Project Overview

This project focuses on analyzing geospatial data using Python libraries such as GeoPandas and Folium. The goal is to map customer distributions, store locations, and traffic flows to identify optimal locations for new retail stores in New York City. By visualizing pedestrian counts and restaurant locations, we aim to provide actionable insights for optimizing store locations and marketing strategies.

Table of Contents

1. Introduction

2. Data Sources

3. Installation

4. Features

5. Visualization Examples

Introduction

In this project, we conduct a comprehensive geospatial analysis using pedestrian count data, restaurant locations, and borough boundaries of New York City. The analysis includes creating heatmaps, interactive maps, and density plots to visualize foot traffic patterns and identify high-potential areas for new retail stores.

Data Sources

• NYC Open Data: Bi-Annual Pedestrian Counts (link)

• OpenStreetMap: Restaurant locations

• NYC Borough Boundaries: GeoJSON file

Installation

To run this project locally, follow these steps:

1. Clone the Repository:

2. Create and Activate a Virtual Environment: python -m venv venv source venv/bin/activate # On Windows use venv\Scripts\activate

3. Install the Required Dependencies

Features

• Heatmaps: Visualize pedestrian counts across different time periods.

• Interactive Maps: Explore foot traffic patterns and restaurant locations interactively.

• Density Plots: Identify high-density areas for potential store locations.

• Proximity Analysis: Analyze the distance between high foot traffic areas and existing restaurants.

Visualizations

Interactive Map