Databricks & Spark: Analyzing SF Fire Calls With Datasets V2

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Databricks & Spark: Analyzing SF Fire Calls with Datasets V2

Let's dive into the world of Databricks, Spark, and datasets by analyzing San Francisco Fire Department (SF Fire) incident data. This is a fantastic way to get hands-on experience with big data tools and learn how to extract valuable insights. Guys, we’ll be using Spark v2 and focusing on the sf-fire-calls.csv dataset. Get ready to roll up your sleeves and get your hands dirty with some real data!

Understanding the Data

Before we jump into the code, let's take a moment to understand the dataset we'll be working with. The sf-fire-calls.csv file contains information about every call made to the San Francisco Fire Department. This includes details such as the type of incident, where it occurred, when it happened, and how long it took to resolve. Each row in the CSV represents a single fire department call, and each column provides specific information about that call. Understanding the structure and content of this dataset is crucial for formulating meaningful queries and extracting valuable insights. For example, we might be interested in identifying the most common types of incidents, the neighborhoods with the highest frequency of calls, or the average response time for different types of emergencies. By carefully examining the dataset, we can gain a deeper understanding of the challenges faced by the San Francisco Fire Department and identify opportunities for improving their operations. To make the most of this analysis, it's essential to explore the dataset's schema, which defines the data type of each column. This will help us avoid common pitfalls, such as attempting to perform numerical calculations on columns that are stored as strings. By taking the time to understand the data thoroughly, we can ensure that our analysis is accurate, reliable, and insightful.

Loading the Data into Spark

First, we need to load the sf-fire-calls.csv file into a Spark DataFrame. Make sure you have the file accessible in your Databricks environment or a location Spark can access (like cloud storage). Here’s how you can do it:

from pyspark.sql import SparkSession

# Create a SparkSession
spark = SparkSession.builder.appName("SF Fire Analysis").getOrCreate()

# Infer schema option enables automatic schema detection
df = spark.read.csv("/FileStore/tables/sf_fire_calls.csv", header=True, inferSchema=True)

# Show the first few rows
df.show()

# Print the schema
df.printSchema()

Let’s break down what's happening in this code snippet. First, we're importing the SparkSession class, which is the entry point to Spark functionality. Then, we're creating a SparkSession instance with the name "SF Fire Analysis". Next, we're using the spark.read.csv() method to load the sf-fire-calls.csv file into a DataFrame. The header=True option tells Spark that the first row of the CSV file contains column headers, and the inferSchema=True option tells Spark to automatically infer the data type of each column based on its contents. After loading the data, we're using the df.show() method to display the first few rows of the DataFrame. This allows us to quickly verify that the data has been loaded correctly and get a sense of its structure and content. Finally, we're using the df.printSchema() method to print the schema of the DataFrame. The schema provides detailed information about each column, including its name, data type, and whether it can contain null values. By examining the schema, we can ensure that Spark has correctly inferred the data types of the columns and identify any potential issues that need to be addressed. Loading the data correctly is a critical step in the analysis process, as it sets the foundation for all subsequent operations. By following these steps carefully, we can ensure that our data is loaded correctly and that we're ready to start exploring and analyzing it.

Basic Data Exploration

Now that we have the data loaded, let's perform some basic exploration to get a feel for what's inside. This includes tasks like counting rows, looking at data types, and checking for missing values. The goal here is to get a sense of the size and structure of the dataset, as well as identify any potential issues that need to be addressed. For example, we might want to know how many fire calls are included in the dataset, what types of data are stored in each column, and whether there are any columns with missing values. By answering these questions, we can gain a better understanding of the data and prepare it for more advanced analysis. In addition to these basic tasks, we can also start to explore the relationships between different variables in the dataset. For example, we might want to see if there is a correlation between the type of incident and the location where it occurred, or between the time of day and the response time. By exploring these relationships, we can begin to uncover insights that might not be immediately apparent. Remember that the goal of data exploration is not to find definitive answers, but rather to generate hypotheses and identify areas for further investigation. By approaching the data with an open mind and a willingness to experiment, we can discover unexpected patterns and gain a deeper understanding of the phenomena that we're studying.

