Karel Charvát

SPATIAL DATA INFRASTRUCTURE AND GEOVISUALIZATION IN EMERGENCY MANAGEMENT

Support for an emergency management (EM) is one of the important requests for contemporary cartography. Map use demands high flexibility during emergency situations and variety of outputs according to changing situations, requested scope of decision making, and various users involved. Electronic maps are offering more flexible possibilities than traditional analogue maps, but nowadays, despite huge data sources for EM are Geographic Information Systems (GIS) based, still many cartographic interfaces are even less efficient copies of former analogue maps. At the base of this analysis, the focus on the role of GIS, geovisualization, and sensor technologies in emergency management is overviewed. Global description of positional accuracy, projection handling, geodata harmonization, and quality management for EM are described.

Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing

Intensive farming on land represents an increased burden on the environment due to, among other reasons, the usage of agrochemicals. Precision farming can reduce the environmental burden by employing site specific crop management practices which implement advanced geospatial technologies for respecting soil heterogeneity. The objectives of this paper are to present the frontier approaches of geospatial (Big) data processing based on satellite and sensor data which both aim at the prevention and mitigation phases of disaster risk reduction in agriculture. Three techniques are presented in order to demonstrate the possibilities of geospatial (Big) data collection in agriculture: (1) farm machinery telemetry for providing data about machinery operations on fields through the developed MapLogAgri application; (2) agrometeorological observation in the form of a wireless sensor network together with the SensLog solution for storing, analysing, and publishing sensor data; and (3) remote sensing for monitoring field spatial variability and crop status by means of freely-available high resolution satellite imagery. The benefits of re-using the techniques in disaster risk reduction processes are discussed. The conducted tests demonstrated the transferability of agricultural techniques to crisis/emergency management domains.

The Plan4business Approach to Transfer Open Data into Real Estate Businesses

Spatial planning data including urban, regional, spatial or zoning plans are not aggregated so far. Creating time series or comparative analyses on these data sets is not yet possible. The EU funded project Plan4business develops a service platform that can serve to users as a full catalogue of spatial planning data linked with other data sources such as statistics, OpenStreetMap, Urban Atlas and Corine Land Cover that are published as Open Data. The Plan4business platform will offer to clients not only the data itself in an integrated, harmonised and thus ready-to-use form, but also rich analysis and visualisation services via an API and an interactive web frontend. The users include mainly citizens, local authorities and real estate agencies. This paper introduces the problems of data integration and selected technical components of the Plan4business platform supporting data reuse and analysis.

The HABITATS Approach to Build the INSPIRE Infrastructure

Different initiatives focused on spatial data in Europe should not be isolated but closely connected. The portfolio of such initiatives is very extensive. On the one hand, it covers all European activities such as INSPIRE or GMES, on the other hand, there are products of modern approaches based on neogeography and Volunteered Geographic Information (e.g. OpenStreetMap). Data are published by various regional or local authorities, non-governmental organisations, public bodies, research projects as well as by different commercial subjects. The focus of the HABITATS project was to build an environment that enables to share and combine data in order to reach new data, information and knowledge. On the basis of different pilots, HABITATS defined and tested harmonisation rules for spatial environmental data and designed the concept of Reference Laboratory as a tool for testing the interoperability and supporting unification of outputs cross different pilots.

Plan4All GeoPortal: web of spatial data

Plan4All project contributes on the harmonization of spatial data and related metadata in order to make them available through Web across a linked data platform. A prototype of a Web search European spatial data portal is already available at http://www.plan4all.eu. The key aim is to provide a methodology and present best practices towards the standardization of spatial data according to the INSPIRE principles and provide results that would be a reference material for linking data and data specification from the spatial planning point of view. The results include methodology and implementation of multilingual search for data and common portrayal rules for content providers. These are critical services for sharing and understanding spatial data across Europe. Plan4All paradigm shows that a clear applicable methodology for harmonization of spatial data on all different topics of interest can be achieve efficiently. Plan4All shows that it is possible to build Pan European Web access, to link spatial data and to utilize multilingual metadata providing a roadmap for linked spatial data across and hopefully beyond Europe. The proposed demonstration based on Plan4All experience aims to show experience, best practices and methods to achieve data harmonization and provision of linked spatial data on the Web.

Sensor-based Database with SensLog: A Case Study of SQL to NoSQL Migration.

