4 Search Results for "Tsou, Ming-Hsiang"

Document
Short Paper
DOI: 10.4230/LIPIcs.COSIT.2024.19
The Senators Problem: A Design Space of Node Placement Methods for Geospatial Network Visualization (Short Paper)

Authors: Arnav Mardia, Sichen Jin, Kathleen M. Carley, Yu-Ru Lin, Zachary P. Neal, Patrick Park, and Clio Andris

Published in: LIPIcs, Volume 315, 16th International Conference on Spatial Information Theory (COSIT 2024)

Abstract
Geographic network visualizations often require assigning nodes to geographic coordinates, but this can be challenging when precise node locations are undefined. We explore this problem using U.S. senators as a case study. Each state has two senators, and thus it is difficult to assign clear individual locations. We devise eight different node placement strategies ranging from geometric approaches such as state centroids and longest axis midpoints to data-driven methods using population centers and home office locations. Through expert evaluation, we found that specific coordinates such as senators’ office locations and state centroids are preferred strategies, while random placements and the longest axis method are least favored. The findings also highlight the importance of aligning node placement with research goals and avoiding potentially misleading encodings. This paper contributes to future advancements in geospatial network visualization software development and aims to facilitate more effective exploratory spatial data analysis.
Cite as

Arnav Mardia, Sichen Jin, Kathleen M. Carley, Yu-Ru Lin, Zachary P. Neal, Patrick Park, and Clio Andris. The Senators Problem: A Design Space of Node Placement Methods for Geospatial Network Visualization (Short Paper). In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 19:1-19:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{mardia_et_al:LIPIcs.COSIT.2024.19,
  author =	{Mardia, Arnav and Jin, Sichen and Carley, Kathleen M. and Lin, Yu-Ru and Neal, Zachary P. and Park, Patrick and Andris, Clio},
  title =	{{The Senators Problem: A Design Space of Node Placement Methods for Geospatial Network Visualization}},
  booktitle =	{16th International Conference on Spatial Information Theory (COSIT 2024)},
  pages =	{19:1--19:9},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-330-0},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{315},
  editor =	{Adams, Benjamin and Griffin, Amy L. and Scheider, Simon and McKenzie, Grant},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2024.19},
  URN =		{urn:nbn:de:0030-drops-208346},
  doi =		{10.4230/LIPIcs.COSIT.2024.19},
  annote =	{Keywords: Spatial networks, Political networks, Social networks, Geovisualization, Node placement}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.CP.2024.38
Frugal Algorithm Selection (Short Paper)

Authors: Erdem Kuş, Özgür Akgün, Nguyen Dang, and Ian Miguel

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
When solving decision and optimisation problems, many competing algorithms (model and solver choices) have complementary strengths. Typically, there is no single algorithm that works well for all instances of a problem. Automated algorithm selection has been shown to work very well for choosing a suitable algorithm for a given instance. However, the cost of training can be prohibitively large due to running candidate algorithms on a representative set of training instances. In this work, we explore reducing this cost by choosing a subset of the training instances on which to train. We approach this problem in three ways: using active learning to decide based on prediction uncertainty, augmenting the algorithm predictors with a timeout predictor, and collecting training data using a progressively increasing timeout. We evaluate combinations of these approaches on six datasets from ASLib and present the reduction in labelling cost achieved by each option.
Cite as

Erdem Kuş, Özgür Akgün, Nguyen Dang, and Ian Miguel. Frugal Algorithm Selection (Short Paper). In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 38:1-38:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{kus_et_al:LIPIcs.CP.2024.38,
  author =	{Ku\c{s}, Erdem and Akg\"{u}n, \"{O}zg\"{u}r and Dang, Nguyen and Miguel, Ian},
  title =	{{Frugal Algorithm Selection}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{38:1--38:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.38},
  URN =		{urn:nbn:de:0030-drops-207239},
  doi =		{10.4230/LIPIcs.CP.2024.38},
  annote =	{Keywords: Algorithm Selection, Active Learning}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2021.I.10
Estimating Hourly Population Distribution Patterns at High Spatiotemporal Resolution in Urban Areas Using Geo-Tagged Tweets and Dasymetric Mapping

