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diff --git a/05-relational-databases.Rmd b/05-relational-databases.Rmd
@@ -270,7 +270,7 @@ knitr::kable(
 )
 ```
 
-Next, we take the set difference between the Cartesian product above and $S$, $(Π_{code(S)×T)-S$, which has the effect of removing the tuples already observed in $S$. This leaves us with a relation that has all the "incorrect" code-species combinations:
+Next, we take the set difference between the Cartesian product above and $S$, $(Π_{code}(S)×T)-S$, which has the effect of removing the tuples already observed in $S$. This leaves us with a relation that has all the "incorrect" code-species combinations:
 
 ```{r 5-set-division-5, echo=FALSE}
 Ds <- Ct[paste0(Ct$Code,Ct$Species)%in%setdiff(paste0(Ct$Code,Ct$Species),paste0(S$Code,S$Species)),]

diff --git a/08-network-analysis.Rmd b/08-network-analysis.Rmd
@@ -8,7 +8,7 @@ library(ggplot2)
 library(networkD3)
 ```
 
-# Network Analysis {#collecting-and-editing-data}
+# Network Analysis {#network-analysis}
 
 Networks are **abstract structures** commonly used to represent patterns of relationships among sets of various *things* [@ajorlouIntroductionNetworkModels2018]. Such structures can be used to represent social connections, spatial patterns, ecological relationships, etc. In GIS, the elements that compose geospatial networks are **geolocated** -- in other words: they have latitude and longitude values attached to them. Network analysis encompasses a series of techniques used to interpret information from those networks. This chapter introduces basic concepts for building, analyzing and applying spatial networks to real-world problems.
 

diff --git a/10-spatial-estimation.Rmd b/10-spatial-estimation.Rmd
@@ -21,6 +21,7 @@ library(raster)
 library(RColorBrewer)
 library(rgdal)
 library(rgeos)
+library(rgl)
 library(scales)
 library(sf)
 library(sp)

diff --git a/15-LiDAR-acquisition-and-analysis.Rmd b/15-LiDAR-acquisition-and-analysis.Rmd
@@ -351,7 +351,7 @@ m = leaflet() %>%
 
 ```{r 15-map, echo=FALSE, fig.cap = fig_cap}
 
-fig_cap <- paste0("Explore different area-based metrics produced in the case study above. Hover over tile icon (top left) to turn different layers on or off. Du Toit, CC-BY-4.").
+fig_cap <- paste0("Explore different area-based metrics produced in the case study above. Hover over tile icon (top left) to turn different layers on or off. Du Toit, CC-BY-4.")
 knitr::opts_chunk$set(fig.width = 12, fig.height = 12) #make the interactive maps bigger/smaller across the document
 
 ```

diff --git a/16-data-integration.Rmd b/16-data-integration.Rmd
@@ -1,4 +1,4 @@
-## Data Integration {#data-integration}
+# Data Integration {#data-integration}
 
 **Data Integration**, This chapter is about some of the more practical aspects implementing GIS in a workflow.  What type of problems and pitfalls might you encounter and how do you account for them?
 

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diff --git a/docs/libs/DiagrammeR-styles-0.2/styles.css b/docs/libs/DiagrammeR-styles-0.2/styles.css
@@ -0,0 +1,14 @@
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+}
+
+.DiagrammeR g .label {
+  font-family: Helvetica;
+  font-size: 14px;
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+}
+