scholarly journals Intelligent Drought Tracking for its Use in Machine Learning: Implementation and First Results

10.29007/klgg ◽  
2018 ◽  
Author(s):  
Vitali Diaz ◽  
Gerald A. Corzo Perez ◽  
Henny A.J. Van Lanen ◽  
Dimitri Solomatine
Keyword(s):  
Machine Learning ◽  
Temporal Dynamics ◽  
Temporal Analysis ◽  
Drought Monitoring ◽  
Grid Data ◽  
Drought Prediction ◽  
Documented Information ◽  
First Results ◽  
Spatio Temporal ◽  
Over Time

Due to the underlying characteristics of drought, monitoring of its spatio-temporal development is difficult. Last decades, drought monitoring have been increasingly developed, however, including its spatio-temporal dynamics is still a challenge. This study proposes a method to monitor drought by tracking its spatial extent. A methodology to build drought trajectories is introduced, which is put in the framework of machine learning (ML) for drought prediction. Steps for trajectories calculation are (1) spatial areas computation, (2) centroids localization, and (3) centroids linkage. The spatio- temporal analysis performed here follows the Contiguous Drought Area (CDA) analysis. The methodology is illustrated using grid data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor over India (1901-2013), as an example. Results show regions where drought with considerable coverage tend to occur, and suggest possible concurrent routes. Tracks of six of the most severe reported droughts were analysed. In all of them, areas overlap considerably over time, which suggest that drought remains in the same region for a period of time. Years with the largest drought areas were 2000 and 2002, which coincide with documented information presented. Further research is under development to setup the ML model to predict the track of drought.

scholarly journals Word embeddings quantify 100 years of gender and ethnic stereotypes

2018 ◽  
Vol 115 (16) ◽  
pp. E3635-E3644 ◽  
Author(s):  
Nikhil Garg ◽  
Londa Schiebinger ◽  
Dan Jurafsky ◽  
James Zou
Keyword(s):  
United States ◽  
Machine Learning ◽  
Temporal Dynamics ◽  
Temporal Analysis ◽  
The United States ◽  
English Word ◽  
Attitudes Toward Women ◽  
Word Embeddings ◽  
Asian Immigration ◽  
Over Time

Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societal shifts—e.g., the women’s movement in the 1960s and Asian immigration into the United States—and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science.

scholarly journals Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China

2020 ◽  
Vol 17 (7) ◽  
pp. 2563 ◽  
Author(s):  
Wentao Yang ◽  
Min Deng ◽  
Chaokui Li ◽  
Jincai Huang
Keyword(s):  
Temporal Dynamics ◽  
Spatial Interaction ◽  
Temporal Trends ◽  
Temporal Patterns ◽  
Temporal Analysis ◽  
Hubei Province ◽  
Incidence Rates ◽  
County Level ◽  
Spatial Clusters ◽  
Spatio Temporal

Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann–Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran’s I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.

Fog climatology analyses in coastal fog ecosystem at the Atacama Desert/Chile – spatio-temporal analysis of fog water characteristics and variability

2020 ◽  
Author(s):  
Juan Carlos Pastene ◽  
Alexander Siegmund ◽  
Camilo del Río ◽  
Pablo Osses
Keyword(s):  
Temporal Dynamics ◽  
Local Level ◽  
Global Radiation ◽  
Atacama Desert ◽  
Temporal Analysis ◽  
Local Network ◽  
Fog Water ◽  
Water Characteristics ◽  
Vertical Range ◽  
Spatio Temporal

