scholarly journals Space-Time Pattern Extraction in Alarm Logs for Network Diagnosis

2020 ◽  
pp. 134-153
Author(s):  
Achille Salaün ◽  
Anne Bouillard ◽  
Marc-Olivier Buob
Keyword(s):  
Space Time ◽  
Pattern Extraction ◽  
Time Pattern ◽  
Network Diagnosis

scholarly journals ANÁLISIS ESPACIO-TEMPORAL DE LA MALARIA EN MATINA, LIMÓN, COSTA RICA

2016 ◽  
Vol 1 (54) ◽  
pp. 67
Author(s):  
Silvia Argüello Vargas ◽  
Elba de la Cruz Malavassi ◽  
Marco V Herrero Acosta
Keyword(s):  
Costa Rica ◽  
Temporal Trend ◽  
Space Time ◽  
Time Analysis ◽  
Time Pattern ◽  
Future Studies ◽  
Household Level ◽  
Factores De Riesgo ◽  
Spatial Attributes ◽  
Los Grupos

<p>El objetivo de este estudio fue establecer el patrón espacio-temporal de la malaria en Matina y relacionarlo con factores ambientales. Se utilizaron tecnologías espaciales para capturar, almacenar, analizar y visualizar información relacionada con localidades y viviendas. Los atributos no espaciales fueron analizados usando pruebas paramétricas y no paramétricas. Los datos fueron obtenidos de las bases de datos de casos clínicos del Área Rectora del Ministerio de Salud en Matina. Se presentan los descriptores puntuales de las localidades positivas para los años 2005 y 2006 y en los grupos de viviendas positivo y negativo en la localidad piloto. Se propone una clasificación de áreas macroambientales en el cantón y se relaciona con la distribución de la Incidencia Parasitaria Anual (IPA). Se identificaron factores de riesgo a nivel de vivienda en la localidad piloto. Se describe la ocurrencia temporal de la actividad malárica en el cantón. El patrón espacio-temporal que se presenta en este informe puede servir de línea base para estudiar cambios que podrían ocurrir en el futuro.</p><p> </p><p>SPACE-TIME ANALYSIS OF MALARIA IN MATINA, LIMÓN, COSTA RICA</p><p><strong>ABSTRACT</strong><br /> The purpose of this study was to describe the space-time pattern of the disease, and relate it to environmental factors. Spatial technologies were used to collect, store, analyze and display information regarding locations and household locations. Non-spatial attributes were analyzed using parametric and non parametric tests. The information was obtained from databases of clinical cases form the Governing Area of the Health Ministry in Matina. Centrographic parameters were calculated for localities within Matina and for households within the pilot location. Parasitic Incidence (IPA) was associated with a proposed environmental classifiation for Matina. At the household level, risk factors were determined. The temporal pattern of the disease in Matina is described. A similar temporal trend is shown for households within the pilot location. This is the fist time that the information collected in the Matina Governing Area is used to describe the spatial patterns of malaria.<br /> This pattern will be useful as a comparative baseline for future studies.</p><p> </p><p><span><br /></span></p>

scholarly journals A spatiotemporal analysis of COVID-19 transmission in Jakarta, Indonesia for pandemic decision support

2022 ◽  
Vol 17 (s1) ◽  
Author(s):  
Agung Syetiawan ◽  
Mira Harimurti ◽  
Yosef Prihanto
Keyword(s):  
Disease Transmission ◽  
Spatiotemporal Analysis ◽  
Space Time ◽  
Policy Makers ◽  
Time Pattern ◽  
Entire Area ◽  
Comprehensive Information ◽  
National Economic ◽  
Referral Hospitals ◽  
Huge Impact

With 25% confirmed cases of the country’s total number of coronavirus disease 2019 (COVID-19) on 31 January 2021, Jakarta has the highest confirmed cases of in Indonesia. The city holds a significant role as the centre of government and national economic activity for which pandemic have had a huge impact. Spatiotemporal analysis was employed to identify the current condition of disease transmission and to provide comprehensive information on the COVID-19 outbreak in Jakarta. We applied space-time analysis to visualise the pattern of COVID-19 hotspots in each time series. We also mapped area capacity of the referral hospitals covering the entire area of Jakarta to understand the hospital service range. This research was conducted in 4 stages: i) disease mapping; ii) spatial autocorrelation analysis; iii) space-time pattern analysis; and iv) areal capacity mapping. The analysis resulted in 144 sub-districts categorised as high vulnerability. Autocorrelation studies by Moran’s I identified cluster patterns and the emerging hotspot results indicated successful interventions as the number of hotspots fell in the first period of social restrictions. The results presented should be beneficial for policy makers.

On the space-time pattern formation of the earthquake strain field

1985 ◽  
Vol 113 (1-2) ◽  
pp. 31-48 ◽  
Author(s):  
Toru Ouchi ◽  
Shinichi Goriki ◽  
Keisuke Ito
Keyword(s):  
Pattern Formation ◽  
Strain Field ◽  
Space Time ◽  
Time Pattern

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.

Analyzing Active Volcanoes in Space and Time on Jupiter's Moon, Io

2020 ◽  
Author(s):  
◽  
Morghan Montez ◽  
Keyword(s):  
Near Infrared ◽  
Pattern Mining ◽  
Time Frame ◽  
Space Time ◽  
The Other ◽  
Time Pattern ◽  
Space And Time ◽  
Concurrent Study ◽  
Point Feature ◽  
Active Volcanoes

Jupiter's moon, Io, is the most volcanic planetary object in the solar system. Io's active volcanoes have only been previously studied independently in space and time, but not simultaneously. With using geographic information systems (GIS), the ArcGIS Pro software has tools in a toolbox called Space Time Pattern Mining that can analyze these active volcanoes in a concurrent study. In order to achieve a concurrent study, the project was constructed in two parts: analysis and visualizations. The analysis part involved using the tools in the Space Time Pattern Mining toolbox provided space-time aspects that would analyze the client􀂶s database. The client􀂶s database is data from the NASA􀂶s Galileo Near-Infrared Mapping Spectrometer (NIMS) instrument that was used in the Rathbun, Lopes, and Spencer (2018) publication. As for the visualization part, this part involved using 3-D symbology from the volcano point feature, to illustrate a time frame in days of the brightness (aka volcano eruption strength) on the Local Scene map and the Globe Scene map. Both parts of the project presented compelling results that identified and achieved in seeing significant clustering in space and time concurrently, along with seeing a particular part on Io that exhibited eruptions more frequently than the other part.

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