Why Does It Always Rain on Me? A Spatio-Temporal Analysis of Precipitation in Austria

It is popular belief that the weather is “bad” more frequently on weekends than on other days of the week and this is often perceived to be associated with an increased chance of rain. In fact, the meteorological literature does report some evidence for such human-induced weekly cycles although these findings are not undisputed. To contribute to this discussion, a modern data-driven approach using structured additive regression modelsis applied to a newly available high-quality data set for Austria. The analysis investigates how an ordered response of rain intensities is influenced by a (potential) weekend effect while adjusting for spatio-temporal structure using spatially varying effects of overall level and seasonality patterns. The underlying data are taken from the HOMSTART project which provides daily precipitation quantities over a period of more than 60 years and a dense netof more than 50 meteorological stations all across Austria.

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A recursive estimation approach to the spatio-temporal analysis and modelling of air quality data

Environmental Modelling & Software ◽

10.1016/j.envsoft.2005.02.004 ◽

2006 ◽

Vol 21 (6) ◽

pp. 759-769 ◽

Cited By ~ 17

Author(s):

R. Romanowicz ◽

P. Young ◽

P. Brown ◽

P. Diggle

Keyword(s):

Air Quality ◽

Temporal Analysis ◽

Recursive Estimation ◽

Quality Data ◽

Spatio Temporal ◽

Air Quality Data

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Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9120752 ◽

2020 ◽

Vol 9 (12) ◽

pp. 752

Author(s):

Anna Kovacs-Györi ◽

Alina Ristea ◽

Clemens Havas ◽

Michael Mehaffy ◽

Hartwig H. Hochmair ◽

...

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Science ◽

Temporal Analysis ◽

Data Sources ◽

Urban Systems ◽

Urban Dynamics ◽

Analysis Methods ◽

Data Driven Approach ◽

Spatio Temporal

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement.

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Spatio-temporal analysis of flood data from South Carolina

Journal of Statistical Distributions and Applications ◽

10.1186/s40488-020-00112-x ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Haigang Liu ◽

David B. Hitchcock ◽

S. Zahra Samadi

Keyword(s):

South Carolina ◽

Temporal Structure ◽

Daily Rainfall ◽

Temporal Analysis ◽

The United States ◽

Environmental Data ◽

Temporal Properties ◽

Proximity Matrix ◽

Spatio Temporal ◽

Emergency Decision

AbstractTo investigate the relationship between flood gage height and precipitation in South Carolina from 2012 to 2016, we built a conditional autoregressive (CAR) model using a Bayesian hierarchical framework. This approach allows the modelling of the main spatio-temporal properties of water height dynamics over multiple locations, accounting for the effect of river network, geomorphology, and forcing rainfall. In this respect, a proximity matrix based on watershed information was used to capture the spatial structure of gage height measurements in and around South Carolina. The temporal structure was handled by a first-order autoregressive term in the model. Several covariates, including the elevation of the sites and effects of seasonality, were examined, along with daily rainfall amount. A non-normal error structure was used to account for the heavy-tailed distribution of maximum gage heights. The proposed model captured some key features of the flood process such as seasonality and a stronger association between precipitation and flooding during summer season. The model is able to forecast short term flood gage height which is crucial for informed emergency decision. As a byproduct, we also developed a Python library to retrieve and handle environmental data provided by some main agencies in the United States. This library can be of general usefulness for studies requiring rainfall, flow, and geomorphological information over specific areas of the conterminous US.

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Multivariate spatio-temporal analysis of the global COVID-19 pandemic

10.1101/2021.02.08.21251339 ◽

2021 ◽

Author(s):

Wen Xiang ◽

Ben Swallow

Keyword(s):

