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 âÂÂpowerâÂÂ, 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.
Abstract: 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.
Abstract: 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.
Abstract: Australia shapes a unique urban system. This paper examines the Australian urban system using data for urban centers and localities in 1996 and 2001. A summary and a basic descriptive analysis of the database are provided, followed by an examination of whether the system follows Zipf's and Gibrat's laws. None of them are found to hold. An Exploratory Spatial Data Analysis (ESDA) as well as a confirmatory analysis are carried out by using some of the most recent developments in spatial econometrics (Heteroskedastic Consistent GM Estimation) to analyze the spatial dimension of city size and growth, finding no influence for the former but a significant one for the latter.
Abstract: Cities form the heart of a dynamic society. In an open space-economy cities have to mobilize all of their resources to remain attractive and competitive. Smart cities depend on creative and knowledge resources to maximize their innovation potential. This study offers a comparative analysis of nine European smart cities on the basis of an extensive database covering two time periods. After conducting a principal component analysis, a new approach, based on a self-organizing map analysis, is adopted to position the various cities under consideration according to their selected âÂÂsmartnessâ performance indicators.
Abstract: This paper addresses the performance of creative firms from the perspective of complex spatial systems. Based on an extensive high-dimensional database on both the attributes of individual creative firms in the Netherlands and a series of detailed regional facilitating and driving factors related, inter alia, to talent, innovation, skills, networks, accessibility and hardware, a new methodology called self-organizing mapping is applied to identify and explain in virtual topological space, the relative differences between these firms and their business performance in various regions. It turns out that there are significant differences in the spatial and functional profile of large firms vis-ÃÂ -vis SMEs across distinct geographical areas in the country.
Abstract: 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
Abstract: 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 graphical 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 propose 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.
Abstract: This article introduces a new approach to measuring neighborhood change. Instead of the traditional method of identifying âneighborhoodsâ 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.
