From b68e88a7451018bdddc6880ba40fb7d0fb4a00fd Mon Sep 17 00:00:00 2001
From: David Beauchesne <david.beauchesne@uqar.ca>
Date: Tue, 12 Nov 2024 13:58:09 -0500
Subject: [PATCH] update analysis for petroleum threat and update report

---
 _targets.R                                    |  39 +-
 docs/1_research_compendium.html               | 593 ++++++++++++++++++
 docs/{1_shipping.html => 2_data.html}         | 112 ++--
 docs/{2_petroleum.html => 3_threats.html}     | 218 ++-----
 docs/index.html                               |  36 +-
 docs/index_files/figure-html/fig-aoi-1.png    | Bin 228631 -> 0 bytes
 docs/search.json                              | 118 +---
 pubs/report/1_research_compendium.qmd         |   5 +
 pubs/report/2_data.qmd                        |  53 ++
 pubs/report/2_petroleum.qmd                   |  98 ---
 pubs/report/3_threats.qmd                     |  53 ++
 pubs/report/_quarto.yml                       |   7 +-
 pubs/report/{3_VIIRS.qmd => del_VIIRS.qmd}    |   0
 .../{1_shipping.qmd => del_shipping.qmd}      |   0
 pubs/report/index.qmd                         |   6 +-
 .../petroleum_pollution_incidents.yaml        |  51 +-
 .../ana_petroleum_pollution_incidents.R       |  30 +-
 17 files changed, 961 insertions(+), 458 deletions(-)
 create mode 100644 docs/1_research_compendium.html
 rename docs/{1_shipping.html => 2_data.html} (70%)
 rename docs/{2_petroleum.html => 3_threats.html} (56%)
 delete mode 100644 docs/index_files/figure-html/fig-aoi-1.png
 create mode 100644 pubs/report/1_research_compendium.qmd
 create mode 100644 pubs/report/2_data.qmd
 delete mode 100644 pubs/report/2_petroleum.qmd
 create mode 100644 pubs/report/3_threats.qmd
 rename pubs/report/{3_VIIRS.qmd => del_VIIRS.qmd} (100%)
 rename pubs/report/{1_shipping.qmd => del_shipping.qmd} (100%)

diff --git a/_targets.R b/_targets.R
index df18f20..ef97f72 100644
--- a/_targets.R
+++ b/_targets.R
@@ -1,11 +1,12 @@
+# Generated by pipedat: do not edit by hand
 # This file defines the pipeline for the ResearchCompendium project.
 library(targets)
 library(tarchetypes)
 library(pipedat) # To remove by the end of the project
 
-tar_option_set(packages = c('curl', 'googleCloudStorageR', 'aws.s3', 'yaml', 'dplyr'), format = 'file')
+tar_option_set(packages = c('curl', 'googleCloudStorageR', 'aws.s3', 'yaml', 'dplyr'), format = 'file') # To edit properly by the end of the project
 
-tar_source('workspace/scripts/')
+tar_source('workspace/scripts/') # To edit properly by the end of the project
 list(
   tar_target(bibtex_master, bibtex_master(), format = "file", cue = tar_cue(mode = "always")),
     list(list(tar_target(tg_835bc0d1_e57a_4e63_a327_908ee871bf2d_Scientific_article, 
@@ -448,35 +449,27 @@ list(
             })
         "workspace/data/harvested/viirs_nighttime_light-1.0.0/raw"
     }, format = "file"))),
-    list(tar_target(tg_a726889e_848e_4d6c_8496_a7a772e70d85_istop_data_preparation, 
+    list(tar_target(tg_a726889e_848e_4d6c_8496_a7a772e70d85_petroleum_pollution_incidents_istop, 
     {
         ana_petroleum_pollution_incidents_istop(output_path = "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0", 
             input_files = list(tg_835bc0d1_e57a_4e63_a327_908ee871bf2d_istop))
-        "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/istop_prep.gpkg"
-    }, format = "file"), tar_target(tg_a726889e_848e_4d6c_8496_a7a772e70d85_istop_diffusive_model, 
-    {
-        ana_petroleum_pollution_incidents_istop_diffusive(output_path = "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0", 
-            input_files = list(tg_a726889e_848e_4d6c_8496_a7a772e70d85_istop_data_preparation))
-        "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/petroleum_pollution_incidents_istop.tif"
-    }, format = "file"), tar_target(tg_a726889e_848e_4d6c_8496_a7a772e70d85_nasp_data_preparation, 
+        "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/petroleum_pollution_incidents_istop.gpkg"
+    }, format = "file"), tar_target(tg_a726889e_848e_4d6c_8496_a7a772e70d85_petroleum_pollution_incidents_nasp, 
     {
         ana_petroleum_pollution_incidents_nasp(output_path = "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0", 
             input_files = list(tg_e566fb8c_42d8_4013_8002_17af44a15a46_nasp))
-        "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/nasp_prep.gpkg"
