About

Luis Bettencourt, PhD, is a Professor of Ecology and Evolution at the University of Chicago and an Associate Faculty member in Sociology. He serves as the Inaugural Director of the Mansueto Institute for Urban Innovation and is an External Professor at the Santa Fe Institute. His research investigates fundamental processes of biological and social organization and evolution within complex systems, with a particular emphasis on urban environments. Bettencourt creates multidisciplinary theories and methods to analyze these systems, often utilizing network structures and processes of learning and adaptation. His work aims to produce quantitative comparisons across populations through time and space, leading to new insights and theories that unify urban themes and advance the understanding of complex systems more broadly. He has significantly contributed to urban science, fostering a new sense of excitement and possibility in the field. Bettencourt teaches courses such as 'Introduction to Urban Sciences' and 'The Mathematics of Evolution' at the University of Chicago. His academic background includes a PhD in Theoretical Physics from Imperial College London, obtained in 1996.

Research topics

• Sociology
• Engineering
• Political Science
• Geography
• Economic geography
• Environmental planning
• Computer Science
• Economics
• Physics
• Statistical physics
• Cartography
• Mathematics
• Economic growth
• Ecology
• Econometrics
• Statistics
• Regional science
• Civil engineering
• Business

Selected publications

• Open-access model for detecting openly dumped dispersed municipal solid waste from crowdsourced UAV imagery in Sub-Saharan Africa

  ArXiv.org · 2026-05-04

  articleOpen access

  Managing municipal solid waste in rapidly urbanizing Sub-Saharan Africa remains challenging due to dispersed informal dumping and limited high-resolution datasets for spatial monitoring. We present an open-access deep learning model for automated detection of openly dumped dispersed solid waste via crowdsourced UAV imagery, trained and evaluated across 29 regions in 10 countries, encompassing diverse environmental contexts. A deep learning model trained on manually annotated image tiles achieved excellent performance in detecting openly dumped dispersed solid waste across all study regions. Predicted distributions reveal heterogeneous accumulation patterns, ranging from localized hotspots - often along waterways, where waste can exacerbate flood and public health risks - to more dispersed litter across urban areas. Waste accumulation is most strongly associated with population density and indicators of lack of local infrastructure access, whereas its relationship with broader measures of regional development is weaker, highlighting the importance of fine-scale data for understanding localized waste dynamics. By releasing the model, this study provides a ready-to-use tool for UAV imagery collected by municipalities and local mapping communities, enabling openly dumped dispersed solid waste monitoring without extensive technical expertise. This approach empowers local practitioners to convert UAV imagery into actionable insights, supporting targeted interventions and improved municipal solid waste management across Sub-Saharan Africa.

  PublisherOA PDF

• Unraveling the intractable trilemma in urban weather and climate modeling

  npj Urban Sustainability · 2026-04-18

  articleOpen access

  Urban weather and climate modeling is challenged by the highly heterogeneous and dynamic nature of cities. It exhibits a persistent trilemma between spatial granularity, spatiotemporal coverage, and physical interpretability. We articulate this challenge and propose a hybrid framework integrating physics-based models, urban observations, and machine learning. Framing this challenge as an integration problem across methods and scales, we provide a structured guide for next-generation, decision-relevant urban weather and climate modeling.

  PublisherOA PDFDOI

• Open-access model for detecting openly dumped dispersed municipal solid waste from crowdsourced UAV imagery in Sub-Saharan Africa

  arXiv (Cornell University) · 2026-05-04

  preprintOpen access

  Managing municipal solid waste in rapidly urbanizing Sub-Saharan Africa remains challenging due to dispersed informal dumping and limited high-resolution datasets for spatial monitoring. We present an open-access deep learning model for automated detection of openly dumped dispersed solid waste via crowdsourced UAV imagery, trained and evaluated across 29 regions in 10 countries, encompassing diverse environmental contexts. A deep learning model trained on manually annotated image tiles achieved excellent performance in detecting openly dumped dispersed solid waste across all study regions. Predicted distributions reveal heterogeneous accumulation patterns, ranging from localized hotspots - often along waterways, where waste can exacerbate flood and public health risks - to more dispersed litter across urban areas. Waste accumulation is most strongly associated with population density and indicators of lack of local infrastructure access, whereas its relationship with broader measures of regional development is weaker, highlighting the importance of fine-scale data for understanding localized waste dynamics. By releasing the model, this study provides a ready-to-use tool for UAV imagery collected by municipalities and local mapping communities, enabling openly dumped dispersed solid waste monitoring without extensive technical expertise. This approach empowers local practitioners to convert UAV imagery into actionable insights, supporting targeted interventions and improved municipal solid waste management across Sub-Saharan Africa.

  PublisherDOI

• Using human mobility data to quantify experienced urban inequalities

  Nature Human Behaviour · 2025-02-17 · 38 citations

  review
  PublisherDOI

• Redefining Fitness: Evolution as a Dynamic Learning Process

  ArXiv.org · 2025-03-12

  preprintOpen access1st authorCorresponding

  Evolution is the process of optimal adaptation of biological populations to their living environments. This is expressed via the concept of fitness, defined as relative reproductive success. However, it has been pointed out that this definition is incomplete and logically circular. To address this issue, several authors have called for new ways to specify fitness explicitly in terms of the relationship between phenotypes and their environment. Here, we show that fitness, defined as the likelihood function that follows from mapping population dynamics to Bayesian learning, provides a general solution to this problem. We show how probabilistic models of fitness can easily be constructed in this way, and how their averages acquire meaning as information. We also show how this approach leads to powerful tools to analyze challenging problems of evolution in variable environments, game theory, and selection in group-structured populations. The approach is general and creates an explicit bridge between population dynamics under selection, statistical learning theory, and emerging models of artificial intelligence.

  PublisherOA PDFDOI

• Infrastructure deficits and informal settlements in sub-Saharan Africa

  Nature · 2025-09-03 · 9 citations

  articleOpen access1st authorCorresponding

  Sustainable development is an imperative worldwide1–3 but metrics and data on poverty and quality of life have remained too coarse and abstract to characterize challenges adequately and guide practical progress4,5. Nowhere is this challenge greater than in Africa4–6, where we still know little about the spatial details of development3,7–9. Here we leverage a comprehensive, high-precision dataset of building footprints to identify infrastructure deficits and infer informal settlements down to the street block level10–12 everywhere in sub-Saharan Africa. We identify a general pattern of informality with cities showing, on average, greater access to infrastructure and services than rural and peri-urban areas. We show that such patterns of informality are characterized by consistent statistical distributions reflecting uneven local development2,13,14. We also show that these physical measures of informality are systematically associated with many indicators of human deprivation, which form a single principal component co-varying predictably with specific changes in street access to buildings. These results demonstrate that the localization of sustainable development is possible down to the street level at a continental scale and provide a general distributed strategy for accelerating progress in infrastructure and service expansion that taps local innovations in systematic, equitable and context-appropriate ways7,11,12,15. A new network approach maps every street block in sub-Saharan Africa using high-resolution building and street data, pinpointing infrastructure needs and revealing development gradients from neighbourhoods to cities and rural areas.

  PublisherOA PDFDOI

• Decoding the city: multiscale spatial information of urban income

  ArXiv.org · 2025-09-26

  preprintOpen access1st authorCorresponding

  Cities are characterized by the coexistence of general aggregate patterns, along with many local variations. This poses challenges for analyses of urban phenomena, which tend to be either too aggregated or too local, depending on the disciplinary approach. Here, we use methods from statistical learning theory to develop a general methodology for quantifying how much information is encoded in the spatial structure of cities at different scales. We illustrate the approach via the multiscale analysis of income distributions in over 900 US metropolitan areas. By treating the formation of diverse neighborhood structures as a process of spatial selection, we quantify the complexity of explanation needed to account for personal income heterogeneity observed across all US urban areas and each of their neighborhoods. We find that spatial selection is strongly dependent on income levels with richer and poorer households appearing spatially more segregated than middle-income groups. We also find that different neighborhoods present different degrees of income specificity and inequality, motivating analysis and theory beyond averages. Our findings emphasize the importance of multiscalar statistical methods that both coarse-grain and fine-grain data to bridge local to global theories of cities and other complex systems.

  PublisherOA PDFDOI

• Urbanization, Economic Development, and Income Distribution Dynamics in India

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

• Decoding the city: multiscale spatial information of urban income

  Research Square · 2025-10-09

  preprintOpen access1st authorCorresponding
  PublisherDOI

• The Changing Character of Chinese Urbanization: 2000 - 2021

  Research Square · 2025-02-11

  preprintOpen accessSenior author
  PublisherOA PDFDOI

Recent grants

• SGER: Collaborative Research: Mapping the Structure and Evolution of Sustainability Science Research

  NSF · $83k · 2008–2011

Frequent coauthors

• José Lobo

  51 shared

• Scott G. Ortman

  University of Colorado Boulder

  28 shared

• Geoffrey B. West

  27 shared

• Garrett T. Kenyon

  Los Alamos National Laboratory

  27 shared

• Anand Sahasranaman

  21 shared

• Marc G. Berman

  University of Chicago

  20 shared

• Deborah Strumsky

  Jönköping University

  19 shared

• Michael Ham

  18 shared

Labs

• Mansueto Institute for Urban InnovationPI

Education

• Ph.D., Theoretical Physics

  Imperial College

  1996

Awards & honors

• Member at Large: Social, Political and Economic Sciences Ame…
• Technology and the Future of Cities Presidential Committee f…
• Slansky Distinguished Postdoctoral Fellow Los Alamos Nationa…
• Director's Postdoctoral Fellow Los Alamos National Laborator…