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A range of applications analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate-sensitive infections (CSIs) and agriculture crop modelling, make use of land surface modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from that account for land processes in different ways and this may introduce predictive uncertainty when LSM outputs are used as inputs to inform a given application. For useful predictions for a specific application, one must therefore understand the inherent uncertainties in the LSMs and the variations between them, as well as uncertainties arising from variation in the climate data driving the LSMs. This requires methods to analyse multivariate spatio-temporal variations and differences. A methodology is proposed based on multiway data analysis, which extends singular value decomposition (SVD) to multidimensional tables and provides spatio-temporal descriptions of agreements and disagreements between LSMs for both historical simulations and future predictions. The application underlying this paper is prediction of how climate change will affect the spread of CSIs in the Fennoscandian and north-west Russian regions, and the approach is explored by comparing net primary production (NPP) estimates over the period 1998–2013 from versions of leading LSMs (JULES, CLM5 and two versions of ORCHIDEE) that are adapted to high-latitude processes, as well as variations in JULES up to 2100 when driven by 34 global circulation models (GCMs). A single optimal spatio-temporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a good approximation to all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset. The next best adjustment to this pattern, capturing an extra 4 % of the overall variability, is essentially a spatial correction applied to ORCHIDEE-HLveg that significantly improves the fit to this LSM, with only small improvements for the other LSMs. Subsequent correction terms gradually improve the overall and individual LSM fits but capture at most 1.7 % of the overall variability. Analysis of differences between LSMs provides information on the times and places where the LSMs differ and by how much, but in this case no single spatio-temporal pattern strongly dominates the variability. Hence interpretation of the analysis requires the summation of several such patterns. Nonetheless, the three best principal tensors capture around 76 % of the variability in the LSM differences and to a first approximation successively indicate the times and places where ORCHIDEE-HLveg, CLM5 and ORCHIDEE-MICT differ from the other LSMs. Differences between the climate forcing GCMs had a marginal effect up to 6 % on NPP predictions out to 2100 without specific spatio-temporal GCM interaction.
Didier G. Leibovici; Shaun Quegan; Edward Comyn-Platt; Garry Hayman; Maria Val Martin; Mathieu Guimberteau; Arsène Druel; Dan Zhu; Philippe Ciais. Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis. Biogeosciences 2020, 17, 1821 -1844.
AMA StyleDidier G. Leibovici, Shaun Quegan, Edward Comyn-Platt, Garry Hayman, Maria Val Martin, Mathieu Guimberteau, Arsène Druel, Dan Zhu, Philippe Ciais. Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis. Biogeosciences. 2020; 17 (7):1821-1844.
Chicago/Turabian StyleDidier G. Leibovici; Shaun Quegan; Edward Comyn-Platt; Garry Hayman; Maria Val Martin; Mathieu Guimberteau; Arsène Druel; Dan Zhu; Philippe Ciais. 2020. "Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis." Biogeosciences 17, no. 7: 1821-1844.
Didier Leibovici. replies to anonymous Referee #1 (updated) and #2 (see also the changes diifferences file in supplement). 2020, 1 .
AMA StyleDidier Leibovici. replies to anonymous Referee #1 (updated) and #2 (see also the changes diifferences file in supplement). . 2020; ():1.
Chicago/Turabian StyleDidier Leibovici. 2020. "replies to anonymous Referee #1 (updated) and #2 (see also the changes diifferences file in supplement)." , no. : 1.
Didier Leibovici. Replies to anonymous Referee #1 (updated) and #2. 2020, 1 .
AMA StyleDidier Leibovici. Replies to anonymous Referee #1 (updated) and #2. . 2020; ():1.
Chicago/Turabian StyleDidier Leibovici. 2020. "Replies to anonymous Referee #1 (updated) and #2." , no. : 1.
Didier Leibovici. I meant partial review, i.e. reviewer #1. 2019, 1 .
AMA StyleDidier Leibovici. I meant partial review, i.e. reviewer #1. . 2019; ():1.
