Abstract
Urban typologies allow areas to be categorised according to form and the social, demographic, and political uses of the areas. The use of these typologies and finding similarities and dissimilarities between cities enables better targeted interventions for improved health, transport, and environmental outcomes in urban areas. A better understanding of local contexts can also assist in applying lessons learned from other cities. Constructing urban typologies at a global scale through traditional methods, such as functional or network analysis, requires the collection of data across multiple political districts, which can be inconsistent and then require a level of subjective classification. To overcome these limitations, we use neural networks to analyse millions of images of urban form (consisting of street view, satellite imagery, and street maps) to find shared characteristics between the largest 1692 cities in the world. The comparison city of Paris is used as an exemplar and we perform a case study using two Australian cities, Melbourne and Sydney, to determine if a “Paris-end” of town exists or can be found in these cities using these three big data imagery sets. The results show specific advantages and disadvantages of each type of imagery in constructing urban typologies. Neural networks trained with map imagery will be highly influenced by the structural mix of roads, public transport, and green and blue space. Satellite imagery captures a combination of both urban form and decorative and natural details. The use of street view imagery emphasises the features of a human-scaled visual geography of streetscapes. However, for both satellite and street view imagery to be highly effective, a reduction in scale and more aggressive pre-processing might be required in order to reduce detail and create greater abstraction in the imagery.
Highlights
The form a city takes and the way land is allocated can have both positive and negative consequences for population health and well-being
These accuracies were calculated at the end of each epoch during the training step, testing the neural network’s skill in correctly identifying the correct city out of the nearly
While this is true for the cities that are like Paris for the Google Street View (GSV)-Baidu Maps Street View (BSV) neural network, it is not the case for the other two neural networks
Summary
The form a city takes and the way land is allocated can have both positive and negative consequences for population health and well-being. Cities with compact forms have been found to lead to better health outcomes [1,2,3] and reductions in per capita emissions [4]. City design can be a factor in encouraging or discouraging the uptake of active transport [5] leading to better health outcomes [6] or locking-in car dependency [7], increasing levels of air pollution [8], obesity [9], and road trauma [10]. Due to the long time scales of urban change and the high stability of city structures [11], we must consider current cities as both a snapshot in time and a culmination of years of construction. The rigid structure of cities makes rapid changes difficult and changes should be undertaken with due care, as the impacts will be very long-lived [12,13]
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