Omkar Jadhav

Omkar Jadhav

(he/him)

GeoData Scientist

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data quality

Data Quality Components for Geospatial Analysis

less than 1 minute read

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Data Quality Components for Geospatial Analysis

The blog “Data Quality Components for Geospatial Analysis” emphasizes the importance of assessing geospatial data quality, which is intangible and defined by properties like completeness, accuracy, precision, and consistency. It highlights the growing concerns over data quality due to increased private sector data production, reliance on secondary sources, and the use of GIS for decision-making, where poor-quality data can lead to serious consequences. The blog also discusses key components of geospatial data quality — space, time, and theme — and stresses the need for users to evaluate fitness-for-use and producers to document data limitations transparently.

Read on Medium →

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Data Quality Components for Geospatial Analysis

less than 1 minute read

Published:

Blog Banner

Data Quality Components for Geospatial Analysis

The blog “Data Quality Components for Geospatial Analysis” emphasizes the importance of assessing geospatial data quality, which is intangible and defined by properties like completeness, accuracy, precision, and consistency. It highlights the growing concerns over data quality due to increased private sector data production, reliance on secondary sources, and the use of GIS for decision-making, where poor-quality data can lead to serious consequences. The blog also discusses key components of geospatial data quality — space, time, and theme — and stresses the need for users to evaluate fitness-for-use and producers to document data limitations transparently.

Read on Medium →

gis

Data Quality Components for Geospatial Analysis

less than 1 minute read

Published:

Blog Banner

Data Quality Components for Geospatial Analysis

The blog “Data Quality Components for Geospatial Analysis” emphasizes the importance of assessing geospatial data quality, which is intangible and defined by properties like completeness, accuracy, precision, and consistency. It highlights the growing concerns over data quality due to increased private sector data production, reliance on secondary sources, and the use of GIS for decision-making, where poor-quality data can lead to serious consequences. The blog also discusses key components of geospatial data quality — space, time, and theme — and stresses the need for users to evaluate fitness-for-use and producers to document data limitations transparently.

Read on Medium →

medium

Data Quality Components for Geospatial Analysis

less than 1 minute read

Published:

Blog Banner

Data Quality Components for Geospatial Analysis

The blog “Data Quality Components for Geospatial Analysis” emphasizes the importance of assessing geospatial data quality, which is intangible and defined by properties like completeness, accuracy, precision, and consistency. It highlights the growing concerns over data quality due to increased private sector data production, reliance on secondary sources, and the use of GIS for decision-making, where poor-quality data can lead to serious consequences. The blog also discusses key components of geospatial data quality — space, time, and theme — and stresses the need for users to evaluate fitness-for-use and producers to document data limitations transparently.

Read on Medium →

remote sensing

Data Quality Components for Geospatial Analysis

less than 1 minute read

Published:

Blog Banner

Data Quality Components for Geospatial Analysis

The blog “Data Quality Components for Geospatial Analysis” emphasizes the importance of assessing geospatial data quality, which is intangible and defined by properties like completeness, accuracy, precision, and consistency. It highlights the growing concerns over data quality due to increased private sector data production, reliance on secondary sources, and the use of GIS for decision-making, where poor-quality data can lead to serious consequences. The blog also discusses key components of geospatial data quality — space, time, and theme — and stresses the need for users to evaluate fitness-for-use and producers to document data limitations transparently.

Read on Medium →

spatial data science

Data Quality Components for Geospatial Analysis

less than 1 minute read

Published:

Blog Banner

Data Quality Components for Geospatial Analysis

The blog “Data Quality Components for Geospatial Analysis” emphasizes the importance of assessing geospatial data quality, which is intangible and defined by properties like completeness, accuracy, precision, and consistency. It highlights the growing concerns over data quality due to increased private sector data production, reliance on secondary sources, and the use of GIS for decision-making, where poor-quality data can lead to serious consequences. The blog also discusses key components of geospatial data quality — space, time, and theme — and stresses the need for users to evaluate fitness-for-use and producers to document data limitations transparently.

Read on Medium →