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When & Where
Mon, December 7, 2020 - 12:30 PM to 3:30 PM
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Wed, December 9, 2020 - 12:30 PM to 3:30 PM
Remote (Zoom information forthcoming)

Geospatial data are an important component of social science and humanities data visualization and analysis. This workshop will introduce basic methods for working with geospatial data in Python using GeoPandas, a relatively new Python library for working with geospatial data that has matured and stabilized in the last few years. In the workshop we will import geospatial data stored in shapefiles and CSV files into geopandas objects. We will explore methods for subsetting and spatial reshaping these objects. We will use geopandas methods for defining and transforming coordinate reference systems. Participants will also join tabular data to geospatial data and create maps based on the data values.

Knowledge Requirements: Basic knowledge of geospatial data is expected. Python experience equivalent to the D-Lab Python Fundamentals workshop series is required to follow along with the tutorial.

Technology Requirements: Bring a laptop with Python 3 and the following Python packages installed: pandas, numpy, matplotlib, geopandas, shapely and folium. If you do not have these installed you can follow along in the Google Collaboratory online python environment.




Software Tools,Python,Geospatial Analysis

Primary Tool: 
Training Host: 
D-lab Facilitator: 
Patty Frontiera
Format Detail: 
Remote, hands-on, interactive
Participant Technology Requirement: 
Laptop, Internet connection, Zoom account

Basic Competency

These workshops are designed for participants with beginner fluency. They already have a little coding, tool or method experience but need to learn more intermediate applications such as conditional subsetting and appropriate data visualizations for their research. 

Examples: Introduction to Pandas, R-wrang, Data Visualization with Python, R-graphics, Survey Sampling, Weighting Data, Introduction to Qualtrics, Finding Health Statistics and Data, Data Viz Theory and Best Practices, Python Machine Learning, Machine Learning in R, Intro to Computational Text Analysis, Geospatial Fundamentals in Python/sf/QGIS/ArcGIS, Intermediate Tableau

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