7th Jun, 2021Gareth Simons

## Untangling urban data signatures: unsupervised machine learning methods for the detection of urban archetypes at the pedestrian scale

### Abstract

Urban morphological measures applied at a high-resolution of analysis may yield a wealth of data describing varied characteristics of the urban environment in a substantial degree of detail; however, these forms of high-dimensional datasets are not immediately relatable to broader constructs rooted in conventional conceptions of urbanism. Data science and machine learning methods provide an opportunity to explore such forms of data by applying unsupervised machine learning methods. The dimensionality of the data can thereby be reduced while recovering latent themes and identifying characteristic patterns which may resonate with urbanist discourse more generally.

Dimensionality reduction and clustering methods, including Principal Component Analysis (PCA), Variational Autoencoders, and an Autoencoder based Gaussian Mixture Model, are discussed and demonstrated for purposes of ‘untangling’ urban datasets, revealing themes bridging quantitative and qualitative descriptions of urbanism. The methods are applied to a morphological dataset for Greater London. The spatial aggregations and morphological measures are computed at pedestrian walking tolerances at a 20m network resolution using the cityseer-apiPython package, which utilises a local windowing-methodology with distances computed directly over the network and with aggregations performed dynamically and with respect to the direction of approach, thus preserving the relationships between the variables and retaining contextual precision.

Whereas the demonstrated methods hold tremendous potential, their power is difficult to convey or fully exploit using conventional lower-dimensional visualisation methods, thus underscoring the need for subsequent research into how such methods may be coupled to interactive visualisation methods to further elucidate the richness of the data and its potential implications.

### Introduction: Detection of urban archetypes with unsupervised machine learning methods

Vibrant pedestrian districts manifest an affinity for complexity and its requisite diversity, as do complex systems more generally1. However, urban master plans have historically demonstrated a proclivity towards reductionism. Cities were increasingly rearranged around motor vehicles and reconceived in the abstract on drawings boards; the more granular, dense, mixed-use, and visually ‘messy’ artefacts of evolved cities were swept-aside for grandiose compositions that were ultimately too large, too homogenous, and too resistant towards change for pedestrian-based forms of urbanism to thrive2|3. Though initially associated with high-modernism, aspects of these patterns are still prevalent in various forms of contemporary urbanism manifesting across the spectrum from suburbia to romanticised smart city master-plans, explicitly or implicitly emphasising idealised efficiencies at the expense of complexity4|5|6. This paradigm can stifle the oft unpredictable and chaotic forms of interaction aiding processes of discovery and diffusion within complex adaptive systems7|8.

The complex systems interpretation of cities, replete with dynamics from emergence to non-linearities to phase-changes9|10|11|12, resists simple averages and crude models13. A dilemma faces architects, urban designers, and planners tasked with the challenge of how to plan for inherently unpredictable processes at the urban scale14|15. Whereas it is not possible to anticipate every last action of city citizens — and how these chains of interaction might bifurcate or coalesce through space — it is possible to gauge, more generally, how that certain forms of urbanism may be more conducive to large numbers of permutations of complex interactions16. Complex systems derived methods, including network centralities and land-use diversity measures, are proxies for urban complexity: they foreshadow networks of potential interactions available to city citizens. Whereas our ability to model the full complexity of urban systems will always be constrained — not least by sensitivity to initial conditions — it remains possible to explore the spatial manifestations of complex processes present in historical cities and to compare these to new forms of development. The question then arises: if we apply pedestrian-scale centrality and mixed-use measures using sufficiently precise and high-resolution analysis, then can emerging forms of data analysis and unsupervised machine learning methods be used to ‘untangle’ and ‘sift out’ signature patterns from the mass of ensuing data? These terms are used in the literal sense because large and high-dimensional datasets require teasing apart to reveal latent themes, which may ultimately help bridge the gap from quantitative forms of urban analytics to qualitatively framed conceptions of cities14.

A knee-jerk reaction may be to reject machine learning out-of-hand for its links to mathematics and statistics more generally; however, on closer scrutiny, the synthesis of locally precise urban morphological metrics combined with machine learning methods affords the use of highly detailed datasets capable of capturing and preserving contextual particularities; facilitates the use of high-dimensional datasets with significant assortments of variables and potentially complex and varied non-linear relationships between them; and, in the form of unsupervised methods combined with deep neural networks, allows for structures to be unearthed directly from within the data without the imposition of reductionist theories or formulas. Compared to traditional statistical methods applied to larger-scale spatial aggregations, machine learning applied to high-resolution and contextually-anchored spatial data resembles an approach akin to proceeding from the particular to the general. Despite the large volumes of information, the data-space is (in effect) explored ‘line-by-line’ with model losses computed and updated over comparatively small batches of data. Patterns are ‘sniffed out’ using exploratory and bottom-up-like procedures with the more prevalent of these congealing over successive iterations to reveal thematic patterns that have arisen directly from the data. Emphatically, this reasoning only holds if working with pedestrian-scale metrics gathered using sufficiently high-resolution analysis, with the measures processed directly from each location. Use of intervening levels of spatial aggregation, interpolation from larger to smaller units of scale, or overly large units of analysis would otherwise result in the attrition of information and, critically, discards or otherwise masks local-scale inter-relationships between the variables.

Traditional forms of urban morphological analysis have been challenging to apply at scale because of reliance on manually collated observations and wearisome calculations. Geographic Information Systems (GIS) have permitted larger scales of quantitative analysis. However, the lack of comprehensive and granular data sources combined with computational constraints meant that these methods have oft been applied against larger units of spatial aggregation and relied on simplified distance metrics19|20. More recently, however, the increased availability of detailed datasets has facilitated a finer scale of analysis while retaining the ability to process larger areal extents21, thus prompting the adoption of multi-variable and multi-scalar workflows. The ensuing large and high-dimensional datasets can be combined with unsupervised exploratory methods and have engendered interest in how urban morphological analysis can be applied not only to the exposition of existing cities but also in the capacity of a rigorous design-aid for newly planned forms of development22|23|24|25.

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