Identifies soundscape service-supply potential in urban areas, suggesting advanced planning methods.
Urban sound has long been managed primarily within a noise-control framework. To support a more resource-oriented planning perspective, reproducible city-scale screening tools are needed to identify where spatial-physical conditions may support soundscape-relevant service-supply pathways and inform early-stage planning prioritisation. This study introduces the Urban Soundscape Service-supply Potential (USSP) framework as a proxy-based, open-data and planning-oriented approach for mapping such conditions without treating them as direct measures of actual acoustic conditions, perceived soundscape experience or realised cultural ecosystem service benefits. Using Munich, Germany, as a testbed, four proxy-based sound-source potential layers were derived from open geospatial data: traffic and industrial sound-source carrier potentials, human-activity sound-source opportunity potential and natural sound-source support potential. These layers were integrated into three screening-level indices representing naturalness-oriented (USSP N ), liveliness-oriented (USSP L ) and balance-oriented service-supply potential (USSP B ). The results revealed distinct spatial configurations of pressure-related and resource-related potentials, while district-level trade-off typologies clarified relative noise-resource conditions for planning diagnosis. At the composite level, USSP N showed a marked core-periphery contrast, USSP L exhibited a heterogeneous mosaic, and USSP B showed a stronger concentration in upper equal-interval classes. All three indices displayed significant positive spatial autocorrelation and local hotspot and coldspot structures. The eBird-based ecological plausibility check provided limited external support for USSP N by showing a positive association with bird species richness after controlling for sampling effort. USSP complements, rather than replaces, perceptual soundscape assessment and exposure-based noise governance by supporting early-stage prioritisation, targeted field verification and planning discussion.
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Zhu et al. (2026) studied this question.
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