scholarly journals datplot

2021 ◽  
pp. 1-11
Author(s):  
Lisa Steinmann ◽  
Barbora Weissova

This article introduces datplot, an R package designed to prepare chronological data for visualization, focusing on the treatment of objects dated to overlapping periods of time. Datplot is suitable for all disciplines in which scientists long for a synoptic method that enables the visualization of the chronology of a collection of heterogeneously dated objects. It is especially helpful for visualizing trends in object assemblages over long periods of time—for example, the development of pottery styles—and it can also assist in the dating of stratigraphy. As both authors come from the field of classical archaeology, the examples and case study demonstrating the functionality of the package analyze classical materials. In particular, we focus on presenting an assemblage of epigraphic evidence from Bithynia (northwestern Turkey), with a microregional focal point in the territory of Nicaea (modern Iznik). In the article, we present the internal methodology of datplot and the process of preparing a dataset of categorically and numerically dated objects. We demonstrate visualizing the data prepared by datplot using kernel density estimation and compare the outcome with more established methods such as histograms and line graphs.

2008 ◽  
Vol 52 (9) ◽  
pp. 4533-4543 ◽  
Author(s):  
Ana Colubi ◽  
Gil González-Rodríguez ◽  
María José Domínguez-Cuesta ◽  
Montserrat Jiménez-Sánchez

2020 ◽  
Author(s):  
Jose Alquicira-Hernandez ◽  
Joseph E. Powell

AbstractSummaryData sparsity in single-cell experiments prevents an accurate assessment of gene expression when visualised in a low-dimensional space. Here, we introduce Nebulosa, an R package that uses weighted kernel density estimation to recover signals lost through drop-out or low expression.Availability and implementationNebulosa can be easily installed from www.github.com/powellgenomicslab/Nebulosa


Urban Science ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 91 ◽  
Author(s):  
Filipe Brandão ◽  
Ricardo Correia ◽  
Alexandra Paio

In the cities of post-industrialized countries, renovation is the main part of building construction activity and has a major urban impact. Measuring this ongoing phenomenon and its distribution is of great usefulness for municipality urban planning and public policies. In this context, it is essential to introduce tools and processes that can allow for describing and predict how building renovation evolves. Open databases have become a valuable resource for observing processes and interactions in urban context. Data-driven analysis methods were used to directly interact with open city data, thus aiming to propose an alternative building renovation approach based on data gathering, parametric modeling, and visualization. Kernel Density Estimation (KDE) is an efficient tool that overcomes incomplete data, as not all renovation is reported to city halls. This article presents a preliminary study on a method of measuring building renovation intensity using the city of Lisbon building permit alphanumerical and spatial database as a case study.


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