DOC: sync readme changes to docs (#482)

docs/source/about.md

@@ -22,7 +22,7 @@ for working with OGR vector data sources. It is **awesome**, has highly-dedicate
 maintainers and contributors, and exposes more functionality than Pyogrio ever will.
 This project would not be possible without Fiona having come first.
 
-Pyogrio uses a vectorized (array-oriented) approach for reading and writing
+Pyogrio uses a bulk-oriented approach for reading and writing
 spatial vector file formats, which enables faster I/O operations. It borrows
 from the internal mechanics and lessons learned of Fiona. It uses a stateless
 approach to reading or writing data; all data are read or written in a single

docs/source/index.md

@@ -1,32 +1,25 @@
-# pyogrio - Vectorized spatial vector file format I/O using GDAL/OGR
-
-Pyogrio provides a
-[GeoPandas](https://github.com/geopandas/geopandas)-oriented API to OGR vector
-data sources, such as ESRI Shapefile, GeoPackage, and GeoJSON. Vector data sources
-have geometries, such as points, lines, or polygons, and associated records
-with potentially many columns worth of data.
-
-Pyogrio uses a vectorized approach for reading and writing GeoDataFrames to and
-from OGR vector data sources in order to give you faster interoperability. It
-uses pre-compiled bindings for GDAL/OGR so that the performance is primarily
-limited by the underlying I/O speed of data source drivers in GDAL/OGR rather
-than multiple steps of converting to and from Python data types within Python.
+# pyogrio - bulk-oriented spatial vector file I/O using GDAL/OGR
+
+Pyogrio provides fast, bulk-oriented read and write access to 
+[GDAL/OGR](https://gdal.org/en/latest/drivers/vector/index.html) vector data
+sources, such as ESRI Shapefile, GeoPackage, GeoJSON, and several others.
+Vector data sources typically have geometries, such as points, lines, or
+polygons, and associated records with potentially many columns worth of data.
+
+The typical use is to read or write these data sources to/from
+[GeoPandas](https://github.com/geopandas/geopandas) `GeoDataFrames`. Because
+the geometry column is optional, reading or writing only non-spatial data is
+also possible. Hence, GeoPackage attribute tables, DBF files, or CSV files are
+also supported.
+
+Pyogrio is fast because it uses pre-compiled bindings for GDAL/OGR to read and
+write the data records in bulk. This approach avoids multiple steps of
+converting to and from Python data types within Python, so performance becomes
+primarily limited by the underlying I/O speed of data source drivers in
+GDAL/OGR.
 
 We have seen \>5-10x speedups reading files and \>5-20x speedups writing files
-compared to using non-vectorized approaches (Fiona and current I/O support in
-GeoPandas).
-
-You can read these data sources into
-`GeoDataFrames`, read just the non-geometry columns into Pandas `DataFrames`,
-or even read non-spatial data sources that exist alongside vector data sources,
-such as tables in a ESRI File Geodatabase, or antiquated DBF files.
-
-Pyogrio also enables you to write `GeoDataFrames` to at least a few different
-OGR vector data source formats.
-
-```{warning}
-Pyogrio is still at an early version and the API is subject to substantial change.
-```
+compared to using row-per-row approaches (e.g. Fiona).
 
 ```{toctree}
 ---