-    }, format = "file"), tar_target(tg_a726889e_848e_4d6c_8496_a7a772e70d85_nasp_diffusive_model, 
-    {
-        ana_petroleum_pollution_incidents_nasp_diffusive(output_path = "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0", 
-            input_files = list(tg_a726889e_848e_4d6c_8496_a7a772e70d85_nasp_data_preparation))
-        "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/petroleum_pollution_incidents_nasp.tif"
-    }, format = "file"), tar_target(tg_a726889e_848e_4d6c_8496_a7a772e70d85_neec_data_preparation, 
+        "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/petroleum_pollution_incidents_nasp.gpkg"
+    }, format = "file"), tar_target(tg_a726889e_848e_4d6c_8496_a7a772e70d85_petroleum_pollution_incidents_neec, 
     {
         ana_petroleum_pollution_incidents_neec(output_path = "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0", 
             input_files = list(tg_56c710c3_1859_4eab_821f_e1e41dbbfd29_neec))
-        "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/neec_prep.gpkg"
-    }, format = "file"), tar_target(tg_a726889e_848e_4d6c_8496_a7a772e70d85_neec_diffusive_model, 
-    {
-        ana_petroleum_pollution_incidents_neec_diffusive(output_path = "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0", 
-            input_files = list(tg_a726889e_848e_4d6c_8496_a7a772e70d85_neec_data_preparation))
-        "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/petroleum_pollution_incidents_neec.tif"
+        "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/petroleum_pollution_incidents_neec.gpkg"
+    }, format = "file"), tar_target(tg_a726889e_848e_4d6c_8496_a7a772e70d85_petroleum_pollution_incidents, 
+    {
+        ana_petroleum_pollution_incidents(output_path = "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0", 
+            input_files = list(tg_a726889e_848e_4d6c_8496_a7a772e70d85_petroleum_pollution_incidents_istop, 
+                tg_a726889e_848e_4d6c_8496_a7a772e70d85_petroleum_pollution_incidents_nasp, 
+                tg_a726889e_848e_4d6c_8496_a7a772e70d85_petroleum_pollution_incidents_neec))
+        "workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/petroleum_pollution_incidents.gpkg"
     }, format = "file"))
 )
diff --git a/docs/1_research_compendium.html b/docs/1_research_compendium.html
new file mode 100644
index 0000000..3e7827c
--- /dev/null
+++ b/docs/1_research_compendium.html
@@ -0,0 +1,593 @@
+<!DOCTYPE html>
+<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
+
+<meta charset="utf-8">
+<meta name="generator" content="quarto-1.4.551">
+
+<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
+
+
+<title>Mapping threats at sea to marine birds throughout their annual cycle to inform conservation and management in Atlantic Canada - 2&nbsp; Research compendium</title>
+<style>
+code{white-space: pre-wrap;}
+span.smallcaps{font-variant: small-caps;}
+div.columns{display: flex; gap: min(4vw, 1.5em);}
+div.column{flex: auto; overflow-x: auto;}
+div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+ul.task-list{list-style: none;}
+ul.task-list li input[type="checkbox"] {
+  width: 0.8em;
+  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
+  vertical-align: middle;
+}
+</style>
+
+
+<script src="site_libs/quarto-nav/quarto-nav.js"></script>
+<script src="site_libs/quarto-nav/headroom.min.js"></script>
+<script src="site_libs/clipboard/clipboard.min.js"></script>
+<script src="site_libs/quarto-search/autocomplete.umd.js"></script>
+<script src="site_libs/quarto-search/fuse.min.js"></script>
+<script src="site_libs/quarto-search/quarto-search.js"></script>
+<meta name="quarto:offset" content="./">
+<link href="./2_data.html" rel="next">
+<link href="./index.html" rel="prev">
+<script src="site_libs/quarto-html/quarto.js"></script>
+<script src="site_libs/quarto-html/popper.min.js"></script>
+<script src="site_libs/quarto-html/tippy.umd.min.js"></script>
+<script src="site_libs/quarto-html/anchor.min.js"></script>
+<link href="site_libs/quarto-html/tippy.css" rel="stylesheet">
+<link href="site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles">
+<script src="site_libs/bootstrap/bootstrap.min.js"></script>
+<link href="site_libs/bootstrap/bootstrap-icons.css" rel="stylesheet">
+<link href="site_libs/bootstrap/bootstrap.min.css" rel="stylesheet" id="quarto-bootstrap" data-mode="light">
+<script id="quarto-search-options" type="application/json">{
