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https://github.com/aakashjhawar/commercial-centers-using-poi
Identify commercial centers using Points of Interest (POI) data by clustering these points into commercial centers/markets
https://github.com/aakashjhawar/commercial-centers-using-poi
commercial-centers commercial-location convex-hull data-analysis data-science machine-learning market-cluster market-data market-location market-segmentation openstreetmap openstreetmap-data overpass-turbo overpy point-of-interest-recommendation
Last synced: 14 days ago
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Identify commercial centers using Points of Interest (POI) data by clustering these points into commercial centers/markets
- Host: GitHub
- URL: https://github.com/aakashjhawar/commercial-centers-using-poi
- Owner: aakashjhawar
- Created: 2019-10-03T12:57:09.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-08-23T15:58:45.000Z (4 months ago)
- Last Synced: 2024-08-23T17:53:46.394Z (4 months ago)
- Topics: commercial-centers, commercial-location, convex-hull, data-analysis, data-science, machine-learning, market-cluster, market-data, market-location, market-segmentation, openstreetmap, openstreetmap-data, overpass-turbo, overpy, point-of-interest-recommendation
- Language: Jupyter Notebook
- Homepage: https://aakashjhawar.github.io/commercial-centers-using-POI
- Size: 3.61 MB
- Stars: 8
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Commercial centers using POI
[](https://travis-ci.org/pages-themes/cayman) [](https://badge.fury.io/rb/jekyll-theme-cayman)
Checkout https://aakashjhawar.github.io/commercial-centers-using-POI/
Identify commercial centers using Points of Interest (POI) data by clustering these points into commercial centers/markets.
## Getting Started
How to use
```
git clone [email protected]:aakashjhawar/commercial-centers-using-POI.git
cd commercial-centers-using-POI
pip3 install -r requirements.txt
jupyter notebook
```
Start the jupyter server. The following should be executed in order.
* gather-data.ipynb
* DBSCAN-remove-noise.ipynb
* create-clusters.ipynb
---
#### 1. gather-data.ipynb
It uses Overpy (Python wrapper for Overpass) to gather all the nodes of a particular location and stores the data into a csv.
It also cleans the dataset based on amenities.
#### 2. DBSCAN-remove-noise.ipynb
It removes the noise/outliers present in the dataset. It also gives the number of clusters that can be formed from the dataset.
#### 3. create-clusters.ipynb
It uses KMeans Clustering to create clusters and plot them on Google Map using gmplot library.
Check out the [Github repo page](https://aakashjhawar.github.io/commercial-centers-using-POI/)