Implementation of "A Parallel Spatial Co-location Mining Algorithm Based on MapReduce" paper
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Implementation of A Parallel Spatial Co-location Mining Algorithm Based on MapReduce paper DOI

Colocation Pattern

A spatial colocation pattern is a set of features that co-occur in space. For example, two crimes, say Robbery and Assault, would form a colocation pattern if they are reported together at many places. Think of spatial colocation pattern mining as association rule mining in the spatial domain.


  • Download and setup Scala, Hadoop (with HDFS) and HBase for versions given here.
  • Refer this for sample values for Hadoop and HBase configurations in pseudo distributed mode and this for some known issues when setting up HBase.
  • Start Hadoop using $HADOOP_HOME/sbin/ and HBase using $HBASE_HOME/bin/
  • Verify that Hadoop and HBase are working propery by opening http://localhost:50070/ and http://localhost:16010/ respectively.
  • Copy src/main/resources/reference.conf.sample to src/main/resources/reference.conf and populate values.
  • Run mvn clean install in project folder.

To download dataset

  • Obtain an application token from Socrata portal and copy it to socrata.key field in reference.conf.
  • Copy schema from scripts/schema.
  • Run python scripts/scrapper/

To load data in HDFS

  • Run scala -cp target/uber-locis-0.0.1-SNAPSHOT.jar com.github.locis.apps.DataLoader <input_path_to_write_raw_data>
  • If no path is provided, it writes to /user/locis/input/data

Dummy Dataset

  • A very small dataset (6 rows) can be found in sampleData/data file. The file can be used for testing the different MapReduce tasks without having to download the socrata dataset.
  • Add the file to hdfs using the put command $HADOOP_HOME/bin/hdfs dfs -put <path_to_locis>/sampleData/data <input_path_to_write_raw_data> and proceed to run MapReduce tasks.

To run Neighbour Search MapReduce task

  • Run $HADOOP_HOME/bin/hadoop jar target/uber-locis-0.0.1-SNAPSHOT.jar com.github.locis.apps.NeighborSearch <input_path_to_read_raw_data> <output_path_to_write_neighbors>

To run Neighbour Grouping MapReduce task

  • Run $HADOOP_HOME/bin/hadoop jar target/uber-locis-0.0.1-SNAPSHOT.jar com.github.locis.apps.NeighborGrouping <input_path_to_read_neighbors> <output_path_to_write_neighbor_groups>

To run Count Instance MapReduce task

  • Run $HADOOP_HOME/bin/hadoop jar target/uber-locis-0.0.1-SNAPSHOT.jar com.github.locis.apps.CountInstance <input_path_to_read_neighbor_groups> <output_path_to_write_instance_count>

To run Colocation Pattern Search MapReduce task

  • Run $HADOOP_HOME/bin/hadoop jar target/uber-locis-0.0.1-SNAPSHOT.jar com.github.locis.apps.PatternSearch <input_path_to_read_neighbor_groups> <output_path_to_write_prevalence_scores> <size_of_colocation>

Note that for running colocation pattern search task for size k, the results for size 1 to k-1 should already be in the db. So to find colocation patterns of size k, run the script for 1 to k and not just k. This task can be easily automated using a bash script.