In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?
Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.
Today: Open Street Maps (OSM) meets CSV Files and Google Geocoding API
The Challenge
Today’s challenge is a geographical one. Do you know which cities are the most populated cities in the world? Do you know where they are? China? USA? By way of contrast, do you know which cities are the smallest cities in the world?
Today we want to show you where you can find the largest and the smallest cities in the world by population on a map. While there is general agreement from trustworthy sources on the web about which are the most populated cities, agreement becomes sparser when looking for the smallest cities in the world. There is general agreement though about which ones are the smallest capitals in the world.
We collected data for the 125 world’s largest cities in a CSV text file and data for the 10 smallest capitals of equally small and beautiful countries in another CSV text file. Data includes city name, country, size in squared kilometers, population number, and population density. The challenge of today is to localize such cities on a world map. Technically this means:
- To blend the city data from the CSV file with the city geo-coordinates from the Google Geocoding API into KNIME Analytics Platform
- Then to blend the ETL and machine learning from KNIME Analytics Platform with the geographical visualization of Open Street Maps.
Topic. Geo-localization of cities on a world map.
Challenge. Blend city data from CSV files and city geo-coordinates from Google Geocoding API and display them on a OSM world map.
Access Mode. CSV file and REST service for Google Geocoding API.
Integrated Tool. Open Street Maps (OSM) for data visualization.
