2 Search Results for "MacEachren, Alan M."

Document
DOI: 10.4230/LIPIcs.GIScience.2025.14
Guiding Geospatial Analysis Processes in Dealing with Modifiable Areal Unit Problems

Authors: Guoray Cai and Yue Hao

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

Abstract
Geospatial analysis has been widely applied in different domains for critical decision making. However, the results of spatial analysis are often plagued with uncertainties due to measurement errors, choice of data representations, and unintended transformation artifacts. A well known example of such problems is the Modifiable Areal Unit Problem (MAUP) which has well documented effects on the outcome of spatial analysis on area-aggregated data. Existing methods for addressing the effects of MAUP are limited, are technically complex, and are often inaccessible to practitioners. As a result, analysts tend to ignore the effects of MAUP in practice due to lack of expertise, high cognitive loads, and resource limitations. To address these challenges, this paper proposes a machine-guidance approach to augment the analyst’s capacity in mitigating the effect of MAUP. Based on an analysis of practical challenges faced by human analysts, we identified multiple opportunities for the machine to guide the analysts by alerting to the rise of MAUP, assessing the impact of MAUP, choosing mitigation methods, and generating visual guidance messages using GIS functions and tools. For each of the opportunities, we characterize the behavior patterns and the underlying guidance strategies that generate the behavior. We illustrate the behavior of machine guidance using a hotspot analysis scenario in the context of crime policing, where MAUP has strong effects on the patterns of crime hotspots. Finally, we describe the computational framework used to build a prototype guidance system and identify a number of research questions to be addressed. We conclude by discussing how the machine guidance approach could be an answer to some of the toughest problems in geospatial analysis.
Cite as

Guoray Cai and Yue Hao. Guiding Geospatial Analysis Processes in Dealing with Modifiable Areal Unit Problems. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 14:1-14:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{cai_et_al:LIPIcs.GIScience.2025.14,
  author =	{Cai, Guoray and Hao, Yue},
  title =	{{Guiding Geospatial Analysis Processes in Dealing with Modifiable Areal Unit Problems}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{14:1--14:18},
  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.2025.14},
  URN =		{urn:nbn:de:0030-drops-238433},
  doi =		{10.4230/LIPIcs.GIScience.2025.14},
  annote =	{Keywords: Machine Guidance, Geo-Spatial Analysis, Modifiable Areal Unit Problem (MAUP)}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.44
Is This Statement About A Place? Comparing two perspectives (Short Paper)

Authors: Alan M. MacEachren, Richard Caneba, Hanzhou Chen, Harrison Cole, Emily Domanico, Nicholas Triozzi, Fangcao Xu, and Liping Yang

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

Abstract
Text often includes references to places by name; in prior work, more than 20% of a sample of event-related tweets were found to include place names. Research has addressed the challenge of leveraging the geographic data reflected in text statements, with well-developed methods to recognize location mentions in text and related work on automated toponym resolution (deciding which place in the world is meant by a place name). A core issue that remains is to distinguish between text that mentions a place or places and text that is about a place or places. This paper presents the first step in research to address this challenge. The research reported here sets the conceptual and practical groundwork for subsequent supervised machine learning research; that research will leverage human-produced training data, for which a judgment is made about whether a statement is or is not about a place (or places), to train computational methods to do this classification for large volumes of text. The research step presented here focuses on three questions: (1) what kinds of entities are typically conceptualized as places, (2) what features of a statement prompt the reader to judge a statement to be about a place (or not about a place) and (3) how do judgments of whether or not a statement is about a place compare between a group of experts who have studied the concept of "place" from a geographic perspective and a cross-section of individuals recruited through a crowdsourcing platform to make these judgments.
Cite as

Alan M. MacEachren, Richard Caneba, Hanzhou Chen, Harrison Cole, Emily Domanico, Nicholas Triozzi, Fangcao Xu, and Liping Yang. Is This Statement About A Place? Comparing two perspectives (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 44:1-44:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

Copy BibTex To Clipboard

@InProceedings{maceachren_et_al:LIPIcs.GISCIENCE.2018.44,
  author =	{MacEachren, Alan M. and Caneba, Richard and Chen, Hanzhou and Cole, Harrison and Domanico, Emily and Triozzi, Nicholas and Xu, Fangcao and Yang, Liping},
  title =	{{Is This Statement About A Place? Comparing two perspectives}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{44:1--44:6},
  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.2018.44},
  URN =		{urn:nbn:de:0030-drops-93720},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.44},
  annote =	{Keywords: geographic information retrieval, spatial language, crowdsourcing}
}
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