Nicholas Giner
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Nick Giner is a Product Manager for Spatial Analysis and Data Science. Prior to joining Esri in 2014, he completed Bachelor’s and PhD degrees in Geography from Penn State University and Clark University, respectively. In his spare time, he likes to play guitar, golf, cook, cut the grass, and read/watch shows about history.

Posts by this author
End-to-end spatial data science 5: Machine learning: Cluster analysis in Python and ArcGIS

This is the fifth in a series of blogs that showcase an end-to-end spatial data science workflow for clustering US precipitation regions.

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ArcGIS Notebook Server 11.3 Now Supports Docker Engine Installed from Binaries on Windows

ArcGIS Notebook Server on Windows now supports free, open-source Docker Engine from binaries.

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Announcing {arcgis}, an R package for ArcGIS Location Services

A new R package created by the R-ArcGIS Bridge team enables integration with ArcGIS location services.

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R-ArcGIS Bridge at the 2024 Esri Developer Summit

Learn about events and workshops featuring R at the 2024 Esri Developer Summit.

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New User Workspace and File Management Options for ArcGIS Notebooks

A new experience for notebook authors and organization administrators to manage content in the notebooks user workspace.

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End-to-end spatial data science 1: Clustering US Precipitation Regions

This is the first in a series of blogs that showcase an end-to-end spatial data science workflow for clustering US precipitation regions.

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End-to-end spatial data science 3: Data preparation and data engineering using Python

This is the third in a series of blogs that showcase an end-to-end spatial data science workflow for clustering US precipitation regions.

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End-to-end spatial data science 4: Data preparation using spatial analysis and automation in ArcGIS

This is the fourth in a series of blogs that showcase an end-to-end spatial data science workflow for clustering US precipitation regions.

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End-to-end spatial data science 2: Data preparation and data engineering using R

This is the second in a series of blogs that showcase an end-to-end spatial data science workflow for clustering US precipitation regions.

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