THE FUNDAMENTAL SPATIAL DATA IN THE PUBLIC ADMINISTRATION REGISTERS

2016 ◽

Vol XLI-B4 ◽

pp. 171-174

Author(s):

V. Čada ◽

K. Janečka

Keyword(s):

Czech Republic ◽

Communication Networks ◽

Spatial Data ◽

High Speed ◽

Spatial Information ◽

Information Source ◽

Public Authorities ◽

The Czech Republic ◽

Data Infrastructure ◽

Private And Public

The system of basic registers was launched in the Czech Republic in 2012. The system provides a unique solution to centralize and keep actual most common and widely used information as a part of the eGovernment. The basic registers are the central information source for information systems of public authorities. In October 2014, the Czech government approved the conception of The Strategy for the Development of the Infrastructure for Spatial Information in the Czech Republic to 2020 (GeoInfoStrategy) that serves as a basis for the NSDI. The paper describes the challenges in building the National Spatial Data Infrastructure (NSDI) in the Czech Republic with focus on the fundamental spatial data and related basic registers. The GeoInfoStrategy should also contribute to increasing of the competitiveness of the economy. Therefore the paper also reflects the Directive 2014/61/EU of the European Parliament and of the Council on measures to reduce the cost of deploying high-speed electronic communication networks. The Directive states that citizens as well as the private and public sectors must have the opportunity to be part of the digital economy. A high quality digital infrastructure underpins virtually all sectors of a modern and innovative economy. To ensure a development of such infrastructure in the Czech Republic, the Register of passive infrastructure providing information on the features of passive infrastructure has to be established.

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Methods for Estimating Avoidable Costs of Excessive Alcohol Consumption

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18094964 ◽

2021 ◽

Vol 18 (9) ◽

pp. 4964

Author(s):

Beata Gavurova ◽

Miriama Tarhanicova

Keyword(s):

Czech Republic ◽

Alcohol Consumption ◽

Data Availability ◽

Excessive Alcohol Consumption ◽

Public Authorities ◽

The Czech Republic ◽

Cost Of Disease ◽

Lost Productivity ◽

Excessive Alcohol ◽

The Cost

Background: Alcohol is a risk factor with serious consequences for society and individuals. This study aims to present methods and approaches that might be used to estimate the costs related to excessive alcohol consumption. It emphasizes the need for general methods and approaches that are easily applicable, because the level of digitalization and data availability vary across regions. The lack of data makes many methods inapplicable and useless. The ease of applicability will help to make cost-of-illness studies and their results comparable globally. Methods: This study is based on data from the Czech Republic in 2017. Drinking alcohol results in costs of healthcare, social care, law enforcement, and administrative costs of public authorities. To quantify the cost of drinking in the Czech Republic, the top-down approach, bottom-up approach, human capital approach and attributable fractions were used. Results: In 2017, the cost related to alcohol was estimated at 0.66% of the national GDP. Lost productivity represented 54.45% of total cost related to alcohol. All cost related to alcohol is considered to be avoidable. Conclusions: The methods and approaches applied to estimate the cost of disease or any other health issue should be generalized regarding the availability of data and specifics of provided services to people who are addicted or have any kind of disability.

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Implementing an open source spatio-temporal search platform for Spatial Data Infrastructures

10.7287/peerj.preprints.2238v7 ◽

2016 ◽

Cited By ~ 1

Author(s):

Paolo Corti ◽

Benjamin G Lewis ◽

Athanasios Tom Kralidis ◽

Ntabathia Jude Mwenda

Keyword(s):

