James Millington

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I am a broadly trained geographer and landscape ecologist with expertise in developing and using quantitative modelling and computational simulation tools to investigate ecological and socio-economic processes and their interaction. Much of my research, publications and teaching have focused on vegetation succession-disturbance dynamics and human decision-making in multifunctional forest and agricultural landscapes of North America and Europe. More recently, I have examined the role geography and space play in UK educational inequalities. My research uses statistical, simulation and spatial modelling tools to analyse and extrapolate from empirical data and theory. I also have interests...

agent-based simulation geography landscape society and culture fieldwork mobile apps ecology land use forest deer statistical models spatial school district explanation narrative

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James Millington

Présentations

Narrative Explanation in Agent-Based Modelling - Millington AAG 2013

Value of ABM for integrating geographical understanding - Millington Auckland 2013

Patterns of Aspiration: An ABM of school choice

Millington Research Overview | EED Seminar Sept 2013

Representing cultural change in ABM of landscape-scale socio-ecological systems

Mobile Apps for Fieldwork

J’aime

Man on Earth – the challenge of discovering viable ecological survival strategies

Lessons fron the Olympic Park- Peter Neal, Green Infrastructure Seminar

Quantitative Methods in Geography Making the Connections between Schools, Universities and Employers

Geographies of ethnicity by school in London

Optimal models of segregation

Fire Paradox - European Project

Context dependency and the development of social institutions

Fieldtrip GB: A customisable mapping and data capture app

"gis us a clue" - quantitative methods teaching in geography

Empirical Generalisations Kent Nov07

andrew.sayer(1992)method.in.social.science a.realist.approach.2ed.ed

Context in Environmental Modelling– the room around the elephant

The Scandal of Generic Models in the Social Sciences

Personal understanding and publically useful knowledge in Social Simulation