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 Unfolding Events in Space and Time: Geospatial Insights into COVID-19 Diffusion in Washington State during the Initial Stage of the Outbreak

2020 ◽  
Vol 9 (6) ◽  
pp. 382 ◽  
Author(s):  
Vaishnavi Thakar
Keyword(s):  
Spatial Data ◽  
The United States ◽  
Space Time ◽  
Washington State ◽  
Data Availability ◽  
Time Pattern ◽  
Web Mapping ◽  
Space And Time ◽  
First Case ◽  
Initial Stage

The world witnessed the COVID-19 pandemic in 2020. The first case of COVID-19 in the United States of America (USA) was confirmed on 21 January 2020, in Snohomish County in Washington State (WA). Following this, a rapid explosion of COVID-19 cases was observed throughout WA and the USA. Lack of access to publicly available spatial data at finer scales has prevented scientists from implementing spatial analytical techniques to gain insights into the spread of COVID-19. Datasets were available only as counts at county levels. The spatial response to COVID-19 using coarse-scale publicly available datasets was limited to web mapping applications and dashboards to visualize infected cases from state to county levels only. This research approaches data availability issues by creating proxy datasets for COVID-19 using publicly available news articles. Further, these proxy datasets are used to perform spatial analyses to unfolding events in space and time and to gain insights into the spread of COVID-19 in WA during the initial stage of the outbreak. Spatial analysis of theses proxy datasets from 21 January to 23 March 2020, suggests the presence of a clear space–time pattern. From 21 January to 6 March, a strong presence of community spread of COVID-19 is observed only in close proximity of the outbreak source in Snohomish and King Counties, which are neighbors. Infections diffused to farther locations only after a month, i.e., 6 March. The space–time pattern of diffusion observed in this study suggests that implementing strict social distancing measures during the initial stage in infected locations can drastically help curb the spread to distant locations.

scholarly journals Space-Time Covariance Structures and Models

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Wanfang Chen ◽  
Marc G. Genton ◽  
Ying Sun
Keyword(s):  
Climate Models ◽  
Space Time ◽  
Covariance Structures ◽  
Annual Review ◽  
Publication Date ◽  
Monitoring Networks ◽  
Data Set ◽  
Temporal Modeling ◽  
Covariance Models ◽  
Spatio Temporal

In recent years, interest has grown in modeling spatio-temporal data generated from monitoring networks, satellite imaging, and climate models. Under Gaussianity, the covariance function is core to spatio-temporal modeling, inference, and prediction. In this article, we review the various space-time covariance structures in which simplified assumptions, such as separability and full symmetry, are made to facilitate computation, and associated tests intended to validate these structures. We also review recent developments on constructing space-time covariance models, which can be separable or nonseparable, fully symmetric or asymmetric, stationary or nonstationary, univariate or multivariate, and in Euclidean spaces or on the sphere. We visualize some of the structures and models with visuanimations. Finally, we discuss inference for fitting space-time covariance models and describe a case study based on a new wind-speed data set. Expected final online publication date for the Annual Review of Statistics, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Inverse modeling of CO<sub>2</sub> sources and sinks using satellite data: a synthetic inter-comparison of measurement techniques and their performance as a function of space and time

2004 ◽  
Vol 4 (2) ◽  
pp. 523-538 ◽  
Author(s):  
S. Houweling ◽  
F.-M. Breon ◽  
I. Aben ◽  
C. Rödenbeck ◽  
M. Gloor ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spatial Scales ◽  
Measurement Techniques ◽  
Data Retrieval ◽  
Sources And Sinks ◽  
Space And Time ◽  
Infra Red ◽  
Temporal And Spatial ◽  
Source And Sink ◽  
Satellite Instruments