<p>The coastal Chilean Atacama Desert comprise some of the driest areas of the world with anual mean precipitation partly less than 1 mm/year, like in the Tarapacá region. It is in these environments, where fog plays a relevant role for local ecosystems, like the so called <em>Tillandsia</em> Lomas. These fog ecosystems contain <em>Tillandsia landbeckii</em> as an endemic species, which covers a vertical range of about 800 to 1,250 m, related to fog availability. The study area “Oyarbide” (20°29’ S, 70°03’ W) is situated inland desert, over a range of 300 m elevation where the advective and orographic fog penetrate far enough to reach the east border of the site at around 1,200 m.</p> <p>On local level, the understanding of the fog climate characteristics and variability is still poor as well as knowledge about the driving parameters, the temporal dynamics and spatial gradients. For this reason, various parameters of fog climate are analysed and characterised on the basis of a local station network in order to determine the local fog climatology.</p> <p>From 2016, several high quality climatological stations (Thies Clima) were installed in “Oyarbide”, located in a transect from ca. 1,160 m to ca. 1,350 m in a distance between 10.3 km to 10.7 km from the coast. The local network of climate stations is generating a high temporal and spatial acquisition of climatological data of standard fog water (2 m), air temperature & humidity (2 m), surface temperature (5 cm), wind speed & direction (10 m & 2 m), air pressure, global radiation, leaf wetness and dew every 10 minutes until nowadays. Additionally, ten mini fog collectors (Mini FCs) were installed at the beginning 2019, covering a surface of ca. 3 km<sup>2</sup>, generating a monthly data of ground fog water collected (50 cm).</p> <p>First spatio-temporal analyses of different parameters of the local fog climate will be presented. The results of the study show a seasonal, monthly and daily variability, with altitudinal and vertical differences and oscillation. The results will serve as input for the understanding of the fog variability into hyperarid zones.</p>

Towards the estimation of DOC from space in the open ocean

2020 ◽  
Author(s):  
Ana Gabriela Bonelli ◽  
Hubert Loisel ◽  
Vincent Vantrepotte ◽  
Daniel Jorge ◽  
Antoine Mangin ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Temporal Dynamics ◽  
Open Ocean ◽  
Colored Dissolved Organic Matter ◽  
Ocean Water ◽  
Chl A ◽  
First Results ◽  
Spatio Temporal ◽  
Input Variables ◽  
Global Carbon

<p>The Dissolved Organic Carbon (DOC) represents the largest pool of organic carbon and the most active carbon compartment in the ocean. Describing the spatio-temporal dynamics of the oceanic DOC in response to variation in the physical of biological forcings is therefore crucial for better understanding the global carbon cycle. The DOC distribution and its temporal dynamics is however currently not well known.</p><p>In the recent years several works have demonstrated the possibility to assess from space the DOC distribution in the coastal ocean thanks to direct relationships between DOC and the optical properties of colored dissolved organic matter (CDOM). Such CDOM-DOC relationships are not applicable for the open ocean water due making more complex the DOC estimation from space in the latter environments. Here we present first results documenting an alternative method for estimating DOC from satellite imagery which rely on the use of a neural network which combines different physical and biogeochemical input variables (SST, SSS, PAR, aCDOM and Chl-a).</p>

scholarly journals Modelling Local Patterns of Children Mortality. A Bayesian Spatio-Temporal Analysis.

2019 ◽  
Author(s):  
Alejandro Lome-Hurtado ◽  
Jacques Lartigue Mendoza ◽  
Juan Carlos Trujillo
Keyword(s):  
High Risk ◽  
Mortality Risk ◽  
Temporal Analysis ◽  
Health Concern ◽  
North West ◽  
The North ◽  
Policy Interventions ◽  
Medium Risk ◽  
Spatio Temporal ◽  
Over Time

Abstract Background: The number of death children at the international scale are still high, but with proper spatially-targeted health public policies this number could be reduced. In Mexico, children mortality is a particular health concern due to its alarming rate all throughout North America. The aims of this study are i) to model the change of children mortality risk at the municipality level, (ii) to identify municipalities with high, medium and low risk over time and (iii) to ascertain potential high-risk municipalities across time, using local trends of each municipality in Greater Mexico City. Methods: The study uses Bayesian spatio-temporal analysis to control for space-time patterns of data. This allow to model the geographical variation of the municipalities within the time span studied. Results: The analysis shows that most of the high-risk municipalities are in the north, west, and some in the east; some of such municipalities show an increasing children mortality risk over time. The outcomes highlight some municipalities which show a medium risk currently but are likely to become high risk along the study period. Finally, the odds of children mortality risk illustrate a decreasing tendency over the 7-year framework. Conclusions: Identification of high-risk municipalities may provide a useful input to policy-makers seeking out to reduce the incidence of children mortality, since it would provide evidence to support geographical targeting for policy interventions.

