When Spatial Analysis Meets OLAP

2010 ◽  
Vol 6 (4) ◽  
pp. 33-60 ◽  
Author(s):  
Sandro Bimonte ◽  
Anne Tchounikine ◽  
Maryvonne Miquel ◽  
François Pinet
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Spatial Dimension ◽  
Geographic Information ◽  
Environmental Data ◽  
Multidimensional Analysis ◽  
Complex Objects ◽  
Semantic Component ◽  
Venetian Lagoon ◽  
Associated Operators

Introducing spatial data into multidimensional models leads to the concept of Spatial OLAP (SOLAP). Existing SOLAP models do not completely integrate the semantic component of geographic information (alphanumeric attributes and relationships) or the flexibility of spatial analysis into multidimensional analysis. In this paper, the authors propose the GeoCube model and its associated operators to overcome these limitations. GeoCube enriches the SOLAP concepts of spatial measure and spatial dimension and take into account the semantic component of geographic information. The authors define geographic measures and dimensions as geographic and/or complex objects belonging to hierarchy schemas. GeoCube’s algebra extends SOLAP operators with five new operators, i.e., Classify, Specialize, Permute, OLAP-Buffer and OLAP-Overlay. In addition to classical drill-and-slice OLAP operators, GeoCube provides two operators for navigating the hierarchy of the measures, and two spatial analysis operators that dynamically modify the structure of the geographic hypercube. Finally, to exploit the symmetrical representation of dimensions and measures, GeoCube provides an operator capable of permuting dimension and measure. In this paper, GeoCube is presented using environmental data on the pollution of the Venetian Lagoon.

When Spatial Analysis Meets OLAP

2011 ◽  
pp. 249-277 ◽  
Author(s):  
Sandro Bimonte ◽  
Anne Tchounikine ◽  
Maryvonne Miquel ◽  
François Pinet
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Spatial Dimension ◽  
Geographic Information ◽  
Environmental Data ◽  
Multidimensional Analysis ◽  
Complex Objects ◽  
Semantic Component ◽  
Venetian Lagoon ◽  
Associated Operators

Introducing spatial data into multidimensional models leads to the concept of Spatial OLAP (SOLAP). Existing SOLAP models do not completely integrate the semantic component of geographic information (alphanumeric attributes and relationships) or the flexibility of spatial analysis into multidimensional analysis. In this chapter, the authors propose the GeoCube model and its associated operators to overcome these limitations. GeoCube enriches the SOLAP concepts of spatial measure and spatial dimension and take into account the semantic component of geographic information. The authors define geographic measures and dimensions as geographic and/or complex objects belonging to hierarchy schemas. GeoCube’s algebra extends SOLAP operators with five new operators, i.e., Classify, Specialize, Permute, OLAP-Buffer and OLAP-Overlay. In addition to classical drill-and-slice OLAP operators, GeoCube provides two operators for navigating the hierarchy of the measures, and two spatial analysis operators that dynamically modify the structure of the geographic hypercube. Finally, to exploit the symmetrical representation of dimensions and measures, GeoCube provides an operator capable of permuting dimension and measure. In this chapter, GeoCube is presented using environmental data on the pollution of the Venetian Lagoon.

A Web-Based Tool for Spatio-Multidimensional Analysis of Geographic and Complex Data

2012 ◽  
pp. 32-58
Author(s):  
Sandro Bimonte
Keyword(s):  
Spatial Data ◽  
Venice Lagoon ◽  
Environmental Data ◽  
Multidimensional Analysis ◽  
Complex Data ◽  
Web Based ◽  
Complex Information ◽  
Semantic Component ◽  
Spatial Dimensions ◽  
Spatial Data Warehouses

Spatial OLAP (SOLAP) integrates spatial data into OLAP systems, and SOLAP models define spatial dimensions while measuring spatio-multidimensional operators. In this paper, the author presents the concepts of geographic and complex measures that allow integrating geographic and complex information as subjects of analysis in spatial data warehouses. The concept of geographic measure extends the concept of spatial measure to the semantic component of geographic information. The concept of complex measure allows introducing complex data as subjects of multidimensional analysis. To reduce the gap in flexibility between spatial and multidimensional analysis, this paper proposes a symmetrical representation of measures and dimensions. Additionally, the author presents a Web-based SOLAP prototype, GeWOlap, that enriches existing SOLAP tools by effectively and easily supporting symmetrical geographic/complex measures and dimensions for modeling and visualization. To validate this approach, the simulated environmental data concerning the pollution of the Venice lagoon is used.

