Scott Cunningham’s presentation focuses on using data and algorithms to tackle social cohesion issues in Scotland, drawing parallels with methods used in the Netherlands. After moving from the Netherlands to Scotland, Scott observed significant socio-political changes, including Brexit and COVID-19, which have impacted social cohesion. He argues that improved data and models can promote inclusive growth in Scotland, a key national agenda.
Scott introduces the "pull-up model," an ecological framework for understanding cities that incorporates four layers: population, organization, environment, and technology. This model, learned at an AMS conference, allows for a comprehensive analysis of how these layers interact. Scott collaborates with Strathclyde's urban design unit, Urban Morphometrics, focusing on spatial structures and clustering within cities. His work integrates these insights with his role as a policy analyst to explore the interplay between human populations and technological environments.
Institutional economics, according to Scott, provides a lens to understand how rules—whether legal, cultural, or self-imposed—shape social and spatial patterns. He uses Scottish data to illustrate these concepts, emphasizing how administrative structures influence data collection and use. The Scottish census organizes data zones to provide a detailed national overview, reflecting the complex political geography of Scotland, a devolved part of the UK with its own budgetary authority in areas like healthcare.
Scott’s data analysis reveals stark contrasts in socio-economic opportunities across Scotland. For example, maps of deprivation zones show significant disparities within short distances, such as between affluent areas and deprived zones in Glasgow. These patterns highlight long-standing issues like pollution and lack of economic opportunities in certain areas, perpetuating social inequalities.
One case study Scott presents involves electric vehicle (EV) charging infrastructure in Scotland. While EVs are seen as a clean and sustainable technology, their adoption faces challenges, especially in remote areas. Publicly owned charge points leased to small enterprises often result in higher costs for remote regions due to installation logistics. This example underscores the spatial disparities in access to sustainable technologies.
Scott advocates for the use of machine learning and innovative data analysis methods to address these inequalities. By recognizing patterns in census data and other demographic information, policymakers can better understand and address social issues. For instance, identifying urban potentials—demographic types based on neighborhood characteristics—can inform targeted interventions.
Scott also discusses the limitations and challenges of using census data, such as outdated information and the need for supplementary data sources like retail data or mobile phone data to accurately capture population dynamics. Despite these challenges, he emphasizes the importance of understanding spatial and social textures to inform urban planning and policy decisions.
In conclusion, Scott’s presentation highlights the potential of advanced data analysis to address social cohesion issues in Scotland. By integrating ecological models, institutional economics, and innovative data techniques, policymakers can develop more inclusive and effective strategies. The session aims to inspire participants to apply these insights in their work, ultimately improving social cohesion and quality of life in Scotland.