Bandingkan metode

	Interrupted Time Series Spasial	Desain Diskontinuitas Regresi Spasial (Spatial RDD)
Bidang	Inferensi Kausal	Inferensi Kausal
Keluarga	Regression model	Regression model
Tahun asal	1990s–2000s	2010s
Pencetus	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)
Tipe	Quasi-experimental causal inference with spatial adjustment	Quasi-experimental causal inference
Sumber perintis	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.
Alias	Spatial ITS, Geospatial ITS, Spatially-adjusted ITS, SITS	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Terkait	6	4
Ringkasan	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.