2 Search Results for "Taylor, Faith"

Document
DOI: 10.4230/LIPIcs.WABI.2025.15
Average-Tree Phylogenetic Diversity of Networks

Authors: Leo van Iersel, Mark Jones, Jannik Schestag, Celine Scornavacca, and Mathias Weller

Published in: LIPIcs, Volume 344, 25th International Conference on Algorithms for Bioinformatics (WABI 2025)

Abstract
Phylogenetic diversity is a measure used to quantify the biodiversity of a set of species. Here, we introduce the "average-tree" phylogenetic diversity score in rooted binary phylogenetic networks and consider algorithms for computing and maximizing the score on a given network. Basically, the score is the weighted average of the phylogenetic diversity scores in all trees displayed by the network, with the weights determined by the inheritance probabilities on the reticulation edges used in the embeddings. We show that computing the score of a given set of taxa in a given network is #P-hard, directly implying #P-hardness of finding a subset of k taxa achieving maximum diversity score and, thereby, ruling out polynomial-time algorithms for these problems unless the polynomial hierarchy collapses. However, we show that both problems can be solved efficiently if the input network is close to being a tree in the sense that its reticulation number is small. More precisely, we prove that we can solve the optimization problem in networks with n leaves and r reticulations in 2^{𝒪(r)}⋅ n⋅ k time. Using experiments on data produced by simulating a reticulate-evolution process, we show that our algorithm runs efficiently on networks with hundreds of taxa and tens of reticulations.
Cite as

Leo van Iersel, Mark Jones, Jannik Schestag, Celine Scornavacca, and Mathias Weller. Average-Tree Phylogenetic Diversity of Networks. In 25th International Conference on Algorithms for Bioinformatics (WABI 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 344, pp. 15:1-15:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{vaniersel_et_al:LIPIcs.WABI.2025.15,
  author =	{van Iersel, Leo and Jones, Mark and Schestag, Jannik and Scornavacca, Celine and Weller, Mathias},
  title =	{{Average-Tree Phylogenetic Diversity of Networks}},
  booktitle =	{25th International Conference on Algorithms for Bioinformatics (WABI 2025)},
  pages =	{15:1--15:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-386-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{344},
  editor =	{Brejov\'{a}, Bro\v{n}a and Patro, Rob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2025.15},
  URN =		{urn:nbn:de:0030-drops-239405},
  doi =		{10.4230/LIPIcs.WABI.2025.15},
  annote =	{Keywords: phylogenetic diversity, phylogenetic networks, network phylogenetic diversity, algorithms, computational complexity}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.94
Digital Injustice: A Case Study of Land Use Classification Using Multisource Data in Nairobi, Kenya (Short Paper)

Authors: Wenlan Zhang, Chen Zhong, and Faith Taylor

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
The utilisation of big data has emerged as a critical instrument for land use classification and decision-making processes due to its high spatiotemporal accuracy and ability to diminish manual data collection. However, the reliability and feasibility of big data are still controversial, the most important of which is whether it can represent the whole population with justice. The present study incorporates multiple data sources to facilitate land use classification while proving the existence of data bias caused digital injustice. Using Nairobi, Kenya, as a case study and employing a random forest classifier as a benchmark, this research combines satellite imagery, night-time light images, building footprint, Twitter posts, and street view images. The findings of the land use classification also disclose the presence of data bias resulting from the inadequate coverage of social media and street view data, potentially contributing to injustice in big data-informed decision-making. Strategies to mitigate such digital injustice situations are briefly discussed here, and more in-depth exploration remains for future work.
Cite as

Wenlan Zhang, Chen Zhong, and Faith Taylor. Digital Injustice: A Case Study of Land Use Classification Using Multisource Data in Nairobi, Kenya (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 94:1-94:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@InProceedings{zhang_et_al:LIPIcs.GIScience.2023.94,
  author =	{Zhang, Wenlan and Zhong, Chen and Taylor, Faith},
  title =	{{Digital Injustice: A Case Study of Land Use Classification Using Multisource Data in Nairobi, Kenya}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{94:1--94:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.94},
  URN =		{urn:nbn:de:0030-drops-189899},
  doi =		{10.4230/LIPIcs.GIScience.2023.94},
  annote =	{Keywords: Data bias, Digital injustice, Multi-source sensor data, Land use classification, Random forest classifier}
}
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