CPLP of the Sea: Towards a Lusophone Maritime Spatial Data Infrastructure (LMSDI)

Aiming in the data interoperation such as access and sharing of heterogeneous spatial data in distributed system, grid technology was brought into spatial information service, and a complete architecture of spatial data interoperability in Grid environment was proposed. We analyzed and designed spatial data management, access and interoperability in the architecture, and offered possible ways for spatial data user to share, analyze and process the distributed data resources. In the end, a prototype system is built based on Globus Toolkit and OGSA-DAI, proving the architecture and methods brought up above are practical.

In the present world of information and communication technologies (ICT) “Green ICT” represents a topic of immense interest. The meaning, sense and scope of Green ICT are quite varied and very wide. Hardware technologies, for example (virtualization of hardware) and corresponding methods are considered initiatives towards environment protection and sustainable growth. At the same time, however, improved development and implementation of existing tools influencing environment by implication (for example due to reducing travel costs or energy savings) are very important in terms of Green ICT. ICT solutions could also work as a device or medium of implementation of new environmentally friendly methods, for instance in agriculture or industry. Spatial data or data with a direct or indirect reference to a specific location or geographic area (INSPIRE Registry, 2009), like digital maps, data in navigation tools, are a significant means of correlating otherwise disparate sources of information. This chapter tries to show the relationship of spatial data and how it can benefit Green ICT. This relationship is vital, as spatial data plays a very important role in system and application (e.g. Geographic Information Systems) with the potential for making direct impact on environmental protection. Spatial data continues to be an integral part of common equipment like mobile phones, car navigation systems and computers. The numbers of these gadgets are constantly growing and so is the corresponding volume of spatial data sets. Within the context of this rapid growth, the costs of data capture, management, updating, processing and distribution are increasing. For example the operation of servers containing the same spatial data sets is energy-consuming and results in burdening the influence on environment. Spatial data sharing, re-use and possibilities of interconnection of existing spatial data sources pose a solution. Therefore, the spatial data interoperability assurance (e.g. by private spatial data providers, state administration etc.) is required. The spatial data interoperability enables more efficient management and use of spatial data sets and achieving of desired savings.The principles of spatial data interoperability are described in the first part of this document. Emphasis is put on spatial data heterogeneities as the main problem of spatial data interoperability. Moreover, technologies focused on elimination of spatial data heterogeneities are discussed here. Subsequent paragraphs introduce selected instruments (metadata, schema languages, ontologies) which are based on data description and support data interoperability. The last section of this document is composed of examples of several international projects focused on spatial data description and processing of well-described spatial data through web services.

In the present world of information and communication technologies (ICT) “Green ICT” represents a topic of immense interest. The meaning, sense and scope of Green ICT are quite varied and very wide. Hardware technologies, for example (virtualization of hardware) and corresponding methods are considered initiatives towards environment protection and sustainable growth. At the same time, however, improved development and implementation of existing tools influencing environment by implication (for example due to reducing travel costs or energy savings) are very important in terms of Green ICT. ICT solutions could also work as a device or medium of implementation of new environmentally friendly methods, for instance in agriculture or industry. Spatial data or data with a direct or indirect reference to a specific location or geographic area (INSPIRE Registry, 2009), like digital maps, data in navigation tools, are a significant means of correlating otherwise disparate sources of information. This chapter tries to show the relationship of spatial data and how it can benefit Green ICT. This relationship is vital, as spatial data plays a very important role in system and application (e.g. Geographic Information Systems) with the potential for making direct impact on environmental protection. Spatial data continues to be an integral part of common equipment like mobile phones, car navigation systems and computers. The numbers of these gadgets are constantly growing and so is the corresponding volume of spatial data sets. Within the context of this rapid growth, the costs of data capture, management, updating, processing and distribution are increasing. For example the operation of servers containing the same spatial data sets is energy-consuming and results in burdening the influence on environment. Spatial data sharing, re-use and possibilities of interconnection of existing spatial data sources pose a solution. Therefore, the spatial data interoperability assurance (e.g. by private spatial data providers, state administration etc.) is required. The spatial data interoperability enables more efficient management and use of spatial data sets and achieving of desired savings.The principles of spatial data interoperability are described in the first part of this document. Emphasis is put on spatial data heterogeneities as the main problem of spatial data interoperability. Moreover, technologies focused on elimination of spatial data heterogeneities are discussed here. Subsequent paragraphs introduce selected instruments (metadata, schema languages, ontologies) which are based on data description and support data interoperability. The last section of this document is composed of examples of several international projects focused on spatial data description and processing of well-described spatial data through web services.

