Geopan at@s: A brokering based gateway to georeferenced historical maps for risk analysis

Importance of ancient and historical maps is nowadays recognized in many applications (e.g., urban planning, landscape valorisation and preservation, land changes identification, etc.). In the last years a great effort has been done by different institutions, such as Geographical Institutes, Public Administrations, and collaborative communities, for digitizing and publishing online collections of historical maps. In spite of this variety and availability of data, information overload makes difficult their discovery and management: Without knowing the specific repository where the data are stored, it is difficult to find the information required. In addition, problems of interconnection between different data sources and their restricted interoperability may arise. This paper describe a new brokering based gateway developed to assure interoperability between data, in particular georeferenced historical maps and geographic data, gathered from different data providers, with various features and referring to different historical periods. The developed approach is exemplified by a new application named GeoPAN Atl@s that is aimed at linking in Northern Italy area land changes with risk analysis (local seismicity amplification and flooding risk) by using multi-Temporal data sources and historic maps

Towards globally customizable ecosystem service models

Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The ARtificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multi-criteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed

A spatial mediator model for integrating heterogeneous spatial data

The complexity and richness of geospatial data create specific problems in heterogeneous data integration. To deal with this type of data integration, we propose a spatial mediator embedded in a large distributed mobile environment (GeoGrid). The spatial mediator takes a user request from a field application and uses the request to select the appropriate data sources, constructs subqueries for the selected data sources, defines the process of combining the results from the subqueries, and develop an integration script that controls the integration process in order to respond to the request. The spatial mediator uses ontologies to support search for both geographic location based on symbolic terms as well as providing a term-based index to spatial data sources based on the relational model. In our approach, application designers only need to be aware of a minimum amount about the queries needed to supply users with the required data. The key part of this research has been the development of the spatial mediator that can dynamically respond to requests within the GeoGrid environment for geographic maps and related relational spatial data

A Spatial Analysis of the Potentials for Offshore Wind Farm Locations in the North Sea Region:Challenges and Opportunities

Over the last decade, the accelerated transition towards cleaner means of producing energy has been clearly prioritised by the European Union through large-scale planned deployment of wind farms in the North Sea. From a spatial planning perspective, this has not been a straight-forward process, due to substantial spatial conflicts with the traditional users of the sea, especially with fisheries and protected areas. In this article, we examine the availability of offshore space for wind farm deployment, from a transnational perspective, while taking into account different options for the management of the maritime area through four scenarios. We applied a mixed-method approach, combining expert knowledge and document analysis with the spatial visualisation of existing and future maritime spatial claims. Our calculations clearly indicate a low availability of suitable locations for offshore wind in the proximity of the shore and in shallow waters, even when considering its multi-use with fisheries and protected areas. However, the areas within 100 km from shore and with a water depth above -120 m attract greater opportunities for both single use (only offshore wind farms) and multi-use (mainly with fisheries), from an integrated planning perspective. On the other hand, the decrease of energy targets combined with sectoral planning result in clear limitations to suitable areas for offshore wind farms, indicating the necessity to consider areas with a water depth below -120 m and further than 100 km from shore. Therefore, despite the increased costs of maintenance and design adaptation, the multi-use of space can be a solution for more sustainable, stakeholder-engaged and cost-effective options in the energy deployment process. This paper identifies potential pathways, as well as challenges and opportunities for future offshore space management with the aim of achieving the 2050 renewable energy targets

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

The Journal of Conventional Weapons Destruction Issue 20.2 (2016)

Mobile Technologies in CWD South and Central Asia Field Notes Research and Developmen