Control Strategies ◽

Temporal Structure ◽

Temporal Trends ◽

Virus Transmission ◽

Temporal Analysis ◽

Global Scale ◽

Global Dynamics ◽

Multivariate Statistical ◽

Wide Range ◽

Spatio Temporal

AbstractThe COVID-19 pandemic has caused significant mortality and disruption on a global scale not seen in living memory. Understanding the spatial and temporal vectors of transmission as well as similarities in the trajectories of recorded cases and deaths across countries can aid in understanding the benefit or otherwise of varying interventions and control strategies on virus transmission. It can also highlight emerging globa trends as they occur. Data on number of cases and deaths across the globe have been made available through a variety of databases and provide a wide range of opportunities for the application of multivariate statistical methods to extract information on similarity or difference from them. Here we conduct spatial and temporal multivariate statistical analyses of global COVID-19 cases and deaths for the period spanning January to August 2020, using a variety of distance based multivariate methods to cluster countries according to similar temporal trends in cases and deaths resulting from COVID-19. We also use novel air passenger data as a proxy for movement between countries. The air passenger movement can act as an important vector of transmission and thus scaling covariance matrices before conducting dimension reduction techniques can account for known structures in the data and help highlight important residual spatial and/or temporal trends that may then be attributable to the success of interventions or other cultural differences. Global temporal structure is found to be of significantly more importance than local spatial structure in terms of global dynamics. Our results highlight a significant global change in case and mortality daynamics from early-August, consistent in timing with the emergence of new strains with highger levels of transmission. We propose the methodology offers great potential in real-time analysis of complex, noisy spatio-temporal data and the extraction of emerging changes in pandemic dynamics that can support policy and decision makers.

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Spatio-Temporal Analysis and Forecasting of Distributed PV Systems Diffusion: A Case Study of Shanghai Using a Data-Driven Approach

IEEE Access ◽

10.1109/access.2017.2694009 ◽

2017 ◽

Vol 5 ◽

pp. 5135-5148 ◽

Cited By ~ 8

Author(s):

Teng Zhao ◽

Ziqiang Zhou ◽

Yan Zhang ◽

Ping Ling ◽

Yingjie Tian

Keyword(s):

Temporal Analysis ◽

Data Driven ◽

Pv Systems ◽

Data Driven Approach ◽

Spatio Temporal

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Spatio-temporal analysis of wildfire ignitions in the St Johns River Water Management District, Florida

International Journal of Wildland Fire ◽

10.1071/wf04034 ◽

2006 ◽

Vol 15 (1) ◽

pp. 87 ◽

Cited By ~ 54

Author(s):

Marc G. Genton ◽

David T. Butry ◽

Marcia L. Gumpertz ◽

Jeffrey P. Prestemon

Keyword(s):

Water Management ◽

River Water ◽

Spatial Scales ◽

Temporal Structure ◽

Temporal Analysis ◽

Irregular Distribution ◽

Point Patterns ◽

North Eastern ◽

Spatio Temporal ◽

Fire Ignitions

We analyse the spatio-temporal structure of wildfire ignitions in the St Johns River Water Management District in north-eastern Florida. We show, using tools to analyse point patterns (e.g. the L-function), that wildfire events occur in clusters. Clustering of these events correlates with irregular distribution of fire ignitions, including lightning and human sources, and fuels on the landscape. In addition, we define a relative clustering index that summarizes the amount of clustering over various spatial scales. We carry our analysis in three steps: purely temporal, purely spatial, and spatio-temporal. Our results show that arson and lightning are the leading causes of wildfires in this region and that ignitions by railroad, lightning, and arson are spatially more clustered than ignitions by other accidental causes.

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Cuban history of CRF19 recombinant subtype of HIV-1

PLoS Pathogens ◽

10.1371/journal.ppat.1009786 ◽

2021 ◽

Vol 17 (8) ◽

pp. e1009786

Author(s):

Anna Zhukova ◽

Jakub Voznica ◽

Miraine Dávila Felipe ◽

Thu-Hien To ◽

Lissette Pérez ◽

...

Keyword(s):

Large Data ◽

Temporal Analysis ◽

Rapid Progression ◽

Resistance Mutations ◽

Data Set ◽

Early Introduction ◽

Spatio Temporal ◽

Sex With Men ◽

Founder Event ◽

Hiv 1

CRF19 is a recombinant form of HIV-1 subtypes D, A1 and G, which was first sampled in Cuba in 1999, but was already present there in 1980s. CRF19 was reported almost uniquely in Cuba, where it accounts for ∼25% of new HIV-positive patients and causes rapid progression to AIDS (∼3 years). We analyzed a large data set comprising ∼350 pol and env sequences sampled in Cuba over the last 15 years and ∼350 from Los Alamos database. This data set contained both CRF19 (∼315), and A1, D and G sequences. We performed and combined analyses for the three A1, G and D regions, using fast maximum likelihood approaches, including: (1) phylogeny reconstruction, (2) spatio-temporal analysis of the virus spread, and ancestral character reconstruction for (3) transmission mode and (4) drug resistance mutations (DRMs). We verified these results with a Bayesian approach. This allowed us to acquire new insights on the CRF19 origin and transmission patterns. We showed that CRF19 recombined between 1966 and 1977, most likely in Cuban community stationed in Congo region. We further investigated CRF19 spread on the Cuban province level, and discovered that the epidemic started in 1970s, most probably in Villa Clara, that it was at first carried by heterosexual transmissions, and then quickly spread in the 1980s within the “men having sex with men” (MSM) community, with multiple transmissions back to heterosexuals. The analysis of the transmission patterns of common DRMs found very few resistance transmission clusters. Our results show a very early introduction of CRF19 in Cuba, which could explain its local epidemiological success. Ignited by a major founder event, the epidemic then followed a similar pattern as other subtypes and CRFs in Cuba. The reason for the short time to AIDS remains to be understood and requires specific surveillance, in Cuba and elsewhere.

