scholarly journals Shape Identification in Temporal Data Sets

2012 ◽  
pp. 305-321 ◽  
Author(s):  
Machon Gregory ◽  
Ben Shneiderman
Keyword(s):  
Data Sets ◽  
Temporal Data ◽  
Shape Identification

scholarly journals Measuring Similarity of Deforestation Patterns in Time and Space across Differences in Resolution

Geomatics ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 464-495
Author(s):  
Desi Suyamto ◽  
Lilik Prasetyo ◽  
Yudi Setiawan ◽  
Arief Wijaya ◽  
Kustiyo Kustiyo ◽  
...  
Keyword(s):  
Geometric Feature ◽  
Data Sets ◽  
Temporal Data ◽  
Spatial And Temporal Patterns ◽  
Data Set ◽  
Point Matching ◽  
Point Patterns ◽  
Visual Sensing ◽  
Point Data ◽  
Point To Point

This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis as an alternative to conventional approaches, which typically utilize straightforward point-to-point matching. Using our approach, in each point data set, two geometric features (i.e., the distance and angle from the centroid) were calculated and represented as probability density functions (PDFs). The PDF similarity of each geometric feature was measured using nine metrics, with values ranging from zero (very contrasting) to one (exactly the same). The overall similarity was defined as the average of the distance and angle similarities. In terms of sensibility, the method was shown to be capable of measuring, at a human visual sensing level, two pairs of hypothetical patterns, presenting reasonable results. Meanwhile, in terms of the method′s sensitivity to both spatial and temporal displacements from the hypothetical origin, the method is also capable of consistently measuring the similarity of spatial and temporal patterns. The application of the method to assess both spatial and temporal pattern similarities between two deforestation data sets with different resolutions was also discussed.

Quantifying Urban Sprawl With Spatial Autocorrelation Techniques Using Multi-Temporal Satellite Data

2019 ◽  
pp. 1624-1644
Author(s):  
Gabriele Nolè ◽  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Beniamino Murgante
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Urban Expansion ◽  
Data Sets ◽  
Temporal Data ◽  
Landsat Images ◽  
Urban Settlements ◽  
Multi Temporal ◽  
Soil Consumption

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

scholarly journals Connection Between Optical and High Frequency Radio Variability in AGN

1994 ◽  
Vol 159 ◽  
pp. 411-411
Author(s):  
M. Tornikoski ◽  
E. Valtaoja ◽  
A.G. Smith ◽  
A.D. Nair
Keyword(s):  
High Frequency ◽  
Optical Data ◽  
Research Station ◽  
Radio Sources ◽  
Data Sets ◽  
Temporal Data ◽  
Extragalactic Radio Sources ◽  
Radio Data

We have been searching for correlated optical and radio variability in large temporal data sets of 22 extragalactic radio sources. The optical data were obtained with the 76-cm reflector at the Rosemary Hill Observatory in Florida, USA. The radio data were obtained at two different sites: 22, 37 and some of the 90 GHz data at the Metsähovi Radio Research Station, Finland, and 90 and 230 GHz data at the Swedish-ESO Submillimetre Telescope (SEST) on La Silla, Chile. Because the SEST data unfortunately reaches only back to 1988, the 90 and 230 GHz data were complemented by the IRAM data from Steppe et al. (A&AS 75, 1988 and A&AS 96, 1992).

CLASSIFYING TEMPORAL MICROARRAY DATA BY SELECTING INFORMATIVE GENES

2013 ◽  
Vol 11 (03) ◽  
pp. 1341006
Author(s):  
QIANG LOU ◽  
ZORAN OBRADOVIC
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Microarray Data ◽  
Data Sets ◽  
Temporal Data ◽  
Expression Data ◽  
Selection Methods ◽  
Temporal Gene Expression ◽  
Single Matrix

