পদ্ধতির তুলনা করুন

	Space-Time Cube	স্থান-কাল কার্নেল ঘনত্ব প্রাক্কলন (ST-KDE)
ক্ষেত্র	Human Geography	স্থানিক বিশ্লেষণ
পরিবার	Process / pipeline	Regression model
উদ্ভবের বছর	1970	2010 (space-time extension); 1956 (KDE origin)
প্রবর্তক	Torsten Hägerstrand (time geography); cube popularized by Menno-Jan Kraak	Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen
ধরন	Spatiotemporal data structure and visualization framework	Non-parametric density estimation
মৌলিক উৎস	Hägerstrand, T. (1970). What about people in regional science? Papers of the Regional Science Association, 24(1), 6–21.	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.
অপর নাম	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
সম্পর্কিত	4	5
সারসংক্ষেপ	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.