Update README.md

README.md

@@ -13,7 +13,7 @@ This is a single (geo-)parquet file based on the 81 individual parquet files fro
 
 As it's just 10Gb, it's fairly easy to handle as a single file and can easily be queried over modern technologies like httpfs. 
 
-## Ways to query the file
+## Ways to query the file & visualize the results
 If you just want to poke around in the data to get an idea of kind of places to expect, I'd recommend DuckDB.
 Huggingface has a DuckDB WASM console integrated but it's too slow (or runs out of memory) when run over the entire file. You can try it [here](https://huggingface.co/datasets/do-me/foursquare_places_100M?sql_console=true&sql=--+The+SQL+console+is+powered+by+DuckDB+WASM+and+runs+entirely+in+the+browser.%0A--+Get+started+by+typing+a+query+or+selecting+a+view+from+the+options+below.%0ASELECT+*+FROM+train+WHERE+name+ILIKE+%27%25bakery%25%27%3B%0A)
 
@@ -60,10 +60,64 @@ Then you can make use of good old pandas query operators and geospatial tools.
 E.g. looking for bakeries again.
 ```python 
 gdf[gdf["name"].str.contains("bakery")]
-
 ```
 I was actually surprised that the string operator in pandas is that efficient! It only takes 11 seconds, so fairly fast for 100M rows!
 
+But to be fair, the actual equivalent to ILIKE in pandas would be this query: 
+
+```python
+gdf[gdf["name"].str.contains("bakery", case=False, na=False)]
+```
+
+It yields exactly the same number of rows like the SQL command but takes 19 seconds, so - as expected - indeed much slower than DuckDB (especially considering that we just count the query time, not the loading time)
+
+### Example 4: Queried fully locally with Geopandas and visualized with Lonboard
+
+If you quickly want to visulize the data, Lonboard is a super convenient Jupyter wrapper for deck.gl. 
+Install it with `pip install lonboard` and you're ready to go. 
+
+```python
+import geopandas as gpd
+from lonboard import viz
+
+gdf = gpd.read_parquet("foursquare_places.parquet")
+
+bakeries = gdf[gdf["name"].str.contains("bakery", case=False, na=False)]
+
+viz(bakeries)
+```
+It created a nice interactive map with tooltips.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c4da8719565937fb268b32/QIolrK2nlrENnkWlE6TFh.png)
+
+If you ask yourself: "Wait, how is it possible that there are so little bakeries in other countries than the US & UK?", just read on!
+
+## Motivation
+
+So why would I create this repo and duplicate the data if you can download it directly from AWS or Source Cooperative? 
+
+It's mainly about convenience: many folks might just want to get an idea of the data and httpfs is perfectly suited for this purpose. 
+However the ACTUAL reason why I'm doing this, is that I created a geospatial semantic search workflow for social media data, Overturemaps and other data sources. 
+The idea is to use text embeddings and visulize the query similarity on a map. Just play with the apps here to get an idea, it's easier to understand once you tried it:
+
+- Geospatial Semantic Search for Instagram Data in Bonn, Germany: https://do-me.github.io/semantic-hexbins/
+- Worldwide Geospatial Semantic Search for Overture Places: https://huggingface.co/datasets/do-me/overture-places
+
+In the above example 4, where we are looking for bakeries around the world, it's clear that non-English-speaking countries probably do not necessarily name their bakeries "bakery" but e.g. in German "Bäckerei". 
+So if we just search in the name column, we won't find it. That's the reason why Foursquare introduced categories! 
+
+In the column `fsq_category_labels` we find `[Dining and Drinking > Bakery]` great right? Well, yes and no. Of course we can use it and we will get back some results. 
+However, looking a bit closer, we can quickly see why these categories do not seem to work that well: 
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c4da8719565937fb268b32/vtDreZy3bdf7UOnQyqWQG.png)
+
+Sometimes there are no categories and sometimes entries like `Beef&bakery` probably should have gotten more than one entry.
+
+So how can we solve this? 
+
+
+
+