Spatial data sharing among multiple GIS (Geographic Information System) platforms is a fundamental requirement of many GIS applications, yet conventional methods of spatial data interoperability don't adequately consider practical application circumstance, which now becomes a primary barrier to more efficient spatial data sharing among multiple GIS platforms. In this paper, after analyzing the disadvantages of conventional methods and the causation of the disadvantages, and analyzing the principle of spatial data access of ArcGIS, MapInfo and GeoStar based on Oracle Spatial storage, the authors propose a new spatial data interoperability method called Different meta information and Same spatial data Method. This method is based on Oracle Spatial, through which spatial data interoperability for multi-platform of GIS is available. The results of experiments demonstrate that this method is a new simple practical approach adapted for current application circumstance, and it provides us a new idea for spatial data interoperability.

The number of multi-sourced heterogeneous spatial datasets continues to grow and the fragmentation of organizational arrangements has caused much technical and non-technical heterogeneity. Spatial Data Infrastructures aim to facilitate spatial data use and sharing, and can be an effective platform to aid in data integration. This paper discusses the technical and non-technical heterogeneities of multi-sourced spatial data within the holistic framework of Spatial Data Infrastructure. The paper capitalizes on research and case studies undertaken within Australia. The paper also introduces Geo-WebServices as a means of facilitating spatial data integration and interoperability. Geo-WebService can provide a platform to assess the level of Integrability and readiness of multi-sourced datasets. The results of this research aim to assist practitioners in developing the necessary technical tools including geo web-services and guidelines for effective data integration.

As the application fields of spatial information system continue to expand, different sectors in different fields are in desperate need of spatial data sharing to reduce the expenses of spatial data acquisition and to enhance its utilized efficiency. And spatial data interoperability is an effective approach to solve this problem. The present paper proposes a grid collaborative model for spatial data interoperability based on OGSA-DAI with the support of Spatial Database Engine (SDE). In order to validate this model, a prototype system is developed and used in the Pubugou dam project. It can be made a conclusion that the model is very useful to share spatial data and is very convenient to aim at spatial data interoperability.

The integration of multisource heterogeneous spatial data is one of the major challenges for many spatial data users. To facilitate multisource spatial data integration, many initiatives including federated databases, feature manipulation engines (FMEs), ontology-driven data integration and spatial mediators have been proposed. The major aim of these initiatives is to harmonize data sets and establish interoperability between different data sources. On the contrary, spatial data integration and interoperability is not a pure technical exercise, and there are other nontechnical issues including institutional, policy, legal and social issues involved. Spatial Data Infrastructure (SDI) framework aims to better address the technical and nontechnical issues and facilitate data integration. The SDIs aim to provide a holistic platform for users to interact with spatial data through technical and nontechnical tools. This article aims to discuss the complexity of the challenges associated with data integration and propose a tool that facilitates data harmonization through the assessment of multisource spatial data sets against many measures. The measures represent harmonization criteria and are defined based on the requirement of the respective jurisdiction. Information on technical and nontechnical characteristics of spatial data sets is extracted to form metadata and actual data. Then the tool evaluates the characteristics against measures and identifies the items of inconsistency. The tool also proposes available manipulation tools or guidelines to overcome inconsistencies among data sets. The tool can assist practitioners and organizations to avoid the time-consuming and costly process of validating data sets for effective data integration.

Significant progresses have been made in the establishment of cadastre databases in developing countries and the use of cadastre as a base to the development of land information systems based on Geographic Information System (GIS). How to interoperate and share these homogenous data is still a big problem in the applications of cadastral databases. This paper presents a GML (Geography Markup Language) approach to model cadastral spatial features for data interoperability and sharing. The advantages of GML in data interoperability are that (1) it provides an open and standard way to share and interoperate spatial data, and (2) it enables on-line and variety of spatial data interoperability on the Web. The cadastral spatial feature models are first defined based on geography markup language. An application schema is then defined, in which the cadastral features are described as CityModel, StreetModel, BlockModel, LandModel and BuildingModel, and the relationships between them are defined as CityMember, StreetMember, BlockMember and Landmember. The creation of a GML document is discussed in details. Furthermore, a GML-based cadastral WebGIS system is developed based on Java, Document Object Model (DOM) and Simple Application Interface for XML (SAX), and the implementation of the system is illustrated through a case study, which shows the feasibility of the proposed spatial feature models and data interoperability based on GML.

