Building a North American Spatial Data Infrastructure

The development of a National Spatial Data Infrastructure (NSDI) represents a critical advancement in the management and accessibility of geospatial data, facilitating informed decision-making across sectors such as government administration, environmental monitoring, and urban planning { (Rajabifard and Williamson 2003), (Rajabifard and Williamson 2002), (Nebert 2001)}.In the context of Armenia, establishing a robust NSDI is particularly important, given the country's need to modernize its spatial data infrastructure and align with international standards, such as the European INSPIRE Directive (European Commission, 2007).One of the key components of this effort is the standardization of spatial data layers, which is essential for ensuring the effective use of data in various applications ranging from urban planning to environmental protection and resource management.This research focuses on developing a methodology for the standardization of basic layers in Armenia's NSDI, which are critical for the country's geospatial data infrastructure.These basic layers include administrative boundaries, real estate data (cadastral data), land valuation, property rights, land use (land cover), addresses, geographical names, surface water data, elevation models, relief, and transport infrastructure.Standardizing these layers involves creating consistent and interoperable datasets that can be utilized across various sectors, including land administration, environmental monitoring, disaster management, and infrastructure planning.The proposed methodology follows best practices outlined in international frameworks such as the INSPIRE Directive (European Parliament and Council, 2007), the ISO 19100 series of geographic information standards (ISO, 2002), and the Open Geospatial Consortium (OGC) standards (OGC, 2020), ensuring that the basic spatial data layers adhere to both national and international interoperability requirements.The methodology developed in this study consists of four key stages: (1) Data Collection and Preparation-gathering raw spatial data from a variety of sources, including orthophotos and existing topographic maps, to ensure comprehensive and up-to-date information; (2) Attribute Definition-establishing the mandatory attributes for basic spatial data layers based on NSDI standards for Armenia, ensuring completeness, consistency, and interoperability with other datasets {(Nogueras-Iso, Zarazaga, and Muro-Medrano 2005), (Tth et al. 2012), (Hadzilakos et al. 2000) }; (3) Data Structuring and Conversion-transforming raw data into standardized formats such as vector and raster formats, ensuring compatibility with NSDI requirements, and using standardized coordinate systems and metadata formats {(Heimovi, Marasovic, and Tavra 2014), (Rao et al. 2002)}; (4) Data Integration-combining basic spatial data layers with each other, such as administrative boundaries, land use, surface water, elevation models, and other relevant data, to create a cohesive and interoperable spatial database that can be used for multi-disciplinary applications.By following these stages, the research aims to contribute to the successful implementation of standardized and accessible basic spatial data layers within Armenia's NSDI framework.This approach not only aims to meet Armenia's national standards but also aligns with international initiatives for spatial data infrastructure development, ultimately enhancing the quality and availability of basic spatial data layers for a wide range of applications {(Craglia and Annoni 2006), (Kotsev et al.

The integration of marine and land based spatial information will support sustainable management and development of the coastal zone. Therefore, the development of a seamless platform covering the land and marine environments would facilitate more efficient and effective decision-making capabilities for any jurisdiction with land-marine interface. This paper discusses the potential for developing a seamless platform covering the land, marine and coastal environments as part of the National Spatial Data Infrastructure (SDI) enabling a more integrated and holistic approach to management of the land-marine interface. It addresses the feasibility of seamless platform towards spatially enabled society and government. Further, it provides an insight to the design and development of the Seamless SDI model by introducing Seamless SDI conceptual model. The Seamless SDI class and its inherited characteristics and properties will be discussed. In addition to the conceptual phase, the development of a Seamless SDI model also consists of two more stages: design phase and implementation phase. The Use Case Diagram and Class Diagram of the Enterprise Viewpoint will be developed. Finally, it highlights the importance of the creation of appropriate Seamless SDI governance structures that are both understood and accepted. This would help to develop an extended framework to support a spatially enabled jurisdiction covering the land-marine interface. Ideally this extended framework would result in harmonised and universal access, sharing and integration of coastal, marine and terrestrial spatial datasets across regions and disciplines.

