Agglomeration of creative industries: An intra‐metropolitan analysis for Barcelona

Coll-Martínez, Eva; Moreno-Monroy, Ana I.; Arauzo‐Carod, Josep‐Maria

doi:10.1111/pirs.12330

Cited by 50 publications

(49 citation statements)

References 67 publications

(104 reference statements)

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“…The effects of localization economies and the necessity of spatial concentration were only limited. On the other hand, the advertising sector was highly spatially concentrated, which suggests the importance of localization economies (reduction of transaction costs) and their probable sharp attenuation with increasing distance [9,73]. The location of individual hubs indicates a positive effect of physical and functional structures, especially in the inner city (Holešovice, Karlín, Žižkov).…”

Section: Resultsmentioning

confidence: 99%

“…Far less is known about the spatial distribution of KIBS at the intra-urban level. There is, however, a consensus: (i) High-order KIBS concentrate in central business districts and inner cities with secondary concentrations in suburban areas [4][5][6][7][8][9]; (ii) their spatial patterns are shaped by the transport infrastructure, such as airports, highways or subways [10,11]; (iii) KIBS with symbolic, synthetic and analytical knowledge may differ significantly from each other in their spatial distribution and collocation patterns [12]. Despite the growing body of research on localization factors of KIBS at the intra-urban level, there is a certain lack of empirical evidence on the question of how localization patterns of KIBS differ between various urban contexts.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Patterns of Knowledge-Intensive Business Services in Cities of Various Sizes, Morphologies and Economies

2020

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We compare intra-urban localization patterns of advertising and IT companies in three large Czech cities. The main aim of our analysis is an empirically-based contribution to the question to what extent do knowledge bases affect the spatial distribution of various knowledge-intensive business industries. The central research question is: To what extent is the localization of these two industries influenced by different modes of innovation/knowledge bases (symbolic vs. synthetic) and to what extent by contextual factors, such as urban size, morphology, position in the urban hierarchy and economic profile of the given city. We found that the urban contexts shape the localization patterns of advertising and IT companies more than differences in knowledge bases-both industries cluster primarily in the inner cities and urban cores. Formation of more suburban IT "scientific neighborhoods" is limited. significantly to the economic transformation of formerly industrial cities (considering IT, Brno is now the most specialized city in Czechia and Ostrava is the third) [13][14][15].Advertising is a representative of KIBS with a purely symbolic knowledge base. These companies should require tight geographical proximity to their customers, suppliers or rivals [16,17], and cluster in urban cores and dense inner cities, close to the headquarters of large corporations and public institutions. By contrast, services with a predominantly synthetic knowledge base (such as IT) rely primarily on knowledge sourcing and innovation collaboration with partners inside value chains, which are not necessarily local [18,19]. IT companies should, thus, exhibit more dispersed spatial patterns (However, Zook [20] documented clustering of internet companies in the inner city, close to the financial institutions, Spencer [12] also noted this possible location pattern), although they may cluster at the neighborhood level as well [12].Méndez-Ortega and Arauzo-Carod [21] stated that " . . . in addition to industry-specific characteristics that determine some external requirements by these firms (such as accessibility to skilled labour or specialized IT suppliers), there are some city-specific characteristics (e.g., urban policies, spatial distribution of economic activity) that also matter, and which shape the location decisions taken by these firms." We aim to analyze exactly these city-specific characteristics and their effects on the spatial distribution of firms. Our central research question is: To what extent are localization patterns of selected KIBS influenced by their distinct modes of innovation (knowledge bases) and to what extent by the city-specific characteristics?Funding: This research wJan Žas funded by the Ministry of Education, Youth and Sports through a research grant "SMART technologies to improve the quality of life in cities and regions" (no.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Patterns of Knowledge-Intensive Business Services in Cities of Various Sizes, Morphologies and Economies

2020

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“…The results showed that service sectors were more geographically agglomerated than manufacturing and agriculture [35], while other studies claimed that manufacturing has a higher degree of spatial agglomeration than service sectors [34]. Due to the confines of natural conditions and agglomeration effects, agriculture and manufacturing tend to be geographically concentrated [12]; labor-intensive industries and industries related to textiles also tend to be spatially concentrated [3,36]; creative industries tend to cluster in a small spatial scale [37].…”

