Linganisha mbinu

	Mfululizo wa Wakati Uliokatizwa wa Kina	Muundo wa Usambazaji wa Regression wa Kijiografia (Spatial RDD)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	1990s–2000s	2010s
Mwanzilishi	Extension of McDowall et al. (1980) ITS framework; spatial adaptations developed in epidemiology and geography through the 1990s–2000s	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)
Aina	Quasi-experimental causal inference with spatial adjustment	Quasi-experimental causal inference
Chanzo asilia	McDowall, D., McCleary, R., Meidinger, E. E., & Hay, R. A. (1980). Interrupted Time Series Analysis. Sage Publications. ISBN: 978-0803913950	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.
Majina mbadala	Spatial ITS, Geospatial ITS, Spatially-adjusted ITS, SITS	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Zinazohusiana	6	4
Muhtasari	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.	Spatial Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local causal effect. The approach is widely used in economics, political science, and public health when policies or institutions change sharply at a border.