arXiv subjects

Gareth D. Simons

Publications and source records attributed to Gareth D. Simons.

5 recordsLinked to original sources

arXiv2022

The cityseer Python package for pedestrian-scale network-based urban analysis

cityseer-api is a Python package consisting of computational tools for fine-grained street-network and land-use analysis, helpful in assessing the morphological precursors to vibrant neighbourhoods. It is underpinned by network-based methods developed specifically for urban analysis at the pedestrian scale. cityseer-api computes a variety of node and segment-based network centrality methods, land-use accessibility and mixed-use measures, and statistical aggregations. Accessibilities and aggregations are computed dynamically over the street-network while taking walking distance thresholds and the direction of approach into account, and can optionally incorporate spatial impedances and network decomposition to increase spatial precision. The use of Python facilitates compatibility with popular computational tools for network manipulation (NetworkX), geospatial topology (shapely), geospatial data state management (GeoPandas), and the NumPy stack of scientific packages. The provision of robust network cleaning tools aids the use of OpenStreetMap data for network analysis. Underlying loop-intensive algorithms are implemented in Numba JIT compiled code so that the methods scale efficiently to larger cities and regions. Online documentation is available from https://cityseer.benchmarkurbanism.com, and the Github repository is available at https://github.com/benchmark-urbanism/cityseer. Example notebooks are available at https://cityseer.benchmarkurbanism.com/examples/.

cs.SI↗

arXiv2022

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

Urban morphological measures applied at a high-resolution of spatial analysis can yield a wealth of data describing characteristics of the urban environment in a substantial degree of detail; however, such forms of high-dimensional numeric datasets are not immediately relatable to broader constructs rooted in conventional conceptions of urbanism. Data science and machine learning (ML) methods provide an opportunity to explore such forms of complex datasets by applying unsupervised ML methods to reduce the dimensionality of the data while recovering latent themes and 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 dataset for Greater London consisting of network centralities, land-use accessibilities, mixed-use measures, and density measures. The measures are computed at pedestrian walking tolerances at a $20m$ network resolution utilising 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 a need for subsequent research into how such methods may be coupled to interactive visualisation tools to further elucidate the richness of the data and its potential implications.

physics.soc-ph↗

arXiv2022

Prediction of 'artificial' urban archetypes at the pedestrian-scale through a synthesis of domain expertise with machine learning methods

The vitality of urban spaces has been steadily undermined by the pervasive adoption of car-centric forms of urban development as characterised by lower densities, street networks offering poor connectivity for pedestrians, and a lack of accessible land-uses; yet, even if these issues have been clearly framed for some time, the problem persists in new forms of planning. It is here posited that a synthesis of domain knowledge and machine learning methods allows for the creation of robust toolsets against which newly proposed developments can be benchmarked in a more rigorous manner in the interest of greater accountability and better-evidenced decision-making. A worked example develops a sequence of machine learning models that distinguishing `artificial' towns from their more walkable and mixed-use `historical' equivalents. The dataset is developed from network centrality, mixed-use, land-use accessibility, and population density measures as proxies for spatial complexity, which are computed at the pedestrian-scale for 931 towns and cities in Great Britain. Using officially designated `New Towns' as a departure point, a series of clues is then developed. First, using an iterative human-in-the-loop procedure, a supervised classifier (Extra-Trees) is cultivated from which 185 `artificial' locations are identified based on data aggregated to respective town or city boundaries. This information is then used to train supervised and semi-supervised (M2) deep neural network classifiers against the higher resolution dataset. The models broadly align with intuitions expressed by urbanists and show potential for continued development to broach ensuing challenges pertaining to: selection of curated training exemplars; further development of techniques to accentuate localised scales of analysis; and methods for the calibration of model probabilities to align with the intuitions of domain experts.

physics.soc-ph↗

arXiv2021

Network centrality measures and their correlation to mixed-uses at the pedestrian-scale

Street network analysis holds appeal as a tool for the assessment of pedestrian connectivity and its relation to the intensity and mix of land-uses; however, application within urban-design triggers a range of questions on implementary specifics due to a variety of theories, methods, and considerations and it is not immediately clear which of these might be the most applicable at the pedestrian scale in relation to land-uses. It is, furthermore, difficult to directly evaluate differing approaches on a like-for-like basis without recourse to the underlying algorithms and computational workflows. To this end, the cityseer-api Python package is here used to develop, compute, and compare a range of centrality methods which are then applied to the Ordnance Survey Open Roads dataset for Greater London. The centralities are correlated to high-resolution land-use and mixed-used measures computed from the Ordnance Survey Points of Interest dataset for the same points of analysis using a spatially precise methodology based on network distances to premise locations. The comparisons show that mixed-uses correlate more strongly against closeness than betweenness centralities; segmented measures tend to offer slightly stronger correlations than node-based equivalents; weighted variants offer correlations similar to unweighted versions, but with a greater degree of spatial specificity; simplest-path methods confer an advantage in the context of local high-street mixed-uses but not necessarily for district-wide mixed-uses or land-use accessibilities; and the application of centrality measures to the dual network does not offer tangible benefits over the primal network.

physics.soc-ph↗

arXiv2021

The application of mixed-use measures at the pedestrian-scale

Mixed-use urbanism affords access to diverse assortments of land-uses within a pedestrian-accessible context. It confers advantages such as reductions to driving, air pollution, and Body Mass Index with associated increases in active transportation and improvements to health. However, whereas mixed-use urbanism is clearly beneficial, methods for measuring and assessing the presence of mixed-uses at a granular level of analysis remain murkier. This work demonstrates techniques for gauging mixed-uses in more spatially precise terms concurring more readily with an urbanist's conception of pedestrian-accessible mixed-uses. It does so through the use of the cityseer-api Python package, which facilitates the use of spatially granular land-use classification data assigned to adjacent street edges and then aggregated dynamically, with distances measured from each point of analysis to each accessible land-use while taking the direction of approach into account. It is argued that Hill Numbers is a suitable measure of diversity because it can mirror the intent of traditional indices while behaving more intuitively. Further, distance-weighted formulations of Hill diversity can be applied with spatial impedances, thus conferring a particularly spatially nuanced gauge of local access to mixed-uses. These methods and indices are demonstrated for Greater London with observations correlated to Principal Component Analysis derived from a range of land-use accessibilities measured from the same locations and for the same point-of-interest dataset. The Hill diversity measures, particularly the distance-weighted formulations, offer the most robust correlations for both expansive mixed-use districts and more local 'high-street' mixes of uses while yielding the most intuitive and spatially precise behaviour in the accompanying plots.

physics.soc-ph↗