Hot Spot Analysis Project

This project implements a spatial data analysis to identify "hot zones" and "hot cells" in a dataset of taxi pickup points. The analysis is performed using Apache Spark and Scala, involving two main components: Hot Zone Analysis and Hot Cell Analysis.

Project Overview

The goal of this project is to perform geospatial analysis to identify areas with dense taxi pickups using distributed computing. The project is divided into two sections:

1. Hot Zone Analysis: Identifies spatial zones with the highest number of taxi pickups.
2. Hot Cell Analysis: Calculates Z-Scores to find statistically significant clusters of high or low pickup counts.

Technology Stack

• Apache Spark: Distributed data processing framework.
• Scala: Programming language used for implementation.
• SQL Queries: Used to perform aggregation and filtering on large datasets.
• Spark DataFrames: Utilized for structured data manipulation and SQL-style querying.
• User-Defined Functions (UDFs): Created to extend the capabilities of SQL and DataFrame queries.
• Geospatial Analysis: Includes calculating Z-Scores for hot cell analysis.

Implementation Details

1. Hot Zone Analysis

This section performs a spatial range join to associate taxi pickup points with predefined rectangular zones. The key steps involved are:

• Spatial Range Join: Using the ST_Contains function, pickup points are matched to the respective rectangular zones.
• Aggregation: The number of pickups in each zone is calculated, and the zones are ranked by "hotness" (i.e., pickup count).
• Sorting: The result is sorted in ascending order based on the pickup count, with the "hottest" zones appearing first.
• SQL Queries: SQL-style queries using Spark’s DataFrame API are used to perform aggregation and filtering.

Key Functionality:

• ST_Contains(rectangle, point): Determines whether a pickup point lies within a given rectangular zone by checking the point's coordinates against the boundaries of the rectangle.

2. Hot Cell Analysis

In the Hot Cell Analysis, a Z-Score is calculated for each cell in a spatial grid to find areas with statistically significant pickup activity. The steps include:

• Data Preparation: Pickup points are assigned to cells based on their coordinates (latitude, longitude, and potentially time).
• Grid Division: The spatial area is divided into a grid of cells, each with unique x, y, z coordinates.
• Z-Score Calculation: For each cell, a Z-Score is computed using the formula for spatial statistics. This score measures the intensity of pickup counts compared to neighboring cells.
• SQL Queries and UDFs: SQL-style queries, extended by User-Defined Functions (UDFs), are used for data transformations and Z-Score calculations within the DataFrames.

Z-Score Formula: Z = (x_i - μ) / σ

Where:

• x_i is the number of pickups in the cell,
• μ is the overall mean pickup count,
• σ is the standard deviation of the counts.

Lessons Learned

• Spark and Scala: Gained proficiency in spatial queries, data handling with DataFrames, SQL-style queries, and functional programming in Scala. Improved understanding of how to use Apache Spark for large-scale data processing.
• Geospatial Analysis: Learned geospatial querying, statistical analysis (Z-Score), and their practical applications in distributed environments.
• Troubleshooting: Overcame technical challenges such as environment setup, and permissions issues that prevented code execution, leading to valuable debugging experience.

How to Run the Project

1. Prerequisites:

   • Apache Spark installed and configured.
   • IntelliJ IDEA with the Scala plugin (recommended for development).

2. Running the Project:

   • Ensure the dataset is loaded and properly formatted.
   • Use spark-submit to run the Scala program, or configure your IDE to execute the main class.

3. Expected Output:

   • Hot Zone Analysis: Sorted list of zones based on pickup count.
   • Hot Cell Analysis: Dataframe of cells with calculated Z-Scores.