Open Source ◽

Search Engine ◽

Language Processing ◽

Spatial Data ◽

Spatial Information ◽

National Endowment ◽

Data Infrastructure ◽

Advanced Search ◽

Data Infrastructures ◽

Spatio Temporal

A Spatial Data Infrastructure (SDI) is a framework of geospatial data, metadata, users and tools intended to provide an efficient and flexible way to use spatial information. One of the key software components of an SDI is the catalogue service which is needed to discover, query, and manage the metadata. Catalogue services in an SDI are typically based on the Open Geospatial Consortium (OGC) Catalogue Service for the Web (CSW) standard which defines common interfaces for accessing the metadata information. A search engine is a software system capable of supporting fast and reliable search, which may use “any means necessary” to get users to the resources they need quickly and efficiently. These techniques may include features such as full text search, natural language processing, weighted results, fuzzy tolerance results, faceting, hit highlighting, recommendations, feedback mechanisms based on log mining, usage statistic gathering, and many others. In this paper we will be focusing on improving geospatial search with a search engine platform that uses Lucene, a Java-based search library, at its core. In work funded by the National Endowment for the Humanities, the Centre for Geographic Analysis (CGA) at Harvard University is in the process of re-engineering the search component of its public domain SDI (WorldMap http://worldmap.harvard.edu ) which is based on the GeoNode platform. In the process the CGA has developed Harvard Hypermap (HHypermap), a map services registry and search platform independent from WorldMap. The goal of HHypermap is to provide a framework for building and maintaining a comprehensive registry of web map services, and because such a registry is expected to be large, the system supports the development of clients with modern search capabilities such as spatial and temporal faceting and instant previews via an open API. Behind the scenes HHypermap scalably harvests OGC and Esri service metadata from distributed servers, organizes that information, and pushes it to a search engine. The system monitors services for reliability and uses that to improve search. End users will be able to search the SDI metadata using standard interfaces provided by the internal CSW catalogue, and will benefit from the enhanced search possibilities provided by an advanced search engine. HHypermap is built on an open source software source stack.

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Implementing an open source spatio-temporal search platform for Spatial Data Infrastructures

10.7287/peerj.preprints.2238v3 ◽

2016 ◽

Author(s):

Paolo Corti ◽

Benjamin G Lewis ◽

Tom Kralidis ◽

Jude Mwenda

Keyword(s):

Open Source ◽

Search Engine ◽

Language Processing ◽

Spatial Data ◽

Search Engines ◽

Spatial Information ◽

Data Infrastructure ◽

Advanced Search ◽

Data Infrastructures ◽

Spatio Temporal

A Spatial Data Infrastructure (SDI) is a framework of geospatial data, metadata, users and tools intended to provide the most efficient and flexible way to use spatial information. One of the key software components of a SDI is the catalogue service, needed to discover, query and manage the metadata. Catalogue services in a SDI are typically based on the Open Geospatial Consortium (OGC) Catalogue Service for the Web (CSW) standard, that defines common interfaces to access the metadata information. A search engine is a software system able to perform very fast and reliable search, with features such as full text search, natural language processing, weighted results, fuzzy tolerance results, faceting, hit highlighting and many others. The Centre of Geographic Analysis (CGA) at Harvard University is trying to integrate within its public domain SDI (named WorldMap), the benefits of both worlds (OGC catalogues and search engines). Harvard Hypermap (HHypermap) is a component that will be part of WorldMap, totally built on an open source stack, implementing an OGC catalogue, based on pycsw, to provide access to metadata in a standard way, and a search engine, based on Solr/Lucene, to provide the advanced search features typically found in search engines.

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Implementing an open source spatio-temporal search platform for Spatial Data Infrastructures

10.7287/peerj.preprints.2238v4 ◽

2016 ◽

Author(s):

Paolo Corti ◽

Benjamin G Lewis ◽

Tom Kralidis ◽

Jude Mwenda

Keyword(s):

Open Source ◽

Search Engine ◽

Language Processing ◽

Spatial Data ◽

Search Engines ◽

Spatial Information ◽

Data Infrastructure ◽

Advanced Search ◽

Data Infrastructures ◽

Spatio Temporal

A Spatial Data Infrastructure (SDI) is a framework of geospatial data, metadata, users and tools intended to provide the most efficient and flexible way to use spatial information. One of the key software components of a SDI is the catalogue service, needed to discover, query and manage the metadata. Catalogue services in a SDI are typically based on the Open Geospatial Consortium (OGC) Catalogue Service for the Web (CSW) standard, that defines common interfaces to access the metadata information. A search engine is a software system able to perform very fast and reliable search, with features such as full text search, natural language processing, weighted results, fuzzy tolerance results, faceting, hit highlighting and many others. The Centre of Geographic Analysis (CGA) at Harvard University is trying to integrate within its public domain SDI (named WorldMap), the benefits of both worlds (OGC catalogues and search engines). Harvard Hypermap (HHypermap) is a component that will be part of WorldMap, totally built on an open source stack, implementing an OGC catalogue, based on pycsw, to provide access to metadata in a standard way, and a search engine, based on Solr/Lucene, to provide the advanced search features typically found in search engines.

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Sharing Datasets of the COVID-19 Pandemic in the Czech Republic (Preprint)

10.2196/preprints.27620 ◽

2021 ◽

Author(s):

Martin Komenda ◽

Jiří Jarkovský ◽

Daniel Klimeš ◽

Petr Panoška ◽

Ondřej Šanca ◽

...

