Content area
Background
Spatial Data Infrastructure (SDI) frameworks provide a foundation for the integration and sharing of geospatial data, essential for public health decision-making. Effective governance of SDIs plays a critical role in improving health outcomes through disease surveillance, resource allocation, and health equity. While well-established SDIs like INSPIRE (Europe), NSDI (USA), and ASDI (Australia) offer functional models, they still face challenges. In contrast, regions like Africa and Asia struggle with SDI development due to disparities in funding, regulatory compliance, and technological integration.
Objectives
This study evaluates the role of government-regulated SDIs in shaping public health outcomes, focusing on gaps in governance, accessibility, interoperability and policies guiding geospatial data in public health contexts.
Methods
A systematic search was conducted across Scopus, PubMed, and Google Scholar on May 30th, 2024, yielding 129 articles. After screening, 75 articles were excluded for being non-SDI specific, editorial, or abstract-only. Data extraction focused on SDI governance and public health outcomes, and thematic analysis was used to assess the impact on disease surveillance, healthcare access, and data sharing.
Results
Out of 127 articles obtained, 33 addressed technological aspects of SDIs, with only two specifically focusing on public health. No studies addressed SDI policies directly within the public health framework, highlighting a significant research gap.
Conclusion
The lack of SDI policy integration in designing its platform in a public health context underscores the need for targeted research. Improved governance, policy frameworks, and collaboration in regions like Africa and Asia are essential to developing SDIs that can enhance public health outcomes.
Introduction
Spatial Data Infrastructure (SDI) refers to the framework of technologies, policies, and institutions that enable the effective creation, sharing, and utilisation of geospatial data. Spatial Data Infrastructures (SDIs) are essential for public health as they provide a framework for organising and sharing geospatial data to enhance health outcomes [1]. SDI governance plays a significant role in public health by influencing the effectiveness of policies, disease surveillance and interventions to improve healthcare delivery and disease prevention. For example, the Jon Snow spatial distribution model for cholera and the “Atlas of Heart Disease and Stroke” project by CDC and WHO using the ArcGIS technology show the distribution of heart disease death rates between 2015 and 2017 in the United States (US) [2].
Geographic Information Systems (GIS) methods have been instrumental in identifying spatial inequities in access to healthcare facilities, offering valuable quantitative evidence for policy analysis and decision-making. These methods enable the visualisation of healthcare distribution patterns, helping to pinpoint areas with inadequate access to services. Despite the successes of GIS in public health, there remains significant room for improvement in terms of spatial data accessibility, data quality, and advanced analysis techniques. These enhancements can be realised through the establishment of an effectively organised and accountable Spatial Data Infrastructure (SDI) framework, which would facilitate better data integration, standardisation, and availability [3].
The government as a foremost actor in promoting and regulating SDIs is crucial for ensuring the standardisation, accessibility, data governance and interoperability of spatial data, which are vital for informed decision-making in public health and ensuring responsible handling of data in the context of the populace [4]. Studies have shown that well-regulated SDIs established through government policies can improve public health outcomes [5]. Nonetheless, the effectiveness of SDI governance varies, raising questions about its impact on health outcomes. Regions with comprehensive SDI policies tend to have better health indicators due to the availability of accurate geospatial information for planning and implementing health programs [6].
Existing research highlights that frameworks such as the Infrastructure for Spatial Information in Europe (INSPIRE), the National Spatial Data Infrastructure (NSDI) coordinated by the Federal Geographic Data Committee (FGDC) in the United States, Canadian NSDI and the Australian Spatial Data Infrastructure (ASDI) are among the more well-established regional Spatial Data Infrastructures (SDIs). These frameworks have improved significantly in promoting the sharing of geospatial data between government agencies and the private sector, providing vital support for public health and other sectors. However, these SDIs face persistent challenges, including issues related to data quality, limited data sharing with private entities and interoperability [7, 8–9].
In contrast, Africa and Asia face greater difficulties in establishing effective collaborative SDIs due to factors such as international disparities, challenges with regulatory compliance, integrating traditional data sources with modern geospatial technologies, and insufficient funding [10, 11]. These obstacles hinder the development and implementation of robust SDIs that could significantly enhance public health outcomes in these regions.
The importance of governance in shaping SDIs to positively affect public health cannot be underestimated. The benefits of government-regulated SDIs extend to policymakers, healthcare providers, and researchers [12]. Existing literature has highlighted the positive relationship between government-regulated SDIs and public health outcomes, supporting evidence-based decision-making in healthcare [13]. Despite these benefits, overregulation has been seen to stifle the development of collaborative SDI which is seen in the experience faced in the development of Asia–Pacific SDI(APSDI) which has yet to come to fruition since the 1990s due to uneven participation by member nations, lack of a central authority, and defining a common approach.
