tweaks to readme

Author: pranav-walimbe

README.md

@@ -47,15 +47,15 @@ result = sf.lazy().filter(pl.col("population") > 100_000).range_query(-10.0, 35.
 
 During my undergrad research, I saw firsthand how spatial dataframe tooling could use performance improvements.
 
-The driving motivator behind creating this library was to provide the optimizations of relational DBs (query planning, indexing, etc) in a fast spatial dataframe interface that abstracts away these complexities for users.
+The driving motivator behind creating this library was to provide the optimizations of relational DBs (query planning, indexing, etc) in a fast, Polars-like interface meant for in-memory spatial work.
 
-Edit [June 19 2026]: Apache Sedona released a cool Python DataFrame API. There are similarities between their API and this tool but some key differences are (1) this query planner interacts with Polars rather than just being an input source and (2) this uses a cost-model approch to dynamic indexing.
+Edit [June 19 2026]: Apache SedonaDB released a cool Python DataFrame API. There are similarities between their API and this tool but some key differences are that this library uses (1) a Polars-native query engine and (2) a cost model that decides whether and how to index.
 
 
 |  | PyCanopy | GeoPandas | DuckDB | SedonaDB | Spatial Polars |
 |:--|:--------:|:---------:|:------:|:--------:|:--------------:|
 | Polars-native API                               | ✓ | ✗ | ✗ | ✗ | ✓ |
-| Spatial query planner (reorder, fuse, pushdown) | ✓ | ✗ | ✓ | ✓ | ✗ |
+| Spatial query planner (reorder, pushdown, etc)  | ✓ | ✗ | ✓ | ✓ | ✗ |
 | Index vs scan decided by cost model             | ✓ | ✗ | ✗ | ✗ | ✗ |
 | Dynamic index selection                         | ✓ | ✗ | ✗ | ✗ | ✗ |