Professor Lex Comber

Professor Lex Comber

Profile

Alexis Comber, Lex, is Professor of Spatial Data Analytics at Leeds Institute for Data Analytics (LIDA) the University of Leeds. He worked previously at the University of Leicester where he held a chair in Geographical Information Science. His first degree was in Plant and Crop Science at the University of Nottingham and he completed a PhD in Computer Science at the Macaulay Institute, Aberdeen (now the James Hutton Institute) and the University of Aberdeen developing expert systems for land cover monitoring. This brought him into the world of spatial data, spatial analysis, and mapping. Lex’s interests span many different application areas including land cover / land use, demographics, public health, agriculture, bio-energy and accessibility, all of which require multi-disciplinary approaches. His research draws from geocomputation, mathematics, statistics and computer science and he has extended techniques in operations research / location-allocation (what to put where), graph theory (cluster detection in networks), heuristic searches (how to move intelligently through highly dimensional big data), remote sensing (novel approaches for classification), handling divergent data semantics (uncertainty handling, ontologies, text mining) and spatial statistics (quantifying spatial and temporal process heterogeneity). 

He has co-authored (with Chris Brunsdon) the first ‘how to book’ for spatial analyses and mapping in R, the open source statistical software, now in its second edition (https://uk.sagepub.com/en-gb/eur/an-introduction-to-r-for-spatial-analysis-and-mapping/book258267). Outside of academic work and in no particular order, Lex enjoys his vegetable garden, walking the dog and playing pinball (he is the proud owner of a 1981 Bally Eight Ball Deluxe). 

Responsibilities

  • Programme Leader for the Data and Society CDT (CDAS - https://datacdt.org)

Research interests

My research in Spatial Data Analytics develops methods that integrate and analyse high volumes of spatial data to uncover hidden patterns / correlations. It provides spatial insight for social and environmental applications. The context for this work is the limitless demand for and capture of spatial data: from smartphone apps, social networks and more traditional activities related to planning, human rights, environmental justice, public health, climate change, agriculture, land use, etc.  This also extends concepts of 'crowd sourcing' into the  'citizen sensor', supporting community empowerment, resilience and adaptability. 

Current research is developing approaches that extends current methods for land use optimisation. These support policy decisions by allowing multiple user-defined ecosystem services (trade-offs and synergies) to be quantified. The initial work has been in China where I have been collaborating with a number of universities and the Chinese Academy of Sciences. China is starting to look “inwards” rather than outside for land based solutions to its needs and the tools I am developing suggest optimal landscape configurations given a) existing landscape patterns / land distributions, b) different underlying social and environmental gradients, and c) different potential landscape scenarios from policy makers. This builds on previous work on methods for land based renewable energy siting that are “resource catchment” aware - essentially extending location-allocation to an area based approach to supply and demand rather than the usual point based ones. 

Current PhD Students

  • Yi-min Chang-Chien (with Steve Carver), Using social media data to develop real-time augmentation to traditional land use mapping (started Sept 2016)
  • Yuanxuan Yang (with Alison Heppenstall), GIS-based spatial data mining and microsimulation for the analysis of elderly care service inequalities (started Sept 2016)
  • Jennie Gray (with Lisa Buckner), Predictive Geo-Demographics (started Sept 2017)
  • Arif Rohman (with Gordon Mitchel;), Evaluating Flood Hazard Impact to Community Resilience in Urban Areas(started Sept 2017)

Teaching

I convened the following module in 2018/19: 

  • Big Data and Consumer Analytics (Level 3)
  • Geocomputation & Location Analysis (MSc)
  • CDAS Internship and Dissertations modules (MSc)

In 2019/20 I will also lead the new Digital Geographies teaching strand: 

  • Digital Geographies (Level 1)
  • Social and Spatial Data Analysis with GIS (Level 2)

Recent Publications (last 2 years)

Comber A, Collins AL, Haro D, Hess T, Smith A, Turner A and Zhang Y (in press). A generic approach for live prediction of agricultural runoff risk: linking parsimonious soil-water models with live weather data APIs in decision tools. Paper accepted for publication in Frontiers (May 2019)

Comber A and Wulder MA (in press) Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use. Paper accepted for publication in Transactions in GIS (May 2019).

Razieh C, Khunti K, Davies M, Edwardson C, Henson J, Darko N, Comber A, Jones A, Yates T (in press). Association of depression and anxiety with clinical, sociodemographic, lifestyle and environmental factors in South Asians and white Europeans. Paper accepted for publication in Diabetic Medicine (April 2019).

LüY, Hu J, Fu B,Harris P, Wu L, Tong X, Bai Y, Sun J, Comber AJ (2019). A framework for the regional critical zone classification of the Loess Plateau, China. National Science Review, 6(1): 14–18. link

Comber A, Harris P and Atkinson PM (in press). The forgotten semantics of regression modelling in Geography. Paper accepted for publication in Geographical Analysis (March 2019).

Li T, Lü Y, Fu B, Hu W and Comber AJ (2019). Bundling ecosystem services for detecting their interactions driven by large-scale vegetation restoration: enhanced services while depressed synergies. Ecological Indicators, 99: 332-342.

