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). 

With the wider use of spatial data in other disciplines (actually all data are spatial – they are collected some-where!), Lex’s collaborations are increasingly with researchers in non-geographical domains. Recent examples include mental health, consumer analytics, market segment dynamics, bio-informatics and spatial transcriptomics. 

He has co-authored (with Chris Brunsdon) the first ‘how to book’ for spatial analyses and mapping in R the open source statistical software, An Introduction to R for Spatial Analysis and Mapping, now in its second edition (https://uk.sagepub.com/en-gb/eur/an-introduction-to-r-for-spatial-analysis-and-mapping/book258267). The follow on to this is Geographical Data Science and Spatial Data Analysis: and Introduction in R, (ISBN: 978-1526449351, was published in 2021 (https://us.sagepub.com/en-us/nam/geographical-data-science-and-spatial-data-analysis/book260671). 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. This work was started in China where I have been collaborating with a number of universities and the Chinese Academy of Sciences. It is now the subject of a NERC funded project. I am also working on methods to support Landscape Decisions to suggest (near) optimal landscape configurations given a) existing landscape patterns and distributions, b) different underlying social and environmental gradients, c) the value placed on different Ecosystem Services and d) different scales of decision making. One of the key challenges in this work is understanding the uncertainties associated with linking data and processes captured at different grains and scales of support. 

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)
  • Jennie Gray (with Lisa Buckner) Predictive Geo-Demographics (started Sept 2017)
  • Arif Rohman (with Gordon Mitchell), Natural flood management in tropical climates (started Sept 2017)
  • Nan Cui (with Nick Malleson) Using social media data to understand urban green space usage (started October 2019)
  • Yiyu Wang (with Jiaqi Ge) A simulation model of pedestrian flow using Bayesian Nash equilibrium in a multi-agent system (started October 2020)
  • Lisma Safitri (with Marcelo Galdos & Andy Challinor) Water use efficiency monitoring using artificial intelligence (started October 2021)
  • Raidah Hanifah (with Nick Malleson) Spatial temporal analysis of tourism travel pattern from social media data (started October 2021)
  • Eleftherios Zormpas (with Simon Cockell, Newcastle University) Mapping the transcriptome: using geocomputational methods to unlock the potential of spatial transcriptomics data (started January 2022)

Teaching

I convene the following modules in 2021/22: 

  • Big Data and Consumer Analytics (GEOG5917)
  • GeoComputation and Spatial Analysis (GEOG3195)
  • Digital Geographies (GEOG1400)

Recent Publications (last 3 years)

Comber A, Brunsdon C, Charlton M, Dong G, Harris R, Lu B, Lü Y, Murakami D, Nakaya T, Wang Y and Harris P (2022). A route map for successful applications of Geographically Weighted Regression. Geographical Analysis https://doi.org/10.1111/gean.12316 

Yang Y, Beecham R, Heppenstall A, Turner A and Comber A (2021). Understanding the impacts of public transit disruptions on bikeshare schemes and cycling behaviours using spatiotemporal and graph-based analysis: A case study of four London Tube strikes. Journal of Transport Geography https://doi.org/10.1016/j.jtrangeo.2021.103255

Gadd SC, Comber A, Tennant P, Gilthorpe MS and Heppenstall AJ (2021). The utility of multilevel models for continuous-time feature selection of spatio-temporal networks. Computers, Environment and Urban Systems https://doi.org/10.1016/j.compenvurbsys.2021.101728 

Cui N, Malleson N, Houlden V and Comber A (2021). Using VGI and social media data to understand urban green space: A narrative literature review. ISPRS International Journal of Geo-Information, 10: 425, https://doi.org/10.3390/ijgi10070425

Gray J, Buckner L, Comber A (2021). Extending Geodemographics Using Data Primitives: A Review and a Methodological Proposal. ISPRS International Journal of Geo-Information, 10(6):386,  https://doi.org/10.3390/ijgi10060386

Yi-Min CC, Carver S, Comber A (2021). An exploratory analysis of the formal and informal landscape aesthetics evaluations: a case study from Wales. Land, 10(2), 192; https://doi.org/10.3390/land10020192 

