Kangaroos, Cities and Space: A First Approach to the Australian Urban System

Australia conforms a unique urban system. This paper examines the Australian urban system using data for urban centres and localities in 1996 and 2001. A summary and a basic descriptive analysis of the database is provided, followed by an examination of whether the system follows Zipf’s and Gibrat’s laws. The latter is found to hold for all but one of the especifiactions used while the former does not seem to apply. A Exploratory Spatial Data Analysis (ESDA) as well as a confirmatory analysis are carried out to analyize the spatial dimension of city size and growth, finding no relation for the former but a significant one for the latter.

Spatial Fixed Effects and Spatial Dependence

We investigate the common conjecture in applied econometric work that the inclusion of spatial fixed effects in a regression specification re- moves 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 DGP takes the form of a spatial lag or spatial error dependence. In addition, we also show that only in the special case where the dependence is group-wise, with all observations in the same group as neighbors of each other, do spatial fixed effects correctly remove spatial correlation.spatial autocorrelation, spatial econometrics, spatial externalities, spatial fixed effects, spatial interaction, spatial weights

Accidental, open and everywhere: Emerging data sources for the understanding of cities

In this paper, I review the recent emergence of three groups of data sources and assess some of the opportunities and challenges they pose for the understanding of cities, particularly in the context of the Regional Science and urban research agenda. These are data collected from mobile sensors carried by individuals, data derived from businesses moving their activity online and government data released in an open format. Although very different from each other, they are all becoming available as a side-effect since they were created with different purposes but their degree of popularity, pervasiveness and ease of access is turning them into interesting alternatives for researchers. Existing projects and initiatives that conform to each class are featured as illustrative examples of these new potential sources of knowledge. © 2013 Elsevier Ltd

Self-Organizing Maps and the US Urban Spatial Structure

This article considers urban spatial structure in US cities using a multi- dimensional approach. We select six key variables (commuting costs, den- sity, employment dispersion/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 pat- terns in such multi-dimensional 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. JEL C45, R0, R12, R14. Keywords Urban spatial structure, self-organizing maps, US metropolitan areas

Improving the Multi-Dimensional Comparison of Simulation Results: A Spatial Visualization Approach

Results from simulation experiments are important in applied spatial econometrics to, for instance, assess the performance of spatial estimators and tests for finite samples. However, the traditional tabular and graphi- cal formats for displaying simulation results in the literature have several disadvantages. These include loss of results, lack of intuitive synthesis, and difficulty in comparing results across multiple dimensions. We pro- pose to address these challenges through a spatial visualization approach. This approach visualizes model precision and bias as well as the size and power of tests in map format. The advantage of this spatial approach is that these maps can display all results succinctly, enable an intuitive interpretation, and compare results efficiently across multiple dimensions of a simulation experiment. Due to the respective strengths of tables, graphs and maps, we propose this spatial approach as a supplement to traditional tabular and graphical display formats. To allow readers to generate maps such as the ones presented in this article, a package (written in Python) has been made available by the authors as free/libre software. The package includes an example as well as a short tutorial for researchers without programming experience and can be downloaded at: https://github.com/darribas/simVizMap.

From manufacturing belt, to rust belt, to college country: a visual narrative of US urban growth

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

How diverse can measures of segregation be? Results from Monte Carlo simulations of an agent-based model

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. </jats:p