Abstract. Currently two polar orbiting satellite instruments measure CO2 concentrations in the Earth's atmosphere, while other missions are planned for the coming years. In the future such instruments might become powerful tools for monitoring changes in the atmospheric CO2 abundance and to improve our quantitative understanding of the leading processes controlling this. At the moment, however, we are still in an exploratory phase where first experiences are collected and promising new space-based measurement concepts are investigated. This study assesses the potential of some of these concepts to improve CO2 source and sink estimates obtained from inverse modelling. For this purpose the performance of existing and planned satellite instruments is quantified by synthetic simulations of their ability to reduce the uncertainty of the current source and sink estimates in comparison with the existing ground-based network of sampling sites. Our high resolution inversion of sources and sinks (at 8°x10°) allows us to investigate the variation of instrument performance in space and time and at various temporal and spatial scales. The results of our synthetic tests clearly indicate that the satellite performance increases with increasing sensitivity of the instrument to CO2 near the Earth's surface, favoring the near infra-red technique. Thermal infrared instruments, on the contrary, reach a better global coverage, because the performance in the near infrared is reduced over the oceans owing to a low surface albedo. Near infra-red sounders can compensate for this by measuring in sun-glint, which will allow accurate measurements over the oceans, at the cost, however, of a lower measurement density. Overall, the sun-glint pointing near infrared instrument is the most promising concept of those tested. We show that the ability of satellite instruments to resolve fluxes at smaller temporal and spatial scales is also related to surface sensitivity. All the satellite instruments performed relatively well over the continents resulting mainly from the larger prior flux uncertainties over land than over the oceans. In addition, the surface networks are rather sparse over land increasing the additional benefit of satellite measurements there. Globally, challenging satellite instrument precisions are needed to compete with the current surface network (about 1ppm for weekly and 8°x10° averaged SCIAMACHY columns). Regionally, however, these requirements relax considerably, increasing to 5ppm for SCIAMACHY over tropical continents. This points not only to an interesting research area using SCIAMACHY data, but also to the fact that satellite requirements should not be quantified by only a single number. The applicability of our synthetic results to real satellite instruments is limited by rather crude representations of instrument and data retrieval related uncertainties. This should receive high priority in future work.

СПЕЦИФИКА ФОРМИРОВАНИЯ ПЕРЦЕПТУАЛЬНОГО ПРОСТРАНСТВА И ВРЕМЕНИ В РАССКАЗЕ И. С. ТУРГЕНЕВА «СОН»

2020 ◽  
Author(s):  
Elena Skudnyakova
Keyword(s):  
Space Time ◽  
Perceptual Space ◽  
Main Character ◽  
Space And Time ◽  
The Fantastic ◽  
Spatio Temporal

В статье рассматривается специфика формирования перцептуального пространства и времени в рассказе И. С. Тургенева «Сон». Автор доказывает, что специфика обусловлена присутствием в произведении категории фантастического и особого типа личности главного героя, который является носителем фантастического (таинственные сны). Установлено, что пространственно-временные искажения перцептуальной сферы происходят в его сознании и подсознании. В ходе анализа выявлено, что сфера перцептуального пространства-времени сначала расширяется (фантастический сон - сон-галлюцинация - сон наяву), а потом органично включается в реальную действительность. Подчеркивается особая значимость сна-галлюцинации, который активизирует динамику последующего событийного ряда.The article discusses the formation of perceptual space and time in the story of I. S. Turgenev «Dream». The author proves that the specificity is due to the presence of the category of fantastic in the work and a special type of personality of the main character, who is the bearer of the fantastic (mysterious dreams). It has been established that the spatio-temporal distortions of the perceptual sphere occur in the consciousness and subconscious of the protagonist. The analysis revealed that the sphere of perceptual space-time first expands (a fantastic dream - a dream-hallucination - a dream in reality), and then it is naturally included in reality. The author stresses the significance of sleep-hallucination which activates the dynamics of the subsequent series of events.

Using Earth Observations to engender a social-ecological systems perspective on rural livelihoods and wellbeing.

2020 ◽  
Author(s):  
Peter Hargreaves ◽  
Gary Watmough
Keyword(s):  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Spatial Scales ◽  
Earth Observation ◽  
Ecological Systems ◽  
Social Ecological Systems ◽  
Social Ecological ◽  
Development Goals ◽  
Temporal And Spatial ◽  
Spatio Temporal