# Count the number of rows
print("Number of rows:", df.count())

# Display column names and data types
df.printSchema()

# Show summary statistics for numeric columns
df.describe().show()

In this snippet, we're using three key functions to explore the data. First, df.count() returns the number of rows in the DataFrame, giving us a sense of the overall size of the dataset. This is useful for understanding how much data we have to work with and for estimating the time it will take to perform various operations. Second, df.printSchema() displays the schema of the DataFrame, showing the name and data type of each column. This is important for verifying that Spark has correctly inferred the data types of the columns and for identifying any potential issues with the data. For example, if a column that should contain numeric data is instead stored as a string, we'll need to convert it to the correct data type before we can perform any calculations. Finally, df.describe().show() calculates and displays summary statistics for the numeric columns in the DataFrame. This includes the mean, standard deviation, minimum, maximum, and quartiles for each column. These statistics can be useful for identifying outliers, understanding the distribution of the data, and comparing different columns. By using these three functions together, we can quickly gain a good understanding of the basic properties of the data and identify any potential issues that need to be addressed. Remember that data exploration is an iterative process, and we may need to revisit these steps as we gain more knowledge about the data.

Analyzing Fire Calls

Time to get into the real analysis! We can start by answering some basic questions like:

What are the most common call types?

To determine the most common call types, we can group the data by the Call Type column and count the number of calls for each type. This will give us a clear picture of the types of incidents that the San Francisco Fire Department responds to most frequently. By understanding the distribution of call types, we can gain insights into the challenges faced by the fire department and identify areas where resources may be needed most. For example, if medical calls are the most common type of call, it may be necessary to allocate more resources to emergency medical services. On the other hand, if fire-related calls are more frequent, it may be important to focus on fire prevention and suppression efforts. In addition to identifying the most common call types, we can also use this analysis to track trends over time. By comparing the distribution of call types in different years, we can identify any significant changes in the types of incidents that the fire department responds to. This information can be valuable for planning and resource allocation, as it allows the fire department to anticipate future needs and adjust its operations accordingly. To ensure the accuracy of our analysis, it's important to consider the potential for missing or incomplete data. If there are a significant number of calls with missing Call Type values, this could skew our results. In such cases, we may need to impute the missing values or exclude them from the analysis altogether. By carefully considering these factors, we can ensure that our analysis is accurate, reliable, and informative.

from pyspark.sql.functions import col

# Group by call type and count
call_counts = df.groupBy("Call Type").count()

# Order by count in descending order
call_counts = call_counts.orderBy(col("count").desc())

# Show the results
call_counts.show(truncate=False)

This code snippet performs a group-by operation on the Call Type column of the DataFrame, counting the number of occurrences of each call type. This allows us to identify the most common types of incidents that the San Francisco Fire Department responds to. The groupBy() function groups the rows of the DataFrame based on the values in the specified column, and the count() function calculates the number of rows in each group. The result is a new DataFrame that contains the call types and their corresponding counts. To make the results more readable, we can order the DataFrame by the count column in descending order. This will display the most common call types at the top of the list. The orderBy() function sorts the rows of the DataFrame based on the values in the specified column, and the desc() function specifies that the sorting should be done in descending order. Finally, we use the show() function to display the results. The truncate=False option ensures that the full call types are displayed, even if they are longer than the default truncation length. By examining the output of this code snippet, we can quickly identify the most common types of incidents that the San Francisco Fire Department responds to. This information can be valuable for resource allocation, training, and prevention efforts. For example, if medical calls are the most common type of call, the fire department may want to invest in additional training for emergency medical services. Similarly, if fire-related calls are more frequent in certain areas, the fire department may want to increase its fire prevention efforts in those areas.

Which neighborhoods have the most fire calls?

To determine which neighborhoods have the most fire calls, we can group the data by the Neighborhoods - Analysis Boundaries column and count the number of calls for each neighborhood. This will give us a clear picture of the areas in San Francisco that experience the highest frequency of fire-related incidents. By understanding the distribution of fire calls across neighborhoods, we can identify areas that may be at higher risk and prioritize resources accordingly. For example, if a particular neighborhood has a consistently high number of fire calls, it may be necessary to implement additional fire prevention measures or increase the availability of fire stations in that area. In addition to identifying high-risk neighborhoods, we can also use this analysis to track trends over time. By comparing the distribution of fire calls across neighborhoods in different years, we can identify any significant changes in the patterns of fire-related incidents. This information can be valuable for planning and resource allocation, as it allows the fire department to anticipate future needs and adjust its operations accordingly. To ensure the accuracy of our analysis, it's important to consider the potential for missing or incomplete data. If there are a significant number of calls with missing Neighborhoods - Analysis Boundaries values, this could skew our results. In such cases, we may need to impute the missing values or exclude them from the analysis altogether. By carefully considering these factors, we can ensure that our analysis is accurate, reliable, and informative.

