Barriers to Spatial Data and GIS Implementation: Are We Making Progress?
Geographic Information Systems (GIS) have dramatically changed since their early conceptual development in the 1960s and 1970s, when they were primarily the domain of government agencies and specialised research institutions. Early systems were expensive, required extensive technical expertise, and often demanded powerful hardware that was not widely accessible. Over time, thanks to advancements in computing power, internet connectivity, and user-friendly software, GIS has transformed into a more inclusive and accessible technology. Today, it is used across multiple sectors including environmental management, urban planning, transportation, health, and business analytics.
However, increased accessibility has not erased the challenges to widespread and effective GIS implementation. In fact, understanding the barriers identified in the past helps us contextualise why certain obstacles persist or re-emerge. By examining how these hurdles have shifted—from primarily technical limitations in earlier decades to organisational, data-related, and institutional challenges today—we can better design solutions that push GIS toward its full potential. This reflection is vital as spatial data drives critical decision-making in fields like biodiversity conservation, climate adaptation, and sustainable development.
Historical Barriers: From Technical to Organisational
In the 1980s, early GIS literature often focused on technical barriers, such as limited software functionality, scarce computing power, and insufficient data processing capabilities (Göçmen & Ventura, 2010). These constraints made it difficult to run complex spatial analyses, limiting GIS applications mostly to basic cartography or simple overlays. As the technology matured into the 1990s, however, attention began to shift from purely technical obstacles to organisational barriers. Researchers noted that even as software improved and hardware costs decreased, institutions struggled to integrate GIS effectively due to a lack of trained personnel, insufficient funding, unclear mandates, and poor interdepartmental coordination (Skidmore, 2017). These organisational factors often proved more stubborn than the early technical challenges. Key challenges identified in earlier research included:
Technical Barriers: Involved unreliable data, inadequate tools, limited interoperability, and insufficient technical know-how.
Organisational Barriers: Included the absence of a clear strategic purpose, mission alignment, and collaboration networks, as well as gaps in training and institutional capacity.
Ye et al. (2014), through a comprehensive literature review and survey, confirmed that these barriers were consistent across multiple sectors—government, transportation, commercial, and public—underlining the pervasive nature of both technical and organisational challenges.
Current Obstacles: Persistent and Emerging Issues
Despite technological leaps, modern GIS practitioners still face significant obstacles that resemble, in some respects, the issues of the past. While improvements in cloud computing, open-source software, and affordable data storage have reduced the technical challenges, many organisational and data-related barriers remain or have evolved in complexity:
Key contemporary issues include:
Data Accessibility: Despite the availability of open-source platforms and global repositories, many spatial datasets remain difficult to access or restricted by licensing agreements. For instance, while platforms like the Global Biodiversity Information Facility (GBIF) and OpenStreetMap provide accessible global datasets, specialised environmental, socio-economic, or infrastructure data often remain behind paywalls or administrative hurdles. The World Bank and the United Nations have repeatedly emphasised the importance of open data policies for sustainable development, yet progress is uneven globally (World Bank, 2021).
Data and Platforms Usability: Data usability is defined as “the degree to which something is able or fit to be used” (Oxford English Dictionary). In software engineering, usability is a quality attribute that assesses how easy user interfaces are to use, defined by five quality components: learnability, efficiency, memorability, errors, and satisfaction. Even when data is accessible, issues with metadata, inconsistent formats, and varying spatial and temporal resolutions persist. Without high-quality, well-documented data, analysts must spend considerable time cleaning and harmonising information rather than conducting meaningful analyses. A systematic literature review by Kurniawan et al. (2023) highlights the critical need for usability evaluations in GIS to develop more user-friendly applications. The study emphasises that usability issues, such as poor metadata and inconsistent data formats, significantly hinder the efficiency of GIS projects by leading to fragmented data landscapes. This fragmentation makes it challenging for users to integrate datasets from various sources, thereby hindering large-scale analyses and comparisons. Furthermore, the authors argue that improved user guidance and more intuitive design are essential for enhancing data usability, particularly for novice users.
Data Validation: Ensuring data accuracy and completeness is a key challenge in GIS Implementation. Diverse and incompatible data collection protocols among agencies and institutions can create inconsistencies that ultimately undermine trust in GIS outputs (Koldasbayeva et al., 2023). In the environmental and biodiversity sectors, globally validated datasets can be rare due to varied and sometimes incompatible data collection methods, resulting in methodological discrepancies that deter decision-makers (U.S. Environmental Protection Agency, 2024). Moreover, differences in resolution, standards, and the absence of standardised validation protocols across regions can further exacerbate these issues.
