Space syntax analysis of the city as a movement economy has made major contributions to our understanding of the spatial structure of cities, particularly the importance of a mapping of network integration in relation to density, functional mix and streetlife vitality. It has focused attention of urban researchers onto the importance of the relations between the sociality and spatiality of the city. The primary methods of syntactic analysis involve a reduction of urban morphology to a set of spatial axes; here, we explore some limits to such analysis for urban design. Topological analysis of axial models has long recognized problems in accounting for distance, scale and sinuous streetscapes. Existing adaptations to axial methods that address such problems are modelled and shown to produce a broad range of results for the same urban morphology. In each case, we also compare different capacities for to-movement and through-movement – the distinction between ‘closeness centrality’ and ‘betweenness centrality’ that shows that network integration is multiple. We argue that axial analyses privilege visibility over accessibility and can produce distorted mapping at walkable scales; only one of the methods tested measures permeability and walkable access. Space syntax analysis is a powerful tool that will be more useful the better such limits are understood.

The estimation of future sea level rise (SLR) is a major concern for cities near coastlines and river systems. Despite this, current modelling underestimates the future risks of SLR to property. Direct risks posed to property include inundation, loss of physical property and associated economic and social costs. It is also crucial to consider the risks that emerge from scenarios after SLR. These may produce one-off or periodic events that will inflict physical, economic and social implications, and direct, indirect and consequential losses. Using a case study approach, this paper combines various forms of data to examine the implications of future SLR to further understand the potential risks. The research indicates that the financial implications for local government will be loss of rates associated with total property loss and declines in value. The challenges identified are not specific to this research. Other municipalities worldwide experience similar barriers (i.e., financial implications, coastal planning predicaments, data paucity, knowledge and capacity, and legal and political challenges). This research highlights the need for private and public stakeholders to co-develop and implement strategies to mitigate and adapt property to withstand the future challenges of climate change and SLR.

Transportation mode distribution has a large implication on the resilience, economic output, social cost of cities and the health of urban residents. Recent advances in artificial intelligence and the availability of remote sensing data have opened up opportunities for bottom-up modeling techniques that allow understanding of how subtle differences in the urban fabric can impact transportation mode share distribution. This project presents a novel neural network-based modeling technique capable of predicting transportation mode distribution. Trained with millions of images labeled with information from a georeferenced transportation survey, the resulting model is able to infer transportation mode share with high accuracy ( R² = 0.58) from satellite images alone. Additionally, this method can disaggregate data in areas where only aggregated information is available and infer transportation mode share in areas without underlying information. This work demonstrates a new and objective method to evaluate the impact of the urban fabric on transportation mode share. The methodology is robust and can be adapted for cases around the world as well as deployed to evaluate the impact of new developments on the transportation mode choice.

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.

Expanding the scope of sustainability from building scale to urban scale has become an important research topic in recent years, since the scope of a single building neglects interdependencies arising in the urban context. Data has been collected assessing the urban development by means of indicators from sociology, economy, and environment. However, these data sets tend not to be centralized, well processed nor connected. This platform stores, represents and evaluates urban data. It systematical