With the advancement of GIS technology since its inception in 1960’s, many educational institutions, government departments, public/ private sectors and individuals have started its use for the production and management of spatial data. Spatial Data Infrastructure (SDI) concept was introduced in the early1990’s and provides a set of technologies, standards, protocols, policies and guidelines on the whole cycle of geospatial data production and distributions, i.e., from data capture to storage and to sharing. SDI initiative at national level, termed as National Spatial Data Infrastructure (NSDI), has been taken by different countries including India. Geospatial community is facing various challenges like handling of large volumes of geospatial data, requirement of high computing resources to process geospatial data, scalability and interoperability. Therefore, need of advanced technologies in the form of SDI and cloud computing is realized to resolve the above challenges. Cloud computing has several characteristics like scalability, elasticity and self-provisioning that offers high-performance computing resources to perform geoprocessing efficiently. The main aim of the present paper is to study SDI and its components along with analysis and comparison of NSDI of various countries as well as to conceptualize and discuss service oriented architecture of cloud enabled SDI. Several challenges of the spatial data handling and processing that occurred due to the high intensity of data and lack of processing capability can be solved by adopting proposed cloud enabled SDI architecture. This research will help geospatial community and SDI developers in various perspectives including data sharing and management, interoperability, security and reliability, fault tolerance, mass market solution, upfront cost and global collaboration.

This research presents an operational framework to assess organizational Spatial Data Infrastructures (SDIs) from a risk perspective to develop a stable SDI. The core of the framework is constructed based on a survey, fuzzy inference system and cluster analysis, providing quantitative indicators to measure and prioritize the risks to SDI. This framework could mainly contribute to identifying, mitigating or avoiding the potential risks of different aspects of an SDI, such as spatial data and information, organizational and technological aspects. Additionally, it could be considered as an approach that supports multi-view SDI assessment framework toward a more comprehensive assessment of SDIs. A prototype implementation to assess and prioritize the risks of the spatial data and information demonstrates the framework merit, flexibility and usability for assessing the risks of SDI initiatives at different levels, such as organizational, local and national levels; however, the risks and SDIs change over time; thus, the development of stable SDI initiatives depends on a continuous process for coping with the risks.

Spatial Data Infrastructure

The application of Geographic Information Systems (GIS) in the work of government bodies, economic subjects and units of regional and local self-government presents the real situation today in every European country, as well as in its regions. The organisation of collecting, storing, distribution and usage of spatial data, in accordance with the principals of spatial data infrastructure (SDI) presents the next (higher) level in the application of spatial data and GIS. Rapid development of GIS application in the last decade highlights two problems in the efficient usage of GIS technologies, entailing the usage of spatial data in general. The first problem is the lack of the concept in organising the spatial data that has been solved basically through the development of IPP concept. The other problem lies in the fact that the subjects and the users do not have enough knowledge of GIS and IPP. Therefore, special attention has been paid within the frame of INSPIRE project to the development of education concept for the subjects and users of IPP, in order to provide the possibility for them to use the advantages offered by the concept of enhanced technologies and IPP itself. The paper presents the current situation in the education referring to GIS and IPP, and describes the activities carried out for the purpose of creating the environment that would provide quicker and more efficient implementation of National SDI (NSDI) based on better information given to the subjects and users.

Recently, Spatial Data Infrastructures (SDIs) has been a rapidly expanding field and broadly assumed to underpin the development of information society and the knowledge economy. In this paper, we argue that the anticipated benefits of SDIs have largely not yet been generally realized in practice due to a lack of a robust theoretical framework as well as insufficient empirical research to guide the implementation efforts. We posit that learnings from the allied discipline of Information Infrastructures, which has matured during the 1990s in response to the proliferation of distributed, complex, and heterogeneous networked systems, can provide a useful theoretical lens to inform the emerging domain of SDIs. We focus on three key concepts identified in the domain of information infrastructure theory, viz. the installed base, reflexive standardization, and cultivation approach to design, to develop a theoretical framework, which is then applied to analyse the ongoing initiative to establish National Spatial Data Infrastructure (NSDI) in India. This analysis helps us to provide the basis to articulate a potential research agenda—for both theory and practice—which, we believe, offers the promise to better conceptualize and implement SDIs, particularly in developing country settings like India.

Most national Spatial Data Infrastructures (SDI) were built based on a clearinghouse concept. The concept requires all of the service providers to maintain a Web (map) service recourse which leads to duplication of efforts from data collectors. The objectives of developing a geo-service broker are to make data services and other services within an SDI centrally organized, available and accessible to consumers. A geo-service broker offers a centralized and harmonized view on resources. The objective of this research is to investigate how a geo-service broker is built as a tool for intelligent search and information retrieval in the semantic Web for better service delivery of geo-services over the Internet.

