4 Search Results for "Andris, Clio"

Document
DOI: 10.4230/LIPIcs.GIScience.2025.5
Analysis of Points of Interests Recommended for Leisure Walk Descriptions

Authors: Ehsan Hamzei, Thi Minh Hoai Bui, Martin Tomko, and Stephan Winter

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
Leisure walking is a physical activity where locomotion through a natural or even urban environment is the goal in itself, e.g., in pursuit of health and wellbeing. In contrast to destination-oriented walks that are focused on navigation efficiency (i.e., shortest or simplest walk from source to destination), leisure walks emphasize experiencing the environment, engaging in activities, and discovering places that may be off route, or intermediate destinations en-route, summarily called points of interest (POIs). POIs are key for recommending leisure walks, yet a detailed analysis of POIs in the context of leisure walking is missing in the literature. This study extracts and annotates POIs of leisure walking recommendations available in WalkingMaps.com.au, creating an annotated dataset to address this research gap and provide a first analysis of leisure walking descriptions. We classify POIs using the verbal description provided in the dataset, match them with data available in OpenStreetMap (OSM), and compare the POIs with nearby alternatives in OSM. Our analysis reveals thematic and spatial patterns in POI selection, offering a machine learning approach to model POI choices for leisure walks. We further evaluate the availability of rich data in OSM for future automated leisure walking recommendation. This study contributes to automated systems for recommending leisure walks, tailoring suggestions based on available information in the spatial open data, and presents an annotated dataset to facilitate future research in this field.
Cite as

Ehsan Hamzei, Thi Minh Hoai Bui, Martin Tomko, and Stephan Winter. Analysis of Points of Interests Recommended for Leisure Walk Descriptions. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 5:1-5:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{hamzei_et_al:LIPIcs.GIScience.2025.5,
  author =	{Hamzei, Ehsan and Bui, Thi Minh Hoai and Tomko, Martin and Winter, Stephan},
  title =	{{Analysis of Points of Interests Recommended for Leisure Walk Descriptions}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{5:1--5:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.5},
  URN =		{urn:nbn:de:0030-drops-238341},
  doi =		{10.4230/LIPIcs.GIScience.2025.5},
  annote =	{Keywords: leisure walks, points of interest, places, platial information}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.6
MODAP: A Multi-City Open Data & Analytics Platform for Micromobility Research

Authors: Grant McKenzie

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
Over the past decade, micromobility services, particularly electric vehicles for personal short-distance trips, have experienced significant growth. Major cities around the world now host extensive fleets of vehicles available for short-term public rental. While previous research has examined usage patterns within and between a few select cities, large, open, and publicly accessible data sets for analyzing mobility across multiple cities are extremely limited. I have collected, curated, and aggregated over twenty million e-scooter and e-bicycle trips across five major cities and are openly releasing aggregated data for use by mobility and sustainable transport researchers, urban planners, and policymakers. To accompany these data, I developed MODAP (Micromobility Open Data & Analytics Platform), a geovisual analytics tool that empowers researchers to explore the temporal and regional patterns of e-mobility trips within our open data set and download the data for offline analysis. My objective is to foster further research into city-scale mobility patterns and to equip researchers, community members, and policymakers with the necessary tools to conduct this work.
Cite as

Grant McKenzie. MODAP: A Multi-City Open Data & Analytics Platform for Micromobility Research. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 6:1-6:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{mckenzie:LIPIcs.GIScience.2025.6,
  author =	{McKenzie, Grant},
  title =	{{MODAP: A Multi-City Open Data \& Analytics Platform for Micromobility Research}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{6:1--6:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.6},
  URN =		{urn:nbn:de:0030-drops-238353},
  doi =		{10.4230/LIPIcs.GIScience.2025.6},
  annote =	{Keywords: open data, mobility, geovisualization, micromobility}
}
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.GIScience.2023.55
Resiliency: A Consensus Data Binning Method (Short Paper)

Authors: Arpit Narechania, Alex Endert, and Clio Andris

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

Abstract
Data binning, or data classification, involves grouping quantitative data points into bins (or classes) to represent spatial patterns and show variation in choropleth maps. There are many methods for binning data (e.g., natural breaks, quantile) that may make the same data appear very different on a map. Some of these methods may be more or less appropriate for certain types of data distributions and map purposes. Thus, when designing a map, novice users may be overwhelmed by the number of choices for binning methods and experts may find comparing results from different binning methods challenging. We present resiliency, a new data binning method that assigns areal units to their most agreed-upon, consensus bin as it persists across multiple chosen binning methods. We show how this "smart average" can effectively communicate spatial patterns that are agreed-upon across binning methods. We also measure the variety of bins a single areal unit can be placed in under different binning methods showing fuzziness and uncertainty on a map. We implement resiliency and other binning methods via an open-source JavaScript library, BinGuru.
Cite as

Arpit Narechania, Alex Endert, and Clio Andris. Resiliency: A Consensus Data Binning Method (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 55:1-55:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{narechania_et_al:LIPIcs.GIScience.2023.55,
  author =	{Narechania, Arpit and Endert, Alex and Andris, Clio},
  title =	{{Resiliency: A Consensus Data Binning Method}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{55:1--55:7},
  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.2023.55},
  URN =		{urn:nbn:de:0030-drops-189509},
  doi =		{10.4230/LIPIcs.GIScience.2023.55},
  annote =	{Keywords: data binning, data classification, choropleth maps, geovisualization, geographic information systems, geographic information science, cartography}
}
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