Products of CORINE Land Cover (CLC), the National Land Cover Dataset (NLCD), the FAO/UNEP Land Cover Classification System (LCCS), etc. currently provide an important source of information used for the assessment of issues such as landscape change, landscape fragmentation and the planning of urbanization. Assuming that the data from these various databases are often used in searching for solutions to environmental problems, it is necessary to know which classes of different databases exist and to what extent they are similar, i.e. their possible compatibility and interchangeability. An expert assessment of the similarity between the CLC and NLCD 1992 nomenclatures is presented. Such a similarity assessment in comparison with the ‘geometric model’, the ‘feature model’ and the ‘network model’ is not frequently used. The results obtained show the similarity of assessments completed by four experts who marked the degree of similarity between the compared land cover classes by 1 (almost similar classes), 0.5 (partially similar classes) and 0 (not similar classes). Four experts agreed on assigning 1 in only three cases; 0.5 was given 33 times. A single expert assigned 0.5 a total of 17 times. Results confirmed that the CLC and NLCD nomenclatures are not very similar.
Background: In Europe, a lot of data portals are emerging on the local, national or interregional levels. These portals have a common objective to share data and information to its citizens and businesses, and to make information more accessible. However, studies showed that people are still facing difficulties in finding and reusing public sector information. To facilitate data reuse, the information should be available in a machine-readable format and agreed metadata standard, so that interoperability and discoverability could be enhanced. Methods: This article focuses on the interoperability and harmonization of spatial and non-spatial data in the transport field. Both the open data and geospatial world have stable standards (such as DCAT and INSPIRE), and the GeoDCAT-AP is the first attempt in combining the two worlds. Through a case study approach, this article aims to provide insights in the implementation of this new standard and other interoperability cases in transport, such as the Data Tank data management system and a harmonized model for road network data.
According to the United Nations' International Strategy for Disaster Reduction, ''natural hazards are processes or phenomena that may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage''. They are at the interface between human and natural systems. From this perspective, natural hazards are a multidimensional domain including environmental issues, the private and public sector and citizens and governance ranging from local to supranational. The vast amount of information and data necessary for comprehensive hazard and risk assessment present many challenges regarding the lack of accessibility, comparability, quality, organisation and dissemination of natural hazards spatial data. In order to mitigate these limitations, an interoperability framework has been developed and published in the INSPIRE Data Specification on Natural Risk Zonestechnical guidelines (DS) document. This framework provides means for facilitating access, integration, harmonisation and dissemination of natural hazard data from different domains and sources. The objective of this paper is twofold. Firstly, the paper highlights 123Nat Hazards (2015) 78:1545-1563 DOI 10.1007/s11069-015-1786 the key aspects of the interoperability to the various natural hazard communities and illustrates the applicability of the interoperability framework developed in the DS. And secondly, the paper ''translates'' into common language the main features and potentiality of the interoperability framework of the DS for a wider audience of scientists and practitioners in the natural hazard domain. In this paper, the four pillars of the interoperability framework will be presented. First, the adoption of a common terminology for the natural hazard domain will be addressed. A common data model to facilitate cross-domain data integration will then follow. Thirdly, the common methodology developed to express qualitative or quantitative assessments of natural hazards is presented. Fourthly, the extensible classification schema for natural hazards developed from a literature review and key reference documents from the contributing community of practice is discussed. Furthermore, the applicability of the interoperability framework for the various stakeholder groups is illustrated. This paper closes discussing main advantages, limitations and next steps regarding the sustainability and evolution of the interoperability framework.
The tourist industry needs an extensive information support to promote their activities. There are many existing information resources that have two main characteristics: focus on local initiatives and are very heterogeneous (using different data models, frequency of update etc). On the other hand users have their own requirements. They want to find interesting, attractive and credible information in a simple and fast way. The concept of SmartTouristData is developed as part of Smart Open Data and SDI4APPS interconnect a view of users and character of data sources. Our approach adds other components such as global and local open data sources and crowd-sourcing initiatives, social media (feedback from users) and the latest technologies and standards. The SmartTouristData provides benefits for two main groups. Users are able to find information in one place and to compare and evaluate information from more sources. They are appreciative of the simple and attractive interface for dealing with information. SmartTouristData also supports business subjects. An easy integration of project system to proprietary solutions, reusing and sharing of existing information resources and tools saves money and time, because it is not necessary to collect or buy data or develop new software. Moreover users are not forced to change or modify their ways of providing of information. SmartTouristData connects both main participants of the tourist industry by providing high-quality information, as satisfied and well-informed users will come back and recommend the destination.
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