2022

Zhang, M., Adepeju, M., and Thomas, R. (2022). “Estimating the Effect of Crime (maps) on House Prices Using a (un)natural Experiment.” SocArXiv. Available here.

Adepeju, M., (2022). R-stppSim: Spatiotemporal Point Patterns Simulation. R Statistical software. Version 1.2.3. Available on CRAN.

2021

Adepeju, M. (2021). R-Opitools - An Opinion Analytical Tool for Big Social Media Data. Journal of Open Source Software, 6(64),3605. Available here. (Misc: [1], [2]).

Adepeju, M., Jimoh, F. (2021). An Analytical Framework for Measuring Inequality in the Public Opinions on Policing - Assessing the impacts of COVID-19 Pandemic using Twitter Data. Journal of Geographic Information System, 33, 122-147 Available here

Adepeju, M., Langton, S. and Bannister, J. (2021). Anchored k-medoids: a novel adaptation of k-medoids further refined to measure long-term instability in the exposure to crime. Journal of Computational Social Science. Available here.

K. Krzemieniewska-Nandwani, J. Bannister, M. Ellison, M. Adepeju (2021) Evaluation of the Impact of Alcohol Minimum Unit Pricing (MUP) on Crime and Disorder, Public Safety and Public Nuisance. Scottish Government. Available here

2020

Adepeju, M., Langton, S. and Bannister, J. (2020). Akmedoids R package for generating directionally-homogeneous clusters of longitudinal data sets. Journal of Open Source Software, 5(56), pp.2379-2379. Available here. (Misc: [1], [2])

Langton, S., Steenbeek, W. and Adepeju, M. (2020). An examination of variability in offender residences across different spatial scales: a study in Birmingham. SocArXiv.

2018

Adepeju, M. and Evans, A., (2018). Determining the optimal spatial and temporal thresholds that maximize the predictive accuracy of the prospective space-time scan statistic (PSTSS) hotspot method. Journal of Spatial Information Science, 2018(16), pp.57-84. Available here.

Adepeju, M. & Evans, A. (2018). A dynamic microsimulation framework for generating synthetic spatiotemporal crime patterns. GIS Research UK conference, Leicester (2018). Available here.

Lovelace, R., Padgham, M., Adepeju, M. & Groot, N. (2018) Estimating cycling potential on route networks in Accra and Kathmandu: A feasibility study using open data and open source software. WHO Meeting on transport and health, UK. Available here

2017

Adepeju, M. (2017). Testing the adequacy of a single-value Monte Carlo simulation for space-time interaction of crime. In Computational Science and Its Applications - ICCSA 2017, pg. 779-786. Available here.

Adepeju, M. & Evans, A. (2017) Comparative analysis of two variants of the Knox test: Inferences from space-time pattern analysis. In Computational Science and Its Applications - ICCSA 2017, pg. 770-778. Available here.

Adepeju, M. (2017) Investigating the Repeat and Near-Repeat Patterns in Sub-categories of burglary crime. International Conference on GeoComputation, Sept. 2017, Leeds, UK. Available here.

Adepeju, M. & Evans, A. (2017) Investigating the impacts of training data set length (T) and the aggregation unit size (M) on the accuracy of the self-exciting point process (SEPP) hotspot method. International Conference on GeoComputation, Sept. 2017, Leeds, UK. Available here

Adepeju, M. & Cheng, T. (2017) Determining the optimal spatial scan extent of Prospective spacetime scan statistics (PSTSS) for crime hotspot prediction. Proceedings of the 24th GIS Research UK conference, Manchester (2017), UK. Available here.

Adepeju, M. (2017) Modelling of Sparse Spatio-temporal Point Process (STPP) - An application in Predictive Policing. Doctoral thesis, University College London.

2016

Adepeju, M., Rosser, G., & Cheng, T. (2016). Novel evaluation metrics for sparse spatiotemporal point process hotspot predictions - a crime case study. International Journal of Geographical Information Science, 1-22. Available here.

Cheng, T., Bowers, K., Longley, P. [..]., Adepeju, M.,.. (2016). CPC: Crime, Policing and Citizenship - Intelligent policing and big data. London: UCL SpaceTimeLab. Available here.

Cheng, T. & Adepeju, M. (2014). Modifiable temporal unit problem (MTUP) and its effect on spacetime cluster detection. PloS one, 9(6), e100465. Available here.