Spatio-Temporal Footprints

2011 ◽  
pp. 51-57
Author(s):  
Hans W. Guesgen ◽  
Stephen Marsland
Keyword(s):  
Ambient Intelligence ◽  
Smart Homes ◽  
Human Behaviour ◽  
Space And Time ◽  
Local Patterns ◽  
Spatio Temporal

The recognition of human behaviour from sensor observations is an important area of research in smart homes and ambient intelligence. In this paper, we introduce the idea of spatio-temporal footprints, which are local patterns in space and time that should be similar across repeated occurrences of the same behaviour. We discuss the spatial and temporal mapping requirements of these footprints, together with how they may be used.

Spatio-Temporal Footprints

2010 ◽  
Vol 2 (1) ◽  
pp. 52-58
Author(s):  
Hans W. Guesgen ◽  
Stephen Marsland
Keyword(s):  
Ambient Intelligence ◽  
Smart Homes ◽  
Human Behaviour ◽  
Space And Time ◽  
Local Patterns ◽  
Spatio Temporal

The recognition of human behaviour from sensor observations is an important area of research in smart homes and ambient intelligence. In this paper, we introduce the idea of spatio-temporal footprints, which are local patterns in space and time that should be similar across repeated occurrences of the same behaviour. We discuss the spatial and temporal mapping requirements of these footprints, together with how they may be used.

Human Behaviour Recognition in Ambient Intelligent Environments

2015 ◽  
pp. 107-121
Author(s):  
Hans W. Guesgen ◽  
Stephen Marsland
Keyword(s):  
Probability Theory ◽  
Ambient Intelligence ◽  
Human Behaviour ◽  
Intelligent Environments ◽  
Dempster Shafer Theory ◽  
Ambient Intelligent ◽  
Local Patterns ◽  
Spatio Temporal ◽  
Shafer Theory ◽  
Behaviour Recognition

The recognition of human behaviour from sensor observations is an important area of research in smart homes and ambient intelligence. In this chapter, the authors introduce the idea of spatio-temporal footprints, which are local patterns in space and time that should be similar across repeated occurrences of the same behaviour. They discuss the spatial and temporal mapping requirements of these footprints, together with how they may be used. As possible formalisms for implementing spatio-temporal footprints, the authors discuss and evaluate probability theory, fuzzy sets, and the Dempster-Shafer theory.

Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales – Geostatistical interpolation framework

2021 ◽  
Author(s):  
Micha Eisele ◽  
Maximilian Graf ◽  
Abbas El Hachem ◽  
Jochen Seidel ◽  
Christian Chwala ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Pearson Correlation ◽  
Urban Catchment ◽  
Space And Time ◽  
Geostatistical Interpolation ◽  
Primary Network ◽  
Ground Measurement ◽  
Catchment Areas ◽  
Important Input ◽  
Spatio Temporal

<p>Precipitation - highly variable in space and time - is the most important input for many hydrological models. As these models become more and more detailed in space and time, high-resolution input data are required. Especially for modeling and prediction in fast reacting catchments, such as urban catchment areas, a higher space-time resolution is needed than the current ground measurement networks operated by national weather services usually provide. With the increasing number and availability of opportunistic sensors such as commercial microwave links (CMLs) and personal weather stations (PWS) in recent years, new opportunities for measuring meteorological data are emerging.</p><p>We developed a geostatistical interpolation framework which allows a combination of different opportunistic sensors and their specific features and geometric properties, e.g. point and line information. In this framework, a combined kriging approach is introduced, taking into account not only the point information of a reliable primary network, e.g., from national weather services, but also the higher uncertainty of the PWS- and CML-based precipitation. The path-averaged information of the CMLs is included through a block kriging-type approach.</p><p>The methodology was applied for two 7-months periods in Germany using an hourly temporal and a 1x1 km spatial resolution. By incorporating CMLs and PWS, the Pearson correlation could be increased from 0.56 to 0.73 compared to using only primary network for interpolation. The resulting precipitation maps also provided good agreement compared to gauge adjusted radar products.</p>

scholarly journals Seasonal survival and migratory connectivity of the Eurasian Oystercatcher revealed by citizen science

