Linganisha mbinu

	Heterogeneity ya Tabaka la Anga	Sampuli Iliyowekwa Ngazi	Sampuli ya Kisistemati
Nyanja	Usampulishaji	Metodolojia ya Dodoso	Metodolojia ya Dodoso
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	2010	1977	Mid-20th century (Cochran 1953; Kish 1965)
Mwanzilishi	Jinfeng Wang	William G. Cochran	William G. Cochran; formalized in survey sampling theory
Aina	Geographical detection and stratification method	Probability-based survey sampling design	Probability sampling design
Chanzo asilia	Wang, J. F., Li, X. H., Christakos, G., Liao, Y. L., Zhang, T., & Gu, X. (2010). Geographical detectors–based health risk assessment and its application in the neural tube defects study for the C–H plane. International Journal of Geographical Information Science, 24(1), 107–127.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Majina mbadala	Geodetector, GeoDetector	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
Zinazohusiana	3	2	5
Muhtasari	Spatial Stratified Heterogeneity, commonly known as Geodetector, is a framework introduced by Jinfeng Wang and colleagues in 2010 for measuring and detecting spatial heterogeneity in data and identifying environmental risk factors. It quantifies the degree to which a given factor (variable) explains spatial variation in an outcome and is particularly valuable for environmental epidemiology, ecology, and geographical analysis where spatial non-stationarity is common.	Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics.	Systematic sampling is a probability sampling technique in which every k-th element is selected from an ordered list of the population after a random starting point. With population size N and desired sample size n, the sampling interval k = N/n is computed and one unit is chosen at random from the first interval; all subsequent units are selected by adding k repeatedly. The method is operationally simple, yields a spread-out sample, and often achieves lower variance than simple random sampling when the list has no harmful periodicity.