Semantic-based Pruning of Redundant and Uninteresting Frequent Geographic Patterns

GeoInformatica, Volume 14, Number 2 / April, 2010

Vania Bogorny, Joao F. Valiati, and Luis O. Alvares

“In geographic association rule mining many patterns are either redundant or contain well known geographic domain associations explicitly represented in knowledge resources such as geographic database schemas and geo-ontologies. Existing spatial association rule mining algorithms are Apriori-like, and therefore generate a large amount of redundant patterns. For non-spatial data, the closed frequent pattern mining technique has been introduced to remove redundant patterns. This approach, however, does not warrant the elimination of both redundant and well known geographic dependences when mining geographic databases. This paper presents a novel method for pruning both redundant and well known geographic dependences, by pushing semantics into the pattern mining task. Experiments with real geographic databases have demonstrated a significant reduction of the total amount of patterns and the efficiency of the method.”

Spatio-temporal Patterns of Pressure over the North Atlantic

International Journal of Climatology, published online 20 Nov 2009

Sílvia Antunes, Oliveira Pires, and Alfredo Roch

“The North Atlantic mean sea level pressure field variability is analysed. A space-time study is performed using multichannel singular spectral analysis, allowing the detection of significant space-time modes of variability with periodicity behaviour. It is shown that there is a space variability associated with the time variability of the pressure field. The oscillation is not quasi-meridional but has different orientations, rotating in a cycle, with a periodicity of about 9 years, from the positive North Atlantic oscillation (NAO) phase through the negative NAO phase and again to the positive phase. This periodicity behaviour was previously detected in the temporal principal components extracted from a principal component analysis but, in the time domain, it was found as not significant. Furthermore, the analysis of a long series of an NAO index had already revealed similar periodicity behaviour. Copyright © 2009 Royal Meteorological Society”

GIS/Remote Sensing Techniques for Resource Management and Biodiversity Protection in Mountainous Regions

Botanica Orientalis: Journal of Plant Science, 6: 93-99 (2009)

John All

“Biodiversity protection in mountainous regions requires effective fact-driven resource management techniques. Geoinformatic tools including GIS and remote sensing can be integrated to provide regional-scale data products across time for use in strategic and management level policymaking. Several principles are discussed to ensure that geoinformatics data and analysis can effectively contribute to resource management by clarifying issues and minimizing misinterpretation. A case study in the Chilean Andes elucidates these principles. Biological impacts of recent climate changes have not been equal across different ecosystems and stable forest ecosystems provide the best response to climate change. Geoinformatics is used to differentiate functional ecological groups and evaluate long-term resilience to climate change.”

Anomaly Detection and Spatio-temporal Analysis of Global Climate System

International Conference on Knowledge Discovery and Data Mining, Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, Paris, France 2009

Mahashweta Das and Srinivasan Parthasarathy

“Knowledge discovery from temporal, spatial and spatio-temporal data is pivotal for understanding and predicting the behavior of Earth’s ecosystem model. An important influence leaving its impact on the ecosystem is the global climate system. In this paper, the Earth Science data that we have analyzed consists of daily global air temperature and precipitation measurements, aggregated from heterogeneous sensors for fifty years (1950–1999). The enormous amount of data that is available for analysis requires employment of data mining techniques for discovering interesting patterns, detecting significant changes and extracting meaningful insights from the data. Our work considers the problem of detecting anomalous (abnormal or unexpected) behavior in the global climate system, discovering teleconnection patterns and providing consequential insights to the analysts.”

