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Cities have become playgrounds for competitive behaviour and rapid economic dynamics. However, in many cities (or urban agglomerations) economic growth is mainly manifested in specific geographic areas, where creative people and innovative entrepreneurs are located. In this paper we offer first the conceptual and operational foundation for analyzing this so-called ‘urban buzz’ and its interlinked primary drivers. We next develop an analytical framework for testing the buzz hypothesis, with a special reference to the importance of social bonds and networks in Amsterdam. In our empirical analysis we use a unique dataset on social network connectivity and spatial concentration in a city, based on location-sharing services through the use of Foursquare data. Our urban buzz model shows clearly that buzz and socioeconomic (cultural) diversity are closely connected phenomena. [ABSTRACT FROM AUTHOR]

This paper presents the notion of spatial signatures as a characterisation of space based on form and function designed to understand urban environments. The spatial configuration of the dif-ferent components of cities is relevant for at least two main reasons. On the one hand, it encodes many aspects of the phenomena that created such an arrangement in the first place. On the other, once in place, this arrangement of urban form and function underpins many outcomes, from economic productivity to environmental sustainability. Our approach unfolds in three main stages. First, we propose a new spatial unit –the Enclosed Tessellation (ET) cell– to delineate space in a way that is exhaustive and matches the underlying processes at which urban form and function operate. Second, we propose to attach a large variety of form and function-based characters to ET cells to describe each of these units. Third, to build spatial signatures, information on ET cells can be clustered using unsupervised learning techniques. This process results in a theory-informed, data-driven typology of space that follows form and function. We demonstrate the flexibility of the approach to a variety of data landscapes and cultural backgrounds by providing five illustrations of spatial signatures for five cities across five continents. These showcases demonstrate the ability to successfully differentiate areas of a city that were built at different points in time and under different technological regimes, but also highlight broader comparisons about the nature of urban fabric in different regions of the world. Our contribution resides in leveraging modern data, tech-nology and methods to propose a detailed, consistent and scalable methodology that characterises urban form and function. The spatial signatures can be used across academic disciplines and by a variety of practitioners and policymakers supporting initiatives such as the Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

The current Oxford English Dictionary defines a neighbourhood as '[a] district or portion of a town, city, or country, esp. considered in reference to the character or circumstances of its inhabitants'. However, a large part of quantitative urban analysis relies on administrative boundaries, such as postcodes, as an approximation of true neighbourhoods. This regional graphic explores the possibility of redefining internal boundaries of a city through the combination of new sources of data, statistics and computation. Using the language of a sample of georeferenced tweets in the Dutch city of Amsterdam, in combination with a regionalization algorithm that groups similar continuous areas, the official postcodes are redrawn into those based on their spoken characteristics. The result is a very different urban landscape that is likely to encapsulate better substantive differences between parts of the city. [ABSTRACT FROM AUTHOR]

Abstract: This paper argues that there is a global trend towards the highest possible performance among functionally specialized and heterogeneous world cities in different parts of our world. It aims to map out the relative disparities in competitive performance among a preselected set of major global cities by offering a hierarchical benchmark analysis of these cities on the basis of a recently completed comparative study on their socio-economic ‘power’, as exerted and/or perceived by various groups of relevant urban stakeholders. The analytical tool employed to highlight and better understand the relative (hierarchical) position of these cities from a topological perspective is based on Self-Organizing Maps (SOMs), which depict in a multidimensional space the similarities among the cities under consideration. The empirical results are presented and interpreted from the perspective of a benchmark ranking of the various cities involved, while finally also an actor-oriented analysis of the distinct performance components of these cities is provided. [Copyright &y& Elsevier]

In this paper we consider urban spatial structure in US cities using a multidimensional approach. We select six key variables (commuting costs, density, employment dispersion and concentration, land-use mix, polycentricity, and size) from the urban literature and define measures to quantify them. We then apply these measures to 359 metropolitan areas from the 2000 US Census. The adopted methodological strategy combines two novel techniques for the social sciences to explore the existence of relevant patterns in such multidimensional datasets. Geodesic self-organizing maps (SOM) are used to visualize the whole set of information in a meaningful way, while the recently developed clustering algorithm of the max-p is applied to draw boundaries within the SOM and analyze which cities fall into each of them. [ABSTRACT FROM AUTHOR]

