Spatial Data Uncertainty

Li, Linna; Ban, Hyowon; Wechsler, Suzanne P.; Xu, Bo

doi:10.1016/b978-0-12-409548-9.09610-x

Cited by 18 publications

(12 citation statements)

References 172 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The transition probability matrix enables an assessment that indicates the likelihood of each pixel in class A of the rst map class converting to another class (e.g. B, C, D, ...) or remaining in class A in the second map class 59 . The Markov chain, which is technically a separate random process, uses transition probability to forecast the next state and all future states based on the current state 60 .…”

Section: Environmental Prediction Based On the Ca-markovmentioning

confidence: 99%

Scenario-based Analysis of the Impacts of Lake Drying on Sustainable Food Production

Feizizzadeh¹,

Lakes²,

Omarzadeh

et al. 2021

Preprint

View full text Add to dashboard Cite

In many parts of the world, lake drying is caused by water management failures, while the phenomenon is exacerbated by climate change. Lake Urmia in Northern Iran is drying up at such an alarming rate that it is considered to be a dying lake, which has dire consequences for the whole region. While salinization caused by a dying lake is well understood and known to influence the local and regional food production, other potential impacts are as yet unknown. Food production in the Urmia region has predominantly been regionally-oriented and sustainable, particularly in terms of water demand. To explore current and projected impacts of the dying lake on sustainable food production (SFP) we investigated changes in climatic conditions, land use, and land degradation for the period 1990-2020. We examined the environmental impacts of lake drought on SFP through an integrated scenario-based geoinformation framework. The results show that the lake drought has significantly affected food production and has reduced the proportion of SFP over the past three decades. Based on a combination of cellular automata and Markov modelling, the conditions are projected for the next 30 years and are predicted to exacerbate further. To mitigate these issues and support SFP, this research provides some policy recommendations and results for tangible action plans. We consider the modification of cropping patterns towards low water demand crops as one the cheapest and most efficient solutions for reducing the overall agricultural water consumption.

show abstract

Section: Environmental Prediction Based On the Ca-markovmentioning

confidence: 99%

Scenario-based Analysis of the Impacts of Lake Drying on Sustainable Food Production

Feizizzadeh¹,

Lakes²,

Omarzadeh

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Sometimes, particularly if a feedstock supplier's data on roadside storage sites is used, the format of the biomass data may already consist of vector points and no conversion from a raster format or extraction of polygon centroids will be needed. Accuracy in GIS indicates how well the object in the model matches the location or attributes of the corresponding object in the real world (Li 2017). In a spatial BSC case, data accuracy can be assessed, for example, by comparing the location of a line segment representing a road in the GIS context with the reckoned real-world location (e.g.…”

Section: Spatial Data In Bsc Studiesmentioning

confidence: 99%

Spatial forest biomass supply chain analysis in Finland

Korpinen¹

2021

Diss. For.

View full text Add to dashboard Cite

The forest biomass supply represents an important part of the value chain for different woodbased products, and its environmental impacts are also frequently crucial. The performance of biomass supply chains (BSCs) can be assessed for various purposes and using a variety of methodological approaches, either including or excluding spatial properties. The purpose of this thesis was to investigate what kind of spatial data are required and available for casespecific BSC analyses in Finland, and what would be suitable levels of spatial precision for the various approaches. This thesis consists of five papers, one of which reviews case studies carried out in various geographical BSC environments around the world, while the remaining four are spatial case studies of BSC systems in Finland, three of them focusing on bioenergy production and one assessing the performance of a novel pulpwood transportation concept. A geographical information system (GIS) was used as the principal tool in one study, while in the other three the role of GIS was to produce spatially analysed data for life-cycle assessment and agent-based simulation. The main conclusion is that a spatial precision of between 1 km and 10 km, where each point of origin represents roughly an area of 1-100 km 2 , is sufficient for forest biomass data in Finnish BSC systems. The final precision should be determined collectively by the setup of the case study, factors leading to complexity in the supply chain system and the geographical extent of the area concerned. Relative to many other parts of the world, Finland has a readily available high quality source of spatial data for BSC research. It is recommended that GIS-based research could be improved by adding dynamic properties and stochasticity to the models, because temporal variations in feedstock supply and demand will probably increase in the future.

show abstract

“…Geospatial data S 1 uses geospatial locations or trajectories L. Here, various attributes A are assigned to such a domain L by a function f ∶ L → A. Therefore, two types of uncertainty, namely spatial uncertainty and attribute uncertainty [62], are found in such datasets. Spatial uncertainty origins from the underlying areas or trajectories that can be displaced or shifted in shape, deviating from the stored data.…”

Section: Data Typesmentioning

confidence: 99%

“…Figure 4a illustrates both types of uncertainty by showing positional and attribute uncertainty. Li et al [62] described how analytic models can be utilized to achieve uncertainty quantification.…”

Section: Data Typesmentioning

confidence: 99%

Uncertainty-aware Visual Analytics - Scope, Oppertunities and Challenges

Maack

Scheuermann

Hagen

et al. 2021

Preprint

View full text Add to dashboard Cite

In many applications, Visual Analytics(VA) has developed into a standard tool to ease data access and knowledge generation. Unfortunately, many data sources, used in the VA process, are affected by uncertainty. In addition, the VA cycle itself can introduce uncertainty to the knowledge generation process. The classic VA cycle does not provide a mechanism to handle these sources of uncertainty. In this manuscript, we aim to provide an extended VA cycle that is capable of handling uncertainty by quantification, propagation, and visualization. Different data types and application scenarios that can be handled by such a cycle, examples, and a list of open challenges in the area of uncertainty-aware VA are provided.

show abstract

Spatial Data Uncertainty

Cited by 18 publications

References 172 publications

Scenario-based Analysis of the Impacts of Lake Drying on Sustainable Food Production

Scenario-based Analysis of the Impacts of Lake Drying on Sustainable Food Production

Spatial forest biomass supply chain analysis in Finland

Uncertainty-aware Visual Analytics - Scope, Oppertunities and Challenges

Contact Info

Product

Resources

About