השוואת שיטות

	עקרון אי-הומוגניות מרחבית מפולחת	דגימת אשכולות	דגימה שכבתית
תחום	דגימה	מתודולוגיית סקרים	מתודולוגיית סקרים
משפחה	Process / pipeline	Process / pipeline	Process / pipeline
שנת המקור	2010	Early-to-mid 20th century; canonical treatment 1953/1977	1977
הוגה השיטה	Jinfeng Wang	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	William G. Cochran
סוג	Geographical detection and stratification method	Probability sampling design	Probability-based survey sampling design
מקור מכונן	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: 978-0471162407	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
כינויים	Geodetector, GeoDetector	cluster random sampling, area sampling, one-stage cluster sampling	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
קשורות	3	5	2
תקציר	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.	Cluster sampling is a probability sampling technique in which the population is divided into naturally occurring groups (clusters), a random sample of clusters is selected, and all — or a random subset of — members within each selected cluster are studied. It is especially practical when a complete population list is unavailable or when units are geographically dispersed, making individual random selection prohibitively expensive. One-stage cluster sampling surveys every member of selected clusters; two-stage designs add a second random draw within clusters.	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.