Chicago/Turabian StyleDidier Leibovici. 2019. "I meant partial review, i.e. reviewer #1." , no. : 1.
Didier Leibovici. New version after the first review. 2019, 1 .
AMA StyleDidier Leibovici. New version after the first review. . 2019; ():1.
Chicago/Turabian StyleDidier Leibovici. 2019. "New version after the first review." , no. : 1.
Understanding the structuration of spatio-temporal information is a common endeavour to many disciplines and application domains, e.g., geography, ecology, urban planning, epidemiology. Revealing the processes involved, in relation to one or more phenomena, is often the first step before elaborating spatial functioning theories and specific planning actions, e.g., epidemiological modelling, urban planning. To do so, the spatio-temporal distributions of meaningful variables from a decision-making viewpoint, can be explored, analysed separately or jointly from an information viewpoint. Using metrics based on the measure of entropy has a long practice in these domains with the aim of quantification of how uniform the distributions are. However, the level of embedding of the spatio-temporal dimension in the metrics used is often minimal. This paper borrows from the landscape ecology concept of patch size distribution and the approach of permutation entropy used in biomedical signal processing to derive a spatio-temporal entropy analysis framework for categorical variables. The framework is based on a spatio-temporal structuration of the information allowing to use a decomposition of the Shannon entropy which can also embrace some existing spatial or temporal entropy indices to reinforce the spatio-temporal structuration. Multiway correspondence analysis is coupled to the decomposition entropy to propose further decomposition and entropy quantification of the spatio-temporal structuring information. The flexibility from these different choices, including geographic scales, allows for a range of domains to take into account domain specifics of the data; some of which are explored on a dataset linked to climate change and evolution of land cover types in Nordic areas.
Didier G. Leibovici; Christophe Claramunt. On Integrating Size and Shape Distributions into a Spatio-Temporal Information Entropy Framework. Entropy 2019, 21, 1112 .
AMA StyleDidier G. Leibovici, Christophe Claramunt. On Integrating Size and Shape Distributions into a Spatio-Temporal Information Entropy Framework. Entropy. 2019; 21 (11):1112.
Chicago/Turabian StyleDidier G. Leibovici; Christophe Claramunt. 2019. "On Integrating Size and Shape Distributions into a Spatio-Temporal Information Entropy Framework." Entropy 21, no. 11: 1112.
Didier Leibovici. Added comment on uncertainty interpretation raised by #1 about Pg13 L10 of the manupscript. 2019, 1 .
AMA StyleDidier Leibovici. Added comment on uncertainty interpretation raised by #1 about Pg13 L10 of the manupscript. . 2019; ():1.
Chicago/Turabian StyleDidier Leibovici. 2019. "Added comment on uncertainty interpretation raised by #1 about Pg13 L10 of the manupscript." , no. : 1.
Didier Leibovici. replies to Anonymous Referee #1. 2019, 1 .
AMA StyleDidier Leibovici. replies to Anonymous Referee #1. . 2019; ():1.
Chicago/Turabian StyleDidier Leibovici. 2019. "replies to Anonymous Referee #1." , no. : 1.
