cctv
This is the code and data to replicate the analysis in Serebrennikov, Skougarevskiy (2023).
Science Score: 49.0%
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Repository
This is the code and data to replicate the analysis in Serebrennikov, Skougarevskiy (2023).
Basic Info
- Host: GitHub
- Owner: irlcode
- License: other
- Language: R
- Default Branch: master
- Homepage: https://link.springer.com/article/10.1007/s42001-024-00323-1
- Size: 30.1 MB
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- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files
README.md
Data and Code For A tale of four cities: Exploring environmental characteristics of CCTV equipment placement
This is the code and data to replicate the analysis in Serebrennikov, Skougarevskiy (2024).
Repository structure
/
├── code
├── 1._Parameter_searching_and_cross-validation.R # Parameters search, block cross-validation, final estimation
├── 2._SHAP_Analysis.R # SHAP-analysis and Plots based on it.
├── Table_1_code.R # Produce Table 1
├── Figure_1_code.R # Produce Figure 1
├── 1a._Robustness_check_Parameter_searching_and_cross-validation.R # Robustness check for city centers
└── 2a._Robustness_check_SHAP_Analysis.R # Robustness check for city centers
├── data
├── BE_100_catboost_best_params.rds # Best parameters for Brussels' model
├── BE_all_osm_objects_Freqtab.RDS # Frequency tab for Brussels
├── BE_buffer_long_100_RandSamp_GEOM.rds # Brussels geodata for block cross-validation
├── BE_buffer_wide_100_RandSamp.csv # Brussels data for analysis
├── data_all_catboost_buffer_100.rdata # Brussels prepared data
├── feature_importance_catboost_buffer_100.rdata # Data for feature importance plot
├── FR_100_catboost_best_params.rds # Best parameters for Paris' model
├── FR_all_osm_objects_Freqtab.RDS # Frequency tab for Paris
├── FR_buffer_long_100_RandSamp_GEOM.rds # Paris data for analysis
├── FR_buffer_wide_100_RandSamp.csv # Paris prepared data
├── RU_100_catboost_best_params.rds # Best parameters for Moscow's model
├── RU_all_osm_objects_Freqtab.RDS # Frequency tab for Moscow
├── RU_buffer_long_100_RandSamp_GEOM.rds # Moscow data for analysis
├── RU_buffer_wide_100_RandSamp.csv # Moscow prepared data
├── SC_100_catboost_best_params.rds # Best parameters for Edinburgh's model
├── SC_all_osm_objects_Freqtab.RDS # Frequency tab for Edinburgh
├── SC_buffer_long_100_RandSamp_GEOM.rds # Edinburgh data for analysis
└── SC_buffer_wide_100_RandSamp.csv # Edinburgh prepared data
Data availability statement
The data that support the findings of this study were derived from the following resources available in the public domain: - OpenStreetMap data under Open Data Commons Open Database License - Moscow CCTV locations open data published by Moscow city administration under Russian Government Decree N 583 of 10.07.2013 allowing free use - Paris CCTV locations open data published by French Ministry of the Interior under Open Licence - Edinburgh CCTV locations open data published by the Scottish Government under Open Government License v3.0 - Kontur Population Dataset by Kontur under Creative Commons Attribution International (CC BY) licence
Licence

Creative Commons License Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
Copyright © the respective contributors, as shown by the AUTHORS file.
Contacts
Dmitriy Serebrennikov, assoсiated researcher at the Institute for the Rule of Law at the European University at St. Petersburg
Owner
- Name: Institute for the Rule of Law at European University at St. Petersburg
- Login: irlcode
- Kind: organization
- Email: ipp@eu.spb.ru
- Location: Saint Petersburg, Russia
- Website: http://enforce.spb.ru
- Repositories: 1
- Profile: https://github.com/irlcode
Public repository