Report writing - SDS320 Spatial Data Analytics

Writing a concise project report that explains what you did and what you found

1. Purpose of the report¶

The SDS320 report documents your project in a concise and structured way.

It should explain:

The report is not a diary of everything you tried. It is a clear account of the project you implemented and what can be learned from it.

The report must include a link to your public GitHub or GitLab repository.

The project report and repository contribute 30% to the final grade.

The report must be submitted as a PDF via the corresponding MS Teams assignment. The deadline is Monday, 14 December 2026, at 17:00.

The report should not exceed 6500 characters including spaces, excluding figures and references.

Use this as a recommendation and adapt it where needed.

Section	Function
Title	Name the project clearly and specifically.
Motivation and research question	Explain why the project matters and state the question early.
Data	Describe datasets, sources, coverage, time period, format and limitations.
Methods and implementation	Explain the workflow and key method choices.
Results	Present the main outputs with key figures or maps.
Discussion and limitations	Explain meaning, reliability, uncertainty and limitations.
Conclusion	Briefly answer the research question.
References	Cite data, methods, literature, code sources and external material.
AI use statement, if applicable	State when and how AI tools supported your work.

For AI guidance, see AI use and integrity.

Good scientific writing is clear, concise and reader-friendly.

Use these principles:

keep it simple,
assume the reader is scientifically literate but not an expert in your exact project,
keep only what is essential,
tell a clear project story,
write short sentences,
make one main point per paragraph,
be precise and consistent,
use the first sentence of a paragraph to signal the paragraph’s purpose,
avoid unnecessary jargon,
explain abbreviations on first use.

Use past tense for what you did and observed. Use present tense for general facts or established knowledge.

For methods, explain why and how the workflow was implemented.

Input data
→ preprocessing
→ analytical method
→ evaluation
→ output generation

Mention software where it matters for reproducibility. Detailed code belongs in the repository, not in the short report.

For results, lead with what matters for the research question. For each key result, ask:

Because the report is short, leave out:

Failed attempts can be mentioned briefly if they explain an important project decision or limitation.

Red flag	First check
The research question appears late	State it near the beginning.
The report describes tools but not decisions	Explain why each key step was needed.
Data sources are vague	Name provider, coverage, time period and limitations.
Figures are shown but not interpreted	Explain what each figure demonstrates.
The discussion hides problems	State limitations clearly and professionally.
The report repeats generic course content	Focus on your project.
The repository link is missing	Add the public repository link.
AI use is not disclosed	Add a short, accurate AI-use statement if applicable.

Draft a five-sentence report outline:

1. This project investigates ...
2. It uses ...
3. The workflow ...
4. The main result shows ...
5. The main limitation is ...

Then check whether the five sentences already form a coherent project story.