국가공간정보체계(NSDI)는 국가 공공기관의 공간정보를 통합 연계하여 공동 활용하기 위한 목적으로 2008년부터 추진되어 2012년 그 사업을 완료하였다. NSDI의 존재에도 불구하고 각 기관의 공간정보와 NSDI의 공간정보가 동시에 갱신되지 않는 방식을 사용하기 때문에 최신성 확보에 어려움이 있다. 공공기관의 공간정보를 활용한 서비스 사용현황을 살펴보면, 재활용이 가능한 비슷한 서비스가 중복 구축 및 운영되고 있으며 이에 따라 국가 예산이 낭비되고 있다. 이러한 이슈들은 전산자원을 공유하기 위한 목적의 클라우드 시스템을 NSDI에 적용함으로써 해결될 수 있다. 본 논문에서는 서비스 클라우드 기반의 NSDI 확대발전 방안을 다룬다. 제안되는 NSDI의 확대 발전 방안은 공간정보 서비스 클라우드 시스템 구축방안과 거버넌스 체계 수립방안을 포함한다. 시스템 구축방안에서는 서비스 시나리오, 목표 시스템의 개념도, 그리고 서비스 기능이 도출된다. 서비스 기능에서는 제안된 두 가지 모듈에 대한 단위 기능과 세부 기능을 도출한다. 거버넌스 체계 수립방안에서는 서비스 클라우드 기반의 NSDI 운영을 위한 서비스 라이프사이클 단계별 조직의 필요역할을 정의하고 업무 프로세스를 도출한다. 제안된 시스템 구축방안을 기반으로 상세 시스템을 설계하고 조직을 구성함으로써 서비스 클라우드 기반의 NSDI 체계가 구축될 수 있을 것으로 기대된다. The construction of National Spatial Data Infrastructure (NSDI) started from 2008 and was completed at 2012, with the aim of co-utilizing public agencies' national spatial information by integrating and linking. Despite the existence of the NSDI, an update of spatial data is not fully reflected because the spatial information has not been updated simultaneously by NSDI and public agencies. By examining the public agencies' services using the spatial information, a national budget is wasted because the re-usable similar services are duplicated. These issues could be solved by adopting a cloud system, whose concept is co-utilizing IT resources, to the NSDI. This study examined the service cloud based expansion and development plan for NSDI. The plan included an implementation strategy for a spatial information service cloud system and an establishment plan for a governance system. The service scenarios, concept diagram, and service functions for the system were derived in a system implementation plan. For the service functions, the unit functions of two modules were proposed and specific functions were derived. In the establishment of a governance system, the business roles and business processes were defined for organizations managing service cloud based NSDI. The business role and process were defined and derived by each service life-cycle step. With the proposed system implementation plan, a detail system design and composition of the organization could be possible, and a service cloud-based NSDI system could be implemented.

Spatial data infrastructure (SDI) is a framework for collecting, storing, managing, and distributing spatial data. It is a critical component of any modern geographic information system (GIS) and is used in a wide range of applications, including land management, environmental monitoring, and emergency response. One important aspect of SDI is the legal component, which includes the laws and regulations that govern the collection, use, and sharing of spatial data. These laws and regulations can vary depending on the jurisdiction and can include issues such as data ownership, privacy and intellectual property rights. In the Republic of Armenia, with the concept of “Integrated Cadastre”, it is planned to implement and develop a National Spatial Data Infrastructure (NSDI). Nevertheless, in the Republic of Armenia, not all stages of the implementation of NSDI have been developed yet, including a legal document defining the availability of data at different levels. For the implementation and effective policy development of NSDI, it is necessary to give attention to the regulation of legal relations regarding spatial data at different levels. For this purpose, we studied the legal documents related to the regulation of the field of spatial data in the Republic of Armenia, identified the contradictions and problems in the existing legal documents, which made it possible to highlight the necessary relevant legal provisions.

