Cluster identification

Abstract

Purpose This paper aims to contribute to the approaches based on traditional industry concentration statistics for identifying clusters by complementing them with the techniques of exploratory spatial data analysis (ESDA). Design/methodology/approach Using a sample with 34,500 observations retrieved from the social information annual report released by Brazil Ministry of Labor and Employment, the methodology was designed to make a comparison between the application of industry concentration statistics and ESDA statistics. Findings As the results show, the geographic distribution measures proved to be fundamental for longitudinal studies on regional dynamics and industrial agglomerations, and the local indicator of spatial association statistic tends to overcome the limitation of the industry concentration approach. Research limitations/implications In the period considered, due to economic, structural and circumstantial questions, activities linked to the transformation industry have been losing ground in the value creation process in Brazil. In this sense, the study of other industries may generate other types of insights that should be considered in the process of regional development. Originality/value This paper offers a critical analysis of empirical approaches and methodological advances with an emphasis on the treatment of special effects: spatial dependence, spatial heterogeneity and spatial scale. However, the regional dynamic presents a temporal dimension and a spatial dimension. The role of space has increasingly attracted attention in the analysis of economic changes. This work has identified opportunities for incorporating spatial effects in regional analysis over time.

Evaluation of County Agricultural Eco-Efficiency in Chongqing and Analysis of Its Spatiotemporal Differentiation under the Dual Carbon Target

“…The Moran scatter plot and the Local Indicators of Spatial Association (LISA) test are frequently employed in academic research and analysis. The formula for the local Moran's I index [38,39] is as follows:…”

Análise de aglomerados empresariais dos setores de manufatura e serviços do estado do Piauí

“…Neste contexto das identificações, a teoria de base econômica, de onde derivam os indicadores de aglomerações empresariais, visa explicar as relações entre diversas regiões geográficas, seu objetivo é estudar e avaliar o impacto das atividades econômicas comparando diversas regiões, sendo que a maioria dos estudos sobre identificação de clusters usa como método quantitativo o Quociente de Locacional (QL) (Manzini;Luiz, 2019;Puga, 2003;Yamada;Kawakami, 2015).…”

“…Este indicador compara uma economia local com uma economia de referência para identificar especializações, a lógica é: quanto mais concentrada uma atividade econômica, maior a probabilidade de ocorrência de aglomerados (Manzini;Luiz, 2019).…”