Mining sensor datasets with spatiotemporal neighborhoods

Many spatiotemporal data mining methods are dependent on how relationships between a spatiotemporal unit and its neighbors are defined. These relationships are often termed the neighborhood of a spatiotemporal object. The focus of this paper is the discovery of spatiotemporal neighborhoods to find automatically spatiotemporal sub-regions in a sensor dataset. This research is motivated by the need to characterize large sensor datasets like those found in oceanographic and meteorological research. The approach presented in this paper finds spatiotemporal neighborhoods in sensor datasets by combining an agglomerative method to create temporal intervals and a graph-based method to find spatial neighborhoods within each temporal interval. These methods were tested on real-world datasets including (a) sea surface temperature data from the Tropical Atmospheric Ocean Project (TAO) array in the Equatorial Pacific Ocean and (b) NEXRAD precipitation data from the Hydro-NEXRAD system. The results were evaluated based on known patterns of the phenomenon being measured. Furthermore the results were quantified by performing hypothesis testing to establish the statistical significance using Monte Carlo simulations. The approach was also compared with existing approaches using validation metrics namely spatial autocorrelation and temporal interval dissimilarity. The results of these experiments show that our approach indeed identifies highly refined spatiotemporal neighborhoods

On Business Analytics: Dynamic Network Analysis for Descriptive Analytics and Multicriteria Decision Analysis for Prescriptive Analytics.

Ferry Jules. Collèges communaux. — Classement des professeurs. In: Bulletin administratif de l'instruction publique. Tome 24 n°467, 1881. pp. 836-842

Profiling and Grouping Space-time Activity Patterns of Urban Individuals

No abstract

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Bayesian networks for spatio-temporal integrated catchment assessment

Includes abstract.Includes bibliographical references (leaves 181-203).In this thesis, a methodology for integrated catchment water resources assessment using Bayesian Networks was developed. A custom made software application that combines Bayesian Networks with GIS was used to facilitate data pre-processing and spatial modelling. Dynamic Bayesian Networks were implemented in the software for time-series modelling

Understanding functional urban centrality: spatio-functional interaction and its socio-economic impact in central Shanghai

A deeper understanding of the structural characteristics of urban settings is a prerequisite to evaluating the effects of urban design and planning proposals more efficiently. This thesis aims at shaping a new, comprehensive approach to uncover the structure of cities through the investigation of a diachronic spatio-functional process and the socio-economic impacts of such a process. It proposes a spatial network-based framework, in which individual street segments, indexed by space syntax centrality measures, are utilised to develop a series of more complex urban function connectivity measures by an analysis of the spatial network and land-use patterns in tandem. The specific application of this approach in Central Shanghai is conducted with a threefold focus: firstly, to trace the evolutionary interdependence between the spatial grids and the land-use distribution; secondly, to explain the varying economic value of the spatio-functional relationship in the housing market; and thirdly, to capture the impact of the spatiol-functional interaction on the variation of co-presence. The outputs confirm that the centrality structures of the spatial network and the land-use distribution affect each other over time; however, certain degrees of inconsistency are observed, suggesting a distinct complementary relationship between these two systems, which is further validated by the improvement of the proposed model’s predictability of urban performance. The findings verify the hypothesis that urban spatio-functional synergy is a strong determinant of the formation of urban function regions, the delineation of housing submarkets, and the discrepancy of the spatial co-presence in the city. These results demonstrate that urban performance is directly affected by the way the spatial and functional structures of the city interact. Such findings support the proposition that understanding the complexities of the spatio-functional interaction in a morphological analysis can enhance the efficiency of urban design and planning interventions, which aim to improve socioeconomic conditions in cities

Evolution and Learning in Heterogeneous Environments

A real-world environment is complex and non-uniform, varying over space and time. This thesis demonstrates the impact of such environmental heterogeneity upon the ways in which organisms acquire information about the world, via a series of individual-based computational models that apply progressively more detailed forms of environmental structure to understand the causal impact of four distinct environmental factors: temporal variability; task complexity; population structure; and spatial heterogeneity. We define a baseline model, comprised of an evolving population of polygenic individuals that can follow three learning modes: innate behaviour, in which an organism acts according to its genetically-encoded traits; individual learning, in which an organism engages in trial-and-error to modify its inherited behaviours; and social learning, in which an individual mimics the behaviours of its peers. This model is used to show that environmental variability and task complexity affect the adaptive success of each learning mode, with social learning only arising as a dominant strategy in environments of median variability and complexity. Beyond a certain complexity threshold, individual learning is shown to be the sole dominant strategy. Social learning is shown to play a beneficial role following a sudden environmental change, contributing to the dissemination of novel traits in a population of poorly-adapted individuals. Introducing population structure in the form of a k-regular graph, we show that bounded and rigid neighbourhood relationships can have deleterious effects on a population, diminishing its evolutionary rate and equilibrium fitness, and, in some cases, preventing the population from crossing a fitness valley to a global optimum. A larger neighbourhood size is shown to increase the effectiveness of social learning, and results in a more rapid evolutionary convergence rate. The research subsequently focuses on spatially heterogeneous environments, proposing a new method of constructing an environment characterised by two key metrics derived from landscape ecology, “patchiness” and “gradient”. We show that spatial complexity slows the rate of genetic adaptation when movement is restricted, but can increase the rate of evolution for mobile individuals. Social learning is shown to be particularly beneficial within heterogeneous environments, particularly when mobility is restricted, suggesting that phenotypic plasticity may act as a substitute for mobility