+  "location": "sidebar",
+  "copy-button": false,
+  "collapse-after": 3,
+  "panel-placement": "start",
+  "type": "textbox",
+  "limit": 50,
+  "keyboard-shortcut": [
+    "f",
+    "/",
+    "s"
+  ],
+  "show-item-context": false,
+  "language": {
+    "search-no-results-text": "No results",
+    "search-matching-documents-text": "matching documents",
+    "search-copy-link-title": "Copy link to search",
+    "search-hide-matches-text": "Hide additional matches",
+    "search-more-match-text": "more match in this document",
+    "search-more-matches-text": "more matches in this document",
+    "search-clear-button-title": "Clear",
+    "search-text-placeholder": "",
+    "search-detached-cancel-button-title": "Cancel",
+    "search-submit-button-title": "Submit",
+    "search-label": "Search"
+  }
+}</script>
+
+
+</head>
+
+<body class="nav-sidebar floating">
+
+<div id="quarto-search-results"></div>
+  <header id="quarto-header" class="headroom fixed-top">
+  <nav class="quarto-secondary-nav">
+    <div class="container-fluid d-flex">
+      <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+        <i class="bi bi-layout-text-sidebar-reverse"></i>
+      </button>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./1_research_compendium.html"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Research compendium</span></a></li></ol></nav>
+        <a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
+        </a>
+      <button type="button" class="btn quarto-search-button" aria-label="" onclick="window.quartoOpenSearch();">
+        <i class="bi bi-search"></i>
+      </button>
+    </div>
+  </nav>
+</header>
+<!-- content -->
+<div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article">
+<!-- sidebar -->
+  <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal quarto-sidebar-collapse-item sidebar-navigation floating overflow-auto">
+    <div class="pt-lg-2 mt-2 text-left sidebar-header">
+    <div class="sidebar-title mb-0 py-0">
+      <a href="./">Mapping threats at sea to marine birds throughout their annual cycle to inform conservation and management in Atlantic Canada</a> 
+    </div>
+      </div>
+        <div class="mt-2 flex-shrink-0 align-items-center">
+        <div class="sidebar-search">
+        <div id="quarto-search" class="" title="Search"></div>
+        </div>
+        </div>
+    <div class="sidebar-menu-container"> 
+    <ul class="list-unstyled mt-1">
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./index.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Introduction</span></span></a>
+  </div>
+</li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./1_research_compendium.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Research compendium</span></span></a>
+  </div>
+</li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./2_data.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Sources Overview</span></span></a>
+  </div>
+</li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./3_threats.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Threat layers</span></span></a>
+  </div>
+</li>
+    </ul>
+    </div>
+</nav>
+<div id="quarto-sidebar-glass" class="quarto-sidebar-collapse-item" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item"></div>
+<!-- margin-sidebar -->
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+        
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+<!-- main -->
+<main class="content" id="quarto-document-content">
+
+<header id="title-block-header" class="quarto-title-block default">
+<div class="quarto-title">
+<h1 class="title"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Research compendium</span></h1>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+    
+  
+    
+  </div>
+  
+
+
+</header>
+
+
+
+
+
+</main> <!-- /main -->
+<script id="quarto-html-after-body" type="application/javascript">
+window.document.addEventListener("DOMContentLoaded", function (event) {
+  const toggleBodyColorMode = (bsSheetEl) => {
+    const mode = bsSheetEl.getAttribute("data-mode");
+    const bodyEl = window.document.querySelector("body");
+    if (mode === "dark") {
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+      bodyEl.classList.remove("quarto-light");
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+      return codeEl.innerText;
+    }
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+      if (!isInternal(link.href)) {