A range of applications analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate sensitive infections (CSIs), agriculture crop modelling, etc., make use of Land Surface Modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from that account for land processes in different ways and, depending on the application, the choice of LSM and its sensitivity will have different impacts. For useful predictions for a specific application, one must therefore understand the inherent uncertainties in the LSMs and the variations between them, as well as uncertainties arising from variation in the climate data driving the LSMs. This requires methods to analyse multivariate spatio-temporal variations and differences. A methodology is proposed based on multi-way data analysis, which extends Singular Value Decomposition (SVD) to multi-dimensional tables, and provides spatio-temporal descriptions of agreements and disagreements between LSMs for both historical simulations and future predictions. The application underlying this paper is prediction of how climate change will affect the spread of CSIs in the Fenno-Scandinavian and north-west Russian regions, and the approach is explored by comparing Net Primary Production (NPP) estimates over the period 1998–2013 from versions of leading LSMs (JULES, CLM5 and two versions of ORCHIDEE) that are adapted to high latitude processes, as well as variations in JULES up to 2100 when driven by 34 global circulation models (GCMs). A single optimal spatio-temporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a good approximation to all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset. The next best adjustment to this pattern, capturing an extra 4 % of the overall variability, is essentially a spatial correction applied to ORCHIDEE-HLveg that significantly improves the fit to this LSM, with only small improvements for the other LSMs. Subsequent correction terms gradually improve the overall and individual LSM fits, but capture at most 1.7 % of the overall variability. Analysis of differences between LSMs provides information on the times and places where the LSMs differ and by how much, but in this case no single spatio-temporal pattern strongly dominates the variability. Hence interpretation of the analysis requires the summation of several such patterns. Nonetheless, the three best principal tensors capture around 76 % of the variability in the LSM differences, and to a first approximation successively indicate the times and places where ORCHIDEE-HLveg, CLM5 and ORCHIDEE-MICT respectively differ from the other LSMs. Differences between the climate forcing GCMs had a marginal effect up to 6 % on NPP predictions out to 2100 without specific spatio-temporal GCM interaction.
Didier G. Leibovici; Shaun Quegan; Edward Comyn-Platt; Gary Hayman; Maria Val Martin; Mathieu Guimberteau; Arsène Druel; Dan Zhu; Philippe Ciais. Spatio-Temporal Variations and Uncertainty in Land Surface Modelling for High Latitudes: Univariate Response Analysis. 2019, 1 -34.
AMA StyleDidier G. Leibovici, Shaun Quegan, Edward Comyn-Platt, Gary Hayman, Maria Val Martin, Mathieu Guimberteau, Arsène Druel, Dan Zhu, Philippe Ciais. Spatio-Temporal Variations and Uncertainty in Land Surface Modelling for High Latitudes: Univariate Response Analysis. . 2019; ():1-34.
Chicago/Turabian StyleDidier G. Leibovici; Shaun Quegan; Edward Comyn-Platt; Gary Hayman; Maria Val Martin; Mathieu Guimberteau; Arsène Druel; Dan Zhu; Philippe Ciais. 2019. "Spatio-Temporal Variations and Uncertainty in Land Surface Modelling for High Latitudes: Univariate Response Analysis." , no. : 1-34.
Jamie Williams; Colin Chapman; Didier Guy Leibovici; Grégoire Loïs; Andreas Matheus; Alessandro Oggioni; Sven Schade; Linda See; Paul Pieter Lodewijk Van Genuchten. Maximising the impact and reuse of citizen science data. Citzen Science 2018, 321 -336.
AMA StyleJamie Williams, Colin Chapman, Didier Guy Leibovici, Grégoire Loïs, Andreas Matheus, Alessandro Oggioni, Sven Schade, Linda See, Paul Pieter Lodewijk Van Genuchten. Maximising the impact and reuse of citizen science data. Citzen Science. 2018; ():321-336.
Chicago/Turabian StyleJamie Williams; Colin Chapman; Didier Guy Leibovici; Grégoire Loïs; Andreas Matheus; Alessandro Oggioni; Sven Schade; Linda See; Paul Pieter Lodewijk Van Genuchten. 2018. "Maximising the impact and reuse of citizen science data." Citzen Science , no. : 321-336.
The design and running of complex geoprocessing workflows is an increasingly common geospatial modelling and analysis task. The Business Process Model and Notation (BPMN) standard, which provides a graphical representation of a workflow, allows stakeholders to discuss the scientific conceptual approach behind this modelling while also defining a machine‐readable encoding in XML. Previous research has enabled the orchestration of Open Geospatial Consortium Web Processing Services (WPS) with a BPMN workflow engine. However, the need for direct access to predefined data inputs and outputs results in a lack of flexibility during composition of the workflow and of efficiency during execution. This article develops metadata profiling approaches, described as two possible configurations, which enable workflow management at the meta‐level through a coupling with a metadata catalogue. Specifically, a WPS profile and a BPMN profile are developed and tested using open‐source components to achieve this coupling. A case study in the context of an event mapping task applied within a big data framework and based on analysis of the Global Database of Event Language and Tone database illustrates the two different architectures.