Authors: Jaehee Park, Hao Zhang, Su Yeon Han, Atsushi Nara, and Ming-Hsiang Tsou

Published in: LIPIcs, Volume 177, 11th International Conference on Geographic Information Science (GIScience 2021) - Part I (2020)

Abstract
This paper introduces a spatiotemporal analysis framework for estimating hourly changing population distribution patterns in urban areas using geo-tagged tweets (the messages containing users’ geospatial locations), land use data, and dasymetric maps. We collected geo-tagged social media (tweets) within the County of San Diego during one year (2015) by using Twitter’s Streaming Application Programming Interfaces (APIs). A semi-manual Twitter content verification procedure for data cleaning was applied first to separate tweets created by humans from non-human users (bots). The next step was to calculate the number of unique Twitter users every hour within census blocks. The final step was to estimate the actual population by transforming the numbers of unique Twitter users in each census block into estimated population densities with spatial and temporal factors using dasymetric maps. The temporal factor was estimated based on hourly changes of Twitter messages within San Diego County, CA. The spatial factor was estimated by using the dasymetric method with land use maps and 2010 census data. Comparing to census data, our methods can provide better estimated population in airports, shopping malls, sports stadiums, zoo and parks, and business areas during the day time.
Cite as

Jaehee Park, Hao Zhang, Su Yeon Han, Atsushi Nara, and Ming-Hsiang Tsou. Estimating Hourly Population Distribution Patterns at High Spatiotemporal Resolution in Urban Areas Using Geo-Tagged Tweets and Dasymetric Mapping. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 10:1-10:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{park_et_al:LIPIcs.GIScience.2021.I.10,
  author =	{Park, Jaehee and Zhang, Hao and Han, Su Yeon and Nara, Atsushi and Tsou, Ming-Hsiang},
  title =	{{Estimating Hourly Population Distribution Patterns at High Spatiotemporal Resolution in Urban Areas Using Geo-Tagged Tweets and Dasymetric Mapping}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
  pages =	{10:1--10:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-166-5},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{177},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.10},
  URN =		{urn:nbn:de:0030-drops-130456},
  doi =		{10.4230/LIPIcs.GIScience.2021.I.10},
  annote =	{Keywords: Population Estimation, Twitter, Social Media, Dasymetric Map, Spatiotemporal}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.70
Assessing Neighborhood Conditions using Geographic Object-Based Image Analysis and Spatial Analysis (Short Paper)

Authors: Chi-Feng Yen, Ming-Hsiang Tsou, and Chris Allen

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
Traditionally, understanding urban neighborhood conditions heavily relies on time-consuming and labor-intensive surveying. As the growing development of computer vision and GIScience technology, neighborhood conditions assessment can be more cost-effective and time-efficient. This study utilized Google Earth Engine (GEE) to acquire 1m aerial imagery from the National Agriculture Image Program (NAIP). The features within two main categories: (i) aesthetics and (ii) street morphology that have been selected to reflect neighborhood socio-economic (SE) and demographic (DG) conditions were subsequently extracted through geographic object-based image analysis (GEOBIA) routine. Finally, coefficient analysis was performed to validate the relationship between selected SE indicators, generated via spatial analysis, as well as actual SE and DG data within region of interests (ROIs). We hope this pilot study can be leveraged to perform cost- and time- effective neighborhood conditions assessment in support of community data assessment on both demographics and health issues.
Cite as

Chi-Feng Yen, Ming-Hsiang Tsou, and Chris Allen. Assessing Neighborhood Conditions using Geographic Object-Based Image Analysis and Spatial Analysis (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 70:1-70:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{yen_et_al:LIPIcs.GISCIENCE.2018.70,
  author =	{Yen, Chi-Feng and Tsou, Ming-Hsiang and Allen, Chris},
  title =	{{Assessing Neighborhood Conditions using Geographic Object-Based Image Analysis and Spatial Analysis}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{70:1--70:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.70},
  URN =		{urn:nbn:de:0030-drops-93983},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.70},
  annote =	{Keywords: neighborhood conditions assessment, geographic object-based image analysis, spatial analysis}
}
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