data ecosystems for geospatial data · 2020. 6. 17. · data ecosystems for geospatial data data...
TRANSCRIPT
Data Ecosystems
for
Geospatial Data
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/27812Data ecosystems for geospatial data -
JRC/IPR/2019/MVP/2781
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Welcome and some hints for participants
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
“Data ecosystems for geospatial data - Evolution of Spatial Data Infrastructures”
• Ongoing study, started in January 2020
• Performed by the Luxembourg Institute of Science and Technology (LIST) in close collaboration with Joint Research Centre of European Commission.
• Identify and analyse a set of successful data ecosystems and to address recommendations in support of the implementation of data-driven innovation in line with the recently published European strategy for data.
Sharing the methodological approach undertaken;
Presenting identified data ecosystems
Call for:
• Ecosystem use cases
• Identifying & reaching ecosystems' experts
Rationale of the session
4
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
1. Landscape of the study « Data Ecosystems for Geospatial Data - Alexander
Kotsev, JRC
2. Study objectives and workplan - LIST
3. Data Ecosystems Identification and Selection - LIST
4. Ecosystem Thinking & Modular methodological framework - LIST
5. Illustration of data ecosystem analysis with experts
Rennes Urban Data Interface - Ghislain Delabie, OuiShare
Machine Learning for Geospatial Data - Sean Wiid, UP42
Data cycles, feedback from the field - Jean-Charles Simonin, ENEO
6. Interactive session
7. Conclusion & Next activities
AGENDA
5
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
1. Landscape of the study « Data Ecosystems for Geospatial Data”
- Alexander Kotsev, JRC
2. Study objectives and workplan - LIST
3. Data Ecosystems Identification and Selection - LIST
4. Ecosystem Thinking & Modular methodological framework - LIST
5. Illustration of data ecosystem analysis with experts
Rennes Urban Data Interface - Ghislain Delabie, OuiShare
Machine Learning for Geospatial Data - Sean Wiid, UP42
Data cycles, feedback from the field - Jean-Charles Simonin, ENEO
6. Interactive session
7. Conclusion & Next activities
AGENDA
6
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
Alexander Kotsev, JRC
Landscape of the study
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
INSPIRE - STATE OF PLAY
8
• The INSPIRE Directive is 13 years old (now formally a teenager)
• Deadlines for full implementation are approaching
• What has changed in the past few years, and what are our
outstanding challenges?
1. Technological perspective
2. Organisational perspective
3. Political perspective
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
We have come a long way!
1. TECHNOLOGICAL PERSPECTIVE
9
Technology in 2007
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
API4INSPIRE
260+ million spatio-temporal, pan-European
measurements
• standard-based access through OGC
SensorThings API
• data provided in a public cloud through
virtualization
• harvested air quality data from multiple sources:
• national INSPIRE download services
• European Environment Agency
1. TECHNOLOGICAL PERSPECTIVE
10
http://www.datacove.eu/ad-hoc-air-quality/
Technology in 2020
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
1. Heterogeneous data sources○ Citizen data / personal data
○ Remote sensing (e.g. Copernicus)
○ IoT
2. New technologies
○ Data handling at the edge/fog
○ Virtualisation and cloud computing
○ From data collection to data connection (APIs)
3. New standards
○ Embracing web best practices
○ Following an agile and inclusive approach
■ SensorThings API
■ OGC API - Features
1. TECHNOLOGICAL PERSPECTIVE
11
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
1) Who does what in the European context?
○ Distributed system
■ 7000+ data providers (tip of an iceberg)
■ Federated governance
■ Excellence on subnational level
○ Emerging agile approaches at multiple levels
■ Hackathons, code sprints
■ wikifikation / Git repositories
2) Resources for SDI and sustainability of infrastructures
○ Many developments are based on projects
■ Projects do end
3) How to modernise/update existing infrastructures
■ Technologies changes are fast
■ Procurement and organisational changes are not
4) “Follow the user”
○ Sure, but how?
2. ORGANISATIONAL PERSPECTIVE
12
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
1) Europe Fit for the Digital age
○ European Strategy for Data
• Establish a pan-European single market for data
• Regulatory sandboxing
• Emphasis on the benefits of different actors
• Sector-specific data spaces
○ White paper on AI
• Extensive reuse of available data
○ Open Data Directive
• High-value datasets (exposed through APIs)
2) European Green Deal
o GreenData4all initiative
• Modernising INSPIRE
o Destination Earth
3. POLITICAL PERSPECTIVE
13
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
• The INSPIRE community is healthy (and growing)
• There are favourable conditions for data-driven
innovation in Europe!
Q: Is spatial still special?