Julian F. Rosser; Mike Jackson; Didier G. Leibovici. Full Meta Object profiling for flexible geoprocessing workflows. Transactions in GIS 2018, 22, 1221 -1237.
AMA StyleJulian F. Rosser, Mike Jackson, Didier G. Leibovici. Full Meta Object profiling for flexible geoprocessing workflows. Transactions in GIS. 2018; 22 (5):1221-1237.
Chicago/Turabian StyleJulian F. Rosser; Mike Jackson; Didier G. Leibovici. 2018. "Full Meta Object profiling for flexible geoprocessing workflows." Transactions in GIS 22, no. 5: 1221-1237.
Environmental policy involving citizen science (CS) is of growing interest. In support of this open data stream of information, validation or quality assessment of the CS geo-located data to their appropriate usage for evidence-based policy making needs a flexible and easily adaptable data curation process ensuring transparency. Addressing these needs, this paper describes an approach for automatic quality assurance as proposed by the Citizen OBservatory WEB (COBWEB) FP7 project. This approach is based upon a workflow composition that combines different quality controls, each belonging to seven categories or “pillars”. Each pillar focuses on a specific dimension in the types of reasoning algorithms for CS data qualification. These pillars attribute values to a range of quality elements belonging to three complementary quality models. Additional data from various sources, such as Earth Observation (EO) data, are often included as part of the inputs of quality controls within the pillars. However, qualified CS data can also contribute to the validation of EO data. Therefore, the question of validation can be considered as “two sides of the same coin”. Based on an invasive species CS study, concerning Fallopia japonica (Japanese knotweed), the paper discusses the flexibility and usefulness of qualifying CS data, either when using an EO data product for the validation within the quality assurance process, or validating an EO data product that describes the risk of occurrence of the plant. Both validation paths are found to be improved by quality assurance of the CS data. Addressing the reliability of CS open data, issues and limitations of the role of quality assurance for validation, due to the quality of secondary data used within the automatic workflow, are described, e.g., error propagation, paving the route to improvements in the approach.
Didier G. Leibovici; Jamie Williams; Julian F. Rosser; Crona Hodges; Colin Chapman; Chris Higgins; Mike J. Jackson. Earth Observation for Citizen Science Validation, or Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations. Data 2017, 2, 35 .
AMA StyleDidier G. Leibovici, Jamie Williams, Julian F. Rosser, Crona Hodges, Colin Chapman, Chris Higgins, Mike J. Jackson. Earth Observation for Citizen Science Validation, or Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations. Data. 2017; 2 (4):35.
Chicago/Turabian StyleDidier G. Leibovici; Jamie Williams; Julian F. Rosser; Crona Hodges; Colin Chapman; Chris Higgins; Mike J. Jackson. 2017. "Earth Observation for Citizen Science Validation, or Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations." Data 2, no. 4: 35.
Environmental policy involving citizen science (CS) is of growing interest. In support of this open data stream, validation or quality assessment of the CS data and their appropriate usage for evidence-based policy making, needs a flexible and easily adaptable data curation process ensuring transparency. Addressing these needs, this paper describes an approach for automatic quality assurance as proposed by the Citizen OBservatory WEB (COBWEB) FP7 project. This approach is based upon a workflow composition that combines different quality controls, each belonging to seven categories or ‘pillars’. Each pillar focuses on a specific dimension in the types of reasoning algorithms for CS data qualification. These pillars attribute values to a range of quality elements belonging to three complementary quality models. Additional data from various sources, such as Earth Observation (EO) data, are often included as part of the inputs of quality controls within the pillars. However, qualified CS data can also contribute to the validation of EO data. Therefore, the question of validation can be considered as ‘two sides of the same coin’. Based on an invasive species CS study, concerning Fallopia japonica (Japanese knotweed), the paper discusses the flexibility and usefulness of qualifying CS data, either when using an EO data for the validation within the quality assurance process, or validating an EO data product that describes the risk of occurrence of the plant. Both validation paths are found to be improved by quality assurance of the CS data. Addressing the reliability of CS open data, issues and limitations of the role of quality assurance for validation, due to the quality of secondary data used within the automatic workflow, are described, e.g. error propagation, paving the route to improvements in the approach.
