Spatial data is one of the core components in all information retrieval processes for decision-making. Spatial data acquisition consumes enormous monetary resources and time. The Integrated Geospatial Information Framework (IGIF) provides a basis and guide for developing, integrating, strengthening, and maximizing geospatial information management and related resources in all countries. To this, governments all over the world are establishing national spatial data infrastructures (SDIs). However, such initiatives face a considerable amount of resistance as organizations often do not want to share their data assets. The present study investigates these barriers in the establishment of national SDI in Pakistan. The constraints studied through the IGIF pathways and past studies were adapted via a pilot study and conceptualized in a hypothesized model. We collected primary data via the administration of 520 questionnaire surveys to 280 public and private organizations. Partial least squares structural equation modeling (PLS-SEM) was applied to statistically confirm the conceptual model of the barriers to disseminating spatial data. The results indicate institutional barriers from the absence of national data policy, lack of specified roles of stakeholders, poor inter-organizational coordination, missing data-sharing policy, and weak organizational partnerships, with coefficients 0.26, 1.555, 1.305, 8.288, and 0.136, respectively, at the p < 0.001 significance level. The PLS-SEM R² 0.65 indicates a good explanatory power of the model. The methodology developed in the present study will allow devising more sustainable policies for spatial data management and dissemination in Pakistan and beyond.

The growth of Pakistan’s agriculture-based economy depends on elevating agriculture production and raising the per-capita income of rural communities. This paper evaluates the value of two simultaneous initiatives for the economic development of Pakistan, i.e., (i) reforming and modernization of the cadastre system, and (ii) the implementation of national spatial data infrastructure (NSDI). Both can provide crucial frameworks to assemble geographic information necessary for effective agriculture policies in the country. Their execution at the national level requires substantial technical, human, and finical resources. These mega initiatives may become highly challenging, due to the already shrinking economy of the country from COVID-19. The study makes use of an evaluation framework, official documents, such as project proposals, minutes of meetings, in addition to data collected through questionnaire and from ministries, such as the ministry of planning, development, and reforms (commonly known as planning commission), as well as Pakistan bureau of statistics. Our findings indicate that both the projects share some commonalities in terms of benefits, problems, and resources. However, the economic benefits of the NSDI project are high compared to the cadastre for the country, especially for effective agricultural policy-making. The results output will help practitioners from both systems to reduce the overlapping value, cost, and scope of the work involved.

Geospatial information is used to regularly estimate agricultural production for improving food security and economic indicators. Particularly, such estimates are vital for agriculture-based economies like Pakistan. However, poorly managed spatial information causes inaccurate agricultural estimates. Consequently, public policies such as agriculture policies often remain unsustainable to secure enough food and to uplift the rural economy. Against this backdrop, the main objective of this paper is to identify types of spatial datasets, categorize them based on relative importance, and propose a framework to seamlessly disseminate those datasets to agricultural policy-makers in Pakistan. To do so, first of all, the literature is reviewed and a preliminary list of data is prepared. Then we make use of the Delphi survey to prepare the final list of the data. The data are also categorized into most important, very important, and important datasets. The results of the study revealed that the four most important spatial datasets include; hydrological, land use, agricultural census, and meteorological data. The datasets in the category of very important include six datasets; cadaster, crops, soil, pest and disease, natural hazards, and climate change data. The three datasets; remote sensing, research, and agroecological zones data fall under the category of important spatial datasets. Through implementing SDI, the identified data can be made available in one place to find and access to inform policies, the paper concludes.

OSM plays a crucial role in advancing sustainable development across the economy, environment, and society. It can foster economic sustainability by enabling infrastructure planning, disaster response, and energy efficiency. Moreover, OSM can contribute to environmental sustainability through resource management, climate change mitigation, and biodiversity protection. Additionally, OSM can promote social sustainability by empowering communities and supporting healthcare, education, and gender equality. Spatial analysis of OSM data can help to identify areas that need essential services, addressing social inequalities. OSM's vast capabilities, coupled with cloud computing, can further enhance its role in driving sustainable progress. Continuous efforts are required to enhance data accuracy and completeness in OSM using advanced techniques like machine learning and artificial intelligence while also promoting inclusive mapping involving marginalized communities. Request access from your librarian to read this chapter's full text.

Transformative discoveries are available across several disciplines because of the convergence of integrated geospatial information management with the Metaverse. Through the study of geospatial data, integrated geospatial information management (GIM) offers useful insights that can support emergency response, transportation, and urban planning. Immersive encounters, virtual travel, brand activation, cultural preservation, education, healthcare, and entertainment can be possible by the metaverse. The Metaverse's integration of GIM can improves visualization, teamwork, decision-making, and data sharing. However, issues like interoperability, data accuracy, and data privacy require special consideration. Accepting the potential of GIM in the metaverse can opens doors for creative developments across many industries, promoting societal improvement and improving user experiences in virtual settings. Request access from your librarian to read this chapter's full text.

This chapter examined the governance framework of National Spatial Data Infrastructures in five South Asian countries: Bangladesh, India, Nepal, Pakistan, and Sri Lanka. It analyzed six key components—participation and consensus orientation, responsiveness and transparency, equity and accountability, effectiveness and efficiency, rule of law, and strategic vision—underpinning NSDI programs. The diverse strategies and structures employed by these countries are explored. Bangladesh's collaborative NSDI Steering and Technical Committees promote geospatial data sharing. India's two-tier structure involves the National Spatial Data Committee and the NSDI Executive Committee for policy setting and implementation. Nepal's lack of a designated NSDI agency hampers coordination. Pakistan's NSDI is in the feasibility study phase, while Sri Lanka emphasizes its NSDI within its Digital Policy. Overall, it was observed that some characteristics of good governance are evident across the majority of the countries examined in this chapter; however, the level of maturity remains relatively weak. Request access from your librarian to read this chapter's full text.