A Web-Based Tool for Spatio-Multidimensional Analysis of Geographic and Complex Data

2010 ◽  
Vol 1 (2) ◽  
pp. 42-67 ◽  
Author(s):  
Sandro Bimonte
Keyword(s):  
Spatial Data ◽  
Venice Lagoon ◽  
Environmental Data ◽  
Multidimensional Analysis ◽  
Complex Data ◽  
Web Based ◽  
Complex Information ◽  
Semantic Component ◽  
Spatial Dimensions ◽  
Spatial Data Warehouses

Spatial OLAP (SOLAP) integrates spatial data into OLAP systems, and SOLAP models define spatial dimensions while measuring spatio-multidimensional operators. In this paper, the author presents the concepts of geographic and complex measures that allow integrating geographic and complex information as subjects of analysis in spatial data warehouses. The concept of geographic measure extends the concept of spatial measure to the semantic component of geographic information. The concept of complex measure allows introducing complex data as subjects of multidimensional analysis. To reduce the gap in flexibility between spatial and multidimensional analysis, this paper proposes a symmetrical representation of measures and dimensions. Additionally, the author presents a Web-based SOLAP prototype, GeWOlap, that enriches existing SOLAP tools by effectively and easily supporting symmetrical geographic/complex measures and dimensions for modeling and visualization. To validate this approach, the simulated environmental data concerning the pollution of the Venice lagoon is used.

scholarly journals Simple rules for an efficient use of Geographic Information Systems in molecular ecology

2017 ◽  
Author(s):  
Kevin Leempoel ◽  
Solange Duruz ◽  
Estelle Rochat ◽  
Ivo Widmer ◽  
Pablo Orozco-terWengel ◽  
...  
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Open Source ◽  
Spatial Data ◽  
Molecular Ecology ◽  
Relevant Information ◽  
Geographic Information ◽  
Environmental Data ◽  
Essential Information ◽  
Open Source Gis

AbstractGeographic Information Systems (GIS) are becoming increasingly popular in the context of molecular ecology and conservation biology thanks to their display options efficiency, flexibility and management of geodata. Indeed, spatial data for wildlife and livestock species is becoming a trend with many researchers publishing genomic data that is specifically suitable for landscape studies. GIS uniquely reveal the possibility to overlay genetic information with environmental data and, as such, allow us to locate and analyze genetic boundaries of various plant and animal species or to study gene-environment associations (GEA). This means that, using GIS, we can potentially identify the genetic bases of species adaptation to particular geographic conditions or to climate change. However, many biologists are not familiar with the use of GIS and underlying concepts and thus experience difficulties in finding relevant information and instructions on how to use them. In this paper, we illustrate the power of free and open source GIS approaches and provide essential information for their successful application in molecular ecology. First, we introduce key concepts related to GIS than are too often overlooked in the literature, for example coordinate systems, GPS accuracy and scale. We then provide an overview of the most employed open-source GIS-related software, file formats and refer to major environmental databases. We also reconsider sampling strategies as high costs of Next Generation Sequencing (NGS) data currently diminish the number of samples that can be sequenced per location. Thereafter, we detail methods of data exploration and spatial statistics suited for the analysis of large genetic datasets. Finally, we provide suggestions to properly edit maps and to make them as comprehensive as possible, either manually or trough programming languages.

