A Spatial-based KDD Process to Better Understand the Spatiotemporal Phenomena

International audienceIn this paper, we present a knowledge discovery process ap- plied to hydrological data. To achieve this objective, we combine succes- sive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre processed in order to obtain different spatial proximities. Later, we apply two algorithms to extract spatiotemporal patterns and compare them. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and rivers monitoring pressure data

Discovery of Spatiotemporal Event Sequences

Finding frequent patterns plays a vital role in many analytics tasks such as finding itemsets, associations, correlations, and sequences. In recent decades, spatiotemporal frequent pattern mining has emerged with the main goal focused on developing data-driven analysis frameworks for understanding underlying spatial and temporal characteristics in massive datasets. In this thesis, we will focus on discovering spatiotemporal event sequences from large-scale region trajectory datasetes with event annotations. Spatiotemporal event sequences are the series of event types whose trajectory-based instances follow each other in spatiotemporal context. We introduce new data models for storing and processing evolving region trajectories, provide a novel framework for modeling spatiotemporal follow relationships, and present novel spatiotemporal event sequence mining algorithms

COPE: Interactive Exploration of Co-occurrence Patterns in Spatial Time Series.

Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term 'event' refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: finding and exploring events that frequently co-occurred with a target class of similar events having occurred repeatedly over a period of time. This type of analysis can provide important clues for understanding the formation and spreading mechanisms of events and interdependencies among spatial locations. We propose a visual exploration framework COPE (Co-Occurrence Pattern Exploration), which allows users to extract events of interest from data and detect various co-occurrence patterns among them. Case studies and expert reviews were conducted to verify the effectiveness and scalability of COPE using two real-world datasets

Graph Mining for Object Tracking in Videos

International audienceThis paper shows a concrete example of the use of graph mining for tracking objects in videos with moving cameras and without any contextual information on the objects to track. To make the mining algorithm efficient, we benefit from a video representation based on dy- namic (evolving through time) planar graphs. We then define a number of constraints to efficiently find our so-called spatio-temporal graph pat- terns. Those patterns are linked through an occurrences graph to allow us to tackle occlusion or graph features instability problems in the video. Experiments on synthetic and real videos show that our method is effec- tive and allows us to find relevant patterns for our tracking application

Spatio-Temporal Data Mining: From Big Data to Patterns

Abstract Technological advances in terms of data acquisition enable to better monitor dynamic phenomena in various domains (areas, fields) including environment. The collected data is more and more complex -spatial, temporal, heterogeneous and multi-scale. Exploiting this data requires new data analysis and knowledge discovery methods. In that context, approaches aimed at discovering spatio-temporal patterns are particularly relevant. This paper 1 focuses on spatio-temporal data and associated data mining methods

Representation learning on heterogeneous spatiotemporal networks

“The problem of learning latent representations of heterogeneous networks with spatial and temporal attributes has been gaining traction in recent years, given its myriad of real-world applications. Most systems with applications in the field of transportation, urban economics, medical information, online e-commerce, etc., handle big data that can be structured into Spatiotemporal Heterogeneous Networks (SHNs), thereby making efficient analysis of these networks extremely vital. In recent years, representation learning models have proven to be quite efficient in capturing effective lower-dimensional representations of data. But, capturing efficient representations of SHNs continues to pose a challenge for the following reasons: (i) Spatiotemporal data that is structured as SHN encapsulate complex spatial and temporal relationships that exist among real-world objects, rendering traditional feature engineering approaches inefficient and compute-intensive; (ii) Due to the unique nature of the SHNs, existing representation learning techniques cannot be directly adopted to capture their representations. To address the problem of learning representations of SHNs, four novel frameworks that focus on their unique spatial and temporal characteristics are introduced: (i) collective representation learning, which focuses on quantifying the importance of each latent feature using Laplacian scores; (ii) modality aware representation learning, which learns from the complex user mobility pattern; (iii) distributed representation learning, which focuses on learning human mobility patterns by leveraging Natural Language Processing algorithms; and (iv) representation learning with node sense disambiguation, which learns contrastive senses of nodes in SHNs. The developed frameworks can help us capture higher-order spatial and temporal interactions of real-world SHNs. Through data-driven simulations, machine learning and deep learning models trained on the representations learned from the developed frameworks are proven to be much more efficient and effective”--Abstract, page iii

Soil fungi, but not bacteria, track vegetation reassembly across a 30-year restoration chronosequence in the northern jarrah forest, Western Australia

Plant communities have been the primary focus of ecological restoration initiatives; however, the integration of the soil microbiome has become of interest to restoration practice and theory. The inter-dependent nature of the above- and belowground biological environments has led to assumptions that reciprocal shifts in community compositions will occur in response to disturbance and restoration. Ecological restoration of post-mining landscapes within the northern jarrah forest re-instates vegetation communities that are representative of those in adjacent reference forest. The limited studies of soil microbial communities have not addressed whether these communities recover along similar trajectories to plant communities aboveground. Here, a 30-year restoration chronosequence of vegetation development was compared with that of the belowground assemblages of bacteria and fungi, identified using environmental DNA methods. Novel findings of this study highlight similarities between restoration trajectories of fungal and vegetation assemblages, though both remained distinct from reference jarrah forest compositions after 27-years. In contrast, soil bacterial assemblages in restored jarrah forest re-assembled rapidly, with substrate depth being a greater driver of composition than vegetation. Explanatory environmental variables, such as litter cover and initial fertiliser application, were significantly associated with vegetation composition. High covariance among physico-chemical factors made it difficult to establish influences of individual variables on bacterial and fungal communities. Litter depth was significantly associated with fungal composition across the restoration chronosequence, whilst available potassium was associated with both bacterial and fungal community composition. My findings add to a growing body of literature which acknowledges the rich diversity of the belowground microbial community, and the potential for their use as predictors of restoration trajectories. Future research could focus on direct associations between fungi and plant communities, such as potential for fungal inoculation to assist in the rapid reinstatement of missing plants which rely on symbiotic associations with the belowground microbiome

wildfire activity and land use drove 20th century changes in forest cover in the colorado front range

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