Provenance Information in Geodata Infrastructures

Henzen, Christin; Mäs, Stephan; Bernard, Lars

doi:10.1007/978-3-319-00615-4_8

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…It is often the case that data are provided without suitable metadata that document provenance or other aspects that allow evaluation of data quality (Henzen et al [2013]). All data management, including the processes employed in arealDB to harmonise and reshape areal data, is prone to (human) error.…”

Section: Documenting Metadatamentioning

confidence: 99%

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Harmonise and integrate heterogeneous areal data with the R package arealDB

Ehrmann¹,

Seppelt²,

Meyer³

2019

Preprint

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Areal data is a common data type to store information such as biodiversity inventories, socioeconomic censuses or cadastral surveys. Many research questions require that areal data are integrated from multiple heterogeneous sources. Inconsistent concepts, terms, definitions, or messy tables makes data wrangling an often tedious and error-prone process. A dedicated tool that assists in organising areal data still is lacking. Here, we introduce the R package arealDB that helps to harmonise and integrate heterogeneous areal data and associated geometries into a consistent database. The package is used to collect metadata, harmonise language and variable names, reshape messy into tidy data and integrate them in a standard data format. arealDB solves the specific problem of integrating disparate regional data sources on a given target variable, which may be published in different languages, with a different table arrangement or provided in various data formats. We guide the user step by step through the individual functions needed to integrate two such datasets using the example of the harvested area of soybean in Brazil and the USA. A database that has been built with arealDB is "tidy", and can thus be accessed easily with powerful and widespread tools such as the R meta-package tidyverse. Moreover, it is accompanied by provenance documentation that traces the full process of creation for each data point in the database. By offering easy-to-use tools for integrating areal data, arealDB promises substantial time-savings to database collation efforts, as well as quality-improvements to downstream scientific, monitoring, and management applications.Keywords disorganised messy data • interoperability • data integration • relational database • census and survey data • metadata • provenance documentation • zonal data IntroductionAreal data capture phenomena of interest at the level of finite spatial units. They are an essential data type in many basic and applied research fields, for example, to project human populations or to analyse the spread of infectious diseases based on census or survey data, or to map global biodiversity patterns based on species checklists. Areal data also play a crucial role in various policy and management applications such as national progress reporting towards Sustainable Development Goals (SDGs), to assess the implications of macroeconomic policies based on international trade statistics or to document land ownership. Through illustrative maps in news or education media, areal data are also an everyday communication tool in civil society.Many critical applications of areal data surpass the spatial, temporal or thematic scope of any unique data source, which makes it necessary to integrate heterogeneous sources into a single, more comprehensive database (Otto et al. [2015]). Efforts of harmonising and integrating areal data are carried out by numerous organisations such as the Food and Agriculture Organization, the World Bank Group and many smaller projects.However, integrating areal data from...

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Section: Documenting Metadatamentioning

confidence: 99%

“…All of this increases transparency by allowing to trace back inconsistencies that might show only later on in the analysis pipeline, and thus invokes more trust when repurposing databases assembled with arealDB for different downstream applications (Henzen et al [2013]).…”

Section: Documenting Metadatamentioning

confidence: 99%

Harmonise and integrate heterogeneous areal data with the R package arealDB

Ehrmann¹,

Seppelt²,

Meyer³

2019

Preprint

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“…The attribute resource discriminates the information resources dataset, service, data series, metadata, and documentation as defined in ISO19115-1:2014 [26]. Here, documentation is derived from the CI_Citation element (ISO19115-1:2014) and can be used to reference (scientific) publications that describe the assigned geodata and lineage in detail and to inform about observations, methods, or simulation models as the origins of the considered geodata [28,29]. The patterns Provide visualization of dataset lineage and Provide visualization of data hierarchy focus on dataset-related problems and visualize certain metadata elements.…”

Section: Sub-attributementioning

confidence: 99%

Building a Framework of Usability Patterns for Web Applications in Spatial Data Infrastructures

Henzen

2018

IJGI

Self Cite

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Web applications in spatial data infrastructures (SDIs) should provide robust and user-friendly user interfaces for geoinformation (GI) discovery, analysis, and usage. Poor usability, e.g., caused by unsuitable information presentation or inappropriate (non) availability of functions, can result in inefficient or faulty usage and can increase the acceptance of the application and provided geoinformation. Until now, a number of usability problems in GI web applications were identified; however, methods to summarize these problems, to provide (software-independent) solutions for them, and to find pairs of problems and related solutions hardly exist. We propose an adapted usability pattern concept for web applications in SDIs to map and categorize usability problems and best practice solutions and we enable a GI context-specific creation and discovery of these problems and solutions. The concept includes developed pattern types, relationships, and rules on how to use the relationships for the different pattern types.

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“…The implementation aims to still allow a mapping to the lineage element of ISO 19115 [2] to provide that information in a standardized way while allowing one to store much more detailed processing information in a flexible way. The mapping to ISO 19115 will also give the opportunity to use existing external tools like MetaViz [16] for graph-based provenance visualizations, similar to the schematic illustration in Figure 6.…”

Section: Processing Datamentioning

confidence: 99%

River Basin Information System: Open Environmental Data Management for Research and Decision Making

Zander

Kralisch

2016

IJGI

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An open, standardized data management and related service infrastructure is a crucial requirement for a seamless storage and exchange of data and information within research projects, for the dissemination of project results and for their application in decision making processes. However, typical project databases often refer to only one research project and are limited to specific purposes. Once implemented, those systems are often not further maintained and updated, rendering the stored information useless once the system stops operating. The River Basin Information System (RBIS) presented here is designed to fit not only the requirements of one research project, but focuses on generic functions, extensibility and standards compliance typically found in interdisciplinary environmental research. Developed throughout more than 10 years of research cooperation worldwide, RBIS is designed to manage different types of environmental data with and without spatial context together with a rich set of metadata. Beside data management and storage, RBIS provides functions for the visualization, linking, analysis and processing of different types of data to support research, decision making, result dissemination and information discovery for all kinds of users. The focus of this paper is on the description of the technical implementation and the presentation of functions. This will be complemented by an overview of example applications and experiences during RBIS development and operation.

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Provenance Information in Geodata Infrastructures

Cited by 8 publications

References 20 publications

Harmonise and integrate heterogeneous areal data with the R package arealDB

Harmonise and integrate heterogeneous areal data with the R package arealDB

Building a Framework of Usability Patterns for Web Applications in Spatial Data Infrastructures

River Basin Information System: Open Environmental Data Management for Research and Decision Making

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