&lt;p&gt;An estimated 70% of the world&amp;#8217;s poorest people live in rural spaces. There is a consistent differentiation between rural and urban contexts, where the former are typically characterised by weak infrastructure, limited services and social marginalisation. At the same time, the world&amp;#8217;s poorest people are most vulnerable to global change impacts. Historic pathways to measuring and achieving poverty reduction must be adapted for an era of increasingly dynamic change, where spatio-temporal blind spots preclude a comprehensive understanding of poverty and its manifestation in rural developing contexts. To catalyse an effective poverty eradication narrative, we require a characterisation of the spatio-temporal anatomy of poverty metrics. To achieve this, researchers and practitioners must develop tools and mobilise data sources that enable the detection and visualisation of economic and social dimensions of rural spaces at finer temporal and spatial scales than is currently practised. This can only be realised by integrating new technologies and non-traditional sources of data alongside conventional data to engender a novel policy landscape.&lt;/p&gt;&lt;p&gt;Cue Earth Observation: the only medium through which data can be gathered that is global in its coverage but also available across multiple temporal and spatial scales. Earth Observation (EO) data (collected from satellite, airborne and in-situ remote sensors) have a demonstrable capacity to inform, update, situate and provide the necessary context to design evidence-based policy for sustainable development. This is particularly important for the Sustainable Development Goals (SDGs) because the nested indicators are based on data that can be visualised, and many have a definitive geospatial component, which can improve national statistics reporting.&lt;/p&gt;&lt;p&gt;In this review, we present a rubric for integrating EO and geospatial data into rural poverty analysis. This aims to provide a foundation from which researchers at the interface of social-ecological systems can unlock new capabilities for measuring economic, environmental and social conditions at the requisite scales and frequency for poverty reporting and also for broader livelihoods and development research. &amp;#160;We review satellite applications and explore the development of EO methodologies for investigating social-ecological conditions as indirect proxies of rural wellbeing. This is nested within the broader sustainable development agenda (in particular the SDGs) and aims to set out what our capabilities are and where research should be focused in the near-term. In short, elucidating to a broad audience what the integration of EO can achieve and how developing social-ecological metrics from EO data can improve evidence-based policymaking.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Key words:&lt;/strong&gt; Earth Observation; Poverty; Livelihoods; Sustainable Development Goals; Remote Sensing&lt;/p&gt;

Research on Data Assimilation Methods of Multi-Space-Time Meteroligical and Hydrological Data

2013 ◽  
Vol 659 ◽  
pp. 118-122
Author(s):  
Lu Kong ◽  
Ying Zi Song ◽  
Da Ming You
Keyword(s):  
Data Assimilation ◽  
Data Integration ◽  
Spatial Data ◽  
Hydrological Model ◽  
Semantic Space ◽  
Space Time ◽  
Key Technology ◽  
Hydrological Data ◽  
Multi Scale ◽  
Spatial Data Integration

Meteorological and hydrological data has the feature of multi-semantic space, multi-space-time, multi-scale and it has a diversed means to be acquired and storaged, which brings the diversity of multi-origin characteristics. This paper will adopt the method of data assimilation to study a variety of data models with different scales and the key technology the grid resolution spatial data integration in order to establish a meteorological and hydrological model of multi-source spatial data assimilation.

scholarly journals Inverse modeling of CO<sub>2</sub> sources and sinks using satellite data: A synthetic inter-comparison of measurement techniques and their performance as a function of space and time

2003 ◽  
Vol 3 (5) ◽  
pp. 5237-5274
Author(s):  
S. Houweling ◽  
F.-M. Breon ◽  
I. Aben ◽  
C. Rödenbeck ◽  
M. Gloor ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spatial Scales ◽  
Measurement Techniques ◽  
Data Retrieval ◽  
Sources And Sinks ◽  
Space And Time ◽  
Infra Red ◽  
Temporal And Spatial ◽  
Source And Sink ◽  
Satellite Instruments

Abstract. Currently two polar orbiting satellite instruments measure CO2 concentrations in the Earth's atmosphere, while other missions are planned for the coming years. In the future such instruments might become powerful tools for monitoring changes in the atmospheric CO2 abundance and to improve our quantitative understanding of the leading processes controlling this. At the moment, however, we are still in an exploratory phase where first experiences are collected and promising new space-based measurement concepts are investigated. This study assesses the potential of some of these concepts to improve CO2 source and sink estimates obtained from inverse modelling. For this purpose the performance of existing and planned satellite instruments is quantified by synthetic simulations of their ability to reduce the uncertainty of the current source and sink estimates in comparison with the existing ground-based network of sampling sites. Our high resolution inversion of sources and sinks (at 8º x 10º allows us to investigate the variation of instrument performance in space and time and at various temporal and spatial scales. The results of our synthetic tests clearly indicate that the satellite performance increases with increasing sensitivity of the instrument to CO2 near the Earth's surface, favoring the near infra-red technique. Thermal infrared instruments, on the contrary, reach a better global coverage, because the performance in the near infrared is reduced over the oceans owing to a low surface albedo. Near infra-red sounders can compensate for this by measuring in sun-glint, which will allow accurate measurements over the oceans, at the cost, however, of a lower measurement density. Overall, the sun-glint pointing near infrared instrument is the most promising concept of those tested. We show that the ability of satellite instruments to resolve fluxes at smaller temporal and spatial scales is also related to surface sensitivity. All the satellite instruments performed relatively well over the continents resulting mainly from the larger prior flux uncertainties over land than over the oceans. In addition, the surface networks are rather sparse over land increasing the additional benefit of satellite measurements there. Globally, rather challenging satellite instrument precisions are needed to compete with the surface network (about 1 ppmv for weekly and 8° × 10° averaged SCIAMACHY columns). Regionally, however, these requirements relax considerably, increasing to 5 ppmv for SCIAMACHY over tropical continents. This points not only to an interesting research area using SCIAMACHY data, but also to the fact that satellite requirements should not be quantified by only a single number. The applicability of our synthetic results to real satellite instruments is limited by rather crude representations of instrument and data retrieval related uncertainties. This should receive high priority in future work.