The review aims at evaluating government-regulated spatial data infrastructures (SDIs) on public health outcomes from existing literature and providing policy recommendations based on case studies and comparative analysis to enhance public health decision-making and resource allocation.
Main text
SDI development
The concept of SDI has evolved significantly, driven by technological advances and the increasing value of spatial data. Initially, SDI development was ad hoc, with individual organisations and governments creating isolated systems. The establishment of the National Spatial Data Infrastructure (NSDI) in the United States in the 1990s marked a shift towards standardised, interoperable systems. The European Union's INSPIRE Directive (2007) further emphasised the need for comprehensive SDIs to support environmental and public health policies [9].
The NSDI and INSPIRE initiatives, despite their promise of interoperability, have fallen short in practice. In the US, the implementation of NSDI has been hindered by inadequate funding and coordination between federal and state levels, resulting in subpar data quality and availability. The INSPIRE Directive's framework, while being one of the most comprehensive and widely implemented SDI frameworks, has also been criticised for its complexity, leading to varying degrees of adoption and compliance among member states.
Despite challenges, there has been some progress with the advocacy for the development of global standards and systems such as the Open Geospatial Consortium (OGC) and the Global Spatial Data Infrastructure (GSDI), to capture intercontinental spatial elements through advancement in technology in GIS and remote sensing, a land stride towards eliminating spatial data inequities [14].
In addition, SDIs are now seen as complex adaptive systems (CAS) that adapt and self-organize through interactions and feedback loops, making them sensitive, nonlinear, and unpredictable. The evolution underscores the importance of effective data management, necessitating a focus on user needs, collaboration, and embracing their complex nature to realise their potential [15] (Figs. 1 and 2).
[See PDF for image]
Fig. 1
SDI components.
Adopted from [38]
[See PDF for image]
Fig. 2
SDI Technological Framework. Web Map Service (WMS), Web Feature Service (WFS), Web Coverage Service (WCS), Catalog Service for Web (CSW), and Web Processing Service (WPS).
Adopted from [32]
Frameworks and indicators to consider in SDI development
Spatial Data Infrastructure (SDI) development is guided by several conceptual frameworks, including the Sustainable Development Goals (SDGs), which highlight the importance of data and information in achieving health and well-being. Currently in development, the Global Spatial Data Infrastructure (GSDI) framework aims to harmonise spatial data sharing and utilisation globally, promising to foster greater collaboration and innovation in the geospatial community [16]. While awaiting GSDI to be fully fledged, the National Spatial Data Infrastructure (NSDI) currently serves as a cornerstone for most national and regional SDI development, providing a foundational framework for accessing and utilising spatial data e.g. the NSDI by Federal Geographic Data Center (FGDC) in the United States, Canadian Spatial Data Infrastructure, Indian NSDI, and the regional INSPIRE for Europe, and so on. The NSDI framework consists of four essential components: Data, Technology, Actors, and Policies, which work together to facilitate effective decision-making and support a wide range of applications [17]. Although the NSDI framework promotes coordination and cooperation among stakeholders, its implementation in low- and middle-income countries (LMICs) is often hindered by resource and technical expertise limitations. Addressing these challenges is crucial to unlock the full potential of SDI in support of sustainable development [18].
Regions with well-established SDI
Europe (INSPIRE directive)
In Europe, the implementation of Spatial Data Infrastructures (SDIs) has been significantly influenced by the Infrastructure for Spatial Information in Europe (INSPIRE) Directive, which aims to facilitate the sharing of environmental geographic information among public sector organisations across member states [19]. The directive has established a framework that encourages the integration of spatial data from various sectors, promoting interoperability and data sharing. However, challenges persist, including the need for harmonisation of data standards across different countries and sectors, which has historically been limited due to varying national regulations and practices. Furthermore, the engagement of private sector stakeholders in SDI initiatives remains a challenge, as data-sharing mechanisms with non-public entities have not been fully developed, hindering the potential for comprehensive data integration [7].
The directive has enabled extensive cross-border data sharing, benefiting public health by improving disease surveillance and environmental health monitoring. For example, Germany and the Netherlands have leveraged INSPIRE to enhance their public health systems significantly. However, other European Union countries lag due to resource constraints and administrative burdens, demonstrating the uneven impact of the directive.
