scholarly journals Geographic Information and Geo-visualisation in support of Disaster Resilience

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Edward Kurwakumire ◽  
Paul Muchechetere ◽  
Shelter Kuzhazha ◽  
Guy Blachard Ikokou
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Disaster Preparedness ◽  
Geographic Information ◽  
Public Access ◽  
Data Sets ◽  
Disaster Resilience ◽  
Data Systems ◽  
Information Infrastructures ◽  
Timely Access

<p><strong>Abstract.</strong> Society continues to become more spatially enabled as spatial data becomes increasingly available and accessible. This is partly due to democratisation of data achieved through open access of framework data sets. On the other hand, mobile devices such as smartphones have become more accessible, giving the public access to applications that use spatial data. This has tremendously increased the consumption of spatial data at the level of the general public. Spatial data has a history in planning and decision making as detailed in literature on promises and benefits of geographic information. We extend these promises to sustainability and disaster resilience. It is our belief that geographic information (GI) and geographic information infrastructures (GIIs) contribute positively towards the achievement of sustainability in cities and nations and in disaster resilience. This study carries out a review of geo-visualisation and GI applications in order to determine their suitability and impact in disaster resilience. Real-time GI are significant for cities to ensure sustainability and to increase disaster preparedness. Geographic information infrastructures need to be integrated with BIG data systems to ensure that local government agencies have timely access to real time geographic information so that decisions on sustainability and disaster resilience can be effectively done.</p>

Untangling origin-destination flows in geographic information systems

2017 ◽  
Vol 18 (1) ◽  
pp. 153-172 ◽  
Author(s):  
Anita Graser ◽  
Johanna Schmidt ◽  
Florian Roth ◽  
Norbert Brändle
Keyword(s):  
Information System ◽  
Information Systems ◽  
Geographic Information System ◽  
Geographic Information Systems ◽  
Spatial Data ◽  
Geographic Information ◽  
Data Sets ◽  
Flow Maps ◽  
Local Strength ◽  
Edge Bundling

Origin–destination flow maps are a popular option to visualize connections between different spatial locations, where specific routes between the origin and destination are unknown or irrelevant. Visualizing origin–destination flows is challenging mainly due to visual clutter which appears quickly as data sets grow. Clutter reduction techniques are intensively explored in the information visualization and cartography domains. However, current automatic techniques for origin–destination flow visualization, such as edge bundling, are not available in geographic information systems which are widely used to visualize spatial data, such as origin–destination flows. In this article, we explore the applicability of edge bundling to spatial data sets and necessary adaptations under the constraints inherent to platform-independent geographic information system scripting environments. We propose (1) a new clustering technique for origin–destination flows that provides within-cluster consistency to speed up computations, (2) an edge bundling approach based on force-directed edge bundling employing matrix computations, (3) a new technique to determine the local strength of a bundle leveraging spatial indexes, and (4) a geographic information system–based technique to spatially offset bundles describing different flow directions. Finally, we evaluate our method by applying it to origin–destination flow data sets with a wide variety of different data characteristics.

Developing geographic information systems platforms for multijurisdictional transportation analyses: framework and techniques for spatial data sharing

2003 ◽  
Vol 30 (5) ◽  
pp. 807-818 ◽  
Author(s):  
Kai Han ◽  
Scott Minty ◽  
Alan Clayton
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Data Sharing ◽  
Spatial Data ◽  
Rural Areas ◽  
Geographic Information ◽  
Data Sets ◽  
Data Interoperability ◽  
Urban And Rural Areas ◽  
Integration Data

Geographic information systems (GISs) have been presented as a powerful analysing tool for civil engineers to help their decision-making processes. Building GIS platforms for transportation analysis involving multiple jurisdictions has been challenging, however, because of the complexity and difficulty associated with conducting data sharing and ensuring spatial data interoperability among GISs for transportation (GIS-T) data sets. In the context of western Canadian urban and rural areas, this paper investigates the issues related to GIS-T data sharing, establishes a conceptual framework, develops techniques supporting the framework by solving recurring data-sharing problems, and constructs a number of GIS-T platforms facilitating comprehensive multijurisdictional transportation analyses. In addition, based on the knowledge gained through solving real-world problems, the authors propose an open GIS-T platform consisting of a series of customized base maps, each being tailored to suit the needs of individual application and, as a whole, linked together by interoperability to better support transportation applications.Key words: transportation engineering analysis, GIS, GIS-T, spatial data, interoperability, integration, data sharing.