In order to more accurately predict an individual's health status, in clinical applications it is often important to perform analysis of high-dimensional gene expression data that varies with time. A major challenge in predicting from such temporal microarray data is that the number of biomarkers used as features is typically much larger than the number of labeled subjects. One way to address this challenge is to perform feature selection as a preprocessing step and then apply a classification method on selected features. However, traditional feature selection methods cannot handle multivariate temporal data without applying techniques that flatten temporal data into a single matrix in advance. In this study, a feature selection filter that can directly select informative features from temporal gene expression data is proposed. In our approach, we measure the distance between multivariate temporal data from two subjects. Based on this distance, we define the objective function of temporal margin based feature selection to maximize each subject's temporal margin in its own relevant subspace. The experimental results on synthetic and two real flu data sets provide evidence that our method outperforms the alternatives, which flatten the temporal data in advance.

scholarly journals Monitoring of the ice caps area changes on Galindez, Winter and Skua Islands (Argentine Islands, West Antarctica)

2020 ◽  
pp. 42-49
Author(s):  
І. Savchyn ◽  
◽  
Ye. Shylo ◽  
Keyword(s):  
Data Sets ◽  
Temporal Data ◽  
Single System ◽  
West Antarctica ◽  
Linear Rate ◽  
Ice Caps ◽  
Basic Parameters ◽  
Spatio Temporal ◽  
The Antarctic

Due to global warming, the glaciers and ice systems of Antarctica and the Antarctic Peninsula have been significantly changing in shape and size in recent decades. Therefore, to control, forecast and prevent such processes, it is necessary to constantly monitor and analyse changes in the basic parameters of glaciers and ice systems. This paper proposes a study of changes in the area of ice caps located on the Galindez, Winter and Skua islands (Argentine Islands, West Antarctica). The study is based on the integration of different spatio-temporal datasets into a single system for retrospective geographical monitoring of changes in the area of Galindez, Winter and Skua islands ice caps. The system for integrating space-time datasets is the UTM coordinate system (zone 20, South). Using transformed archival cartographic materials, as well as recently obtained orthophotos, the boundaries of the glaciers in different periods of research were digitized. Based on the identified boundaries, the significance of changes in the area of island glaciers, as well as the rate of their change during 1935–2019, were determined. Based on the integration of different spatio-temporal datasets into a single system, retrospective-geographical monitoring of changes in icecaps area during 1935–2019 was performed. The ice caps were found to be experiencing systematic decrease in area with average linear rate of decrease from –0.30%/year to –0.37%/year. A detailed analysis of all integrated spatio-temporal data sets including determination of the cause of changes in the area of the Galindez, Winter and Skua islands' ice caps is a promising topic for further research.

Development of Spatial-Temporal Pipeline Integrity and Risk Management System Based on 4 Dimensional GIS (4D-GIS)

2008 ◽  
Author(s):  
Kazuaki Iwamura ◽  
Akira Mochizuki ◽  
Yoshiki Kakumoto ◽  
Eiji Toyama ◽  
Shoei Takahashi
Keyword(s):  
Risk Management ◽  
Management System ◽  
Data Sets ◽  
Temporal Data ◽  
Risk Management System ◽  
New Type ◽  
Construction Specification ◽  
Safety Diagnosis ◽  
Health Diagnosis ◽  
Corrosion Data

A new type pipeline integrity and risk management system based on 4D-GIS (4Dimensional Geographic Information System) is proposed in this paper. 4D-GIS is a unique GIS platform which can manage 3D plus time change data and processes spatial-temporal pipeline integrity data effectively. The utilization purposes of the developed system are: 1) To understand the current/future physical status of pipeline, 2) To detect anomalies of pipelines and to propose solutions for replacing or repair, 3) To transport natural gas safely and stably. For 1), the methods for spatial-temporal data integration and data input are proposed. The developed system integrates a variety of pipeline integrity data sets such as pipeline maps, construction specification, documents, corrosion data, and cathodic protection voltage data. For 2), two new type diagnosis functions, which process integrated data, are proposed. One is the pipe safety diagnosis based on corrosion spreading prediction. The other one is insulation health diagnosis. For 3), stress distribution on corrosion and HCA (High Consequence Areas) is calculated. Thus, the system can be utilized for the decision making for pipeline-specific- problem solving by both pipeline operators and field pipeline managers. The processing results for realized functions are shown. Furthermore, the architecture of pipeline integrity and risk management system is described.

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