WILDSPACE: Wildlife data integration in a government context · 2017-12-21 · WILDSPACE: Wildlife...
32
WILDSPACE: Wildlife data integration in a government context OFWIM Annual Meeting Schoodic Education and Research Center 2013-10-03 Eric Woodsworth Canadian Wildlife Service, Saskatoon
Transcript of WILDSPACE: Wildlife data integration in a government context · 2017-12-21 · WILDSPACE: Wildlife...
WILDSPACE: Wildlife data integration
in a government context
OFWIM Annual Meeting
Schoodic Education and Research Center
2013-10-03
Eric Woodsworth
Canadian Wildlife Service, Saskatoon
Page 2 – October-8-13
Overview
• Environment Canada (EC) mandate for wildlife data
management
• Purpose of Project WILDSPACE
• Data management approach
• Scope of data content
• Client service and accessibility
• Context
• New Development
Page 3 – October-8-13
Data management mandate
• EC wildlife programs generate data in response to specific population and habitat management objectives.
• Combination of long term monitoring programs and short term synoptic surveys.
• National level population and habitat reporting requires synthesis of regional datasets and interoperability with partners
• Potential efficiency in an approach that addresses needs of multiple programs (silos) in a common system.
• Federal data management requirements
EC wildlife programs:
Species at Risk Act
Migratory Bird Convention Act
Canada Wildlife Act
additional programs/policies
Page 4 – October-8-13
Purpose:
To improve data management at the project level while capturing data and applying standards at the corporate level to facilitate integration and access.
Project level: transition from fragmented spreadsheets to standardized formats and software; avian monitoring review showed 70% of bird data not managed
Corporate level: facilitate integrated discovery and access to data via ODBC and web services
Page 5 – October-8-13
Design drivers
Wildlife and habitat observations
Habitat assessments
Site versus sub-site resolution
Informal surveys
Formal population surveys
presence/absence
Nest records and productivity
Individual-based histories
abundance
density
• Lack of standardization
Pervasive problem in ecology heterogeneity & silos
Solution 1: Data content standards
– Datasets classified based on content
– Core data elements in each class plus project-specific variables
– Standardized documentation of search effort, observer skill,
sampling design etc
Solution 2: Data formatting standards
- Common species taxonomy, synonomy across standards and time
- Controlled vocabularies – existing standards or create and publish
- Sampling sites, sub-sites referenced via centroids; linkage to spatial
features
- Decimal lat/long as per Darwin Core
- Date/Time as per ISO 8601
Page 6 – October-8-13
Design Drivers …
• Small space-time extents of studies data integration
Solution: EAV Design
– dynamic database design (Entity-Attribute-Value) --developed by
the epidemiology discipline.
– All core variables available as integrating factors
– GIS: integration (overlays & analysis) by geography
– WILDSPACE: integration by any core variable subset including
geography
Page 7 – October-8-13
SiteID NWA1
Date 2009-06-12
Time 05:05
ObserverID hawk, r g
Latitude 49.543
Longitude -110.456
Datum NAD83
TempStart 8
WindStart 0
PrecipStart fog/mist
CloudCovStart 1
Page 8 – October-8-13
SiteID NWA1
Date 2009-06-12
Time 05:05
ObserverID hawk, r g
Latitude 49.543
Longitude -110.456
Datum NAD83
TempStart 8
WindStart 0
PrecipStart fog/mist
CloudCovStart 1
2009-06-12 NWA1 1 Hawk, r g 05:05 0 Fog/mist 8 1
Page 9 – October-8-13
Page 10 – October-8-13
Page 11 – October-8-13
• Template now specifies information on:
– Date
– Time
– Observation conditions
– General location (Site)
– Specific location (Station)
– Coverage
-geospatial
analysis
-trend analysis
-Survey effort
Page 12 – October-8-13
Survey Effort
• Survey effort records (Site Surveys table) relate to
Species Observation Log in a 1 : many relationship
• Site Surveys template sets the stage for observation
records
• Like the Survey effort template Species Observation Log
is extensible…
• Song bird point count example
SEOW
Page 14 – October-8-13
SEOW
Page 19 – October-8-13
O
Page 20 – October-8-13
Keith Hurley: “ Technology is less important than the questions you ask”
Eric Woodsworth: “It’s also less important than the standards you use”
Page 21 – October-8-13
WILDSPACE Best Practices
Page 22 – October-8-13
Data management approach
• WILDSPACE is a DM workbench that
formalizes the DM life cycle.
• Content standards
– metadata and data
• Standards of practice
– georeferencing, codification and data
checking
– guidance on data mining and synthesis
Page 23 – October-8-13
Components of the WILDSPACE system
Page 24 – October-8-13
WILDSPACE content types
Observations resulting from EC projects:
– Wildlife population surveys
– Habitat assessments
– Productivity studies
– Mark-recapture studies
• Incidental observations and targeted studies
Page 25 – October-8-13
Volume of content
Core/custom observations resulting from EC projects:
– Recordsets: 233
– Observers: 752
– Sites: 9K
– Site Surveys: 130K / 330K
– Species-counts: 400K / 1.3M
– Habitat assessments: 30K / 280K
– Productivity studies (nest visits): 5K / 12K
– Marking events: 5K / 27K
– Mark recaptures :6K / 30K
Page 26 – October-8-13
Geographic Scope
Page 27 – October-8-13
Client service and accessibility
• Accessibility by user group – Dependent on multi-faceted sensitivity, at observation resolution
• Data dissemination methods – Data views developed in consultation with user communities (e.g. NS, OGD’s)
– Availability in multiple formats e.g. WS content standards as Excel, CSV, but also exchange formats such as BMDE, Darwin Core
Page 28 – October-8-13
Corporate Environment
• IT context always changing: client-server synchronization
• IM context changing
– EC Data Management Program
▪ Governance: stewardship, data standards & policies, architecture
▪ Data Catalogue, CSDGM to ISO, linkage to Open Data
▪ Data Access & Sharing: WMS/WFS, consolidated dissemination
▪ Data Consolidation: Master Data
▪ Data Integration: support reporting & decision making
• How to adapt?
– Little opportunity for independent projects
– If you can’t beat them…
Page 29 – October-8-13
In development….
• ISO-compliant metadata profile & relevant CV’s
– Keyword fields
• Data Security/Sensitivity Model
– Ecological & landowner issues vs. legislation & policy
• Spatial enablement / linkage
• Data Licensing and Partnership Agreements
• Project management & engagement
• Succession & data rescue – low priority
• QA/QC, quality metrics
• Platform independence
• Bilingual interface and CV’s
Page 30 – October-8-13
Current Status
• Updating CV’s, including species list
• Consultations on security model
• Clearer definition of system requirements, use cases,
test data
• Supporting users
– Data, metadata standards
– Prep for bulk data entry
– Awareness of changes
• Improved management of software development
- internal development capacity; contracting rules inconsistency
• And of course: Conflicting priorities, uncertain funding
Page 31 – October-8-13
Conclusions
• WILDSPACE takes a life cycle approach to data management
• It can provide integrated access to most biodiversity data in EC
• It balances Project Officer needs with Government of Canada requirements for standardization and dissemination
• Data standards increase quality and relevance to internal and external clients
• It gains scientific relevance through conceptual development by biologists, but….
• It needs more rigor from a development & project management perspective
Page 32 – October-8-13
Questions?