Keyword(s):

Czech Republic ◽

General Public ◽

Open Data ◽

Online News ◽

Public Authorities ◽

The Czech Republic ◽

Aggregated Data ◽

Open Datasets ◽

Epidemiological Characteristics ◽

The Impact

BACKGROUND At the time of the COVID-19 pandemic, the impact of providing access to data plays a crucial role in providing the general public and media with up-to-date information. Open datasets also represent one of the means for evaluation of the pandemic on a global level. OBJECTIVE The primary aim of this paper is to describe the methodical and technical framework for publishing datasets describing basic and advanced epidemiological characteristics related to the COVID-19 epidemic in the Czech Republic, including the use of these datasets in practice. METHODS As a reaction to the epidemic situation, a new portal COVID‑19: Current Situation in the Czech Republic was developed and launched in March 2020 to provide a fully-fledged and trustworthy source of information for the public and media. The portal also contains a section for the publication of (i) public open datasets available for download in CSV and JSON formats and (ii) authorized-access-only section where the authorized persons can (through an online generated token) safely visualize or download regional datasets with aggregated data at the level of the individual municipalities and regions. The data are also provided to the local open data catalogue of the Ministry of Health and to the National Catalogue of Open Data. RESULTS The datasets have been published in various authentication regimes and widely used by general public, scientists, public authorities and decision-makers. The total number of API calls since its launch in March 2020 to 15th December 2020 exceeded 13 million. The datasets have been adopted as an official and guaranteed source for outputs of third parties, including public authorities, non-governmental organizations, scientists and online news portals. CONCLUSIONS Datasets currently published as open data meet the 3-star open data requirements, which makes them machine-readable and facilitates their further usage without restrictions. This is essential for making the data more easily understandable and usable for data consumers. In conjunction with the strategy of the MH in the field of data opening, additional datasets meeting the already implemented standards will be also released, both on COVID-19 related and unrelated topics.

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Developing the Niger Delta Transport System Using Adequate Geo-Spatial Information

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.18-19.365 ◽

2007 ◽

Vol 18-19 ◽

pp. 365-373 ◽

Cited By ~ 2

Author(s):

J.O. Ehiorobo ◽

Henry A.P. Audu

Keyword(s):

Spatial Data ◽

Niger Delta ◽

Spatial Information ◽

Transportation System ◽

Delta Region ◽

Data Infrastructure ◽

Goods And Services ◽

Niger Delta Region ◽

Water Transportation ◽

Good Schools

The Niger Delta Region, although the back bone of Nigerian economy, has been neglected by successive governments over time. There are no good schools, no health facilities, no water and other facilities within this area. The problem of the area is compounded by lack of good transportation system. This has been a hindrance both for security agencies and the people in the restive area. A good transportation system should ensure that people, goods and services move speedily, conveniently, safely and cost effectively. To have an efficient ground and water transportation system needed in this area requires adequate and accurate Geo-spatial information. In this paper, the benefits of adequate transportation system in the Niger Delta Region are highlighted. The roles of Geo-spatial data and Geo-spatial information system in the planning, development and management of an efficient transportation system are discussed. The paper also examines Geospatial Data infrastructure (GDI) in the development of the Niger Delta transportation sector Master plan, with data generated for Edo and Delta States presenting case study scenarios.

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Geospatial Semantic Query Engine for Urban Spatial Data Infrastructure

International Journal on Semantic Web and Information Systems ◽

10.4018/ijswis.2019100103 ◽

2019 ◽

Vol 15 (4) ◽

pp. 31-51

Author(s):

Sunitha Abburu

Keyword(s):

Spatial Data ◽

Spatial Information ◽

Semantic Relations ◽

Semantic Relationship ◽

Data Infrastructure ◽

Semantic Query ◽

Semantic Relationships ◽

Query Engine ◽

Spatial Method ◽

User Friendly

The research aims at design and develop a special semantic query engine “CityGML Spatial Semantic Web Client (CSSWC)” that facilitates ontology-based multicriteria queries on CityGML data in OGC standard. Presently, there is no spatial method, spatial information infrastructure or any tool to establish the spatial semantic relationship between the 3D city objects in CityGML model. The present work establishes the spatial and semantic relationships between the 3DCityObjects and facilitates ontology-driven spatial semantic query engine on 3D city objects, class with multiple attributes, spatial semantic relations like crosses, nearby, etc., with all other city objects. This is a novel and original work practically implemented generic product for any 3D CityGML model on the globe. A user-friendly form-based interface is designed to compose effective ontology based GeoSPARQL query. CSSWC enhances CityGML applications performance through effective and efficient querying system.

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