Didier Leibovici; Jamie Williams; Julian Rosser; Crona Hodges; Colin Chapman; Chris Higgins; Mike Jackson. Earth Observation for Citizen Science Validation, or, Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations. 2017, 1 .
AMA StyleDidier Leibovici, Jamie Williams, Julian Rosser, Crona Hodges, Colin Chapman, Chris Higgins, Mike Jackson. Earth Observation for Citizen Science Validation, or, Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations. . 2017; ():1.
Chicago/Turabian StyleDidier Leibovici; Jamie Williams; Julian Rosser; Crona Hodges; Colin Chapman; Chris Higgins; Mike Jackson. 2017. "Earth Observation for Citizen Science Validation, or, Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations." , no. : 1.
The paper retraces the GRASPgfs endeavor (Geospatial Resource for Agricultural Species and Pests with integrated workflow modelling to support Global Food Security) between multiple disciplines around a common objective of facilitating research and model simulations for sustainable food security. Within this endeavor, the geospatial media has been the enabler for multidisciplinary research in crop modelling. Geospatial genetic-trait variations and associations with environmental forecasting were the main focus of the GRASPgfs. Designing the platform achieving this objective generated a transdisciplinary vision of modelling and forecasting for sustainable agriculture. Based on interoperability principles, seamless access as well as sharing for data, metadata and processing models, the design is described in this paper. This geospatial binding facilitates and supports new types of hypotheses and analysis as illustrated in the paper with a landscape genetic case study (bambara groundnut) and a crop disease modelling (eyespot disease). The approach and the eGRASP platform are generic enough to accommodate further complexity into the integrated modelling that this geospatial binding enables.
Didier G. Leibovici; Suchith Anand; Roberto Santos; Sean Mayes; Rumiana Ray; Masoud Al-Azri; Abdul Baten; Graham King; Asha S. Karunaratne; Sayed Azam-Ali; Mike J. Jackson. Geospatial binding for transdisciplinary research in crop science: the GRASPgfs initiative. Open Geospatial Data, Software and Standards 2017, 2, 20 .
AMA StyleDidier G. Leibovici, Suchith Anand, Roberto Santos, Sean Mayes, Rumiana Ray, Masoud Al-Azri, Abdul Baten, Graham King, Asha S. Karunaratne, Sayed Azam-Ali, Mike J. Jackson. Geospatial binding for transdisciplinary research in crop science: the GRASPgfs initiative. Open Geospatial Data, Software and Standards. 2017; 2 (1):20.
Chicago/Turabian StyleDidier G. Leibovici; Suchith Anand; Roberto Santos; Sean Mayes; Rumiana Ray; Masoud Al-Azri; Abdul Baten; Graham King; Asha S. Karunaratne; Sayed Azam-Ali; Mike J. Jackson. 2017. "Geospatial binding for transdisciplinary research in crop science: the GRASPgfs initiative." Open Geospatial Data, Software and Standards 2, no. 1: 20.
