Code and Dataset for Thesis "Bayesian Analysis of Spatial Log-Gaussian Cox Processes"
These files contain the relevant code and data to produce the results presented in the thesis titled "Bayesian Analysis of Spatial Log-Gaussian Cox Processes" by Nadeen Khaleel.
These files contain the input data and the output results for the implementation of the models and exploratory analysis as well as the implementation of the Grid Mesh Optimisation method and the INLA within MCMC algorithms. Some of the input data corresponds to the processed crime data in US cities, in particular incidences of homicide and motor vehicle theft in Los Angeles, New York and Portland, aggregated to census-tract level or discretisation grids. The raw third party data is not included; however, a document detailing how to access the relevant data is provided and all of the code used to clean and extract the necessary data from the raw data is included.
These files additionally contain the relevant code for the data tidying, manipulation and simulation as well as the code to implement the Grid Mesh Optimisation method and the INLA within MCMC algorithms.
Cite this dataset as:
Khaleel, N.,
2022.
Code and Dataset for Thesis "Bayesian Analysis of Spatial Log-Gaussian Cox Processes".
Bath: University of Bath Research Data Archive.
Available from: https://doi.org/10.15125/BATH-01133.
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Data
THESIS_DATA.zip
application/zip (2GB)
Creative Commons: Attribution 4.0
This directory contains the input and output data for the results in the thesis. We have aggregated crime data for LA, NYC and Portland over census tracts as well as grids of different resolutions. These and simulated data were used for fitting GLMs as well as the implementation of the Grid-Mesh Optimisation method and the INLA within MCMC algorithm. All input and output data for the code can be found in these directories. The file hierarchy matches that of THESIS_CODE.zip for ease of merging.
Code
THESIS_CODE.zip
application/zip (655kB)
Software: GNU GPL 3.0
This directory contains all of the relevant code to produce the results found in the thesis. The code for cleaning the data as well as the model fits, the Grid-Mesh Optimisation method and INLA within MCMC implementations can be found in relevant directories. The file hierarchy directly matches that of the THESIS_DATA.zip file and so combining the matching sub-directories of these files provides the necessary input data for the code as well as the output data originally produced.
Creators
Nadeen Khaleel
University of Bath
Documentation
Technical details and requirements:
R Software was solely used within the data tidying/manipulation and simulation as well as for the code and any results output. Any tidied or simulated data is stored under the .rda or .rds format and any code is available in the .R or .Rmd format with output from the latter in the form .html.
Documentation Files
README.pdf
application/pdf (404kB)
Creative Commons: Attribution 4.0
This README contains the details of the data and code found in the relevant directories of THESIS_DATA and THESIS_CODE. While the code and the data are split between the two directories, the hierarchical structure of the sub-directories for both match. Therefore, the README file contains information on the data and code within each sub-directory together, and is not split for data and code.
DataAccessInformation.pdf
application/pdf (127kB)
Creative Commons: Attribution 4.0
This file contains the data access information for the raw, third party data that is used within the relevant R code files do generate the processed data. These data sets cannot be archived and so we provide information for the reader to access the data and any data manipulation can be found in the relevant .R files which are also discussed in the README.pdf.
Funders
Engineering and Physical Sciences Research Council (EPSRC)
https://doi.org/10.13039/501100000266
EPSRC Centre for Doctoral Training in Statistical Applied Mathematics at Bath (SAMBa)
EP/L015684/1
Publication details
Publication date: 26 September 2022
by: University of Bath
Version: 1
DOI: https://doi.org/10.15125/BATH-01133
URL for this record: https://researchdata.bath.ac.uk/id/eprint/1133
Related theses
Khaleel, N., 2022. Bayesian Analysis of Spatial Log-Gaussian Cox Processes. Thesis (PhD). University of Bath. Available from: https://researchportal.bath.ac.uk/en/studentTheses/bayesian-analysis-of-spatial-log-gaussian-cox-processes.
Contact information
Please contact the Research Data Service in the first instance for all matters concerning this item.
Contact person: Nadeen Khaleel
Faculty of Science
Mathematical Sciences
Research Centres & Institutes
EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa)