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+      },
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+      theme: 'quarto',
+      placement: 'bottom-start',
+    };
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+  for (var i=0; i<noterefs.length; i++) {
+    const ref = noterefs[i];
+    tippyHover(ref, function() {
+      // use id or data attribute instead here
+      let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
+      try { href = new URL(href).hash; } catch {}
+      const id = href.replace(/^#\/?/, "");
+      const note = window.document.getElementById(id);
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
+    });
+  }
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+  const processXRef = (id, note) => {
+    // Strip column container classes
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+      el.classList.remove("page-full", "page-columns");
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+      if (note.children && note.children.length > 2) {
+        container.appendChild(note.children[0].cloneNode(true));
+        for (let i = 1; i < note.children.length; i++) {
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+          if (child.tagName === "P" && child.innerText === "") {
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+          } else {
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+        }
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+      } else {
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+        }
+        return note.innerHTML;
+      }
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+      if (anchorLink) {
+        anchorLink.remove();
+      }
+      if (window.Quarto?.typesetMath) {
+        window.Quarto.typesetMath(note);
+      }
+      // TODO in 1.5, we should make sure this works without a callout special case
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+        return note.outerHTML;
+      } else {
+        return note.innerHTML;
+      }
+    }
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+          .then(res => res.text())
+          .then(html => {
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+            timer = setTimeout(() => {
+              fn.apply(this, args);
+              timer = throttle = false;
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+      // Attach click handler to the DT
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+});
+</script>
+<nav class="page-navigation">
+  <div class="nav-page nav-page-previous">
+      <a href="./index.html" class="pagination-link" aria-label="Introduction">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Introduction</span></span>
+      </a>          
+  </div>
+  <div class="nav-page nav-page-next">
+      <a href="./2_data.html" class="pagination-link" aria-label="Data Sources Overview">
+        <span class="nav-page-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Sources Overview</span></span> <i class="bi bi-arrow-right-short"></i>
+      </a>
+  </div>
+</nav>
+</div> <!-- /content -->
+
+
+
+
+</body></html>
\ No newline at end of file
diff --git a/docs/1_shipping.html b/docs/2_data.html
similarity index 70%
rename from docs/1_shipping.html
rename to docs/2_data.html
index cedb966..e2d89a7 100644
--- a/docs/1_shipping.html
+++ b/docs/2_data.html
@@ -7,7 +7,7 @@
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>Mapping threats at sea to marine birds throughout their annual cycle to inform conservation and management in Atlantic Canada - 2&nbsp; Shipping-based pollution</title>
+<title>Mapping threats at sea to marine birds throughout their annual cycle to inform conservation and management in Atlantic Canada - 3&nbsp; Data Sources Overview</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -50,8 +50,8 @@
 <script src="site_libs/quarto-search/fuse.min.js"></script>
 <script src="site_libs/quarto-search/quarto-search.js"></script>
 <meta name="quarto:offset" content="./">
-<link href="./2_petroleum.html" rel="next">
-<link href="./index.html" rel="prev">
+<link href="./3_threats.html" rel="next">
+<link href="./1_research_compendium.html" rel="prev">
 <script src="site_libs/quarto-html/quarto.js"></script>
 <script src="site_libs/quarto-html/popper.min.js"></script>
 <script src="site_libs/quarto-html/tippy.umd.min.js"></script>
@@ -101,7 +101,7 @@