Volunteer geographical information (VGI), either in the context of citizen science or the mining of social media, has proven to be useful in various domains including natural hazards, health status, disease epidemics, and biological monitoring. Nonetheless, the variable or unknown data quality due to crowdsourcing settings are still an obstacle for fully integrating these data sources in environmental studies and potentially in policy making. The data curation process, in which a quality assurance (QA) is needed, is often driven by the direct usability of the data collected within a data conflation process or data fusion (DCDF), combining the crowdsourced data into one view, using potentially other data sources as well. Looking at current practices in VGI data quality and using two examples, namely land cover validation and inundation extent estimation, this paper discusses the close links between QA and DCDF. It aims to help in deciding whether a disentanglement can be possible, whether beneficial or not, in understanding the data curation process with respect to its methodology for future usage of crowdsourced data. Analysing situations throughout the data curation process where and when entanglement between QA and DCDF occur, the paper explores the various facets of VGI data capture, as well as data quality assessment and purposes. Far from rejecting the usability ISO quality criterion, the paper advocates for a decoupling of the QA process and the DCDF step as much as possible while still integrating them within an approach analogous to a Bayesian paradigm.
Didier G. Leibovici; Julian F. Rosser; Crona Hodges; Barry Evans; Michael J. Jackson; Chris I. Higgins. On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies. ISPRS International Journal of Geo-Information 2017, 6, 78 .
AMA StyleDidier G. Leibovici, Julian F. Rosser, Crona Hodges, Barry Evans, Michael J. Jackson, Chris I. Higgins. On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies. ISPRS International Journal of Geo-Information. 2017; 6 (3):78.
Chicago/Turabian StyleDidier G. Leibovici; Julian F. Rosser; Crona Hodges; Barry Evans; Michael J. Jackson; Chris I. Higgins. 2017. "On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies." ISPRS International Journal of Geo-Information 6, no. 3: 78.
Flood events cause substantial damage to urban and rural areas. Monitoring water extent during large-scale flooding is crucial in order to identify the area affected and to evaluate damage. During such events, spatial assessments of floodwater may be derived from satellite or airborne sensing platforms. Meanwhile, an increasing availability of smartphones is leading to documentation of flood events directly by individuals, with information shared in real-time using social media. Topographic data, which can be used to determine where floodwater can accumulate, are now often available from national mapping or governmental repositories. In this work, we present and evaluate a method for rapidly estimating flood inundation extent based on a model that fuses remote sensing, social media and topographic data sources. Using geotagged photographs sourced from social media, optical remote sensing and high-resolution terrain mapping, we develop a Bayesian statistical model to estimate the probability of flood inundation through weights-of-evidence analysis. Our experiments were conducted using data collected during the 2014 UK flood event and focus on the Oxford city and surrounding areas. Using the proposed technique, predictions of inundation were evaluated against ground-truth flood extent. The results report on the quantitative accuracy of the multisource mapping process, which obtained area under receiver operating curve values of 0.95 and 0.93 for model fitting and testing, respectively.
Julian F. Rosser; Didier Leibovici; M. J. Jackson. Rapid flood inundation mapping using social media, remote sensing and topographic data. Natural Hazards 2017, 87, 103 -120.
AMA StyleJulian F. Rosser, Didier Leibovici, M. J. Jackson. Rapid flood inundation mapping using social media, remote sensing and topographic data. Natural Hazards. 2017; 87 (1):103-120.
Chicago/Turabian StyleJulian F. Rosser; Didier Leibovici; M. J. Jackson. 2017. "Rapid flood inundation mapping using social media, remote sensing and topographic data." Natural Hazards 87, no. 1: 103-120.
We retrace the construction of AgriGIS framework between multiple disciplines around a common objective of facilitating research on model simulations for sustainable food security. The geospatial media enabling multidisciplinary research in crop modelling but also supporting new types of hypothesis and analysis, is described with interoperability principles and seamless access and sharing for data, metadata and processing models. Designing the platform achieving this main objective generated a transdisciplinary vision of modelling and forecasting for sustainable agriculture.
Roberto Santos; Dai Huynh; Suchith Anand; Rumiana V Ray; Sean Mayes; Didier Leibovici. A geoprocessing modelling interoperable framework for AgriGIS using open data and open standards. 2016, 1 .
