding and
Referencing
 Vector Product Format (VPF) – National Geospatial-Intelligence
Agency (NGA)'s format of vectored data for large geographic
databases
Grid formats (for elevation)
 USGS DEM – The USGS' Digital Elevation Model
 GTOPO30 – Large complete Earth elevation model at 30 arc seconds,
delivered in the USGS DEM format
 DTED – National Geospatial-Intelligence Agency (NGA)'s Digital Terrain
Elevation Data, the military standard for elevation data
 GeoTIFF – TIFF variant enriched with GIS relevant metadata
 SDTS – The USGS' successor to DEM
Other formats
 Dual Independent Map Encoding (DIME) – A historic GIS file format,
developed in the 1960s
 Geographic Data Files (GDF) — An interchange file format for
geographic data
 GeoPackage (GPKG) – An standards-based open format based on the
SQLite database format for both vector and raster data
 Well-known text (WKT) – A text markup language for representing
feature geometry, developed by Open Geospatial Consortium
 Well-known binary (WKB) – Binary version of Well-known text
 World file – Georeferencing a raster image file (e.g. JPEG, BMP)

Spatial Data describes the
world -> Metadata
describes the spatial data
Abstract/summary,
methodology, date, author,
revision history, access,
references, search tags.
Every layer should have
Metadata

The Challenges of Spatial Data
Who updates which layers, using what standards?
Who pays for it?
What are the rights to access and to sell it?
Can trust the data I receive? Can I trust others to
have my data?
Where can I find the data I need? Who needs my
data?

Spatial data is created by one, but used by many.
e.g. Forest boundaries: Ecology, climate change, timber industry…
Satellite imagery: Agriculture, planning, real estate, hydrology…

How do we deal with these challenges?
Spatial Data Infrastructure!
(technology, policies, criteria, standards and people)

What should (N)SDI look like?
NSDI Index, Justification and Design - Smith School of Enterprise and the Environment. 2016.

Research Questions
What is the current state of Kyrgyzstan’s
SDI?
What are the major shortfalls and barriers, and what
are the implications for climate change adaptation
efforts?

Methodology
Literature review
Qualitative Interviews
9 GIS practitioners and international development experts
National Statistical Committee of the Kyrgyz Republic
Mountain Societies Research Institute (UCA)
World Agroforestry Centre (UCA)
Food and Agriculture Organization
Department of Water Resources and Land Improvement
Gesellschaft für Internationale Zusammenarbeit (GIZ) / CIM

Theoretical Framework
Many SDI assessment methodologies
 Developing vs. developed countries
 Goal-based vs. performance-based
 Technology oriented
 Qualitative
 Quantitative
 National vs. Sectoral
Eelderink, L., Crompvoets, J., & Erik de Man, W. H. (2008). Towards key
variables to assess National Spatial Data Infrastructures (NSDIs) in
developing countries.
- 14 key variables of importance for developing countries

Theoretical Framework
Eelderink, L., Crompvoets, J., & Erik de Man, W. H. (2008). Towards key
variables to assess National Spatial Data Infrastructures (NSDIs) in
developing countries.
- 14 key variables of importance for developing countries
 Willingness to share,
 Availability of digital data
 Access mechanism
 Funding
 Leadership
 Vision
 Initiatives connected to SDI
 Institutional arrangements
 Socio-political stability
 Interoperability
 Metadata
 Capacity building
 Human capital
 SDI awareness

Willingness to share &
Availability of digital data
 Very low willingness to share data, data availability is
inadequate
 Very difficult to determine which data sets exist, and where
 “Soviet mentality”, “Silo mentality”, “Culture of exchange”
 Corruption? - “Data is power”
 “If you ask, you can get it. It’s possible, but it takes effort”
Sharing seems to be based on relationships and personal
discretion.

Access Mechanism
No central access mechanism (website, ‘geo-
portal’, request form/contact)
Several ‘Geonodes’ (website template for
downloading layers) created – e.g. CAIAG &
Disaster Risk Data Platform
 Incomplete, low support

Leadership, Vision, Initiatives
connected to SDI
National government is currently antagonistic to SDI
development
Current “Champion” of geospatial information:
GosRegister
Two world bank projects -> GNSS Reference Network, Cadaster
mapping, open source data system.
One of 9 ministries representing the 2014 NSDI roadmap effort
Only informal level of leadership, initiative, vision:
Unofficial working group, meetings & discussions
Interest on behalf of many organizations to improve SDI

Funding
Reason for not supporting 2014 NSDI initiative
Good potential for access to aid for SDI development.
Sweden, Norway, German, Korea-- previously provided
support, or have voiced interested in providing it.
Free open-source options exist for hosting, sharing,
analyzing spatial data
Public and international funding has been used to
generate data and purchase satellite imagery, but the
data is not available to the public.