Geographic Information Systems: A Critical Look at the Commercialization of Public Information

2011 ◽  
Vol 4 (3) ◽  
pp. 88-105 ◽  
Author(s):  
Safiya Umoja Noble
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Spatial Data ◽  
Public Information ◽  
Geographic Information ◽  
The Internet ◽  
Data Sets ◽  
Software Projects ◽  
For Profit ◽  
Huge Data

This paper is a political economic critique and exploration of the ways that private-sector companies in the Geographic Information Systems (GIS) industry have emerged and consolidated themselves. This includes a discussion about buying, analyzing and selling spatial data mined from the Internet and other public resources, and how this is packaged and sold to other corporations for profit. I detail GIS research and development projects and the activities that are fueling growth. One of the fastest growing sectors of the GIS business is data mining and information processing, where companies are able to capitalize on the flow of information through proprietary systems or public networks like the Internet, and as such, are accumulating great wealth. GIS software projects are often the outgrowth of direct political and economic policy and funding, and industry giants are afforded greater access to purchasing huge data sets and labor to analyze and re-sell it. Public adoption and usage of GIS tools via the Internet is creating competitive tensions within the GIS industry and producing complex new partnerships. What is most critical to explore at this moment are the details of the industry, who it serves, and in whose interest. An understanding of the GIS terrain will better equip the public in making informed decisions about how state policies and consumer practices are contributing to, or disrupting, these activities.

scholarly journals Kreatorzy, gestorzy i internauci – od baz danych przestrzennych do map numerycznych i wirtualnych globusów

2013 ◽  
Vol 36 (2) ◽  
pp. 239-250
Author(s):  
Piotr Werner Piotr Werner
Keyword(s):  
Information And Communication Technologies ◽  
Spatial Data ◽  
Spatial Databases ◽  
Distributed Processing ◽  
Communication Technologies ◽  
Geographic Information ◽  
Public Access ◽  
Positioning Systems ◽  
Information And Communication ◽  
Access Data

The basic elements of Geographic Information Systems are spatial databases. There are multiply interfaces of views and queries as well as methods of reporting. They are multi-resolution and multi-representations. Additional elements are standardized metadata. Currently they are developing as the technologies of distributed processing using wireless networks and global positioning systems. The procedures of spatial data bases creation are based on well recognized and defined methodology. Recent development of Information and Communication Technologies (ICT) is the cause that traditional division of work concerning spatial databases among authors, administrators and users changes itself. Directional propagation of information (according to Shannon theory) from authors through administrators to users is changing. Users are simultaneously authors and administrators, sharing their own collections of spatial data and vice versa, sometimes professionals use such collections supporting and updating professional spatial databases using public access data. Creation, assembling and dissemination of spatial data provided voluntarily by individuals has been defined as Volunteered Geographic Information. There are a lot of impacts of this new trend involving essential, legal and economic aspects as well as creating the new qualities in culture of the societies.

Techniques for Building Multijurisdictional Geographic Information System Platforms for Transportation Analysis

2002 ◽  
Vol 1804 (1) ◽  
pp. 187-195
Author(s):  
Kai Han ◽  
Jeannette Montufar ◽  
Scott Minty ◽  
Alan Clayton
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Data Sharing ◽  
Spatial Data ◽  
Geographic Information ◽  
Data Sources ◽  
Data Sets ◽  
Data Interoperability ◽  
Gis Data ◽  
Transportation Applications

Transportation analysis involving multiple jurisdictions requires data sharing and spatial data interoperability among geographic information system (GIS) data sets. Data sharing and spatial data interoperability issues related to multijurisdictional transportation analysis are discussed. Specific techniques based on practical data-sharing, problem-solving experience are developed. To further enhance the data-sharing process, a conceptual framework is established to guide technique implementations. Regional GIS transportation (GIS-T) platforms integrated from various data sources by applying the framework and the associated techniques are also presented. To better support different transportation applications, an open GIS-T platform is proposed, consisting of a series of customized base maps, each tailored to suit individual applications and, as a whole, linked together by inherently established interoperability.

Geographic Information Systems as Decision Tools

2005 ◽  
pp. 1274-1277 ◽  
Author(s):  
Martin D. Crossland
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Spatial Data ◽  
Information Science ◽  
Large Data ◽  
Geographic Information Science ◽  
Geographic Information ◽  
Data Sets ◽  
Decision Tools ◽  
University Consortium

Geographic information systems (GISs) as a technology have been studied and reported extensively and, not unexpectedly, in the field of geography. The various ways of capturing spatial data, arranging attribute data into appropriate database structures, and making the resulting large data sets efficient to store and query have been extensively researched and reported (Densham, 1991). However, the geographic research community has only recently noted the need to study how GISs are used as decision tools, especially with regard to how such decision making might be related to a decision maker’s cognitive style (Mennecke, Crossland, et al., 2000). As an example, the University Consortium for Geographic Information Science called for research examining how geographic knowledge is acquired through different media and by users with different levels of experience and training (University Consortium for Geographic Information Science, 1996).

Spatial Data Infrastructures in Morocco (From the Diagnosis to the Success of the Implementation)

2019 ◽  
pp. 64-81
Author(s):  
Elmostaphi Elomari ◽  
Hassan Rhinane
Keyword(s):  
Spatial Data ◽  
Geographic Information ◽  
Spatial Data Infrastructure ◽  
Geographical Information ◽  
Collection Management ◽  
Data Systems ◽  
Data Infrastructure ◽  
Information Governance ◽  
Data Infrastructures ◽  
Current Context

A spatial data infrastructure (SDI) is a platform for coordinating the exchange and sharing of spatial data between several producers or users of spatially referenced data. In Morocco, there is a massive production of spatial data and several generally public administrations are starting to feel the need for geographic information governance through a mechanism of exchange and management of data to optimize their efforts and avoid a redundant production. The purpose of this chapter is to draw up an inventory of the state of the art of geo-spatial data, systems, and tools existing in the central administrations in Morocco in relation with the collection, management, storage, and dissemination of geographical information. Through this study, it was found that the problem is more a question of global governance, and that the current context has assets for the establishment of a spatial data infrastructure in Morocco.

