adds tiny improvements tot the preface

preface.qmd

@@ -10,7 +10,7 @@ We want to avoid reinventing the wheel and provide something that fills an 'ecol
 Key features include:
 
 1. Doing basic operations well
-2. Integration of vector and raster datasets and operations
+2. Integration of vector and raster datasets operations
 3. Clear explanation of each line of code in the book to minimize surprises
 4. Exercises at the end of each chapter with reproducible and open solutions
 5. Provision of lucid example datasets and meaningful operations to illustrate the applied nature of geographic research
@@ -20,8 +20,8 @@ This book is complementary with, and adds value to, other projects in the ecosys
 - [Learning Geospatial Analysis with Python](https://www.packtpub.com/product/learning-geospatial-analysis-with-python/9781783281138) and [Geoprocessing with Python](https://www.manning.com/books/geoprocessing-with-python) are books in this space that focus on processing spatial data using low-level Python interfaces for GDAL, such as the **gdal**, **gdalnumeric**, and **ogr** [packages](https://gdal.org/api/python_bindings.html) from **osgeo**. This approach requires writing more lines of code. We believe our approach is more ["Pythonic"](https://rasterio.readthedocs.io/en/latest/intro.html#philosophy) and future-proof, in light of development of packages such as **geopandas** and **rasterio**.
 - [Introduction to Python for Geographic Data Analysis](https://pythongis.org/) (in progress) seeks to provide a general introduction to 'GIS in Python', with parts focusing on Python essentials, using Python with GIS, and case studies. Compared with this book, which is also open source, and is hosted at pythongis.org, *Geocomputation with Python* has a narrower scope (not covering [spatial network analysis](https://pythongis.org/part3/chapter-11/index.html), for example) and more coverage of raster data processing and raster-vector interoperability.
 - [Geographic Data Science with Python](https://geographicdata.science/book/intro.html) is an ambitious project with chapters dedicated to advanced topics, with Chapter 4 on [Spatial Weights](https://geographicdata.science/book/notebooks/04_spatial_weights.html) getting into complex topics relatively early, for example.
-- [Python for Geospatial Data Analysis](https://www.oreilly.com/library/view/python-for-geospatial/9781098104788/) introduces a wide range of approaches to working with geospatial data using Python, including automation of proprietary and open-source GIS software, as well as standalone open source Python packages (which is what we focus on and explain comprehensively in our book). Geocompy is shorter, simpler and more introductory, and cover raster and vector data with equal importance (1 to 4).
+- [Python for Geospatial Data Analysis](https://www.oreilly.com/library/view/python-for-geospatial/9781098104788/) introduces a wide range of approaches to working with geospatial data using Python, including automation of proprietary and open-source GIS software, as well as standalone open source Python packages (which is what we focus on and explain comprehensively in our book). Geocompy is shorter, simpler and more introductory, and cover raster and vector data with equal importance.
 
 Another unique feature of the book is that it is part of a wider community.
 *Geocomputation with Python* is a sister project of [Geocomputation with R](https://r.geocompx.org/), a book on geographic data analysis, visualization, and modeling using the R programming language that has 60+ contributors and an active community, not least in the associated [Discord group](https://discord.gg/PMztXYgNxp).
-Links with the vibrant 'R-spatial' community, and other communities such as [GeoRust](https://georust.org/) and [JuliaGeo](https://juliageo.org/), will lead to many opportunities for mutual benefit across open source ecosystems.
+Links with the vibrant 'R-spatial' community, and other communities such as [GeoRust](https://georust.org/) and [JuliaGeo](https://juliageo.org/), lead to many opportunities for mutual benefit across open source ecosystems.