UMAP
UMAP is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Architecture, Planning & Design sample dataset — how the analysis is run, what the MerQur output looks like, and how the result is reported in APA 7 format.
🎯 What is it for?
UMAP automatically applies in the background all the assumption checks required for the relevant data type (normality, homogeneity of variance, etc.) and presents the results with a clear table + chart. Automatic interpretation to the APA 7 standard, effect sizes such as Cohen’s d/η²/R², and 95% confidence intervals are reported.
📌 When is it used?
- Statistical analysis of measurements in the Architecture, Planning & Design domain
- To produce APA 7-compatible result tables for academic publications
- Hypothesis testing and decision-making processes
- Undergraduate / master’s / PhD theses after the appropriate method has been selected
📐 Assumptions
- Appropriate scale — Variables must be at the measurement level required by the analysis (nominal/ordinal/interval/ratio)
- Independent observations — Observations must come from individuals independent of one another
- Sufficient sample size — The minimum n requirement for the analysis must be met
- Outlier check — Outliers must be detected and evaluated
If assumptions are violated, MerQur automatically suggests a non-parametric or robust alternative.
🛠 How to do it in MerQur
Load the data. Select the sample file from File → Open. MerQur auto-detects column types.
Select the analysis. From the left side select UMAP.
Panel assignments (form fields in the program):
- Ozellik Sutunlari:
[kanal_01, kanal_02, kanal_03, ... +22 adet] - Bilesen sayisi:
2 - Komsu:
15 - Min mesafe:
0.1
Optional settings. Effect size ✓ · 95% confidence interval ✓ · Assumption checks (automatic).
▶ Run — click the button. Results are produced automatically as a table + chart.
📄 Export to Word. APA 7-formatted report with italic statistical symbols.
📊 Sample Dataset — Architecture, Planning & Design
ℹ Note: The scenario, MerQur output and interpretation below were produced by actually running the real example dataset in MerQur. Numeric results on your own data will differ; the goal is to show how the analysis is set up and interpreted end-to-end.
🎬 Example File
This analysis is demonstrated on the following example dataset for Architecture, Planning & Design:
Peyzaj_Mimarligi/72_umap_spectral_5tip.xlsx
🎬 Scenario
We explore land-cover types defined by 25 spectral channels on a 2D map with UMAP.
⚙️ Variable Selection
- Variables: channel_01..channel_25
Data Preview (First 5 Rows)
| area_id | lower_type | channel_01 | channel_02 | channel_03 | channel_04 | channel_05 | channel_06 | channel_07 | channel_08 | channel_09 | channel_10 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | type-A | 1.654 | -0.357 | -0.023 | 0.792 | -0.661 | -0.328 | -0.382 | 0.09 | -0.985 | -0.502 |
| 2 | type-A | 2.076 | -0.209 | 0.524 | -0.469 | 2.248 | 0.867 | -1.913 | 0.804 | -1.192 | -1.895 |
| 3 | type-A | -0.287 | -0.498 | 0.566 | 0.767 | -1.294 | -0.059 | 0.512 | -0.261 | 0.139 | -1.019 |
| 4 | type-A | -0.605 | -1.798 | -1.436 | 0.411 | 0.968 | 1.539 | -0.761 | -0.687 | 0.497 | 1.493 |
| 5 | type-A | -0.286 | -0.949 | 2.943 | 0.163 | 0.134 | 0.407 | -0.127 | -0.756 | 0.396 | -0.198 |
n = 200 · Columns (first 12 columns): area_id, lower_type, channel_01, channel_02, channel_03, channel_04, channel_05, channel_06, channel_07, channel_08, channel_09, channel_10
📈 MerQur Output
💬 Interpretation
UMAP is a modern dimension-reduction method similar to t-SNE but it preserves both local and global structure better and is faster. We projected area profiles of 25 spectral channels (satellite/hyperspectral) into a two- dimensional map; similar spectral signatures (land-cover types) cluster. The n_neighbors parameter tunes the local-global balance, and min_dist the tightness of clusters. UMAP is increasingly preferred for discovering hidden land-cover classes in high-dimensional remote-sensing/spectral data. In landscape/GIS it is valuable for land-cover classification and visualisation.
⚠ Common Mistakes
- Misidentifying the data type (e.g., loading a categorical variable as numeric)
- Skipping assumption checks and going straight to the p-value
- Failing to report effect size — APA 7 requires both p and effect size
- Failing to apply a Type I error correction (Bonferroni/Tukey) in multiple comparisons
- Not switching to a non-parametric alternative when n is insufficient
📹 Video Walkthrough
Watch the video below for an end-to-end walkthrough of this analysis on a Architecture, Planning & Design file.
▶ UMAP — video walkthrough
This section is part of the Kümeleme ve Boyut İndirgeme video (7 analyses in one video). The link below jumps straight to 9:49, where this analysis begins. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities & Admin Sciences · 🏃 Sport Sciences · 🌾 Agriculture, Forestry & Aquatic
📚 If You Used This Analysis, Cite MerQur
If you performed this analysis using MerQur in a scientific study, please use the citation below as part of your academic citation obligations (APA 7):
Örücü, Ö. K. (2026). MerQur: Integrated Academic Data Analysis & Reporting Platform [Computer software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001
For BibTeX, RIS, EndNote and the English citation form: all citation formats →
- American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
- Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.