WLIA 2025 Annual Conference Appleton

Generative AI is becoming an invaluable tool for GIS professionals, streamlining everyday tasks such as project planning, workflow automation, and documentation. This presentation explores how AI can assist with automating workflows, drafting and reviewing scripts, answering geoprocessing questions, and refining written content. We will examine the strengths and limitations of AI models like ChatGPT, Gemini, and GovAI, while also sharing best practices for crafting effective prompts. Additionally, we will address key concerns such as data privacy and job security. By focusing on practical applications, this session will demonstrate how GIS professionals can leverage AI to boost productivity and efficiency in their daily work.

Artificial intelligence is transforming the way we approach complex tasks, but integrating AI into everyday workflows can often feel elusive. In this talk I will demonstrate how ChatGPT has become an indispensable tool in Calumet County GIS workflows, specifically in automating data processes and streamlining Python scripting. From simplifying routine tasks in ArcGIS to developing efficient, reusable code for spatial data management, I will share practical examples of how AI bridges the gap between innovation and productivity. Join me to see how AI can move from being a fascinating concept to a powerful ally in enhancing efficiency and accuracy in geospatial workflows.

Much interest and speculation is being focused on using Artificial Intelligence (AI) and how it can help GIS Practitioners in their daily work. This presentation will discuss the possibilities and limitations related to parcel mapping, the generation of digital representation of land ownership, what you can expect, and how to best prepare for its impact.

On January 12, 2024 seven proposals for Wisconsin’s legislative districts were submitted to the Wisconsin Supreme Court in the case Clarke v. Wisconsin Elections Commission. One was generated by a computer algorithm, and six were created by expert human mapmakers. In this presentation, we examine a major development from the case: the map proposal generated by the FastMap algorithm significantly outperformed the other proposals. Relevant background information, an algorithm summary, and map visualizations/comparisons are provided. The case likely marks the moment when computer algorithms surpassed humans in overall mapmaking ability, but it was not the first time that FastMap outperformed a group of expert human mapmakers. A previous version of the algorithm did the same in December 2021 when it created a map proposal for Wisconsin’s legislative districts that better adhered to “least change” principles than any of the six proposals that were submitted to the Wisconsin Supreme Court in the case Johnson v. Wisconsin Elections Commission.

Going forward, algorithmic mapmaking promises to enhance American democracy by ensuring that congressional, state legislative, and local election districts better reflect constitutional requirements, the will of the voters, and other criteria specified by courts and legal teams. However, better maps will become the norm only if high-quality redistricting data is readily available and if redistricting commissions, nonprofit organizations, courts, lawmakers, and legal teams are interested in fairness and are open to learning from experts in mathematical optimization and algorithm design.