Geospatial Analysis in Public Health¶

The goal of this lab is to guide your LLM agent (Claude, Cline, etc.) through a reproducible workflow that turns open geospatial data into an interactive story map.
Copy-and-paste the prompts below in order. Adjust ONLY the bracketed values (<…>) to match your environment.

Prerequisites (checklist)¶

✔︎	Requirement	Notes
	Frontier-class LLM access (API or Desktop)	Claude 4, GPT-4.5, Gemini 2.5 Pro, etc.
	IDE with Cline or Roo Code extension or Claude Desktop	Enables local tool use & file ops
	Filesystem MCP server running	Gives the AI read/write access
	Git & GitHub account (optional but recommended)	For version control & sharing

Step 0 – Add a System Instruction or Workspace Rules file.¶

These are sometimes called an EigenPrompt -- because they come before any other prompts.

EigenPrompts

We are using Cline on VS Code, so we will create a unique Workspace Rules file, these are located in the .clinerules folder -- clicking on the :material-scales: icon will take you to Cline Rules

Copy/Paste

**Eigenprompt: Rigorous Code Generation & Automated Validation**

**Objective:** Generate [code for a specific function/module/class | architectural outline] for [project/feature description] with a focus on correctness, testability, maintainability, and automated verification via Cline Workspace Rules.

**I. Code Generation Specifications:**

1. **Functionality:**  
  - Clearly define input(s), output(s), and the intended behavior.

2. **Language/Framework:**  
  - Specify the programming language and version clearly (e.g., Python 3.10, JavaScript ES2022, Go 1.18).

3. **Dependencies:**  
  - Explicitly list external libraries or modules required.

4. **Error Handling:**  
  - Define expected errors explicitly with handling methods (exceptions, error codes, fallbacks).

5. **Performance Constraints (Optional):**  
  - Describe any important time or memory constraints clearly.

6. **Code Style:**  
  - Follow defined style guides (e.g., PEP 8, Google Java Style).  
  - Clearly document non-obvious or complex logic concisely, specifying reasons ("why") and behavior ("what").

**II. Testing & Validation Requirements:**

1. **Unit Tests:**  
  - Specify testing framework explicitly (e.g., unittest, Jest, Mocha).  
  - List and implement critical test cases clearly:
    - Typical valid inputs.
    - Edge cases.
    - Invalid inputs and related error-handling tests.
  - Indicate desired code coverage clearly [% of coverage as applicable].

2. **Validation Criteria:**  
  - Clearly describe measurable criteria for successful test results.  
  - Specify validation datasets, criteria, or methods if needed.

**III. Automated Execution, Validation, and Bug-Fixing Workflow (Cline Workspace Rules):**

1. **Terminal Execution Validation:**
  - After execution of generated code or tests via ChatGPT API in VS Code Terminal, automatically inspect the outputs.
  - Verify explicitly that the commands have exited without errors or warnings.

2. **Error & Warning Inspection:**
  - Check VS Code's "Problems" pane for reported errors, warnings, or alerts promptly after running code or tests.

3. **Automated Re-examination on Errors:**
  - In case of any detected terminal output issues or problems pane alerts:
    - Automatically re-inspect the relevant code and identify root causes clearly.
    - Promptly propose corrected or improved code, addressing identified issues directly.
    - Re-run tests and terminal commands, verifying fixes iteratively until no critical issues persist.

4. **Final Confirmation:**
  - Explicitly confirm successful execution (no persistent errors or warnings) before finishing the task.

**IV. Project Structure & Documentation (Initialize/Update):**

1. **`README.md`:**
  - **Project Title:**
  - **Description:** Succinct description.
  - **Setup Instructions:** Clearly outlined installation and execution steps.
  - **Usage:** Simple demonstration or examples.
  - **Testing Instructions:** Exact commands to run provided unit tests.

2. **`prompts/` directory:**
  - Log initial eigenprompt clearly as `prompts/001_initial_eigenprompt.md`.
  - Log ChatGPT API's full responses (code, documentation, README) as `prompts/001_response.md`.
  - Future interactions follow sequential convention (e.g., `002_refinement_prompt.md`, `002_response.md`).

