Standards and Data Infrastructure

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15 Νοε 2013 (πριν από 3 χρόνια και 9 μήνες)

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Statistical and Spatial
Frameworks,
Standards and Data
Infrastructure


Work Session on Statistical Metadata 2013

WP11


Alistair Hamilton

alistair.hamilton@abs.gov.au

Australian Bureau of Statistics (ABS)

Content


1.
Space is big…and getting bigger

2.
The emergence of spatial data & metadata
standards

3.
Global Geospatial Information Management
(GGIM) Initiative and UNSC Programme Review

4.
Toward an international statistical geospatial
framework.

5.
A national example from Australia

6.
Conclusions, and possible topics for consideration

Space : The universal frontier


Space and time are the framework within which the mind is
constrained to construct its experience of reality


Immanuel Kant


Statistics has traditional focused more on time series analysis than spatial
analysis?



Connecting information with space assists in connecting it with
experienced reality


Location


experience of place (point)


E
xperience of movement through (or connection across) space (line)


Spatial extent


experience of domain (polygon)


Eg

neighbourhood, boundaries of countries , service (and sales) regions



Along with official statistics, geospatial information is an indispensable
element in the information system of a democratic society

Relevance of spatial information


Spatial information


describes
the
natural and built environment in which citizens live their lives
and consume goods and
services


represents a
key dimension when considering equity of access to

services
and
when measuring efficiency and effectiveness of service delivery
.


p
rovides a meaningful
point of common reference when "overlaying" and
analysing data from multiple sources


official statistics, data about infrastructure and services, data from administrative datasets, data
from social
media

Explosion over the past decade in availability &
use of spatial information


If the first big bang created space and time, a second big bang has resulted in
prevalence of spatial information?



Availability


GPS (Global Positioning System)


Cellular networks


RFID (Radio
-
frequency identification)


Automated geocoding for street and IP addresses



Use


Google Maps


Including Navigation and Transit


Google Earth


(Relatively) user friendly & community supported open source geospatial tools


Including
GeoTools

and
GeoServer


Spatial Data Infrastructure (SDI)


A
data infrastructure implementing a framework of
geographic
data
,
metadata
,
users

and
tools

that are interactively connected
in order to use spatial data in an efficient and flexible
way



Examples


INSPIRE


16 national examples listed on GGIM website

Standardisation driven by industry needs


Use of GIS (Geographic Information Systems) became prevalent
within the industry during the 1980s.



Led to a need to be able to exchange data and metadata between
different GIS.


Defacto

industry standards emerged in the early 1990s (
eg

shapefiles
)



Rise of the Open Geospatial Consortium (OGC) from 1994


Currently comprises 475
companies, government agencies and
universities


Consensus
process to develop publicly available interface
standards


Aim is
to enable
geoprocessing

technologies to interoperate, or
plug & play


OGC has standardised 50+ specifications,


Includes GML, KML (used widely by Google), WMS (Web Map Service), WFS (Web Feature Service)


Also includes registry services, catalogue services, access control, semantic web

Focus on metadata


Standardisation of geographic metadata at the national level
during the 1990s.


International harmonisation begins in 1999


ISO
19115 ("Geographic Information
-

Metadata“) released 2003


ISO
19139
released 2007
to provide
standard
XML representation
for metadata that corresponded semantically with ISO
19115


Some analogies with work to represent GSIM using SDMX and DDI


Many national and transnational implementations use or profile
the ISO metadata standard(s),
eg


INSPIRE


FGDC


Covers identification of resources (can be used for cataloguing
traditional library resources
)
, spatial extent (
eg

bounding box),
temporal extent, data quality
etc







ISO
19115 ("Geographic Information
-

Metadata“) released 2003


ISO 19139 released 2007 to provide standard XML representation
for metadata that corresponded semantically with ISO 19115








Use
of GIS (Geographic Information Systems) became prevalent
within the industry during the 1980s.



Led to a need to be able to exchange data and metadata between
different GIS.


Defacto

industry standards emerged in the early 1990s (
eg

shapefiles
)



Rise of the Open Geospatial Consortium (OGC) from 1994


Currently comprises 475
companies, government agencies and
universities


Consensus
process to develop publicly available interface
standards


Aim is
to enable
geoprocessing

technologies to interoperate, or
plug & play


OGC has standardised 50+ specifications,


Includes GML, KML (used widely by Google), WMS (Web Map Service), WFS (Web Feature Service)


Also includes registry services, catalogue services, access control, semantic web

Global Geospatial Information Management
(GGIM) initiative


Arose from UNSC 2010 paper from Brazilian NSI requesting focus
on GGIM


UNSC
recognized the importance of the integration of geographic and
statistical information


GGIM operates under auspices of the UNSD & UN Cartographic
Section


In
their second meeting, in August 2012, GGIM Committee of
Experts
concluded


One
of nine areas for focus in the future was linking geospatial information to
statistics
.


A
Programme Review by the UNSC would be helpful to support the
development of a Statistical Geospatial Framework in National Statistical
Systems.


