National Spatial Data Infrastructure as a Catalyst for Good Governance and Policy Improvements in Pakistan

Abstract

This study explores the potential of National Spatial Data Infrastructure (NSDI) to strengthen governance and policy processes in Pakistan. Drawing on the UNESCAP principles of good governance and the EGU policy cycle model, this research applies a dual-method approach combining thematic document analysis of 23 national policy frameworks and a stakeholder survey (n = 28). The results reveal that while many policies reference spatial data conceptually, critical components such as standardised datasets, spatial dashboards, and institutional coordination mechanisms remain underdeveloped. Spatial references are largely confined to early policy stages, with limited integration in evaluation and maintenance, thereby limiting adaptive governance. Conversely, survey findings reflect strong recognition of NSDI’s value across governance principles, policy integration, and spatial awareness dimensions. The composite endorsement score highlights institutional demand for geospatial tools, data standards, and capacity-building platforms. The study concludes that embedding NSDI within policy and planning systems can bridge critical governance gaps, enhance implementation fidelity, and support inter-agency coordination for long-term policy effectiveness.

1. Introduction

Pakistan is confronted with continuous governance as well as policy challenge, thereby preventing its socio-economic development [1,2]. These challenges include an incomplete institutional framework [3,4], poor transparency [5], lack of public involvement [6,7], unjustified implementation of policies [8,9], and poor check and balance [10,11]. The country is still struggling with ineffective spending of resources [12,13], ineffective service delivery [12,14,15], and lack of evidence-based policymaking [16,17,18]. Above challenges explain why it is necessary to use modern governance tools to drive the provision of coordinated, transparent, responsive practices in the public sector.

National Spatial Data Infrastructure (NSDI) is a kind of transformative framework that heterogeneously incorporates spatial data, technologies, standards, policies, and institutional arrangements to enhance the capacity for sharing and utilising data efficiently and making informed decisions [19,20,21,22,23,24,25]. NSDI has been established in numerous nations so as to facilitate the efficient collection, management, sharing, and implementation of geospatial data in extensive amounts of sectors [26,27]. NSDI can also help in locating schools in education and planning access to learning facilities on the basis of equity [28,29]. In the healthcare sector, NSDI data can aid in disease outbreak monitoring, control of healthcare-related infrastructure, and enhancement of emergency responses [30,31,32,33,34]. In agriculture, NSDI can facilitate smart farming, crop monitoring, and irrigation planning [35,36]. NSDI datasets can be useful in marine and coastal management for monitoring ecosystems, managing fisheries, and making decisions about sustainable development [37,38,39]. The mapping of watersheds, flood risk areas, and the availability of groundwater proves beneficial in water resource management [40]. Similarly, geospatial data can be used in urban planning and the design of the infrastructure, such as land use planning, transport network design, and selecting areas where investments are to be made [41,42]. NSDIs can also play critical roles in addressing environmental issues, such as environmental monitoring, tracking deforestation, monitoring changes in land use, and informing climate adaptation and conservation strategies [43,44]. Modern NSDIs can facilitate disaster risk management where spatial data can help with mapping the hazards and developing early warning mechanisms as well as post-disaster assessment [45,46,47,48]. NSDI datasets are also useful in land administration, offering better cadastral mapping [49,50,51], energy planning for renewable site selection, and transportation for optimisation of traffic. Within the framework of public administration and planning, NSDI can be utilised as a transformational enabler, since it can provide accurate and current geospatial data that is imperative in generating knowledge of extensive development challenges. Other uses of NSDI datasets may include tourism enhancement, cultural heritage conservation, national security support, and natural resources [52], including minerals and biodiversity management.

In the case of a developing nation like Pakistan, where planning is often reactionary and fragmented, an efficient NSDI can significantly enhance the outcomes of governance [53]. Through the harmonisation and unification of spatial information across federal, provincial, and local jurisdictions, the NSDI can enhance cross-agency collaboration, eliminate overlap and duplicate operations, and foster accountability through publicly available spatial data. It facilitates more effective, immediate, and accountable policy responses, shifting governance to more data-driven, strategic decision-making, rather than ad hoc. Moreover, the use of geospatial data across the sectors could provide a complete picture of the development requirements and, thus, let planners and policymakers find the gaps in the delivery of services, streamline the distribution of resources, and understand the overall effects of the policies or projects. To illustrate this, through spatial analysis, one may find underserved neighbourhoods in need of healthcare or education, create effective routes in the network of the city’s public transport system, or even determine the degree to which city construction or expansion is affecting the surrounding environment. By doing so, NSDI not only contributes to the efficiency and effectiveness of using public resources but also helps to develop the resilience to climate change, disasters, and social inequalities. NSDI can be placed at the foundation of modern governance, enabling Pakistan to progress to the future of reactive, consolidated, and future-proof planning. Actual applicability, impact and significance may vary depending on the governance challenges, institutional capacity, and sector-specific priorities within Pakistan. By tailoring NSDI deployment to the governance gaps of each sector, whether transparency deficits, lack of participatory planning, or weak monitoring mechanisms, the infrastructure can deliver targeted improvements in accountability, service delivery, and policy evaluation, thereby making governance reforms more impactful and sustainable.

Although the above discussion has marked the spherical applicability of NSDI in making governance and policy actions, particularly in a developing economy, such a conceptual justification is not enough for any academic investigation. To confirm and enrich its meaning, it is necessary to analyse the connection of NSDI with the effectiveness of a governance process and policy formulation with the help of systematic theoretical and analytical models. Thus, this paper will use a two-framework approach by combining two established and interrelated models. The first framework is the Good Governance Framework practiced by the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP) [54], consisting of eight main characteristics of good governance, namely participation, rule of law, transparency, responsiveness, consensus orientation, equity and inclusiveness, effectiveness, and efficiency and accountability. The second one is the Policy Cycle Model of the European Geosciences Union (EGU) [55], which views policymaking as an evolutionary and repetitive process that consists of six major stages, namely agenda setting, policy formulation, decision-making, implementation, evaluation, and termination or renewal. These two frameworks were chosen because of their complementary and inclusive nature, where on the one hand, the governance framework emphasises the institutional quality and decision-making principles, and on the other hand, the policy cycle describes the procedural and operational process through which decisions can be crafted and implemented. Collectively, they offer a synthetic perspective through which NSDI contributes to the traditions of good governance, and the process of efficient policy implementation can be considered.

The fundamental rationale of this study is to examine how the implementation of National Spatial Data Infrastructure may serve as an engine of governance and policy reform in Pakistan. In particular, this paper aims to analyse the extent to which NSDI capabilities are in keeping with the concepts of good governance and how they could be used to maximise the effectiveness of every step of the policy cycle. In this dual approach, the study hopes to unite the concerns between the spatial data infrastructure and institutional reform.

Research Questions

The preceding discussion leads to the formulation of the following research questions.

RQ1. 

What role can NSDI play at every step of the policy cycle in Pakistan?

RQ2. 

What is the position of NSDI, and how does it support the principles of good governance in Pakistan?

This research study blends theories about governance and practical uses of geospatial information to provide some insights into the emerging debate surrounding digital governance transformation in the Global South. The results will provide lessons for policymakers, development practitioners, and experts in the field of technology, who will be provided with the strategic benefits of the involvement of geospatial infrastructure in promoting an inclusive, efficient, and accountable governance. Additionally, the paper promotes a system-level view of digital transformation of the functioning of the public institution by incorporating the spatial information into the national policy. This research will not only cast intellectual light but it will also formulate solutions in support of a better governance mechanism in a developing environment such as Pakistan.

This study’s novelty lies in developing and applying an integrated analytical framework that positions NSDI across all stages of the policy cycle and assesses its alignment with core good-governance principles, offering a system-level perspective beyond the technical focus of most South Asian and Global South studies. Conceptually, we introduce a policy cycle–governance matrix that embeds geospatial functions within institutional processes; empirically, we apply it to Pakistan to reveal role, mechanism, and outcome linkages; and comparatively, we advance NSDI literature by shifting from infrastructure readiness to policy-use pathways, yielding a transferable template for other developing contexts. The framework informs policymakers about where NSDI investments deliver the highest governance returns, guides practitioners and donors in prioritising capacity-building tied to outcomes, and helps technologists identify institutional conditions for inclusive, efficient, and accountable services, offering lessons for digital governance transformation across the Global South.

