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)

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.)

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)

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

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?

The Challenges of Spatial DataSpatial 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…

The Challenges of Spatial Data

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.

Examples

Examples

Examples

Examples

Examples

Examples

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