The COVID-19 pandemic caused significant changes in day-to-day mobility patterns. Much research has revealed the socioeconomic disparities in travel behavior during the pandemic, focusing on the differences between different socioeconomic groups. By contrast, relatively little research has been done to explore the potentially different responses to the pandemic even within the same socioeconomic group. Based on data collected from a map-based online survey conducted in Sweden from 2020 to 2021, we applied multivariate clustering to identify distinct types of travel behavior for people with different income levels and from different geographic areas in the City of Stockholm. Our results show that seven distinct clusters can be identified by simultaneously taking into account income, accessibility, and travel distance. These distinct clusters may speak of distinct modes of living during the pandemic. By acknowledging and recognizing the heterogeneity within the high-income or low-income population group, this study provides more angles and adds more depth to discussions of equal and resilient cities.

Modeling spaces and their relationships is at the core of syntactic analysis, including reach analysis. In a syntactic model, two spaces can be described as close together or far apart based on the directional distance between them. In this study, we compare three different ways of measuring directional distance—namely, by number of steps, by number of direction changes, and by angular deviation—in the context of accessibility and reach analysis. By graphically showing how choosing a different way of measuring directional distance can result in a different reach or accessibility pattern, we provide an intuitive understanding of the different natures of the syntactic measures. By demonstrating how the modeling conventions and the geometric composition of lines at a local scale can have a huge impact on the results of syntactic analysis at a larger scale, we call for more attention to the conventions and principles used for modeling street networks.

The shape and distribution of the most integrated streets, collectively called the integration core, is critical to the characterization of local and global street network types in the space syntax literature. The description of the shape, position, and distribution of integration cores relative to the underlying street networks, however, has remained largely intuitive. We propose analytic and algorithmic definitions of integration core types. We then study empirical and experimental superblock designs with rectangular boundaries, as a particular kind of urban spatial syntax. The analysis leads to a clear understanding of the different ways in which the local street network, internal to the superblock, is structured and interfaced with the perimeter. When used as part of an automated sorting and query process applied to a universe of experimentally generated designs, our definitions and algorithms provide new insights about the interplay between the local generators of street network differentiation and the emergent syntactic structures of the superblock as a whole.

Dockless e-scooter sharing, as a new shared micromobility service, has quickly gained popularity in recent years. In this paper, we present a practical approach to estimating e-scooter flow patterns without knowing the actual routes taken by the e-scooter riders. Our method takes advantage of a huge open dataset that contains the origins and destinations of millions of trips. We show that our models can help cities better support the emerging shared micromobility service. The additional information generated in the modeling process can also be useful for a more refined analysis of e-scooter trips.

The patterns of syntactic differentiation and their causes and effects are fundamental to space syntax analysis. Often, however, differentiation is taken for granted with no reference to the dynamic process that brings it about. Here, we first show that by measuring the amount of syntactic differentiation, we can better distinguish between types of street networks. We then show that repeated local transformations of a regular street grid lead to different yet largely predictable trajectories of differentiation depending upon the rules used. Finally, we show that different paths to differentiation entail different costs in terms of undesirable properties. This allows us to better assess the likely consequences of design moves and their appropriateness relative to design intentions.

Unequal living conditions is a core challenge in contemporary societies and addressed in the Sustainability Development Goals. It is argued that unequal living conditions create and reproduce urban segregation. Having good access to different resources is especially critical for disadvantaged groups. One way of increasing the understanding of urban segregation and the role of architecture, urban design and planning that reach beyond housing segregation is to define living conditions created by the distribution of, and the accessibility to, various resources and opportunities in an urban environment. We argue that analysis of living conditions needs to acknowledge urban form since such descriptions highlight accessibility through public space and are closer to what people may perceive and experience in their everyday life. This paper presents a comprehensive approach that uncover urban inequalities, using the city of Uppsala in Sweden as a case study. Configurative analysis identifies spatial segregation and foregrounds the spaces that have a high network centrality. Accessibility to local resources and amenities is measured with a high spatial resolution based on both metric distance and topological distance. Analyses that measure the socioeconomic diversity within the catchment areas of schools reveal the potential diversity among pupils. Further, the study explores methods for comparison between neighbourhoods. Superimposing socio-economic layers of the population illustrates how analyses may inform decisions of future urban design strategies with the aim to counteract segregation and realize the just city. The results reveal an urban landscape characterised by unequal living conditions. However, the situation in many of the neighbourhoods may be improved through urban design interventions and investments.