The heterogeneity of spatial planning data is a well known problem that admittedly limits its use and provokes numerous difficulties to planners, investors as well as decision makers. Spatial planning is confronted with a huge problem concerning the diversity in data collection, storing, processing and provision. These challenges constitute the main concern of the Infrastructure for Spatial Information in the European Community Directive (INSPIRE). Spatial infrastructure processes can be supported by increasing transparency and developing shared methodologies. This on-going research presents the current situation in Greece and the EU Initiative on spatial data interoperability and its design effects on GIS. It identifies the problems that need to be encountered and recommends information and communication (ICT) approaches and tools suitable to overcome them.

Sharing data across diverse thematic disciplines is only the next step in a series of hard-fought efforts to ensure barrier-free data availability. The Plan4all project is one such effort, focusing on the interoperability and harmonisation of spatial planning data as based on the INSPIRE protocols. The aims are to support holistic planning and the development of a European network of public and private actors as well as Spatial Data Infrastructure (SDI). The Plan4all and INSPIRE standards enable planners to publish and share spatial planning data. The Malta case tackled the wider scenario for sharing of data, through the investigation of the availability, transformation and dissemination of data using geoportals. The study is brought to the fore with an analysis of the approaches taken to ensure that data in the physical and social domains are harmonised in an internationally-established process. Through an analysis of the criminological theme, the Plan4all process is integrated with the social and land use themes as identified in the CRISOLA model. The process serves as a basis for the need to view sharing as one part of the datacycle rather than an end in itself: without a solid protocol the foundations have been laid for the implementation of the datasets in the social and crime domains.

This paper reviews the progress of the research on spatial data interoperability and several methods currently used to achieving spatial data interoperability, puts the emphasis on analyzing the advantages, as well as the handicaps and existing conditions of interoperability based on shared database and the discrepancy of data models specific to different GIS, which is a main obstacle to spatial data interoperability, and brings forward two methods for achieving spatial data interoperability based on shared spatial database: one is realizing spatial data interoperability among different clients by using the same set of data model in database, the other is interoperability among clients through objects translation in a shared database. The paper also makes a comparison between these two methods in feasibility and difficulty of carrying out. Based on the analysis, this paper makes an experiment with Oracle Spatial in a local network using the second method, the emphasis of the experiment is put on the interoperability of oriented point objects and text, or annotation objects, as the two types of objects have no standard data model in Oracle Spatial and data models brought forward by different GIS vendor used to describe these two types of objects are specific to their GIS software. Two GIS software, MapInfo and Geomedia, are used as client in the experiment. The experiment shows that this method to achieve spatial data interoperability is practical and could be used inside an organization by employees in different departments, who are familiar with different software in their work, to obtain data sharing and interoperability. This method can also lower data redundancy and improve work efficiency.