Digital transformation is the key to turning public authorities into organisations that make decisions based on data-driven insights. Big geodata analysis can enable public authorities to tackle complex sustainability issues in order to achieve long-term goals. However, the efficient management of large amounts of geodata through the implementation of viable and state-of-the-art data infrastructures represents a major challenge for public authorities. In this article, we propose a cloud-integrated decentralised Spatial Data Infrastructure (SDI) to meet the needs of public authorities mandated to provide data products and services based on Earth Observation (EO) imagery. We describe the SDI setup, the implementation process, big geodata components, and the integration of the Copernicus Data and Exploitation Platform—DE (CODE-DE), drawing on the specific SDI implementation in a federal agricultural authority in Germany. Two practical applications are illustrated, underpinning the added value of a cloud-integrated SDI. We elaborate on lessons learnt from the SDI-implementation by summarising key findings that may facilitate the effective establishment and use of the SDI, namely i) the need for an organisational strategy, ii) identifying stakeholders, including their participatory roles, and iii) planning of long-term financial and human resources. The SDI proposed serves as a blueprint for public authorities helping them on their way to become data service providers, leveraging the potential of big geodata, including EO imagery.

The spatial data plays a vital role in any developmental activities whether it is natural resource management or socio-economic development. Most land-related government departments in Botswana have over the years since independence in 1966 developed systems to support their principal areas of operations as regards to spatial data. The adequacy and currency of spatial data in government operations improved leading to a need for integrated systems. This has progressively led to issues of building a National Spatial Data Infrastructure (NSDI) and an initiative modeled around Federal Geographic Data Committee (FGDC) has been established. Several facilitative committees were set and several meetings held in attempt to develop the idea to a realizable level and integrate it into the greater workings of the national economy. This noble idea has stalled for some time now and it is the intention of this paper to report on how the idea was initiated in Botswana and look at the probable causes for its stalling. The paper will then go ahead and suggest what could be done to revitalize the idea by relating it to what is considered the best practices in Spatial Data Infrastructure (SDI) programmes globally.

Over the past years, efforts have been made by the NSDI to re-engineer the geospatial feature data sets and compile and serve their metadata as per ISO/OGC specifications. These are required for provision of interoperable geospatial data services in a service-oriented architecture and automated development of GIS applications. Identification, registration and maintenance of metadata of the feature data sets and related services from the national- and state-level partnering agencies in a central metadata registry, called the National Data Registry (NDR), are essential for improving data utilization. Registering spatial data sets and services may typically involve capturing their semantics (definition), making each data item persistently and uniquely identifiable by assigning unique identifiers and organizing/maintaining their schema and associated classification systems (code lists) in a set of standard registers. In line with these requirements, a proof-of-concept (PoC) of a National Data Registry (NDR) for the Indian NSDI has been implemented and demonstrated using data services from Survey of India, National Remote Sensing Centre and the Ministry of Railways’ train running information. The PoC successfully demonstrated the utility of a sample registry and technical architecture and led to the development of a strategy for selection of a suitable system integrator for a real-life NDR development for the NSDI.

Cartography and spatial data infrastructures (SDIs) are mutually dependent on each other: SDIs provide geographic information for maps; and maps help to understand geographic information provided by an SDI. SDIs emerged in the 1990s when paper maps were replaced by map production from digital geographic information. They evolved from government-funded initiatives within the geographic information science (GISc) community to SDIs with a variety of data producers and an audience much beyond the relatively small GISc community. SDIs continue to evolve in response to new technologies and developments. This chapter traces SDI evolution over the last three decades and considers how SDI technical developments have impacted map production and how they are likely to impact map production in the future.

The need for spatially explicit thematic data is currently increasing in parallel to the development of observing, storing and processing capabilities. This requires an integrated data management structure in which human and institutional aspects play a key role as part of a Spatial Data Infrastructure (SDI). We focus in this study on the African continent to evaluate the status of its SDI implementation. Because assessing SDI at a continental scale in a traditional way (i.e. following methods developed for national assessments) requires financial resources and mechanisms only affordable to developed countries (e.g. European Union), alternative ways have been explored based on fourteen key SDI indicators that were validated by SDI experts in a previous study. Data was collected for each African country through the African leading SDI institution (UN Economic Commission for Africa) and through Internet searches. We found relatively weak scores of the fourteen SDI indicators for African countries compared to the rest of the World, but with notable differences within Africa. We discuss the implication of the lack of information available on the Internet to assess SDI status in Africa. We conclude that it is necessary to improve statistical information in most African countries. This requires an agreed-on geospatial data structure and organization between concerned institutions that is only achievable through a shared global vision on geospatial data governance. To this end, we suggest a few quick wins and several new mechanisms that would enhance the flow of SDI statistical information and improve data management structure in Africa.