United States (NSDI)
The National Spatial Data Infrastructure (NSDI) in the U.S., coordinated by the Federal Geographic Data Committee (FGDC), provides a framework for sharing geospatial data across governmental levels and sectors [9]. Launched in the 1990s, the NSDI enhances the accessibility and interoperability of spatial data for applications in urban planning, public health, and more. Its successes include establishing data standards and creating initiatives like the National Map, improving geospatial data quality and integration [20].
However, challenges such as inconsistent data-sharing practices among stakeholders and insufficient funding hinder the full realisation of NSDI’s potential. The evolving nature of technology, requiring updates in geospatial tools, further compounds the need for resources. Additionally, concerns about data privacy and security arise as geospatial data is increasingly applied in sensitive areas like law enforcement and public health [14]. In comparison, similar infrastructures globally also struggle with aligning local, regional, and national priorities and ensuring sustainable funding [21]. Despite these obstacles, the NSDI's ongoing evolution toward cloud computing and big data analytics ensures its adaptability in a dynamic data environment [14].
The NSDI framework supports numerous public health applications, from emergency response to disease tracking. The CDC's use of SDI for tracking disease outbreaks has been instrumental [22]. However, disparities in data quality and accessibility across states reveal gaps in the NSDI's implementation.
Australia (ASDI)
Australia’s Spatial Data Infrastructure (ASDI), a national geographical information system in Australia, has significantly evolved, driven by the need to leverage geospatial data across various sectors, notably public health [23]. The ASDI enhances the accessibility, quality, and interoperability of spatial data to support informed decision-making [8, 24, 39]. Additionally, the Australian Government's Geospatial Strategy promotes standardised data practices, improving data integration across sectors [8].
ASDI's successes are evident in public health applications, such as the Western Australia Data Linkage System (WADLS), which integrates health datasets including routine geocoding for research and policy development [24]. ASDI also supports geospatial solutions in disease tracking and environmental monitoring, enhancing public health responses.
However, challenges persist, including inconsistent data-sharing practices among agencies and limited funding for technology upgrades. Additionally, privacy concerns necessitate robust data governance to build public trust and ensure ethical data use which is quite essential for health data.
Actors in SDI governance in public health
According to Sjoukema, SDI Actors are classified into four groups: SDI executives, data providers, platform providers, and users. The SDI executive oversees policy-making and coordination, shaping the mission and vision of the Spatial Data Infrastructure (SDI). SDI data providers deliver or produce spatial data, while platform providers maintain the infrastructure and bridge data between providers and users. SDI users utilise the data either as end-users or resellers [18]. In lieu of this, governance of Spatial Data Infrastructure (SDI) within public health, multiple actors play crucial roles, each contributing unique resources, expertise, and challenges to the overall system. These actors include governments, non-governmental organisations (NGOs), communities, and the private sector, all of whom must collaborate to ensure effective data utilisation and governance.
Governmental role
In the context of this research, governments refer to the political representatives, institutions, and agencies responsible for top-level decision-making, policy formulation, funding, and execution of public health initiatives related to Spatial Data Infrastructure (SDI) development. These bodies operate at national or regional levels and play a critical role in establishing and implementing SDI frameworks that support public health objectives. Their involvement is essential for providing the necessary financial resources, setting legal and regulatory guidelines, and ensuring the overall coordination of SDI efforts to enhance the management of health data and geospatial information.
Governments play a pivotal role in the governance of Spatial Data Infrastructures (SDIs) by providing policy direction, funding, and infrastructure support. Most operational SDIs globally are coordinated and maintained by national governmental bodies, underscoring the importance of executive and legislative stakeholders in the health sector at both national and local levels. The effectiveness of SDI policies is heavily influenced by the political landscape, including leadership changes that can shift funding allocations and policy priorities, potentially disrupting long-term initiatives aimed at improving public health outcomes [25].
Institutions such as the Centers for Disease Control and Prevention (CDC) in the United States and the UK Health Security Agency (formerly Public Health England) are responsible for coordinating national and regional efforts in the collection, management, and dissemination of spatial data for public health purposes [18]. These agencies establish data standards, ensure interoperability, and promote the integration of spatial data into public health decision-making processes. However, their effectiveness is often hampered by bureaucratic inefficiencies, budget constraints, and shifting political priorities. For instance, changes in political leadership can alter funding allocations and policy focus, disrupting long-term SDI initiatives aimed at improving public health [25]. Moreover, the centralised nature of governmental control can slow down decision-making processes, particularly when multiple departments or levels of government are involved.
