השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| רגרסיה מרחבית מקומית× | מודל ההֶגֶר המרחבי המקומי× | |
|---|---|---|
| תחום | ניתוח מרחבי | ניתוח מרחבי |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 1996 | 1988 (global); 2000s (local extensions) |
| הוגה השיטה≠ | Brunsdon, Fotheringham & Charlton | Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002) |
| סוג≠ | Spatially varying coefficient regression | Spatially varying regression model |
| מקור מכונן≠ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737215 |
| כינויים | locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression | local SLM, geographically weighted spatial lag model, GW-SLM, spatially varying lag model |
| קשורות≠ | 6 | 5 |
| תקציר≠ | Local Spatial Regression fits a separate regression model at each location in a study area, allowing regression coefficients to vary continuously across space. Rather than forcing one global slope on all observations, it reveals where and how the relationship between predictors and an outcome changes geographically — producing a map of coefficients rather than a single number. | The Local Spatial Lag Model extends the classical spatial lag model by allowing both the spatial autocorrelation parameter and the regression coefficients to vary across geographic locations. Instead of one global estimate of how neighboring outcomes influence each observation, the model fits location-specific parameters using kernel-weighted local estimation, revealing spatial heterogeneity in spatial dependence. |
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