Added link to slides

sdesabbata · Sep 12, 2023 · 889c297 · 889c297
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diff --git a/docs/README.md b/docs/README.md
@@ -5,6 +5,10 @@ Graph theory has long provided the basis for the computational modelling of urba
 Our preliminary results (see [results and supplementary materials](#results-supplementary) below) illustrate how a model trained on a 1% random sample of street junctions in the UK can be used to explore the urban form of the city of Leicester, generating embeddings which are similar but distinct from classic metrics and able to capture key aspects such as the shift from urban to suburban structures. 
 
 
+## Slides
+
+The slides for our presentation at the [2nd International Workshop on Geospatial Knowledge Graphs and GeoAI: Methods, Models, and Resources](https://geokg-geoai2023.github.io/) (12th September, 2023, Leeds, UK) can be found [here](https://sdesabbata.github.io/gnn-urban-form/slides/geokg-geoai2023/learning-urban-form-gnn.html).
+
 
 ## Data 
 

diff --git a/docs/slides/geokg-geoai2023/learning-urban-form-gnn.html b/docs/slides/geokg-geoai2023/learning-urban-form-gnn.html
diff --git a/docs/slides/geokg-geoai2023/learning-urban-form-gnn.qmd b/docs/slides/geokg-geoai2023/learning-urban-form-gnn.qmd
@@ -22,6 +22,8 @@ bibliography: biblography-geokg-geoai2023.bib
 citations-hover: true
 ---
 
+
+
 ## Urban form (and function)
 
 @ARRIBASBEL2022102641 define urban form as *what a space "looks like"* compared to urban function, which focuses on *"what it is used for"*.
@@ -97,7 +99,6 @@ Unsupervised learning of *nodes representations*
 
 
 
-
 ## Graph AutoEncoder (some details) {.smaller}
 
 
@@ -159,19 +160,23 @@ Leicester (UK)
 :::
 ::::
 
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 ## Results
 
 ::: {layout="[16,-1,16]" layout-valign="center"}
-![](images/gnnuf_ea_v0-5-emb_Leicester_scatter-bivar.png)
+![<span style="font-size: 12px;">Street network data by OpenStreetMap, under ODbL, and by @DVN/KA5HJ3_2020, under CC0 1.0</span>](images/gnnuf_ea_v0-5-emb_Leicester_scatter-bivar.png)
 
 ![](images/gnnuf_ea_v0-5-emb_Leicester_streetmap-bivar.png)
+
 :::
 
 
+
 ## Results (embedding clustering)
 
 ::: {layout="[16,-1,16]" layout-valign="center"}
-![](images/gnnuf_ea_v0-5-emb_Leicester_scatter-clust.png)
+![<span style="font-size: 12px;">Street network data by OpenStreetMap, under ODbL, and by @DVN/KA5HJ3_2020, under CC0 1.0</span>](images/gnnuf_ea_v0-5-emb_Leicester_scatter-clust.png)
 
 ![](images/gnnuf_ea_v0-5-emb_Leicester_streetmap-clust.png)
 :::
@@ -181,7 +186,7 @@ Leicester (UK)
 ## Results (ego-graph pooled)
 
 ::: {layout="[16,-1,16]" layout-valign="center"}
-![](images/gnnuf_ea_v0-5-emb-pooled_Leicester_scatter-bivar.png)
+![<span style="font-size: 12px;">Street network data by OpenStreetMap, under ODbL, and by @DVN/KA5HJ3_2020, under CC0 1.0</span>](images/gnnuf_ea_v0-5-emb-pooled_Leicester_scatter-bivar.png)
 
 ![](images/gnnuf_ea_v0-5-emb-pooled_Leicester_streetmap-bivar.png)
 :::
@@ -255,7 +260,7 @@ GNNs can be used as an unsupervised framework to explore urban form
 
 [sdesabbata.github.io/gnn-urban-form](https://sdesabbata.github.io/gnn-urban-form/)
 
-![](images/gnnuf_ea_v0-5-emb_Leicester_streetmap-bivar.png)
+![<span style="font-size: 12px;">Street network data by OpenStreetMap, under ODbL, and by @DVN/KA5HJ3_2020, under CC0 1.0</span>](images/gnnuf_ea_v0-5-emb_Leicester_streetmap-bivar.png){width=80%}
 
 :::
 ::: {.column width="5%"}