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.

Urban CO₂ mitigation strategies typically aim at particular zones or sectors but do not account for spatial interdependencies among different components within the city. Understanding how land uses emit within and across districts can reveal systemic leverage points for climate-resilient urban planning. This study applies a bipartite spatial network approach using high-resolution Urban Atlas land-use data and a hierarchical spatial framework for emissions and sequestration estimation. The approach links urban land uses to their emissions profiles, offering a structural view of how different areas interconnect within urban carbon dynamics, moving beyond fragmented emission accounting. Using the Reggio Calabria Functional Urban Area in Italy as a case study, the analysis identifies influential areas and emission-intensive land uses. Subsequently, using centrality metrics highlights the spatial units with strong connections to emission-dense land uses, marking them as points of intervention. Results show that although 53% of districts act as net carbon sinks, their sequestration capacity is outweighed by the intensity of a smaller group of emitter districts. Among these, five central districts (IDs 94, 82, 107, 108, and 72) emit over 500 million kg CO₂ per year, making them leverage points for systemic mitigation. The integration of bipartite spatial network and multiscale territorial analysis provides a replicable, data-driven framework for urban CO₂ mitigation. Ultimately, the study demonstrates that mapping emissions through spatial interdependencies enables planners to target interventions where localized action yields the greatest network-wide climate impact.

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.

Integrating ecosystem services into urban planning requires analytical tools that connect spatial land-use data with environmental performance. This paper applies a multi-scale, data-driven approach to assess urban carbon dynamics using spatial units that reflect both ecological functions and planning relevance. The study examines the Reggio Calabria Functional Urban Area (FUA) in Southern Italy, using Copernicus Urban Atlas land-use data to characterize spatial patterns and estimate CO₂ emissions and sequestration using parameters derived from established literature and institutional sources. A Spatial Durbin Model (SDM) identifies land uses with direct and spillover effects, revealing how spatial organization shapes urban carbon outcomes. Results reveal a net emission imbalance of approximately 1.85 billion kg CO₂ per year, confirming the region’s role as a net emitter. Transport corridors and discontinuous low-density urban areas show the strongest positive SDM coefficients (+3.48 and +0.78 kg CO₂ m⁻² yr⁻¹, respectively). Forests and agricultural lands show negative effects, indicating potential sequestration functions, though not statistically significant. This suggests that natural and semi-natural land uses contribute little to measurable CO₂ reduction within the FUA. Emissions and sinks display a polarized spatial pattern, with coastal urban zones acting as hotspots and inland areas serving as potential sinks. These findings underscore the need to strengthen ecological connectivity and integrate green infrastructure within dense urban areas to enhance mitigation capacity. The proposed framework shows how spatially explicit, hierarchical analysis can bridge ecosystem services and urban planning, offering a replicable basis for data-informed, climate-responsive strategies.

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.