Bhargavi_Kadiyala_CSE511_Project2_HotSpotAnalysisReport

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Arizona State University *

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511

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Computer Science

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Jan 9, 2024

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pdf

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6

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Reflection for HotZoneAnalysis Function and HotCellAnalysis Function HotZoneAnalysis Function: The HotZoneAnalysis function is a key component of this project, focusing on the calculation of hotspots within rectangles concerning New York taxi trip pick-up points. Rectangles are defined by the longitude and latitude of opposing corners, while points represent taxi pick-up locations. The function utilizes the ST_Contains(rec_string, point_string) function to determine if a point is within a given rectangle. The input format is ("2.3, 5.1, 6.8, 8.9", "5.5, 5,5"). To correctly implement this function, we identify the minimum and maximum corners of the rectangle using "math. min" and "math. max" for latitude and longitude. A user- defined function (ST_contains) is created and registered for use in a SQL query to join the rectangle and point datasets using the ST_contains UDF in a WHERE clause. The final step involves returning each rectangle's coordinates and the count of points inside it from the joinResult view using the SQL query: "SELECT rectangle, COUNT(point) AS count FROM joinResult GROUP BY rectangle ORDER BY rectangle." HotCellAnalysis Function: The HotCellAnalysis function calculates the hotness of a given cell, defined by latitude, longitude, and DateTime. The objective is to compute the Getis-Ord statistic, indicating hotness based on the number of pickups for a specific location on a particular day. Analysis/Lessons Learned: 1. Setup and Utilization of Apache Spark: Gained familiarity with setting up Apache Spark, creating User Defined Functions (UDF), and working with DataFrames. 2. Execution of SQL Queries on Spark: Learned the process of executing SQL queries on Spark. 3. Proficiency in Structuring Scala Projects: Developed proficiency in structuring and composing a Scala project, including SBT commands for compilation, cleaning, and packaging. 4. Scala Code Construction: Acquired experience in constructing a Scala project, manipulating SBT commands, and working with Scala code. 5. Handling Geospatial Data: Gained hands-on exposure to geospatial data, including determining if a point falls within a zone and retrieving zone boundaries.
6. Local Testing Procedures: Enhanced proficiency in local testing procedures, encompassing the creation of input files and the specification of the test output directory. Demonstrated expertise in configuring and executing tests, ensuring the seamless validation of code logic and functionality in a local environment. 7. Optimization Techniques: Mastered optimization techniques, notably employing strategies like coalesce(1) to effectively minimize the number of partitions in a Data Frame. Particularly valuable when generating multiple CSV outputs, these techniques contribute to the streamlined processing and enhanced performance of data operations. Analysis/Lessons Learned: 1. Familiarity was gained in setting up Apache Spark, creating and utilizing User Defined Functions (UDF), and working with DataFrames. 2. The process of executing SQL queries on Spark was learned. 3. Proficiency was developed in structuring and composing a Scala project. This involved aspects like initiating a Scala project, utilizing SBT commands, compiling, cleaning, and packaging. 4. A novel experience with Scala code encompassed learning how to construct a simple project and manipulate SBT commands for various project tasks. 5. Hands-on exposure to geospatial data involved understanding how to ascertain if a point falls within a zone, retrieving zone boundaries, and handling longitude and latitude. 6. Successfully acquired skills in local testing procedures, encompassing tasks such as the creation of input files and the establishment of the test output directory. Demonstrated competence in executing comprehensive tests to ensure code functionality in a local testing environment.Techniques such as using coalesce(1) to reduce the number of partitions in a Data Frame, especially when generating multiple CSV outputs, were acquired. 7. Developed expertise in optimization techniques, including the utilization of coalesce(1) to minimize the number of partitions in a Data Frame. Particularly beneficial when generating multiple CSV outputs, these techniques contribute to efficient data processing and improved overall performance. 8. Implementation: a. Overview of Hot Zone Analysis: b. Overview of Hot Cell Analysis. Overview of Hot Zone Analysis(def ST_Contains(queryRectangle: String, pointString: String )
The HotzoneAnalysis is written in Scala using Apache Spark. The purpose of the code is to perform a Hot Zone Analysis on spatial data, specifically, it seems to be dealing with points and rectangles. ST_Contains function: spark.udf.register("ST_Contains",(queryRectangle:String,pointString:String)=>(HotzoneUtils.ST_Contains (queryRectangle, pointString))) This line registers a User Defined Function (UDF) named ST_Contains in Spark. A UDF is a feature in Spark that allows you to define your own functions and use them in SQL expressions. The ST_Contains UDF takes two parameters: queryRectangle : A string representing a rectangle. pointString : A string representing a point. The UDF delegates the actual implementation to HotzoneUtils.ST_Contains(queryRectangle, pointString). This implies that there is a companion object or class named HotzoneUtils where the ST_Contains method is defined. In spatial databases and GIS (Geographic Information Systems), ST_Contains is a common spatial predicate that checks whether one geometry (in this case, a rectangle) contains another geometry (in this case, a point). If the point is inside the rectangle, ST_Contains returns true; otherwise, it returns false. The details of the HotzoneUtils.ST_Contains method would be in the HotzoneUtils class or object, which is not provided in the code snippet you shared. You would need to look into the HotzoneUtils code to understand the specifics of how the containment check is implemented. In summary, the HotzoneAnalysis code reads point and rectangle data, registers a UDF for spatial containment check (ST_Contains), performs a join between points and rectangles based on the containment condition, and finally calculates and returns the count of points within each rectangle as a result of the Hot Zone Analysis. Overview of Hot Cell Analysis.(write the multiple steps required before calculating the z-score.) Here are the multiple steps required before calculating the Z-score in the runHotcellAnalysis function: 1) Load Data: Load the original data from the specified data source (CSV file in this case) using Spark, and create a temporary view named "nyctaxitrips." var pickupInfo = spark.read.format("com.databricks.spark.csv") .option("delimiter", ";") .option("header", "false") .load(pointPath)
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