2. Literature Review

This study uses a dual framework approach to analyse how National Spatial Data Infrastructure can be used to enhance the governance and policymaking at the national level of Pakistan by fusing two existing models that supplement each other: the Good Governance Framework prepared by the United Nations Economic and Social Commission for Asia and the Pacific (UN-ESCAP) [54], and the Policy Cycle Model suggested by the European Geosciences Union (EGU) [55]. In combination, these frameworks offer a unitary analytical plan to evaluate both the normative concepts of governance and the technical procedures of the policymaking process to provide a methodical measurement of the strategic worth of NSDI. This twofold use of the UNESCAP governance principles and the EGU Policy Cycle Model reflects the normative and procedural aspects of the role of NSDI in governance. OECD and UNDP policy cycle approaches offer intuitive and useful insights, but they are highly generic and do not refer to data-driven or geospatial policy processes. In contrast, the EGU model highlights the science–policy interface and the role of evidence iteration in the policy cycle, which makes it suitable for analysing how NSDI contributes to governance functions. This complementarity that occurs between the UNESCAP governance principles forms a strong analytical lens for the study.

2.1. Good Governance Framework UNESCAP

Eight dimensions of good governance identified by UN-ESCAP can be taken up as standards against which governance systems and the performance of the public sector can be assessed, especially when it comes to sustainable development and digital transformation. These principles are listed below.

2.1.1. Participation

Participation entails the active and inclusive participation of all the stakeholders concerned, which are the government agencies, civil society, private sector, and marginalised groups, in the course of governance and decision-making. NSDI can support participatory governance using instruments such as crowd-sourced mapping, citizen science, and community mapping initiatives, which can magnify the voices of disadvantaged groups, particularly in urban planning or in disaster risk reduction studies [56,57,58]. For example, in the context of participatory GIS, the community can draw markers on environmental hazard zones or land boundaries that can be pulled into some formal data to affect planning and disaster mitigation.

2.1.2. Rule of Law

The rule of law demands that laws should be applied equally and that there should be equality and predictability in the rule of law. In the NSDI context, the rule of law has an advantage in standardised geospatial records of data on land administration and cadastral systems, which limit disputes, corruption, and overlapping interests, which are critical problems in the Pakistani land governance sector. Geospatial laws have been incorporated in various countries to control usage and ensure the integrity of cadastral and spatial information through control systems [59,60]. Strong geospatial law also facilitates the protection of the distortion or unauthorised modification of national spatial databases, such as the cadastral database.

2.1.3. Transparency

Transparency refers to the accessibility of information, so that the citizenry is free to observe any post-governmental activity. By using open data platforms, metadata standards, and real-time geospatial dashboards, NSDI can provide a system of transparency by enabling citizens and other stakeholders to view, examine, and track government activities associated with service provision, infrastructure, and environmental hazard situations [61]. As an illustration, systems such as the European INSPIRE geoportal enable people to view environmental and planning information at the member state level, making cross-border examination and cooperation possible.

2.1.4. Responsiveness

Responsiveness relates to institutions’ actions, which allow them to respond to community needs and aspirations in adequate ways. NSDI also contributes to this aspect of good governance, as it allows real-time data collection and spatial analysis to support emergency responses, interventions, and service delivery in public health and service provision. After natural disasters, geospatial systems can assist in finding the affected people, optimising the relief process, and tracking recovery progress. During the COVID-19 pandemic, nations with NSDI systems monitored the cases and mobilisation restrictions with the help of spatial data.

2.1.5. Consensus Orientation

Consensus orientation aims at reconciling issues of different interests to create agreeable policies and decisions. This can be facilitated by NSDI through the provision of common data platforms, where various stakeholders, ministries, municipalities, and civil society can access and interpret common spatial data, which can further ease inter-agency coordination and communication among stakeholders at all levels of governance, thereby minimising the severity of information asymmetry [62]. Integrated spatial data allows mediation between competing land uses or development goals in the context of a multi-stakeholder urban planning project.

2.1.6. Equity and Inclusiveness

This principle states that all the groups of people in a population, including marginalised people, must be given an equal chance to access or become involved in governance. NSDI also promotes equity by providing a means for the spatial disaggregation of data, to analyse the division in service delivery as well as marginalised groups. An example is an informal settlement mapping that can identify health service gaps in remote places so that the interventions can be conducted in a more focused manner [63]. Inclusive mapping practices also allow women and indigenous citizens to describe their spatial realities.

2.1.7. Effectiveness and Efficiency

Effectiveness and efficiency are the characteristics that allow institutions to achieve public goods and services through the careful application of resources. NSDI can improve effectiveness and efficiency with a reduction in the duplication of data collection, incorporating spatial data in decision-making in the fields of environment, health, education, transport, and agriculture, evidence-based, targeted interventions, and simplification of service delivery implemented based on location intelligence [64]. To illustrate, the harmonised application of geospatial information in the development of taxes, the utility provision system, and agricultural planning in many countries has been successful in terms of its cost-saving and positive performance.

2.1.8. Accountability

Accountability involves making institutions and the functioning of officials in a society accountable for their decisions and performance. NSDI adds to this process through its ability to provide spatial audit trials, performance dashboards, and data-based reporting facilities that enable both internal and external monitoring, and metrics of performance that are spatially visualised [65]. Within Pakistan, geo-tagging of publicly funded development projects has been deployed by the Government of Sindh to keep track of the ongoing progress as well as to identify anomalies in fund use [66].

With geospatial data currently playing a key role in digital governance, NSDI has great potential for transforming the institutional practice and citizen interaction in the country.

Table 1. Mapping NSDI components to UNESCAP good governance principles.

UNESCAP Governance Principle	Corresponding NSDI Functionality	References
Participation	Participatory GISs, citizen mapping platforms, open feedback loops	[56,67]
Rule of Law	Standardised cadastral systems and legal geospatial data registries	[23]
Transparency	Open data portals, metadata catalogues, and real-time dashboards	[68,69]
Responsiveness	Real-time spatial analytics and emergency response systems	[70]
Consensus Orientation	Inter-agency data sharing platforms and collaborative mapping	[71]
Equity and Inclusiveness	Spatial inclusion mapping and rural–urban data disaggregation	[72]
Effectiveness and Efficiency	Integrated spatial decision-support systems	[73]
Accountability	Audit trails for spatial data updates and performance visualisations	[61]

provides an overview of mapping NSDI components to UNESCAP good governance principles. Corresponding NSDI functionalities (as indicated in Table 1) are derived with the use of thematic synthesis of SDI literature, as cited in the table. This mapping of SDI and governance principles incorporates existing practices (e.g., open data portals to support transparency and similarly participatory GISs to incorporate citizens).

2.2. Policy Cycle Model

The Policy Cycle Model conceptualised by the European Geosciences Union (EGU) offers a dynamic, iterative process with six interconnecting phases as agenda setting, policy formulation, decision-making, implementation, evaluation, and termination or renewal [55]. It is a structured model through which we can analyse how spatial data infrastructures in general, including NSDI, can be leveraged to bring informed, timely, and answerable decisions throughout the policy development process.

2.2.1. Agenda Setting

Agenda setting is considered to be the first and the most crucial part of the policy cycle, in which problems are defined and given priority by the government. The process requires timely and accurate information to create a perception and make the people see that the issue at hand needs a sense of urgency. At this stage, NSDI can contribute to the change by providing the spatially referenced evidence, which can be used by policymakers to recognise the current trends, see threats, and identify poor areas. With the help of spatial dashboards, thematic maps, and early warning systems, the geospatial intelligence provided by NSDI can enable it to shape the policy discourse. For example, climate risk maps and flood vulnerability models can raise questions about environmental adaptation to a policy agenda, whereas high-resolution imagery, including imagery of informal settlements, can dampen the issue of action on urban planning. NSDI makes sure that the issues are not only observed but also justified with geographic data that appeals to stakeholders and decision-makers. Spatial data, besides describing the narrative in the context of important challenges, also gives these challenges a higher status on the political agenda by presenting captivating visuals [74].

2.2.2. Policy Formulation

Policy formulation is the phase whereby possible strategies are formulated, discussed, and improved to form presentable policy proposals. It entails several choices, weighing their possible impacts and determining the most effective and circumstantially appropriate interventions. NSDI can also promote this by providing spatial data for spatial scenario analysis, integrated mapping of socio-economic and environmental indicators, and visualisation of target populations. Policymakers can simulate the impacts of different proposed policies via the use of NSDI platforms; some of the processes include determining the most appropriate location for expanding infrastructure by superimposing demographic, topography, and land use information. As an illustration, the housing policy may be better defined by the locations of slums, property, as well as utility networks being visualised simultaneously to determine the areas that are underserved and design inclusive development. In addition, spatial data infrastructures can enable consultations with the stakeholders to present options in the form of maps and dashboards, enabling common ground to be established with regard to possible solutions. NSDI datasets support policymaking not only in theory but also in practice, by grounding policy options in the specific conditions and needs of each location.

