Reference Data as a Basis for National 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.

Ordnance Survey is the national mapping agency of Great Britain We are always developing our product line and are currently investigating the addition of 3D data. In line with our role as a national mapping agency the approach is to do this, such that any data is part of a coherent range of products that are sympathetic with the terrain and existing 2D topographic data, are of national cover and are maintained. Such intentions create unique challenges to a mapping agency. These challenges include: 1. Specification determination and an appropriate capture methodology. 2. Data modelling and storage of 3D data. 3. Maintenance of the database. 4. Supply of products in a useful format. This paper will discuss those issues most pertinent to the development of a potential 3D product within Ordnance Survey. Subsequently, a better understanding of the complexity of implementing a 3D dataset into a national mapping agency's product portfolio can be achieved.

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.

A protocol for the collection of vector data in Volunteered Geographic Information (VGI) projects is proposed. VGI is a source of crowdsourced geographic data and information which is comparable, and in some cases better, than equivalent data from National Mapping Agencies (NMAs) and Commercial Surveying Companies (CSC). However, there are many differences in how NMAs and CSC collect, analyse, manage and distribute geographic information to that of VGI projects. NMAs and CSC make use of robust and standardised data collection protocols whilst VGI projects often provide guidelines rather than rigorous data collection specifications. The proposed protocol addresses formalising the collection and creation of vector data in VGI projects in three principal ways: by manual vectorisation; field survey; and reuse of existing data sources. This protocol is intended to be generic rather than being linked to any specific VGI project. We believe that this is the first protocol for VGI vector data collection that has been formally described in the literature. Consequently, this paper shall serve as a starting point for on-going development and refinement of the protocol.

Land use and land cover (LULC) mapping is often undertaken by national mapping agencies, where these LULC products are used for different types of monitoring and reporting applications. Updating of LULC databases is often done on a multi-year cycle due to the high costs involved, so changes are only detected when mapping exercises are repeated. Consequently, the information on LULC can quickly become outdated and hence may be incorrect in some areas. In the current era of big data and Earth observation, change detection algorithms can be used to identify changes in urban areas, which can then be used to automatically update LULC databases on a more continuous basis. However, the change detection algorithm must be validated before the changes can be committed to authoritative databases such as those produced by national mapping agencies. This paper outlines a change detection algorithm for identifying construction sites, which represent ongoing changes in LU, developed in the framework of the LandSense project. We then use volunteered geographic information (VGI) captured through the use of mapathons from a range of different groups of contributors to validate these changes. In total, 105 contributors were involved in the mapathons, producing a total of 2778 observations. The 105 contributors were grouped according to six different user-profiles and were analyzed to understand the impact of the experience of the users on the accuracy assessment. Overall, the results show that the change detection algorithm is able to identify changes in residential land use to an adequate level of accuracy (85%) but changes in infrastructure and industrial sites had lower accuracies (57% and 75 %, respectively), requiring further improvements. In terms of user profiles, the experts in LULC from local authorities, researchers in LULC at the French national mapping agency (IGN), and first-year students with a basic knowledge of geographic information systems had the highest overall accuracies (86.2%, 93.2%, and 85.2%, respectively). Differences in how the users approach the task also emerged, e.g., local authorities used knowledge and context to try to identify types of change while those with no knowledge of LULC (i.e., normal citizens) were quicker to choose ‘Unknown’ when the visual interpretation of a class was more difficult.

The presented article discusses some legal issues regarding spatial data as a crucial tool of information society. As the introduction, the author defines some basic terms and their relation (geographic information systems, spatial data, and their infrastructures). Next, his attention concentrates briefly on the legal framework for access to such data within the European Union – especially the INSPIRE Directive, its aims and structure. In the third and main part of this article, the author analyses the quality of transposition process considering the aforementioned directive in the Czech Republic. He puts stress on the provisions transposing the national infrastructure for spatial data into Czech Act No. 123/1998 Sb., on the Right to Information on the Environment. Not underestimating the undisputable improvements made by amending Act No. 83/2015 Sb., there are still points to enhance, particularly in connection with section 11d limiting access to spatial data. The article concludes with some other obligations enabling the overall control and evaluation of the implementation in next phases are mentioned.

This paper presents the results from a quality and usability analysis of participatory land registration (PaLaR) in Indonesia’s rural areas, focusing on data quality, cost, and time. PaLaR was designed as a systematic community-centered land titling project collecting requisite spatial and legal data. PaLaR was piloted in two communities situated in Tanggamus and Grobogan districts in Indonesia. The research compared spatial data accuracy between two approaches, PaLaR and the normal systematic land registration approach (PTSL) with respect to point accuracy and polygon area. Supplementary observations and interviews were undertaken in order to evaluate the effectiveness of the spatial and legal data collection, as well as logical consistency of the data collected by the community committee, using a mobile application. Although the two pilots showed a lower spatial accuracy than the normal method (PTSL), PaLaR better suited local circumstances and still delivered complete spatial and legal data in a more effective means. The accuracy and efficiency of spatial data collection could be improved through the use of more accurate GNSS antennas and a seamless connection to the national land databases. The PaLaR method is dependent on, amongst other aspects, inclusive and flexible community awareness programs, as well as the committed participation of the community and local offices.

