Spatial data trusts: an emerging governance framework for sharing spatial data

Figures & data

Figure 1. An example of traditional data sharing challenges.

Figure 2. A schematic view of data Trust structure.

Figure 3. A schematic view of data Trust life-cycle (Open Data Institute 2018a).

Figure 4. Overview of MIDATA framework (MIDATA 2022).

Figure 5. Overview of TRUATA data Trust workflow (Lee and Behan 2019).

Figure 6. Proposed pilot climate data Trust project for Indigenous people’s migration in Peru (Global Partnership on Artificial Intelligence 2022).

Figure 7. Overview of PLACE data Trust (Place 2022a).

Table 1. Summary of data Trust initiatives.

Sector	Data Trust	Trustee	Trustors	Beneficiaries	Target uses
Health and human services	Johns Hopkins	Johns Hopkins Institute (JHI)	Patients at JHI	JH School of Medicine and Health System Corporation	Data analytics, medical and clinical research
	MIDATA	ETH Zurich and Bern University	Patients	Public	Medical research and clinical studies
	Silicon Valley Regional Data Trust (SVRDT)	SVRDT, University of California, Local counties	Educational organizations, well-being and human services	Students and schools	Improve educational school and life outcomes
	Open Humans	Open Humans Foundation	Individuals and organizations	Individuals and organizations	Research, education and medical research
Finance and infrastructure	TRUATA	TRUATA	Customers	Business users and scientists	Data analytics services and tools
	Construction Data Trust	Sir Robert McAlpine and their partners	Building companies and government organizations	Building companies and government organizations	Improve performance, productivity and safety issues
	Beneva	Beneva	Insured customers	Customers, employees, and marketers	Simple, secure, and personalized customer insurance services
Civic and social	Commonwealth Data Trust	Office of Data Governance and Analytics	Individuals and organizations	State Virginia agencies and organizations	Data discovery and analytics
	GLA and RBG	GLA and RBG	London citizens	Individuals, companies and organizations	Smart city solutions
	Food Data Trust	University of Lincoln, Food Standards Agency in UK	Stakeholders in the food sector	Stakeholders in the food sector	Safe food production supply networks
	DATA TRUST	DATA TRUST	Individuals	Political parties, companies and non for profit organizations	Political, advocacy, educational and other commercial and non-commercial purposes
	WIF	WIF and funding partners	Workers, trade unions and organizations	Workers, trade unions and organizations	Protection of workers’ digital rights and improving work conditions
First Nations	Maranguka Community Hub	Bourke Tribal Council	Federal and local government organizations	Indigenous people in Bourke (NSW, Australia)	Indigenous data sovereignty, well-being and services
	Proposed by GPAI	Indigenous people and representative organizations	Indigenous, government and NGOs	Indigenous Government, NGOs, and others	Protect data on the migration of Indigenous people affected by climate change
Spatial	PLACE	PlACE Board	Local companies	Government and other PLACE members	Produce, protect and share ultra-high resolution optical imagery data
	Proposed by GPAI	London Cycling Data Trust	Cycling community and individuals	Government and transport authority	Improve cycling network

Figure 8. Five special characteristics of spatial data Trust.

Figure 9. Graphical overview of proposed spatial data Trust and its effective role in collecting, managing, protecting and sharing personal location data.

Figure 10. Data valuation framework (Deloitte 2020).

Table 2. Five special characteristics, challenges, and general recommendations associated to spatial data Trusts.

Characteristics	Key challenges	General recommendations
Domain specificity	Unique structure and provenance information of spatial data compared to other data types	Required professional expertise to manage, assess, protect and share spatial data
Data quality	Spatial data quality depends on special methods for capturing, protecting, managing and sharing	Enable estimation and assessment of spatial data quality based on provenance information and its fitness for use (i.e. purpose)
Data privacy	Location data such as home address is a unique personal identifier; for example, a frequent visit to the same location (hospital, school, office) can reveal a person’s sensitive information and identity	Provide agreed privacy protection measures, rules, policies, and processes for deposit, access, monitoring, management, anonymization, and auditing to ensure the required location privacy and to enable sharing personal and sensitive location data
Data sovereignty	Insufficient control and participation by Indigenous People in managing, protecting and sharing spatial data; protection of national interests	Ensure careful management, protection and control over sharing Indigenous data; and impose stronger control for protecting spatial data with national interest while also enabling multi-national data sharing
Data value	High growth rate of spatial data sources, analysis tools, and applications	Include a data valuation framework to define and quantify the value of spatial data

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