Code for the paper "Spatial relationship between residential segregation and urban fragmentation"
Datacite citation style
Spierenburg, Lucas; Sander van Cranenburgh; Cats, Oded (2025): Code for the paper "Spatial relationship between residential segregation and urban fragmentation". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/be216b07-ba91-41ad-98c7-3115d319fda7.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
Version 2 - 2025-07-09 (latest)
Version 1 - 2025-07-03
Categories
Geolocation
Western Europe (France, Germany, Ireland, Italy, the Netherlands, Portugal, Spain, the United Kingdom)
Licence MIT
Interoperability
Collection
This code is used for computing the results of the paper "Does residential segregation align with urban barriers?".
This code quantifies the spatial alignment between residential segregation patterns and urban fragmentation across 520 European cities using a Monte Carlo approach. The analysis generates synthetic urban fragmentation patterns using Voronoi tessellation and compares observed spatial overlap against null distributions to test whether urban barriers (railways, motorways, waterways) act as social frontiers more than expected by chance.
History
- 2025-07-03 first online, published, posted
Publisher
4TU.ResearchDataFormat
.py, .shFunding
- Delft AI Initiative
Organizations
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport and PlanningDATA
Files (19)
- 3,431 bytesMD5:
d84b0b0d5db08ab978ed2e7842a3ddffREADME_code.md - 2,686 bytesMD5:
11068e882b4e8366689245baa950d02ebuild_adjacency_matrix.py - 5,129 bytesMD5:
3428fa0a8c798a0dd30772143a76b1efbuild_covariance_matrix.py - 9,106 bytesMD5:
f68ec180b02abafa822d0412f5b48942build_urban_fragments.py - 7,082 bytesMD5:
ed15641bf1272d958905b3e1db0bcccbcoef_variance.py - 4,343 bytesMD5:
008856a09dc50bf1c43c727d15612fdedemographic_preprocess.py - 8,672 bytesMD5:
acf78a6b6b4567060a73adc3c24a2949environment.yml - 6,026 bytesMD5:
7c1440d989ee8d08e3f46529657f6192extract_pbf.sh - 19,898 bytesMD5:
6e9ee4da53fa064eb58b37dd840a34b4generate_synthetic.py - 11,274 bytesMD5:
6a097fc5791a4d43a97fbc711663e5cbmeasure_city_indicators.py - 3,343 bytesMD5:
f2e393c605afc61f247b668e71d353e1merge_data.py - 4,859 bytesMD5:
c5edb82a77cb8ab22cedd0cb5d0924cemoving_average.py - 6,881 bytesMD5:
701c0732a7ff802046d03482520b6c6coverlap_analysis.py - 21,086 bytesMD5:
419e3cb1a704e4522fd97ae830f03b2eperimeter_study.py - 19,574 bytesMD5:
48f09227917f7e92c725f090c4d790cbperimeter_study_Northern_Ireland.py - 6,303 bytesMD5:
c4d1ba720caf1ae16bebaf7a2156a2d1proximity_table.py - 7,490 bytesMD5:
b0266c60fb7feb91c1141757bade521equantile_dist.py - 8,885 bytesMD5:
6df98f8352f5811f7d770a87b3c43294regionalization.py - 7,314 bytesMD5:
0ba38d74a6b75c7b25c213dde9a6d48aregression.py -
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