2.2.3. Policy Adoption

During the decision-making phase, the evaluation of policy alternatives and the formal adoption of the decision is carried out through legislature, executive decision, or administrative approval. This step usually involves a trade-off between political, technical, and budgetary lines. This process can be improved by NSDI by providing data to support evidence of visuals and simulation interactivity that allows trade-offs to be apparent and paves the way towards more informed discussion. Decision-makers can then also view the differences between the effects of different options through the use of geospatial platforms, such as where a proposed road would have run down the protected wetlands or driven communities out. Such spatial clarity enhances transparency and responsibility for policy decisions. In the urban transport case, for example, NSDI tools can support the visualisation of the possibility of a new travel path to reduce congestion while impacting air quality in nearby areas. Political leaders, as well as citizens, can use such visualisations to support data-informed deliberation, which increases their trust in the validity of the decision-making process. NSDI, therefore, acts as a director linking the technical policy analysis and decision tools that are friendlier and accessible to stakeholders.

2.2.4. Policy Implementation

Implementation of policy turns formal decisions into effective programmes and services and usually involves the coordinated efforts of many institutions and the deployment of resources at the right time, in addition to monitoring. With NSDI, this step can be augmented by the provision of real-time geospatial tracking systems, allowing agencies to coordinate with each other with high accuracy and facilitating geo-tagged reporting of project progress. Infrastructure projects such as the construction of a new highway, for example, can also be tracked with the help of NSDI, which combines satellite images, field-based data, and administrative data to track whether construction is on track at the specified locations. GISs supported by NSDI datasets can also be employed to guide humanitarian supplies and map the delivery of supplies with spatial precision, especially in post-disaster situations [75]. In this way, NSDI can transform implementation through the incorporation of location intelligence into the system of service delivery and administrative responsibility.

2.2.5. Policy Evaluation

Policy evaluation is a process based on the systematic analysis of the outcomes of a policy in terms of effectiveness, efficiency, and equity. It helps governments know what worked and what did not, and it can help inform future decisions. This stage can be facilitated by NSDI, which allows one to evaluate spatial performance with regard to various regions and population groups, resulting in an understanding of the geographical inequities in the effect of policies. Evaluators can assess quantitative and qualitative dimensions of program implementation using remote sensing, spatial statistics, as well as geo-tagged feedback systems. Considering the estimation of the effectiveness of a sanitation programme as one of the examples, NSDI tools can allow the comparison of images before and after the intervention, overlaying them with health statistics, and correlating this information with the demographic metrics to offer a detailed description of the programme’s efficiency. Furthermore, NSDI datasets can enable the real-time assessment based on dashboards that monitor the development of service coverage, the environmental transformation, or the quality of the infrastructure over time. Through spatial feedback mechanisms, NSDI datasets can support evaluation that transcends the measuring output process to the unveiling of spatial injustices and supporting the remedial process.

2.2.6. Support/Maintenance

The last activity of the policy cycle is what comes next after evaluating the current policies, so that decisions about whether to continue, adopt, change, or abandon a current policy are made depending on the outcomes of the policy evaluation and evolving needs. The NSDI dataset can support the possibility of performing an ongoing trend analysis and creation of spatial evidence regarding trends in the long-term relevance and effects of policies. As conditions change in the environment, demographically or economically, spatial data through the NSDI resource can be used to identify emerging inconsistencies between policy goals and reality on the ground. As an example, an urban development policy can be updated in case satellite evidence indicates the tendencies of uncontrolled sprawling or abuse of natural resources within the areas of interest. NSDI is also useful for carrying out a comparison over time, so that policymakers can visualise this to see whether the starting objectives were maintained or need updating. Moreover, it has feedback loops through which the learning continues, and the lessons of past policy cycles can be utilised to design new or renewed interventions. In this manner, NSDI is empowering adaptive governance by enhancing the dynamics of policy cycles in a way that is evidence-based and attentive to geographically diverse areas. Table 2 offers an outline of the integration of NSDI across the EGU policy cycle stages.

It is worth mentioning that Policy Cycle Models, despite their analytic value, have always been criticised as a patchy representation of later processes of evaluation and feedback/maintenance. These stages are poorly institutionalised in practice compared to agenda setting or formulation and thus are not visible in policy analysis or governance outputs [76,77,78]. This is also a concern that has been raised in the NSDI literature, with researchers observing a paucity of approaches toward systematic evaluation and sustainability [24,68,79]. In the use of the EGU model, this limitation is recognised, so that the underrepresentation of these stages, in the context of the NSDI, is not only a matter of unrealized methodological opportunity, but also of substantive governance concern. Table 2 provides an overview of the integration of NSDI in policy cycle stages of the EGU. These connections between stages of the policy cycle and geospatial infrastructures are mentioned in the literature of governance and spatial planning, which are noted in Table 2.

Table 2. Integration of NSDI across the EGU policy cycle stages.

Policy Cycle Stage	Purpose	NSDI Contribution
Agenda Setting	Identifying and prioritising issues for policy attention	NSDI provides spatial data to detect trends, visualise risks, and identify vulnerable areas [24,62]
Policy Formulation	Designing policy options and strategies	Supports scenario analysis, mapping of affected populations, and visualisation of socio-economic indicators [80]
Policy Adoption	Selecting among alternatives based on feasibility and impacts	Facilitates impact visualisation, spatial simulations, and trade-off analysis to guide evidence-based decision-making [68,76]
Policy Implementation	Executing decisions through programmes and services	Enables real-time monitoring, geo-tagged field reporting, and inter-agency coordination using a shared geospatial platform [23,81]
Policy Evaluation	Assessing policy impact, efficiency, and equity	Spatial analysis and visualisation of performance metrics help identify geographic disparities and programme effectiveness [82]
Support/Maintenance	Adjusting, replacing, or terminating policies based on evolving realities	NSDI allows time-series analysis, trend detection, and continuous feedback to revise or renew policies in response to changing spatial conditions [24]

Table 2. Integration of NSDI across the EGU policy cycle stages.

Policy Cycle Stage	Purpose	NSDI Contribution
Agenda Setting	Identifying and prioritising issues for policy attention	NSDI provides spatial data to detect trends, visualise risks, and identify vulnerable areas [24,62]
Policy Formulation	Designing policy options and strategies	Supports scenario analysis, mapping of affected populations, and visualisation of socio-economic indicators [80]
Policy Adoption	Selecting among alternatives based on feasibility and impacts	Facilitates impact visualisation, spatial simulations, and trade-off analysis to guide evidence-based decision-making [68,76]
Policy Implementation	Executing decisions through programmes and services	Enables real-time monitoring, geo-tagged field reporting, and inter-agency coordination using a shared geospatial platform [23,81]
Policy Evaluation	Assessing policy impact, efficiency, and equity	Spatial analysis and visualisation of performance metrics help identify geographic disparities and programme effectiveness [82]
Support/Maintenance	Adjusting, replacing, or terminating policies based on evolving realities	NSDI allows time-series analysis, trend detection, and continuous feedback to revise or renew policies in response to changing spatial conditions [24]

The two frameworks were chosen to be complementary, where the governance framework focuses on the quality of institutions and normative values, whereas the policy cycle provides a procedural guide for policy activities and transformation. Their combination provides a dual perspective, evaluating the contribution of NSDI datasets to the quality of governance on one hand and the effectiveness of policy implementation on the other. This is a comprehensive strategy that provides a strong analytical basis for the comprehension of the role of NSDI in facilitating data-driven, transparent, and responsive reforms of the Pakistani public sector.

3. Research Methodology

This research involves a mixed-methods research design utilising the conceptual modelling, document-based analysis and the survey-based stakeholder analysis. The cross-layered approach takes the form of investigating how the NSDI can trigger the development of governance and policies in Pakistan, both with the help of empirical knowledge and theoretical underpinnings.

3.1. Conceptual Framework Development

The conceptual framework of the paper consists of the dual-theoretical approach whereby the good governance principles, which are propounded by the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP), and the Policy Cycle Model advocated by the European Geosciences Union (EGU) are integrated. NSDI is envisioned as a socio-technical solution including legal protocols, institutional arrangements, standards, and digital infrastructure to collect, manage, distribute, and utilise spatial information. According to the framework, it is assumed that:

• At the high level, NSDI has the capability of refining each phase of the policy process, including the identification of geography-centred issues; the evaluation of policies and corresponding adaptation of policies through geospatially interconnected feedback.
• NSDI can improve governance through transparency, accountability, participation, and efficiency in government service because of enhanced access to spatial information and inter-agency cooperation.

To explain why and how certain NSDI tools can be matched with governance principles and policy-cycle stages, a visual integration model (Figure 1) was designed to visualise the relationship between particular tools of the NSDI (e.g., open data portals, metadata catalogues, geospatial dashboards) and governance principles, as well as policy cycle stages. This conceptual framework is used to collect and code data as well as to interpret the data.

3.2. Data Sources and Materials

Three main data sources are used, which are listed below.

