From individuals to collective spatial truth: data characteristics in digital participatory mapping

Abstract. The collection of individual spatial insights through participatory mapping using structured digital sketch maps through self-administrated geo-questionnaires has gained increased visibility as an effective method for capturing subjective data, including people’s perceptions, opinions, experiences, and knowledge. This approach gathers individual contributions and aggregates them into a unified dataset, enabling the identification of collective insights derived from diverse inputs. This aggregation process facilitates the generation of knowledge from a collective perspective.

The aim of this paper is to describe five key characteristics of this data: unique inputs that transform into collective narratives, varying levels of bias, data assessment, multiscale data, and spatial representation uncertainty. These characteristics highlight the strengths of this technique, including its proven potential and widespread acceptance across a wide range of applications. However, they also reveal weaknesses and opportunities for improvement, such as the uncertainty that permeates the entire data lifecycle—from participant recruitment and technical proficiency to task understanding and data representation. The outlined characteristics are intended to be enunciative rather than exhaustive and serve as a starting point for more in-depth exploration of the methodological, technical, and conceptual aspects of collecting and analyzing subjective spatial data through digital participatory mapping.