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diff --git a/.nojekyll b/.nojekyll
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-<title>quarto-input82c0c333a3decec6 – geocompx</title>
+<title>quarto-input4864d1e90984f633 – geocompx</title>
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diff --git a/guestbook.html b/guestbook.html
diff --git a/post.html b/post.html
diff --git a/post/2025/geocompy-bp2/index.html b/post/2025/geocompy-bp2/index.html
@@ -209,14 +209,14 @@ <h1 class="title"><em>Geocomputation with Python</em> has arrived</h1>
 <figcaption>The book cover</figcaption>
 </figure>
 </div>
-<p><em>Geocomputation with Python</em> (<strong><em>geocompy</em></strong>) is an open-source introductory resource for working with geographic data with Python. It covers the entire range of standard GIS operations for both vector and raster data models and provides a consistent learning curve. The book is designed to minimize surprises, with each section and chapter building on the previous. If you’re just starting out with Python to work with geographic data, we hope that the book will be an excellent place to start.</p>
+<p><em>Geocomputation with Python</em> (<strong><em>geocompy</em></strong>) is an open-source, introductory resource for working with geographic data with Python. It covers the entire range of standard GIS operations for both vector and raster data models and provides a consistent learning curve. The book is designed to minimize surprises, with each section and chapter building on the previous. If you’re just starting out with Python to work with geographic data, we hope that the book will be an excellent place to start.</p>
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
 <p><img src="geocompy2.png" class="img-fluid figure-img"></p>
 <figcaption>An image from the book</figcaption>
 </figure>
 </div>
-<p>The book is open-source, and you can find the code underlying the <em>geocompy</em> project on <a href="https://github.com/geocompx/geocompy">GitHub</a>. This ensures that the content is reproducible, transparent, and accessible. It also lets you interact with the project by opening issues and submitting pull requests.</p>
+<p>The book is open-source and you can find the code on <a href="https://github.com/geocompx/geocompy">GitHub</a>. This ensures that the content is reproducible, transparent, and accessible. It also lets you interact with the project by opening issues and submitting pull requests.</p>
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
 <p><img src="geocompy3.png" class="img-fluid figure-img"></p>

diff --git a/search.json b/search.json
@@ -438,7 +438,7 @@
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-    "text": "Geocomputation with R\n\nModules using the book\n\nCASA0005 Geographic Information Systems and Science, Andy MacLachlan and Adam Dennett, Centre for Advanced Spatial Analysis, Univeristy College London\nECS530 Analysing spatial data, Roger Bivand, Norwegian School of Economics\nGEOG 495 and 595: Geographic Data Analysis, University of Oregon\nESPM 288: Reproducible and Collaborative Data Science, Berkeley University of California \nENVR_SCI 390-0 “R Data Science” - Special Topics in Environmental Sciences Northwestern University\nPD-15 - R for Geospatial Analysis and Mapping, University Consortium for Geographic Information Science \nCRD 298: Spatial Methods in Community Research, Professor Noli Brazil, University of California, Davis\nCP6521 Advanced GIS, Yongsun Lee, Georgia Tech\nUrban Data Analytics in R, Nikhil Kaza, University of North Carolina at Chapel Hill\nGeospatial Analytics, taught by Dr Nick Tate as part of the MSc in Geographical Information Science at the Unversity of Leicester, UK.\nGEOG 28402 & 28403: Geographic Information Science II & III, Marynia Kolak at the Center for Spatial Data Science, Unversity of Chicago\n\n\n\nOther teaching courses/lectures/guides using the book\n\nData Science for Economists, EC 607\n11th International Summer School2019: Spatial and digital Epidemiology: Leveraging geo-referenced social media in the context of urban health\nData Carpentry: Introduction to Geospatial Raster and Vector Data with R\nR: Mapping and Geospatial guide, Duke University\n\n\n\nBook reviews\n\nI first read the first edition around 3 years ago and was astounded that everything I had been doing piecemeal in Q, Arc, and with some sf functions could be done in an orderly manner and finally the geospatial libraries were accessibly explained. Sadly at the time I did not get past the first part and thought “this is great! but need to move on!”. A month or so ago I sat down to reread the book in its entirety, with the move to terra etc.\n\n\nBeautiful book: A must-read if you’re into data analytics/science with R and do geospatial analysis.\n\nSee the rest of these reviews and maybe add your own review here.\n\n\nSocial media\n\n\n\n\n\n\n;document.getElementById(\"tweet-31570\").innerHTML = tweet[\"html\"];\n\n\n\n;document.getElementById(\"tweet-54998\").innerHTML = tweet[\"html\"];\n\n\n;document.getElementById(\"tweet-77937\").innerHTML = tweet[\"html\"];\n\n\n\n\n\n\n;document.getElementById(\"tweet-40589\").innerHTML = tweet[\"html\"];"
+    "text": "Geocomputation with R\n\nModules using the book\n\nCASA0005 Geographic Information Systems and Science, Andy MacLachlan and Adam Dennett, Centre for Advanced Spatial Analysis, Univeristy College London\nECS530 Analysing spatial data, Roger Bivand, Norwegian School of Economics\nGEOG 495 and 595: Geographic Data Analysis, University of Oregon\nESPM 288: Reproducible and Collaborative Data Science, Berkeley University of California \nENVR_SCI 390-0 “R Data Science” - Special Topics in Environmental Sciences Northwestern University\nPD-15 - R for Geospatial Analysis and Mapping, University Consortium for Geographic Information Science \nCRD 298: Spatial Methods in Community Research, Professor Noli Brazil, University of California, Davis\nCP6521 Advanced GIS, Yongsun Lee, Georgia Tech\nUrban Data Analytics in R, Nikhil Kaza, University of North Carolina at Chapel Hill\nGeospatial Analytics, taught by Dr Nick Tate as part of the MSc in Geographical Information Science at the Unversity of Leicester, UK.\nGEOG 28402 & 28403: Geographic Information Science II & III, Marynia Kolak at the Center for Spatial Data Science, Unversity of Chicago\n\n\n\nOther teaching courses/lectures/guides using the book\n\nData Science for Economists, EC 607\n11th International Summer School2019: Spatial and digital Epidemiology: Leveraging geo-referenced social media in the context of urban health\nData Carpentry: Introduction to Geospatial Raster and Vector Data with R\nR: Mapping and Geospatial guide, Duke University\n\n\n\nBook reviews\n\nI first read the first edition around 3 years ago and was astounded that everything I had been doing piecemeal in Q, Arc, and with some sf functions could be done in an orderly manner and finally the geospatial libraries were accessibly explained. Sadly at the time I did not get past the first part and thought “this is great! but need to move on!”. A month or so ago I sat down to reread the book in its entirety, with the move to terra etc.\n\n\nBeautiful book: A must-read if you’re into data analytics/science with R and do geospatial analysis.\n\nSee the rest of these reviews and maybe add your own review here.\n\n\nSocial media\n\n\n\n\n\n\n;document.getElementById(\"tweet-77731\").innerHTML = tweet[\"html\"];\n\n\n\n;document.getElementById(\"tweet-13179\").innerHTML = tweet[\"html\"];\n\n\n;document.getElementById(\"tweet-60904\").innerHTML = tweet[\"html\"];\n\n\n\n\n\n\n;document.getElementById(\"tweet-34124\").innerHTML = tweet[\"html\"];"
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