scholarly journals Spatio-temporal dynamics of dengue in Brazil: seasonal travelling waves and determinants of regional synchrony

2018 ◽  
Author(s):  
Mikhail Churakov ◽  
Christian J. Villabona-Arenas ◽  
Moritz U.G. Kraemer ◽  
Henrik Salje ◽  
Simon Cauchemez
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Mosquito Control ◽  
Human Mobility ◽  
Travelling Waves ◽  
Seasonal Pattern ◽  
Temporal Analysis ◽  
Vector Ecology ◽  
Spatio Temporal ◽  
Dengue Epidemics

AbstractDengue continues to be the most important vector-borne viral disease globally and in Brazil, where more than 1.4 million cases and over 500 deaths were reported in 2016. Mosquito control programmes and other interventions have not stopped the alarming trend of increasingly large epidemics in the past few years.Here, we analyzed monthly dengue cases reported in Brazil between 2001 and 2016 to better characterize the key drivers of dengue epidemics. Spatio-temporal analysis revealed recurring travelling waves of disease occurrence. Using wavelet methods, we characterised the average seasonal pattern of dengue in Brazil, which starts in the western states of Acre and Rondônia, then travels eastward to the coast before reaching the northeast of the country. Only two states in the north of Brazil (Roraima and Amapá) did not follow the countrywide pattern and had inconsistent timing of dengue epidemics throughout the study period.We also explored epidemic synchrony and timing of annual dengue cycles in Brazilian regions. Using gravity style models combined with climate factors, we showed that both human mobility and vector ecology contribute to spatial patterns of dengue occurrence.This study offers a characterization of the spatial dynamics of dengue in Brazil and its drivers, which could inform intervention strategies against dengue and other arboviruses.Author summaryIn this paper we studied the synchronization of dengue epidemics in Brazilian regions. We found that a typical dengue season in Brazil can be described as a wave travelling from the western part of the country towards the east, with the exception of the two most northern equatorial states that experienced inconsistent seasonality of dengue epidemics.We found that the spatial structure of dengue cases is driven by both climate and human mobility patterns. In particular, precipitation was the most important factor for the seasonality of dengue at finer spatial resolutions.Our findings increase our understanding of large scale dengue patterns and could be used to enhance national control programs against dengue and other arboviruses.

scholarly journals Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis (Preprint)

2021 ◽  
Author(s):  
Siru Liu ◽  
Jili Li ◽  
Jialin Liu
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Transfer Learning ◽  
Behavioral Intentions ◽  
Temporal Analysis ◽  
Public Understanding ◽  
Support Vector ◽  
Learning Models ◽  
The Public ◽  
Over Time

BACKGROUND The COVID-19 vaccine is considered to be the most promising approach to alleviate the pandemic. However, in recent surveys, acceptance of the COVID-19 vaccine has been low. To design more effective outreach interventions, there is an urgent need to understand public perceptions of COVID-19 vaccines. OBJECTIVE Our objective was to analyze the potential of leveraging transfer learning to detect tweets containing opinions, attitudes, and behavioral intentions toward COVID-19 vaccines, and to explore temporal trends as well as automatically extract topics across a large number of tweets. METHODS We developed machine learning and transfer learning models to classify tweets, followed by temporal analysis and topic modeling on a dataset of COVID-19 vaccine–related tweets posted from November 1, 2020 to January 31, 2021. We used the F1 values as the primary outcome to compare the performance of machine learning and transfer learning models. The statistical values and <i>P</i> values from the Augmented Dickey-Fuller test were used to assess whether users’ perceptions changed over time. The main topics in tweets were extracted by latent Dirichlet allocation analysis. RESULTS We collected 2,678,372 tweets related to COVID-19 vaccines from 841,978 unique users and annotated 5000 tweets. The F1 values of transfer learning models were 0.792 (95% CI 0.789-0.795), 0.578 (95% CI 0.572-0.584), and 0.614 (95% CI 0.606-0.622) for these three tasks, which significantly outperformed the machine learning models (logistic regression, random forest, and support vector machine). The prevalence of tweets containing attitudes and behavioral intentions varied significantly over time. Specifically, tweets containing positive behavioral intentions increased significantly in December 2020. In addition, we selected tweets in the following categories: positive attitudes, negative attitudes, positive behavioral intentions, and negative behavioral intentions. We then identified 10 main topics and relevant terms for each category. CONCLUSIONS Overall, we provided a method to automatically analyze the public understanding of COVID-19 vaccines from real-time data in social media, which can be used to tailor educational programs and other interventions to effectively promote the public acceptance of COVID-19 vaccines.