NGOs and their role in SDI governance
Non-governmental organisations (NGOs) serve as critical intermediaries in the governance of SDI, facilitating data sharing and capacity building while bridging the gap between governments and local communities. Organisations such as Médecins Sans Frontières (MSF) utilise spatial data to inform their humanitarian health interventions, particularly in low- and middle-income countries (LMICs) [26]. NGOs often possess localised knowledge and can respond more swiftly to health crises, using spatial data to identify disease outbreaks or track the delivery of health services in real time. However, integrating their spatial data systems with government-run infrastructures can be challenging due to differences in data standards, priorities, and available resources. Furthermore, while NGOs may provide vital services during health crises, they frequently lack sustained funding and technical support to maintain robust SDI systems over time, limiting their long-term effectiveness in public health governance.
Private sector’s contribution
The private sector contributes significantly to SDI governance by providing technological innovations, tools, and expertise that enhance the functionality and utility of spatial data infrastructures. Companies like ESRI which developed the ArcGIS platform, and Google with services like Google Maps and Google Earth have developed advanced platforms that support the collection, visualisation, and analysis of spatial data, which public health officials and researchers widely use. These platforms offer cutting-edge features such as real-time mapping and big data analytics, crucial in addressing complex public health challenges, such as disease surveillance and environmental health monitoring [27]. However, the profit-driven motives of private companies can sometimes conflict with public health goals. For instance, proprietary software and data access limitations can hinder the sharing of vital spatial data across different stakeholders, particularly in public health crises where open data access is crucial [27].
Collaboration models in SDI governance
Collaboration models such as public–private partnerships (PPPs) and community-based participatory research (CBPR) have emerged as essential frameworks for effective SDI governance in public health. PPPs enable the pooling of resources and expertise from both the public and private sectors, ensuring that technological advancements align with public health needs. CBPR, on the other hand, emphasises the inclusion of community voices in the research process, ensuring that local knowledge and priorities are reflected in the data governance structures. These collaboration models are essential for balancing diverse interests and ensuring efficient resource utilisation [27]. However, they require careful management to ensure that public health outcomes remain the primary focus, avoiding potential conflicts of interest that can arise when private sector or political motives dominate the decision-making process.
Establishing concrete public health SDI governance
Establishing a robust Public Health Spatial Data Infrastructure (SDI) involves designing comprehensive frameworks that ensure efficient management of spatial health data, involving every aspect of SDIs: actors, infrastructures, policies and protocols [18]. Public Health SDIs should integrate Geographic Information Systems (GIS) into health management not restricted to just environmental but also considering other epidemiological factors and the evolving electronic health systems in specified areas, enhancing the capacity to address critical health challenges such as disease surveillance, resource allocation, and the analysis of environmental health risks while ensuring every aspect of public health ethics is ensured. To create an effective SDI, several policies and strategies must be implemented.
Establish clear public health goals
It is essential to integrate public health objectives within SDI frameworks to enhance disease surveillance, outbreak tracking, and response times. Aligning SDIs with specific goals—such as accurate disease tracking, rapid data sharing, and timely decision-making—will optimise resource allocation, improve healthcare infrastructure, and monitor health system performance. Prioritising health equity by incorporating goals that address disparities will ensure that vulnerable populations are considered in data collection and analysis while maintaining data ownership, privacy and security. By aligning SDIs with public health objectives, health systems can be strengthened, outcomes improved, and disparities reduced.
Data standardization and interoperability
Effective SDI policies require clear standards for collecting, storing, and exchanging spatial health data. This ensures that various health systems and platforms—ranging from hospitals to government agencies—can share and utilise spatial data seamlessly. Adopting internationally recognized standards, such as International Organization for Standardization (ISO) TC211, ISO 19115 for geospatial metadata or Health Level 7 (HL7) for health data exchange, is essential. This step ensures consistency and compatibility across different health information systems, facilitating better data integration and analysis [28].
Data governance and ownership
To maintain clarity and accountability, it is critical to establish protocols for data governance and ownership. This involves defining who owns the data, who is responsible for its management, and under what conditions it can be accessed. Public health institutions, government bodies, and private stakeholders must collaborate to create transparent governance frameworks. These protocols ensure that spatial data is managed responsibly and that all parties involved adhere to agreed-upon regulations [29]. In addition, there should be balance in the regulation such that innovations in the space be fostered.
