Analysis of article using Artificial Intelligence tools
|Author||Currid, E., ; Williams, S.,|
|Title||Two cities, five industries: Similarities and differences within and between cultural industries in New York and Los Angeles|
Currid, E., Williams, S. (2010) Two cities, five industries: Similarities and differences within and between cultural industries in New York and Los Angeles. Journal of Planning Education and Research, 29(3): 322‑335.
|Keywords||Cultural industries; GIS analysis; Economic and social development; Los Angeles; New York City
|Link to article|| https://doi.org/10.1177/0739456X09358559
|Abstract||Recent work has pointed towards the possibility that industries are not tied to their specific urban location as much as to their linkages with particular types of infrastructure and to their social and economic networks. Industries have similar clustering patterns even in very different cities. Using Bureau of Labor Statistics data, we conducted geographic information systems (GIS) analysis to compare cultural industries in Los Angeles and New York City, two cities with very different types of geography and urban environments. Two distinct findings emerged: (1) when cultural industries are disaggregated into distinct industrial subsectors (art, fashion, music, design), important differences among them emerge; and (2) cultural industries “behave” similarly in each city because their subsectors tend to colocate (e.g., art with design; music with film) in similar ways, and this colocation pattern remains consistent in both locations. Such notable clustering tendencies of cultural industries help inform future research and further enlighten our understanding of their location patterns.
|Metodology||They use 2005 Bureau of Labor Statistics (BLS) firm location data at the zip code level, they have conducted geographical information systems (GIS) analysis, including spatial autocorrelation and Pearson correlations to compare Los Angeles and New York City, two very different types of geography and urban environment that are significant centers of artistic and cultural production in the United States. Likewise used the global Moran’s I statistical tests to determine whether spatial autocorrelation (clustering) occurs based on feature locations and attributes and then to identify particular “hot spots” (Getis-Ord or G*i Stat), or localities in which there was a significantly higher presence of a particular industry subsector. Spatial correlation tests, using the Pearson correlation method, were used to analyze the interplay of the arts and culture industries by measuring whether the industry subsectors tended to colocate. The scale of the analysis, both geographically and in terms of the industry variables, allowed for a neighborhood level investigation, where localities within each city were identified as having high concentrations of particular arts and culture industry subsectors.
||Technique||Data mining; Statistical analysis; Spatial analysis|