Spatial Data Infrastructures as Complex Adaptive Systems

International Journal of Geographical Information Science, Volume 24, Issue 3 March 2010 , pages 439 – 463

L. Grus; J. Crompvoets; A. K. Bregt

“Many researchers throughout the world have been struggling to better understand and describe spatial data infrastructures (SDIs). Our knowledge of the real forces and mechanisms behind SDIs is still very limited. The reason for this difficulty might lie in the complex, dynamic and multifaceted nature of SDIs. To evaluate the functioning and effects of SDIs we must have a proper theory and understanding of their nature. This article describes a new approach to understanding SDIs by looking at them through the lens of complex adaptive systems (CASs). CASs are frequently described by the following features and behaviours: complexity, components, self-organization, openness, unpredictability, nonlinearity and adaptability, scale-independence, existence of feedback loop mechanism and sensitivity to initial conditions. In this article both CAS and SDI features are presented, examined and compared using three National SDI case studies from the Netherlands, Australia and Poland. These three National SDIs were carefully analysed to identify CAS features and behaviours. In addition, an Internet survey of SDI experts was carried out to gauge the degree to which they consider SDIs and CASs to be similar. This explorative study provides evidence that to a certain extent SDIs can be viewed as CASs because they have many features in common and behave in a similar way. Studying SDIs as CASs has significant implications for our understanding of SDIs. It will help us to identify and better understand the key factors and conditions for SDI functioning. Assuming that SDIs behave much like CASs, this also has implications for their assessment: assessment techniques typical for linear and predictable systems may not be valid for complex and adaptive systems. This implies that future studies on the development of an SDI assessment framework must consider the complex and adaptive nature of SDIs.”

Delineating a Managed Fire Regime and Exploring its Relationship to the Natural Fire Regime in East Central Florida, USA: A Remote Sensing and GIS Approach

Forest Ecology and Management, 258 (2), p.132-145, Jun 2009

Duncan, B.W. / Shao, G. / Adrian, F.W.

“A managed fire regime on John F. Kennedy Space Center, Florida and surrounding federal properties was mapped using time series satellite imagery and GIS techniques. Our goals were to: (1) determine if an image processing technique designed for individual fire scar mapping could be applied to an image time series for mapping a managed fire regime in a rapid re-growth pyrogenic system; (2) develop a method for labeling mapped fire scar confidence knowing a formal accuracy analysis was not possible; and (3) compare results of the managed fire regime with regional information on natural fire regimes to look for similarities/differences that might help optimize management for persistence of native fire-dependent species. We found that the area burned by managed fire peaked when the drought index was low and was reduced when the drought index was high. This contrasts with the expectations regarding the natural fire regime of this region. With altered natural fire regimes and fire-dependent species declining in many pyrogenic ecosystems, it is important to manage fire for the survival of fire-adapted native species. The remote sensing and GIS techniques presented are effective for delineating and monitoring managed fire regimes in shrub systems that grow rapidly and may be appropriate for other fire-dependent systems world wide.”

A Qualitative Approach to Understanding the Role of Geographic Information Uncertainty during Decision Making

Cartography and Geographic Information Science, Volume 36, Number 4, October 2009 , pp. 315-330(16)

Roth, Robert E.

“Much recent research in GIScience is focused on developing deep comprehension of the underlying nature of uncertainty in order to design effective uncertainty representations that support informed decision-making. As it is impossible to eliminate all uncertainty from an abstracted representation, it is important to understand the involvement of uncertainty in the geographic information life cycle, the many forms that uncertainty can take, and the influence these forms have on decision-making. This paper examines the involvement and influence of geographic information uncertainty during decision-making, using the case study domain of floodplain mapping. A set of focus groups composed of floodplain mapping experts was conducted to provide initial insight into the following research questions: (1) How is uncertainty involved in the creation, representation, and use of geographic information in the domain of floodplain mapping and how can this practice be improved? (2) Is the MacEachren et al. typology an appropriate categorization of the many geographic information uncertainties in the domain of floodplain mapping or are there categories that must be added, removed, or revised? (3) Which categories of uncertainty are the most influential on the decision-making process in the domain of floodplain mapping? Although the focus groups revealed that the current involvement of geographic information uncertainty is less than ideal, there was clear consensus that the MacEachren et al. typology is an appropriate categorization of geographic information uncertainty for the domain of floodplain mapping and that the categories accuracy/error, precision/resolution, and currency are the most influential on the decision-making process. ”