A brief history of the analysis of crime concentration

2010 ◽  
Vol 21 (4-5) ◽  
pp. 349-370 ◽  
Author(s):  
SHANE D. JOHNSON
Keyword(s):  
Resource Allocation ◽  
Recent Work ◽  
Spatial Scales ◽  
Space Time ◽  
Crime Concentration ◽  
Crime Prediction ◽  
Time Patterns ◽  
Formal Mathematics ◽  
History Of ◽  
Spatio Temporal

Decades of research demonstrate that crime is concentrated at a range of spatial scales. Such findings have clear implications for crime forecasting and police resource allocation models. More recent work has also shown that crime clusters in space and time with a regularity that might improve methods of crime prediction. In this paper I review some of the available evidence and provide illustrations of the types of analysis – spatial and spatio-temporal – conducted hitherto. With a few exceptions, the application of formal Mathematics in the study of space–time patterns of crime has been rather limited, and so a central aim of the paper is to stimulate interest in this area of research.

Temporal and spatial variation of catastrophic river floodings in the Lower Yellow River from AD 960 to 1938

The Holocene ◽  
2017 ◽  
Vol 27 (9) ◽  
pp. 1359-1369 ◽  
Author(s):  
Xiangping Zhang ◽  
Xiuqi Fang
Keyword(s):  
Yellow River ◽  
Spatial Scales ◽  
Alluvial Fan ◽  
Lower Yellow River ◽  
The Lower Yellow River ◽  
Spatial Changes ◽  
Temporal And Spatial ◽  
Spatio Temporal ◽  
Downstream Movement ◽  
Past 1000 Years

This study is intended to investigate the patterns for the temporal and spatial changes of catastrophic river floodings which took place in the Lower Yellow River, based on the available records collected from historical documents, and rearranged in a GIS database. A series of catastrophic river floodings from AD 960 to 1938 was reconstructed, and their temporal and spatial variations were analyzed, which leads to the conclusions, among others. (1) The increasing trend of frequency of catastrophic river floodings in the Lower Yellow River is not so significant in the past 1000 years. (2) Most dike breachings and overtoppings occurred near the apex of the Yellow River Alluvial Fan, and the number of dike breaching and overtopping was gradually reduced as the elevation decreased. (3) Under different spatio-temporal backgrounds, dike breaching and overtopping developed either downstream or upstream, which is evidenced by both the downstream movement for large temporal and spatial scales in dike breaching and overtopping places in AD 1128–1344 and 1391–1447 and the upstream movement for small temporal and spatial scales in AD 960–969, 1730–1761, and 1807–1819.

scholarly journals The implications of disconnectivity for the study of contemporary geomorphic processes

2017 ◽  
Vol 19 (1) ◽  
pp. 5-15
Author(s):  
SLAYMAKER Olav
Keyword(s):  
Human Activities ◽  
Spatial Scales ◽  
Scale Problem ◽  
Conservation Of Mass ◽  
Natural Processes ◽  
The Past ◽  
Temporal And Spatial ◽  
Spatio Temporal ◽  
Geomorphic Processes

The emphasis on the understanding of contemporary geomorphic processes that has dominated Anglophone geomorphological literature over the past 50 years has seen huge progress but also some set-backs. We now have reliable measurements of mean rates of operation of all subaerial processes responsible for modification of landforms and landscapes and have made good progress in estimating the role of human activities as compared with “natural” processes. Some limited progress has been achieved in understanding the scale problem but problems remain. Perhaps the single most surprising development has been the recognition of the ubiquity of disconnectivity in geomorphic systems, the need to calculate virtual velocities of whole geomorphic systems and the relevance of this understanding to the general spatio-temporal scale problem. We have always known that most geomorphic processes operate intermittently but we have continued to depend on models that imply that mass and energy move freely through geomorphic systems and that conservation of mass and energy occurrs uninterruptedly at all temporal and spatial scales.

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