Analyzing Recycling Infrastructure Disparities in Costa Rica Through Geospatial Analysis and Monte Carlo Simulations

Python Code Files (.py):
These Python scripts were used for Monte Carlo simulations and statistical analyses of recycling infrastructure disparities in different regions of Costa Rica, specifically focusing on Limon and San Jose. The scripts generate simulation models to assess the variability of recycling rates, simulate the financial costs associated with recycling infrastructure, and produce relevant visualizations such as recycling rate figures. The simulations and analyses support the research paper by providing detailed statistical insights and graphical representations of recycling performance.

PDF Files (.pdf):
The uploaded PDF files include both infographics and reference materials:

Infographics for Limon, Puntarenas, and San Jose present regional data on population and housing estimates, which were used as input for the recycling rate and infrastructure simulations.
Reference documents on solid waste management in Latin America and the Caribbean provide context on regional waste management practices, while documents on tariffs and costs for January 2024 supply financial data used in the simulations for evaluating the cost efficiency of recycling infrastructure.

Python代码文件 (.py): 这些Python脚本用于对哥斯达黎加不同地区，尤其是利蒙和圣何塞地区的回收基础设施差异进行蒙特卡洛模拟和统计分析。脚本生成模拟模型以评估回收率的变异性，模拟与回收基础设施相关的财务成本，并生成相关的可视化图表，例如回收率数据图。这些模拟和分析通过提供详细的统计洞察和回收性能的图形表示，支持研究论文。
PDF文件 (.pdf): 上传的PDF文件包含了信息图表和参考资料：针对利蒙、蓬塔雷纳斯和圣何塞的信息图表展示了区域人口和住房估计数据，这些数据被用作回收率和基础设施模拟的输入。关于拉丁美洲和加勒比地区固体废物管理的参考资料提供了区域废物管理实践的背景，而关于2024年1月的关税和成本文件则提供了用于评估回收基础设施成本效率的模拟财务数据。