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Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Space-Time Cube× | Priestorovo-časový odhad hustoty jadra (ST-KDE)× | |
|---|---|---|
| Odbor≠ | Human Geography | Priestorová analýza |
| Rodina≠ | Process / pipeline | Regression model |
| Rok vzniku≠ | 1970 | 2010 (space-time extension); 1956 (KDE origin) |
| Tvorca≠ | Torsten Hägerstrand (time geography); cube popularized by Menno-Jan Kraak | Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen |
| Typ≠ | Spatiotemporal data structure and visualization framework | Non-parametric density estimation |
| Pôvodný zdroj≠ | Hägerstrand, T. (1970). What about people in regional science? Papers of the Regional Science Association, 24(1), 6–21. DOI ↗ | Nakaya, T., & Yano, K. (2010). Visualising crime clusters in a space-time cube: An exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Transactions in GIS, 14(3), 223-239. DOI ↗ |
| Ďalšie názvy | Hägerstrand Space-Time Cube, Space-Time Aquarium, Spatiotemporal Cube, Time-Geographic Cube | ST-KDE, spatiotemporal kernel density estimation, space-time KDE, 3D kernel density estimation |
| Príbuzné≠ | 4 | 5 |
| Zhrnutie≠ | The space-time cube is a framework from time geography for representing and analyzing phenomena that move and change over both space and time. Two horizontal axes carry geographic location and a vertical axis carries time, so each observation becomes a point in a three-dimensional x–y–t volume and a moving object traces a continuous 'space-time path' through the cube. Introduced conceptually by Torsten Hägerstrand in 1970 and turned into a practical analytic and cartographic tool by Menno-Jan Kraak, it underpins modern spatiotemporal hot-spot and trajectory analysis. | Space-Time Kernel Density Estimation extends classical KDE into three dimensions — two spatial and one temporal — to reveal how the intensity of point events (crimes, accidents, disease cases) varies continuously across both geographic space and time. It produces a smooth probabilistic surface that highlights where and when events concentrate most densely. |
| ScholarGateDátová sada ↗ |
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