EV Station Location Analysis to Maximize EV Adoption and Reduce Emissions.

Posted on Aug 14, 2023


In our pursuit of a sustainable future, the electrification of transportation is a crucial step. Electric vehicles (EVs) have emerged as a groundbreaking solution, offering a pathway to minimize our reliance on fossil fuels and significantly reduce our carbon footprint. What was once deemed a distant dream is now a tangible reality, thanks to remarkable advancements in battery technology, breakthroughs in electrical engineering concepts, and the power of cutting-edge computational systems. In this blog post, we embark on a data-driven journey to explore the transformative potential of EVs, focusing on the critical relationship between EV stations and EV sales.

The Promise of Electric Vehicles

Electric vehicles represent a paradigm shift in the automotive industry, revolutionizing the way we think about transportation. By replacing internal combustion engines with high-capacity batteries and electric motors, EVs offer numerous advantages. They provide a cleaner and more sustainable mode of transportation, emitting zero tailpipe emissions and contributing to improved air quality in our cities. EVs also offer reduced operating costs, lower maintenance requirements, and the potential for energy independence through renewable energy sources. With their impressive acceleration and advanced features, EVs are redefining the driving experience and paving the way for a greener future.

The Role in Reducing Emissions

The adoption of electric vehicles (EVs) in Colorado plays a pivotal role in the state's journey toward a sustainable and clean energy future. Unlike their gasoline-powered counterparts, EVs produce zero tailpipe emissions, which means that every EV on the road directly reduces the amount of harmful pollutants released into the atmosphere. As Colorado sees an upward trend in EV adoption, it stands to significantly decrease emissions from the transportation sector, which is often one of the largest contributors to greenhouse gas emissions. Furthermore, as the state's energy grid becomes increasingly powered by renewable sources like wind and solar, the environmental benefits of EVs will be magnified, ensuring that not only are vehicles cleaner, but the electricity powering them is as well. This transition to electric mobility is crucial for Colorado's efforts to combat climate change, improve air quality, and safeguard the health of its residents as a quarter of the states total emissions come from transportation.

The Importance of Charging Infrastructure

To fully harness the benefits of EVs, a robust and accessible charging infrastructure is paramount. As EV adoption continues to grow, it is crucial to ensure that charging stations are strategically placed to maximize convenience, coverage, and accessibility. Building an extensive charging network that spans urban centers, rural areas, and underserved communities is essential to overcome range anxiety and enable long-distance travel. By leveraging data science and analytics, we can uncover valuable insights to guide the expansion of EV charging infrastructure, promote EV adoption, and accelerate the transition to a sustainable transportation ecosystem.

  • Map of U.S. Charger Network

Exploring the Relationship Between EV Stations and EV Sales

In this project, we dive deep into the intricate relationship between EV stations and EV sales in the state of Colorado. Colorado was chosen because of its diverse urban centers, expansive rural areas, commitment to environmental sustainability, and rapidly growing population, presents a unique environment to study and optimize EV infrastructure. Its blend of metropolitan and countryside regions offers a comprehensive setting to explore strategies for increasing EV adoption, both in bustling cities and in remote areas. By analyzing comprehensive datasets sourced from reliable repositories, including the Alt Fuel Stations database and the CO EV Registrations dataset, we aim to uncover the driving forces behind the growth of the EV market. We leverage the power of data science and statistical modeling techniques to investigate the impact of EV station count on EV sales, identify potential correlations, and unveil the dynamics that underpin this transformative relationship.

Data-Driven Insights for a Sustainable Future

Our data-driven exploration goes beyond simple observation; it empowers us to make informed decisions that pave the way for a sustainable and electrified future. By leveraging advanced analytics and machine learning algorithms, we aim to provide actionable insights that guide the optimal placement of EV stations, facilitate the transition to electric vehicles, and mitigate environmental impacts. Through our findings, we seek to support policymakers, stakeholders, and individuals alike in making informed choices and driving meaningful change in the transportation sector

Potentials models for the project

ARIMA (AutoRegressive Integrated Moving Average): This model is a staple in time series forecasting. ARIMA captures various structures of time dependencies in the data. However, it requires the series to be stationary (i.e., properties do not depend on the time at which the series is observed). Given its parameters, it can cater to seasonality, trend, and noise in datasets.