Data privacy and security
As the electronic medical record(EMR) and health record(EHR) grow in adoption globally, integration with spatial data becomes paramount. Public health SDIs must prioritise stringent data privacy and security measures to protect sensitive public health information [30]. Safeguarding individual health data while allowing for the analysis of spatial patterns is a delicate balance. Policies should include encryption, anonymisation techniques, and compliance with international data protection laws like the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA) and ISO 19113 principles, one of the core protocol that guides the Canada GeoData Infrastructure [18, 28]. Therefore, ensuring the security of spatial health data builds public trust and prevents potential misuse.
Open data and accessibility
Promoting open access to non-sensitive health data encourages transparency and enhances public health decision-making. Providing researchers, policymakers, and the general public with access to spatial health data can improve disease surveillance and resource allocation. Creating data-sharing platforms that provide access to open datasets, such as environmental health indicators and disease incidence maps, will enable stakeholders to use this data effectively [31].
Capacity building and training
An effective SDI requires continuous training programs aimed at public health officials, GIS professionals, and healthcare workers. These programs should focus on developing technical skills for collecting, analysing, and applying spatial data in public health decision-making. Collaborating with universities, research institutions, and health agencies can help create specialised training and certification programs that enhance capacity in health GIS [18].
Technological infrastructure development
A strong technological infrastructure is essential for supporting the storage, analysis, and dissemination of spatial health data. Investments in databases, software, and hardware are necessary to ensure that health organisations can effectively utilise spatial data. Partnering with governments and technology providers to establish centralised data repositories and cloud-based GIS platforms will support this infrastructure [19, 32]. For example, the INSPIRE implementation focused on the web reporting of air quality across Belgium and Netherlands with technological infrastructure involving a reconstructed data stream provided by the Web feature service(WFS) and Sensor Observation Service (SOS), data transformation using HUMBOLDT Alignment Editor (HALE) while ensuring compliance to the ethical frameworks across involved states [33].
Legal and regulatory frameworks
Developing legal frameworks to regulate the use of spatial data in public health is crucial for ensuring accountability and protecting public health interests. These frameworks must include provisions on data use, liability, and ethical considerations. Collaborating with legal experts and policymakers is necessary to draft legislation that governs spatial data practices, ensuring they align with public health priorities [12, 34, 35].
Public–private partnerships
Fostering collaborations between government agencies, private companies, and non-governmental organisations (NGOs) can strengthen SDI initiatives. Public–private partnerships (PPPs) leverage expertise, technology, and resources from different sectors, helping to enhance SDI implementation. Memorandums of Understanding (MoUs) and strategic partnerships can integrate spatial data into public health projects such as disease tracking and emergency response [27].
Monitoring and evaluation
Establishing mechanisms for ongoing monitoring and evaluation is essential for ensuring the continuous improvement of the SDI. By setting key performance indicators (KPIs) and regularly assessing the infrastructure's performance, public health institutions can refine their SDI systems in response to technological advancements and evolving health needs [7]. In conclusion, implementing a robust Public Health Spatial Data Infrastructure requires a multifaceted approach that encompasses data standardisation, governance, privacy, accessibility, capacity building, infrastructure development, legal frameworks, public–private partnerships, and continuous evaluation. By addressing these key areas, public health systems can enhance their ability to respond to health challenges effectively and improve overall health outcomes.
Challenges and possible solutions of implementing SDI in public health
The implementation of Spatial Data Infrastructure (SDI) faces several critical challenges that can hinder its effectiveness across different regions. These challenges, ranging from funding limitations to issues with data quality and security, impact both low- and middle-income countries (LMICs) and high-income nations, albeit in distinct ways.
Funding
A primary obstacle to effective SDI policy implementation is the availability of funding. Many countries, particularly in Africa, struggle with inadequate financial resources, which restrict the development and long-term maintenance of SDIs. Insufficient funding leads to incomplete and outdated geospatial data systems crucial for public health and other applications [11]. While high-income countries typically have more stable financial structures, they are not immune to budget cuts or reallocations that can slow down or derail SDI projects. For example, even in wealthier regions, economic downturns or changing political priorities may divert resources away from essential infrastructure upgrades or capacity-building initiatives.
One key strategy for increasing investment in SDI is advocating for increased funding from both government and private sectors. Investment in SDIs is crucial for their development, especially in public health, where geospatial data can enhance disease surveillance and response. International aid and development grants are valuable sources of funding for LMICs, which often face financial constraints. For example, global initiatives such as the World Bank and the Bill & Melinda Gates Foundation can provide financial assistance to underfunded regions to support SDI development in health applications.
