方法对比
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| Huff 模型× | 区位-分配模型× | 空间交互(引力)模型× | |
|---|---|---|---|
| 领域 | 空间分析 | 空间分析 | 空间分析 |
| 方法族≠ | Regression model | Process / pipeline | Regression model |
| 起源年份≠ | 1964 | 1963 | 1971 |
| 提出者≠ | David Huff | Leon Cooper; S. L. Hakimi | Alan Wilson (entropy-maximizing family) |
| 类型≠ | Probabilistic spatial interaction model | Spatial facility-location optimization | Model of flows between spatial origins and destinations |
| 开创性文献≠ | Huff, D. L. (1964). Defining and estimating a trading area. Journal of Marketing, 28(3), 34–38. DOI ↗ | Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343. DOI ↗ | Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and Planning A, 3(1), 1–32. DOI ↗ |
| 别名 | Huff Gravity Model, Probabilistic Retail Gravity Model, Huff Trade Area Model, Huff Çekim Modeli | facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri | gravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeli |
| 相关≠ | 3 | 4 | 4 |
| 摘要≠ | Proposed by David Huff in 1964, the Huff Model is a probabilistic spatial interaction model that estimates the likelihood that consumers located in a given geographic zone will choose to shop at a particular retail outlet. It extends deterministic gravity models by assigning each consumer zone a probability of patronage across all competing stores, weighting store attractiveness (typically measured by floor area) against the friction of travel time or distance. The model is widely used in retail site selection, trade area delineation, and market share forecasting. | Location-allocation models decide where to place a set of facilities and simultaneously assign demand points to them so as to optimize an objective such as total travel cost, worst-case distance, or population covered. Rooted in the operations-research work of Cooper (1963) and Hakimi (1964) and central to network GIS, they answer questions like where to site warehouses, hospitals, fire stations, or schools to best serve a spatially distributed population. | Spatial interaction models predict the volume of flows — migrants, commuters, shoppers, trade, trips — between origins and destinations as a function of the size of each place and the distance or cost separating them. By analogy to Newton's gravity, interaction rises with the 'mass' of origin and destination and falls with separation, and Wilson's 1971 entropy-maximizing family put these models on a rigorous footing for transport, migration, and retail analysis. |
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