       <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
         <i class="bi bi-layout-text-sidebar-reverse"></i>
       </button>
-        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./1_shipping.html"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Shipping-based pollution</span></a></li></ol></nav>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./2_data.html"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Sources Overview</span></a></li></ol></nav>
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         </a>
       <button type="button" class="btn quarto-search-button" aria-label="" onclick="window.quartoOpenSearch();">
@@ -134,14 +134,20 @@
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./1_shipping.html" class="sidebar-item-text sidebar-link active">
- <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Shipping-based pollution</span></span></a>
+  <a href="./1_research_compendium.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Research compendium</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./2_petroleum.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Petroleum pollution based on past incidents</span></span></a>
+  <a href="./2_data.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Sources Overview</span></span></a>
+  </div>
+</li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./3_threats.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Threat layers</span></span></a>
   </div>
 </li>
     </ul>
@@ -154,10 +160,13 @@
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#tasks" id="toc-tasks" class="nav-link active" data-scroll-target="#tasks"><span class="header-section-number">2.1</span> Tasks</a></li>
-  <li><a href="#global-fishing-watch" id="toc-global-fishing-watch" class="nav-link" data-scroll-target="#global-fishing-watch"><span class="header-section-number">2.2</span> Global Fishing Watch</a></li>
-  <li><a href="#vessel-density-mapping" id="toc-vessel-density-mapping" class="nav-link" data-scroll-target="#vessel-density-mapping"><span class="header-section-number">2.3</span> Vessel Density Mapping</a></li>
-  <li><a href="#notes-references" id="toc-notes-references" class="nav-link" data-scroll-target="#notes-references"><span class="header-section-number">2.4</span> Notes &amp; references</a></li>
+  <li><a href="#data-sources-overview" id="toc-data-sources-overview" class="nav-link active" data-scroll-target="#data-sources-overview"><span class="header-section-number">4</span> Data Sources Overview</a>
+  <ul class="collapse">
+  <li><a href="#integrated-satellite-tracking-of-pollution-istop-istop" id="toc-integrated-satellite-tracking-of-pollution-istop-istop" class="nav-link" data-scroll-target="#integrated-satellite-tracking-of-pollution-istop-istop"><span class="header-section-number">4.1</span> Integrated Satellite Tracking of Pollution (ISTOP) {istop}</a></li>
+  <li><a href="#national-aerial-surveillance-program-nasp-nasp" id="toc-national-aerial-surveillance-program-nasp-nasp" class="nav-link" data-scroll-target="#national-aerial-surveillance-program-nasp-nasp"><span class="header-section-number">4.2</span> National Aerial Surveillance Program (NASP) {nasp}</a></li>
+  <li><a href="#national-environmental-emergency-centre-neec-neec" id="toc-national-environmental-emergency-centre-neec-neec" class="nav-link" data-scroll-target="#national-environmental-emergency-centre-neec-neec"><span class="header-section-number">4.3</span> National Environmental Emergency Centre (NEEC) {neec}</a></li>
+  </ul></li>
+  <li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references"><span class="header-section-number">5</span> References</a></li>
   </ul>
 </nav>
     </div>
@@ -166,7 +175,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Shipping-based pollution</span></h1>
+<h1 class="title"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Sources Overview</span></h1>
 </div>
 
 
@@ -183,42 +192,55 @@ <h1 class="title"><span class="chapter-number">2</span>&nbsp; <span class="chapt
 </header>
 
 
-<section id="tasks" class="level2" data-number="2.1">
-<h2 data-number="2.1" class="anchored" data-anchor-id="tasks"><span class="header-section-number">2.1</span> Tasks</h2>
-<ol type="1">
-<li>Access to shipping AIS data through Global Fishing Watch</li>
-<li>Prepare monthly petroleum pollution and disturbance due to vessel traffic threat layers in the Atlantic Ocean
