Complexity-Informed Analysis in Data-Scarce Cities

Abstract

Urban change often unfolds in ways that are difficult to observe and explain, particularly in data-scarce cities. To address this, this paper presents a complexity-informed exploratory framework for spatial analysis using globally consistent Earth observation data. Here, data scarcity refers not to absence but to limitations in validation, temporal continuity, resolution, and proxy alignment. Guided by non-linearity, emergence, and distributed agency, the framework compares multiple indicators: built-up extent, density, vegetation, and land consumption across time and space, and examines their alignment and divergence. It applies explicit stopping rules to bound interpretation and treats ambiguity and non-detection as valid analytical outcomes; illustrated through an application to La Paz–El Alto. The framework establishes a disciplined basis for interpreting spatial evidence and supporting planning-relevant orientation under constrained observation.