GWR (Geographically Weighted Regression)
v1.0.2
Architecture, Planning & Design context — regression in which the coefficients vary by location. At each point a local model is fit with an AICc-optimal bandwidth; a map of β₁(s), β₂(s) is obtained.
🎯 What is it for?
Standard OLS estimates a single global β; GWR produces a location-varying β, answering questions such as “Is the effect of X1 stronger in the north or the south?” For Architecture, Planning & Design, it visualizes how the sensitivity of emlak_fiyati_TL_m2 to ulasim_yakinligi and yesil_alan_orani varies regionally.
📌 When is it used?
- Spatial clustering in the OLS residuals (Moran’s I significant)
- The discipline-specific question “does the effect differ by geography?”
- A desire to visualize spatial heterogeneity
⚙ Assumptions
- Lat/lon coordinates, a continuous DV (emlak_fiyati_TL_m2), continuous X’s.
- Sufficient density: the number of points within the bandwidth ≥ 30.
- AICc / CV for bandwidth selection (AICc by default).
📊 How to Run It in MerQur?
Panel assignments (form fields in the program):
- Columns:
{'y': 'park_deger_TL', 'x': ['alan_m2', 'yesil_orani'], 'lat': 'lat', 'lon': 'lon'} - Parameters:
{'bandwidth_method': 'AICc'}
📊 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/107_gwr_local_park_value.xlsx
🎬 Scenario
We map how the park-value relationship varies in space (local coefficients) with GWR.
⚙️ Variable Selection
- Dependent: park_value_TL | Predictors: area_m2, green_ratio | Coordinates: lat, lon
Data Preview (First 5 Rows)
| park_id | lat | lon | area_m2 | green_ratio | park_value_TL |
|---|---|---|---|---|---|
| 1.0 | 41.049 | 31.4253 | 28657.0 | 0.317 | 1448261.0 |
| 2.0 | 39.6093 | 36.155 | 31366.0 | 0.201 | 668219.0 |
| 3.0 | 39.8738 | 37.2388 | 4166.0 | 0.435 | 213982.0 |
| 4.0 | 36.9336 | 30.3355 | 22328.0 | 0.657 | 466261.0 |
| 5.0 | 40.3021 | 34.0883 | 25210.0 | 0.565 | 1920724.0 |
n = 110 · Columns: park_id, lat, lon, area_m2, green_ratio, park_value_TL
📈 MerQur Output
─────────────────────────────────────────────
R2 = 0.831 (local/location-specific regression)
Spatial variation of the park-value relationship
💬 Interpretation
Ordinary regression assumes a single park-value relationship for the whole city; but this relationship can vary in
space — in one region green ratio may strongly affect value while in another it is weak. GWR (Geographically
Weighted Regression) captures this spatial heterogeneity by fitting a separate local regression at each location
(R2 = 0.83). The output is a map of coefficients: a surface showing where green ratio/area’s effect on park value
is strong and where it is weak. This tests “is the relationship the same everywhere” and sets local investment/
renewal priorities. In urban-landscape value analysis, GWR reveals what the global model hides for mapping spatial
inequalities and place-specific value.
⚠ Common Mistakes
- If too few points fall within the bandwidth in low-density regions, the standard error becomes large.
- If you find the same effect across the entire region, global OLS is sufficient — the complication of GWR is not needed.
- Multicollinearity may vary locally in GWR; checking the VIF at each bandwidth is recommended.
📚 MerQur’a Atıf
Örücü, Ö. K. (2026). MerQur: Integrated Academic Data Analysis & Reporting Platform [Computer software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001
📝 Üretim Notu — Bu sayfadaki örnek veri sentetik olarak üretilmiştir (sabit SEED=42, generator: samples/Ileri_Duzey_v102/_generate_v102_datasets.py). Sayfa içeriği Anthropic Claude desteği ile hazırlanmış, akademik doğruluk yazar tarafından kontrol edilmiştir.