-<ol type="1">
-<li>All vessels</li>
-<li>Cargo/bulk vessels</li>
-<li>Fishing vessels</li>
-</ol></li>
-</ol>
+<section id="data-sources-overview" class="level1" data-number="4">
+<h1 data-number="4"><span class="header-section-number">4</span> Data Sources Overview</h1>
+<section id="integrated-satellite-tracking-of-pollution-istop-istop" class="level2" data-number="4.1">
+<h2 data-number="4.1" class="anchored" data-anchor-id="integrated-satellite-tracking-of-pollution-istop-istop"><span class="header-section-number">4.1</span> Integrated Satellite Tracking of Pollution (ISTOP) {istop}</h2>
+<p>The <a href="https://www.canada.ca/en/environment-climate-change/services/ice-forecasts-observations/latest-conditions/oil-pollution-monitoring-overview/satellite-spills-overview.html">Integrated Satellite Tracking of Pollution (ISTOP)</a> program <span class="citation" data-cites="eccc2024">(<a href="#ref-eccc2024" role="doc-biblioref">Environment and Climate Change Canada, 2024</a>)</span> utilizes satellite imagery to monitor and detect oil spills in coastal waters. Since 2006, daily satellite analyses have been conducted to identify and report both illegal and accidental marine oil pollution. These analyses detect sea-surface anomalies, which are vectorized into polygons representing affected areas. The data obtained span 2006 to 2023 and include 637 recorded incidents. The data was transferred manually to the Canadian Wildlife Service and are currently stored on a secure Google Cloud Storage bucket accessible through an authentication key and maintained by inSileco.</p>
+<ul>
+<li><strong>Source</strong>: Environment Canada Integrated Satellite Tracking of Pollution (ISTOP) Program</li>
+<li><strong>Data Type</strong>: Vectorized Polygons</li>
+<li><strong>Coverage</strong>: North American coastal waters, 2006–2023</li>
+<li><strong>Processing Script</strong>: <code>prc_istop.R</code></li>
+<li><strong>Output File</strong>: <code>istop.gpkg</code></li>
+</ul>
 </section>
-<section id="global-fishing-watch" class="level2" data-number="2.2">
-<h2 data-number="2.2" class="anchored" data-anchor-id="global-fishing-watch"><span class="header-section-number">2.2</span> Global Fishing Watch</h2>
-<p>The purpose of Global Fishing Watch is to create and publicly share knowledge about human activity at sea to enable fair and sustainable use of our ocean. They use cutting-edge technology to turn big data into actionable information. They believe human activity at sea should be common knowledge in order to safeguard the global ocean for the good of all. Global Fishing Watch’s open-access technology products, datasets, and code accelerate research and innovation and support sustainable ocean management. They offer a variety of products related to human activity at sea, including shipping and fisheries, but also data on ocean conditions and biology. Access to their data can be achieved through their <a href="https://globalfishingwatch.org/our-apis/">API</a>, which is also accessible through an R Package called <a href="https://github.com/GlobalFishingWatch/gfwr"><code>gfwr</code></a>.</p>
-<p>The <code>gfwr</code> R package offers wrapper functions to access data through the GFW APIs. It provides convenient functions to freely pull GFW data directly into R in tidy formats. The package currently works with the following APIs:</p>
+<section id="national-aerial-surveillance-program-nasp-nasp" class="level2" data-number="4.2">
+<h2 data-number="4.2" class="anchored" data-anchor-id="national-aerial-surveillance-program-nasp-nasp"><span class="header-section-number">4.2</span> National Aerial Surveillance Program (NASP) {nasp}</h2>
+<p>The <a href="https://tc.canada.ca/en/programs/national-aerial-surveillance-program">National Aerial Surveillance Program (NASP)</a> <span class="citation" data-cites="tc2024">(<a href="#ref-tc2024" role="doc-biblioref">Transport Canada, 2024</a>)</span> is a country-wide aerial surveillance initiative designed to detect pollution and protect Canadian waters. The program, covering pollution detections in the Atlantic region from April 2012 to March 2023, aims to safeguard marine environments, endangered marine life, and promote safe maritime transport along Canada’s coastlines. The dataset includes 3578 incidents. The data was transferred manually to the Canadian Wildlife Service and are currently stored on a secure Google Cloud Storage bucket accessible through an authentication key and maintained by inSileco.</p>