**V. Output Format (Concise & Complete):**

- Clearly named source code files according to module criteria (e.g., `module_name.py`).
- Clearly named unit test files aligned with testing framework (e.g., `test_module_name.py`).
- Complete and concise README.md file content.
- Confirmation that automated validation via Cline Workspace Rules has executed successfully or corrections documented explicitly.
- Confirmation of structured prompt logging.

---

**Illustrative Usage Example:**

**Objective:** Generate efficient Python code for calculating Fibonacci numbers with memoization, fully tested and automatically validated via Cline Workspace Rules.

- **Code Specifications:**
  - Input: non-negative integer `n`; Output: nth Fibonacci number.
  - Use memoization for efficiency, with clear descriptive comments.
  - Error Handling: Raise explicit `ValueError` on negative input.
  - Python version: 3.10; Adhere strictly to PEP 8 style.

- **Unit Testing:**
  - Framework: `unittest`.
  - Test cases: `fib(0)`→`0`, `fib(1)`→`1`, `fib(10)`→`55`, `fib(20)`→`6765`; negative inputs raise `ValueError`.

- **Automated Validation (Cline Workflow):**
  - Upon running tests in terminal through ChatGPT API integration with VS Code, check terminal output immediately.
  - Automatically examine the "Problems" pane for errors or warnings.
  - If issues detected, automatically re-inspect code, clearly identify and implement fixes, and iteratively rerun validation steps until no problems remain.

- **Project Structure & Logs:**
  - Create README.md, `prompts/` structure and log prompts/responses precisely as described.

- **Final Output:**
  - Files: `fibonacci.py`, `test_fibonacci.py`, `README.md`.
  - Explicit confirmation that code and tests execute without errors or warnings and validation is automated successfully.

Step 1 – Initialize project folders¶

TASK
Create the following directory structure in the current repo  
  data/  
  map/  
  code/  
  prompts/
Acknowledge when folders exist.

Step 2 – Download & unzip “snow” dataset¶

TASK
1. Download https://geodacenter.github.io/data-and-lab/data/snow.zip into data/  
2. Unzip it in place, then delete the original .zip  
3. Write a brief summary of extracted files 

Use Python scripting; save the script as code/download_data.py

Step 3 – Organize GeoJSON layers¶

TASK
In the unzipped snow dataset, locate every *.geojson file.  
Move the .geojson files into the map/ folder

Ignore all other file types.  

Confirm moves as successful.

Step 4 – Summarize accompanying PDFs¶

TASK
Within data/snow/, there are several PDF documentation files.

1. Extract their plain-text content and generate a markdown summary (≤ 200 words) of key variables & metadata.  
2. Save these markdown text as data/snow_docs_summary.md

Step 5 – Build a storytelling Leaflet map¶

TASK
Using Leaflet HTML, CSS, and JavaScript, create  
  map/snow_storymap.html
Requirements:
  • The HTML must scroll like a Story Map,
  • Layers appear when scrolled to and disappear when they are scrolled past
  • Summarized text explains the relevance and meaning of each data set
  • Use chloropleth colors for presence or absence of observations, such as 'deaths' and 'deathdens' for deaths and death density
  • add the death count to polygons but not point layers
  • Run local python web server on a high random port (e.g., 51234) to avoid conflicts

Step 6 – Iterate for aesthetics¶

TASK
Open map/snow_storymap.html and critique its look (colors, fonts, layout).  
Suggest up to three improvements.  
Wait for user approval, then implement changes inside the same file.

Step 7 – Log every prompt¶

TASK
Create code/log_prompts.py that appends each user & assistant message  
from this session into prompts/session_<timestamp>.md  
Ensure it runs automatically at the end of each assistant response.

Step 8 – Commit & push¶

TASK
Git add all new/modified files  
Commit with message "Add snow GIS story-map lab"  
Push to <your-GitHub-remote>  
Report the commit URL on success.

Next Steps¶

• Experiment with alternative datasets (CDC SVI, Tiger/Line, etc.)
• Swap in QGISMCP to automate desktop GIS steps.
• Deploy the map via GitHub Pages or Netlify.