cff-version: 1.2.0
abstract: "<p>This code is used for computing the results of the paper "Characterizing residential segregation in cities using intensity, separation, and scale indicators". </p>
<p><br></p>
<p>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.</p>
<p><br></p>
<p>This source code should be stored in a folder named code. The folder code and the folder data (see https://doi.org/10.4121/19597258) should be located in the same directory.</p>
<h4>FORMAT</h4>
<p>*.mkd;</p>
<p>*.ipynb;</p>
<p>*.txt;</p>
<p>*.csv</p>
<h4>RECOMMENDED HARDWARE</h4>
<p>1. Processor: Intel® Core™ i5-10210U CPU</p>
<p>2. RAM: 32GiB of RAM (DDR4)</p>
<p>3. GPU: Intel® UHD Graphics GPU</p>
<h4>RECOMMENDED OPERATING SYSTEM</h4>
<p>Ubuntu 21.10, 64-bit</p>
<h4>REQUIRED VERSION OF PYTHON</h4>
<p>3.9.7</p>
<h4>REQUIRED LIBRARIES USED</h4>
<p>see requirements.txt</p>
<h4>EXTRA FILE</h4>
<p>parameter.csv specifies some parameters used in the analysis.</p>
<h4>SEQUENCE OF SCRIPTS</h4>
<p>The scripts should be run in the following order:</p>
<p><br></p>
<p>1. demographics_preprocess.ipynb</p>
<p>2. extract_city_boundary.ipynb</p>
<p>3. extract_street_network.ipynb</p>
<p>4. extract_zones_in_gemeente.ipynb</p>
<p>5. shortest_path.ipynb</p>
<p>6. adjacency_matrix.ipynb</p>
<p>7. correlation_matrix.ipynb</p>
<p>8. exposure.ipynb</p>
<p>9. cluster_analysis.ipynb</p>
<p>10. descriptive_stats.ipynb</p>
<p>11. combine_zones_into_municipalities.ipynb</p>"
authors:
  - family-names: Spierenburg
    given-names: Lucas
    orcid: "https://orcid.org/0000-0002-7806-6961"
title: "Code for the paper &#34;Characterizing residential segregation in cities using intensity, separation, and scale indicators&#34;"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/21286653.v1
license: MIT
date-released: 2022-10-19