Publication & data

List of selected recent publications

Guo H., Python, A. and Liu Y. Extending regionalization algorithms to explore spatial process heterogeneity. International Journal of Geographic Information Science. 2023. DOI: 10.1080/13658816.2023.2266493

• Paper: click here

Kefyalew A., Python, A. et al. Mapping tuberculosis prevalence in Ethiopia using geospatial meta-analysis. International Journal of Epidemiology. 2023. DOI: 10.1093/ije/dyad052

• Paper: click here

Seufert J., Python, A., Weisser C., Cisneros E., Kis-Katos K., and Kneib T.  Mapping ex-ante risks of COVID-19 in Indonesia using a Bayesian geostatistical model on airport network data. Journal of the Royal Statistical Society: Series A (Statistics in Society). 2022. DOI: 10.1111/rssa.12866

• Paper: click here

Linfei Y., Guoyong L., and Python, A. Attribution of the spatial heterogeneity of Arctic surface albedo feedback to the dynamics of vegetation, snow and soil properties and their interactions. Environmental Research Letters. 2022. 17(1):014036

• Paper: please contact the corresponding author.

B. Qin, F. Huang, S. Huang, Python, A., Y. Chen, and J ZhangZhou. Machine Learning Investigation of Clinopyroxene Compositions to Evaluate and Predict Mantle Metasomatism Worldwide. Journal of Geophysical Research: Solid Earth. 2022. 127(5).

• Paper: please contact the corresponding author.

Zhenqian Chen, Yongheng Shang, Python, André, Yuxiang Cai, and Jianwei Yin. DB-BlendMask: Decomposed Attention and Balanced BlendMask for Instance Segmentation of High-Resolution Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 2022. 60:1-15.

• Paper: please contact the corresponding author.

Brandsch, J. and Python, A. Provoking ordinary people. The effects of terrorism

on civilian violence. Journal of Conflict Resolution. 2021. DOI: 10.1177/0022002720937748

• Paper: click here

• Data replication: click here

Python, A., Bender A., Illian, J., Blangiardo, M., Lin, Y., Liu, B., Lucas, T., Tan, Wen Y., Svanidze, D., and Yin., J. A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales. Journal of the Royal Statistical Society: Series A (Statistics in Society). 2021. DOI: 10.1111/rssa.12738.

• Paper: click here

Lucas, Tim & Nandi, Anita & Chestnutt, Elisabeth & Twohig, Katherine & Keddie, Suzanne & Collins, Emma & Howes, Rosalind & Nguyen, Michele & Rumisha, Susan & Python, Andre & Arambepola, Rohan & Bertozzi‐Villa, Amelia & Hancock, Penelope & Amratia, Punam & Battle, Katherine & Cameron, Ewan & Gething, Peter & Weiss, Daniel. Mapping malaria by sharing spatial information between incidence and prevalence data sets. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2021. DOI: 10.1111/rssc.12484.

• Paper: click here

Python, A. Debunking Seven Terrorism Myths with Statistics. Statistical Reasoning in Science and Society. American Statistical Association and CRC Press. 2020. ISBN 9780367472245. 

• To order the book:

  • Routledge: click here

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Python, A. What we know - and don't know - about terrorism. Significance Magazine. Published by the Royal Statistical Society (JRSS) and the American Statistical Association (ASA). 2020. DOI:  10.1111/1740-9713.01418.

• Paper: click here 

Python, A., Brandsch J., Illian, J., Jones-Todd, C., and Blangiardo, M. Statistics and Terrorism: Getting insights into terrorism lethality through Bayesian modeling. Wiley StatsRef: Statistics Reference Online. DOI: 10.1002/9781118445112.stat08250. 

• Paper: click here

Python, A., Illian, J., Jones-Todd, C., and Blangiardo, M. The deadly facets of terrorism. Significance Magazine. Published by the Royal Statistical Society (JRSS) and the American Statistical Association (ASA). 2019 (16) 28-31. DOI: 10.1111/j.1740-9713.2019.01300.x. 

• Paper: click here

Python, A., Illian, J., Jones-Todd, C., and Blangiardo, M. A Bayesian approach to modelling subnational spatial dynamics of worldwide non-state terrorism, 2010–2016. Journal of the Royal Statistical Society: Series A (Statistics in Society). 2019 (182)1 323-344. DOI: 10.1111/rssa.12384. 

• Paper: click here

• Replication code & data: click here

Python, A., Brandsch, J., and Tskhay, A. Provoking local ethnic violence–A global study on ethnic polarization and terrorist targeting. Political Geography. May 2017(58): 77-89. DOI: 10.1016/j.polgeo.2017.02.001. 

