The Spatial Disaggregation Problem: Simulating Reasoning Using a Fuzzy Inference System

Verstraete, Jörg

doi:10.1109/tfuzz.2016.2567452

Cited by 13 publications

(18 citation statements)

References 28 publications

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“…The regridding is usually performed by either assuming an underlying distribution or by trying to estimate an underlying distribution. This contribution expands on the work in Verstraete (2016) which, using a fuzzy inference system (an approach from the field of artificial intelligence) in combination with proxy data (other available data that provides information on the spatial distribution), is capable of providing a solution to the spatial disaggregation problem.…”

Section: Regridding Versus Spatial Disaggregationmentioning

confidence: 99%

“…In Verstraete (2014), the authors presented the concept of using a rule-based system for grid remapping. Their detailed algorithm to use proxy data to generate a fuzzy rule-based system that performs the spatial disaggregation is elaborated on in Verstraete (2016).…”

Section: Remapping Gridded Datamentioning

confidence: 99%

“…Most of the above methods, with the exception of the methods in Horabik and Nahorski (2014) and Verstraete (2016) are directly applicable for regridding. In areal weighting, the spatial distribution is assumed per grid cell, but its definition using the amount of overlap does not pose problems with partial overlap and can thus directly be used for regridding.…”

Section: Remapping Gridded Datamentioning

confidence: 99%

“…The method in Horabik and Nahorski (2014) was designed specifically for spatial disaggregation. The method described in Verstraete (2016) was intended for regridding but limited to spatial disaggregation. The reason is that it was difficult to maintain the constraint (2).…”

Section: Remapping Gridded Datamentioning

confidence: 99%

“…The generation and use of a fuzzy rule-based system to perform a spatial disaggregation is explained in detail in Verstraete (2016) and summarized here. In this method, the weights as mentioned in the previous section are calculated by means of a fuzzy rule-based system.…”

Section: Spatial Disaggregation Using the Fuzzy Rule-based Approachmentioning

confidence: 99%

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Solving the general map overlay problem using a fuzzy inference system designed for spatial disaggregation

Verstraete

2018

Mitig Adapt Strateg Glob Change

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Identification of mitigation and adaptation strategies in any situation has to be a well informed decision. This decision not only has to be based on quantifiable data (e.g. amounts or concentrations of emissions), but it also needs to consider spatial aspects such as the source of emissions, the impact of policies on the population or the identification of responsible parties. As such, it is important that the decisions are based on accurate reports made by experts in the field. Many data are available to the experts, and combining this data tends to increase the inherently contained uncertainty. Novel operations that exhibit a lower increase of uncertainty can yield outcomes that contain less uncertainty, which subsequently improves the accuracy of the resulting reports. At the same time, the decisionmakers are confronted with different reports, and the comparison of the contained spatial aspects requires combining the data, which exhibits similar issues relating to uncertainty. With data usually represented in gridded structures, comparing them often requires a process called regridding: this is the process of mapping one grid onto a second grid, a process which increases spatial uncertainty. In this contribution, a novel regridding algorithm is presented. While mainly intended as a preprocessing tool for the experts, it is also applicable for supporting the comparison of gridded datasets as used by decision-makers. In the context of this article, a grid can be irregular, allowing the presented algorithm to also be used for remapping a grid onto, e.g. administrative borders or vice versa. The presented algorithm for regridding is a modification that is generally applicable on spatial disaggregation algorithms. It was developed in parallel with a novel method that uses artificial intelligence (in the form of fuzzy rule-based systems) to involve proxy data to obtain better results, and it will be demonstrated using this approach for spatial disaggregation. Mitig Adapt Strateg Glob Change to perform regridding using an algorithm designed for spatial disaggregation is detailed in this article and the performance of the combination with the artificial intelligent system for disaggregation is illustrated by means of an example.

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Section: Regridding Versus Spatial Disaggregationmentioning

confidence: 99%

Section: Remapping Gridded Datamentioning

confidence: 99%

Section: Remapping Gridded Datamentioning

confidence: 99%

Section: Remapping Gridded Datamentioning

confidence: 99%

Section: Spatial Disaggregation Using the Fuzzy Rule-based Approachmentioning

confidence: 99%

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Solving the general map overlay problem using a fuzzy inference system designed for spatial disaggregation

Verstraete

2018

Mitig Adapt Strateg Glob Change

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Consequences and Modeling Challenges Connected with Atmospheric Pollution

Nahorski

Holnicki

2020

Studies in Systems, Decision and Control

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Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources

Bun

Nahorski

Horabik-Pyzel

et al. 2018

Mitig Adapt Strateg Glob Change

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Greenhouse gas (GHG) inventories at national or provincial levels include the total emissions as well as the emissions for many categories of human activity, but there is a need for spatially explicit GHG emission inventories. Hence, the aim of this research was to outline a methodology for producing a high-resolution spatially explicit emission inventory, demonstrated for Poland. GHG emission sources were classified into point, line, and area types and then combined to calculate the total emissions. We created vector maps of all sources for all categories of economic activity covered by the IPCC guidelines, using official information about companies, the administrative maps, Corine Land Cover, and other available data. We created the algorithms for the disaggregation of these data to the level of elementary objects such as emission sources. The algorithms used depend on the categories of economic activity under investigation. We calculated the emissions of carbon, nitrogen sulfure and other GHG compounds (e.g., CO 2 , CH 4 , N 2 O, SO 2 , NMVOC) as well as total emissions in the CO 2 -equivalent. Gridded data were only created in the final stage to present the summarized emissions of very diverse sources from all categories. In our approach, information on the administrative assignment of corresponding emission sources is retained, which makes it possible to aggregate the final results to different administrative levels including municipalities, which is not possible using a traditional gridded emission approach. We demonstrate that any grid size can be chosen to match the aim of the spatial inventory, but not less than 100 m in this example, which corresponds to the coarsest resolution of the input datasets. We then considered the uncertainties in the statistical data, the calorific values, and the emission factors, with symmetric and asymmetric (lognormal) distributions. Using the Monte Carlo method, uncertainties, expressed using 95% confidence intervals, were estimated for high point-type emission sources, the provinces, and the subsectors. Such an approach is flexible, provided the data are available, and can be applied to other countries.

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The Spatial Disaggregation Problem: Simulating Reasoning Using a Fuzzy Inference System

Cited by 13 publications

References 28 publications

Solving the general map overlay problem using a fuzzy inference system designed for spatial disaggregation

Solving the general map overlay problem using a fuzzy inference system designed for spatial disaggregation

Consequences and Modeling Challenges Connected with Atmospheric Pollution

Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources

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