• Policy documents (n = 23): Official policies from key sectors (e.g., energy, climate, environment, education, cybersecurity) were selected and downloaded from authenticated government portals and public repositories.
• Conceptual frameworks: Eight UNESCAP governance principles and six EGU policy cycle stages guided the thematic analysis.
• A questionnaire survey: A structured questionnaire was administered to 28 experts from public sector organisations, academia, and the private sector.

3.3. Documents Reviewed

A qualitative review of documents was conducted on a broad level to evaluate the state and institutionalisation level of NSDI in Pakistan. The reviewed documents included policy documents as well as strategy-related documents created by various ministries and organisations, such as the Ministry of Planning, Development and Reform; the Ministry of National Food Security and Research; the Ministry of Information Technology and Telecommunication; the Ministry of Housing & Works; the Ministry of Climate Change; the Ministry of Energy; the Ministry of Science and Technology; the Ministry of Water Resources; and other ministries. This step provided context for the mapping of NSDI with governance and policy objectives.

Many documents from all the ministries’ websites were extracted. A total of 23 national-level policy documents were selected for in-depth document analysis. These documents span key development sectors, including education, security, ICT, climate, environment, energy, water, biodiversity, transport, and health, all of which are significantly influenced by spatial data and inter-agency coordination. The rationale for selecting these policies lies in their strategic relevance to governance and public service delivery, and the potential for SDI integration to enhance planning, monitoring, and implementation within their respective domains. The selection was guided by three primary criteria:

• The documents represent sectors where geospatial data plays a critical role in policy outcomes (e.g., climate adaptation, infrastructure, energy planning, and natural resource management).
• Only national-level policies and strategies with recognised importance or recent updates were included to ensure contemporary relevance.
• All documents were sourced from official government portals and endorsed by relevant ministries or agencies, ensuring their authenticity and traceability.

Table 3. Documents reviewed.

Index	Document	Year
1	National Education Policy (https://pbit.punjab.gov.pk/system/files/National%20Educaton%20Policy%202017.pdf) (accessed on 1 June 2025).	2017
2	National Security Policy of Pakistan (https://dnd.com.pk/wp-content/uploads/2022/01/National-Security-Policy-2022-2026.pdf) (accessed on 1 June 2025).	2022
3	Pakistan Cloud First Policy (https://moitt.gov.pk/SiteImage/Misc/files/Pakistan%20Cloud%20First%20Policy-Final-25-02-2022.pdf) (accessed on 5 June 2025).	2022
4	National Clean Air Policy (https://mocc.gov.pk/SiteImage/Misc/files/NCAP%20(28-02-2023)%20v1.pdf) (accessed on 5 June 2025).	2023
5	National Hazardous Waste Management Policy (https://mocc.gov.pk/SiteImage/Misc/files/National%20Hazardous%20Waste%20Management%20Policy%202022.pdf) (accessed on 5 June 2025).	2022
6	National Climate Change Policy (https://mocc.gov.pk/SiteImage/Policy/NCCP%202021.pdf) (accessed on 18 June 2025).	2021
7	National Forest Policy (https://pbit.punjab.gov.pk/system/files/National%20Forest%20Policy%202016.pdf) (accessed on 18 June 2025).	2015
8	National Rangeland Policy (https://mocc.gov.pk/SiteImage/Policy/NationalRangelandPolicy.doc) (accessed on 18 June 2025).	2010
9	National Drinking Water Policy (https://mocc.gov.pk/SiteImage/Policy/DrinkingWaterPolicy.doc) (accessed on 1 July 2025).	2009
10	National Environmental Policy (https://envitechal.com/downloaddocs/NationalLawsOfPK/National%20Environmental%20Policy%202005.pdf) (accessed on 1 July 2025).	2005
11	National Cyber Security Policy (https://moitt.gov.pk/SiteImage/Misc/files/National%20Cyber%20Security%20Policy%202021%20Final.pdf) (accessed on 1 July 2025).	2021
12	Digital Pakistan Policy (https://moitt.gov.pk/SiteImage/Misc/files/DIGITAL%20PAKISTAN%20POLICY.pdf) (accessed on 1 July 2025).	2018
13	National Electricity Plan 2023-27 (https://www.power.gov.pk/SiteImage/Policy/National%20Electricity%20Plan%202023-27.pdf) (accessed on 1 July 2025).	2023
14	National Electricity Policy (https://www.power.gov.pk/SiteImage/Policy/1-NationalElectricityPolicy2021.pdf) (accessed on 1 July 2025).	2021
15	Alternative and Renewable Energy Policy (https://nepra.org.pk/Policies/ARE_Policy_2019_-_Gazette_Notified.pdf) (accessed on 1 July 2025).	2019
16	5Es Framework (https://pc.gov.pk/uploads/downloads/5Es_Framework.pdf) (accessed on 1 July 2025).	2023
17	National Health Vision (https://extranet.who.int/countryplanningcycles/sites/default/files/planning_cycle_repository/pakistan/national_health_vision_2016-25_30-08-2016.pdf) (accessed on 1 July 2025).	2016
18	National Food Security Policy (https://pbit.punjab.gov.pk/system/files/National%20Food%20Security%20Policy%202018.pdf) (accessed on 1 July 2025).	2018
19	National Water Policy (https://water.muet.edu.pk/wp-content/uploads/2019/03/National-Water-Policy.pdf) (accessed on 1 July 2025).	2018
20	National Water Conservation Strategy for Pakistan (2023-27) (https://pcrwr.gov.pk/wp-content/uploads/2023/02/National-Water-Conservation-Strategy-for-Pakistan-2023-27.pdf) (accessed on 1 July 2025).	2023
21	National Transport Policy of Pakistan (https://pc.gov.pk/uploads/downloads/policy.pdf) (accessed on 1 July 2025).	2018
22	Pakistan National Biodiversity Strategy and Action Plan (https://www.cbd.int/doc/world/pk/pk-nbsap-v2-en.pdf) (accessed on 1 July 2025).	2017
23	National Sustainable Development Strategy (https://policy.asiapacificenergy.org/sites/default/files/National%20Sustainable%20Development%20Strategy%20%28NSDS%29.pdf) (accessed on 1 July 2025).	2012

lists the selected documents along with their publication years.

3.3.1. Analytical Metrics

Each document is analysed to extract the Excerpt of the document, Inferred NSDI Function, Linked EGU Policy Cycle Stage, and Linked UNESCAP Governance Principle. Each inferred NSDI Function corresponds to one or more of the five analytical metrics. The metrics are partially based on the NSDI components defined by the Federal Geographic Data Committee (https://www.fgdc.gov/components, accessed on 1 July 2025), ensuring conceptual validity and international alignment. The metrics are

• Spatial Data (SD): The presence of references to the use of spatial or geospatial data in planning, monitoring, or decision-making.
• Geoportal/Mapping Tools (GMTs): Mention of interactive platforms, dashboards, or GIS tools for spatial visualisation or dissemination.
• Data Sharing/Integration (DSI): References to mechanisms or institutional provisions for inter-agency or cross-platform data exchange.
• Spatial Standards/Metadata (SSM): Any mention of adherence to geospatial data standards, metadata protocols, or interoperability frameworks.
• Capacity Building (CB): Indicators of investment in spatial literacy, workforce training, or institutional development related to geospatial data.

These five indicators were selected to systematically assess the presence and maturity of SDI components across various documents. By operationalising key NSDI dimensions, this study ensures objective coding of complex text, reproducible measurement of NSDI relevance, and a cross-sectoral lens for comparison.

3.3.2. Spatial Readiness Score (SRS)

The SRS is calculated for each document through binary coding based on the presence (1) or absence (0) of the above indicators. This score represents the degree to which a document implicitly or explicitly aligns with NSDI principles. Equation (1) is used to calculate the SRS to obtain a normalised score ranging from 0 to 1 for each document.

SRS = (SD + GMT + DSI + SSM + CB)/5

(1)

The SRS provides a composite index that quantifies the overall alignment of each policy document with NSDI principles. It transforms binary-coded indicators into a continuous score (0–1), enabling comparative ranking and aggregation across documents. This quantitative simplification highlights strengths and gaps in NSDI readiness and supports longitudinal or cross-country benchmarking. As an example, the National Climate Change Policy states “Promote the use of GIS/RS-based studies to assess and quantify past temporal trends and monitor future changes in snow cover, glacial volume, glacial lake formation and burst, deforestation, land degradation (salinity, water logging), soil erosion, inundation of Indus deltaic region and other coastal areas”. This was coded as “1” for the “Formulation” and “Implementation” stages of the policy cycle, as it not only guides the design of strategies, but also helps in terms of oversight during implementation. Regarding the governance principles, this text was coded as ‘1’ for the “effectiveness and efficiency” principle because the incorporation of geospatial data leads to increased coverage of evidence-informed policymaking.

