Towards a SOLAP-based public participation GIS

McHugh, Rosemarie; Roche, Stéphane; Bédard, Yvan

doi:10.1016/j.jenvman.2008.01.020

Cited by 21 publications

(12 citation statements)

References 19 publications

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“…SOLAP. While On-Line Analytical Processing (OLAP) technologies allow fast, easy, and interactive exploration and analysis of data without any expert assistance [8,7], they are not optimized to spatial data. On the other hand, Geographic Information Systems (GIS) are powerful tools for detailed spatial data analysis, but they are not meant to support analytical needs which mostly require summarized information, aggregated data, trends analysis, spatio-temporal comparisons, interactive exploration of data, geographic knowledge discovery in large amounts of data, etc.…”

Section: Multiple-level Analysis Of Spatial Informationmentioning

confidence: 99%

Mining Spatial Gradual Patterns: Application to Measurement of Potentially Avoidable Hospitalizations

Ngo

Georgescu

Laurent

et al. 2017

SOFSEM 2018: Theory and Practice of Computer Science

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Gradual patterns aim at automatically extracting covariations between variables of data sets in the form of "the more/the less" such as "the more experience, the higher salary". This data mining method has been applied more and more in finding knowledge recently. However, gradual patterns are still not applicable on spatial data while such information have strong presence in many application domains. For instance, in our work we consider the issue of potentially avoidable hospitalizations. Their determinants have been studied to improve the quality, efficiency, and equity of health care delivery. Although the statistical methods such as regression method can find the associations between the increased potentially avoidable hospitalizations with its determinants such as lower density of ambulatory care nurses, there is still a challenge to identify how the geographical areas follow or not the tendencies. Therefore, in this paper, we propose to extend gradual patterns to the management of spatial data. Our work is twofold. First we propose a methodology for extracting gradual patterns at several hierarchical levels. In addition, we introduce a methodology for visualizing this knowledge. For this purpose, we rely on spatial maps for allowing decision makers to easily notice how the areas follow or not the gradual patterns. Our work is applied to the measure of the potentially avoidable hospitalizations to prove its interest.

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Section: Multiple-level Analysis Of Spatial Informationmentioning

confidence: 99%

Mining Spatial Gradual Patterns: Application to Measurement of Potentially Avoidable Hospitalizations

Ngo

Georgescu

Laurent

et al. 2017

SOFSEM 2018: Theory and Practice of Computer Science

View full text Add to dashboard Cite

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“…Currently, some spatial extensions have been proposed for OLAPs so as to view the geographical data of data warehouses on maps (Bimonte et al 2006;McHugh et al 2009). These tools are of significant interest in fields such as agriculture and the environment.…”

Section: Integrity Of Datamentioning

confidence: 99%

Precise design of environmental data warehouses

Pinet

Schneider

2009

Oper Res Int J

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People use data warehouses to help them make decisions. For example, public policy decision-makers can improve their decisions by using this technology to analyze the environmental effects of human activity. In production systems, data warehouses provide structures for extracting the knowledge required to optimize systems. Designing data warehouses is a complex task; designers need flexible and precise methods to help them create data warehouses and adapt their analysis criteria to developments in the decision-making process. In this paper, we introduce a flexible method based on UML (Unified Modeling Language). We introduce a UML profile for building multi-dimensional models and for choosing different criteria according to analysis requirements. This profile makes it possible to specify integrity constraints in OCL (Object Constraint Language). We apply our method to the construction of an environmental system for analyzing the use of certain agricultural fertilizers. We integrate various data sources into a multi-dimensional model showing several categories of analysis, and the consistency of data can be checked with OCL constraints

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“…Participatory and collaborative GIS have, for instance, raised this challenge (Jankowski and Nyerges, 2001;Balram and Dragićević, 2006). The next generation of tools requires easy-to-use interactive interfaces so that GIS analysts are not needed to formulate queries, produce charts, and generate maps on behalf of the users (McHugh et al, 2009). MCDA methods must also be easy to use and understand, yet many available methods are perceived by decision makers as being a black box (Belton and Stewart, 2002;Kangas and Kangas, 2005;Løken, 2007).…”

Section: Introductionmentioning

confidence: 99%