How Can You Map Current EV Charger Infrastructure through Big Data and Web Scraping?

happens. Unfortunately, the majority of data gets distributed across different websites and locations and stored in various formats (web pages, pdf files, excel sheets, etc.), making this analysis very time-intensive. While some studies and datasets about clean transitions are frequently published—e.g., they become published yearly more often.

At Actowiz Solutions, we are continuously working on different tools to collect and analyze current data on cleaner energy transitions in actual time. All the tools help us provide network building, evidence-based policy advisory, communications, etc.

The tools we are dealing with identify the locations of current EV charging infrastructure within the country. EVs will be a crucial part of clean energy evolution. Globally, the transportation sector is responsible for around 40% of energy consumption and 28% of total energy-related CO2 emissions. In 2019, the transport sector accounted for about 18% of CO2 emissions in ASEAN. So, moving towards EVs and substituting renewable energy sources would significantly decrease carbon emissions.

One method of evaluating the acceptance of electric vehicles is to map charging ports' availability, locations, and which companies supply chargers. If you go through the given map carefully, you will get insights into how easy it is to get an EV in a city or a country.

Using programming codes in Python, we can scrape information from EV chargers provided in Google Maps and save it in excel datasets.

We all know how to find EV chargers nearby. You must open your smartphone, write the "EV charger" text in Google Maps, and press the search button. Now, you can see the neighboring 30 charging stations.

So, in rule, you can:

Run a search

Write the valid search results

Walk the distance of 10 km in a single route

and repeat

This is a helpful way of striking your daily step count, but still, it is not considerable. Fortunately, a form of writing code influences Google Maps when you are on longer walks and car chargers.

The leading case study provided focuses on Hong Kong, so we have gathered the list of public EV chargers in Hong Kong and, with Python, saved the scraped data in the Google Sheet. The given Python code might be re-run at any time to keep the datasets reorganized.

The tool used here is the early step to using data and developing a complete representation of EV infrastructure. This Hong Kong case study shows that you can easily continue analysis using writing code that scrapes data from Hong Kong Transport Department to list EV models currently approved for the road. After that, we can use the listings to trail 'EV car companies in Hong Kong, go through each company's page, and scrape required EV data from the website, including car sales, EV charger maps, EV models sold, etc.

You can use various data scraping tools for multiple facets of the apparent energy transition, including energy competence perspective and renewable energy policy status and development. As more exploration is needed, it could be possible to scrape data daily about renewable pipeline sites while also using code to analyze and map grid transformation. For energy proficiency, map scraping can give us more accurate data on making areas and building capability in different urban areas. To summarize, using Python to collect and analyze data might open doors for quicker and more regulatory analysis of the energy transition.

You can comment or contact us through the mail if you have any queries about this blog. You can also contact us for your mobile app scraping and web scraping services requirements.