Institutional Arrangements
November 2014 Decree on spatial information:
Topographic, photographic maps, in digital and analog
formats, “in the coordinate system of 1942 at a scale larger than
1:50000” are classified!
Other data sets are unclassified, if they do not display “military
and industrial facilities of significant importance”
Institutions are creating and using spatial data largely
without coordination.
October 2010 decree officially established Kyrgyz06 as
the National Coordinate Reference System, replacing
the classified 1942 system.

Socio-Political Stability
Concerns: recent revolution, boundary disputes
with neighbors, corruption.
It’s possible that more accurate mapping could
lead to property disputes
Data can be valuable for profit or political
influence

Interoperability, Metadata
Kyrg-06 is based on the International Terrestrial Reference
Frame ITRF-2005, UTM projection with five 3-degree zones
No systematic process for quality assurance, no
concerted effort to follow standards or protocols for data
and metadata.
Different results between institutions
E.g. Forest data
Delay and added costs due to encoding issues, missing
metadata, etc.

Capacity building, human capital, SDI
awareness
GIS courses at the University of Central Asia, American University of
Central Asia (AUCA), and Central Asian Institute for Applied
Geosciences for some time. Curriculum development is underway.
Need for more widespread awareness, especially in national
government, for what GIS can do.
Awareness of SDI and GIS is a prerequisite for willingness to share and
for institutional arrangements.
Language of the spatial decree demonstrates poor understanding
of modern cartography.

No NSDI.
Silo-mentality, data islands, poor coordination, duplication of efforts
Low willingness to share data, difficult to find and access data.
Some informal leadership and coordination-- SDI “embryo”
Some important technical infrastructure and capacity (Kyrgyz06,
GNSS Network)
Summary

Implications
Unnecessary time and resource expenditure, and severely limiting
the quality and quantity of important GIS analyses
Climate adaptation efforts are difficult, because the most relevant
datasets are missing, difficult to access, or of low or indeterminable
quality.
Topology, recent satellite imagery, climate and weather data,
Boundaries of villages, forests, agriculture, habitats, floodplains and
other hazards.
Effective monitoring and reporting of progress in the climate sector,
and of progress towards achieving the 17 United Nations Sustainable
Development Goals, will also require quality and up-to-date versions
of these spatial data sets.

Opportunities
The benefits of an NSDI would be clear and immediate.
Existing projects related to climate adaptation already stand to benefit
from improved data access
Corruption can be dependent on lack of transparency
Should improve the confidence of foreign donors and private investors.
Gains from job creation and added efficiency—in the United States, cost
savings and benefits from geospatial services =15-20x the value of the
American geospatial sector itself [2]
Catalonia, Spain: total amount invested in set-up and operation was
recovered in just over 6 months [3]

Recommendations
 Climate and adaptation projects: be aware of the “data
reality” in Kyrgyzstan– extra costs, delays, and uncertainties.
 Donors should consider spending on SDI for larger net-benefit in
the field of sustainable development.
 Support from the government to establish an NSDI is imperative
Establish new funding models, instead of charging for data
 Focus on the policy and support, not the technology

Next Steps
 Formalize a working group to oversee NSDI development
Take advantage of existing networks, expertise and
leadership
Memorandums of Understanding between institutions
Pooling of financial resources
 Improve awareness of SDI and it’s benefits, especially in the
national government and donor institutions.
 Data Survey – Which institutions have what data?
 Look for best practices from around the world; Europe’s
INSPIRE SDI, American National Spatial Data Infrastructure…

Final thoughts
COP21, Paris: anticipated mobilization of
$100.000.000.000 in climate finance– a large portion for
adaptation and developing countries.
Sustainable Development Goal 17.28:
“By 2020 enhance capacity building support to
developing countries… to increase significantly the
availability of high-quality, timely and reliable data
disaggregated by income, gender, age, race, ethnicity,
migratory status, disability, geographic location and other
characteristics relevant in national contexts.”

References
 [1] Intended Nationally Determined Contribution for Kyrgyzstan, 2015.
http://www4.unfccc.int/Submissions/INDC/
 [2] Federal Geograhic Data Committee. (2014). National Spatial
Data Infrastructure Strategic Plan 2014-2016.
 [3] Almirall, P. G., & Bergadà, M. M. (2008). The socio-economic
impact of the spatial data infrastructure of Catalonia.
 Nepal's NSDI Initiative
 The SDI Cookbook – Global Spatial Data Infrastructure Association
 NSDI Index, Justification and Design – Discussion Paper, Smith School
of Enterprise and the Environment

Spatial data infrastructure in Kyrgyzstan

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