diff --git a/mapping_draft.Rmd b/mapping_draft.Rmd
index ca26538..8e2fd7d 100644
--- a/mapping_draft.Rmd
+++ b/mapping_draft.Rmd
@@ -468,20 +468,11 @@ The key question which this analysis seeks to answer is whether ECS, or the othe
 
 Of all the groups surveyed in this study, Eco-Congregation Scotland is the most heavily concentrated in large urban areas (33.53%), exceeding by almost 50% the rate for all places of worship (22.96% in large urban areas). Transition is a much more modest 20% and development trusts a bit lower at 15%. It is interesting to note that the rate of ECS concentration in these large urban areas matches the level of overall population distribution (34.5%). On the other end of the scale, Eco-Congregation Scotland is the least concentrated in remote rural areas (with 3.93% on level 7 and 5.44% on level 8 on the urban-rural scale), though again, they correlate roughly to the general population distribution (3.2% and 2.9% respectively). Places of worship outpace both the population of Scotland and the footprint of Eco-Congregation Scotland, with 14.98% in very remote rural areas, but this is exceeded by transition at 16.47% and both by Scottish community development trusts at 32.14%. So while Eco-Congregation Scotland correlates roughly with Scottish population distribution across the urban-rural scale, it has a considerably more urban profile than either of the other two groups surveyed.
 
-```{r}
+```{r create_urbanrural_ecs_chart_choropleth}
 # Create bar chart
 # TODO: use ggplot here to generate stacked bar chart showing representation across 8 urban/rural bands from urbanrural_df
-# TODO: generate map based on urbanrural
+# TODO: generate map based on urbanrural polygons, shade based on 8-fold categories, draw transparent dots onto polygons
 
-var01 <- admin_lev1$ecs_count
-bins <- unique(quantile(var01, seq(0,1,length.out=30)))
-admin_lev1$binId01 <- findInterval(var01, bins)
-colSet01 <- rev(heat.colors(length(bins)))
-plot(admin, col=colSet01[admin_lev1$binId01], border="grey", lwd=0.25)
-par(mar=c(5,3,2,2)+0.1)
-```
-
-```{r create_urbanrural_ecs_choropleth}
 pdf(file="figures/urbanrural_choropleth_ecs.pdf", width=4, height=4)
 bins <- unique(quantile(urbanrural$ecs_count, seq(0,1,length.out=30)))
 urbanrural$binId01 <- findInterval(urbanrural$ecs_count, bins)
@@ -496,8 +487,7 @@ title(main="Figure 5", sub="Eco-Congregation Scotland\nconcentrations in Urban R
 dev.off()
 ```
 
-
-[Figure6 map of ECS concentration by UR Areas Urban - Rural via Brown to Green]
+![][Figure6 map of ECS concentration by UR Areas Urban - Rural via Brown to Green]
 
 
 # Wealth, Employment, and Literacy