Suchergebnisse

This resource contains the materials and structure suggested to run a mini course of approximately 14 hours on spatial data, analysis and regression. The course is structured along four lectures and four labs that require the use of computers.

This resource describes WooW-II, a two-day workshop on open workflows for quantitative social scientists. The workshop is broken down in five main parts, where each of them typically consists of an introductionary tutorial and a hands-on assignment. The specific tools discussed in this workshop are Markdown, Pandoc, Git, Github, R, and Rstudio, but the theoretical approach applies to a wider range of tools (e.g., LATEX and Python). By the end of the workshop, participants should be able to reproduce a paper of their own and make it available in an open form applying the concepts and tools introduced.

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.

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.