Code for the paper "Evolution of residential segregation patterns in the Netherlands between 2015 and 2020"
收藏DataCite Commons2023-10-06 更新2024-07-03 收录
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https://data.4tu.nl/datasets/dfe5f48a-b13c-441c-9d8a-345b89599093/1
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资源简介:
This code is used for computing the results of the paper "Evolution of residential segregation patterns in the Netherlands between 2015 and 2020". <br>This code identifies and characterizes residential segregation patterns from demographic data. It is applied in a Dutch case study. It is written in python, using notebooks.<br>This source code should be stored in a folder named code. The folder code and the folder data (see https://doi.org/10.4121/78ca3d23-2846-499d-8448-d0a1812bc378) should be located in the same directory.<br>FORMAT*.mkd;*.ipynb;*.txt;*.csv<br>RECOMMENDED HARDWARE1. Processor: Intel® Core™ i5-10210U CPU2. RAM: 32GiB of RAM (DDR4)3. GPU: Intel® UHD Graphics GPU<br>RECOMMENDED OPERATING SYSTEMUbuntu 21.10, 64-bit<br>REQUIRED VERSION OF PYTHON3.9.7<br>REQUIRED LIBRARIES USEDsee requirements.txt<br>EXTRA FILEparameter.csv specifies some parameters used in the analysis.<br>SEQUENCE OF SCRIPTSThe scripts should be run in the following order:<br>1. demographics_preprocess.ipynb2. extract_city_boundary.ipynb3. extract_street_network.ipynb4. extract_zones_in_gemeente.ipynb5. shortest_path.ipynb6. adjacency_matrix.ipynb7. covariance_matrix.ipynb8. mean_var_city_Bernoulli.ipynb7. exposure.ipynb8. regionalization.ipynb9. indicators_city.ipynb10. data_analytics.ipynb
本代码用于复现论文《2015至2020年荷兰居住隔离格局的演变》中的计算结果。
本代码可从人口统计数据中识别并刻画居住隔离格局,并应用于荷兰的案例研究。该代码基于Python语言编写,采用Notebook运行环境。
本源代码需存储于名为`code`的文件夹中。`code`文件夹与`data`文件夹(详见https://doi.org/10.4121/78ca3d23-2846-499d-8448-d0a1812bc378)需置于同一目录下。
支持文件格式:*.mkd;*.ipynb;*.txt;*.csv
推荐硬件配置:1. 处理器:Intel® Core™ i5-10210U中央处理器(CPU);2. 内存:32GiB DDR4内存;3. 图形处理器(GPU):Intel® UHD Graphics GPU
推荐操作系统:64位Ubuntu 21.10
所需Python版本:3.9.7
所需依赖库:详见requirements.txt文件
额外文件:parameter.csv文件用于指定分析过程中使用的部分参数。
脚本运行顺序:需按照以下次序执行各脚本:
1. demographics_preprocess.ipynb
2. extract_city_boundary.ipynb
3. extract_street_network.ipynb
4. extract_zones_in_gemeente.ipynb
5. shortest_path.ipynb
6. adjacency_matrix.ipynb
7. covariance_matrix.ipynb
8. mean_var_city_Bernoulli.ipynb
7. exposure.ipynb
8. regionalization.ipynb
9. indicators_city.ipynb
10. data_analytics.ipynb
提供机构:
4TU.ResearchData
创建时间:
2023-10-06



