Greenery from apartments: quantifying and comparing views with residents' perceptions

Abstract

With urban densification and the proliferation of high-rise structures, residents’ apartment views are getting obstructed from surrounding nature, especially greenery. Existing approaches often rely on simplified proxies or aggregated building- or floor-level metrics which does not capture individual-level variation. Most of them use coarse spatial data or subjective self-reports, lacking the granularity and precision to quantify the greenery visible to each resident from their own living space. This study introduces a conceptual and methodological framework for objectively modelling green views at the individual apartment level. Our Apartment Greenery View Measure was developed and assessed by (1) geocoding individual observer positions at window-level within apartment buildings, (2) implementing GIS-based three-dimensional viewshed analysis using high-resolution environmental datasets to objectively quantify views, and (3) examining agreement between modelled views and 445 residents’ self-reported perceptions using the green-to-grey ratio. The method was applied to 30 apartment buildings across Melbourne, Australia. Findings reveal variability in green view exposure by building height, floor-level, and apartment orientation. A moderate correlation (r = 0.556, ICC = 0.521) shows the agreement between objective and perceived view measures, with 39.1% participants overestimating and 60.9% underestimating their views. This underscores the need for objective, standardised measures that move beyond perception alone. The workflow supports aggregation at multiple spatial scales, from individual units to floors and buildings, providing a flexible framework for assessing visual green equity citywide. This provides a scalable, low-cost tool for planners, designers, and health researchers seeking to integrate visual greenery into urban housing, policy, and equity-focused interventions.

Keywords

Green view index / Visibility analysis / Subjective environmental perception / Apartment window views / Semantic building model / Urban environment

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