Reproducibility and replicability: opportunities and challenges for geospatial research

Kedron, Peter; Li, Wenwen; Fotheringham, A. Stewart; Goodchild, Michael F.

doi:10.1080/13658816.2020.1802032

Cited by 56 publications

(28 citation statements)

References 57 publications

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“…Different from the index-based mobility data that are highly aggregated without OD information, these data sources are big in volume, variety or heterogeneous in format and spatiotemporal resolutions, and noisy and inconsistent in data quality, which highlights the well-defined Big Data challenges [ 28 ]. Besides, these data also pose shareability, reproducibility, and replicability challenges in the Big Data era that draw attention to the scientific community [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility

Huang

et al. 2021

PLoS ONE

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In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics.

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Section: Methodsmentioning

confidence: 99%

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility

Huang

et al. 2021

PLoS ONE

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“…If the processes do vary across space, however, local models will generate much better predictions of y and generate useful information on the nature of the spatially varying processes by mapping the local parameter estimates and by examining the optimized bandwidth values. Further, if some processes do exhibit spatial nonstationarity, this has profound implications for the current interest in the reproducibility and replicability of geographic research (Goodchild et al, 2020;Kedron et al, 2019Kedron et al, , 2020Sui & Kedron, 2020). If processes are spatially varying, then we cannot expect a model calibrated in one location to be replicated exactly in another location -the processes being modelled might be different in the two locations.…”

Section: Summary and Implicationsmentioning

confidence: 99%

Modelling spatial processes in quantitative human geography

Fotheringham

Sachdeva

2021

Annals of GIS

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We discuss the nature of processes relating to human behaviour and how to model such processes when they vary over space. In so doing, we describe the role of local modelling and how the bandwidth parameter, a component of multiscale geographically weighted regression, can inform on the spatial scale over which processes are relatively constant. To do this, we translate properties of spatial data, such as heterogeneity and spatial dependency into the realm of spatial processes. We argue that the modelling of spatially varying processes has important ramifications for how we see the world.

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“…Replicability is the capacity to obtain consistent findings across studies that aim to answer the same question but with each study collecting and using its own data. Reproducibility is central to the scientific method based on the skeptical evaluation of claims, to scrutinizing methodologies for possible identification and correction of mistakes, and to appraising scientific explanations [30]. This paper proposes to extend MD_Usage class to include explicit code and additional descriptions of the processing chains (see Figure 3).…”

Section: Figurementioning

confidence: 99%

Geospatial User Feedback: How to Raise Users’ Voices and Collectively Build Knowledge at the Same Time

Zabala

Masó

Bastin

et al. 2021

IJGI

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Geospatial data is used not only to contemplate reality but also, in combination with analytical tools, to generate new information that requires interpretation. In this process data users gain knowledge about the data and its limitations (the user side of data quality) as well as knowledge on the status and evolutions of the studied phenomena. Knowledge can be annotations on top of the data, responses to questions, a careful description of the processes applied, a piece of software code or scripts applied to the data, usage reports or a complete scientific paper. This paper proposes an extension of the current Open Geospatial Consortium standard for Geospatial User Feedback to include the required knowledge elements, and a practical implementation. The system can incrementally collect, store, and communicate knowledge elements created by users of the data and keep them linked to the original data by means of permanent data identifiers. The system implements a Web API to manage feedback items as a frontend to a database. The paper demonstrates how a JavaScript widget accessing this API as a client can be easily integrated into existing data catalogues, such as the ECOPotential web service or the GEOEssential data catalogue, to collectively collect and share knowledge.

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Reproducibility and replicability: opportunities and challenges for geospatial research

Cited by 56 publications

References 57 publications

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility

Modelling spatial processes in quantitative human geography

Geospatial User Feedback: How to Raise Users’ Voices and Collectively Build Knowledge at the Same Time

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