Infrastructure
The technological infrastructure necessary for SDI implementation is another significant challenge, particularly in LMICs. Regions with underdeveloped internet and technological resources often find it difficult to support the complex data systems that SDIs require. This infrastructure gap is a stark contrast to high-income regions like Europe and North America, where robust internet and technological networks provide a more solid foundation for SDI development [36]. Without reliable technological infrastructure, the potential for SDIs to improve public health outcomes through spatial data analysis remains limited in many parts of the world.
Improving technological infrastructure is essential to bridging the gap between high- and low-income regions. Investing in internet connectivity, data storage facilities, and reliable technological systems in underserved areas will enable effective SDI implementation. By focusing on infrastructure development, regions with weaker technological bases can better participate in data collection, sharing, and analysis. Public–private partnerships can play a pivotal role here, as companies with technological expertise, such as Google and Microsoft, can contribute to advancing infrastructure in regions that lack robust systems.
Data quality and standards
Maintaining high-quality data and adhering to standardised formats present considerable challenges, particularly when integrating data from multiple sources. Inconsistent data quality between regions, such as disparities between urban and rural areas, complicates the aggregation of accurate and actionable information. Data gaps and discrepancies often arise in regions with weaker governance or infrastructure, making it difficult for SDIs to provide a comprehensive view of public health trends or other critical issues. Establishing and enforcing universal data standards remains a priority but is challenging when various stakeholders, each with differing capabilities, contribute data [12].
Data quality can be improved by enforcing stringent data protocols and adopting international standards to ensure consistency and interoperability. Establishing data quality frameworks that apply across different sectors and regions will enhance the reliability of SDI outputs. Additionally, promoting the use of standardised formats and metadata will ensure that data from various sources can be seamlessly integrated into a unified system. This is particularly critical in public health, where timely and accurate data can lead to better interventions and policy decisions.
Privacy and security
Data privacy and security are central concerns for the effective implementation of SDI policies, particularly when dealing with sensitive health data. Balancing the need for accessibility with robust security measures is essential, as breaches of health data can lead to significant privacy violations and erode public trust in the systems [37]. For instance, large-scale data breaches experienced by prominent health organisations highlight the vulnerabilities that exist even within well-established SDIs. Ensuring compliance with data protection regulations and investing in advanced cybersecurity measures are necessary to mitigate these risks [36].
Developing comprehensive data security policies and implementing advanced security technologies can help mitigate risks associated with breaches and unauthorised access. Encryption, anonymization, and secure data-sharing protocols are essential tools to protect sensitive health data, thereby fostering public trust. Governments and institutions must also prioritise regular audits and compliance with data protection laws to maintain security and transparency [37].
Capacity building
A final challenge is the lack of technical expertise required to effectively manage and utilise SDIs. Capacity-building programs, which provide training and education to improve data management and analytical skills, are essential but often underfunded and inconsistently applied. In LMICs, this shortage of skilled personnel exacerbates other challenges, such as data integration and infrastructure limitations. Comprehensive and well-supported training programs are crucial to ensuring that SDIs can be used to their full potential.
Capacity building is vital for ensuring long-term sustainability in SDI governance. Implementing training programs and fostering collaborations with academic and research institutions can create local expertise and enhance the technical capacity of individuals responsible for SDI management. These efforts can include workshops, certifications, and formal education programs that focus on geospatial technologies, data management, and analysis. Strengthening local capacity will ensure that SDI initiatives are not reliant on external expertise and can be maintained and expanded over time.
Conclusion
Spatial Data Infrastructure governance plays a crucial role in public health by enabling the effective collection, sharing, and utilisation of geospatial data. While there are significant challenges to implementing SDI policies, particularly in terms of funding, infrastructure, and capacity, the benefits are substantial. Effective SDI governance can enhance disease surveillance, inform health policy, and improve health outcomes. By adopting best practices, strengthening policy frameworks, and leveraging emerging technologies, stakeholders can maximise the impact of SDI on public health and contribute to a healthier, more equitable world. Future research and policy efforts should continue to focus on addressing challenges and harnessing new opportunities to further integrate SDI into public health strategies.
Acknowledgements
The authors would like to acknowledge THE LIND LEAGUE, Nigeria for providing the invaluable resources to kick start, culminate and leverage this research project while also enabling our capacities.
Author contributions
Conceptualisation, Writing of Initial and Final Draft, Initial Review: V.M.A, P.A Writing, Editing, Data Collation: All authors Final review, Validation and Supervision: P.A
Funding
Not applicable.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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