InSpace3D: A Middleware for Built Environment Data Access and Analytics

The article explores current challenges associated with the methodology adopted in Ukraine in the year two thousand twenty-one for determining the normative monetary value of land. A central issue identified is a methodological inconsistency: although the approach is presented as unified, it uses two distinct methods to determine the coefficient that accounts for location – one based on simplified formulas and another based on spatial modeling. This contradiction undermines the principle of methodological consistency and creates disparities in valuation results. As spatial analysis technologies become more widely used in land management and valuation processes, there is a growing need for unified standards, clear legal frameworks, and technical guidelines. In particular, the broader use of computerized spatial analysis systems in land registry activities calls for stronger institutional and methodological support. This article argues that the application of European experience and best practices can offer effective solutions to these challenges. The primary aim of this study is to examine contemporary approaches to determining the monetary value of land in Ukraine, with a focus on the integration of spatial analysis systems into the valuation process. The article also evaluates the experiences of Germany, Poland, and France concerning legal, technical, and professional mechanisms that support the use of spatial data and technologies in land valuation procedures. This comparative analysis is intended to inform practical recommendations for Ukraine. Recent studies highlight the urgent need to revise and improve Ukraine's land valuation methodology, particularly through the greater application of spatial data technologies. Prior research emphasizes the advantages of using spatial analysis systems for land valuation purposes. These systems contribute to improved accuracy, transparency, and efficiency, and support the revision of valuation zones and the standardization of procedures. A detailed review of the existing valuation methodology reveals significant weaknesses, many of which are related to the varied roles and functions of different land categories. For example, the current approach to urban land often relies on outdated data and previous assessments that do not reflect current urban conditions. In the case of agricultural land, the methodology fails to distinguish between different types of land use, leading to generalized and sometimes misleading valuation results. European countries offer valuable models. Ukraine’s legal environment already permits the use of digital tools in land valuation, and land registry data can serve as a foundational layer for integrating spatial analysis technologies. In the European Union, legal instruments such as the directive establishing a shared spatial information framework and the regulation on data specifications ensure consistency and interoperability of spatial data across member states. Additionally, data protection is addressed through legislation that safeguards personal information in digital systems. Other legal instruments promoting open access to data and mortgage financing further support the use of spatial technologies in land-related decision-making. Germany has established a highly integrated system where spatial technologies are regularly used to determine standard land values, ensuring transparency and reliability. Poland has built a robust framework that incorporates spatial data into mass valuation processes and relies on professional standards that mandate the use of digital technologies. France has embedded spatial analysis into its urban planning codes, allowing for detailed land valuation and efficient access to land registry data. Reforms such as the Law on the Evolution of Housing, Planning, and Digital Technologies have advanced the digitization of the property valuation system in France, offering open access to transaction records to support market-based analysis. In conclusion, countries such as Germany, Poland, and France provide practical, tested models of how spatial technologies can be fully integrated into land valuation systems. For Ukraine, the adoption and adaptation of such models could significantly enhance the accuracy, consistency, and transparency of its land valuation framework, supporting broader goals of land reform and sustainable territorial development.

Abstract. So far, thousand kinds of Spatial Data were acquired, produced, published and used for specialized applications. Heterogeneity is obviously one key problem for spatial data in the process of its creating, structuring or managing. The heterogeneity keeps the complete and variety of spatial data, but induces the difficulty of describing, publishing, sharing, interoperability or metadata harvesting. Decentralized, multi-organization and synchronization are the major features of the next generation spatial data service infrastructure. Standard research on geospatial metadata harvesting and data service is becoming a research focus. More and more organizations and consortiums (e.g. ISO/TC 211, OGC) drafted and released their geospatial metadata and service standards. However, these standards are diverse in order to fit to different spatial data format and research fields, which makes spatial data owner hard to choose standard. The paper focuses on analyzing metadata harvesting from multi-organizations archives, including Spatial Data description, publishing, sharing, and interoperability. Several of most widely used Spatial Data standards are introduced, and compared. Advices on how to choose the standards are given for Spatial Data owner.

In the wake of the so-called information technology revolution, many stakeholders from the public and private sectors (including citizens) have indeed grown accustomed to the promise and usability of spatial data infrastructures (SDI) for data access, use, and sharing. Analyzing the obstacles as well as the processes and mechanisms of integration and implementation, Spatial Data Infrastructures in Context: North and South investigates the technological and the non-technological aspects of the widespread adoption of spatial data infrastructures. Supporting theoretical issues with empirical studies, the editors pay particular attention to the non-technological aspects of organizational, financial, and legal issues including owner rights, liability, copyrights, and compatibility with precedent and supercedent laws. The authors also highlight the importance of understanding the local environment and circumstances in the process of tailoring the approaches to the conditions that characterize societies of different cultural, institutional, and economic settings. Designed to improve the accessibility, interoperability, and affordability of spatial data, the book focuses on the increasing challenges associated with integrating individuals and organizations into a network to support (1) public authorities and administrations at various levels, (2) thematic user communities, (3) enterprises, and (4) citizen-oriented society as a whole. It addresses the implementation and development of spatial data infrastructures for a wide range of themes, applicable technical standards and protocols, and specific organizational issues unique to data policy. Highlighting the potential for profound changes to the access, use, and exchange of spatial data for citizens, organizations, and geographically related applications, and therefore to the role and interaction of the stakeholders from the public and private sectors, this timely contribution provides new insights into improving our understanding of the increasing relevance, applicability, and value of spatial data infrastructures.