Subjective vs Objective Data
Exploring the interplay between measurable facts and lived perceptions in geospatial research and walking systems.
Exploring the interplay between measurable facts and lived perceptions in geospatial research and walking systems.
Geospatial systems rely on data to model, analyse, and represent the world. This data can be broadly divided into two categories: objective data, which captures measurable, verifiable facts about the environment, and subjective data, which reflects human perceptions, experiences, and values. Both forms are crucial for understanding walking environments. While objective measures provide consistency and comparability, subjective accounts reveal how spaces are actually lived and interpreted by walkers.
Balancing subjective and objective perspectives remains a key challenge in geospatial research. Systems that privilege only one risk producing either abstract but disconnected outputs, or rich narratives that lack replicability. Walking systems must therefore integrate both to design routes, maps, and tools that are accurate, meaningful, and user-centred.
Objective data refers to information that can be measured, observed, and validated independently of individual interpretation. In walking contexts, this includes datasets such as street networks, elevation models, traffic counts, and land cover classifications. Sources may be derived from government surveys, satellite imagery, or open data platforms such as OpenStreetMap.
These data enable precise modelling of distances, connectivity, and accessibility. They are vital for tasks such as route optimisation or infrastructure planning, where reproducibility and comparability are necessary. However, objective data often struggles to capture qualities such as safety, comfort, or aesthetics, which are central to the walking experience.
Subjective data captures how individuals and communities perceive, evaluate, and narrate their environments. This includes reported experiences of safety, beauty, noise, or social atmosphere, gathered through surveys, interviews, participatory mapping, or digital traces such as reviews and comments. These perspectives reveal the intangible qualities of place that objective datasets cannot represent on their own.
Subjective data introduces variability, bias, and context-specific interpretation. Yet it is precisely these qualities that make it valuable, as they provide insight into how environments are lived and felt. For walking systems, subjective data is indispensable for designing routes that are not only efficient but also enjoyable, inclusive, and meaningful.
Effective walking systems integrate both data types, creating hybrid representations of space and place. For example, a route might be calculated using objective measures of distance and surface type, but weighted by subjective ratings of safety or scenic value. Similarly, mapping tools can overlay sensor-derived air quality data with community perceptions of comfort or tranquillity, highlighting both environmental and experiential qualities.
Integration requires careful methodological design. Subjective and objective data must be aligned to the same spatial units, weighted transparently, and validated through user feedback. Done well, this integration strengthens both scientific rigour and experiential relevance, making geospatial systems more reflective of real-world walking practices.