GWR (Geographically Weighted Regression)
v1.0.2
Health Sciences context — regression in which coefficients vary by location. At each point a local model is fit with an AICc-optimal bandwidth, yielding a β₁(s), β₂(s) map.
🎯 What is it for?
Standard OLS estimates a single global β; GWR produces location-varying β, answering questions such as “Is the effect of X1 stronger in the north or the south?” For Health Sciences, it visualizes how the sensitivity of diyabet_prevalans_pct to yas_ort and obezite_pct 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, continuous DV (diyabet_prevalans_pct), continuous X’s.
- Sufficient density: number of points within the bandwidth ≥ 30.
- AICc / CV for bandwidth selection (default AICc).
📊 How to Run It in MerQur
Panel assignments (form fields in the program):
- Columns:
{'y': 'vaka_orani', 'x': ['sosyoekonomik', 'yas_ortalama'], 'lat': 'lat', 'lon': 'lon'} - Parameters:
{'bandwidth_method': 'AICc'}
📊 Sample Dataset — Health Sciences
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 Health Sciences:
Tip/107_gwr_local_risk_factor.xlsx
🎬 Scenario
Assuming the relationship is not constant in space, we estimate separate
coefficients per province. For spatial heterogeneity, GWR is appropriate.
⚙️ Variable Selection
- Dependent variable: case_ratio
- Predictor(s): socioeconomic
- Predictor(s): age_mean
- Latitude: lat
- Longitude: lon
Data Preview (First 5 Rows)
| province_id | lat | lon | socioeconomic | age_mean | case_ratio |
|---|---|---|---|---|---|
| 1.0 | 39.3442 | 31.3475 | 58.8 | 44.8 | 502.1 |
| 2.0 | 39.6177 | 29.0262 | 68.5 | 46.2 | 365.1 |
| 3.0 | 36.8704 | 33.7026 | 48.6 | 30.3 | 474.9 |
| 4.0 | 38.7048 | 38.1436 | 36.6 | 50.4 | 522.7 |
| 5.0 | 40.3021 | 34.0883 | 54.9 | 44.7 | 419.7 |
n = 110 · Columns: province_id, lat, lon, socioeconomic, age_mean, case_ratio
📈 MerQur Output
─────────────────────────────────────────────
R^2 = 0.811 local coefficients vary by location outcome: case_ratio, predictors: socioeconomic, age_mean
💬 Interpretation
Assuming the relationship is NOT constant across space, we estimated SEPARATE coefficients for each province/location
(R^2 = 0.81). Unlike “global” regression, GWR fits a separate model at each point with local overlap/weights; this
answers “how does this variable’s effect vary by region?” and maps spatial heterogeneity. In medicine/epidemiology
it is powerful where the socioeconomic-disease relationship differs by region.
⚠ 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.