A. Yes, everything that happens happens
somewhere. Location information is fundamental
for an increasing number of use cases.
B. No, SDI developments should be merged into
mainstream ICT.
● We should
○ Avoid that SDIs become a big silo
○ Focus on sustainability & scalability
○ Learn from existing ecosystems
The context for the evolution of SDIs
14
FROM SDIs TO DATA ECOSYSTEMS?
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
1. Landscape of the study « Data Ecosystems for Geospatial Data - Alexander Kotsev,
JRC
2. Study objectives and workplan - LIST
3. Data Ecosystems Identification and Selection - LIST
4. Ecosystem Thinking & Modular methodological framework - LIST
5. Illustration of data ecosystem analysis with experts
Rennes Urban Data Interface - Ghislain Delabie, OuiShare
Machine Learning for Geospatial Data - Sean Wiid, UP42
Data cycles, feedback from the field - Jean-Charles Simonin, ENEO
6. Interactive session
7. Conclusion & Next activities
AGENDA
15
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
Luxembourg Institute of Science and Technology (LIST)
Prune GAUTIER
Sébastien MARTIN
Slim TURKI
Research and Technology Organization (RTO)
Develops innovative and competitive solutions in
response to the key needs of Luxembourgish and
European economies.
• Employees: ~600 | Budget: EUR 66 millions
• Activities:
• Fundamental and applied scientific research,
development of knowledge and competences;
• Experimental development, incubation and transfer of
new technologies, competences, products and services;
• Scientific support to the policies of the Luxembourgish
government, businesses and society in general;
• Doctoral and post-doctoral training, in partnership with
universities.
LUXEMBOURG INSTITUTE OF SCIENCE AND
TECHNOLOGY
17
Interdisciplinary portfolios
• Smart cities
• Spatial sector
• Industry 4.0
• FinTech and RegTech
Fields of activity
• Digital innovation
• Ecological innovation
• Materials innovation
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
Objectives of the study
18
Investigate how Spatial Data Infrastructures (SDIs) can evolve into data ecosystems to support the goals of digital government in Europe.
• Take into account factors such as relevant actors, their responsibilities and data value chains, emerging data sources (e.g. the Internet of Things) and technical/architectural approaches (e.g. digital platforms, mobile-by-default, Application Programming Interfaces).
Address the interoperability between data ecosystems for different sectors and/or different countries and cross-cutting requirements for geospatial data.
Provide an input into the discussion on the future evolution of INSPIRE after the conclusion of the current implementation programme in 2020.
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
Identify existing Data Ecosystems and case studies for interoperability between such Data Ecosystems
Analyse / compare characteristics / requirements of Data Ecosystems and their interoperability
Analyse in depth a subset of Data Ecosystems
Develop recommendations for setting up Data Ecosystems and to enable interoperability between them
Work plan
19
> Desk research> Academic literature
> Reports (official reports, projects reports, etc.)
> Companies' documentation
> Qualitative data (interviews)
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
Technology Policies Standards, and Human resources
Necessary too Acquireo Processo Storeo Distribute, ando Improve utilization of Geospatial data
• Linear process in which data is published and made discoverable and usable
• No feedback loop between users and providers - critical to the potential sustainability and evolution of data ecosystems.
SPATIAL DATA INFRASTRUCTURES (SDIs)
Definition
20
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
DATA ECOSYSTEMS
21
Eco
syste
mD
ata
Eco
syste
m
Evolve and adapt through a cycle of
data creation and sharing, data analytics,
and value creation in the form of new products,
services, or knowledge, which, when used,
produce new data feeding back into
the ecosystem.
Complex socio-technical system of People,
Organizations, Technology, Policies and Data In
specific Area/Domain, that Interact with one
another and their surrounding environment to
achieve a specific Purpose
Data ecosystem =
Ecosystem analysed with a strong focus on data issues
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
• How Ecosystem Thinking may contribute to identify and
trigger the uptake of SDIs?
• What is the Position and Role of SDIs in Self-sustainable Data
Ecosystems?
• Addressing interoperability between Data Ecosystems
WHAT?
22
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
1. Landscape of the study « Data Ecosystems for Geospatial Data - Alexander Kotsev,
JRC
2. Study objectives and workplan - LIST
3. Data Ecosystems Identification and Selection - LIST
4. Ecosystem Thinking & Modular methodological framework - LIST
5. Illustration of data ecosystem analysis with experts
Rennes Urban Data Interface - Ghislain Delabie, Simon Saint Georges
Machine Learning for Geospatial Data - Sean Wiid, UP42
Data cycles, feedback from the field - Jean-Charles Simonin, ENEO
6. Interactive session
7. Conclusion & Next activities
AGENDA
23
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
• Looking for cases studies illustrating shifts from Data Infrastructures to
Self-sustainable Data Ecosystems
• With potential to enrich recommendations
• Diversity of the case studies/use cases is important.
