Professor Lex Comber
- Position: Chair in Spatial Data Analytics
- Areas of expertise: spatial analysis; GeoComputation; land cover / land use; spatial data quality; spatial planning; uncertainty; evidence combination; search heuristics; location-allocation
- Email: A.Comber@leeds.ac.uk
- Phone: +44(0)113 343 9225
- Location: 10.118 Irene Manton Building
- Website: Twitter | LinkedIn | Googlescholar | Researchgate | ORCID
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 the high volumes of spatial data that are being ubiquitously generated by our digital transactions, in order to uncover hidden patterns / correlations. It provides spatial insight for both 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. Some of the key challenges in this work are understanding the uncertainties associated with linking data describing different processes but captured over different spatial supports (scales), and dealing with issues of data bias and representativeness.
My current research is focused on 2 main areas: 1) Developing methods to support decision making at different scales, that are able to quantify cross scale trade-offs with any decision. These are being applied to spatial planning and land use in both urban / peri-urban and rural contexts, and to sample designs that support multiple decision scales (e.g. from household to community to settlement, and from field to farm to catchment). 2) Developing methods to quantify process spatial heterogeneity (how and where processes vary). These are being used to examine how the drivers of processes vary spatially and are being applied to understand neighbourhood change (such as gentrification), spatial inequalities in health outcomes and the emergence of house price bubbles. An example of how these research themes combine is the development of a tools to support spatial decisions and spatial planning given a) existing spatial patterns and distributions, b) different underlying social and environmental gradients, c) the value placed on different activities and process, and d) different scales of decision making.
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 & Vikki Houlden) 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 & Vikki Houlden) 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 2022/23:
- Big Data and Consumer Analytics (GEOG5917)
- GeoComputation and Spatial Analysis (GEOG3195)
- Digital Geographies (GEOG1400)
Recent Publications (last 3 years)
Comber A, Harris P and Brunsdon C (2022). A Rejoinder to the Commentaries on “A Route Map for Successful Applications of Geographically Weighted Regression” by Comber et al. (2022). Geographical Analysis, https://doi.org/10.1111/gean.12352
Cui N, Malleson N, Houlden V and Comber A (2022). The impact of the COVID-19 pandemic on urban green space use. Urban Forestry & Urban Greening, https://doi.org/10.1016/j.ufug.2022.127677
Comber A, Callaghan M, Harris P, Lu B, Malleson N and Brunsdon C (2022). gwverse: a template for a new generic Geographically Weighted R package. Geographical Analysis, https://doi.org/10.1111/gean.12337
Lu B, Hu Y, Murakami D, Brunsdon C, Comber A, Charlton M and Harris P (2022). High-performance solutions of geographically weighted regression in R. Geo-spatial Information Science, https://doi.org/10.1080/10095020.2022.2064244
Song Y, Wang Y, Jin L, Shi W, Aryal J and Comber A (2022). Quantitative contribution of the Grain for Green project to vegetation greening and its spatiotemporal variation across the Chinese Loess Plateau. Land Degradation & Development, https://doi.org/10.1002/ldr.4269
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 Planning, https://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 Science, https://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 Systems, 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 Systems, https://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 GIS, https://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 Systems, 77, 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. Frontiers, https://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
<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