Identifying Commercial Centers/Markets

Identifying commercial centers using Points of Interest (POI) OSM data for New Delhi using DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm.

The following map highlights the results (all the commercial centers/markets) obtained after analysis marked on New Delhi's map.

The following scatter plot provides an overview of the markets(clusters) identified during analysis.

x-axis - Longitude, y-axis - Latitude

Setup

1. Setup a conda environment or virtualenv.

2. Install requirements.txt.

   Steps:-

   $ pip install virtualenv

   $ virtualenv myenv

   $ myenv\Scripts\activate

   $ pip install -r requirements.txt

Description of the files

• clustered.csv - Contains cluster labels for all the commercial nodes.
• markets.csv - Contains centre points for all the clusters/markets identified in New Delhi region.
• data_manip.ipynb - Collection, cleaning and visualization of data obtained using overpass API, pandas and numpy.
• clustering.ipynb - Algorithm for clustering. Analysis and visualization of results using Scatter Plots, Bar Plots, gmplot and Reverse Geocoding.
• map_delhi.html - Market coordinates plotted on the map of New Delhi.
• map_delhi_{1,2,3,4}.html - Markets identified for different values of epsilon during analysis.
• market_coor_shape_files.zip - zip file containing shape file for coordinates of identified markets.
• requirements.txt - Dependencies required for running the scripts.
• All other json and pickle files contain data obtained after different stages during the analysis.