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Using a multilevel modelling approach, this study investigates the impact of urban inequalities on changes to rail ridership across Chicago's "L" stations during the pandemic, the mass vaccination rollout, and the full reopening of the city. Initially believed to have an equal impact, COVID-19 disproportionally impacted the ability of lower socioeconomic status (SES) neighbourhoods' to adhere to non-pharmaceutical interventions: working-from-home and social distancing. We find that "L" stations in predominately Black or African American and Hispanic or Latino neighbourhoods with high industrial land-use recorded the smallest behavioural change. The maintenance of higher public transport use at these stations is likely to have exacerbated existing health inequalities, worsening disparities in users' risk of exposure, infection rates, and mortality rates. This study also finds that the vaccination rollout and city reopening did not significantly increase the number of users at stations in higher vaccinated, higher private vehicle ownership neighbourhoods, even after a year into the pandemic. A better understanding of the spatial and socioeconomic determinants of changes in ridership behaviour is crucial for policymakers in adjusting service routes and frequencies that will sustain reliant neighbourhoods' access to essential services, and to encourage trips at stations which are the most impacted to revert the trend of declining public transport use. [ABSTRACT FROM AUTHOR]

Existing empirical work has focused on assessing the effectiveness of nonpharmaceutical interventions on human mobility to contain the spread of COVID‐19. Less is known about the ways in which the COVID‐19 pandemic has reshaped the spatial patterns of population movement within countries. Anecdotal evidence of an urban exodus from large cities to rural areas emerged during early phases of the pandemic across western societies. Yet, these claims have not been empirically assessed. Traditional data sources, such as censuses offer coarse temporal frequency to analyse population movement over infrequent time intervals. Drawing on a data set of 21 million observations from Meta‐Facebook users, we aim to analyse the extent and evolution of changes in the spatial patterns of population movement across the rural–urban continuum in Britain over an 18‐month period from March 2020 to August 2021. Our findings show an overall and sustained decline in population movement during periods of high stringency measures, with the most densely populated areas reporting the largest reductions. During these periods, we also find evidence of higher‐than‐average mobility from high‐density population areas to low‐density areas, lending some support to claims of large‐scale population movements from large cities. Yet, we show that these trends were temporary. Overall mobility levels trended back to precoronavirus levels after the easing of nonpharmaceutical interventions. Following these interventions, we found a reduction in movement to low‐density areas and a rise in mobility to high‐density agglomerations. Overall, these findings reveal that while COVID‐19 generated shock waves leading to temporary changes in the patterns of population movement in Britain, the resulting vibrations have not significantly reshaped the prevalent structures in the national pattern of population movement. As of 2021, internal population movements sit at an intermediate level between those observed pre‐ and early phases of the pandemic. [ABSTRACT FROM AUTHOR]

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It is widely acknowledged that the emergence of "Big Data" is having a profound and often controversial impact on the production of knowledge. In this context, Data Science has developed as an interdisciplinary approach that turns such "Big Data" into information. This article argues for the positive role that Geography can have on Data Science when being applied to spatially explicit problems; and inversely, makes the case that there is much that Geography and Geographical Analysis could learn from Data Science. We propose a deeper integration through an ambitious research agenda, including systems engineering, new methodological development, and work toward addressing some acute challenges around epistemology. We argue that such issues must be resolved in order to realize a Geographic Data Science, and that such goal would be a desirable one. [ABSTRACT FROM AUTHOR]