3.3.3. Cross-Indicator Correlation Matrix

To examine relationships between the five indicators across documents, a Cross-Indicator Correlation Matrix is developed using Pearson’s correlation coefficient. This helps identify which indicators tend to co-occur, signalling potential policy clustering or systemic integration. This statistical layer adds depth by revealing structural relationships across documents and identifying patterns of policy coherence. Equation (2) is used for calculating the Pearson correlation r between two indicators, such as SD and DSI.

r (SD, DSI) = cov (SD, DSI)/(σ_SD * σ_DSI)

(2)

3.3.4. Integrated Document Analysis Matrix

The matrix aggregates indicator scores, governance principles, and EGU policy stages in one structured table. This matrix is used to enable multi-dimensional comparison and link raw content analysis to theoretical frameworks. This comparative matrix serves as the foundational dataset for further thematic synthesis. The matrix includes

• The title of the document.
• Computed SRS.
• Relevant EGU policy cycle stage(s).
• The implications of the UNESCAP good governance principle(s).

3.4. Questionnaire Survey Analysis

To triangulate the findings and capture insights into the experience, a survey through a questionnaire was conducted. The questionnaire was distributed to 28 geospatial and IT professionals who belonged to government organisations, research institutes, and academia, as well as the private sector.

Table 4. Respondents by stakeholder category.

Stakeholder Category	Number of Respondents
Government Agencies	19
Academia and Research Institutes	5
Private Sector	4

shows the profiles of questionnaire respondents. The questionnaire contained closed-ended questions which were aimed at evaluating the role of NSDI, as perceived by the respondents, in achieving good governance, and the existing and possible roles of NSDI in different stages of the policy cycle. The survey was conducted anonymously to allow openness and reduce response bias. To capture the diversity of the interests of key stakeholder groups, purposive sampling was used to select respondents in such a way that government organisation officials, representatives of the private sector, and academicians were included as participants, reflecting the difference in the involvement level in the usage of NSDI datasets. No personal information (including names, designations, or institutional affiliations) was requested, and the replies were received through an online form to add further anonymity. Using this methodology improved the validity of the findings because the participants were not afraid of sharing their honest opinions, and their sources could not be identified.

Government (19), Private (4), and Academia (5) represent the proportionate relative relevance and concerned participation of each sector regarding the development of NSDI in Pakistan. Since NSDI is predominantly government-agency driven, and as government agencies are data custodians and policy authorities, they were regarded as significant, warranting high representation to reflect institutional views. The private sector and academia, which are fewer in number, have also been included to provide a mix of views, and in light of their contributions to innovation, capacity building, and service provision. The questionnaire included 20 items across four sections, as elaborated in

Table 5. Survey respondent profile.

Section	Focus	Metric Computed
A	Respondent Background (designation, domain, organisation)	—
B	Spatial Awareness (Q1–Q6): Understanding of spatial data, GIS, and NSDI	NSDI Awareness Index
C	Governance Linkage (Q7–Q14): Perceived impact of NSDI on governance values	Governance Alignment Perception Score
D	Policy Integration Potential (Q15–Q20): Perceived usefulness of NSDI across policy processes	Policy Linkage Score

. Each question was structured using a 5-point Likert scale, where responses ranged from 1 (Strongly Disagree) to 5 (Strongly Agree). Mean scores were calculated for each index.

3.4.1. NSDI Awareness Index (NAI)

The NAI measures the general understanding of spatial data systems, GIS platforms, and the NSDI concept. NSDI awareness is a prerequisite for policy adoption, infrastructure development, and training programmes. Equation (3) is employed to estimate the NAI.

NAI = Σ(R_i)/6

(3)

where R_i = response to question i in Section B (spatial awareness).

3.4.2. Governance Alignment Perception Score (GAPS)

The GAPS assesses respondents’ perceptions about how NSDI supports key governance principles (e.g., accountability, transparency, participation, efficiency and rule of law). It gauges institutional and normative alignment. The calculation of GAPS is completed through the use of Equation (4).

GAPS = Σ(R_i)/8

(4)

where R_i = response to question i in Section C (Governance Linkage).

3.4.3. Policy Linkage Score (PLS)

The PLS evaluates the perceived alignment of NSDI with sectoral policies (e.g., energy, health, climate, environment), and its relevance in supporting decision-making and monitoring. Equation (5) is used to compute the PLS.

PLS = Σ(R_i)/6

(5)

where R_i = response to question i in Section D (policy relevance).

3.4.4. Weighted Endorsement Score (WES)

The WES is a composite metric combining spatial awareness, governance alignment, and policy relevance. The weightings (30-40-30) emphasise governance as the central dimension, while also considering technical knowledge and sectoral applicability. The weighting scheme (30-40-30) utilised at WES is based on the relative importance of the three-dimensional factors to the success of the NSDI. Governance alignment was given the most weight (40%), since it contributes to institutional coordination, legal mandates, and accountability. Equitable weights were assigned to NSDI awareness (30%) and sectoral policy alignment (30%) because both have an important enabling role, but much of their value depends on the design of governance structures. WES is computed with the help of Equation (6).

WES = (0.3 × NAI) + (0.4 × GAPS) + (0.3 × PLS)

(6)

3.5. Scope and Limitations

This paper is restricted to an analysis of national level of strategy and policy documents. Although the mixed-method type increases robustness, the research method is exploratory, and it does not include quantitative impact assessment or prediction modelling. Still, it provides a solid foundation for further empirical studies and policy formulation on NSDI in Pakistan.

4. Results

This section shows the results based on the combined methodological approach, highlighting the following two research objectives: (1) to evaluate to what extent NSDI can support different stages of the policy cycle in Pakistan; and (2) to estimate the compatibility of NSDI with the eight principles of good governance envisioned by UNESCAP. Triangulation of data was employed through analysis of 23 national-level documents and a survey involving 28 respondents from the public sector, academia, and the private sector to gather responses from diverse professionals.

4.1. Document Analysis

This section presents the findings from the document analysis of 23 national policy documents, which was conducted to evaluate the integration and readiness of NSDI components. The analysis was performed using the analytical framework described in the Methodology Section, encompassing three key components: (i) Spatial Readiness Score (SRS), (ii) the Cross-Indicator Correlation Matrix, and (iii) the Integrated Document Analysis Matrix.

4.1.1. Spatial Readiness Score Results

To assess the extent to which each national policy document aligns with the conceptual and operational dimensions of an NSDI, the SRS was calculated. This composite score aggregates five binary indicators: SD, GMT, DSI, SSM, and CB, as defined in the Methodology Section. Each document was assigned a normalised score between 0 and 1, reflecting the presence or absence of these key NSDI components. The resulting scores range from 0.2 to 0.6, indicating a moderate level of integration of NSDI components across most documents, with no policy achieving a perfect score.

Figure 2 presents the SRS for all 23 analysed documents. The chart reveals a moderate clustering pattern, with most policies scoring between 0.4 and 0.6, indicating partial but inconsistent integration of spatial infrastructure considerations. Policies such as the Digital Pakistan Policy, National Climate Change Policy, Hazardous Waste Management Policy, and National Electricity Plan achieved the highest scores of 0.6, suggesting a more mature spatial orientation. In contrast, foundational documents like the National Education Policy, National Drinking Water Policy, and Alt. and Renewable Energy Policy had lower scores (0.2), underscoring significant spatial integration gaps and suggesting minimal recognition of spatial dimensions in policy design and governance.

To complement the SRS trend, Figure 3 visualises the same data using a binary heatmap, displaying the presence or absence of the five NSDI indicators across all documents. This visual representation reinforces the insight that SSM is the least addressed component (only 1 out of 23), while SD is the most acknowledged (present in 19 documents). Despite this, most policies exhibit fragmentation, with few integrating more than three of the five indicators. The heatmap thus illustrates not just which policies score high overall, but which specific spatial infrastructure components are systematically overlooked.

Taken together, these figures underscore a critical gap: while many policy documents acknowledge spatial data implicitly, few embed the structural or institutional elements needed for a fully functioning NSDI. The low scores and missing indicators call attention to a lack of standardised frameworks, interoperable systems, and investment in spatial data capacity—factors which are necessary for aligning national governance with geospatial decision-making paradigms.

4.1.2. Cross-Indicator Correlation Matrix Results

To evaluate the degree of interdependence among the five NSDI readiness indicators across the reviewed policy documents, a Pearson Correlation Matrix is computed. This analysis aims to identify whether the presence of one NSDI element (e.g., spatial data) implies the inclusion or omission of another (e.g., capacity-building or data-sharing infrastructure).

Figure 4 displays the resulting heatmap, where darker shades indicate stronger positive correlations. The analysis yields many key observations.