• Paper: click here

• Replication code & data: click here

Python, A., Illian, J., Jones-Todd, C., and Blangiardo, M. Explaining the Lethality of Boko Haram’s Terrorist Attacks in Nigeria, 2009–2014: A Hierarchical Bayesian Approach. International Conference on Bayesian Statistics in Action. June 2016 231-239. DOI: 10.1007/978-3-319-54084-9_22.

• Paper: click here

• Replication code & data: click here

Python, A. and Brandsch. A Case Study of Spatial Analysis: Approaching a Research Question With Spatial Data. SAGE Research Methods Cases. 2019(58): 77-89. DOI: 10.4135/9781526467454. 

• Book chapter: click here

Rathmes, G., Rumisha, S. F., Lucas, T. C., Twohig, K. A., Python, A., Nguyen, M., Gibson H. S., Chestnutt E. G., Battle K. E., Humphreys G., Amratia P., Arambepola R., Bertozzi-Villa A., Hancock P., Millar J. J., Symons T. L., Bhatt S., Cameron E., Guerin P. J., Gething P. W. & Weiss D. J. (2020). Global estimation of anti-malarial drug effectiveness for the treatment of uncomplicated Plasmodium falciparum malaria 1991–2019. Malaria Journal. 2020. 19(1): 1-15. DOI: 10.1186/s12936-020-03446-8

• Paper: click here

Lucas, T.C., Nandi, A., Nguyen, M., Rumisha, S., Battle, K.E., Howes, R.E., Hendriks, C., Python, A., Hancock, P., Cameron, E. and Gething, P. Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence. Spatial and Spatio-Temporal Epidemiology. 2020. DOI: 10.1016/j.sste.2020.100357.

• Paper: click here

Zehua L., Fontana F., Python, A., T. H. Jouni, and Santos H. A. Microfluidics for production of particles: mechanism, methodology and applications. Small. 2019(1904673): 1-24. DOI:  10.1002/smll.201904673

• Paper: click here

Hendriks C., Gibson H., Trett A., Python A., Weiss D., Vrieling A., Coleman, M., Gething P., Hancock P., and Moyes C. Mapping geospatial processes affecting the environmental fate of agricultural pesticides in Africa. Int. J. Environ. Res. Public Health. 2019 16(3523): 1–22. DOI: 10.3390/ijerph16193523. 

• Paper: click here

Liu, Zehua; Wang, Shiqi; Tapeinos, Christos; Torrieri, Giulia; Känkänen, Voitto; El-Sayed, Nesma; Python, Andre; Hirvonen, Jouni T; Santos, Hélder A; Non-viral nanoparticles for RNA interference: Principles of design and practical guidelines. Advanced Drug Delivery Reviews. 2021.

Yu, Linfei; Leng, Guoyong; Python, Andre; Varying response of vegetation to sea ice dynamics over the Arctic. Science of The Total Environment (799). 2021.

Hall S., Illian J., Makuta I., McNabb K., Murray S., O’Hare B AM, Python, A. Zaidi SHI, Bar-Zeev N. Government Revenue and Child and Maternal Mortality. Open Economies Review. 2021 (9/12): 1–17. DOI:  10.1007/s11079-020-09597-0. 

• Paper: click here

Kozlowski, G., Rion, S., Python, A., and Riedo, S. Global conservation status assessment of the threatened aquatic plant genus Baldellia (Alismataceae): challenges and limitations. Biodiversity and conservation. August 2009 18(9): 2307-2325. DOI: 10.1007/s10531-009-9589-3.

• Paper: click here 

• Replication code & data: please contact gregor.kozlowski(at)unifr.ch 

Lucas, T., Redding, D., and Python A. Utility Functions for INLA, R Package: INLAutils. Version 0.0.7. (May 2018).

• A number of utility functions for R-INLA. Additional diagnostic plots and support for 'ggplot2'. Step wise regression with 'INLA'. Species distribution models. A spatial leave-one-out cross validation based on R-INLA outputs and other helper functions. 

Nandi A., Arambepola R., Lucas, T., and Python A. Disaggregation Modelling, R Package: disaggregation. Version 0.1.1. (December 2019).

• Fits disaggregation regression models using 'TMB' ('Template Model Builder'). This package fits models when the response data are aggregated to polygon level but the predictor variables are at a higher resolution. These are an extension of regression models with spatial random fields.  

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