Developing An Objective Approach for Context-Sensitive Building Line Determination: A Planning and Spatial Decision-Support Tool

Building lines are a fundamental development control used to regulate the spatial relationship between roads and adjacent buildings, influencing safety, infrastructure expansion and streetscape character. In Sri Lanka, building lines are typically determined using fixed distances or professional judgement, resulting in inconsistent and non-contextual decisions. The absence of a systematic analytical method limits transparency and weakens the effectiveness of planning regulation. This study proposes an objective, context-sensitive approach for building line determination using spatial data and computational modelling.

A mixed-methods framework was adopted. First, legislative documents and international practices were reviewed to establish the conceptual basis of building line regulation. Expert interviews were analysed through thematic analysis to identify influential determinants, followed by a Delphi-informed validation with multi-institutional practitioners. The validated factors were translated into spatial variables using GIS datasets and incorporated into a machine-learning-based predictive model to estimate appropriate building line distances.

Six key determinants were confirmed: road geometry and traffic capacity, development pressure and density, land use and zoning, road hierarchy and connectivity, future development proposals and population, and urban form characteristics. The model successfully generated context-responsive building line values, demonstrating improved consistency compared with fixed-distance practices. Sensitivity and expert validation indicated that the approach better reflects urban character and regulatory intent.

The research establishes a data-driven decision-support tool for building line determination, enhancing transparency, reliability and contextual responsiveness in planning control. The framework provides a replicable methodology for integrating expert knowledge, spatial analysis and machine learning into regulatory planning practice, supporting more coherent and adaptable urban development.