ScholarGate
المساعد

قارن الطرق

راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.

الترجيح بالمسافة العكسية (IDW)×الانحدار الموزون جغرافيًا (GWR)×
المجالالتحليل المكانيالتحليل المكاني
العائلةRegression modelRegression model
سنة النشأة19682002
صاحب الطريقةDonald ShepardFotheringham, Brunsdon & Charlton
النوعDeterministic spatial interpolationLocal spatial regression
المصدر التأسيسيShepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524. DOI ↗Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
الأسماء البديلةIDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyonGWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
ذات صلة35
الملخصInverse distance weighting is a simple, deterministic method for estimating values at unsampled locations by taking a weighted average of nearby measured points, where closer points carry more weight. Introduced by Donald Shepard in 1968, it embodies the first law of geography — near things are more related than distant things — and is one of the most widely used interpolation methods in GIS for mapping continuous fields such as rainfall, elevation, or pollution from scattered samples.Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.
ScholarGateمجموعة البيانات
  1. v1
  2. 2 المصادر
  3. PUBLISHED
  1. v1
  2. 1 المصادر
  3. PUBLISHED

انتقل إلى البحث تنزيل الشرائح

ScholarGateقارن الطرق: Inverse Distance Weighting · Geographically Weighted Regression. استُرجع بتاريخ 2026-06-19 من https://scholargate.app/ar/compare