Understanding urban sub-centers with heterogeneity in agglomeration economies-Where do emerging commercial establishments locate?

This paper investigates the formation of employment sub-centers from a new perspective of heterogeneity in agglomeration economies. Using highly granular commercial and residential land-use data (2001–2011) in Chicago, we measure how the locations of jobs, population, quality-of-life amenities, and transportation networks shape specific and heterogenous sub-centers. First, the results suggest that the CBD as it was traditionally defined is no longer the primary source of agglomeration externalities for the new economic sectors; sub-centers with sector-specific positive agglomeration externalities have stronger correlations with new commercial establishments. Secondly, residents appear to give the highest weight to quality-of-life amenities in choosing where to live. Both trends imply dis-incentives for CBD agglomeration. These findings connect the heterogeneous production theories with land use planning and urban design, through new empirical insights into how urban sub-centers grow. Furthermore, we put forward a method for forecasting of future sub-center growth through measuring changes in the probability of commercial development, and discuss its practical implications for planning and design in Chicago

A Prediction Model of the Project Life-Span in Open Source Software Ecosystem

© 2018, Springer Science+Business Media, LLC, part of Springer Nature.In nature ecosystems, animal life-spans are determined by genes and some other biological characteristics. Similarly, the software project life-spans are related to some internal or external characteristics. Analyzing the relations between these characteristics and the project life-span, may help developers, investors, and contributors to control the development cycle of the software project. The paper provides an insight on the project life-span for a free open source software ecosystem. The statistical analysis of some project characteristics in GitHub is presented, and we find that the choices of programming languages, the number of files, the label format of the project, and the relevant membership expressions can impact the life-span of a project. Based on these discovered characteristics, we also propose a prediction model to estimate the project life-span in open source software ecosystems. These results may