השוואת שיטות

	סדרות עתיות מקוטעות מרחביות	ניתוח סדרות עתיות מופרעות (Interrupted Time Series - ITS)
תחום	הסקה סיבתית	הסקה סיבתית
משפחה	Regression model	Regression model
שנת המקור	1990s–2000s	2002
הוגה השיטה	Extension of McDowall et al. (1980) ITS framework; spatial adaptations developed in epidemiology and geography through the 1990s–2000s	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
סוג	Quasi-experimental causal inference with spatial adjustment	Quasi-experimental segmented regression
מקור מכונן	McDowall, D., McCleary, R., Meidinger, E. E., & Hay, R. A. (1980). Interrupted Time Series Analysis. Sage Publications. ISBN: 978-0803913950	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.
כינויים	Spatial ITS, Geospatial ITS, Spatially-adjusted ITS, SITS	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
קשורות	6	5
תקציר	Spatial Interrupted Time Series (Spatial ITS) extends the classic ITS design to settings where units are geo-referenced and outcomes in one location may spill over into or correlate with outcomes in neighbouring locations. It estimates the causal effect of a discrete intervention on an outcome time series while explicitly modelling geographic autocorrelation, preventing biased standard errors and enabling detection of spatial spillovers.	Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.