Connecting Physical and Socio-Economic Spaces for Multi-Scale Urban Modelling: A Dataset for London

Abstract

Versatile approaches for urban modelling need to simultaneously consider the physical characteristics of a city (urban form) and urban function as a manifestation of economically, socially, and culturally motivated human activities. Exposure and risk assessment studies concerning urban heat or air pollution can greatly benefit from modelling that dynamically connects physical and socio-economic urban spaces and represents humans as active components of the urban system (e.g., agent-based modelling). The spatio-temporal complexity and variability of urban form, function, human behaviour, and micro-climate put high demands on input data of such models. We present a general methodology for creating a suite of data connecting and harmonising available information for high-resolution modelling. This is demonstrated for London, UK. The multi-scale database covers urban neighbourhoods (at 500 m grid-cell resolution), localised microenvironments of activity, buildings, and extends down to the scale of individuals. Data include neighbourhood land-cover fractions that provide boundary conditions for urban land-surface models and building typologies generated by assessing building function, form, and materials (via building age) that are suitable for building energy modelling. Urban populations (residential, workplace) and demographic composition of households in building typologies are derived. Temporal profiles (10 min resolution) of human activities by age cohort, household size, day type, work patterns, and season derived from time-use survey data are mapped to various socio-economic microenvironments, alongside assessments of activity-dependent electrical energy consumption and human metabolic output. A transport database provides available travel options (1 min resolution) between London neighbourhoods by mode, making use of public transport schedules, road networks, and traffic speeds.