• The strongest negative association was found between DSI and GMT (r = −0.36) and DSI and SD (r = −0.35), indicating that policies emphasising data-sharing infrastructure often neglect basic spatial data framing or geo-management tools.
• The generally low positive correlations (e.g., SD–GMT at 0.13 or CB–GMT at 0.17) suggest that most policies treat NSDI components in isolation rather than as part of an integrated ecosystem.
• Notably, no valid correlation coefficients could be computed for SSM due to its absence in nearly all policies. This reinforces the findings of Section 4.1.1 that standards and metadata practices are almost universally neglected, making it a structural blind spot in national policy planning.

These findings collectively indicate that the spatial policy environment in Pakistan is fragmented and functionally disconnected. Rather than progressing toward an integrated NSDI architecture, most policy documents isolate spatial functions, failing to align data acquisition, sharing, tools, and capacity development into a coherent whole.

Figure 4, the heatmap visualisation of this matrix, further emphasises the lack of strong positive relationships among NSDI components. The blue-to-red gradient highlights weak coherence and low integration intensity across the policy landscape, especially concerning interoperability-oriented functions. In short, Figure 4 shows Pakistan’s NSDI policy environment is fragmented, with notable negative correlations between DSI and GMT (r = –0.36) and DSI and SD (r = –0.35), indicating that data-sharing priorities often bypass core spatial data and management tools. Weak positive correlations (e.g., SD–GMT at 0.13, CB–GMT at 0.17) and the absence of SSM data highlight neglect of standards and metadata, creating a major structural blind spot. This poor integration undermines interoperability, reduces efficiency, and limits governance impact; without coordinated policy design, NSDI reforms risk-producing siloed capabilities instead of enabling evidence-based decision-making and sustainable digital governance.

4.1.3. Integrated Document Analysis Matrix Results

A detailed evaluation of each document is synthesised in the Integrated Document Analysis Matrix (

Table 6. Integrated document analysis matrix.

No.	Document	SRS	Key EGU Stages	Governance Principles
1	National Education Policy	0.20	Agenda Setting, Formulation, Implementation	Transparency, Equity and Inclusiveness, Effectiveness and Efficiency
2	National Security Policy	0.40	Agenda Setting, Formulation, Implementation	Responsiveness, Equity and Inclusiveness, Effectiveness and Efficiency
3	Pakistan Cloud First Policy	0.40	Formulation, Implementation	Consensus Orientation, Effectiveness and Efficiency
4	National Clean Air Policy	0.40	Agenda Setting, Implementation, Adoption	Transparency, Equity and Inclusiveness, Participation
5	Hazardous Waste Management Policy	0.60	Agenda Setting, Formulation, Implementation, Adoption	Transparency, Equity and Inclusiveness, Accountability, Effectiveness and Efficiency, Participation
6	National Climate Change Policy	0.60	Formulation, Implementation	Transparency, Equity and Inclusiveness, Rule of Law, Effectiveness and Efficiency, Participation
7	National Forest Policy	0.60	Agenda Setting, Formulation, Implementation, Adoption	Transparency, Equity and Inclusiveness, Consensus Orientation, Effectiveness and Efficiency, Participation
8	National Rangeland Policy	0.40	Agenda Setting, Formulation, Implementation	Equity and Inclusiveness, Effectiveness and Efficiency
9	National Drinking Water Policy	0.20	Agenda Setting, Formulation	Equity and Inclusiveness, Rule of Law, Participation
10	National Environmental Policy	0.40	Implementation, Adoption	Responsiveness, Equity and Inclusiveness, Consensus Orientation, Effectiveness and Efficiency
11	National Cyber Security Policy	0.40	Agenda Setting, Formulation, Implementation	Equity and Inclusiveness, Effectiveness and Efficiency
12	Digital Pakistan Policy	0.60	Agenda Setting, Formulation, Implementation	Transparency, Equity and Inclusiveness, Accountability, Effectiveness and Efficiency
13	National Electricity Plan	0.60	Agenda Setting, Implementation	Transparency, Equity and Inclusiveness, Rule of Law, Effectiveness and Efficiency, Participation
14	National Electricity Policy	0.20	Formulation, Implementation	Transparency
15	Alt. & Renewable Energy Policy	0.20	Formulation, Implementation	Transparency, Rule of Law, Effectiveness and Efficiency, Participation
16	5Es Framework	0.60	Implementation	Transparency, Equity and Inclusiveness, Responsiveness, Effectiveness and Efficiency, Participation
17	National Health Vision	0.20	Agenda Setting, Formulation, Implementation	Accountability, Responsiveness, Effectiveness and Efficiency, Participation
18	National Food Security Policy	0.40	Formulation, Implementation	Accountability, Responsiveness, Transparency
19	National Water Policy	0.40	Agenda Setting, Formulation, Adoption, Implementation, Evaluation	Accountability, Transparency, Effectiveness and Efficiency, Participation, Consensus Orientation
20	National Water Conservation Strategy	0.60	Agenda Setting, Implementation	Transparency, Equity and Inclusiveness, Responsiveness, Effectiveness and Efficiency
21	National Transport Policy	0.40	Implementation	Equity and Inclusiveness, Effectiveness and Efficiency
22	Biodiversity Strategy and Action Plan	0.40	Formulation, Implementation	Accountability, Equity and Inclusiveness, Responsiveness, Effectiveness and Efficiency, Participation
23	Sustainable Development Strategy	0.60	Agenda Setting, Formulation, Implementation, Evaluation	Accountability, Equity and Inclusiveness, Responsiveness, Effectiveness and Efficiency, Participation, Consensus Orientation, Transparency

). This matrix triangulates SRS, EGU policy cycle stages, and UNESCAP Governance Principles for each document.

This matrix offers a multi-dimensional comparison of the spatial data governance potential across national policies. A few insights are listed below:

• Policies in the energy and ICT sectors (e.g., National Electricity Plan, Digital Pakistan Policy) show relatively high NSDI integration and alignment with stages such as formulation and implementation.
• Environmental and natural resource policies (e.g., Climate Change Policy and Forest Policy) demonstrate moderate spatial readiness but lack standardisation and service-level components.
• Social sector policies such as the National Education Policy and National Health Vision are spatially underdeveloped, despite articulating strong commitments to equity and inclusion.
• The most frequently cited governance principles include Effectiveness and Efficiency, Equity and Inclusiveness, Transparency, and Participation, all of which are foundational to spatial data policy frameworks.

The matrix also reveals how these documents correspond to specific stages in the EGU policy cycle and to key governance principles such as transparency, effectiveness, and accountability. Figure 5 presents EGU policy cycle stage frequencies, offering insight into which policy stages are most commonly associated with spatial references.

Based on the extracted EGU stages from each policy, Figure 5 depicts the frequency distribution across the 23 analysed documents. The findings indicate that

• Implementation (21 mentions) is the most dominant stage, indicating that spatial data references are often tied to execution and delivery mechanisms rather than planning or evaluation.
• The Formulation (19) and Agenda Setting (15) stages are also well-represented, suggesting growing awareness of geospatial needs in early-stage policy design.
• In contrast, the Adoption (4) and, in particular, Evaluation (2) stages show limited engagement.
• No policy explicitly addressed Support/Maintenance, indicating a critical gap in long-term NSDI planning and revision.

This skewed distribution highlights a critical gap in post-implementation monitoring and sustainability planning, an area where NSDI could contribute significantly through spatial dashboards, temporal tracking, and location-based auditing.

Further, Figure 6 shows the frequency of governance principles across all 23 documents, helping to illuminate which principles dominate and which are underrepresented.

Figure 6 presents the occurrence of the UNESCAP good governance principles across the documents, based on thematic coding. The radar chart demonstrates that

• Effectiveness and Efficiency (21) and Equity and Inclusiveness (19) are the most frequently invoked governance dimensions, suggesting strong normative commitments to service delivery and social fairness.
• Transparency (16) and Participation (13) are also notable but somewhat less emphasised, indicating only partial adoption of open data or participatory spatial platforms.
• Meanwhile, Rule of Law (4) and Consensus Orientation (5) are significantly underrepresented, pointing to governance challenges in standard setting, legal frameworks, and institutional coordination, which are core tenets of NSDI architecture.

This uneven pattern suggests that while spatial data is often framed in terms of technical efficiency, its role in institutional legitimacy, inclusivity, and accountability remains underdeveloped. These results indicate that while many policies emphasise delivery and inclusion, there is a structural absence of principles like legality, participatory consensus, and procedural transparency that are essential for sustainable NSDI implementation. Moreover, Figure 7 introduces comparing presence vs. absence counts for each NSDI indicator, visually emphasising thematic gaps in metadata and governance tooling.

