@article{duque2018s,
title = {S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem},
author = {Duque, Juan C. and Laniado, Henry and Polo, Adriano},
url = {https://doi.org/10.1371/journal.pone.0207377},
doi = {10.1371/journal.pone.0207377},
issn = {1932-6203},
year = {2018},
date = {2018-11-27},
journal = {PloS one},
volume = {13},
number = {11},
pages = {1-25},
abstract = {This work presents a nonparametric statistical test, S-maup, to measure the sensitivity of a spatially intensive variable to the effects of the Modifiable Areal Unit Problem (MAUP). To the best of our knowledge, S-maup is the first statistic of its type and focuses on determining how much the distribution of the variable, at its highest level of spatial disaggregation, will change when it is spatially aggregated. Through a computational experiment, we obtain the basis for the design of the statistical test under the null hypothesis of non-sensitivity to MAUP. We performed an exhaustive simulation study for approaching the empirical distribution of the statistical test, obtaining its critical values, and computing its power and size. The results indicate that, in general, both the statistical size and power improve with increasing sample size. Finally, for illustrative purposes, an empirical application is made using the Mincer equation in South Africa, where starting from 206 municipalities, the S-maup statistic is used to find the maximum level of spatial aggregation that avoids the negative consequences of the MAUP.},
keywords = {Análisis espacial},
pubstate = {published},
tppubtype = {article}
}

@article{she2017network,
title = {The network-max-P-regions model},
author = {She, Bing and Duque, Juan C. and Ye, Xinyue},
url = {https://doi.org/10.1080/13658816.2016.1252987},
doi = {10.1080/13658816.2016.1252987},
issn = {1362-3087},
year = {2016},
date = {2016-11-04},
journal = {International Journal of Geographical Information Science},
volume = {31},
number = {5},
pages = {962-981},
abstract = {This paper introduces a new p-regions model called the Network-Max-P-Regions (NMPR) model. The NMPR is a regionalization model that aims to aggregate n areas into the maximum number of regions (max-p) that satisfy a threshold constraint and to minimize the heterogeneity while taking into account the influence of a street network. The exact formulation of the NMPR is presented, and a heuristic solution is proposed to effectively compute the near-optimized partitions in several simulation datasets and a case study in Wuhan, China.},
keywords = {Análisis espacial, Diseño de regiones},
pubstate = {published},
tppubtype = {article}
}