 <ul>
-<li><a href="https://globalfishingwatch.org/our-apis/documentation#vessels-api">Vessels API</a>: vessel search and identity based on AIS self-reported data</li>
-<li><a href="https://globalfishingwatch.org/our-apis/documentation#events-api">Events API</a>: encounters, loitering, port visits and fishing events based on AIS data</li>
-<li><a href="https://globalfishingwatch.org/our-apis/documentation#map-visualization-4wings-api">Map Visualization (4Wings API</a>: apparent fishing effort based on AIS data</li>
+<li><strong>Source</strong>: Transport Canada National Aerial Surveillance Program (NASP)</li>
+<li><strong>Data Type</strong>: CSV</li>
+<li><strong>Coverage</strong>: Atlantic Region, Canada, 2012–2023</li>
+<li><strong>Processing Script</strong>: <code>prc_nasp.R</code></li>
+<li><strong>Output File</strong>: <code>nasp.gpkg</code></li>
+</ul>
+</section>
+<section id="national-environmental-emergency-centre-neec-neec" class="level2" data-number="4.3">
+<h2 data-number="4.3" class="anchored" data-anchor-id="national-environmental-emergency-centre-neec-neec"><span class="header-section-number">4.3</span> National Environmental Emergency Centre (NEEC) {neec}</h2>
+<p>The <a href="https://www.canada.ca/en/environment-climate-change/services/environmental-emergencies-program/national-centre.html">National Environmental Emergency Centre (NEEC)</a> <span class="citation" data-cites="neec2024">(<a href="#ref-neec2024" role="doc-biblioref">National Environmental Emergency Centre, 2024</a>)</span>, a program under Environment and Climate Change Canada, provides an inventory of environmental emergency incidents across Canada. The NEEC dataset provided to the Canadian Wildlife Service covers incidents impacting waterbodies in the Atlantic region, including Québec, from 2016 to 2023, comprising 9570 recorded incidents. The data was transferred manually to the Canadian Wildlife Service and are currently stored on a secure Google Cloud Storage bucket accessible through an authentication key and maintained by inSileco.</p>
+<ul>
+<li><strong>Source</strong>: National Environmental Emergency Centre (NEEC)</li>
+<li><strong>Data Type</strong>: CSV</li>
+<li><strong>Coverage</strong>: Atlantic Region, Canada (including Québec), 2016–2023</li>
+<li><strong>Processing Script</strong>: <code>prc_neec.R</code></li>
+<li><strong>Output Files</strong>: <code>neec.csv</code> and <code>substances.csv</code></li>
 </ul>
-<p>The incentive of fetching data from such an API is that the current project could have been built so that continuous integration of new data could be performed, ensuring an updated assessment of this particular threat layer through time. However, after exploring the APIs and contacting representatives of GFW, we had to come to the conclusion that the data needed for this project were not and would likely not be available through their API. They stated that other products that might be useful are planned to be released in the future, such as vessel presence raster resources. However, the release dates for these resources extends beyond the timelines of the current project. We therefore had to pivot to an alternative data source to complete this part of the contract.</p>
 </section>
-<section id="vessel-density-mapping" class="level2" data-number="2.3">
-<h2 data-number="2.3" class="anchored" data-anchor-id="vessel-density-mapping"><span class="header-section-number">2.3</span> Vessel Density Mapping</h2>
-<p>During the same timeframe of this project, <span class="citation" data-cites="veinot2023">Veinot et al. (<a href="#ref-veinot2023" role="doc-biblioref">2023</a>)</span> published a Technical Report detailing a project seeking to map vessel density in the Northwest Atlantic in 2019 using Automatic Identification System (AIS) data. Their work covers our study area, but was not available publicly yet. Since, their work has been extended to include vessel density mapping for the years 2013 through 2022 and has been made public on the Open Government data platform as a series entitled <a href="https://open.canada.ca/data/en/dataset/e60a7e32-5a67-45d6-900d-e6ab6b581a51"><em>Vessel Density Mapping Series in the Northwest Atlantic</em></a>. These data provide vessel density raster layers monthly for all vessel types combined, and yearly for different vessel types. This does not match perfectly with the requirements for this project. Namely, we would need to differentiate between daytime and nighttime vessel traffic, and use monthly data for each vessel type. In light of this, we agreed on a strategy to address this issue.</p>