A Survey of Spatial Data Mining Methods Databases and Statistics Point of Views

2011 ◽  
pp. 229-242 ◽  
Author(s):  
Karine Zeitouni
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Spatial Analysis ◽  
Geographic Information Systems ◽  
Spatial Statistics ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Data Mining ◽  
Geographic Information ◽  
Mining Methods

This chapter reviews the data mining methods that are combined with Geographic Information Systems (GIS) for carrying out spatial analysis of geographic data. We will first look at data mining functions as applied to such data and then highlight their specificity compared with their application to classical data. We will go on to describe the research that is currently going on in this area, pointing out that there are two approaches: the first comes from learning on spatial databases, while the second is based on spatial statistics. We will conclude by discussing the main differences between these two approaches and the elements they have in common.

THE NEXT GENERATION SPATIAL DATA WAREHOUSE

2002 ◽  
Vol 42 (1) ◽  
pp. 633
Author(s):  
A.J. Yardley
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Essential Element ◽  
Spatial Location ◽  
Geographic Information ◽  
Environmental Data ◽  
Next Generation ◽  
Data Types ◽  
Political Boundaries ◽  
Access To Data

Woodside Energy, based in Perth, Western Australia, has commenced the implementation of its next generation spatial data warehousing and visualisation system. The warehouse facilitates access to data in various corporate geoscience data sets, as well as up-to-date cultural and environmental data. It expands the capabilities of the existing geoscience database by providing a facility to handle spatial data at the database level rather than in files and maps. Spatial data can now be kept in the database, in its correct spatial location, and with a known provenance.Woodside’s worldwide exploration, development and production activities require the use of a wide variety of geographic data such as seismic, bathymetry, wells, permits, coastlines, political boundaries, navigation charts, remote sensing and geological interpretations.Geo-spatial data comes to Woodside in a variety of formats, datums and conditions. The Geomatics Department, through the Geoscience Database and Spatial Information Management teams, loads, maintains and manages all data considered to be corporate. It is quality controlled and placed into the warehouse, where it is readily accessible to technical and administrative staff.Location is an essential element in most Woodside decisions. Because of the new spatial capabilities, a number of geographic information processes are now possible. Additionally information can also be made available through the internet if required.Reliable geographic information will become more widely available in the organisation, and be more easily merged with traditional data types, enhancing the decision-making process.

New Design Approach to Handle Spatial Vagueness in Spatial OLAP Datacubes

2016 ◽  
pp. 1859-1880
Author(s):  
Elodie Edoh-Alove ◽  
Sandro Bimonte ◽  
François Pinet ◽  
Yvan Bédard
Keyword(s):  
Spatial Data ◽  
End Users ◽  
Environmental Data ◽  
Decision Makers ◽  
Multidimensional Analysis ◽  
Multidimensional Data ◽  
New Approach ◽  
New Models ◽  
Different Types ◽  
Spatial Vagueness

Spatial-OLAP (SOLAP) technologies are dedicated to multidimensional analysis of large volumes of (spatial) data. Spatial data are subject to different types of uncertainty, in particular spatial vagueness. Although several researches propose new models to cope with spatial vagueness, their integration in SOLAP systems is still in an embryonic state. Also, analyzing multidimensional data with metadata brought by the exploitation of the new models can be too complex and demanding for decision-makers. To help reduce spatial vagueness consequences on the exactness of SOLAP analysis queries, the authors present a new approach for designing SOLAP datacubes based on end-users' tolerance to the risks of misinterpretation of fact data. An experimentation of the new approach on agri-environmental data is also proposed.

scholarly journals Perangkat Sistem Informasi Geografis (SIG) dalam Penelitian dan Penyajian Informasi Arkeologi

2016 ◽  
Vol 12 (1) ◽  
pp. 29 ◽  
Author(s):  
Muhammad Al Mujabuddawat
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Spatial Analysis ◽  
Spatial Data ◽  
Spatial Information ◽  
Geographic Information ◽  
Archaeological Research ◽  
Gis Tools ◽  
The Government ◽  
Data Context