• Regional/City, if possible with Public, Private sectors and Citizen
involvement.
• Thematic: Mobility, Agriculture, Insurance, etc.
• Generic, involving user data and feedback, such as recommendation in
tourism
• Community oriented, such open science networks
• Place / role of spatial data
• Where expert(s), documentation or standards are available and
accessible
DATA ECOSYSTEMS IDENTIFICATION
24
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
Evaluation
DATA ECOSYSTEMS IDENTIFICATION
25
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
DATA ECOSYSTEMS IDENTIFICATION
26
Rennes Urban
Data Interface
Tracking Technologies
for Supply Chain
Hotel reviews Tripadvisor, Yelp,
etc.
VOD Entertainment
(Netflix, Disney, etc.)
Vehicles and planes
fleets predictive
maintenance
Circular
economy
Pandemic
Data
Weather
Forecast
Digital patient record
(e-health)
B2C
e-commerce
Data
Marketplace
Smart
Agriculture
ML services on
space imagery
Pan-European Invasive
Alien Species Monitoring
and Reporting
Crowdsourced traffic
information (Waze)
Energy efficiency of
buildings
Location aware
dating services
National wide
data ecosystem
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
• Sought expertise
• General overview of the Data Ecosystem
• Specific (actual ecosystem participants speaking from their perspective)
• Kinds of experts
• Data owners (public and private);
• Data re-users (public and private)
• Platform actors
• Academic
• (End-users)
• Available for some online/onsite interviews
Looking for Experts
27
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
Q1: Do you know about Self-sustainable Data Ecosystems?
• With potential to enrich recommendations
• Where expert(s), documentation or standards are available and
accessible
Q2: Would you kindly recommend experts of already identified data
ecosystems?
Please share your suggestions
• Slim TURKI, LIST, [email protected]
• Alexander KOTSEV, JRC, [email protected]
• Using the Chat
IDENTIFICATION of DATA ECOSYSTEMS
28
Suggestions?
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
1. Landscape of the study « Data Ecosystems for Geospatial Data - Alexander Kotsev,
JRC
2. Study objectives and workplan - LIST
3. Data Ecosystems Identification and Selection - LIST
4. Ecosystem Thinking & Modular methodological framework -
LIST
5. Illustration of data ecosystem analysis with experts
Rennes Urban Data Interface - Ghislain Delabie, OuiShare
Machine Learning for Geospatial Data - Sean Wiid, UP42
Data cycles, feedback from the field - Jean-Charles Simonin, ENEO
6. Interactive session
7. Conclusion & Next activities
AGENDA
29
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
• How the ecosystem thinking may contribute to identify and trigger the uptake of spatial data infrastructures?
• (Oliveira & Loscio, 2018) : “Lack of well-accepted definition of the term Data Ecosystem”
• Ecosystem is a paradigm, to analyse a network and to act on it.
• Ecosystem emergence (Thomas, 2015)
• Ecosystem health
• Increasing data-reuse beyond the original purpose
• New modes of data creation
• Case of non-geospatial data
ECOSYSTEM THINKING
30
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
(Self-) sustainability:
• Well-balanced ecosystem
• Able to function and development without
direct government support
Combination of supportive factors:
• Value creation and business model
• Value distribution
FOCUS ON BUSINESS MODELS &
VALUE CREATION
31
Value Creation
Business
Models
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
• Orchestrating an ecosystem means managing the network relations.
• Originating from innovation networks (Gawer & Cusumano, 2014).
• Keystone actor(s) / actor(s) leadership• Orchestration concepts
• Ecosystem membership (size, diversity)
• Ecosystem structure (density, autonomy)
• Ecosystem position (centrality, status)
• Appropriability regime
• Knowledge mobility
• Ecosystem stability
• Kinds of orchestration• Organizational orchestration
• Technical orchestration• Standard / industry standard adoption
• Internal / external interoperability
• But intertwined
FOCUS ON ORCHESTRATION
32
Orchestration
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
Modular
Analysis
Framework
A MODULAR ANALYSIS FRAMEWORK
33
1
2
3
> Desk research> Academic literature
> Reports (official repor
ts, projects reports, etc.)
> Companies'
documentation
> Qualitative data
(interviews)
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
A MODULAR ANALYSIS FRAMEWORK
34
Ecosystem Summary
Provides an overall representation of the components of the ecosystem.
