Urban density and the rate of invention

Carlino, Gerald A.; Chatterjee, Satyajit; Hunt, Robert M.

doi:10.1016/j.jue.2006.08.003

Cited by 436 publications

(122 citation statements)

References 40 publications

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“…For example, Kline & Boyd [33] show that both population size and island interconnectedness correlates with number of tools and tool complexity among oceanic islands. Carlino et al [89] show that urban density (a proxy for interconnectivity) predicts the rate of innovation. Similarly, Bettencourt et al [90] [35]).…”

Section: (A) Increasing Innovation Ratesmentioning

confidence: 99%

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Innovation in the collective brain

Muthukrishna

Henrich

2016

Phil. Trans. R. Soc. B

240

265

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One contribution of 15 to a theme issue 'Innovation in animals and humans: understanding the origins and development of novel and creative behaviour'. Innovation is often assumed to be the work of a talented few, whose products are passed on to the masses. Here, we argue that innovations are instead an emergent property of our species' cultural learning abilities, applied within our societies and social networks. Our societies and social networks act as collective brains. We outline how many human brains, which evolved primarily for the acquisition of culture, together beget a collective brain. Within these collective brains, the three main sources of innovation are serendipity, recombination and incremental improvement. We argue that rates of innovation are heavily influenced by (i) sociality, (ii) transmission fidelity, and (iii) cultural variance. We discuss some of the forces that affect these factors. These factors can also shape each other. For example, we provide preliminary evidence that transmission efficiency is affected by sociality-languages with more speakers are more efficient. We argue that collective brains can make each of their constituent cultural brains more innovative. This perspective sheds light on traits, such as IQ, that have been implicated in innovation. A collective brain perspective can help us understand otherwise puzzling findings in the IQ literature, including group differences, heritability differences and the dramatic increase in IQ test scores over time.

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Section: (A) Increasing Innovation Ratesmentioning

confidence: 99%

“…R. Soc. B 371: 20150192 occur in larger, more interconnected collective brains [33,89,90] and among individuals with access to more and diverse information [83]. But what about differences between cultural brains?…”

Section: The Collective Brain Fuels the Cultural Brainmentioning

confidence: 99%

Innovation in the collective brain

Muthukrishna

Henrich

2016

Phil. Trans. R. Soc. B

240

265

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“…Our approach of modelling agglomeration economies as a function of density is a common empirical strategy [59]. Agglomeration economies manifest via dense customer-supplier linkages, labor pooling, knowledge spill overs, and high quality infrastructure.…”

Section: Agglomeration Location Factorsmentioning

confidence: 99%

Analyzing and Predicting Micro-Location Patterns of Software Firms

Kinne

Resch

2017

IJGI

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While the effects of non-geographic aggregation on statistical inference are well studied in economics, research on the effects of geographic aggregation on regression analysis is rather scarce. This knowledge gap, together with the use of aggregated spatial units in previous firm location studies, results in a lack of understanding of firm location determinants at the microgeographic level. Suitable data for microgeographic location analysis has become available only recently through the emergence of Volunteered Geographic Information (VGI), especially the OpenStreetMap (OSM) project, and the increasing availability of official (open) geodata. In this paper, we use a comprehensive dataset of three million street-level geocoded firm observations to explore the location pattern of software firms in an Exploratory Spatial Data Analysis (ESDA). Based on the ESDA results, we develop a software firm location prediction model using Poisson regression and OSM data. Our findings offer novel insights into the mode of operation of the Modifiable Areal Unit Problem (MAUP) in the context of a microgeographic location analysis: We find that non-aggregated data can be used to detect information on location determinants, which are superimposed when aggregated spatial units are analyzed, and that some findings of previous firm location studies are not robust at the microgeographic level. However, we also conclude that the lack of high-resolution geodata on socio-economic population characteristics causes systematic prediction errors, especially in cities with diverse and segregated populations.

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“…Entre sus muchas ventajas, las ciudades densas ayudan a preservar las tierras fértiles para usos rurales (Jenks & Burgess, 2000), disminuir los tiempos dedicados a traslados (Gaigne, Riou & Thisse, 2012) y contribuyen a reducir las emisiones de gases de efecto invernadero (Stone, Mednick, Holloway & Spak, 2007). Además, la densidad se correlaciona positivamente con la acumulación de capital humano (Glaeser, 1999), la tasa de innovación (Carlino, Chatterjee & Hunt, 2007), la productividad del trabajo (Ciccone & Hall, 1996) y la inclusión social (Burton, 2000), siendo la aglomeración de actividades econó-micas en el territorio un factor determinante de la formación de concentraciones humanas (Krugman, 1991). En general, las áreas de mayor desarrollo económico están representadas como un sistema de economías polarizadas vinculadas a una aglomeración central (Scott, 2006).…”

Section: Introductionunclassified