NACIS 2019

Making Maps with Python
Presenter: Sergio Sanchez, Public Policy Institute of California
In this presentation we will go over some open source tools available in the python ecosystem for geographic data visualization and analysis. We will also go over a couple open data portals to access geographic data for the United States and other countries.
We will then explore how a social scientist may typically approach a data analysis project using these tools and data to extract insights.

Challenges of migrating automated map production from ArcMap to ArcGIS Pro
Presenter: Andrew Stauffer, US Geological Survey
The US Geological Survey currently leverages ArcPy Python libraries to manipulate ArcMap files and create US Topo PDF maps. A recent evaluation explored the possibility of migrating from ArcMap to ArcGIS Pro to support a modernized cartographic environment and improved map production throughput. In a testbed, our evaluation focused on the migration of several elements – geospatial data, map symbology, map labels and production code – and on the compatibility of these elements with ArcGIS Pro. I summarize our results of what could be migrated smoothly, what quirks were encountered, and how we could mitigate these challenges.

Spatial Patterns in Commute to Workplace Census Data
Presenter: Mark Cruse, University of Kentucky
This session demonstrates the use of Python and Jupyter notebooks to process and transform Census Bureau data for an interactive web map that provides insight into commuters' residence, workplace, earnings, age ranges, and industry type. Attendees will gain insight into best practices and lessons learned when wrangling large data sets through a data-to-map pipeline of tools including Tippecanoe, Mapbox, and Leaflet-VectorGrid.

Open source command line mapping
Presenter: Boyd Shearer, New Maps Plus
The command line interface has been compared to a Swiss Army knife. This presentation demonstrates how to use a variety of command line applications (primarily the GDAL library and AWS) to manipulate high-resolution NOAA GOES satellite imagery and produce raster tilesets for JavaScript mapping libraries like Leaflet. I conclude by showing how this process can be automated using Python and shell scripts to produce an updated map every 15 minutes.