Sensors gained a significant role in the Internet of Things (IoT) applications in various industry sectors. The information retrieved from the sensors are generally stored in the database for post-processing and analysis. This sensor database could grow rapidly when the data is frequently collected by several sensors altogether. It is thus often required to scale databases as the volume of data increases dramatically. Cloud computing and new database technologies has become key technologies to solve these problems. Traditionally relational SQL databases are widely used and have proved reliable over time. However, the scalability of SQL databases at large scale has always been an issue. With the ever-growing data volumes, various new database technologies have appeared which proposes performance and scalability gains under severe conditions. They have often named as NoSQL databases as opposed to SQL databases. One of the challenges that have arisen is knowing how and when to migrate existing relational databases to NoSQL databases for performance and scalability. In the current paper, we present a work in progress with the DataBio project for the SensLog application case study with some initial success. We will report on the ideas and the migration approach of SensLog platform and the performance benchmarking.

The SensLog Platform – A Solution for Sensors and Citizen Observatories

SensLog is an integrated server side Web based solution for sensor data management. SensLog consists of a data model and a server-side application which is capable of storing, analyzing and publishing sensor data in various ways. This paper describes the technical advancements of the SensLog platform. SensLog receives measured data from nodes and/or gateways, stores data in a database, pre-processes data for easier queries if desired and then publishes data through the system of web-services. SensLog is suitable for sensor networks with static sensors (e.g. meteorological stations) as well as for mobile sensors (e.g. tracking of vehicles, human-as-sensor). The database model is based on the standardized data model for observations from OGC Observations & Measurements. The model was extended to provide more functionalities, especially in the field of users’ hierarchy, alerts and tracking of mobile sensors. The latest SensLog improvements include a new version of the database model and an API supporting citizen observatories. Examples of pilot applications using SensLog services are described in the paper.

Open Farm Management Information System Supporting Ecological and Economical Tasks

A Farm Management Information System (FMIS) is a sophisticated tool managing geospatial data and functionalities as it provides answers to two basic questions: what has happened and where. The presented FOODIE (Farm-Oriented Open Data in Europe) and DataBio (Data-Driven Bioeconomy) approach may be recognized as an OpenFMIS, where environmental and reference geospatial data for precision agriculture are provided free of charge. On the other hand, added-value services like yield potential, sensor monitoring, and/or machinery fleet monitoring are provided on a paid basis through standardised Web services due to the costs of hardware and non-trivial computations. Results, i.e. reference, environmental and farm-oriented geospatial data, may be obtained from the FOODIE platform. All such results of whatever kind are used in the European DataBio project in order to minimise the environmental burden while maximising the economic benefits.

Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction

Clear and straightforward communication is a key aspect of all human activities related to crisis management. Since crisis management activities involve professionals from various disciplines using different terminology, clear and straightforward communication is difficult to achieve. Semantics as a broad science can help to overcome communication difficulties. This research focuses on the evaluation of available semantic resources including ontologies, thesauri, and controlled vocabularies for disaster risk reduction as part of crisis management. The main idea of the study is that the most appropriate source of broadly understandable terminology is such a semantic resource, which is accepted by—or at least connected to the majority of other resources. Important is not only the number of interconnected resources, but also the concrete position of the resource in the complex network of Linked Data resources. Although this is usually done by user experience, objective methods of resource semantic centrality can be applied. This can be described by centrality methods used mainly in graph theory. This article describes the calculation of four types of centrality methods (Outdegree, Indegree, Closeness, and Betweenness) applied to 160 geographic concepts published as Linked Data and related to disaster risk reduction. Centralities were calculated for graph structures containing particular semantic resources as nodes and identity links as edges. The results show that (with some discussed exceptions) the datasets with high values of centrality serve as important information resources, but they also include more concepts from preselected 160 geographic concepts. Therefore, they could be considered as the most suitable resources of terminology to make communication in the domain easier. The main research goal is to automate the semantic resources evaluation and to apply a well-known theoretical method (centrality) to the semantic issues of Linked Data. It is necessary to mention the limits of this study: the number of tested concepts and the fact that centralities represents just one view on evaluation of semantic resources.

SDI4Apps Points of Interest Knowledge Base

The SDI4Apps project has collected a large number of points of interest (POIs). The Smart Points of Interest (SPOI) represents a seamless and open resource of POIs covering all the world. Its principal target has been to provide information for cycling as Linked data together with other data sets containing road network. But the current version can be used for any purposes related to tourism. The article presents the data model for POIs as a basis for harmonization of external data sources into this data model. The current version of the SPOI data set includes a harmonized combination of selected OpenStreetMap data, GeoNames.org, experimental geo-ontologies developed at the University of West Bohemia and local data. The data model follows the recommendations for RDF data sets, semantic data, and Linked Data as well as the data model published in Points of Interest Core. The SPOI knowledge base complies with the 5-star rating system of Linked Open Data. The data model re-uses several important, respected and standardized formats and vocabularies such as XML, XML Schema, RDF, RDFS, SKOS (Simple Knowledge Organization System), GeoSPARQL or FOAF (Friend of a Friend).