Capacity Gaps: Governments, NGOs, and private-sector organisations, particularly in regions with limited resources, often lack the human capital and training needed to implement GIS effectively. The lack of trained professionals means that many organisations struggle to fully leverage GIS capabilities, which can impede data collection, analysis, and decision-making processes. Academic studies highlight that the gap in GIS capacity is not just about the number of trained individuals but also about the depth of their training and the availability of continuous professional development. For instance, a study by Murthy and Kishore (2018) emphasises the need for comprehensive training programs to build a robust geospatial workforce capable of meeting the demands of modern GIS applications. Similarly, Dakis (2016) points out that the absence of institutional support and infrastructure further exacerbates these capacity gaps, making it difficult for organisations to sustain GIS initiatives. Moreover, the disparity in GIS capacity is often more pronounced in developing regions, where educational and training opportunities are limited. This creates a significant barrier to the effective use of GIS in addressing local and regional challenges. The need for targeted capacity-building interventions is critical to bridge these gaps and ensure that all regions can benefit from the advancements in GIS technology.
Organisational Inertia and Policy Gaps: A state-of-the-art GIS system cannot deliver results if organisational culture resists change, fails to incentivise data sharing, or lacks clear policies guiding GIS integration into strategic planning. These institutional barriers, noted decades ago, remain pertinent today (Göçmen & Ventura, 2010; Skidmore, 2017).
Have We Improved?
Reflecting on the past, some challenges like technical constraints have been alleviated through modern technology. However, many organisational barriers endure.
What has changed:
Cloud platforms like ArcGIS Online democratise access to spatial tools.
Open-source initiatives (e.g., QGIS) and collaborative platforms like GBIF offer opportunities to overcome data cost issues.
Training opportunities and global communities enhance networking and capacity-building.
What remains:
Data quality, validation, and access.
Organisational inertia in adopting GIS for complex, interdisciplinary challenges.
Funding and political will to drive systemic change.
Where Do We Go From Here?
To break through these barriers, organisations and governments must:
Invest in Capacity-Building: Training workshops, e-learning modules, and professional development programs build the necessary human capital.
Encourage Collaboration and Networking: Partnerships between government agencies, academia, and the private sector can foster knowledge exchange and resource sharing.
Enforce and Adopt Standards: Adhering to international standards like those developed by the OGC and implementing frameworks like INSPIRE can improve data interoperability and trust.
Promote Transparency and Participation: Engaging stakeholders and the public in data collection and validation can improve trust and relevance, while also encouraging data sharing and open access.
Only by tackling these organisational, institutional, and data-related barriers can we unlock the full potential of GIS. Understanding historical challenges and current realities enables us to craft more effective solutions that go beyond technological fixes, ultimately strengthening GIS’s role in shaping sustainable policies and outcomes.
References
Dakis, K. J. (2016). Capacity-building intervention for geographic information system (GIS): Developing spatial data infrastructure to support urban development in CDI cities. USAID/SURGE Project. Retrieved from https://pdf.usaid.gov/pdf_docs/PA00X6TP.pdf
European Commission. (2021). INSPIRE Directive. Retrieved from https://inspire.ec.europa.eu/
Koldasbayeva, D., Tregubova, P., Gasanov, M., Zaytsev, A., Petrovskaia, A., & Burnaev, E. (2023). Challenges in data-based geospatial modeling for environmental research and practice. arXiv. https://arxiv.org/abs/2311.11057
Kurniawan, D., Indah, D. R., Sari, P., & Alif, R. (2023). Understanding the landscape of usability evaluation in geographic information systems: A systematic literature review. Journal of Applied Science, Engineering, Technology, and Education, 5(1), 35-45. https://doi.org/10.35877/454RI.asci1815
Murthy, R., & Kishore, J. K. (2018). Capacity building for RS & GIS technology applications: Student remote sensing and geospatial programme in academic institutions. ISPRS Archives, XLII-5, 907-914. https://doi.org/10.5194/isprs-archives-XLII-5-907-2018
OGC. (2021). OGC Standards and Best Practices. Open Geospatial Consortium. Retrieved from https://www.ogc.org/standards
Oxford English Dictionary. (n.d.). Usability. In Oxford English Dictionary. Retrieved December 9, 2024, from https://www.oed.com/
UNDP, UNEP-WCMC & CBD Secretariat. (2021). UN Biodiversity Lab 2.0. https://unbiodiversitylab.org/
U.S. Environmental Protection Agency. (2024, May 22). Guidance on environmental data verification and data validation. Retrieved from https://www.epa.gov/quality/guidance-environmental-data-verification-and-data-validation
World Bank. (2021). World Development Report 2021: Data for Better Lives. Washington, DC: World Bank. https://www.worldbank.org/en/publication/wdr2021
