diff --git a/_sources/notebooks/spill/spillover-README.md b/_sources/notebooks/spill/spillover-README.md
index a8a9f92..67ee44b 100644
--- a/_sources/notebooks/spill/spillover-README.md
+++ b/_sources/notebooks/spill/spillover-README.md
@@ -27,7 +27,8 @@ For this analysis, we filtered the global dataset for exports to all Yemen ports
 
 ![](images/capacity.png)
 
-
+<br>
+<br>
 
 |     | Origin Country       | Daily Capacity at Risk (metric tons) | Capacity at Risk - 90-Day Disruption (metric tons) |
 | --- | -------------------- | ------------------------------------ | -------------------------------------------------- |
diff --git a/notebooks/spill/spillover-README.html b/notebooks/spill/spillover-README.html
index f01c221..5461c0d 100644
--- a/notebooks/spill/spillover-README.html
+++ b/notebooks/spill/spillover-README.html
@@ -469,6 +469,8 @@ <h2>Methodology<a class="headerlink" href="#methodology" title="Permalink to thi
 <h3>Estimating At-Risk Capacity of a Port Disruption (Metric Tons) by Country<a class="headerlink" href="#estimating-at-risk-capacity-of-a-port-disruption-metric-tons-by-country" title="Permalink to this heading">#</a></h3>
 <p>For this analysis, we filtered the global dataset for exports to all Yemen ports. We then identified the top ten countries whose at-risk daily capacity (aggregated across all ports in a country) were the highest if any of the Yemen ports were disrupted. We then followed the paper’s guidance on the linear relationship between daily at-risk capacity and the number of days of disruption and multipled the at-risk capacity by 90 days. With these steps, we produced the following bar chart and table.</p>
 <p><img alt="" src="../../_images/capacity.png" /></p>
+<br>
+<br>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p></p></th>