Copyright of Professional Geographer is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A geodemographic classification aims to describe the most salient characteristics of a small area zonal geography. However, such representations are influenced by the methodological choices made during their construction. Of particular debate are the choice and specification of input variables, with the objective of identifying inputs that add value but also aim for model parsimony. Within this context, our paper introduces a principal component analysis (PCA)-based automated variable selection methodology that has the objective of identifying candidate inputs to a geodemographic classification from a collection of variables. The proposed methodology is exemplified in the context of variables from the UK 2011 Census, and its output compared to the Office for National Statistics 2011 Output Area Classification (2011 OAC). Through the implementation of the proposed methodology, the quality of the cluster assignment was improved relative to 2011 OAC, manifested by a lower total within-cluster sum of square score. Across the UK, more than 70.2% of the Output Areas (OAs) occupied by the newly created classification (i.e. AVS-OAC) outperform the 2011 OAC, with particularly strong performance within Scotland and Wales. [ABSTRACT FROM AUTHOR]

Record linkage is a frequent obstacle to unlocking the benefits of integrated (spatial) data sources. In the absence of unique identifiers to directly join records, practitioners often rely on text‐based approaches for resolving candidate pairs of records to a match. In geographic information science, spatial record linkage is a form of geocoding that pertains to the resolution of text‐based linkage between pairs of addresses into matches and non‐matches. These approaches link text‐based address sequences, integrating sources of data that would otherwise remain in isolation. While recent innovations in machine learning have been introduced in the wider record linkage literature, there is significant potential to apply machine learning to the address matching sub‐field of geographic information science. As a response, this paper introduces two recent developments in text‐based machine learning—conditional random fields and word2vec—that have not been applied to address matching, evaluating their comparative strengths and drawbacks. [ABSTRACT FROM AUTHOR]

Cities with more roads are more productive. However, it can be unclear whether roads increase productivity directly, through improved intra-urban connections, or indirectly, by attracting more people. Our theory suggests that population responses may obscure the direct connectivity effects of roads. Indeed, conditional on population size, highway density does not affect productivity in a sample of US metropolitan areas. However, when exploiting exogenous variation in urban populations, we find that highway density improves agglomeration benefits: moving from the 50th to the 75th percentile of highway density increases the productivity-to-population elasticity from 2% to 4%. Moreover, travel-based measures outperform population size as a measure of agglomeration externalities. [ABSTRACT FROM AUTHOR]

Our understanding of the spatial structure of cities has been traditionally shaped by the availability of static data. In the last few years, thanks to improvements in geospatial technology as well as computing storage and power, there has been an explosion of geo‐referenced data, which monitor cities and urban activities in real time. Although this shift in the data landscape promises to change and augment the way we measure, understand, and act on cities, it poses significant methodological challenges and uncovers substantial gaps in the analytics required. This article contributes in this direction by delivering insights on two fundamental fronts: first, we compare several methods that conceptualize both space and time in rather different ways, highlighting their main advantages and limitations; second and more important, we propose a novel approach the Space‐Time Calendar that uncovers, characterizes and visualizes in an explicitly spatial way both fast and slow urban dynamics. We illustrate its advantages using a data set derived from over 2 years of mobile phone activity in Amsterdam (the Netherlands). Our findings highlight the advantages of the Space‐Time Calendar approach, but also the benefits of appropriately matching the methodological approach to the nature of the data at hand. [ABSTRACT FROM AUTHOR]

This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively. [ABSTRACT FROM AUTHOR]

Cultural diversity is a complex and multi-faceted concept. Commonly used quantitative measures of the spatial distribution of culturally defined groups—such as segregation, isolation or concentration indexes—have been designed to capture just one feature of this distribution. The strengths and weaknesses of such measures under varying demographic, geographic and behavioral conditions can only be comprehensively assessed empirically. This has been rarely done in the case of multigroup cultural diversity. This paper aims to fill this gap and to provide evidence on the empirical properties of various segregation indexes by means of Monte Carlo replications of agent-based modelling simulations under widely varying assumptions. Schelling’s classical segregation model is used as the theoretical engine to generate patterns of spatial clustering. The data inputs include the initial population, the assumed geography, the number and shares of various cultural groups, and their preferences with respect to co-location. Our Monte Carlo replications of agent-based modelling data generating process produces output maps that enable us to assess the sensitivity of the various measures of segregation to parameter assumptions by means of response surface analysis. We find that, as our simulated city becomes more diverse, stable residential location equilibria require the preference for co-location with one’s own group to be not much more than the group share of the smallest demographic minority. When equilibria exist, the values of the various segregation measures are strongly dependent on the composition of the population across cultural groups, the assumed preferences and the assumed geography. Index values are generally non-decreasing in increasing preference for within-group co-location. More diverse populations yield—for given preferences and geography—a greater degree of spatial clustering. The sensitivity of segregation measures to underlying conditions suggests that meaningful analysis of the impact of segregation requires spatial panel data modelling. [ABSTRACT FROM AUTHOR]