SARIMA (Seasonal AutoRegressive Integrated Moving Average): An extension of the ARIMA model, SARIMA includes an additional seasonal component. This is particularly useful for datasets that exhibit seasonality, like perhaps an increase in EV sales during specific times of the year.

Holt-Winters: Another model tailored for time series forecasting, Holt-Winters captures trends and seasonality more explicitly. It's especially powerful for datasets where seasonal patterns evolve over time.

Prophet: Developed by Facebook, Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. Its ability to handle missing data, outliers, and large datasets makes it particularly versatile. For our project, Prophet outshined the other models in predicting the uptick in EV sales, making it the star player of our analysis.

OLS (Ordinary Least Squares) Regression: OLS is a method used in linear regression to estimate the unknown parameters by minimizing the sum of the squared differences between the observed and predicted values. In our project, OLS helped in understanding the correlation between the number of EV stations and the sales of EVs.

Each of these models was chosen for a specific reason and catered to particular characteristics of the data. While Prophet emerged as the most accurate for our needs, the insights and learning from each model were invaluable.

Models Implemented

The Facebook Prophet and OLS models excelled in this project due to their inherent strengths tailored to the data at hand. Prophet seamlessly handled the time series data, capturing its seasonal patterns and yearly effects without extensive tuning. Meanwhile, OLS capitalized on the apparent linear relationship between EV stations and sales, offering clear and interpretable insights. Their combined simplicity, adaptability, and robustness made them particularly effective tools for analyzing the relationship and forecasting trends in electric vehicle adoption in Colorado.

FaceBook Prophet Model

The Prophet model's results provided an insightful lens into the evolving landscape of electric vehicle (EV) adoption in Colorado. When applied to the data, Prophet was able to accurately capture the inherent seasonality and yearly growth trends in EV sales. The forecasts generated by the model depicted a promising uptick in EV sales, aligning with the rising trajectory observed in the historical data. Moreover, the model's ability to seamlessly integrate external regressors, like the number of EV stations, demonstrated the direct influence of infrastructure development on vehicle adoption rates. The shaded uncertainty intervals in the Prophet's predictions also offered a valuable perspective on the potential variability in future sales, emphasizing the model's capacity to anticipate fluctuations and provide a holistic view of the market's potential trajectory.

The OLS Model

The Ordinary Least Squares (OLS) regression analysis presented a compelling narrative about the relationship between the number of EV stations and EV sales in Colorado. The model's coefficient for EV stations indicated a positive correlation, suggesting that for every new EV charging station introduced, there was an associated increase of approximately 1100 EV sales. This quantifiable relationship underscores the importance of infrastructure development in facilitating EV adoption. The model's high R-squared value of 0.905 further validated its accuracy, implying that over 90% of the variance in EV sales could be explained by the number of charging stations. Additionally, the statistical significance of the predictor, indicated by the p-value, reaffirms the hypothesis that the proliferation of EV stations plays a crucial role in influencing consumer purchasing decisions. In essence, the OLS model quantitatively confirmed the symbiotic relationship between infrastructure expansion and the growth in EV adoption.


In recent years, Colorado has witnessed a significant uptrend in the adoption of electric vehicles (EVs), as evidenced by the consistent rise in monthly sales. Our analysis, employing models like Facebook's Prophet and Ordinary Least Squares (OLS) regression, suggests a clear positive correlation between the number of EV charging stations and EV sales. Specifically, the OLS model indicates that for every new EV charging station in Colorado, we can expect an increase of approximately 1,100 EV sales. The robustness of the Prophet model further emphasizes this relationship, accurately forecasting the surge in EV sales. These findings underscore the importance of infrastructure development in promoting sustainable transportation. As Colorado continues to invest in and expand its EV charging network, it not only supports the current EV owners but also incentivizes more residents to make the switch to electric, thereby advancing the state's environmental goals and reducing its carbon footprint.

About Author

Kane Monaco

Business major looking to bring a unique perspective to the field of Data Science.
View all posts by Kane Monaco >

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI