Detecting spatial economic clusters using kernel density and global and local Moran's I analysis in Ekurhuleni metropolitan municipality, South Africa

Agglomeration economies are credited for providing the needed catalyst for economic growth and development. This paper uses approximately 14,000 firm-level data and employs several spatial data analysis approaches to examine evidence of types of spatial sectoral clusters and their footprints in Ekurhuleni Metropolitan Municipality, a major subregional economy in Gauteng metropolis, South Africa. The results of four selected industrial sectors show evidence of varying global and localized clustering. Localized clustering is statistically significant. This research suggests policies that ensure that regional economy's economic growth and development benefit from agglomeration economies. A las economías de aglomeración se les atribuye la función catalizadora necesaria para el crecimiento económico y el desarrollo. Este artículo utiliza aproximadamente 14.000 datos a nivel de empresa y emplea varios enfoques de análisis de datos espaciales para examinar las pruebas del tipo de agrupaciones sectoriales espaciales y su huella en el municipio metropolitano de Ekurhuleni, una importante economía subregional de la metrópolis de Gauteng y de Sudáfrica. Los resultados de cuatro sectores industriales seleccionados muestran evidencias de una agrupación global y localizada variable. Se estableció que la agrupación localizada también era estadísticamente significativa. Esta investigación sugiere políticas que garanticen que el crecimiento económico y el desarrollo de la economía regional se beneficien de las economías de aglomeración. 集積経済は、経済成長と発展に必要な触媒的役割を与えていると考えられている。本稿では、約14,000社レベルの企業データを使用し、いくつかの空間データ解析手法を用いて、ハウテン州の都市圏および南アフリカの主要な地方経済域であるエクルレニ都市圏における部門別の空間クラスターのタイプのエビデンス及びその足跡を検討する。選択した4つの産業部門の結果から、様々なグローバルおよび局地的なクラスター形成のエビデンスが示される。また、局地的なクラスター形成は統計的に有意であることも確認される。本研究から、経済成長と地域経済の発展が、集積経済から確実に利益を得られる政策が示唆される。 The study of the economics of agglomerations, either focusing on commercial/industrial districts within cities, industrial clusters at the regional level, or the imbalances between regions/countries, can be traced to Marshall's (1890) work relating to industrial districts in nineteenth-century England (Fujita Hoover, 1948; Marshall, 1890). Urbanization economies—also known as Jacobian externalities, after Jacobs (1969)—refer to the advantage enjoyed by diverse firms when they colocate in a large urban area with large and heterogeneous markets. In the literature, several theoretical and empirical studies can be found focusing on developed and some developing countries. In South Africa, limited empirical studies have focused on the subnational regions—either provinces or cities (Fedderke Krugell Naudé Pillay Pisa et al., 2015; Vom Hofe if so, what kind of spatial economic clusters are they, and what is the footprint of these spatial business clusters. Thus, this paper explores clustering using the number of firms per square kilometer, independent of firm size (Duranton Pillay see Fujita and Thisse (2002) for a theoretical survey, and Duranton and Kerr (2015) and Rosenthal and Strange (2001) for empirical surveys. In South Africa, academic work on clusters or agglomeration is limited. Pisa et al. (2015) undertook cluster analysis in South Africa's North West Province, a rich platinum and gold mining region that is highly specialized and dependent on a few sectors. Employing structural path analysis and power-of-pull methods, they identified 10 industrial clusters that offer the greatest economy-wide benefits, while also creating opportunities for cross-sectoral collaboration. Vom Hofe and Cheruiyot (2018), while employing principal component analysis on Gauteng's social accounting matrix, showed evidence of a few but nonetheless critical masses of economic clusters in Gauteng's regional economy. Their analysis led to the identification of six distinctive industrial clusters: service and trade, food products, metal products, chemical products and petroleum, building and metal products, and light manufacturing products. Rogerson (1998), using University of South Africa's Bureau of Market Research data, found that Gauteng province dominated agglomeration activities in the high-technology clusters, with locations such as Johannesburg, Boksburg, and Kempton Park emerging as the largest foci of high-technology manufacturing (p. 889). Other studies have found evidence that different South African cities are agglomeration hot spots—places that offer urban diversity, industry specialization, dynamism and inclusivity, and opportunities for migrants as well as hosting growing industries, such as finance, business and consumer services, and high-level professional and technical occupations (Krugell Naudé Turok, 2011). As such, these cities (e.g., Johannesburg, Tshwane, and Cape Town) have experienced significant growth. All the aforementioned South African research focused on the subregional levels and were aspatial, meaning they did not spatially allocate the identified clusters or evidence of agglomeration economies in the respective study areas. One paper with a spatial focus, like the current one, is that of Pillay and Geyer (2016), who used aerial photography, zoning, and cadastral data as well as field surveys to show evidence of business clusters along one of the transport corridors in Gauteng. Pillay and Geyer (2016) employed a distributional directional analysis tool (part of ESRI's ArcGIS software's spatial statistical analysis; ESRI, 2022) to measure the geographic distribution of the data, thus producing a visual interpretation of how business areas along the M1–N3–N1 corridor between Johannesburg, Germiston, and Pretoria have densified between 2003 and 2012. Finally, a triangulation survey of the number of businesses revealed multiple and different business clustering across the A1–A15 business clustering zones (see Pillay and Geyer, 2016, pp. 349–352 for a complete description of their methodology and the different identified clusters). This paper complements existing research by analyzing spatial sectoral clustering in EMM. Beyond Pillay and Geyer's (2016) research, the present paper employs advanced spatial statistical techniques, including exploratory spatial data analysis (ESDA), kernel density analysis, global Moran's I, and Anselin's (1995) local indicators of spatial association (LISA) tests. These techniques were implemented in ArcGIS and GeoDa (Anselin et al., 2010). In doing so, the present paper not only identifies both global and local spatial sectoral clusters through spatial dependence but also their statistical significance. This was possible since point data (captured from individual firms’ geocodes) used in the analysis were sufficiently large and detailed. Often spatial clustering or agglomeration is underestimated when data is only available for some defined discrete space that merely allows aspatial analysis (Guillain for details of the Ellison and Glaeser concentration index, see Lafourcade and Mion (2007, pp. 3–5). As suggested by Duranton and Overman (2005, p. 1079), an ideal test of localization should rely on a measure which “(i) is comparable across industries; (ii) controls for the overall agglomeration of manufacturing; (iii) controls for industrial concentration; (iv) is unbiased with respect to scale and aggregation … (v) gives an indication of the significance of the results.” This paper focuses on global Moran's and spatial distribution and dependence in the data within defined spatial for spatial identification firms are and the statistical significance of (Duranton Guillain if what kind of spatial economic clusters are they, and what is the footprint of these spatial business clusters. This results on point firm have the of a statistically significant concentration of firms in EMM. 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