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Estimation of Moran’s I in the Context of Uncertain Mobile Sensor Measurements

Authors Dominik Bucher , Henry Martin, David Jonietz, Martin Raubal , René Westerholt

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Author Details

Dominik Bucher
  • Institute of Cartography and Geoinformation, ETH Zurich, Switzerland
Henry Martin
  • Institute of Cartography and Geoinformation, ETH Zurich, Switzerland
David Jonietz
  • HERE Technologies Switzerland, Zurich, Switzerland
Martin Raubal
  • Institute of Cartography and Geoinformation, ETH Zurich, Switzerland
René Westerholt
  • School of Spatial Planning, TU Dortmund University, Germany

Funding

This research was supported by the Swiss Data Science Center (SDSC) and by the Swiss Innovation Agency Innosuisse within the Swiss Competence Center for Energy Research (SCCER) Mobility.

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Dominik Bucher, Henry Martin, David Jonietz, Martin Raubal, and René Westerholt. Estimation of Moran’s I in the Context of Uncertain Mobile Sensor Measurements. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 2:1-2:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.GIScience.2021.I.2

Abstract

Measures of spatial autocorrelation like Moran’s I do not take into account information about the reliability of observations. In a context of mobile sensors, however, this is an important aspect to consider. Mobile sensors record data asynchronously and capture different contexts, which leads to considerable heterogeneity. In this paper we propose two different ways to integrate the reliability of observations with Moran’s I. These proposals are tested in the light of two case studies, one based on real temperatures and movement data and the other using synthetic data. The results show that the way reliability information is incorporated into the Moran’s I estimates has a strong impact on how the measure responds to volatile available information. It is shown that absolute reliability information is much less powerful in addressing the problem of differing contexts than relative concepts that give more weight to more reliable observations, regardless of the general degree of uncertainty. The results presented are seen as an important stimulus for the discourse on spatial autocorrelation measures in the light of uncertainties.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
  • Information systems → Sensor networks
  • Mathematics of computing → Statistical paradigms
  • Applied computing → Earth and atmospheric sciences
Keywords
  • mobile sensors
  • Moran’s I
  • uncertainty
  • probabilistic forecasting

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