Mapping the Invisible Boundaries A Data-Driven Approach to City Delineation

According to estimates, 67% of the world's population is expected to live in urban and sub-urban areas by 2040, primarily due to ongoing migration from rural areas. This pattern is also very noticeable in Albania, where a highly populated metropolis that frequently stretches beyond its administrative borders is the result of rapid urbanisation. For a long time, researchers and policymakers have struggled to define urban areas. Some of the traditional methods rely on administrative borders, which often fail to capture the actual economic and spatial dynamics of cities. Others depend on urban morphology, missing the population behaviours and needs. To better understand and manage the urban dynamics, this research aims to try a different method for calculating Tirana's borders. This method will be based on population distribution, utilising a density-based clustering technique in conjunction with a digital representation of the urban form. In comparison to the original administrative boundaries, can a digital, data-driven, density-based algorithm provide a more functionally correct and policy relevant delineation of metropolitan areas in a mid-sized city like Tirana? And how can we encode the urban morphology in a digitalised representation that can be both fed into an algorithm and understood by urban planners? This project aims to develop a machine learning-based approach that clusters buildings into urban zones defined by metrics such as density, urban morphology, and geographic distribution. This approach will lead to the identification of a group of strongly interconnected urban clusters that better represent the physical environment and distribution of economic activity in Tirana. These groups will reflect the real functional extent of the city, taking into account its urban form, and excluding low-density, outlying zones. Additionally, we believe that the vertical land indicator will provide fresh perspectives on Tirana's urban polycentricity and compactness, which will influence the design of spatial policies and infrastructure development. These newly drawn lines will serve as a foundation for further study, enabling more accurate plans for sustainable development, more focused urban planning, quick detection and reaction to change, as well as novel opportunities for economic analysis and policymaking.