AMA StyleRoberto Santos, Dai Huynh, Suchith Anand, Rumiana V Ray, Sean Mayes, Didier Leibovici. A geoprocessing modelling interoperable framework for AgriGIS using open data and open standards. . 2016; ():1.
Chicago/Turabian StyleRoberto Santos; Dai Huynh; Suchith Anand; Rumiana V Ray; Sean Mayes; Didier Leibovici. 2016. "A geoprocessing modelling interoperable framework for AgriGIS using open data and open standards." , no. : 1.
Didier G Leibovici. Research resource review: An Introduction to R for Spatial Analysis and Mapping. Progress in Physical Geography: Earth and Environment 2016, 40, 362 -364.
AMA StyleDidier G Leibovici. Research resource review: An Introduction to R for Spatial Analysis and Mapping. Progress in Physical Geography: Earth and Environment. 2016; 40 (2):362-364.
Chicago/Turabian StyleDidier G Leibovici. 2016. "Research resource review: An Introduction to R for Spatial Analysis and Mapping." Progress in Physical Geography: Earth and Environment 40, no. 2: 362-364.
The Open Geospatial Consortium (OGC) Web Processing Service (WPS) standard enables access to a centralized repository of processes and services from compliant clients. A crucial part of the standard includes the provision to chain disparate processes and services to form a reusable workflow. To date this has been realized by methods such as embedding XML requests, using Business Process Execution Language (BPEL) engines and other external orchestration engines. Although these allow the user to define tasks and data artifacts as web services, they are often considered inflexible and complicated, often due to vendor specific solutions and inaccessible documentation. This paper introduces a new method of flexible service chaining using the standard Business Process Markup Notation (BPMN). A prototype system has been developed upon an existing open source BPMN suite to illustrate the advantages of the approach. The motivation for the software design is qualification of crowdsourced data for use in policy-making. The software is tested as part of a project that seeks to qualify, assure, and add value to crowdsourced data in a biological monitoring use case.
Sam Meek; Mike Jackson; Didier G Leibovici. A BPMN solution for chaining OGC services to quality assure location-based crowdsourced data. Computers & Geosciences 2015, 87, 76 -83.
AMA StyleSam Meek, Mike Jackson, Didier G Leibovici. A BPMN solution for chaining OGC services to quality assure location-based crowdsourced data. Computers & Geosciences. 2015; 87 ():76-83.
Chicago/Turabian StyleSam Meek; Mike Jackson; Didier G Leibovici. 2015. "A BPMN solution for chaining OGC services to quality assure location-based crowdsourced data." Computers & Geosciences 87, no. : 76-83.
SIEL : système intégré pour la modélisation et l’évaluation du risque de désertification SIEL: Integrated system for modeling and assessment of desertification risk
Maud Loireau; Mongi Sghaier; Bertrand Guerrero; Farah Chouikhi; Mondher Fétoui; Didier Leibovici; Stéphane Debard; Jean-Christophe Desconnets; Nabil Ben Khatra. SIEL : système intégré pour la modélisation et l’évaluation du risque de désertification. Ingénierie des systèmes d information 2015, 20, 1 .
AMA StyleMaud Loireau, Mongi Sghaier, Bertrand Guerrero, Farah Chouikhi, Mondher Fétoui, Didier Leibovici, Stéphane Debard, Jean-Christophe Desconnets, Nabil Ben Khatra. SIEL : système intégré pour la modélisation et l’évaluation du risque de désertification. Ingénierie des systèmes d information. 2015; 20 (3):1.
Chicago/Turabian StyleMaud Loireau; Mongi Sghaier; Bertrand Guerrero; Farah Chouikhi; Mondher Fétoui; Didier Leibovici; Stéphane Debard; Jean-Christophe Desconnets; Nabil Ben Khatra. 2015. "SIEL : système intégré pour la modélisation et l’évaluation du risque de désertification." Ingénierie des systèmes d information 20, no. 3: 1.