The location of the local network of firms impacts, positively or negatively, their economic performance. The interactions between different sectors in a territory are still not easily observable. We test the complexity of the economic structure at a local level, given the availability of data at a very granular scale. This could greatly assist in observing sectors or/and locations that play a dominant role in the regional economy. Thus, in order to interpret the economic structure of a territory, we used cluster-based analysis. The analysis helps in evaluating the interconnections among sectors that constitute a cluster. A novel method of describing the territorial economic structure is presented by applying Social Network Analysis (SNA) within cluster-based analysis to characterize the importance of both location and economic interconnections. In this study, we focus on the industrial agglomerations in Calabria, Italy, to underpin the potential of the region’s industries by using social networking analysis metrics. This research put forward new interpretations of SNA metrics that describe regional economic compositions. Our findings reveal that territorial social networks are a potential instrument for understanding interactions in regional systems and economic clusters and might help in highlighting local industrial potentials. We believe that this study’s results could be considered as the initial steps for a pioneer data-driven place-based structural analysis model.
Geographic proximity might not be the only factor influencing the connections between neighborhoods within the same city. Most likely, the community’s needs and behaviors play a role in facilitating or hindering any connections between these urban areas. Accordingly, relationships between communities may differ or be similar based on their respective characteristics. This paper aims to demonstrate that communities are close based on the needs they share, regardless of their ethnicity or geographic location. In this study, a time series analysis of neighborhoods’ needs is explored to gain a deeper understanding of the communities’ network. The study takes into account the co-occurrence of complaints/reports from residents regarding the same issue. The dataset was retrieved from the Boston Area Research Initiative (BARI) and the 311 system that describe the features of neighborhoods regarding non-emergency issues. Subsequently, the connection between neighborhoods in the City of Boston was analyzed using a mixture of PCA, K-means, association rule mining, and a network creation tool. Moreover, clustering coefficients and degrees of centrality were used as significant factors in identifying the members of groups and marking crucial nodes in the network. A series of graphs were generated to show how the neighborhoods are linked based on their socioeconomic concerns. The results prove that even geographically disconnected neighborhoods within Boston have similar social needs, despite their distance from one another. Furthermore, it revealed that some neighborhoods can act as linking bridges for other neighborhoods, while others may be isolated within the network graph. This study has increased awareness of urban aspects. The authorities may consider other dimensions than the traditional ones regarding neighborhood development and addressing problems. Finally, it helps to identify common characteristics between neighborhoods, which facilitates the policy making process.
The economy is a complex system, and the interactions between different agents are still not easy to quickly see-through. This complexity should reflect in a spatial dimension; in this way, tracking the tradeoffs opens a new window to the nexus of place and flow. Due to the fact, the economic systems often go through transitions and end up in another state, and this evolution is embedded in cities as the new motor of paradigm shift. To adequately represent and study these dynamics, we aim to develop an integrated method based on network analysis science and geographic economy synthesis to detect a multiscale navigator to track the transition from regional to the local level. This paper seeks to explore the specialization of regional clusters and their innovative behaviour in a particular lagging region, hence unfolding the innovation ecosystem to the smallest granularity then simulating the emergence phase of this complex system. First, our findings reveal that the local scale is relevant to start a bottom-up planning approach on policy implementation. Second, the global challenges could be addressed on a regional scale if we investigate the local complexity to unfold the innovation flow over its complex ecosystem and lead the knowledge as a critical element for inclusive transition, most probably into cities. Finally, the innovation network is an existing fact which can translate as a host for prosperity; In this line of reasoning, we intend to spatialize the track of the innovation flow to achieve transition hotspots and respond adequately to upcoming world concerns.
Cities, public authorities, and private organizations respond to climate change with various green policies and strategies to enhance community resilience. However, these community-level transition processes are complex and require deliberate and collective planning. Under this context, the purpose of this study is to understand the energy actions taken at the local level, as well as to analyze the differences between the neighborhoods’ green energy transitions in terms of their socio-economic aspects, using a big data perspective. The paper is addressing the following question: what was the role that the pandemic played in accelerating or slowing Boston’s green investments, and to what extent do different racial and socioeconomic groups invest in green technologies during this period? The study aims to answer these research questions using the City of Boston as a case study to reveal different neighborhoods’ paths in achieving the transformation of city ecosystems towards green neutrality. Next, the theoretical framework builds the linkages among the city’s measures, climate actions proposed by the City of Boston, and their associated contexts and outcomes in shaping new policy and planning models for higher ‘green’ performance. Following the understanding of the actions, the neighborhoods’ socio-economic and building permit data were assessed to understand whether economic disparities exacerbated during the pandemic have affected neighborhoods’ performance in green transition. This method is applied in a comparative study of its 23 neighborhoods, using a dataset provided by Boston Area Research Initiative (BARI). Intriguingly, the paper’s findings show that racial differences within the city have no significant impact on tech-related expenditures. There is a clear negative correlation between poverty rate and investment, which indicates the reverse relationship between these socio-economic factors. The study concludes that city authorities will need to address the challenges of each community achieving green transition with more targeted programs based on its needs.
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