Confronta i metodi

	Eterogeneità Spaziale Stratificata	Campionamento a grappoli	Campionamento Sistematico
Campo	Campionamento	Metodologia delle indagini	Metodologia delle indagini
Famiglia	Process / pipeline	Process / pipeline	Process / pipeline
Anno di origine	2010	Early-to-mid 20th century; canonical treatment 1953/1977	Mid-20th century (Cochran 1953; Kish 1965)
Ideatore	Jinfeng Wang	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	William G. Cochran; formalized in survey sampling theory
Tipo	Geographical detection and stratification method	Probability sampling design	Probability sampling design
Fonte seminale	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.). John Wiley & Sons. ISBN: 978-0471162407
Alias	Geodetector, GeoDetector	cluster random sampling, area sampling, one-stage cluster sampling	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
Correlati	3	5	5
Sintesi	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.	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.