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https://github.com/cortext/geoclust

Software to build geographical clusters based on weighted couples of coordinates (longitude and latitude)
https://github.com/cortext/geoclust

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Software to build geographical clusters based on weighted couples of coordinates (longitude and latitude)

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README 

        
# GeoClust

GeoClust is a simple solution to study the geographical aggregation of activities. This is done by grouping coordinates (latitude and longitude) into clusters. This has been developed by members of the [platform CorText](http://www.cortext.net/) for the [RISIS project](http://risis.eu/).



##  The software is based on two different approaches

First we use a density-based algorithm, DBSCan (Ester, Kriegel, & Sander, 1996), to identify the areas where the activities are concentrated. Following the original algorithm two parameters are fixed before the calculation: all points of a cluster are surrounded by at least X points in a circle with a diameter of Y km. We have added the ability to weighted each X points; the software examines the quantity of activities for each couple of coordinates. This step builds initial clusters.  

The second step builds the final clusters by using the CHAMELEON method (Karypis, Han, & Kumar, 1999). The software compares two different dimensions of the initial clusters (relational and structural analysis) to merge them if the thresholds are reached:



1. RI or Relative Interconnectivity: how intense are the relations between the initial clusters (with less than 100 km between the centroids)? 

2.	RC or Relative Closeness: does the final cluster will have a similar profile of collaborations than the two initial clusters taken separately (to avoid large variations of density of links in the final cluster)? 



##  How to use

* Import the project into Eclipse;

* Add the jar libraries found into the folder external-libs;

* Be sure you created a MySQL database, and that the MySQL server is running; 

* Run the java class InterfaceBD.



##  Input CSV file format

The input csv file should be tab separated with a \n at the end of the lines. Have a look in the Examples folder to see more.



To use the csv file you must follow the next format : Article id, article year, latitude, longitude.

* IDb : a unic number to identify the articles (repeated if there are some relations inside articles);

* Year : if the a temporal (year) information does not exist or if you do not want tu use it, you should fill-in this column by 0;

* Latitude : this field is request;

* Longitude : this field is request.



##  Export the results from MySQL server

The GeoClust software creates some useful files to understand the geographical clusters produced. See the example folder. But if you want to directly link the ID of your documents and the results you can query the final data and produce a table with all the relevant information you will need to work outside GeoClust. Use one of these queries directly in your MySQL instance: 

```sql

-- All information for clusters with a weight for each latitude-longitude couples

SELECT 

    a.IDc,

    a.Latitude,

    a.Longitude,

    a.nbArticles AS NbDocuments,

    a.IdClusterDbScan,

    a.IdClusterCham,

    a.isFusion

FROM

    geoclust.ww_resultatclustering AS a;

-- All clusters's information for each document

SELECT 

    b.IDb,

    a.IDc,

    a.Latitude,

    a.Longitude,

    a.IdClusterDbScan,

    a.IdClusterCham,

    a.isFusion

FROM

    geoclust.ww_resultatclustering AS a

        INNER JOIN

    geoclust.ww_pruebadata AS b ON a.IDc = b.IDc;

```



##  Examples of outputs and performace

All the results files are stored in the example folder. The parameters used are: 25km with a minimum of 150 for the weight of the longitude-latitude couples, and 0.28 for RI and 0.32 for RC.



On a computer with a 2.4GH processor (i7) and 8 GO RAM, with tow rounds of merging (chameleon):

* it takes around 8 minutes for around 15 000 relations between coordinates with 6 000 distinct weighted coordinate couples. 

* it takes around 30 minutes for around 30 000 relations between coordinates with 18 000 distinct weighted coordinate couples. 



##  More information on the algorithms

Ester, M., Kriegel, H.-P., & Sander, J. (1996). *A density-based algorithm for discovering clusters in large spatial databases with noise.* In S. Evangelos, J. Han, & U. M. Fayyad (Eds.), 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96) (pp. 226–231). AAAI Press.  

Karypis, G., Han, E.-H., & Kumar, V. (1999). *Chameleon: Hierarchical clustering using dynamic modeling.* IEEE Computer, 32(8), 68–75.



##  Licence

MIT License (MIT), Copyright (c) 2015 CorText  

Lionel Villard, ESIEE Paris, CorText Platform, Paris Est University  

Michel Revollo, ESIEE Paris, Paris Est University