Ojämlika livsvillkor reproducerar och driver urban segregation. Planerings-­ och stadsbyggnadspraktiken har stor rådighet över vilka livsvillkor som skapas i staden. Genom stadsbyggandet bestäms stadens rumsliga struktur, markanvändning och bebyggelsetäthet vilket får konsekvenser för tillgängligheten till olika resurser samt till andra människor och olika sociala grupper. Stadsbyggandet formar det offentliga rummet som är en viktig arena för utbyte och samnärvaro med potential att stödja processer som kan främja tillit, social sammanhållning och uppbyggandet av sociala nätverk. Denna studie innehåller en kartläggning av livsvillkor i Uppsala stad utifrån ett stadsbyggnads-­ och planeringsperspektiv. Med ökad kunskap om stadsbyggandets och arkitekturens roll i relation till ojämlika livsvillkor och segregation kan planeringspraktiken vidareutvecklas och bli mer träffsäker i strävan mot skapandet av socialt hållbara städer. Kartläggningen kan också bidra till relevanta och rimliga målformuleringar i översiktsplaner, handlingsplaner och olika kommunala styrdokument.

Estimating health outcomes at a neighborhood scale is important for promoting urban health, yet costly and time-consuming. In this paper, we present a machine-learning-enabled approach to predicting the prevalence of six common non-communicable chronic diseases at the census tract level. We apply our approach to the City of Austin and show that our method can yield fairly accurate predictions. In searching for the best predictive models, we experiment with eight different machine learning algorithms and 60 predictor variables that characterize the social environment, the physical environment, and the aspects and degrees of neighborhood disorder. Our analysis suggests that (a) the sociodemographic and socioeconomic variables are the strongest predictors for tract-level health outcomes and (b) the historical records of 311 service requests can be a useful complementary data source as the information distilled from the 311 data often helps improve the models' performance. The machine learning models yielded from this study can help the public and city officials evaluate future scenarios and understand how changes in the neighborhood conditions can lead to changes in the health outcomes. By analyzing where the most significant discrepancies between the predicted and the actual values are, we will also be ready to identify areas of best practice and areas in need of greater investment or policy intervention.

By asking how much street length can be reached from a given origin within a specified distance limit, and by defining distance in different ways as a function of the physical or cognitive effort required to move in cities, the analysis of reach produces measures that effectively characterize street density, connectivity, and the associated urban potential. While the conceptual foundation for reach analysis has already been laid, the computational aspects have not been sufficiently addressed. We introduce the different graph representations and algorithms we developed to analyze metric reach, directional reach, and intersection reach—a new addition to the existing measures. The graph representation we developed for directional reach analysis also sets the foundation for more advanced graph-based street network analysis. We also provide formulae for computing the mean directional and intersection reach. Finally, we discuss common street network modeling issues that can be addressed by consistent mapping protocols.

Syntactic types are defined as sets of designs with particular properties. We are specifically interested in the definition of deformed grids, a particular type of street network internal to superblocks. The superblocks under study are surrounded by arterial streets and have traversing local main streets and internal street infills. The argument takes advantage of three kinds of rules or algorithms: generative rules that produce a universe of designs, analytical algorithms that can be applied to the description of properties of interest for each member of the universe, and query or sorting rules that allow us to identify those members of the universe that have particular ranges and combinations of properties of interest. The iterative application of analytical and sorting algorithms assists the transition from an intuitive to formalized definitions of type.