This study explores spatial patterns and mechanisms of industrial growth in Salt Lake County, Utah, which vary with sectoral divisions and scales in the context of urban sprawl. We find that besides the center growth, producer services prefer newly developed areas, and manufacturing has a suburbanizing growth pattern. The spatial regressions indicate neighborhood effects among mechanisms: At the block group level, producer services are more sensitive to community characteristics than manufacturing. At the neighborhood level, whereas producer services are still profoundly affected by community characteristics such as population density and higher education rates, manufacturing only responds to agglomeration effects. The model sets further uncover the variety of mechanisms among industry sectors. These results illustrate different location preferences of industry sectors and challenge the traditional view regarding producer services as an entire group. Highlighting the preference of firms to newly developed areas, our research discloses the growth pattern in Salt Lake County and illuminates the effects of sprawl development, such as low population density and car-based development, in the firm suburbanization process. It also reveals that growth patterns and mechanisms might vary with industry sectors and due to neighborhood effects. Thus, these effects should be carefully considered in research and policies.

This paper studies the relationships between network capabilities and innovation development in the context of two types of innovation networks: scientific knowledge networks (SKN) and technological knowledge networks (TKN). Focusing on two types of network capabilities, namely acquisition capability and control capability, the paper uses spatial regime models to compare the impacts of multiple factors on different spatial regimes. The main conclusions are the following. First, as regards SKN, the political-administrative hierarchy has shaped the spatial evolution of acquisition capacity, forming a pattern consisting of three dominant cities (Beijing, Shanghai, Nanjing), three subsidiary cities (Guangzhou, Hangzhou, Wuhan), and multiple lesser centers (Tianjin, Chengdu, Xi’an). Moreover, high control capability cities are mainly clustered in the coastal areas, specifically, one monocentric city (Beijing) and two polycentric metropolises (Shanghai, Wuhan). Second, for TKN, cities with high acquisition and control also are mainly found in coastal areas, with Shanghai and Beijing dominating network capabilities. The model’s analysis confirms the positive effect of network capabilities on innovation development, especially in scientific knowledge networks, and the driver for regional innovation development appears to have shifted from global pipeline (globalization) to local buzz (localized talents). This paper concludes with suggestions regarding network capabilities’ potential to reduce regional inequality and achieve sustainable development of regional economies.