Limitations, opportunities, and implications for climate adaptation. Brendan McGill, 2016 October

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

2. 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

3. 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

4. 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].

5. 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

6. 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.

7. 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

8. 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

9. 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)

10. What Is Spatial Data?
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.)

11. What Is Spatial Data?
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)

12. What Is Spatial Data?
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

13. 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?

14. The Challenges of Spatial 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…

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

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

17. 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?

18. 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

19. 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

20. 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

21. 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.

22. 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

23. 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

24. 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.

25. 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.

26. 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

27. 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.

28. 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.

29. 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

30. 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.

31. Examples

32. Examples

33. Examples

34. Examples

35. Examples

36. Examples

37. 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]

38. 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

39. 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…

40. 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.”

41. 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