The EU’s INSPIRE Directive (EC 2007a) is to create a pan-European Spatial Data Infrastructure (SDI) focused on environmental data. INSPIRE’s implementing EC Regulations and EU Decisions are intended to provide data harmonization and information services interoperability for spatial data required by many other EU Directives. In the marine community, INSPIRE has been proposed as the supporting SDI for the Water Framework Directive WFD (EC 2000), the Marine Strategy Framework Directive MSFD (EC 2008a), the Flood Risk Assessment Directive (EC 2007b), and other environmentally oriented Directives in many different areas, e.g. air quality, noise, health, etc. INSPIRE harmonized data is also expected to support the principles and data requirements of the Water Information System for Europe (WISE) (water.europa.eu) and its marine component (WISE-Marine) (webgate.ec.europa.eu/maritimeforum/category/554) as well as the Shared Environmental Information System (SEIS) (EC 2008b).

The observation that spatial and environmental data relating to specific locations or geographic areas in the European Union (EU) have been difficult to find online is undisputed. Whether the EU INSPIRE Directive of 2007 which establishes an infrastructure to enable the sharing of spatial data and services to support European and national environmental policies will achieve its objectives is a topic of debate. The INSPIRE Directive was conceived and implemented to address identifiable challenges relating to environmental policies and data. Before the complete implementation of the Directive, it may be necessary to reflect on what progress have been achieved at a legal and policy level. The central argument of this paper is that legal and policy issues may have hindered the rapid and smooth implementation of the Directive. The implementation of the legal framework of the Directive provides salutary lessons. Evaluation reports from the European Commission and the four case studies demonstrably paint an encouraging future. The conclusion is that INSPIRE should foster good governance, use and sharing of environmental data and information for the common good of all Europeans.

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.

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.

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.

Motives: The need for sustainable urban development, including an improvement in residents’ quality of life, requires ongoing urban diagnostics. Assessments of multi-family housing estates play a very important role in this process. Population growth influences the expansion of housing estates in limited urban space. The extent to which spatial and functional structures (SFS) in housing developments meet the residents’ current needs should be evaluated. These needs undergo dynamic change and are influenced by economic, socio-cultural, sanitary, and ecological factors. Aim: The main objective of this study was to develop a methodology for assessing SFS solutions in open spaces in multi-family residential estates (MFREs) based on a complete list of SFS indicators, and to determine the potential of GIS tools and selected open data sources for automating this process. GIS was used to represent data. The intermediate goal was to determine differences in the SFS solutions of two MFREs that were built with different technologies and urban layouts in the last 70 years in the city of Olsztyn (Poland). Methods: An empirical study was conducted with the use of qualitative and quantitative methods based on a review of the literature, the results of a resident survey, and an analysis of spatial data in ArcGIS and QGIS software. Results: The residents’ needs for SFS in MFREs were identified. A list of 26 SFS indicators and their values (on a 3-point scale) was developed to assess multi-family housing. The applicability of GIS software and spatial data from the national spatial data infrastructure (NSDI) and other sources was assessed in the process. The research method was tested to reveal differences in SFS solutions in the compared MFREs.

Oracle Spatial and Graph is a geographic information system (GIS) which provides users the ability to store spatial data alongside conventional data in Oracle. As a result of the coexistence of spatial and other data, we observe a trend towards users performing increasingly complex queries which involve spatial as well as non-spatial predicates. Accurate selectivity values, especially for queries with multiple predicates requiring joins among numerous tables, are essential for the database optimizer to determine a good execution plan. For queries involving spatial predicates, this requires that reasonably accurate statistics collection has been performed on the spatial data. For extensible data cartridges such as Oracle Spatial and Graph, the optimizer expects to receive accurate predicate selectivity and cost values from functions implemented within the data cartridge. Although statistics collection for spatial data has been researched in academia for a few years; to the best of our knowledge, this is the first work to present spatial statistics collection implementation details for a commercial GIS database. In this paper, we describe our experiences with implementation of statistics collection methods for complex geometry objects within Oracle Spatial and Graph. Firstly, we exemplify issues with previous partitioning-based algorithms in presence of complex geometry objects and suggest enhancements which resolve the issues. Secondly, we propose a main memory implementation which not only speeds up the disk-based partitioning algorithms but also utilizes existing R-tree indexes to provide surprisingly accurate selectivity estimates. Last but not the least, we provide extensive experimental results and an example study which displays the efficacy of our approach on Oracle query performance.

As the largest archipelagic nation with thousands of islands, the establishment of infrastructure for marine and coastal spatial data is indispensable. Six technical working groups have been formed as a component of spatial data information management in the Marine and Coastal Resources Management Project (MCRMP). The groups’ responsibilities are to determine the standard of spatial database, the data clearinghouse and custodianship among stakeholders and improvement of human resource. After the discussion with stakeholders, some standards have been established and some agreements have been reached to be implemented in the further stage of national spatial data infrastructure.