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uci's Introduction

ipea

uci: Urban Centrality Index

CRAN status CRAN/METACRAN Total downloads R-CMD-check Codecov test coverage DOI Lifecycle: experimental

uci is an R package to calculate the Urban Centrality Index (UCI) originally proposed by Pereira et al., (2013). The UCI measures the extent to which the spatial organization of a city or region varies from extreme polycentric to extreme monocentric in a continuous scale from 0 to 1. Values close to 0 indicate more polycentric patterns and values close to 1 indicate a more monocentric urban form. More info on this vignette.

Installation

# from CRAN
install.packages('uci')

# or use the development version with latest features
utils::remove.packages('uci')
devtools::install_github("ipeaGIT/uci")

Basic Usage

library(uci)

# load data
data_dir <- system.file("extdata", package = "uci")
grid <- readRDS(file.path(data_dir, "grid_bho.rds"))

head(grid)
#> Simple feature collection with 6 features and 4 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -43.96438 ymin: -19.97414 xmax: -43.93284 ymax: -19.96717
#> Geodetic CRS:  WGS 84
#>                id population jobs schools                       geometry
#> 1 89a881a5a2bffff        439  180       0 POLYGON ((-43.9431 -19.9741...
#> 2 89a881a5a2fffff        266  134       0 POLYGON ((-43.94612 -19.972...
#> 3 89a881a5a67ffff       1069  143       0 POLYGON ((-43.94001 -19.972...
#> 4 89a881a5a6bffff        245   61       0 POLYGON ((-43.9339 -19.9728...
#> 5 89a881a5a6fffff        298   11       0 POLYGON ((-43.93691 -19.971...
#> 6 89a881a5b03ffff        555 1071       0 POLYGON ((-43.96136 -19.970...

# calculate UCI
df <- uci(
       sf_object = grid,
       var_name = 'jobs',
       bootstrap_border = FALSE,
       showProgress = TRUE
       )

head(df)
#>         UCI location_coef spatial_separation spatial_separation_max
#> 1 0.2538635     0.5278007           3880.114               7475.899

Citation ipea

The 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:

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}
}

Acknowledgement

The Hex image above illustrates Christaller’s Central Place Theory. It was adapted from an image originally created by Christaller and adapted by Becerra, 2015.

uci's People

Contributors

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Watchers

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uci's Issues

Make required R Version more explicit

First of all, thanks for another great package.

One small suggestion, though: make the R version requirement (>= 4.2.0) clearer to the users in your GitHub page or vignette.

Thanks!

allow user to input custom distance matrix

For the cases where the study area is a concave polygon (like a bay area), using a distance matrix based on Euclidean distance can be problematic. Allowing users to input a pre-calculated distance matrix based on along road network distances would solve this problem.

Function uci() does not work

Hi!

My student has been using the package uci to calculate the indicator for her master's dissertation. She has experimented with the code provided as an example with success and has calculated the indicator for the metropolitan areas she is investigating.

Nevertheless, after the recent actualization, the function uci() is not working, not even for the example.

I have attached some pictures of my screen with the trials.

Please, let me know if she's doing something wrong or if there is an issue with the function!

2023-06-20 16_27_21-centrality - RStudio
2023-06-20 16_27_52-centrality - RStudio

Thank you so much!

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