Spatial Data Science

Bação, Fernando; Santos, Maribel Yasmina; Behnisch, Martin

doi:10.3390/ijgi9070428

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Whilst broadly similar to what is discussed in this chapter, the latter differs conceptually, particularly in terms of modelling spatial weights (e.g., Gao 2015). In terms of future trends, one current research direction is towards a deeper integration of spatial analysis with computer science, leading to the notion of 'spatial' or 'geographical' data science (Bacao et al 2020;Singleton/ Arribas-Bel 2021). A related direction deals with a stronger integration of spatial analysis and machine learning (Klemmer/Neill 2020;Klemmer et al 2019).…”

Section: Discussionmentioning

confidence: 87%

Exploratory Statistical Analysis of Spatial Structures in Urban Datasets

Westerholt¹

2022

Metropolitan Research

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Section: Discussionmentioning

confidence: 87%

Exploratory Statistical Analysis of Spatial Structures in Urban Datasets

Westerholt¹

2022

Metropolitan Research

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“…The economic sector is facing a significant transformation due to the penetration of data-driven innovation in the business core. A similar transformation is underway within many scientific disciplines [3,4]. This is especially true of geodesy, environmental sciences and Earth sciences.…”

Section: Introductionmentioning

confidence: 91%

A Python Library for the Jupyteo IDE Earth Observation Processing Tool Enabling Interoperability with the QGIS System for Use in Data Science

Bednarczyk

2021

GaEE

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This paper describes JupyQgis – a new Python library for Jupyteo IDE enabling interoperability with the QGIS system. Jupyteo is an online integrated development environment for earth observation data processing and is available on a cloud platform. It is targeted at remote sensing experts, scientists and users who can develop the Jupyter notebook by reusing embedded open-source tools, WPS interfaces and existing notebooks. In recent years, there has been an increasing popularity of data science methods that have become the focus of many organizations. Many scientific disciplines are facing a significant transformation due to data-driven solutions. This is especially true of geodesy, environmental sciences, and Earth sciences, where large data sets, such as Earth observation satellite data (EO data) and GIS data are used. The previous experience in using Jupyteo, both among the users of this platform and its creators, indicates the need to supplement its functionality with GIS analytical tools. This study analyzed the most efficient way to combine the functionality of the QGIS system with the functionality of the Jupyteo platform in one tool. It was found that the most suitable solution is to create a custom library providing an API for collaboration between both environments. The resulting library makes the work much easier and simplifies the source code of the created Python scripts. The functionality of the developed solution was illustrated with a test use case.

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“…USDS should be able to explore and predict “what if” scenarios that can validate reality with the fusion of old-new OR new-new (data and models). It should not have to be just physical – rather it needs a combined understanding of socio-demographic-economic-environmental dimensions; where a option of spatial link/consideration will be always valuable (e.g Bacao et al, 2020 ; Bensmann et al, 2020 ). Of course, fundamental efforts need to continue to detect the changes and even causality, the scale effect of data, uncertainty, privacy and legal bindings.…”

mentioning

confidence: 99%