Скачать или смотреть Geospatial Analysis with GeoPandas

Dive deep into Geospatial Analysis with Geopandas! This comprehensive 49-minute tutorial is your complete guide to building interactive, statistical maps in Python.

You will learn the fundamental steps required to transform raw data into powerful geographic visualizations:

Environment Setup: Install all necessary dependency libraries (Contextually, Folium, etc.).

Spatial Data Management: Understand the GeoDataFrame structure and the crucial geometry column.

Data Transformation: Learn how to merge spatial (GeoDataFrame) and attribute (Pandas DataFrame) data, as demonstrated with US state shapes and COVID-19 case data.

Interactive Mapping: Master the explore() method to create dynamic, zoomable Choropleth maps with hover-over tooltips displaying specific statistics.

This is an essential guide for anyone looking to analyze, manipulate, and visualize geographic data in Python.

Video Chapters (Timestamps)
Introduction to Choropleth Maps and Interactivity 0:00:05

CRITICAL SETUP: Installing Geopandas and Dependency Libraries (!pip install) 0:02:33

Understanding the GeoDataFrame and the Geometry Column 0:08:34

Exploring Geometry Shapes (Polygons, Multi-Polygons) 0:09:43

Initial Map Visualization using state_shapes.plot() 0:12:01

Understanding the Coordinate Reference System (CRS) (EPSG 4269) 0:14:07

Subsetting and Plotting Individual Regions (Texas Example) 0:16:11

Data Transformation: Changing the CRS (e.g., 4269 to 3857) 0:19:07

Calculating Area and Distance (Texas vs. Virginia) 0:23:40

Loading and Preparing the Attribute Data (COVID-19 Cases) 0:31:11

Aggregating Data from County Level to State Level using groupby 0:38:25

Merging the GeoDataFrame and the State DataFrames 0:40:48

Creating the Interactive Choropleth Map using gdf.explore() 0:45:25

Customizing Tooltips and Map Display 0:46:30

Conclusion on Geospatial Analysis Approach 0:47:39

Keywords/Tags:
geospatial analysis, geopandas, python gis, choropleth map, interactive map, folium, spatial data, geometry column, pandas merge, coordinate reference system, CRS, data visualization python, contextually