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

Abstract

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.

Combined Defuzzification Under Shared Constraint

“…Exploring type-2 fuzzy sets is also considered as a future generalisation with a particular focus on exploiting the additional uncertainty. no 3,5 no 3,5 no 3,5 no 5 no 5 no 5 no 5 One-Element no 3,5 no 3,5 no 3,5 no 3,5 no 5 no 5 no 5 no 5 Monotony -6 -6 -6 -6 symmetry yes 3,7,8 yes 3,7,8 yes 3,7,8 yes 3,7,8 yes 3,7,8 yes 3,7,8 yes 3,7,8 yes 3,7,8 x-translation yes 3,7,9 yes 3,7,9 yes 3,7,9 yes 3,7,9 yes 3,7,9 yes 3,7,9 yes 3,7,9 yes 3,7,9 x-scaling yes 3,7,8 yes 3,7,8 yes 3,7,9 yes 3,7,9 yes 3,7,9 yes 3,7,9 yes 3,7,9 yes 3,7,9 µ-translation yes 3,8 no yes 3,9 no yes 3,8 no yes 3,8 no µ-scaling yes 3,8 no yes 3,9 no yes 3,8 no yes 3,8 no note 1 Uniqueness has to be considered in the context of a given input. G.4 is more likely to support unicity than G.3 in practical applications.…”

“…G.4 is more likely to support unicity than G.3 in practical applications. 2 for convex fuzzy sets 3 if uniqueness is fulfilled 4 if the support is an interval 5 yes only under very strict conditions and not generally 6 not applicable 7 requires an appropriate change of the constraining function 8 only when changing all sets in the same way 9 it is possible to change a single set for any use that may be made of the information it contains. S. Bykuć is funded by the project Greenheat.…”

“…In [1], the authors presented an approach that employs a type-1 fuzzy inference system to perform spatial disaggregation, mapping the data to a more refined partitioning, using additionally available information. This approach was later extended to perform a general regridding ( [3]). The inference system is applied once for each new partition and yields a fuzzy set which contains the possible values for the result of the disaggregation, a disaggregation thus requires a rulebase application for each partition.…”

Quantifying greenhouse gas emissions

“…The papers by La Notte et al (2018), Verstraete (2018), Gusti et al (2018), Jarnicka and Żebrowski (2019), and Jonas and Żebrowski (2018) provide beyond-inventory or methodological support on aspects of uncertainty that have not been addressed to date, have been overlooked, or have emerged over time. The research described in these papers contributes equally to key issues 1 (one-sided perspective), 3, and 4.…”

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

“…These grids can differ in cell size, they can be displaced in any latitude and longitude direction, and they can even be rotated by a certain angle. To address this overlay problem, approaches based on fuzzy logic and artificial intelligence techniques can be used (Verstraete 2017(Verstraete , 2018. Another problem is that most GHG gridded emission calculations do not fully take into account the state and provincial administrative boundaries, and usually, a cell is assigned to an administrative unit based on where the majority of the area falls.…”