To contrast presence and absence of core spatial functions across the policy set, a diverging bar chart (Figure 7) was constructed using counts of the five NSDI indicators.

As shown in the figure,

• The majority of documents demonstrate explicit reference to SD, DSI, and CB, indicating baseline spatial awareness.
• However, the virtual absence of GMT and complete omission of Spatial Standards and Metadata (SSM) reveal a major gap in technical interoperability, cataloguing practices, and data governance frameworks.

This reflects an inconsistent and often fragmented understanding of NSDI as a full system, with clear emphasis on raw data availability but a near absence of structured tools, standards, and institutional coordination for spatial interoperability. Together, these visualisations provide a multi-dimensional profile of NSDI alignment across the policy corpus, highlighting where institutional awareness exists, where it is fragmented, and where it is missing altogether. Importantly, the integration of governance principles with policy cycle stages offers a unique framework to assess governance-enabling capacity for spatial data infrastructure, a foundational step toward effective national NSDI planning.

4.2. Survey Questionnaire Analysis

To complement the document analysis, a structured survey was conducted with 28 public sector respondents representing participants in policymaking, spatial planning, and data management across public sector organisations. The survey aimed to gauge perceptions regarding the relevance, understanding, and governance alignment of the NSDI in Pakistan. Twenty Likert-scale questions were grouped into three analytical sections, generating three indices:

• The NSDI Awareness Index (NAI), measures understanding of spatial data, GIS platforms, and the NSDI concept.
• The Governance Alignment Perception Score (GAPS) assesses perceptions of how NSDI contributes to good governance values (e.g., transparency, accountability, participation).
• The Policy Linkage Score (PLS) evaluates the perceived utility of NSDI across different policy cycle stages.

Additionally, a composite Weighted Endorsement Score (WES) was calculated to reflect holistic stakeholder endorsement of NSDI, assigning 30% weight to NAI, 40% to GAPS, and 30% to PLS, in line with the centrality of governance in NSDI success.

Figure 8 presents the NAI scores across respondents. Mostly ranging from 3.4 to 4.2, indicating a moderate to strong familiarity with spatial data systems, GIS, and NSDI concepts. Notably, only a few respondents scored below 3.5, suggesting a generally high baseline of technical awareness. This supports the inference that while technical familiarity exists, it may not be uniformly translated into policy practice, especially where NSDI-specific institutional mandates are lacking.

Similarly, Figure 9 displays the GAPS values, indicating consistently high perceptions of NSDI’s alignment with governance principles, with most scores falling between 3.6 and 4.3. The strongest perceptions were linked to NSDI’s role in enhancing transparency, accountability, and efficiency in government decision-making. These responses align with the document analysis, where effectiveness, inclusiveness, and transparency emerged as key principles. The positive GAPS trend suggests institutional openness toward spatial governance integration, though operational mechanisms remain underdeveloped.

In contrast, Figure 10 shows relatively lower PLSs, which reflect stakeholder perceptions of NSDI’s practical integration into policy cycles. Most values fall between 3.2 and 3.8, with a slightly broader variation than NAI or GAPS. These findings align with the document analysis results, where policy formulation and implementation stages showed some presence of spatial components, but adoption, evaluation, and maintenance stages were almost absent. This indicates a need to institutionalise NSDI utility across the full spectrum of policy development and lifecycle monitoring.

Figure 11 presents the Weighted Endorsement Score, combining the three indices. Scores cluster tightly between 3.5 and 3.9, reflecting strong, consistent support across awareness, governance alignment, and policy integration. The absence of extreme outliers reflects a broad consensus among stakeholders that NSDI is both valuable and relevant across governance and planning domains. However, the absence of scores above 4.3 suggests that practical implementation barriers, especially regarding interoperability, centralised geoportals, and standardisation, may temper full-scale endorsement.

The survey results yield several critical insights:

• A high average NAI score suggests that the concept of NSDI is no longer novel among relevant professionals, and awareness-building efforts may now shift toward sector-specific operationalisation and training.
• GAPS results reveal strong belief in NSDI’s capacity to improve governance processes. This is particularly aligned with the document analysis findings, where effectiveness and efficiency, equity, and transparency emerged as key policy priorities.
• The relatively lower PLS values indicate a gap between recognition of NSDI’s importance and its practical incorporation into sectoral policy stages. Institutional fragmentation, absence of standards, and limited spatial data mandates are likely to contribute to this lag.
• The tightly clustered WES distribution suggests broad agreement among stakeholders about the relevance of NSDI. However, the modest maximum scores point to an underlying hesitation, possibly stemming from inconsistent institutional support, limited cross-agency coordination, or insufficient capacity-building frameworks.

Overall, the survey reinforces the document analysis findings and highlights the urgency of moving from conceptual alignment to operational execution. Strategic investment in governance-based spatial data integration and institutional capacity-building will be essential to fully realise the potential of NSDI in Pakistan.

The combined findings of the document and survey analysis point to several key observations:

• Both documents and respondents emphasise NSDI’s role in policy implementation, highlighting a shared understanding of its operational importance.
• Despite strong normative support (via GAPS), documents reveal weak emphasis on the rule of law, transparency, and consensus, indicating institutional and legal barriers.
• The absence of GMT and SSM in both document analysis and stakeholder feedback confirms systemic weaknesses in technical standards and tools.
• Moderate NAI scores and frequent CB mentions in documents point to a need for structured training and advocacy for NSDI adoption.

This integrated Results Section reveals that while NSDI is partially acknowledged across Pakistan’s policy architecture, its institutionalisation, infrastructural backbone, and legal underpinnings remain inadequate. Stakeholder perceptions largely validate document findings, though some optimism about governance alignment suggests a potential entry point for targeted policy interventions.

5. Discussions

This research evaluated how an NSDI can contribute to improved governance and more effective policy processes in Pakistan. Guided by two analytical frameworks—the UNESCAP Principles of Good Governance and the EGU Policy Cycle Model—the study employed a dual-track methodology: (i) content analysis of 23 national policies to trace NSDI indicators and alignment patterns, and (ii) a structured stakeholder survey assessing perceptions related to spatial data awareness, governance alignment, and policy relevance. A synthesis of findings from both evidence streams reveals several converging and diverging trends:

• Convergence was observed in the recognition of spatial data as an enabling tool for policy formulation and implementation. Both policy documents and survey respondents emphasised NSDI’s potential to support evidence-based planning, resource allocation, and service delivery.
• Divergence appeared in the institutionalisation and operationalisation of spatial frameworks. While survey participants generally perceived NSDI as critical to governance and policy integration, the document analysis revealed major gaps in terms of legal frameworks, interoperability protocols, metadata standards, and capacity-building mechanisms.
• The survey data highlighted optimism, with relatively high scores for NSDI’s perceived relevance across governance functions and policy cycles. In contrast, the document analysis exposed structural inertia, with only superficial inclusion of spatial concepts in many policies and minimal attention to long-term support or spatial feedback loops.

Together, these patterns suggest a mismatch between perceived value and actual institutional practice, reinforcing the need for coordinated efforts to embed spatial thinking not only in technical environments but also within governance architectures and policy cycles.

5.1. Gaps in NSDI Institutionalisation

While the document analysis revealed frequent references to spatial data across sectors, particularly in areas such as water management, climate resilience, and digital transformation, these mentions often lacked the depth and institutional anchoring necessary for fully operational NSDI. Despite the rhetorical recognition of spatial needs, most policy texts did not articulate concrete implementation frameworks, standards, or cross-sectoral mandates. This indicates a form of superficial spatial integration, where spatial concepts are acknowledged but not structurally embedded.

A key explanatory factor is institutional inertia, which inhibits the translation of spatial awareness into institutional action. Many government departments continue to operate within rigid bureaucratic hierarchies, relying on traditional data collection and paper-based reporting mechanisms. In such environments, the integration of interoperable spatial data systems requires significant shifts, not only technological but also cultural and procedural. These changes demand high-level political commitment, legal reforms, and sustained investment in capacity development, all of which remain sporadic or underdeveloped in the current policy landscape.

Another critical barrier is the persistence of policy silos, wherein each ministry or department develops and implements strategies with limited coordination or data sharing. This fragmentation was reflected in the survey results: while respondents acknowledged the importance of spatial data (NAI = 3.74) and endorsed its governance utility (GAPS = 3.90), many also noted the lack of shared platforms and joint mechanisms to operationalise spatial collaboration. The negligible presence of indicators like spatial standards and metadata (SSM = 0) and geo-mapping tools (GMT = 1) across the document corpus reinforces this diagnosis of institutional compartmentalisation.

Moreover, without a centralised NSDI authority or legal mandate, there is no enforcement mechanism to ensure that policies align with a common spatial framework. As a result, spatial data generation, maintenance, and application remain dispersed, ad hoc, and inconsistent, undermining the potential of NSDI as a backbone for coordinated governance.

