This repository accompanies the following paper: https://doi.org/10.1111/rssc.12431

Povala, Jan, Seppo Virtanen, and Mark Girolami. 2020. ‘Burglary in London: Insights from Statistical Heterogeneous Spatial Point Processes’. Journal of the Royal Statistical Society. Series C (Applied Statistics) forthcoming. DOI: 10.1111/rssc.12431.

These codes have been tested to work on Ubuntu and OSX. It requires Python 3.

Note that, all the commands below need to be run from the root of this repository.

Create a conda environment with the required libraries and activate it.

conda env create -f conda-python-env.yml
conda activate london-crime-mixtures

python src/z_sampler/setup.py  build_ext --build-lib ./

Codes for the three models that we consider in the paper are available in the ./src/models folder. The naming conventions follow the variable names in the paper.

Running any of the three scripts as described below will produce regular snapshots of the respective model. The snapshots are saved in a .npy file with the name based on the model context and the UID. These snapshots can the be processed using the src/experiment/results_processing.py script. The attached notebook called paper-results.ipynb utilises these scripts to produce figures in the paper.

If the scripts are run with --verbose flag, they will regularly inform the user about the state of the inference algorithm. One of the most useful information it gives is the acceptance ratio for each HMC sample as well as the 'momentum' and the 'potential' parts of the ratio. Also, it regularly plots the traceplots for the quantities it samples. This gives an indication if something is very wrong. More chains are recommended to be run if higher degree of belief of convergence is sought.

1. --model: specifies the YAML filename with the configuration of the covariates. The YAML file needs to be placed inside ./models/config/ directory.
2. --type: specifies the crime type. The data provides has counts for other crime types as well, but we only work with burglary.
3. --resolution: the provided data file has only 400m resolution so this is the only valid option. If it is to be changed then it needs to regenerated as described below.
4. --uid: identifier used to generate file names for the inference snapshots. This uid(s) need to be supplied when processing the results using src/experiment/results_processing.py.

python -m src.models.lgcp_matern --verbose --year 12013-122015 --type burglary --resolution 400 --model_name burglary_raw_1  --uid "LGCP_MODEL_UID"

python -m src.models.block_mixture_gp_softmax --verbose --type burglary --model_name burglary_raw_4  --resolution 400  --block_type msoa --year 12013-122015  --num_mixtures 3 --lengthscale 1000  --uid "BLOCK_MIX_GP_SOFTMAX_UID"

python -m src.models.block_mixture_flat --verbose --year 12013-122015 --type burglary --resolution 400 --model_name burglary_raw_1   --num_mixtures 3 --block_type msoa --uid "BLOCK_MIXTURE_MODEL_UID"

The notebook in ./notebooks/paper-results.ipynb has codes to produce summaries and plots after the models have been run. To a large extent, the notebook utilises src/experiment/results_processing.py script to produce the plots or the supporting data for the plots. Some of the plots are eps, but many of them are standalone LaTeX files which after compiling with LaTeX can be converted to a desired format. PAI/PEI section produces .dat tabular data file which can be rendered by gnuplot, Excel, etc.

The notebook also has additional plotting that were used for diagnostics, e.g. traceplots for the samples.

To be able to correctly link the code to the required libraries on a MAC, you should run this command:

export CPATH=/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/

One might want to create datasets that cover different time period, or use different spatial resolution. To do that, one can utilise the script src/data/r-spatial-data-extensive-retriever.R. For example, one may want to run:

./src/data/r-spatial-data-extensive-retriever.R --resolution 300 --startdate "2013-01-01" --enddate "2015-12-31"

Note that this might take time as for many covariates it downloads them from the internet. Not all data sources will work out of the box. For example, point of interest data, which I have obtained form Ordnance Survey data must be downloaded by the user separately and the path to such file must be specified in r-spatial-data-extensive-retriever.R. Either, do not use POI data, or provide an alternative source and adjust the POI retrieval functions accordingly.

One might also want to process new crime data as reported on police.uk portal. To do so, place the downloaded CSVs into ./data/raw/crime/ folder and run ./src/data/create_crime_db.py script. This will create SQLite database that the R script above used.