Enhanced Geospatial Analysis and Algorithm Optimization

2 min read

📅 2023-07-23 — Session: Enhanced Geospatial Analysis and Algorithm Optimization

🕒 02:30–19:00
🏷️ Labels: Geospatial, Python, Algorithm, Modularization, Permutation
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance geospatial data analysis for villages in the Democratic Republic of Congo and optimize algorithmic procedures in Python.

Key Activities

  • Developed a geospatial analysis notebook utilizing Python libraries to fetch elevation data and compute terrain ruggedness using GeoJSON input.
  • Planned and outlined a strategy for modularizing Python code to improve organization, readability, and reusability.
  • Updated the geodesic buffer function in Pyproj to replace deprecated pyproj.transform with pyproj.Transformer to avoid FutureWarnings.
  • Modified the RANDOMLY_PERMUTE procedure to ensure correct application to nonempty subarrays, including an updated proof of Lemma 5.4.
  • Implemented the PERMUTE_WITHOUT_IDENTITY procedure in Python to generate permutations excluding the identity permutation.
  • Analyzed the PERMUTE-WITH-ALL algorithm, highlighting its limitations in producing uniform random permutations due to fixed points.

Achievements

  • Successfully enhanced the geospatial analysis capabilities by integrating Python-based solutions.
  • Improved code structure and organization through modularization strategies.
  • Resolved potential FutureWarnings in geodesic buffer functions, ensuring forward compatibility.
  • Clarified algorithmic procedures and proofs, enhancing the robustness of permutation algorithms.

Pending Tasks

  • Further testing and validation of the updated geospatial analysis notebook and algorithmic procedures to ensure comprehensive functionality and accuracy.

Graph View

Backlinks