5.2. The Policy Cycle Disconnect

One of the central aims of this study (RQ1) was the asymmetric integration of NSDI across different stages of the policy cycle, as conceptualised by the European Geospatial Union (EGU). The frequency analysis of policy documents indicates that while NSDI-relevant content appears moderately in the early stages—including Agenda Setting (15), Formulation (19), and Implementation (21)—its presence sharply declines when it comes to Adoption (4) and Evaluation (2), and is absent in Support/Maintenance (0).

This pattern highlights a critical disconnect between initial policy ambition and long-term policy adaptability, exposing a structural gap in spatial data institutionalised across the full policy lifecycle. While spatial references are often used to justify policy direction or support planning activities (e.g., in environmental or infrastructure policies), the same emphasis is not extended to post-implementation review or iterative improvement processes.

This has significant implications for adaptive governance, which relies on continuous feedback, learning, and evidence-based adjustments—functions that NSDI is inherently well-suited to support. The absence of NSDI elements in evaluation and maintenance stages indicates that spatial data is not yet institutionalised as a feedback mechanism, limiting capacity to monitor outcomes, validate assumptions, or revise interventions using geospatial intelligence.

The survey data further reinforces this concern. While stakeholders largely affirmed the relevance of NSDI in agenda setting, formulation, and implementation (D1–D3), the lowest mean scores were associated with Evaluation (D4) and Maintenance (D6). These patterns highlight a misalignment between NSDI’s potential as a dynamic governance tool and the static ways in which it is currently referenced in policy design.

Several factors may contribute to this deficiency:

• Lack of policy mandates integrating spatial data into monitoring and evaluation (M and E) systems.
• Institutional silos that separate technical NSDI units from policy review bodies.
• Limited investment in longitudinal spatial data collection, which is crucial for assessing change over time.

5.3. NSDI and Governance Principles

Another key insight emerging from this study (RQ2) was to evaluate how well the concept of an NSDI aligns with the UNESCAP principles of good governance and whether this alignment is reflected both in policy documents and stakeholder perceptions. The findings reveal a significant discrepancy between perceived potential and actual practice.

As highlighted in the Governance Alignment Perception Score (GAPS), survey respondents exhibited a strong belief in NSDI’s capacity to support governance values. The average GAPS stood at 3.90, suggesting a broad consensus among professionals that spatial data systems can enhance transparency, accountability, equity, participation, responsiveness, and the rule of law. Particularly high ratings were observed for items such as transparency (C1) and service delivery efficiency (C7), reinforcing the belief that NSDI can play a transformative role in public sector governance.

However, the document analysis presents a more constrained reality. While references to effectiveness, efficiency, and inclusiveness were frequent in policy texts (with respective frequencies of 21, 19, and 16), other key governance principles were marginally present or entirely underdeveloped. For example, accountability was addressed in only 7 documents, participation in 13, and the rule of law in just 4. Consensus orientation, a principle central to collaborative spatial governance, appeared in only 5 documents, despite stakeholders affirming its importance in the survey.

This contradiction between perception and documentation suggests that while technical experts and policy implementers recognise the strategic value of NSDI, this recognition has yet to be translated into normative or operational commitments within official policy frameworks. In essence, the governance principles that NSDI can strengthen are acknowledged in theory but underrepresented in legal and procedural language.

Several explanations underpin this misalignment. First, governance language in many national policies remains generic, with few mechanisms linking principles like participation or accountability to specific spatial data strategies. Second, in the absence of an NSDI legal framework or governance charter, policy drafters lack clear institutional mandates or templates for embedding spatial governance values.

5.4. Policy and Institutional Implications

The insights emerging from this study carry significant implications for the strategic planning, regulatory development, and institutional coordination necessary for effective NSDI implementation in Pakistan. By juxtaposing the policy content analysis with the perceptions of key stakeholders, this study highlights both the structural constraints and latent opportunities that must inform future NSDI initiatives.

• The document analysis confirms that while spatial data concepts are increasingly acknowledged across sectoral policies, they are rarely framed within a nationally coherent geospatial strategy. The survey further reinforces this gap, as respondents recognised the importance of NSDI across all stages of the policy cycle, yet the formal documents fall short in reflecting this lifecycle integration, particularly in areas of evaluation, adaptation, and maintenance.
• One of the most critical findings is the absence of spatial standards, metadata protocols, and enforceable mandates within existing policy texts. This institutional vacuum creates ambiguity around roles, responsibilities, and compliance, limiting the enforceability and interoperability of spatial systems across agencies. To address this, the following strategies should be implemented:

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  Pakistan must develop binding regulatory frameworks for spatial data governance, modelled on international NSDI principles but adapted to local legal and institutional contexts.

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  Legal instruments must define minimum spatial data standards, establish data custodianship roles, and ensure public access and licencing protocols to maximise both use and trust.

• Survey results, particularly the WES, show moderate to strong belief in NSDI’s cross-sectoral utility. However, policy fragmentation, as revealed in the analysis, continues to limit institutional integration. No single entity currently has the mandate or operational capacity to coordinate spatial efforts across ministries, leading to duplicative efforts, siloed datasets, and incompatible platforms. This points to the urgent need to

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  Establish a National Geospatial Coordination Body, ideally under the Planning Commission or Cabinet Division, with statutory authority to guide and enforce NSDI implementation.

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  Foster horizontal and vertical integration mechanisms, such as inter-ministerial working groups, provincial spatial units, and public–private data partnerships, to build consensus and interoperability.

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  Institutionalise capacity-building programmes across all levels of government to ensure that spatial data literacy, technological understanding, and governance applications are uniformly strengthened.

• The near-total absence of references to geoportals and spatial infrastructure (e.g., only one mention of a Geo-Mapping Tool in policy texts) underscores a major infrastructure deficit. Even as survey participants acknowledge the potential of NSDI platforms, their implementation remains hindered by limited investments in digital infrastructure, cloud computing, and secure data sharing environments. Future efforts must include the following:

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  Development of a federated national geoportal, enabling standardised data access, visualisation, and analytics for all stakeholders.

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  Investment in cloud-first, interoperable platforms for real-time spatial monitoring and decision support.

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  Strategic alignment with ICT, digital governance, and cybersecurity policies to ensure the security, scalability, and resilience of spatial data systems.

By addressing these institutional and policy-level implications, Pakistan can transition from a fragmented and reactive spatial data environment toward a strategic, coordinated, and governance-driven NSDI framework. Such a shift is not only critical for enhancing policy coherence and service delivery but is also essential for ensuring data sovereignty, resilience, and inclusive development in an increasingly digital and climate-sensitive era.

6. Conclusions

This study explored the role of National Spatial Data Infrastructure (NSDI) as a catalyst for improved governance and policy integration in Pakistan, drawing upon two analytical lenses: the UNESCAP Good Governance Framework and the EGU Policy Cycle Model. Through a comprehensive document analysis of 23 national policies and a stakeholder survey across public sector institutions, the research yielded a dual-layered perspective, one capturing the textual presence of spatial data indicators, and the other reflecting institutional awareness, perception, and endorsement of NSDI.

The results reveal a marked discrepancy between policy-level acknowledgement of spatial data and the actual institutional mechanisms needed for NSDI realisation. While spatial data indicators (SD, DSI, CB) were present in a significant number of policies, interoperability standards, centralised platforms, and spatial governance protocols were largely absent. Simultaneously, the survey findings underscore a moderately high level of awareness and a strong belief in the relevance of NSDI for improving governance, transparency, inclusivity, and sectoral policy support.

Critically, the disconnect between the policy texts and stakeholder perceptions signals the need for a more deliberate, structured, and cross-sectoral approach to NSDI development in Pakistan. This disconnect not only inhibits the institutionalisation of spatial data governance but also undermines the potential for data-driven policy reform, monitoring, and adaptive governance.

The establishment of an NSDI holds transformative potential for addressing the current limitations in policy implementation, monitoring, and inter-agency coordination. Many national policies reference the need for data-driven evaluation, standardised metrics, and institutional capacity building, yet lack the tools or datasets necessary to operationalise these aims. By providing interoperable platforms, geospatial dashboards, and harmonised data standards, NSDI can bridge this gap, enabling effective policy evaluation, targeted implementation, and continuous policy maintenance. Furthermore, NSDI enhances data quality, fosters inter-agency collaboration, and institutionalises spatial capacity across sectors, thereby translating policy intent into measurable governance outcomes.

This study provides a conceptual and empirical foundation for understanding the role of NSDI in governance and policy coherence. Future research should conduct longitudinal studies to track the evolution of NSDI adoption across policy cycles. Researchers may explore provincial-level policy integration to understand sub-national spatial governance dynamics.