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PENYEBARAN POLUTAN DALAM KASUS KEBAKARAN HUTAN DAN LAHAN DI SUMATERA SELATAN TAHUN 2015

Jurnal Sains & Teknologi Modifikasi Cuaca ◽

10.29122/jstmc.v18i2.2611 ◽

2017 ◽

Vol 18 (2) ◽

pp. 61

Author(s):

Erwin Mulyana

Keyword(s):

Air Quality ◽

Black Carbon ◽

Temporal Analysis ◽

Quality Data ◽

Display System ◽

Rain Intensity ◽

Grid Analysis ◽

Spatio Temporal ◽

Air Quality Data ◽

Area Data

IntisariKebakaran hutan dan lahan di Sumatera Selatan tahun 2015 menimbulkan bencana kabut asap yang sangat masif sehingga kualitas udara dalam beberapa hari mencapai kategori berbahaya. Dalam tulisan ini dibahas penyebaran polutan di wilayah Sumatera Selatan akibat kebakaran hutan dan lahan yang terjadi di wilayah tersebut. Data yang digunakan dalam penelitian ini adalah data hotspot dari satelit MODIS dengan tingkat kepercayaan 70 %, curah hujan TRMM serta curah hujan dari penakar yg ada di Sumatera Selatan,data kualitas udara (ISPU), data black carbon dari MERRA-2 Model M2T1NXAER v5.12.4. dengan resolusi 0.5o x 0.625o, serta arah dan kecepatan angin lapisan 925 mb. Analisis spasio temporal penyebaran black carbon yang dipadukan dengan arah dan kecepatan angin menggunakan perangkat lunak Grid Analysis and Display System (GrADS). Intensitas hujan dari 16 penakar hujan sejak minggu kedua bulan Agusus 2015 hingga akhir Oktober 2015 sebesar 36 mm. Selama bulan Juni-November 2015, Jumlah hotspot terbanyak terjadi pada bulan September (6.839 titik) dan Oktober (7.709 titik). Lokasi hotspot sebagian besar berada di Kabupaten OKI dengan jumlah mencapai 10.581 titik. Kualitas udara pada bulan September 2015 dominan masuk kategori tidak sehat sedangkan bulan Oktober 2015 dominan masuk kategori sangat tidak sehat – berbahaya. Angin pada lapisan 925 mb umumnya bertiup dari arah tenggara hingga timur sehingga black carbon dari kebakaran di daerah OKI menyebar ke arah wilayah Kabupaten Musi Banyuasin serta Kabupaten Banyuasin. AbstractIn 2015, Forest and Land fires inflict serious and massive smoke disaster so that air quality in several days laid in dangerous category. This paper discussed pollutant dispersed in South Sumatera due to forest and land fire in this area. Data that used in this paper were MODIS satellite hotspot data with 70 % confidence level, rainfall from TRMM satellite and from ground observation at South Sumatera, Air quality data (ISPU), MERRA-2 Model M2T1NXAER v5.12.4 black carbon data, also wind direction and speed at 925 mb height. Spatio temporal analysis of black carbon dispersion combined with wind speed and direction using Grid Analysis and Display System (GrADS) software. Rain intensity from 16 rainfall gauge since week two of August 2015 until end of October 2015 was 36 mm. During June-November 2015, the number on highest hotspot observed was in September (6.839) and October (7.709). Hotspot location mainly in OKI district as much as 10.581. Air quality in September 2015 mainly laid in unhealthy category, meanwhile in October 2015 laid mainly stated as unhealthy to dangerous. Wind at 925 mb height generally came from South East and East so black carbon came from fires at OKI district dispersed to Musi Banyuasin and Banyuasin district.

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