Wisconsin, USA

2010 ◽  
pp. 145-155
Author(s):  
David Hart
Keyword(s):  
Spatial Data ◽  
Management Decision ◽  
Multiple Sources ◽  
Research Topics ◽  
Data Infrastructure ◽  
Web Mapping ◽  
Data Archives ◽  
Institutional Issues ◽  
Data Infrastructures ◽  
Spatial Data Integration

Simply stated, a coastal web atlas (CWA) is a means of organizing, presenting, and sharing spatial data for the coast. Once in place, a CWA can function as a coastal spatial data infrastructure and a platform for developing coastal management decision support tools. While Wisconsin has been actively applying geospatial technologies to coastal issues since 1994, development of a CWA is in its infancy. Wisconsin Sea Grant has learned much about key components of a CWA during the past decade through its role leading four coastal spatial data integration projects. Several technical and institutional issues surfaced as the projects moved from discovery, acquisition, and integration of spatial data from multiple sources to analyze regional coastal issues to the development of interoperable web mapping services and spatial data catalogs. These issues are associated with the following research topics: web portal design and evaluation, choosing appropriate web mapping technologies, GIS cartography, domain spatial data infrastructures, geospatial data archives, and spatial ontologies. Building the Wisconsin Coastal Atlas will provide insight on these important research topics.

Geospatial Digital Rights Management

2019 ◽  
pp. 126-142
Author(s):  
Titus M. Ng'ang'a ◽  
Peter M. Wachira ◽  
Tim J. L. Wango ◽  
Joseph M. Ndung'u ◽  
Margaret N. Ndungo
Keyword(s):  
Intellectual Property Rights ◽  
Spatial Data ◽  
Digital Rights Management ◽  
Data Infrastructure ◽  
Web Mapping ◽  
Digital Rights ◽  
Rights Management ◽  
Mapping Technology ◽  
Data Infrastructures ◽  
Spatial Data Infrastructures

This Chapter introduces the need for general Digital Rights Management (DRM) requirements. Further, it intertwines DRM with its spatial counterpart, Geospatial DRM (GeoDRM). However, unlike DRM, GeoDRM is far much complicated due to issues such as the development of Web Mapping technology among other issues. The Chapter discusses the ability of GeoDRM to mitigate transgression of Intellectual Property Rights (IPR). Highlighting economical and environmental wellbeing and other benefits of Spatial Data Infrastructure (SDI) geared towards global sustainable developments, the Chapter focuses on challenges of National Spatial Data Infrastructures (NSDIs) and Regional SDIs and the need to harmonize their standards for the upward mobility of global SDI (GSDI). Emphasizing the undisputed need for Local, Regional and Global Spatial Data Infrastructures (SDIs), in the presence of various Geo-communities and different GeoDRM models, the Chapter concludes that capacity building need to be urgently but carefully harnessed across all levels in order to develop cohesive GeoDRM policies.

Geospatial Digital Rights Management

2017 ◽  
pp. 291-307
Author(s):  
Titus M. Ng'ang'a ◽  
Peter M. Wachira ◽  
Tim J. L. Wango ◽  
Joseph M. Ndung'u ◽  
Margaret N. Ndungo
Keyword(s):  
Intellectual Property Rights ◽  
Spatial Data ◽  
Digital Rights Management ◽  
Data Infrastructure ◽  
Web Mapping ◽  
Digital Rights ◽  
Rights Management ◽  
Mapping Technology ◽  
Data Infrastructures ◽  
Spatial Data Infrastructures

This Chapter introduces the need for general Digital Rights Management (DRM) requirements. Further, it intertwines DRM with its spatial counterpart, Geospatial DRM (GeoDRM). However, unlike DRM, GeoDRM is far much complicated due to issues such as the development of Web Mapping technology among other issues. The Chapter discusses the ability of GeoDRM to mitigate transgression of Intellectual Property Rights (IPR). Highlighting economical and environmental wellbeing and other benefits of Spatial Data Infrastructure (SDI) geared towards global sustainable developments, the Chapter focuses on challenges of National Spatial Data Infrastructures (NSDIs) and Regional SDIs and the need to harmonize their standards for the upward mobility of global SDI (GSDI). Emphasizing the undisputed need for Local, Regional and Global Spatial Data Infrastructures (SDIs), in the presence of various Geo-communities and different GeoDRM models, the Chapter concludes that capacity building need to be urgently but carefully harnessed across all levels in order to develop cohesive GeoDRM policies.

scholarly journals The Evolution of National Spatial Data Infrastructure in Pakistan - Implementation Challenges and the Way Forward

2021 ◽  
Author(s):  
Asmat Ali
Keyword(s):  
Spatial Data ◽  
National Level ◽  
Stakeholder Analysis ◽  
Grid Method ◽  
Distributed Environment ◽  
Data Infrastructure ◽  
Implementation Challenges ◽  
Institutional Issues ◽  
Share Data ◽  
Data Infrastructures

Geospatial data are produced by several organizations located at various places, and that is clearly a distributed environment. Many technical and institutional issues need to be resolved to share data in such an environment and to eventually enable regional development. For this matter, many countries implement Spatial Data Infrastructures (SDIs) for the last 40 years. Since 2010, also Pakistan is striving to implement an SDI at the national level (NSDI). However, so far, the promised benefits have not yet been achieved. This study explores the evolution of the NSDI in Pakistan from 2010 till 2020 to reveal what kind of challenges the country is facing. Given the importance of stakeholders' support for the implementation of SDIs, we conducted a stakeholder analysis and a dedicated survey. We adopted the power-interest grid method to classify stakeholders' interests based on their authority to influence the NSDI development. Among other, the results show that stakeholders’ low participation due to insufficient technological, financial, and human resources impedes NSDI implementation efforts in the country.