1
Focuses on Data Ecosystems key aspects
• Goal / purposes
• Main actors, their exchanges and communication
• Legal context and governance
• Technology specific aspects
• Cost and revenues / benefits
• Faced Barriers and incentives
First level of assessment, inspired by the
Business Model Canvas from Alexander
OSTERWALDER
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
A MODULAR ANALYSIS FRAMEWORK
35
Ecosystem Dynamics
Represents the interactions between stakeholders.
2
The graphical representation of this second layer of the
Framework finds its inspiration in network modelling tools.
While illustrating the resources exchanged between the
stakeholders, and their value, we can:
- follow the value creation,
- highlight the orchestration
- and evaluate the sustainability of the ecosystem ( balanced
counterparts missing, actor missing, value lost).
Goal centred, it gives a representation of
the actors commitment level and strength of interactions
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
A MODULAR ANALYSIS FRAMEWORK
36
Ecosystem Data flows
Focuses on the associated data flows / Data life cycles
3
• Data flows as proxy of the maturity and health of an
ecosystem
• Data cycle: (Pollock, 2011) "infomediaries — intermediate
consumers of data such as builders of apps and data
wranglers — should also be publishers who share back
their cleaned / integrated / packaged data into the
ecosystem in a reusable way — these cleaned and
integrated datasets being, of course, often more valuable
than the original source.“
• Approach and graphical representation are derived from
product lifecycle model
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
Q: Do you agree on the dimensions?
Q: Do you see missing dimensions?
A MODULAR ANALYSIS FRAMEWORK
37
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
A MODULAR ANALYSIS FRAMEWORK
38
Ecosystem Summary
Provides an overall representation of the components of the ecosystem.
1
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
A MODULAR ANALYSIS FRAMEWORK
39
Ecosystem Dynamics
Represents the interactions between stakeholders.
2
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
A MODULAR ANALYSIS FRAMEWORK
40
Ecosystem Data flowsFocuses on the associated data flows.
3
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
1. Landscape of the study « Data Ecosystems for Geospatial Data - Alexander Kotsev,
JRC
2. Study objectives and workplan - LIST
3. Data Ecosystems Identification and Selection - LIST
4. Ecosystem Thinking & Modular methodological framework - LIST
5. Illustration of data ecosystem analysis with experts
Rennes Urban Data Interface - Ghislain Delabie, OuiShare
Machine Learning for Geospatial Data - Sean Wiid, UP42
Data cycles, feedback from the field - Jean-Charles Simonin,
ENEO
6. Interactive session
7. Conclusion & Next activities
AGENDA
41
RENNES URBAN DATA INTERFACE
42
Ghislain Delabie, OuiShare
MACHINE LEARNING FOR GEOSPATIAL DATA
Sean Wiid, UP42
DATA CYCLES, FEEDBACK FROM THE FIELD
Jean-Charles Simonin, ENEO
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
1. Landscape of the study « Data Ecosystems for Geospatial Data - Alexander Kotsev,
JRC
2. Study objectives and workplan - LIST
3. Data Ecosystems Identification and Selection - LIST
4. Ecosystem Thinking & Modular methodological framework - LIST
5. Illustration of data ecosystem analysis with experts
Rennes Urban Data Interface - Ghislain Delabie, OuiShare
Machine Learning for Geospatial Data - Sean Wiid, UP42
Data cycles, feedback from the field - Jean-Charles Simonin, ENEO
6. Interactive session
7. Conclusion & Next activities
AGENDA
45
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
Questions?
Discussion - Interactive session
46
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
1. Landscape of the study « Data Ecosystems for Geospatial Data - Alexander Kotsev,
JRC
2. Study objectives and workplan - LIST
3. Data Ecosystems Identification and Selection - LIST
4. Ecosystem Thinking & Modular methodological framework - LIST
5. Illustration of data ecosystem analysis with experts
Rennes Urban Data Interface - Ghislain Delabie, OuiShare
Machine Learning for Geospatial Data - Sean Wiid, UP42
Data cycles, feedback from the field - Jean-Charles Simonin, ENEO
6. Interactive session
7. Conclusion & Next activities
AGENDA
47
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
Conclusion & call for participation
48
Next steps:
• Experts interviews, desk analysis, documentation
review
• Selection and in analysis depth analysis of 5
ecosystems
• Recommendations for setting up self-sustainable data
ecosystems
• Stakeholders workshop to present and challenge the
findings (Fall 2020)
Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781
• Joint Research Centre:
• Alexander KOTSEV, [email protected]
• Luxembourg Institute of Science and Technology
• Slim TURKI, [email protected]
Contacts
49