Gosal AS, Giannichi ML, Beckmann M, Comber A, Massenberg JR, Palliwoda J, Roddis P, Schägner JP, Wilson J and Ziv G (2021). Do drivers of nature visitation vary spatially? The importance of context for predicting visitation of nature areas in Europe and North America. Science of the Total Environment https://doi.org/10.1016/j.scitotenv.2021.145190

Gadd SG, Comber A, Gilthorpe MS, Suchak K and Heppenstall A (2021). Simplifying the interpretation of continuous time models for spatio-temporal networks. Geographical of Systems https://doi.org/10.1007/s10109-020-00345-z 

Fu Y, Deng J, Wang H, Comber A, Yang W, Wu W, You S, Lin Y and Wang K (2021).  A new satellite-derived dataset for marine aquaculture in the China’s coastal region. Earth System Science DataEarth System Science Data, 13(5): 1829-1842, https://doi.org/10.5194/essd-2020-122 

Brunsdon C and Comber A (2020). Big Issues for Big Data: challenges for critical spatial data analytics. Journal of Spatial Information Science, 21: 89–98, https://doi.org/10.5311/JOSIS.2020.21.625 

Chien Y-MC, Carver S and Comber A (2020). Using geographically weighted models to explore how crowdsourced landscape perceptions relate to landscape physical characteristics. Landscape and Urban Planninghttps://doi.org/10.1016/j.landurbplan.2020.103904.

Brunsdon C and Comber A (2020). Opening practice: supporting Reproducibility and Critical spatial data science. Journal of Geographical Systems (https://doi.org/10.1007/s10109-020-00334-2

Harris P, Lanfranco B, Lu B and Comber A (2020). Influence of geographical effects on hedonic pricing models for grass-fed cattle in Uruguay. Agriculture, 10, 299, https://doi.org/10.3390/agriculture10070299  

Li, Z., White, J.C., Wulder, M.A., Hermosilla, T., Davidson, A.M., Comber, A.J. (2020). Land cover harmonization using Latent Dirichlet Allocation. International Journal of Geographical Information Sciencehttps://doi.org/10.1080/13658816.2020.1796131

Bell MJ and Comber A (2020). Smarter Farming: New Approaches for Improved Monitoring, Measurement and Management of Agricultural Production and Farming Systems. Frontiers in Sustainable Food Systemshttps://www.frontiersin.org/articles/10.3389/fsufs.2020.00121/full(https://doi.org/10.3389/fsufs.2020.00121

Yang Y, Heppenstall A, Turner A, Comber A (2020). Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems. Computers, Environment and Urban Systemshttps://doi.org/10.1016/j.compenvurbsys.2020.101521

Westerholt R, Mocnik F-B and Comber A (2020). A place for place – Modelling and analysing platial representations. Transactions in GIShttps://doi.org/10.1111/tgis.12647

Comber A, Brunsdon C, Charlton M, Dong G, Harris R, Lu B, Lü Y, Murakami D, Nakaya T, Wang Y and Harris P (submitted). The GWR route map: a guide to the informed application of GWR. Paper posted on arXiv, https://arxiv.org/abs/2004.06070  (April, 2020).

Belete M, Deng J, Teshome M, Wang K, Woldetsadik M, Zhu E, Comber A, Gudo A and Abubakar GA (2020). Partitioning the Impacts of Land Use/Land Cover Change and Climate Variability on Water Supply over the Source Region of Blue Nile Basin. Land Degradation and Development, https://doi.org/10.1002/ldr.3589

Beecham R, Williams N and Comber A (2020). Regionally-structured explanations behind area-level populism: An update to recent ecological analyses. PLoS ONE 15(3): e0229974. https://doi.org/10.1371/journal.pone.0229974

Comber A, Chi K, Huy MQ, Nguyen Q, Lu B, Phe HH and Harris P (2020). Distance metric choice can both reduce and induce collinearity in geographically weighted regression. Environment and Planning B, 47(3): 489–507 https://doi.org/10.1177/2399808318784017

Ren Y, Lü Y, Fu B, Comber A, Li T, Hu J (2020). Driving factors of land change in China’s Loess Plateau: quantification using Geographically Weighted Regression and management implications. Remote Sensing 12(3),453; https://doi.org/10.3390/rs12030453

Ye Z, Fu Y, Gan M, Deng J, Comber A, Wang K (2019). Building extraction from very high resolution aerial imagery using joint attention deep neural network. Remote Sensing, 11, 2970; https://doi.org/10.3390/rs11242970

