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

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/start-dfs.sh and HBase using $HBASE_HOME/bin/start-hbase.sh.
• 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.

• 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/socrata.py.

• 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

• 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.

• 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>

• 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>

• 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>

• 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.

MIT