Ren Y, Lü Y, ComberA, Fu B, HarrisP and Wu L (2019). Spatially explicit simulation of land use/land cover changes: Current coverage and future prospects. Earth Science Reviews https://doi.org/10.1016/j.earscirev.2019.01.001

Luo Y, Lü Y, Fu B, Zhang Q, Li T, Hu W and Comber A(2019). Half century change of interactions among ecosystem services driven by ecological restoration: quantification and policy implications at a watershed scale in the Chinese Loess Plateau. Science of The Total Environment, 651(2): 2546-2557.

Tsutsumida N, Rodríguez-Veiga P, Harris P, Balzter H, and Comber A (2019). Investigating spatial error structures in continuous raster data. International Journal of Applied Earth Observation and Geoinformation, 74: 259-268.

Xu R, Lin H, LüY, LuoY, Ren Y and ComberA (2018). A modified change vector approach for quantifying land cover change. Remote Sensing, 10: 1578, https://doi.org/10.3390/rs10101578

Comber Aand Harris P (2018). Geographically weighted elastic net logistic regression. Journal ofGeographical Systems, 20(4), 317-341, DOI:10.1007/s10109-018-0280-7 https://rdcu.be/7R4Y

Tao X, Fu Z and Comber AJ (2018).  An Analysis of Modes of Commuting in Urban and Rural Areas. Applied Spatial Analysis and Policyhttps://doi.org/10.1007/s12061-018-9271-9

Chukwusa, E and Comber A (2018). The Impact of residential and non-residential demand on location-allocation decision-making: a case study of modelling suitable locations for EMS in Leicester and Leicestershire, England UK.Journal of Geographic Information System, 10(4): 381. 10.4236/jgis.2018.104020

Comber A, Wang Y, LüY, Zhang X and Paul Harris (2018). Hyper-local geographically weighted regression: extending GWR through local model selection and local bandwidth optimization. Journal of Spatial Information Science, 17:63-84.

ComberA, ChiK, HuyMQ, NguyenQ, LuB, PheHH and HarrisP (2018). Distance metric choice can both reduce and induce collinearity in geographically weighted regression. Environment and Planning Bhttps://doi.org/10.1177/2399808318784017

Benitez-Paez F, Comber A, Trilles S and Huerta J (in press). Creating a conceptual framework to assess and improve the re-usability of open geographic data in cities. Paper accepted for publication in Transactions in GIS(April 2018) https://doi.org/10.1111/tgis.12449

Comber Aand Kuhn W (2018). Fuzzy difference and data primitives: a transparent approach for supporting different definitions of forest in the context of REDD+. Paper accepted for publication in Geographica Helvetica,  73: 151-163 https://doi.org/10.5194/gh-73-151-2018, Paper: https://www.geogr-helv.net/73/151/2018/ Data and code at https://doi.org/10.5281/zenodo.1188392

Fu W, Lü Y, Harris P, Comber Aand Wu L (2018). Peri-urbanization may vary with vegetation restoration: A large scale regional analysis. Urban Forestry and Urban Greening, 29: 77-87. https://doi.org/10.1016/j.ufug.2017.11.006

Luo Y, Lü Y, Fu B, Harris P, Wu L and Comber A (2018). When multi-functional landscape meets critical zone science: advancing multi-disciplinary research for sustainable human well-being. National Science Review, https://doi.org/10.1093/nsr/nwy003

Hu, J, Lü Y, Fu B, Comber AJand Harris P (2017). Quantifying the effect of ecological restoration on runoff and sediment yields: A meta-analysis for the Loess Plateau. Progress in Physical Geography, DOI:10.1177/0309133317738710

Harris, R., O’Sullivan, D., Gahegan, M., Charlton, M., Comber, L., Longley, P., Brunsdon, C., Malleson, N., Heppenstall, A., Singleton, A. Arribas-Bel, D. and Evans A (2017). More bark than bytes? Reflections on 21+ years of geocomputation. Environment and Planning B: Urban Analytics and City Science44(4), pp.598-617

Li T, Lü Y, Fu B, Comber AJ, Harris P and Wu L (2017). Gauging policy-driven large-scale vegetation restoration programmes under a changing environment: their effectiveness and socioeconomic relationships. Science of the Total Environment, 607-608: 911-919, DOI: 10.1016/j.scitotenv.2017.07.044

Tsutsumida N, Harris P and Comber A (2017). The application of a geographically weighted principal components analysis for exploring 23 years of goat population change across Mongolia. Annals of the American Association of Geographers, 107(5): 1060-1074, http://dx.doi.org/10.1080/24694452.2017.1309968

Comber A, Brunsdon CF, Charlton M and Harris P (2017). Geographically weighted correspondence matrices for local change analyses and error reporting: mapping the spatial distribution of errors and change. Remote Sensing Letters, 8(3): 234-243, http://dx.doi.org/10.1080/2150704X.2016.125812

<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://environment.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>

Research groups and institutes

  • Centre for Spatial Analysis and Policy
<h4>Postgraduate research opportunities</h4> <p>We welcome enquiries from motivated and qualified applicants from all around the world who are interested in PhD study. Our <a href="https://environment.leeds.ac.uk/research-opportunities">research opportunities</a> allow you to search for projects and scholarships.</p>