Data and code underlying the publication: Spatial optimization of circular timber hubs

This dataset contains the data and code used for a paper currently being peer reviewed, titled "Spatial optimization of circular timber hubs". For this research, we identified the optimal location and scale of circular construction hubs in Amsterdam using a spatial simulated annealing as an optimization algorithm. The zipped file contains two folders, "code" and "data". The "code" folder contains:costEffectiveness_v3.py - python script for calculating the cost effectiveness (euros / tCO2eq reduction) of a given configuration of circular construction hubs.spatialAnnealing_parallel_hpc.R - R script for running the spatial simulated annealing algorithm on a high performance computing cluster.resultsVisualization_v2.ipynb - a jupyter notebook visualizing the results of the research.<br>The "data" folder contains:ams_edges.shp - shp file of major street network in Amsterdam.candiHubs_ams.shp - shp file of potential locations for circular construction hubsmatGrid_ams.shp - shp file showing the predicted future supply and demand for timber (for construction) in Amsterdam. This shpfile was modified from a dataset created by the Planbureau voor de Leefomgeving and researchers from Leiden University, see their publication here: https://www.sciencedirect.com/science/article/pii/S0921344921007138本数据集收录了一篇当前处于同行评审阶段、题为《循环木材枢纽的空间优化》（Spatial optimization of circular timber hubs）的学术论文所使用的数据与代码。本研究以空间模拟退火算法（spatial simulated annealing）作为优化工具，确定了阿姆斯特丹地区循环建筑枢纽的最优选址与规模。压缩包内含"code"与"data"两个文件夹。 "code"文件夹包含： costEffectiveness_v3.py：用于计算给定循环建筑枢纽配置的成本效益（欧元/吨二氧化碳当量减排量）的Python脚本。 spatialAnnealing_parallel_hpc.R：用于在高性能计算集群（high performance computing cluster）上运行空间模拟退火算法的R脚本。 resultsVisualization_v2.ipynb：用于可视化本研究结果的Jupyter笔记本（Jupyter Notebook）。 "data"文件夹包含： ams_edges.shp：阿姆斯特丹主要街道网络的Shapefile（.shp）文件。 candiHubs_ams.shp：循环建筑枢纽潜在选址的Shapefile（.shp）文件。 matGrid_ams.shp：展示阿姆斯特丹建筑用木材未来供需预测的Shapefile（.shp）文件。该数据集修改自荷兰环境规划局（Planbureau voor de Leefomgeving）与莱顿大学研究者构建的原始数据集，相关研究文献可参见：https://www.sciencedirect.com/science/article/pii/S0921344921007138