scholarly journals Hot spot detection and spatio-temporal dynamics of dengue in Queensland, Australia

2014 ◽  
Vol XL-8 ◽  
pp. 197-204 ◽  
Author(s):  
S. Naish ◽  
S. Tong
Keyword(s):  
Temporal Variation ◽  
Temporal Dynamics ◽  
Hot Spot ◽  
Seasonal Pattern ◽  
Temporal Distribution ◽  
Health Concern ◽  
Spatial And Temporal Variation ◽  
Dengue Case ◽  
Spatio Temporal ◽  
Over Time

Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992&ndash;1993. This study explored spatio-temporal distribution and clustering of locally-acquired dengue cases in Queensland State, Australia and identified target areas for effective interventions. A computerised locally-acquired dengue case dataset was collected from Queensland Health for Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. Dengue hot spots were detected using SatScan method. Descriptive spatial analysis showed that a total of 2,398 locally-acquired dengue cases were recorded in central and northern regions of tropical Queensland. A seasonal pattern was observed with most of the cases occurring in autumn. Spatial and temporal variation of dengue cases was observed in the geographic areas affected by dengue over time. Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in tropical Queensland, Australia. There is a clear evidence for the existence of statistically significant clusters of dengue and these clusters varied over time. These findings enabled us to detect and target dengue clusters suggesting that the use of geospatial information can assist the health authority in planning dengue control activities and it would allow for better design and implementation of dengue management programs.

scholarly journals Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning

2021 ◽  
Vol 10 (12) ◽  
pp. e452101220804
Author(s):  
Cecilia Cordeiro da Silva ◽  
Clarisse Lins de Lima ◽  
Ana Clara Gomes da Silva ◽  
Giselle Machado Magalhães Moreno ◽  
Anwar Musah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Aedes Aegypti ◽  
Temporal Analysis ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Monitoring And Control ◽  
Linear Regression Models ◽  
Vector Machines ◽  
Spatio Temporal

Dengue has become a challenge for many countries. Arboviruses transmitted by Aedes aegypti spread rapidly over the last decades. The emergence chikungunya fever and zika in South America poses new challenges to vector monitoring and control. This situation got worse from 2015 and 2016, with the rapid spread of chikungunya, causing fever and muscle weakness, and Zika virus, related to cases of microcephaly in newborns and the occurrence of Guillain-Barret syndrome, an autoimmune disease that affects the nervous system. The objective of this work was to construct a tool to forecast the distribution of arboviruses transmitted by the mosquito Aedes aegypti by implementing dengue, zika and chikungunya transmission predictors based on machine learning, focused on multilayer perceptrons neural networks, support vector machines and linear regression models. As a case study, we investigated forecasting models to predict the spatio-temporal distribution of cases from primary health notification data and climate variables (wind velocity, temperature and pluviometry) from Recife, Brazil, from 2013 to 2016, including 2015’s outbreak. The use of spatio-temporal analysis over multilayer perceptrons and support vector machines results proved to be very effective in predicting the distribution of arbovirus cases. The models indicate that the southern and western regions of Recife were very susceptible to outbreaks in the period under investigation. The proposed approach could be useful to support health managers and epidemiologists to prevent outbreaks of arboviruses transmitted by Aedes aegypti and promote public policies for health promotion and sanitation.

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