Using your favorite Python packages in ArcGIS Pro
python
gis
data science
Last week, I gave a workshop on working with geographic data in Python with the University of Michigan’s Social Science Data Analysis Network. The workshop focused on pygris, my new Python package for working with US Census Bureau geographic data resources. I was asked by multiple attendees if pygris works within ArcGIS Pro Python notebooks. I did not know the answer at the time, but it seemed like pygris - and other geospatial data packages in Python - combined with ArcGIS Pro could be quite powerful. I tested this process out, and it turns out that pygris and ArcGIS Pro work quite well together! Read on for a tutorial for how to use pygris (and your other favorite Python packages) in ArcGIS Pro.
Distance and proximity analysis in Python
python
gis
data science
navigation
Spatial data science projects frequently require the calculation of proximity to resources. Analysts in fields like health care, real estate, retail, education, and more are commonly tasked with finding out what resources are near to a given location, and potentially develop strategies to fill identified gaps in resource access. In Chapter 7 of my book Analyzing US Census Data, I illustrate a workflow that shows how to analyze accessibility from Census tracts to major trauma hospitals in Iowa. In this post, I’ll show you how to reproduce the first part of that analysis in Python for the neighboring state of South Dakota.
Analyzing labor markets in Python with LODES data
python
gis
data science
In Chapter 11 of my book Analyzing US Census Data, I explore a sampling of the variety of government datasets that are available for the United States. One of the most useful of these datasets is LODES (LEHD Origin-Destination Employment Statistics). LODES is a synthetic dataset that represents, down to the Census block level, job counts by workplace and residence as well as the flows between them.
Exploratory spatial data analysis with Python
python
gis
data science
In early 2023, the print copy of my book Analyzing US Census Data: Methods, Maps, and Models in R will be available for purchase. The response to the free online version of the book has been fantastic thus far. One question I commonly get asked, however, is “will you re-produce this for Python? I’d love to use this work but I don’t use R.”
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