# Group by neighborhood and count
neighborhood_counts = df.groupBy("Neighborhoods - Analysis Boundaries").count()

# Order by count in descending order
neighborhood_counts = neighborhood_counts.orderBy(col("count").desc())

# Show the results
neighborhood_counts.show(truncate=False)

This code snippet performs a group-by operation on the Neighborhoods - Analysis Boundaries column of the DataFrame, counting the number of fire calls in each neighborhood. This allows us to identify the neighborhoods with the highest frequency of fire-related incidents. The groupBy() function groups the rows of the DataFrame based on the values in the specified column, and the count() function calculates the number of rows in each group. The result is a new DataFrame that contains the neighborhoods and their corresponding counts. To make the results more readable, we can order the DataFrame by the count column in descending order. This will display the neighborhoods with the most fire calls at the top of the list. The orderBy() function sorts the rows of the DataFrame based on the values in the specified column, and the desc() function specifies that the sorting should be done in descending order. Finally, we use the show() function to display the results. The truncate=False option ensures that the full neighborhood names are displayed, even if they are longer than the default truncation length. By examining the output of this code snippet, we can quickly identify the neighborhoods with the highest frequency of fire-related incidents. This information can be valuable for resource allocation, fire prevention efforts, and community outreach programs. For example, if a particular neighborhood has a consistently high number of fire calls, the fire department may want to increase its fire prevention efforts in that area and work with community organizations to educate residents about fire safety.

What is the average response time?

To calculate the average response time, we first need to convert the Call Date and Arrival DtTm columns to timestamps. Then, we can calculate the difference between these timestamps to determine the response time for each call. Finally, we can calculate the average response time across all calls. This will give us a sense of the overall efficiency of the San Francisco Fire Department's response to emergencies. By tracking the average response time, we can identify areas where improvements can be made and measure the effectiveness of interventions aimed at reducing response times. For example, if the average response time is consistently high in a particular neighborhood, it may be necessary to increase the availability of fire stations in that area or improve traffic flow to allow fire trucks to reach the scene more quickly. In addition to calculating the overall average response time, we can also calculate the average response time for different types of calls or in different neighborhoods. This can help us identify specific areas where response times are particularly high and target our interventions accordingly. To ensure the accuracy of our analysis, it's important to consider the potential for missing or incomplete data. If there are a significant number of calls with missing Call Date or Arrival DtTm values, this could skew our results. In such cases, we may need to impute the missing values or exclude them from the analysis altogether. By carefully considering these factors, we can ensure that our analysis is accurate, reliable, and informative.

from pyspark.sql.functions import to_timestamp, avg, round

# Convert columns to timestamp
df = df.withColumn("Call_Timestamp", to_timestamp(col("Call Date"), "MM/dd/yyyy hh:mm:ss AA"))
df = df.withColumn("Arrival_Timestamp", to_timestamp(col("Arrival DtTm"), "MM/dd/yyyy hh:mm:ss AA"))

# Calculate response time in seconds
df = df.withColumn("Response_Time", (col("Arrival_Timestamp").cast("long") - col("Call_Timestamp").cast("long")))

# Calculate average response time
average_response_time = df.select(round(avg(col("Response_Time")), 2))

# Show the result
average_response_time.show()

In this code, we're calculating the average response time for fire calls. First, we convert the Call Date and Arrival DtTm columns to timestamps using the to_timestamp() function. This allows us to perform calculations on these columns as dates and times, rather than as strings. The to_timestamp() function takes two arguments: the column to convert and the format of the timestamp. In this case, the format is "MM/dd/yyyy hh:mm:ss AA", which specifies the month, day, year, hour, minute, second, and AM/PM indicator. Next, we calculate the response time for each call by subtracting the Call_Timestamp from the Arrival_Timestamp. Since the timestamps are stored as seconds since the epoch, the result of this subtraction is also in seconds. Finally, we calculate the average response time across all calls using the avg() function. The avg() function calculates the average value of a column, and the round() function rounds the result to two decimal places. The select() function selects the column containing the average response time, and the show() function displays the result. By examining the output of this code, we can quickly determine the average response time for fire calls in San Francisco. This information can be valuable for evaluating the performance of the fire department and identifying areas where improvements can be made. For example, if the average response time is higher than desired, the fire department may want to invest in additional resources or optimize its dispatch procedures.

Conclusion

Analyzing the SF Fire Calls dataset using Databricks and Spark is a great way to understand how to process and gain insights from real-world data. We've covered loading data, basic exploration, and answering specific questions about call types, neighborhood distribution, and response times. Remember, this is just the beginning! There's so much more you can do with this dataset, including building predictive models or creating interactive dashboards. So keep exploring and happy coding, guys!