Zeng W and Comber A (2020). Using household counts as ancillary information for areal interpolation of population: comparing formal and informal, online data sources. Computers, Environment and Urban Systems, 20 https://doi.org/10.1016/j.compenvurbsys.2019.101440

Yasumoto S, Jones A, Kanasugi H, Sekimoto Y, Shibasaki R, Comber A and Watanbe C (2019). Heat exposure assessment based on individual daily mobility patterns in Dhaka, Bangladesh. Computers Environment and Urban Systems77, p.101367, https://doi.org/10.1016/j.compenvurbsys.2019.101367 

Yang Y, Heppenstall A, Turner AGD and Comber A (2019). A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile. Computers Environment and Urban Systems, 77: 101361, https://doi.org/10.1016/j.compenvurbsys.2019.101361

Comber A and Zeng W (2019). Spatial interpolation using areal features: a review of methods and opportunities using new forms of data with coded illustrations. Geography Compass, e12465, https://doi.org/10.1111/gec3.12465

Yang Y, Heppenstall A, Turner A and Comber A (2019). Who, where, why and when? Using smart card and social media data to understand urban mobility. ISPRS International Journal of Geo-Information 8:6, https://doi.org/10.3390/ijgi8060271  

Zhou, M., Deng, J., Lin, Y., Belete, M., Wang, K., Comber, A., Huang, L. and Gan, M. (2019). Identifying the effects of land use change on sediment export: Integrating sediment source and sediment delivery in the Qiantang River Basin, China. Science of the Total Environment, 686:38-49; https://doi.org/10.1016/j.scitotenv.2019.05.336

Comber A, Collins AL, Haro D, Hess T, Smith A, Turner A and Zhang Y (2019). A generic approach for live prediction of agricultural runoff risk: linking parsimonious soil-water models with live weather data APIs in decision tools. Frontiershttps://doi.org/10.3389/fsufs.2019.00042

Comber A and Wulder MA (2019) Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use. Transactions in GIS, 23(5): 879-891; https://doi.org/10.1111/tgis.12559

Razieh C, Khunti K, Davies M, Edwardson C, Henson J, Darko N, Comber A, Jones A, Yates T (2019). Association of depression and anxiety with clinical, sociodemographic, lifestyle and environmental factors in South Asians and white Europeans. Diabetic Medicine https://doi.org/10.1111/dme.13986

Lü Y, 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; https://doi.org/10.1093/nsr/nwy147

Comber A, Harris P and Atkinson PM (2019). The forgotten semantics of regression modelling in Geography. Geographical Analysis https://doi.org/10.1111/gean.12199

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;  https://doi.org/10.1016/j.ecolind.2018.12.041

Ren Y, Lü Y, Comber A, Fu B, Harris P 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; https://doi.org/10.1016/j.scitotenv.2018.10.116

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; https://doi.org/10.1016/j.jag.2018.09.020

Luo Y, Lü Y, Fu B, Harris P, Wu L and Comber A (2019). When multi-functional landscape meets critical zone science: advancing multi-disciplinary research for sustainable human well-being. National Science Review, 6(2) 349–358; https://doi.org/10.1093/nsr/nwy003

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

Comber A and Harris P (2018). Geographically weighted elastic net logistic regression. Journal of Geographical Systems, 20(4), 317-341;  https://doi.org/10.1007/s10109-018-0280-7  

Tao X, Fu Z and Comber AJ (2018).  An Analysis of Modes of Commuting in Urban and Rural Areas. Applied Spatial Analysis and Policy https://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; https://doi.org/10.4236/jgis.2018.104020

Comber A, Wang Y, Lü Y, 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; https://doi.org/10.5311/JOSIS.2018.17.422

Benitez-Paez F, Comber A, Trilles S and Huerta J (2018). 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 https://doi.org/10.1111/tgis.12449

Comber A and Kuhn W (2018). Fuzzy difference and data primitives: a transparent approach for supporting different definitions of forest in the context of REDD+. Geographica Helvetica,  73: 151-163; https://doi.org/10.5194/gh-73-151-2018

Fu W, Lü Y, Harris P, Comber A and 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

 

<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

Current postgraduate researchers

<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://phd.leeds.ac.uk">research opportunities</a> allow you to search for projects and scholarships.</p>