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]

What has shaped the US urban landscape? Probably, different forces worked at different magnitudes, times, and locations. In this paper we develop a methodology to disaggregate some of the engines of US city growth over time and across space. To understand the results we propose a visualization approach based on what we term storyboards, which create an intuitive and dynamic narrative on the effect of several factors of urban success. This allows us to show that the role of growth engines differs greatly: the rise and decline of manufacturing were very localized; industrial specialization is counterproductive, particularly so in the 1990s; service sectors used to be a consumption amenity, but now serve as a production amenity; and highly educated cities unambiguously and increasingly attract firms in any part of the US. We also note that the arguments for our visualization and its lessons bear implications for visualization in the social sciences beyond this particular example. [ABSTRACT FROM AUTHOR]

This paper highlights the importance of migrant entrepreneurs as change agents for economic vitality (‘health angels’) in cities in the developed world. The focus will in particular be on transition strategies of second-generation migrant entrepreneurs, as their ‘break-out’ growth and risk strategies may lead to a convergence of migrant entrepreneurship with mainstream entrepreneurship. The present study will address both the driving forces of this ‘new entrepreneurship’ and the socio-economic implications for modern cities. To test the above proposition, a unique extensive micro-database is used for a diversified set of migrant entrepreneurs in the four largest cities in the Netherlands. Detailed information on their risk and growth strategies was obtained from personal interviews with these entrepreneurs. The research first offers statistical findings from this data-set, and subsequently it uses a multidimensional classification tool from artificial intelligence, namely self-organizing maps, to identify and present patterns of manifest differences and similarities in the migrant entrepreneurs’ behaviour and attitudes. Finally, a causal econometric model is designed and estimated to assess the importance of various behavioural variables and control factors for the business performance of the entrepreneurs concerned. [ABSTRACT FROM PUBLISHER]

This study tackles the description of the size distribution of urban employment centers or, in other words, the size of areas within cities with significantly high densities of workers. Certainly, there exists a branch of urban economics that has paid substantial attention to urban employment centers, but the efforts have been focused on identification methodologies. In this paper we build on such body of research and combine it with insights from the latest contributions in the sister subfield of city size distributions to push the agenda forward in terms of the understanding of these phenomena. We consider the 359 Metropolitan Statistical Areas (MSAs) in the United States in the year 2000 and reach three main conclusions: first, employment center sizes are more unevenly distributed than city sizes; second, the two functions that best describe city size distributions, namely the lognormal and the double Pareto-lognormal, also offer a good fit for the case of centers, particularly the latter; and third, several interesting statistically significant relationships (correlations) between variables related to centers and MSAs are deduced. Further experiments with a different technique of center identification suggest that the results are fairly robust to the method of choice. [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]

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]

We investigate the common conjecture in applied econometric work that the inclusion of spatial fixed effects in a regression specification for a single cross-sectional data set removes spatial dependence. We demonstrate analytically and by means of a series of simulation experiments how evidence of the removal of spatial autocorrelation by spatial fixed effects may be spurious when the true data generating processes ( DGP) takes the form of a spatial lag or spatial error dependence. In addition, we also show that spatial fixed effects correctly remove spatial correlation only in the special case where the dependence is group-wise, with all observations in the same group as neighbours of each other. [ABSTRACT FROM AUTHOR]