In this blog, we will practice web data scraping by scraping names, addresses, cuisine types, and star ratings of Michelin-Star restaurants in Singapore to know cuisine-type distribution and geolocations.
There isn’t a single website that effortlessly contains both; therefore, we recognized a news website with an address and an official Michelin website having cuisine types. We have used rvest and selector gadgets to gather data and made some cleaning, like a minority of lessons get prearranged with various CSS ids in a frame, and cuisine types comprise both Chinese and English words.
Then we used BatchGeo to get geocoding using addresses that are somewhat accurate and conveniently offer a map, excluding it isn’t apparent whether it offers latitude or longitude data in the output.
![Then-we-used-BatchGeo](assets\img\blog\scrape-listing-data-of-michelin-star-restaurants-in-singapore\Then-we-used-BatchGeo.png)
They are mainly centrally positioned in about 3 clusters:
From Tanjong Pagar to City Hall (city center)
Within the Botanic or Orchard Gardens area (touristy/shopping area)
3 within Sentosa, one each from 1-star (named Osia), 2-star (named L’Atelier de Joel Robuchon), and 3-star (named Joel Robuchon), all are touristy or rich residential areas
![within-Sentosa](assets\img\blog\scrape-listing-data-of-michelin-star-restaurants-in-singapore\within-Sentosa.png)
In the next step, we can match restaurant names (different spellings) from any two sources to get cuisine type, group them through geographical districts and recognize distances to various residential areas.
![hank-you-for-reading.png](assets\img\blog\scrape-listing-data-of-michelin-star-restaurants-in-singapore\Thank-you-for-reading.png)
You can find the scraped data here. Thank you for reading this blog. You can always contact Food Data Scrape for more information or all food data scraping service requirements.