Provides the Saylac Shiny application for multidisciplinary analysis of global, national, and regional indicators observed across places and years. SAYLAC abbreviates Spatial Analysis of Yearly, Longitudinal, and Areal Change. The platform supports spatial diagnostics, longitudinal data exploration, time-series diagnostics, comparative forecasting, and automated reporting for development, health, education, economic, environmental, and social indicators. The application implements three connected modules: Spatial Analysis Workflow for exploratory spatial data analysis, Single Model Diagnostics for stationarity testing, trend diagnostics, and comparative forecasting, and Spatial Uncertainty and Reporting Analysis Dashboard for forecast mapping, spatial clustering, and reporting. The application supports choropleth mapping, Moran's I, Geary's C, Local Indicators of Spatial Association, Getis-Ord Gi star statistics, spatial correlograms, Theil-Sen trend estimation, Mann-Kendall testing, Autoregressive Integrated Moving Average models, Exponential Smoothing State Space models, neural network autoregression, BATS, TBATS, theta forecasting, symmetric mean absolute percentage error model comparison, and report generation. The platform was first applied in Touryare and Mohamud (2026) <doi:10.1007/s43621-026-04022-x> for integrated spatial-temporal forecasting of educational attainment in Eastern Africa toward Sustainable Development Goal 4.