shanbhag10 / crime-predictor-chicago

One of the most important questions people ask before moving to a new place or traveling is: Is this locality safe? We answer this question using co-location pattern mining. Our crime predictor gives the possibility of all crimes that can happen given the presence of certain geographical entities and certain crimes in the locality. Given a set of boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. We have used a transaction free approach for this using Haversine distance as a metric for finding proximity neighborhood and measuring all spatial autocorrelations. We also propose an improvement to distance measure approach while calculating R-proximity neighborhood using boundary window. We used distance and participation thresholds for determining the correlation between features and crimes. Our algorithm exploits the anti-monotone property and optimizes the co-location pattern discovery algorithm by prevalence based pruning.

Crime Predictor: Chicago

Pre-requisites

• Third party Python modules
  • panadas
  • numpy
  • tabulate

STEP 1: Clone the repository

Please run the command git clone https://github.com/shanbhag10/Crime-Predictor-Chicago.git

STEP 2: Change the directory to scripts

Please run the command cd <PATH_OF_CLONE_FOLDER>/Crime-Predictor-Chicago/scripts

STEP 3: Pre process raw data [ONLY IF REQUIRED]

Please run the command python3 preProcessData.py ../config/<PRE_PROCESS_FILE>.config ../config/<MAIN_PROCESS_FILENAME>.config

Example:

python3 preProcessData.py ../config/preprocess_all.config ../config/main.config

The structure of <PRE_PROCESS_FILE>.config is as follows

First Line: <OUTPUT_FILE_FOR_MAIN_ALGO> Second Line<RAW_FILE_PATH>,<ROW_FILTER_KEY>,<COL_NAME_HAVING_THE_FILTER_KEY>,<COL_NAME_FOR_LATITUDE>,<COL_NAME_FOR_LONGITUDE>

For multiple files put each entry on a new line

NOTE: The ROW_FILTER_KEY and COL_NAME_HAVING_THE_FILTER_KEY can be empty if you don't want to filter rows

Example content:

../data/input/allFeatures.csv
../data/overlap/crime.csv,HOMICIDE,PRIMARY DESCRIPTION,LATITUDE,LONGITUDE
../data/overlap/bars.csv,,,LATITUDE,LONGITUDE
../data/overlap/church.csv,,,"LAT","LONG"
../data/overlap/library.csv,,,LAT,LONG
../data/overlap/school.csv,,,Lat,Long
../data/overlap/police.csv,,,LATITUDE,LONGITUDE

STEP 4: Create the <MAIN_PROCESS_FILENAME> [ONLY IF Pre processing step is skipped]

Create a file <MAIN_PROCESS_FILENAME>.config in the config directory with the names of clean combined input file separated by ','

Example Content:

../data/input/allFeatures.csv

Please make sure that the file above only has a UID, latitude, longitude and Feature values for each record for the corresponding feature in a new line

Example input file content: ../data/input/allFeatures.csv

1,41.86922,-87.66249,bars
2,41.86923,-87.66249,bars
3,41.86921,-87.66248,school
4,41.86923,-87.66248,school
5,41.86920,-87.66247,police

STEP 5: Run the Main algorithm

Run the following command

python3 colocationAlgo.py ../config/<MAIN_PROCESS_FILENAME>.config > ../data/output/<OUTPUT_FILE_NAME>.txt

Example:

python3 colocationAlgo.py ../config/main.config > ../data/output.txt