The Auk ◽  
2019 ◽  
Vol 136 (1) ◽  
Author(s):  
Andrew M Allen ◽  
Bruno J Ens ◽  
Martijn Van de Pol ◽  
Henk Van der Jeugd ◽  
Magali Frauendorf ◽  
...  
Keyword(s):  
Citizen Science ◽  
Avian Species ◽  
Future Research ◽  
Migratory Connectivity ◽  
Space And Time ◽  
Demographic Rates ◽  
Breeding Populations ◽  
Spatio Temporal ◽  
Using Data ◽  
Model Structures

Abstract Migratory connectivity describes linkages between breeding and non-breeding areas. An ongoing challenge is tracking avian species between breeding and non-breeding areas and hence estimating migratory connectivity and seasonal survival. Collaborative color-ringing projects between researchers and citizen scientists provide opportunities for tracking the annual movements of avian species. Our study describes seasonal survival and migratory connectivity using data from more than 4,600 individuals with over 51,000 observations, predominantly collected by citizen scientists. Our study focuses on the Eurasian Oystercatcher (Haematopus ostralegus), a species that has experienced a substantial and ongoing decline in recent decades. Multiple threats have been described, and given that these threats vary in space and time, there is an urgent need to estimate demographic rates at the appropriate spatio-temporal scale. We performed a seasonal multi-state (5 geographical areas within The Netherlands) live- and dead-recoveries analysis under varying model structures to account for biological and data complexity. Coastal breeding populations were largely sedentary, while inland breeding populations were migratory and the direction of migration varied among areas, which has not been described previously. Our results indicated that survival was lower during winter than summer and that survival was lower in inland areas compared with coastal areas. A concerning result was that seasonal survival of individuals over-wintering in the Wadden Sea, an internationally important site for over-wintering shorebirds, appeared to decline during the study period. We discuss the outcomes of our study, and how citizen science was integral for conducting this study. Our findings identify how the demographic rates of the oystercatcher vary in space and time, knowledge that is vital for generating hypotheses and prioritizing future research into the causes of decline.

A spatio-temporal analysis of non-farm enterprise performance in Uganda: 2010-2012

2016 ◽  
Vol 7 (4) ◽  
pp. 535-546
Author(s):  
Nkechi Srodah Owoo
Keyword(s):  
Firm Performance ◽  
Information Exchange ◽  
Business Performance ◽  
Spillover Effects ◽  
Developed Countries ◽  
Enterprise Performance ◽  
Content Type ◽  
Space And Time ◽  
Knowledge Transfers ◽  
Spatio Temporal

Purpose Recent research into enterprise performance has focussed on the importance of firm proximity to total productivity. Using spatial correlation of firm performance as a proxy for knowledge transfers and diffusion, the purpose of this paper is to examine the evidence for these spatial effects in non-farm enterprise performance in Uganda, across space and time. Design/methodology/approach The author uses data from the geo-referenced Uganda National Panel Survey from 2010 to 2012, and employs explicit spatial techniques in the analysis of rural non-farm enterprise performance. Spatial autocorrelation of firm performance are used as proxies for knowledge transfers and information flows among enterprises across space and over time. Findings The study finds evidence of spatial spillover effects across space and time in Uganda. This implies that, as existing studies of developed countries have found, social infrastructure and firm proximity contribute significantly to the performance of rural economies, through information exchange and knowledge transfers. Practical implications Given the communal nature of rural households in the African setting, knowledge exchange and transfers among neighbouring firms should be encouraged as studies have found they have strong effects on business performance. Additionally, business “leaders” could also be useful in disseminating useful new technologies and applications to neighbouring enterprises in order to boost performance and productivity. Social implications There should be better targeting of policy interventions to clusters of particularly needy enterprises. Originality/value To the best of the author’s knowledge, this is the first time that spatio-temporal effects of business performance have been explored. While spatial analyses of business performance have been carried out in developed countries, studies using explicit spatial techniques in the developing country setting have been conspicuously absent.

scholarly journals Re-Arranging Space, Time and Scales in GIS: Alternative Models for Multi-Scale Spatio-Temporal Modeling and Analyses

2019 ◽  
Vol 8 (2) ◽  
pp. 72 ◽  
Author(s):  
Yi Qiang ◽  
Nico Van de Weghe
Keyword(s):  
Spatial Data ◽  
Spatial Scales ◽  
Space Time ◽  
Unified Framework ◽  
Temporal Modeling ◽  
Multi Scale ◽  
Space And Time ◽  
Pyramid Model ◽  
Temporal And Spatial ◽  
Spatio Temporal

