AESOP Complexity and Planning 2.-3.5. 2013 Aveiro Empirical Indicators for Self-Organisation Jenni Partanen Tampere University of Technology Urban Planning and Design Tampere, Finland University of Washington Department of Urban Design and Planning Seattle, WA, USA Research problem • Complex city and planning • Self-organization from a metaphor towards empiria Self-organization • studying non-linear systems A. M. Lyapunov’s work at the turn of the 20th century. • Growing interest in the work of mathematicians such as N. Kryloff or N. Bogoliuboff began in the West in the 1950s in the field of control theory, • rapidly expanding in the 1960s to mathematics, physics, meteorology and biology. - Manfred Eigen Hypercycle - Herman Haken: Synergetics - Varela Et Al: Autopoiesis - Prigogine’s Dissipative Structures • Variety of aspects; yet similair premises Applied indicators for s-o • In Literature: • - micro- and macrolevel of the structure; feedback • - dissipative structure (open, far-from equilibrium/multiple equilibria -system) • - interactions between coherent agents • - emergence of internal order • - continuous flow of energy (open system) • - boundary conditions • - increesing complexity of the system (Eigen, Haken, Prigogine, Varela et al Heylighen , Shalizi et al etc) Part 1. Indicators for s-o in the city: Study Of Local Scale Eclaves • ENERGY FLOW, INCREASING COMPLEXITY, INTERNAL ORDER; INTERACTORS, FEED BACK • RESILIENCE: evidence and a typical consequence of the system’s positive continuity and self-organisation 1. ENERGY FLOW • Analogical to high potential accessibility • measured as generic accessibility • RESULTS: high generic accessibility in all scales for the case area 2. INCREASING COMPLEXITY • increasing unpredictability/decreasing entropy: entities’ re-organization from bottom up • SHANNON’S INFORMATION 3. Internal order • E.g. agglomeration • Follow RANK SIZE DISTRIBUTION – – SELF-CRITICAL STATES imply self-organization Part 2: VALIDATION: other affecting mechanisms; WORK IN PROGRESS 1. DIVERSITY AND CO-EVOLUTION • Study of co-existing, dynamic nets of activities • diversity of activities in clusters vs. in total • Results: Statistical differences in the general progress for two groups (mean, standard deviation and the timely patterns of the relations of these) 2. SPATIAL FEATURES: ISOVISTS, RANKED • Isovists of clusters differ from total and • random distribution, ranked by size • Refers to self-organization Æ A CA-MODEL - Rules are based on these findings ÆNEXT STEP: CLOSER LOOK TO THE MECHANISM: EXPLORING OTHER FACTORS SUCH AS SPATIAL, FUNCTIONAL, REGULATORY ISSUES … CHALLENGES AND POTENTIAL FOR BUILDING THE COMPLEX PLANNING PRAXIS THANK YOU! Jenni Partanen Tampere University of Technology [email protected].