Understanding Drive Time and Drive Distance in Tableau: A Comprehensive Guide

In today’s fast-paced world, understanding the nuances of drive time versus drive distance is crucial for efficient planning and decision-making. Whether it’s for urban planning, logistics, or simply daily commutes, leveraging data visualization tools like Tableau can provide deep insights into these metrics. In this blog, we’ll explore how to calculate drive time and drive distance in Tableau, and why these calculations are more valuable than you might think.

Why Drive Duration Matters More Than Distance

When planning a journey, the shortest route by distance isn’t always the fastest. This discrepancy arises due to various real-world obstacles such as traffic, road conditions, and infrastructure limitations. The image below illustrates this concept:

As depicted, the “as the crow flies” distance might be a mere 10 miles. However, real-world obstacles can increase this to 17 miles, or road infrastructure might adjust it to 14 miles. Hence, drive time becomes a more practical metric for real-world applications.

Leveraging Isochrone Maps

Isochrone maps are an excellent tool for visualizing drive times. These maps show areas that can be reached within a specific time frame from a starting point, accounting for all possible routes and obstacles. The first know isochrone map dates back to 1881 with colors depicting 10 day of travel time:

Here’s an example from a Tableau project:

This map highlights the areas reachable within a 30-minute drive from a central point in Washington, DC considering real-world road networks and traffic conditions and how that compares to as the crow flies milage.

While Isochrone do a very good job of visualizing time in the space of the real world, they are not necessary for all analytical questions. Sometimes a simple number suffices, gives you more flexibility and is a more efficient from a performance stand point.

Calculating Duration and Distance

In a recent post, I shared a means of doing proximity based filtering in Tableau. An experience that many are familiar with when looking for stores, clinics, or various locations in their area. One example of this can be seen here by OASH for Find a Family Planning Clinic where an end user inputs their address so below. While this gets an end user most of the way there, the issues I listed above are still present. So let’s figure out a way to calculate drive distance rather than distance as the crow flies.

To calculate driving distance and duration I have to look outside of Tableau’s native functionality. For today’s example I am going to leverage Mapbox’s APIs but you could use a similar process with Google Maps or other similar services. For this example, I have created a simple dataset where there are 5 column: Origin_Lat, Origin_Lon, Destination_Lat, Destination_Lon and ID where all of the Origin Latitudes and Longitudes are the same. From there I added a new Table Extension in the data model leveraging TabPy. This is the code I wrote to calculate driving duration and distance:

import requests
import pandas as pd

# Define the function to calculate driving distance and time using Mapbox API
def calculate_driving_info(origin_lat, origin_lon, destination_lat, destination_lon):
    mapbox_token = 'ENTER YOUR TOKEN HERE'  # Replace with your Mapbox access token
    url = f"https://api.mapbox.com/directions/v5/mapbox/driving/{origin_lon},{origin_lat};{destination_lon},{destination_lat}"
    
    params = {
        'access_token': mapbox_token,
        'geometries': 'geojson',
        'overview': 'full',
        'annotations': 'duration,distance'
    }
    
    response = requests.get(url, params=params)
    
    if response.status_code == 200:
        data = response.json()
        route = data['routes'][0]
        distance = (route['distance']/1000)/1.609  # in miles
        duration = route['duration']/60  # in minutes
        return distance, duration
    else:
        return None, None

# Function that will be used as in Tableau for specific dataframe
def get_driving_info(table):
    origin_lat = table['Origin_Lat'].tolist()
    origin_lon = table['Origin_Lon'].tolist()
    destination_lat = table['Destination_Lat'].tolist()
    destination_lon = table['Destination_Lon'].tolist()
    id = table['ID'].tolist()
    
    distances = []
    durations = []
    
    for o_lat, o_lon, d_lat, d_lon in zip(origin_lat, origin_lon, destination_lat, destination_lon):
        distance, duration = calculate_driving_info(o_lat, o_lon, d_lat, d_lon)
        distances.append(distance)
        durations.append(duration)
        
    if not distances or not durations:
        return pd.DataFrame({
            'Distance': [0],
            'Duration': [0]
        })

    return pd.DataFrame({
        'Distance': distances,
        'Duration': durations,
        'Origin_Lat': origin_lat,
        'Origin_Lon': origin_lon,
        'Destination_Lat': destination_lat,
        'Destination_Lon': destination_lon,
        'ID': id
    })

data = pd.DataFrame(_arg1)
result_df = get_driving_info(data)

return result_df.to_dict(orient='list')

This code outputs all the original input data with the additional outputs of Duration and Distance (both based on average traffic in area).

Now with this output we can use these values just like we would any other field within Tableau. In this example I have created a visual representation of distance as the crow flies and a table that distance all of the values for duration in minutes and then distance in miles and the difference in milage from the script and native function.

Once built, all this data can be filtered and interacted with just like any normal data within Tableau .

Conclusion

Incorporating the calculation of drive time and drive distance into data visualization projects using tools like Tableau enhances the analytical power available to businesses and public agencies. Here are several use cases and the inherent value of these tools:

Urban Planning: Planners can utilize drive time data to optimize the location of essential services like hospitals and schools to ensure they are within a reasonable driving distance for all residents.
Logistics and Supply Chain Management: Companies can streamline operations by using drive time analysis to choose optimal routes, reducing fuel costs and improving delivery times.
Real Estate and Housing Development: Drive time analysis can influence decisions on where to develop new housing projects by analyzing proximity to key amenities and employment hubs.
Emergency Response Optimization: By analyzing drive times, emergency management services can ensure that they are positioned to respond as quickly as possible to incidents across a city or region.

Ultimately this process can help with improved decision-making by understanding the practical implications of drive time versus simple distance metrics, organizations can make more informed decisions that consider real-world variables like traffic and road conditions. Additionally, optimizing routes and placements based on drive time data can lead to significant cost savings and operational efficiency.
By embedding these sophisticated data analyses into everyday operational strategies, businesses and public entities can not only achieve greater efficiency but also offer enhanced services that meet the needs of their communities more effectively.