Daniel Arribas-Bel

Multidimensional Urban Sprawl in Europe: A Self-Organizing Map Approach

The present paper addresses the issue of urban sprawl in Europe from a multidimensional point of view, identifying the most sprawled areas and characterizing them in terms of population size. The literature is reviewed to categorize and extract the most relevant six dimensions that define the concept and several indices are specified to implement them. These are then calculated for a sample of the main European cities that uses several sources to obtain the best possible dataset to measure urban sprawl. All this information is brought together using the self-organizing map (SOM) algorithm to be visualized and further studied, taking advantage of its properties as a data-reduction as well as a clustering technique. The analysis locates the hot-spots of urban sprawl in Europe in the centre of the continent, around Germany, and characterizes such urban areas as small, always half the size of the average city of the sample.

The validity of the monocentric city model in a polycentric age: US metropolitan areas in 1990, 2000 and 2010

In this article, we use local indicators of spatial association (LISA) and other spatial analysis techniques to analyze the distribution of centers with high employment density within metropolitan areas. We examine the 359 metropolitan areas across the United States at three points in time (1990, 2000, and 2010) to provide a spatio-temporal panoramic of urban spatial structure. Our analysis highlights three key findings. (1) The monocentric structure persists in a majority of metropolitan areas: 56.5% in 1990, 64.1% in 2000, and 57.7% in 2010. (2) The pattern of employment centers remains stable for most metropolitan areas: the number of centers remained the same for 74.9% of metropolitan areas between 1990 and 2000 and for 85.2% between 2000 and 2010. (3) Compared with monocentric metropolitan areas, polycentric metros are larger and more dense, with higher per-capita incomes and lower poverty rates.

Spatial Variation in the Quality of American Community Survey Estimates

Social science research, public and private sector decisions, and allocations of federal resources often rely on data from the American Community Survey (ACS). However, this critical data source has high uncertainty in some of its most frequently used estimates. Using 2006–2010 ACS median household income estimates at the census tract scale as a test case, we explore spatial and nonspatial patterns in ACS estimate quality. We find that spatial patterns of uncertainty in the northern United States differ from those in the southern United States, and they are also different in suburbs than in urban cores. In both cases, uncertainty is lower in the former than the latter. In addition, uncertainty is higher in areas with lower incomes. We use a series of multivariate spatial regression models to describe the patterns of association between uncertainty in estimates and economic, demographic, and geographic factors, controlling for the number of responses. We find that these demographic and geographic patterns in estimate quality persist even after we account for the number of responses. Our results indicate that data quality varies across places, making cross-sectional analysis both within and across regions less reliable. Finally, we present advice for data users and potential solutions to the challenges identified.

Self-organizing maps and the US urban spatial structure

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.

The sociocultural sources of urban buzz

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.

The Size Distribution of Employment Centers within the US Metropolitan Areas

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.

The Creative Urban Diaspora Economy: A Disparity Analysis Among Migrant Entrepreneurs

This paper highlights the ‘magic of diasporas’ − as a source of progress in a globalizing world − with special attention for migrant (or ethnic) entrepreneurship. The present study aims to identify and examine the critical critical success factors of migrant enterprises and their socio-economic implications in modern cities. We will assess the business performance of migrant entrepreneurs by employing a new analytical instrument, coined Super-Efficient Data Envelopment Analysis (Super-DEA). Next, we will offer a multidimensional visualization of the relative differences in the performance of migrant entrepreneurs by introducing a recently developed technique from the cognitive sciences, coined Self-Organizing Maps (SOMs). This analytical apparatus will be tested on the basis of a sample of Moroccan entrepreneurs in four Dutch cities, namely Amsterdam, Rotterdam, The Hague and Utrecht. The study will be concluded with some strategic conclusions.

Concrete agglomeration benefits: do roads improve urban connections or just attract more people?

ABSTRACT 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.

Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning

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.

The spoken postcodes

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.