“Making Moves” in a Cardiac ICU: An Epistemology of Rhythm, Data Richness, and Process Certainty

2020 ◽  
Vol 34 (3) ◽  
pp. 344-360 ◽  
Author(s):  
Scott D. Stonington
Keyword(s):  
Data Richness

scholarly journals UAV-Based Mapping of Banana Land Area for Village-Level Decision-Support in Rwanda

2021 ◽  
Vol 13 (24) ◽  
pp. 4985
Author(s):  
Regina Kilwenge ◽  
Julius Adewopo ◽  
Zhanli Sun ◽  
Marc Schut
Keyword(s):  
Decision Support ◽  
Crop Production ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Support Vector ◽  
Ground Truth Data ◽  
Crop Monitoring ◽  
Landcover Classification ◽  
Optimal Productivity ◽  
Data Richness

Crop monitoring is crucial to understand crop production changes, agronomic practice decision-support, pests/diseases mitigation, and developing climate change adaptation strategies. Banana, an important staple food and cash crop in East Africa, is threatened by Banana Xanthomonas Wilt (BXW) disease. Yet, there is no up-to-date information about the spatial distribution and extent of banana lands, especially in Rwanda, where banana plays a key role in food security and livelihood. Therefore, delineation of banana-cultivated lands is important to prioritize resource allocation for optimal productivity. We mapped the spatial extent of smallholder banana farmlands by acquiring and processing high-resolution (25 cm/px) multispectral unmanned aerial vehicles (UAV) imageries, across four villages in Rwanda. Georeferenced ground-truth data on different land cover classes were combined with reflectance data and vegetation indices (NDVI, GNDVI, and EVI2) and compared using pixel-based supervised multi-classifiers (support vector models-SVM, classification and regression trees-CART, and random forest–RF), based on varying ground-truth data richness. Results show that RF consistently outperformed other classifiers regardless of data richness, with overall accuracy above 95%, producer’s/user’s accuracies above 92%, and kappa coefficient above 0.94. Estimated banana farmland areal coverage provides concrete baseline for extension-delivery efforts in terms of targeting banana farmers relative to their scale of production, and highlights opportunity to combine UAV-derived data with machine-learning methods for rapid landcover classification.

Reducing Underreporting by Aggregating Budgeted Time

2019 ◽  
Vol 95 (5) ◽  
pp. 299-319
Author(s):  
Kim I. Mendoza
Keyword(s):  
Mediation Analysis ◽  
Audit Quality ◽  
Low Cost ◽  
Negative Consequences ◽  
Audit Firms ◽  
Data Richness

ABSTRACT Underreporting, or reporting fewer hours than actually worked, is a prevalent behavior among auditors at all levels. Underreporting can result in negative consequences, such as tight budgets and reductions in future audit quality. In this paper, I propose a low-cost budget formatting procedure that reduces underreporting. Using an experiment, I document that individuals with higher underreporting incentives underreport less when given an aggregated budget relative to a disaggregated budget. When individuals have lower underreporting incentives, aggregating the budget has a smaller effect on underreporting. I also provide evidence of the process by performing a mediation analysis. In a second experiment, I examine a budget formatting procedure that reduces underreporting while also mitigating the loss of data richness that results from aggregation. This study provides important insights to audit firms, partners, managers, and regulators who rely on audit hours for budgets, measures of staff efficiency, and measures of audit quality.

Envisioning Deep Maps: Exploring the Spatial Navigation Metaphor in Deep Mapping

2013 ◽  
Vol 7 (1-2) ◽  
pp. 201-227 ◽  
Author(s):  
Katie Oxx ◽  
Allan Brimicombe ◽  
Johnathan Rush
Keyword(s):  
Subject Matter ◽  
Spatial Data ◽  
Spatial Navigation ◽  
Spatial Dimension ◽  
Spatial Turn ◽  
Balanced Approach ◽  
Spatial Narrative ◽  
Data Richness

The spatial turn within the humanities and need for data richness has led to the re-conceptualisation and exploration of maps as ‘deep maps.’ Building narratives of place is becoming increasingly contingent on data landscapes as opposed to the physical landscapes within which they are situated. To make the assumption that GIS can form the basis for deep maps is to privilege the spatial dimension (and spatial data) over all others. We have sought in our experimentation to take a more open, balanced approach as to how a deep map might be organised as a way of learning/reflecting on what elements a framework should contain. Our subject matter here necessitated attention to the challenges and potentialities of deep mapping ‘things deemed religious.’ We found spatial navigation to be useful for visualizing physical and metaphysical linkages, integrating the geographical portions of our spatial narrative as well as organizing thoughts off the map.

The data richness estimation framework for federated data warehouse integration

2020 ◽  
Vol 513 ◽  
pp. 397-411
Author(s):  
Rafał Kern ◽  
Adrianna Kozierkiewicz ◽  
Marcin Pietranik
Keyword(s):  
Data Warehouse ◽  
Richness Estimation ◽  
Data Richness

scholarly journals Towards understanding the influence of data-richness on interpretational confidence in image interpretation

2013 ◽  
Vol 2013 (1) ◽  
pp. 1-4
Author(s):  
Alan R.A. Aitken ◽  
Mike C. Dentith ◽  
Eun-Jung Holden
Keyword(s):  
Image Interpretation ◽  
Data Richness

Semiautomated quantification of the influence of data richness on confidence in the geologic interpretation of aeromagnetic maps

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. J1-J13 ◽  
Author(s):  
A. R. A. Aitken ◽  
E-J. Holden ◽  
M. C. Dentith
Keyword(s):  
Data Collection ◽  
Structural Complexity ◽  
Statistical Error ◽  
Geologic Mapping ◽  
Relative Level ◽  
Sources Of Uncertainty ◽  
Confidence Assessment ◽  
Additional Data Collection ◽  
Moderate Range ◽  
Data Richness

Geologic interpretations of aeromagnetic maps are highly subjective but are rarely accompanied by a quantitative confidence assessment, which is a key limitation on the usefulness of the results. Here, we outline a method with which the relative level of data richness can be assessed quantitatively, leading to an improved understanding of spatial variations in interpretational confidence. Simple rules were used to quantify the likely influence of several major sources of uncertainty. These were: (1) the level of geologic constraint, using the local abundance of outcropping rock and the quality of geologic mapping; (2) the interpretability of the aeromagnetic data, considering the strength of edge-like features and the degree of directionality of these features, a proxy for structural complexity; (3) data collection and processing errors, including gridding errors, derived from the statistical error returned during kriging, and the influence of anisotropic line data collection on the detection of gradients. From these individual sources of uncertainty, an overall data richness map was generated through a weighted summation of these grids. Weightings were assigned so as to best match the result to the interpreter’s perception of interpretational confidence. This method produced a map of data richness, which reflects the opportunity that the data provided to the interpreter to make a correct interpretation. An example from central Australia indicated that the data influences were preserved over a moderate range of weighting factors, and that strong bias was required to override these. In addition to providing a confidence assessment, this method also provides a way to test the potential benefits of additional data collection.

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