The statistical geoportal and the “cartographic added value” – creation of the spatial knowledge infrastructure

Abstract

The wide access to source data, published by numerous websites, results in situation, when information acquisition is not a problem any more. The real problem is how to transform information in the useful knowledge. Cartographic method of research, dealing with spatial data, has been serving this purpose for many years. Nowadays, it allows conducting analyses at the high complexity level, thanks to the intense development in IT technologies, The vast majority of analytic methods utilizing the so-called data mining and data enrichment techniques, however, concerns non-spatial data. According to the Authors, utilizing those techniques in spatial data analysis (including analysis based on statistical data with spatial reference), would allow the evolution of the Spatial Information Infrastructure (SII) into the Spatial Knowledge Infrastructure (SKI). The SKI development would benefit from the existence of statistical geoportal. Its proposed functionality, consisting of data analysis as well as visualization, is outlined in the article. The examples of geostatistical analyses (ANOVA and the regression model considering the spatial neighborhood), possible to implement in such portal and allowing to produce the “cartographic added value”, are also presented here.

Geographic Information Systems (GIS) as Supporting Tools in the Monitoring and Water Management of Lakes in Poland: A Review

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Integrating Cartographic Knowledge Within a Geoportal: Interactions and Feedback in the User Interface

“…Consequently, there is no "one size fits all" interface (van Elzakker and Wealands 2007), but even so, aiming to cater to universal usability can help (Shneiderman and Plaisant 2005; see S2 in Figure 1). Suggestions from previous work are to design methods of interaction that can be adapted to the end user in terms of complexity (Slocum et al 2001;Fiedukowicz et al 2012;Jenny et al 2010) and to provide flexibility in unfamiliar situations (MacEachren and Kraak 1997). Increasing interface complexity or its degrees of freedom can render tasks more difficult for users and thus alienate them (Slocum et al 2001;Jones et al 2009;Andrienko and Andrienko 2006).…”

Fine scale population density data and its application in risk assessment

“…Sharing data on flood risk along with information about the number of people exposed to danger enables a wide and open access for all citizens (Fiedukowicz et al 2012) and it facilitates people protection measures both in crisis situations and risk mitigation. Regardless the size and character of the hazard, people are always first and foremost affected locally, that is, where they live and conduct their daily activities (Freire 2010).…”