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

2016 ◽  
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.

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

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.

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

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.

Application of Geospatial Mashups in Web GIS for Tourism Development

2018 ◽  
pp. 3403-3418
Author(s):  
Somnath Chaudhuri ◽  
Nilanjan Ray
Keyword(s):  
Spatial Data ◽  
Rapid Development ◽  
Tourism Development ◽  
Web Gis ◽  
Multiple Sources ◽  
Web Mapping ◽  
Tourism Management ◽  
Mapping Technology ◽  
Spatial Display

This paper examines current development in Web GIS with the implementation of Geospatial Mashup technologies, such as Google Map in the context of map Mashups, and presents a classification of map Mashups and their application in tourism management and promotion. On the Web GIS context, Mashup is the process of merging multiple sources of data, both spatial and non-spatial, into a single integrated spatial display. It is about extracting spatial data from a non-spatial source and combining with other spatial data and finally displaying it on a map. This paper demonstrates that Geospatial Mashup has great potential to facilitate and widen the rapid development of the future web mapping technology in Web GIS in tourism development. It also highlights on the basic architecture and working principles of Map Mashups in context to tourism management. The final section of this research paper emphasizes on some issues and limitations inherent to the current Mashup technologies like privacy protection, copyright issues etc. which need to be worked out before its wider adoption.

scholarly journals Geospatial IoT—The Need for Event-Driven Architectures in Contemporary Spatial Data Infrastructures

2018 ◽  
Vol 7 (10) ◽  
pp. 385 ◽  
Author(s):  
Matthes Rieke ◽  
Lorenzo Bigagli ◽  
Stefan Herle ◽  
Simon Jirka ◽  
Alexander Kotsev ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Geospatial Information ◽  
Data Infrastructure ◽  
Event Driven ◽  
Data Infrastructures ◽  
Spatial Data Infrastructures ◽  
Time Critical ◽  
Real Time Information ◽  
Time Information

The nature of contemporary spatial data infrastructures lies in the provision of geospatial information in an on-demand fashion. Although recent applications identified the need to react to real-time information in a time-critical way, research efforts in the field of geospatial Internet of Things in particular have identified substantial gaps in this context, ranging from a lack of standardisation for event-based architectures to the meaningful handling of real-time information as “events”. This manuscript presents work in the field of event-driven architectures as part of spatial data infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of spatial data infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches—developed in different research projects—to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall event-driven spatial data infrastructure.

scholarly journals Development of project-oriented spatial data infrastructure using cloud technologies

2019 ◽  
Vol 29 (3) ◽  
pp. 79-90
Author(s):  
S. A. Yamashkin ◽  
A. A. Yamashkin ◽  
S. A. Fedosin
Keyword(s):  
Social Services ◽  
Data Storage ◽  
Spatial Data ◽  
Design Development ◽  
Data Infrastructure ◽  
Regulatory Frameworks ◽  
Analysis And Synthesis ◽  
Data Infrastructures ◽  
Spatial Data Infrastructures ◽  
Cloud Technologies

The article includes the issues of design, development and introduction of project-oriented spatial data infrastructures (SDIs) that build the information space to solve pressing challenges in economy, ecology, social services, in the field of preparation of pre-investment, urban planning, pre-project, project documentation, and natural disaster forecasting.It also provides an overview of a historical development of spatial data infrastructures in Russia and in the world. Based on an analysis of a historical landscape within the challenging area, authors have identified the following system components of SDIs: users and professionals, data, technologies, standards, regulatory frameworks, and institutional procedures. There is a proposed platform solution architecture to build SDI, summarized in a form of a structure-component scheme. It rests upon the hypothesis that in order to optimize spatial data storage and application-related processes, the project-oriented SDI needs to include loosely bound and closely bound subsystems for spatial data storage (cloud or local storages), analysis and synthesis modules, as well as modules for visualization and distribution of spatial data (as geoportal systems).

scholarly journals GeoSpatial IoT - the Need for an Event-Driven Spatial Data Infrastructure

2018 ◽  
Author(s):  
Matthes Rieke ◽  
Lorenzo Bigagli ◽  
Stefan Herle ◽  
Simon Jirka ◽  
Alexander Kotsev ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Spatial Data Infrastructure ◽  
Geospatial Information ◽  
Data Infrastructure ◽  
Event Driven ◽  
Data Infrastructures ◽  
Spatial Data Infrastructures ◽  
Real Time Information ◽  
Time Information

The nature of contemporary Spatial Data Infrastructures lies in the provision of geospatial information in an on-demand fashion. Though recent applications identified the need to react to real-time information in a time-critical way. In particular, research efforts in the field of geospatial Internet of Things have identified substantial gaps in this context, ranging from a lack of standardization for event-based architectures to the meaningful handling of real-time information as ''events''. This manuscript presents work in the field of Event-driven Spatial Data Infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of Spatial Data Infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches - developed in different research projects - to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall Event-driven Spatial Data Infrastructure.

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