Urban Livability Assessment and Planning

Urban livability assessment and planning encompass the systematic evaluation of factors that influence the quality of life in cities. This multidisciplinary field integrates social, economic and environmental dimensions through composite indices and indicator frameworks. Planners draw on diverse data sources—surveys, remote sensing, open-source datasets and participatory inputs—to generate spatially explicit maps of livability. Machine learning and geospatial modelling techniques have enhanced predictive capacity, enabling assessments at neighbourhood to city scales and allowing for scenario-based foresight. These insights guide policy interventions in housing, transport, public space and environmental management, aiming to foster equitable, healthy and resilient urban environments. As urbanisation intensifies globally, robust assessment tools are vital for identifying priority areas, monitoring change over time and aligning development with sustainability goals.

Research from Nature Portfolio

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Urban Livability Assessment and Planning publication trend

The graph below shows the total number of articles in urban livability assessment and planning across all publications each year (not limited to Nature Index journals).

Technical terms

Liveability: The capacity of an urban environment to fulfil residents’ needs for health, comfort, opportunity and wellbeing.

Semantic bottleneck model: A deep learning architecture that first predicts interpretable intermediate scores (domains) from imagery before estimating overall livability.

Open-source data synthesis: The integration of freely available datasets—such as housing, population distribution, transport networks and points of interest—to inform spatial analyses.

Participatory mixed methods: A research approach combining qualitative and quantitative techniques—surveys, interviews, observation—to capture stakeholder perspectives on urban conditions.

Multi-criteria decision analysis (MCDA): A framework that evaluates multiple, often conflicting, indicators by assigning weights and aggregating them into a composite index.

References

1. Predicting the liveability of Dutch cities with aerial images and semantic intermediate concepts. Remote Sensing of Environment (2023).
2. Assessing urban livability in Shanghai through an open source data-driven approach. npj Urban Sustainability (2024).
3. Liveability and vitality: an exploration of small cities in Bangladesh. Cities (2023).