This article introduces a new approach to measuring neighborhood change. Instead of the traditional method of identifying “neighborhoods” a priori and then studying how resident attributes change over time, this approach looks at the neighborhood more intrinsically as a unit that has both a geographic footprint and a socioeconomic composition. Therefore, change is identified when both aspects of a neighborhood transform from one period to the next. The approach is based on a spatial clustering algorithm that identifies neighborhoods at two points in time for one city. The authors also develop indicators of spatial change at both the macro (city) level and the local (neighborhood) scale. The authors illustrate these methods in an application to an extensive database of time-consistent census tracts for 359 of the largest metropolitan areas in the United States for the period 1990-2000. [ABSTRACT FROM AUTHOR]

• Deep learning based approach to derive clusters describing visual features of retail environments. • Characterising scene attributes different retail environments project to consumers. • Demonstrating utility of unsupervised deep learning methods to urban planning. Visual characteristics of leisure and retail environments provide sensory cues that can influence how consumers experience and behave within these spaces. In this paper, we provide a computational method that summarises the "visual features" of shopping districts by analysing a national database of geocoded store frontage images. While the traditional focus of social scientific research explores how drivers such as proximity to shopping environments factor into location choice decisions, the visual characteristics that describe the enclosing urban area are often neglected. This is despite the assumption consumers translate visual appearance of a retail area into a judgement of its functional utility which mediates consumer behaviour, patronage intention and the image a retail location projects to passers-by. Such judgements allow consumers to draw fine distinctions when evaluating between competing destinations. Our approach introduces a deep learning model known as Convolutional Autoencoders to extract visual features from storefront images of leisure and retail amenities. These features are partitioned into five clusters before several measures describing the environment around the leisure and retail properties are introduced to differentiate between the clusters and assess which variables are distinctive for particular groupings. Our empirical strategy unpacks different groupings from the clusters, which implies the existence of relationships between visual features of shopping areas and functional characteristics of the surrounding urban environment. Ultimately, using the example of retail landscapes, the core contribution of this paper demonstrates the utility of unsupervised deep learning methods to research questions in urban planning. [ABSTRACT FROM AUTHOR]

Urban areas of interest (AOIs) represent areas within the urban environment featuring high levels of public interaction, with their understanding holding utility for a wide range of urban planning applications. Within this context, our study proposes a novel space-time analytical framework and implements it to the taxi GPS data for the extent of Manhattan, NYC to identify and describe 31 road-constrained AOIs in terms of their spatiotemporal distribution and contextual characteristics. Our analysis captures many important locations, including but not limited to primary transit hubs, famous cultural venues, open spaces, and some other tourist attractions, prominent landmarks, and commercial centres. Moreover, we respectively analyse these AOIs in terms of their dynamics and contexts by performing further clustering analysis, formulating five temporal clusters delineating the dynamic evolution of the AOIs and four contextual clusters representing their salient contextual characteristics. • An enhanced analytical framework is proposed to integratedly explore urban AOIs in New York City. • ST-DBSCAN algorithm is applied on taxi GPS data to detect spatiotemporal hotspots. • Road network is used as the carrier of the identified taxi trip hotspots, formulating road-constrained AOIs. • Spatiotemporal dynamics and contextual characteristics of the AOIs are investigated through further clustering analysis. [ABSTRACT FROM AUTHOR]

This study develops a Geographic Data Science framework that transforms the Foursquare check-in locations and user origin-destination flows data into knowledge about the emerging forms and characteristics of cities' neighbourhoods. We employ a longitudinal mobility dataset describing human interactions with Foursquare venues in ten global cities: Chicago, Istanbul, Jakarta, London, Los Angeles, New York, Paris, Seoul, Singapore, Tokyo. This social media data provides spatio-temporally referenced digital traces left by human use of urban environments, giving us access to the intangible aspects of urban life, such as people behaviours and preferences. Our framework capitalizes on these new data sources, bringing about a novel Geographic Data Science and human-centered methodological approach. Combining network science – a study area with great promise for the analysis of cities and their structure – with geospatial analysis methods, we model cities as a series of global urban networks. Through a spatially weighted community detection algorithm, we uncover functional neighbourhoods for the ten global cities. Each neighbourhood is linked to hyper-local characterisations of their built environment for the Foursquare venues that compose them, and complemented with a range of measures describing their diversity, morphology and mobility. This information is used in a clustering exercise that uncovers a set of four functional neighbourhood types. Our results enable the profiling and comparison of functional neighbourhoods, based on human dynamics and their contexts, across the sample of global cities. The framework is portable to other geographic contexts where interaction data are available to bind different localities into functional agglomerations, and provide insight into their contextual and human dynamics. [ABSTRACT FROM AUTHOR]