The representations of space and time are fundamental issues in GIScience. In prevalent GIS and analytical systems, time is modeled as a linear stream of real numbers and space is represented as flat layers with timestamps. Despite their dominance in GIS and information visualization, these representations are inefficient for visualizing data with complex temporal and spatial extents and the variation of data at multiple temporal and spatial scales. This article presents alternative representations that incorporate the scale dimension into time and space. The article first reviews a series of work about the triangular model (TM), which is a multi-scale temporal model. Then, it introduces the pyramid model (PM), which is the extension of the TM for spatial data, and demonstrates the utility of the PM in visualizing multi-scale spatial patterns of land cover data. Finally, it discusses the potential of integrating the TM and the PM into a unified framework for multi-scale spatio-temporal modeling. This article systematically documents the models with alternative arrangements of space and time and their applications in analyzing different types of data. Additionally, this article aims to inspire the re-thinking of organizations of space, time, and scales in the future development of GIS and analytical tools to handle the increasing quantity and complexity of spatio-temporal data.

scholarly journals Spatio-temporal information in an artificial olfactory mucosa

2008 ◽  
Vol 464 (2092) ◽  
pp. 1057-1077 ◽  
Author(s):  
Manuel A Sánchez-Montañés ◽  
Julian W Gardner ◽  
Timothy C Pearce
Keyword(s):  
Analytical Model ◽  
Temporal Dynamics ◽  
Stationary Phases ◽  
Formal Analysis ◽  
Discrimination Performance ◽  
Olfactory Mucosa ◽  
Temporal Information ◽  
Information Measure ◽  
Space And Time ◽  
Spatio Temporal

Deploying chemosensor arrays in close proximity to stationary phases imposes stimulus-dependent spatio-temporal dynamics on their response and leads to improvements in complex odour discrimination. These spatio-temporal dynamics need to be taken into account explicitly when considering the detection performance of this new odour sensing technology, termed an artificial olfactory mucosa. For this purpose, we develop here a new measure of spatio-temporal information that combined with an analytical model of the artificial mucosa, chemosensor and noise dynamics completely characterizes the discrimination capability of the system. This spatio-temporal information measure allows us to quantify the contribution of both space and time to discrimination performance and may be used as part of optimization studies or calculated directly from an artificial mucosa output. Our formal analysis shows that exploiting both space and time in the mucosa response always outperforms the use of space alone and is further demonstrated by comparing the spatial versus spatio-temporal information content of mucosa experimental data. Together, the combination of the spatio-temporal information measure and the analytical model can be applied to extract the general principles of the artificial mucosa design as well as to optimize the physical and operating parameters that determine discrimination performance.

scholarly journals Violence in the prehistoric period of Japan: the spatio-temporal pattern of skeletal evidence for violence in the Jomon period

2016 ◽  
Vol 12 (3) ◽  
pp. 20160028 ◽  
Author(s):  
Hisashi Nakao ◽  
Kohei Tamura ◽  
Yui Arimatsu ◽  
Tomomi Nakagawa ◽  
Naoko Matsumoto ◽  
...  
Keyword(s):  
Human Nature ◽  
Human Evolution ◽  
Temporal Pattern ◽  
Selective Pressure ◽  
Human Behaviour ◽  
Hunter Gatherers ◽  
Lethal Violence ◽  
Ethnographic Data ◽  
Spatio Temporal

Whether man is predisposed to lethal violence, ranging from homicide to warfare, and how that may have impacted human evolution, are among the most controversial topics of debate on human evolution. Although recent studies on the evolution of warfare have been based on various archaeological and ethnographic data, they have reported mixed results: it is unclear whether or not warfare among prehistoric hunter–gatherers was common enough to be a component of human nature and a selective pressure for the evolution of human behaviour. This paper reports the mortality attributable to violence, and the spatio-temporal pattern of violence thus shown among ancient hunter–gatherers using skeletal evidence in prehistoric Japan (the Jomon period: 13 000 cal BC–800 cal BC). Our results suggest that the mortality due to violence was low and spatio-temporally highly restricted in the Jomon period, which implies that violence including warfare in prehistoric Japan was not common.

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