Managing oceanographic data for the Marine Strategy Framework Directive in Spain supported by the Spatial Data Infrastructure of the Spanish Institute of Oceanography (IEO) and the INSPIRE Directive

2020 ◽  
Author(s):  
Gerardo Bruque ◽  
Olvido Tello
Keyword(s):  
Marine Environment ◽  
Spatial Data ◽  
Geographic Information ◽  
Spatial Data Infrastructure ◽  
Data Sets ◽  
Marine Strategy Framework Directive ◽  
Data Infrastructure ◽  
Continuous Evaluation ◽  
Oceanographic Data ◽  
Fish And Shellfish

&lt;p&gt;In Europe, the Marine Strategy Framework Directive (MSFD) seeks to achieve a good environmental status of European marine waters and protect the resource base on which economic and social activities related to the sea depend. With this legislative tool the European Parliament recognizes the vital importance of the management of human activities that have an impact on the marine environment, integrating the concepts of environmental protection and sustainable use.&lt;br&gt;MSFD establishes a monitoring program of different descriptors for continuous evaluation and periodic updating of the objectives. In Spain, the Ministry of Ecological Transition (MITECO) is responsible and coordinator of carrying out the MSFD, but it is the Spanish Institute of Oceanography (IEO) that performs the research and study of the different indicators and therefore the tasks of collecting oceanographic data.&lt;br&gt;The Geographic Information Systems Unit of the IEO is responsible for storing, debugging and standardizing this data by including them in the IEO Spatial Data Infrastructure (IDEO). IDEO has useful and advanced tools to discover and manage the oceanographic, spatial or non-spatial data that the IEO manages. To facilitate access to IDEO, the IEO Geoportal was developed, which essentially contains a catalog of metadata and access to different IEO web services and data viewers.&lt;br&gt;Some examples of priority dataset for the MSFD are: Species and Habitat distribution, commercially-exploited fish and shellfish species distribution, Nutrients, Chlorophyll a, dissolved oxygen, spatial extent of loss of seabed, Contaminants, litter, noise, etc.&lt;br&gt;The correct preparation and harmonization of the mentioned data sets following the Implementing Rules adopted by the INSPIRE Directive is essential to ensure that the different Spatial Data Infrastructures (SDI) of the member states are compatible and interoperable in the community context.&lt;br&gt;The INSPIRE Directive was born with the purpose of making relevant, concerted and quality geographic information available in a way that allows the formulation, implementation, monitoring and evaluation of the impact or territorial dimension policies of the European Union.&lt;br&gt;The geographic data sets, together with their corresponding metadata, constitute the cartographic base on which the information collected for the update of the continuous evaluation of the different descriptors of the MSFD is structured.&lt;br&gt;Thus, although these datasets are intended for use by public institutions responsible for decision-making on the management of the marine environment, they can also be very useful for a wide range of stakeholders and reused for multiple purposes.&lt;br&gt;With all this in mind, the INSPIRE Directive is extremely interesting and essential for the tasks required for the MSFD. As with work on our projects related to the Marine Space Planning Directive (MSP).&lt;/p&gt;

scholarly journals Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Arvind Sharma ◽  
R. K. Gupta ◽  
Akhilesh Tiwari
Keyword(s):  
Data Mining ◽  
Information System ◽  
Geographic Information System ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Geographic Information ◽  
Data Sets ◽  
Clustering Methods ◽  
Data Objects

There are many techniques available in the field of data mining and its subfield spatial data mining is to understand relationships between data objects. Data objects related with spatial features are called spatial databases. These relationships can be used for prediction and trend detection between spatial and nonspatial objects for social and scientific reasons. A huge data set may be collected from different sources as satellite images, X-rays, medical images, traffic cameras, and GIS system. To handle this large amount of data and set relationship between them in a certain manner with certain results is our primary purpose of this paper. This paper gives a complete process to understand how spatial data is different from other kinds of data sets and how it is refined to apply to get useful results and set trends to predict geographic information system and spatial data mining process. In this paper a new improved algorithm for clustering is designed because role of clustering is very indispensable in spatial data mining process. Clustering methods are useful in various fields of human life such as GIS (Geographic Information System), GPS (Global Positioning System), weather forecasting, air traffic controller, water treatment, area selection, cost estimation, planning of rural and urban areas, remote sensing, and VLSI designing. This paper presents study of various clustering methods and algorithms and an improved algorithm of DBSCAN as IDBSCAN (Improved Density Based Spatial Clustering of Application of Noise). The algorithm is designed by addition of some important attributes which are responsible for generation of better clusters from existing data sets in comparison of other methods.

Sign in / Sign up

Export Citation Format

Share Document