A few weeks ago we had a heatwave and we were thinking if the hotter a place the poorer it is. We could collect data to test our hypothesis thanks to the recent advances in open data and scraping technologies.

We restricted ourselves to Budapest since we had no income data on the level we needed. But we built a scraper to get data on square meter prices from a property listing site. You can check our scraper on github. We think property prices are good proxies of income levels. Thanks to the EU, we can get high-quality open remote sensing Earth observation data from the WEkEO portal. We chose the following datasets:

• Level 2 Land – Sea and Land Surface Temperature Radiometer (SLSTR) (Celsius)

• Global 10-daily Leaf Area Index 333m (LAI)

• Global 10-daily Fraction of Vegetation Cover 333m (FCOVER)

We aggregated the data on level 7 of the H3 geospatial indexing system and made a few maps.

Square meter prices

Temperature

Fraction of Vegetation Cover

Leaf Area Index

The maps suggested that there might be some relation between our variables. However, we didn’t find a significant correlation between the variables on level 7. Zooming out to level 5, we found the followings.

It turned out that our data on higher resolutions contains many missing data points due to several factors. Probably the most important factor is that we chose a short time period to investigate temperature and vegetation covers. Stay tuned, we are working on these issues!

The original source of the header image can be found here.