Efficient regionalization techniques for socio‐economic geographical units using minimum spanning trees

By Renato Martins Assunção, M. C. Neves, G. Câmara, C. Freitas. International Journal of Geographical Information Science, 2006

439 citations recorded by the source corpus.

Subfields: Building and Construction, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f18b72118eac4bae5fc84943e51e67bb11b1deee

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Luc Anselin
2. Seth E. Spielman
3. David C. Folch

In Information Systems

1. Luc Anselin
2. Sergio J. Rey

In Signal Processing

1. Song Gao
2. Krzysztof Janowicz
3. Luc Anselin
4. Helen Couclelis
5. Juan C. Duque
6. Sergio J. Rey

Citations in the corpus

• Spatial Generalization and Aggregation of Massive Movement Data (2011, neutral): “The approaches are based on various clustering techniques [4].”
• Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017, methodology): “More implementation details about this clustering algorithm can be found at Assunçäo et al. (2006).”
• The Max‐P‐Regions Problem (2012, background): “…needs to be broken into connected subgraphs, while maximizing some intraregional homogeneity criterion Maravalle et al. (1997); Hansen et al. (2003); Assunção et al. (2006); and, (e) Formulation of exact optimization models are where a subset of constraints are responsible for satisfying the…”