Leveraging Machine Learning to Understand Urban Change with Net Construction


A key indicator of urban change is construction, demolition, and renovation. Although these development activities are often interrelated, they are typically studied independent of one another. Analytic methods relying on a strict set of modeling assumptions limit our ability to understand this change holistically. Machine learning has demonstrated the potential when combined with big data to discover patterns and relationships between seemingly unrelated variables. This research explores urban change through net construction, a composite value that treats demolition as a deductive process that is subtracted from construction activity which provides for a more holistic and nuanced understanding of development activity. Once validated through a visual analysis of its reliability as a measure of urban change, we then used a series of random forest regression models to evaluate the predictive accuracy of net construction compared with independent models of construction and demolition. Applying the approaches to an urban county in the United States, we compiled 122 independent variables to provide a comprehensive view of individual neighborhoods from multi-disciplinary data sources such as socioeconomic, built environment characteristics, and landscape metrics. We then analyze the feature importance scores derived from the random forest models in an effort to assess the similarities and differences between the variables that have the greatest influence on model accuracy. The net construction model produced more accurate results than models that used construction and demolition activity independently. While many of the most important features aligned with those from the independent models, land use mix drawn from landscape metrics appeared as the most important, representing a departure from previous studies. This study provides a scalable method for modeling urban change using machine learning techniques and reveals the importance of applying data-driven algorithms that can help communities become more informed about their pressing issues.

Landscape and Urban Planning