Previous studies have predominantly focused on analyzing the relationship between the built environment and urban vitality

The gap

Evidence profile

Sourced from the stated research gap and future-work section and limitations section of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 2 journals. Those papers have been cited 79 times in total.

Supporting evidence — 5 representative gaps

• Explaining Intra-Urban Spatial Interaction with Theory-Informed Interpretable Machine Learning: Nonlinear Contributions of Complementarity, Intervening Opportunities, and Transferability (2026) · ISPRS International Journal of Geo-Information · doi

  Systematically translating theoretical concepts into quantifiable indicators remains a significant methodological challenge. Traditional spatial interaction models typically rely on prespecified functional forms and fixed parametric assumptions, overlooking important socioeconomic and institutional factors. Recent advances in machine learning have raised persistent concerns regarding interpretability in urban studies.

  generalstated research gapevidence 5/5
  Keywords: systematically translating theoretical concepts quantifiable indicators remains significant
• Exploring Urban Spatial Quality Through Street View Imagery and Human Perception Analysis (2025) · Buildings · cited 16× · doi

  Traditional methods for assessing urban spatial quality have limitations in capturing human-scale, perceptual nuances. The paper identifies a gap in understanding how micro-scale spatial features shape human perception in the context of rapid urbanization. The study highlights the need for a data-driven framework to explore the relationship between urban spatial characteristics and subjective perceptions.

  generalstated research gapevidence 5/5
  Keywords: traditional methods assessing urban spatial quality have limitations
• Exploring Urban Spatial Quality Through Street View Imagery and Human Perception Analysis (2025) · Buildings · cited 16× · doi

  expand the range of spatial features, integrating data related to social infrastructure and service accessibility to develop a more comprehensive spatial quality evaluation system - extend the analysis to cities of different scales and developmental stages to test the robustness and applicability of the results - incorporate multisource heterogeneous data and adopt more interpretable models, thereby improving model precision, transparency, and operational applicability and providing stronger theoretical and practical support for perception-oriented urban spatial optimization

  generalfuture-work sectionevidence 5/5
  Keywords: expand range spatial features integrating data related social
• Exploring Urban Spatial Quality Through Street View Imagery and Human Perception Analysis (2025) · Buildings · cited 16× · doi

  the analysis primarily relied on street-view imagery and subjective perception data, which may have been affected by temporal, locational, and environmental factors during data collection, potentially leading to bias or incompleteness - the inherent 'black-box' nature of machine learning models limits the interpretability of complex multi-variable interactions - the study focused primarily on physical spatial characteristics such as building density and green visibility, without systematically incorporating social functional factors like transportation networks or public service facilities - the study area was limited to the central district of Xi'an, the generalizability of the findings to other cities or regions remains to be verified

  generallimitations sectionevidence 5/5
  Keywords: analysis primarily relied street-view imagery subjective perception data
• Investigating the Nonlinear Relationship Between the Built Environment and Urban Vitality Based on Multi-Source Data and Interpretable Machine Learning (2025) · Buildings · cited 31× · doi

  Previous studies have predominantly focused on analyzing the relationship between the built environment and urban vitality at either a macro or micro-scale. The assumption of a predefined linear relationship between the built environment and urban vitality is a limitation. The lack of interpretability of machine learning models is another gap addressed by this study.

  generalstated research gapevidence 5/5
  Keywords: previous studies have predominantly focused analyzing relationship between

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