Archaeology is closely associated with spatial or spatial aspects. Because the material archeological data such as artifacts, features, buildings, and sites containing the inherent spatial information in order to keep the data context. The themes of the archaeological research nowadays often reconstructing the spatial aspects of history and culture. Device Geographic Information System (GIS) is clearly greatly assist the process of archaeological research both in the field and during the process of analysis and presentation of information related to the results of the research. GIS has become the main choice for researchers to update the development of archeology that have been all-digital, practical, and effective. Although the use of GIS in archaeological research is very popular in many countries, in fact the use of GIS in archaeological research in Indonesia is still not that popular. This paper presents the use of GIS tools that allowed to be applied by archaeologists that can be adopted in the analysis and presentation of information and research results, conditions of application of GIS in the current archaeological research, as well as the constraints faced. This paper shows that recently the archaeologists in Indonesia is very enthusiactic in using the GIS for the effective spatial analysis tools. The government is also concerned about the importance of GIS in mapping the spatial data of heritage as well archaeological research locations in order to support the acceleration of One Map Policy.Ilmu arkeologi sangat erat kaitannya dengan aspek keruangan atau spasial. Karena materi data arkeologi seperti artefak, fitur, bangunan, dan situs mengandung informasi spasial yang melekat agar tidak kehilangan data konteksnya. Tema-tema penelitian arkeologi dewasa ini tidak sedikit yang bertemakan aspek spasial dalam merekonstruksi sejarah dan budaya. Perangkat Sistem Informasi Geografis (SIG) jelas sangat membantu proses penelitian arkeologi baik di lapangan maupun saat proses analisis dan penyajian informasi terkait hasil penelitian semacam itu. SIG menjadi pilihan bagi peneliti arkeologi dalam mengikuti perkembangan dunia riset yang serba digital, praktis, dan efektif. Walaupun penggunaan perangkat SIG dalam penelitian arkeologi sangat populer di banyak negara, namun kenyataannya penggunaan perangkat SIG dalam penelitian arkeologi di Indonesia belum cukup polpuler. Penelitian ini menyajikan penggunaan perangkat SIG yang memungkinkan diterapkan oleh peneliti arkeologi yang dapat membantu dalam proses analisis dan penyajian informasi hasil penelitian, kondisi penerapan perangkat SIG di dalam penelitian arkeologi saat ini, serta kendala-kendala yang dihadapi. Penelitian ini menunjukkan bahwa dewasa ini perhatian peneliti arkeologi di Indonesia terhadap peran SIG cukup terbuka mengingat kebutuhan perangkat analisis spasial yang efektif. Pemerintah juga menaruh perhatian akan pentingnya SIG dalam memetakan data spasial Cagar Budaya dan Lokasi penelitian arkeologi dalam rangka mendukung percepatan kebijakan One Map Policy atau kebijakan Satu Peta.

New Design Approach to Handle Spatial Vagueness in Spatial OLAP Datacubes

2015 ◽  
Vol 6 (3) ◽  
pp. 29-49 ◽  
Author(s):  
Elodie Edoh-Alove ◽  
Sandro Bimonte ◽  
François Pinet ◽  
Yvan Bédard
Keyword(s):  
Spatial Data ◽  
End Users ◽  
Environmental Data ◽  
Decision Makers ◽  
Multidimensional Analysis ◽  
Multidimensional Data ◽  
New Approach ◽  
New Models ◽  
Different Types ◽  
Spatial Vagueness

Spatial-OLAP (SOLAP) technologies are dedicated to multidimensional analysis of large volumes of (spatial) data. Spatial data are subject to different types of uncertainty, in particular spatial vagueness. Although several researches propose new models to cope with spatial vagueness, their integration in SOLAP systems is still in an embryonic state. Also, analyzing multidimensional data with metadata brought by the exploitation of the new models can be too complex and demanding for decision-makers. To help reduce spatial vagueness consequences on the exactness of SOLAP analysis queries, the authors present a new approach for designing SOLAP datacubes based on end-users' tolerance to the risks of misinterpretation of fact data. An experimentation of the new approach on agri-environmental data is also proposed.

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