21 papers · ranked by Valyu relevance
Trang VoPham, Jaime E. Hart, Francine Laden, Yao-Yi Chiang
Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology…
Siran Li, Suzana Dragićević, François Anton, Monika Sester + 7 more
'Stephan Winter' 'Arzu Çöltekin' 'Christopher Pettit' 'Bin Jiang' 'James Haworth' 'Alfred Stein' 'Tao Cheng'] Songnian Li *, Ryerson University, Toronto, Canada, snli@ryerson.ca Suzana Dragicevic, Simon Fraser University, Vancouver, Canada, suzanad@sfu.ca François Anton, Technical University of Denmark, Lyngby…
Rakesh Kumar Lenka, Rabindra K. Barik, Noopur Gupta, Syed Mohd Ali + 2 more
'Amiya Kumar Rath' 'Harishchandra Dubey'] In this digitalised world where every information is stored, the data a are growing exponentially. It is estimated that data are doubles itself every two years. Geospatial data are one of the prime contributors to the big data scenario. There are numerous tools of the big data…
Chris Brunsdon, Alexis Comber
This paper reflects on a number of trends towards a more open and reproducible approach to geographic and spatial data science over recent years. In particular it considers trends towards Big Data, and the impacts this is having on spatial data analysis and modelling. It identifies a turn in academia towards coding as…
Daniel A. Griffith, Yongwan Chun, Monghyeon Lee
Small areas refer to small geographic areas, a more literal meaning of the phrase, as well as small domains (e.g., small sub-populations), a more figurative meaning of the phrase. With post-stratification, even with big data, either case can encounter the problem of small local sample sizes, which tend to inflate local…
Rabindra K. Barik, Harishchandra Dubey, Arun Baran Samaddar, R. D. Gupta + 1 more
'R. D. Gupta' 'Prakash K. Ray'] Abstract— Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client. This paper developed a Fog…
Martin Werner
This paper provides an abstract analysis of parallel processing strategies for spatial and spatio-temporal data. It isolates aspects such as data locality and computational locality as well as redundancy and locally sequential access as central elements of parallel algorithm design for spatial data. Furthermore, the…
Tin Vu, Ahmed Eldawy
The rapid growth of big spatial data urged the research community to develop several big spatial data systems. Regardless of their architecture, one of the fundamental requirements of all these systems is to spatially partition the data efficiently across machines. The core challenges of big spatial partitioning are…
Martin Sudmanns, Dirk Tiede, Stefan Lang, Helena Bergstedt + 4 more
'Georg Trost' 'Hannah Augustin' 'Andrea Baraldi' 'Thomas Blaschke'] Title: ABSTRACT Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term ‘big Earth data’ emerged to describe massive EO datasets that…
Luca Marconato, Giovanni Palla, Kevin A. Yamauchi, Isaac Virshup + 10 more
Spatially resolved omics technologies are transforming our understanding of biological tissues. However, handling uni- and multi-modal spatial omics datasets remains a challenge owing to large volumes of data, heterogeneous data types and the lack of unified spatially-aware data structures. Here, we introduce…
Md Mahbub Alam, Luı́s Torgo, Albert Bifet
Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and…
Isam Mashhour Al Jawarneh, Luca Foschini, Paolo Bellavista, Jose Manuel Molina López
'Jose Manuel Molina López'] The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urban planning. However, processing and…
Jocelyne Shimin Sze, Laura Aileen Sauls
Conservation has embraced advances in big data and related digital technologies as key to preventing biodiversity loss, especially in the identification of areas of conservation priority based on spatial data, which we call the big geospatial data turn. This turn has led to the proliferation of useful methods and…
Christoffer M. Langseth, Bastien Hervé, Hanna P. Piechaczyk, Yuk Kit Lor + 2 more
Spatial omics technologies enable high-resolution mapping of molecular and cellular organization within tissues, yet interactive exploration of these data remains challenging due to computational bottlenecks, and reliance on proprietary software infrastructures. We present KaroSpace, a framework for cell-centric…
Gohta Aihara, Kalen Clifton, Mayling Chen, Lyla Atta + 2 more
Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells. To enhance the scalability of spatial omics…
Lambda Moses, Alik Huseynov, Joseph M Rich, Lior Pachter
SpatialFeatureExperiment is a Bioconductor package that leverages the versatility of Simple Features for spatial data analysis and SpatialExperiment for single-cell -omics to provide an expansive and convenient S4 class for working with spatial -omics data. SpatialFeatureExperiment can be used to store and analyze a…
Yannick Mahlich, Harkirat Sohi, Paul Piehowski, Jason E McDermott + 1 more
Spatial omics is a young and evolving field and as such shows rapid development of novel technologies and analysis methods to measure transcripts, proteins, metabolites, and post-translational modifications at high spatial resolution. These advances in technology have enabled the simultaneous generation of abundance…
Theodoros Visvikis, Wouter-Michiel Vierdag, Luca Marconato, Ron M.A. Heeren + 1 more
Mass Spectrometry Imaging (MSI) is a powerful technique for mapping molecular distributions, and its integration with other imaging modalities is crucial for comprehensive understanding of molecular systems. Fragmented data formats and the limitations of existing standards like imzML, challenge spatial biology centric…
Ling Huang, Shannon Stokes, Qining Chen, Felipe Cardoso-Saldaña + 1 more
Total column loadings of methane measured by satellites are increasingly used to estimate methane emission rates, using inversion calculations. Forward calculations of methane column loadings, based on detailed emission inventories at fine spatial resolution, coupled with fine spatial scale gridded chemical transport…
Daniel Goldberg, Benjamin de Foy, M. Omar Nawaz, Jeremiah Johnson + 2 more
Air quality managers in areas exceeding air pollution standards are motivated to understand where there are further opportunities to reduce NOx emissions to improve ozone and PM2.5 air quality. In this project, we use a combination of aircraft remote sensing (i.e., GCAS), source apportionment models (i.e., CAMx), and…
Yasin El Abiead, Michael Strobel, Thomas Payne, Eoin Fahy + 13 more
Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, we've developed pan-repository universal identifiers and harmonized cross-repository metadata. This novel ecosystem…