Section: Theoretical and Empirical Controversiesmentioning

confidence: 99%

The Evolution of Industrial Agglomerations and Specialization in the Yangtze River Delta from 1990–2018: An Analysis Based on Firm-Level Big Data

Song

et al. 2019

Sustainability

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Although industrial agglomeration and specialization have been studied for more than 100 years, it is still a controversial field. In the era of big data, it is of great significance to study industrial agglomeration and regional specialization by using firm-level data. Based on 3,053,024 pieces of firm-level big data, the spatial evolution and spatial patterns of industrial agglomeration and specialization of 9 major industries in the Yangtze River Delta, China were revealed. Results show that: (1) the degree of industrial agglomeration is highly related to industrial attributes; industries which are directly related to production tend to be geographically concentrated, while industries that serve for production tend to be spatially dispersed; (2) the evolution characteristics and trajectories of industrial agglomeration vary by industries: wholesale and retail trade and real estate are becoming more spatially dispersed; information industries, leasing and commercial services, scientific research and polytechnic services, as well as finance are experiencing continuous spatial agglomeration; construction and manufacturing show a tendency of transfer from spatial agglomeration to spatial dispersion; (3) since 1990, most industries in the Yangtze River Delta have formed distinct spatial patterns of industrial specialization. Most core cities have experienced obvious deindustrialization processes; and high-end industries are clustering to the three biggest core cities of Shanghai, Nanjing, and Hangzhou.

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“…Cultural industries mainly attract well-located areas where "something is happening" (i.e. social and network events) [9], which allows to establish the fact of the significance of a territorial attribute in the development of creative industries. Thus, creative industries in the Arctic regions are underdeveloped.…”

Section: Problem Statementmentioning

confidence: 99%

Modern University as a driver for the development of creative industries in the Arctic

Belevskikh¹,

Ignatenko²,

Statsenko³

et al. 2019

Proceedings of the International Scientific-Practical Conference “Business Cooperation as a Resource of Sustainable Economic De

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This article observes the potential role and prospects of the modern university in the development of creative industries in the Arctic region, as well as formation of a creative economy behind the Polar Circle with consideration of the territorial socioeconomic conditions. First of all, the key characteristics of the Arctic economy as a whole are highlighted. The negative (constraining) factors of the development of creative industries in the region are identified: namely the industrial nature of the economy, focused on mining and flaccid processing of territorial natural resources; remoteness of production sites from the consumption points and high transportation costs; small and declining population. However, economic activities in the modern Arctic regions are conducted in the context of transnationalization and globalization. In turn it facilitates new opportunities and prospects for the Arctic territory and the Arctic economy in general and the creative industries in particular. In the present work, the conclusion has been drawn which identifies the transformation and formation of new global Arctic conditions. Hence, the restructuring of the Arctic economy is needed, which most importantly will result in the formation of prerequisites for the development of Arctic creative practice. Some of these processes are already running. However the full development of a creative economy is not possible without the development of creative industries and a creative class that will be able to put forward bold creative ideas and implement (commercialize) creative solutions. Modern University is identified as a driver for the development of a creative economy in the Arctic, provides conditions for the formation and retention of a territorial creative class and the development of entrepreneurial activity in the field of creative industries. The University can initiate a reboot of a social life and economic activity of an individual in the region.

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Agglomeration of creative industries: An intra‐metropolitan analysis for Barcelona

Cited by 50 publications

References 67 publications

Spatial Patterns of Knowledge-Intensive Business Services in Cities of Various Sizes, Morphologies and Economies

Spatial Patterns of Knowledge-Intensive Business Services in Cities of Various Sizes, Morphologies and Economies

The Evolution of Industrial Agglomerations and Specialization in the Yangtze River Delta from 1990–2018: An Analysis Based on Firm-Level Big Data

Modern University as a driver for the development of creative industries in the Arctic

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