ABS volunteered to assist
and
to prepare a paper for consideration by the UNSC in 2013.

UNSC Programme Review


Objectives


To
present a review of current geospatial capabilities and capacity within
NSOs


To
propose roles for NSOs in geospatial activities, with a particular focus on
integrating statistical and geospatial
information


To
propose how geospatial activities could be further developed by NSOs
within countries, and understand user needs driving particular geospatial data
developments


To
explore how NSOs do, or should be doing, geocoding of their data;
and


To
explore how to set standards that integrate data between the two
communities
.


Survey
of Linking Geospatial Information to Statistics, circulated in
October
2012


50 questions


53 NSOs responded


Results reported to UNSC, including detailed analysis.

Developing an international statistical geospatial
framework (1)


Main paper from ABS to UNSC 2013 addressed


The
need for linking socioeconomic information to a
location


The
current situation (summarising the findings of the survey
)


The
future information agenda (such as requirements beyond the Millennium
Development Goals and to inform Sustainable Development
)


Proposed
future directions


Developing an international statistical geospatial
framework (2)


Recommendations (fundamentals were endorsed by UNSC)


An
international conference be convened to identify and address common issues relating
to linking socioeconomic information to a location, including developing best practice
principles


Linkages
between relevant statistical and geospatial organizations be formalized, building
on the efforts of the UN Committee of Experts on GGIM and working with other relevant
international entities, including HLG.


The
approach used in Australia through the Statistical Spatial Framework
(SSF) be
examined as a possible methodology to guide a common global approach to linking
socioeconomic information to a
location.


A
group of experts be established at an international level to further the development of
a common approach to linking socioeconomic information to a location.


In
developing national statistics plans, countries be encouraged to consider the
possibilities for linking statistical and spatial information, consistent with their
development
priorities


As
national statistics offices undertake information management infrastructure
transformation activities, consideration be given to adding geospatial capability, including
the geocoding of
addresses

Bringing it back home


HLG Projects for 2013


Frameworks
and Standards for Statistical Modernization
Project


Common
Statistical Production Architecture Project ("Plug and Play
")


Under “Frameworks and Standards” Work Package 6 is


provide an initial assessment of the role of geo
-
spatial standards in the
modernisation of official statistics, including how they may relate to the GSBPM
and the GSIM
.


Examples
of possible areas for
review


How
should relevant attribute components within unit and dimensional data structures
used in statistics be denoted as geospatial in nature (
eg

if they contain geographical co
-
ordinates or codes for geographic areas) and linked to relevant geospatial metadata (
eg

spatial representation information, reference system information
)?


Both
statistical and geospatial metadata include data quality information. Quality in the
latter case typically refers to precision of spatial positioning. How can we manage the
two types of quality information, making the right information available to the right
user?


Both statistical and geospatial metadata support the identification, description and
subsequent discovery of resources. How can they best work together?


Both statistical and geospatial metadata standards refer to the temporal extent of data
.

Australian Statistical Spatial Framework (SSF)

Context

Key points


The
concept of location or ‘place’ is now a key driver for the ABS
and other organisations collecting, compiling, analysing and
disseminating socio
-
economic statistical
information


SSF
is aimed at providing a consistent and common spatial
approach for all providers of socio
-
economic information
.


Using
a common approach will greatly simplify the process of linking socio
-
economic data sets to help better understand a wide range of complex issues,
improving the ability of government and the community to make more
informed decisions
.


SSF
is essentially a bridge
-

a bridge between the statistical and spatial
communities and the systems in which they
operate.


The common element in this bridge is geography. Geography draws the
socio
-
economic into the spatial community's environment, and makes it
available for use within that environment
.


SSF
needs to meet ABS, Australian Government and National needs and
support International interoperability.

Expected outcomes


By
standardising the process of integrating a range of socio
-
economic information within a location context, the SSF is
expected to
enable


Improved planning for regional economies and
communities


Targeted service delivery at the small area level;
and


Community level decision
making



In
addition, the Framework will support the considerable efforts
currently being made to bring a range of data together to better
understand the causes, impacts and responses at the local level to
national and global concerns such as climate change and
sustainable development

Conclusions


Spatial
data is increasingly prevalent and increasingly used by
governments and the community
.


UNSC and others
recognise the importance
and value of the
integration of geospatial information and statistics in supporting
social, economic and environmental policy
decision
-
making


Developing
, agreeing and applying statistical spatial frameworks
can facilitate this integration at the national
&
international
levels


UNSC
, UNSD and HLG are committed to taking practical steps to
establish such frameworks


The
expertise and experience of specialists in statistical
information management, including statistical metadata,
will
make a vital contribution to defining frameworks which serve as
the "bridge" between the spatial and
statistical communities

Possible considerations for METIS participants


What
additional information and/or actions might help them
better to contribute to this work at the national and/or
international level
?



How
might contributions from the statistical metadata
community best be
progressed
?



What
collaboration arrangements would be best across the
statistical metadata community and beyond that community (
eg

with geospatial data and metadata experts
)?