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.

本数据集包含构建论文《基于贝叶斯分析的时空对数高斯柯克斯过程》中呈现的结果的相关代码和数据。该论文由Nadeen Khaleel所著。数据集内含模型实现和探索性分析所需的输入数据与输出结果，以及网格网格优化方法及INLA（集成贝叶斯分析）在马尔可夫链蒙特卡洛（MCMC）算法中的应用实现代码。部分输入数据对应于美国城市处理后的犯罪数据，尤其是洛杉矶、纽约和波特兰市区的谋杀和机动车辆盗窃事件，数据已汇总至人口普查区或离散网格级别。原始第三方数据未包含在内；然而，提供了一份说明如何获取相关数据的文档，以及所有用于从原始数据中清理和提取必要数据的代码。
此外，数据集还包含了数据整理、操作和模拟的相关代码，以及实现网格网格优化方法和在MCMC算法中应用INLA的代码。