Martin Fleischmann - A Gentle Introduction to Spatial Data in the Pandas Ecosystem [PyData Prague]

Automation

Discover the basics of spatial data with GeoPandas, an extension of Pandas that integrates geospatial data and provides spatial operations and analysis capabilities in the Pandas ecosystem.

Key takeaways

GeoPandas is an extension of Pandas that integrates geospatial data, providing spatial operations and analysis capabilities.
There are various types of geometry, including points, lines, and polygons, each with different properties and methods.
A pedestrian can walk between neighboring polygons, but it’s not the same as a pedestrian walking between different groups or clusters.
Geopandas can perform spatial joins, which allow data from multiple sources to be merged based on spatial relationships.
The University of Liverpool has a strong relationship with a specific Geospatial Data Science program.
Geopandas can be used for spatial clustering, recognizing patterns and grouping data points based on spatial proximity.
There are various coordinate reference systems (CRS) that define the spatial relationships between points.
Chocolatey is used to automate the installation of libraries and frameworks.
Packages in the PySAL family, such as libPySAL and pycell, provide additional geospatial functionality.
The DASK Geopandas library uses the DASK infrastructure to scale up geospatial computing.
OSMnx is a library used to load OpenStreetMap data directly into Pandas.
The geometry attribute of a GeoPandas DataFrame can be used to perform spatial operations and analysis.
read_file is used to read spatial data files, such as GeoJSON, into a GeoPandas DataFrame.
Spatial weights can be used to analyze spatial relationships between data points.
There are different levels of spatial hierarchy, including global, national, regional, and local.
Polygons can be split into different parts or partitions based on spatial relationships.
Geopandas provides various methods for spatial operations, including distance, length, and area.
There are various spatial predicates, such as touches, intersects, and within.
Spatial data can be analyzed using various libraries and frameworks, including those from the PySAL family.
Geopandas provides various tools for visualizing spatial data, including plotting and map-making capabilities.

Martin Fleischmann - A Gentle Introduction to Spatial Data in the Pandas Ecosystem [PyData Prague]

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