Calculate Urban Centrality Index (UCI) as described in Pereira et al. (2013).
The UCI quantifies the spatial clustering of a city or region based on the distribution of a chosen dimension, such as employment, population, or other points of interest. The index is measured on a continuous scale from 0 to 1, where values closer to 0 indicate a more polycentric pattern, and values near 1 suggest a more monocentric urban structure.
The Python implementation is based on the R
package uci by Pereira et al.
pip install git+https://github.com/ai4up/urban-centrality-index@v0.1.0
>>> import uci
>>> uci.uci(gdf, 'column_of_interest')
UCI 0.089
location_coef 0.492
proximity_index 0.181
spatial_separation 146.196
spatial_separation_max 179.015
dtype: float64
Build from source using poetry:
poetry build
pip install dist/urban_centrality_index-*.whl
For more information of how the index is calculated, see R
docs.
The original R
package uci is developed by a team at the Institute for Applied Economic Research (Ipea), Brazil. If you use this package in research publications, please cite it as:
- Pereira, R. H. M., Nadalin, V., Monasterio, L., & Albuquerque, P. H. (2013). Urban centrality: a simple index. Geographical analysis, 45(1), 77-89. https://www.doi.org/10.1111/gean.12002
BibTeX:
@article{pereira2013urbancentrality,
title = {Urban {{Centrality}}: {{A Simple Index}}},
author = {Pereira, Rafael H. M. and Nadalin, Vanessa and Monasterio, Leonardo and Albuquerque, Pedro H. M.},
year = {2013},
journal = {Geographical Analysis},
volume = {45},
number = {1},
pages = {77--89},
issn = {1538-4632},
doi = {10.1111/gean.12002}
}