Exploratory Analysis of Massive Movement Data

Gräser, Axel; Dragaschnig, Melitta; Koller, Hannes

doi:10.1007/978-3-030-55462-0_12

Cited by 6 publications

(4 citation statements)

References 90 publications

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“…This characteristic was not directly included in these past frameworks, most likely because, as mentioned in the Introduction, trajectories are commonly recorded with precise sensors. Forms of general movement would likely be most important to trajectory research once the analysis is performed to aggregate movement into overall patterns, such as analyses on big data movement patterns (Graser, Dragaschnig, & Koller, 2021). Therefore, although this characteristic was not included in earlier frameworks, we saw the potential importance of this characteristic in our own analysis of movement statements and in recent research to aggregate big movement data.…”

Section: Experiments Results: Important Characteristics Of Movement S...mentioning

confidence: 99%

Differentiating geographic movement described in text documents

Pezanowski

MacEachren

Mitra

2022

Transactions in GIS

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Understanding movement described in text documents is important, since text descriptions of movement contain a wealth of geographic and contextual information about the movement of people, wildlife, goods, and much more. Our research makes several contributions to improve our understanding of movement descriptions in text. First, we show how interpreting geographic movement described in text is challenging because of general spatial terms, linguistic constructions that make the thing(s) moving unclear, and many types of temporal references and groupings, among others. Next, as a step to overcome these challenges, we report on an experiment with human subjects through which we identify multiple important characteristics of movement descriptions (found in text) that humans use to differentiate one movement description from another. Based on our empirical results, we provide recommendations for computational analysis using movement described in text documents. Our findings contribute toward an improved understanding of the important characteristics of the underused information about geographic movement that is in the form of text descriptions.

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Section: Experiments Results: Important Characteristics Of Movement S...mentioning

confidence: 99%

Differentiating geographic movement described in text documents

Pezanowski

MacEachren

Mitra

2022

Transactions in GIS

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“…Além disso, essa abordagem pode fazer uso de informac ¸ões como acelerômetro, quando disponíveis. Para validar a ferramenta proposta, o conjunto de dados reunidos no STAGA foi utilizado para realizar uma comparac ¸ão de resultados com métodos implementados em duas bibliotecas, Scikit-Mobility [Pappalardo et al 2022] e MovingPandas [Graser and Dragaschnig 2020].…”

Section: Stop Go Classifier [Spang Et Al 2022a]unclassified

“…No trabalho [Duarte and Sakr 2023], é apresentado um método para gerar ground-truth, ou seja, um método para gerar um conjunto de targets ou labels que posteriormente podem ser usados para treinar e testar um ou mais modelos, utilizando validac ¸ão cruzada. Especificamente, esse método serve para gerar labels de 'outliers' ou 'ruído' em quatro conjuntos de dados distintos, que serão utilizados para avaliar os métodos de detecc ¸ão de outliers, e limpeza de trajetória, implementados em sete bibliotecas: (i)Movetk [Custers et al 2021]; (ii) Moving Pandas [Graser and Dragaschnig 2020]; (iii) Scikit-mobility [Pappalardo et al 2022]; (iv) Ptrail [Haidri et al 2021]; (v) Pymove [Sanches 2019, Bráz 2020]; (vi) Argosfilter [Freitas and Freitas 2022]; (vii) Stmove [Seidel et al 2019].…”

Section: Outlier Detection and Cleaning In Trajectories: A Benchmark ...unclassified

Uma análise comparativa de técnicas de detecção de pontos de parada em ambientes urbanos

Oliveira,

Celes,

Oliveira

et al. 2023

Anais Da XI Escola Regional De Computação Do Ceará, Maranhão E Piauí (ERCEMAPI 2023)

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Este artigo apresenta um framework para a criação de conjuntos de dados de referência (ground-truth) destinados à detecção automatizada de pontos de parada. O framework utiliza dados do OpenStreetMap e o SUMO (Simulation of Urban MObility) como fontes de informação essenciais. Além disso, são implementados e comparados métodos amplamente discutidos na literatura para a detecção de pontos de parada, utilizando conjuntos de dados gerados por meio desse framework. Os resultados da análise confirmam a confiabilidade dos métodos estudados. O estudo também introduz novos algoritmos à análise, que demonstram ser promissores na detecção de pontos de parada, além de identificar áreas para melhorias futuras. Destacam-se a necessidade de explorar análises adicionais que considerem métodos alternativos de aquisição de dados e avaliem seus impactos na detecção de pontos de parada.

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“…Existing geographic movement research has improved analysis methods (Dodge et al, 2012;Dodge, 2016a;Dodge et al, 2016;Dodge, 2016b;Graser et al, 2021Soares Junior et al, 2017;Huang, 2017) and shown how these methods can derive valuable information about human movement and wildlife movement (Wang et al, 2020a;Dodge et al, 2014;Miller et al, 2019;Zhu et al, 2021;Li et al, 2021). However, partly due to the challenges and because computational techniques to address them are relatively new, this prior research focused on geographic movement in precise movement trajectories from sensors such as GPS and mostly ignored movement described in text documents.…”

Section: Introductionmentioning

confidence: 99%

Erforschung von Bewegungsbeschreibungen durch geovisuelle Analytik

Pezanowski

Mitra

MacEachren

2022

KN J. Cartogr. Geogr. Inf.

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Sensemaking using automatically extracted information from text is a challenging problem. In this paper, we address a specific type of information extraction, namely extracting information related to descriptions of movement. Aggregating and understanding information related to descriptions of movement and lack of movement specified in text can lead to an improved understanding and sensemaking of movement phenomena of various types, e.g., migration of people and animals, impediments to travel due to COVID-19, etc. We present GeoMovement, a system that is based on combining machine learning and rule-based extraction of movement-related information with state-of-the-art visualization techniques. Along with the depiction of movement, our tool can extract and present a lack of movement. Very little prior work exists on automatically extracting descriptions of movement, especially negation and movement. Apart from addressing these, GeoMovement also provides a novel integrated framework for combining these extraction modules with visualization. We include two systematic case studies of GeoMovement that show how humans can derive meaningful geographic movement information. GeoMovement can complement precise movement data, e.g., obtained using sensors, or be used by itself when precise data is unavailable. Supplementary Information The online version contains supplementary material available at 10.1007/s42489-022-00098-3.

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Exploratory Analysis of Massive Movement Data

Cited by 6 publications

References 90 publications

Differentiating geographic movement described in text documents

Differentiating geographic movement described in text documents

Uma análise comparativa de técnicas de detecção de pontos de parada em ambientes urbanos

Erforschung von Bewegungsbeschreibungen durch geovisuelle Analytik

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