-<p>First, we built the code necessary to access the openly available data and integrate it in our study grid.; this data serves as an acceptable backup plan in case we cannot access more detailed data. The <a href="https://github.com/inSilecoInc/ceacws/blob/main/project-data/pipedat/vessel_density_mapping_dfo-98916b4a/vessel_density_mapping_dfo-98916b4a.R">code</a> that fetches the data from the Open Government platform and integrates it in our study grid has therefore been developed and is available in the <em>research compendium</em>.</p>
-<p>Second, the team from the CWS contacted the authors of <span class="citation" data-cites="veinot2023">Veinot et al. (<a href="#ref-veinot2023" role="doc-biblioref">2023</a>)</span> to discuss the possibility of either accessing the raw AIS data that they used to generate their own vessel density mapping, collaborating on a refined version of their work that addresses the needs of the CWS, or a combination of both. After discussing with their team, it was agreed that we would meet with a representative from their team to discuss the approach that they used to prepare their vessel density layers and potentially collaborate on updating their code to generate the layers that are required for this project. It was also agreed that the Northwest Atlantic region track lines and AIS data points, with MMSI data and times could be shared with us, under condition of a signed agreement and the transfer of anonymized MMSI numbers to ensure confidentiality. The data were transferred to the CWS on March 20th and we are currently waiting to have access to continue with our work. The threat layers using shipping data will therefore be completed during the second part of this project.</p>
 </section>
-<section id="notes-references" class="level2" data-number="2.4">
-<h2 data-number="2.4" class="anchored" data-anchor-id="notes-references"><span class="header-section-number">2.4</span> Notes &amp; references</h2>
+<section id="references" class="level1" data-number="5">
+<h1 data-number="5"><span class="header-section-number">5</span> References</h1>
 
 
 <div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="1" data-line-spacing="5" role="list">
-<div id="ref-veinot2023" class="csl-entry" role="listitem">
-Veinot, T., Nicoll, A., Rozalska, K., and Coffen-Smout, S. (2023). Vessel <span>Density Mapping</span> of 2019 <span>Automatic Identification System</span> (<span>AIS</span>) <span>Data</span> in the <span>Northwest Atlantic</span>. <span>Can</span>. <span>Tech</span>. <span>Rep</span>. <span>Fish</span>. <span>Aquat</span>. <span>Sci</span>. 3520: Vi + 29 p. <span>Department of Fisheries and Oceans</span>.
+<div id="ref-eccc2024" class="csl-entry" role="listitem">
+Environment and Climate Change Canada (2024). Integrated satellite tracking of pollution (ISTOP) program. <span>Environment and Climate Change Canada</span>.
+</div>
+<div id="ref-neec2024" class="csl-entry" role="listitem">
+National Environmental Emergency Centre (2024). Atlantic (including <span>Quebec</span>) notifications impacting or potentially impacting a waterbody with select substances, 2016-2023. <span>Extracted</span> by <span>Philippe Muise</span> 2024-01-29. <span>Environment and Climate Change Canada</span>.
+</div>
+<div id="ref-tc2024" class="csl-entry" role="listitem">
+Transport Canada (2024). National <span>Aerial Surveillance Program</span> (<span>NASP</span>). <span>Data</span> extraction for period between <span>April</span> 2012 to <span>March</span> 2023. <span>Transport Canada</span>.
 </div>
 </div>
 </section>
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+      <a href="./1_research_compendium.html" class="pagination-link" aria-label="Research compendium">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Research compendium</span></span>
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-<title>Mapping threats at sea to marine birds throughout their annual cycle to inform conservation and management in Atlantic Canada - 3&nbsp; Petroleum pollution based on past incidents</title>
+<title>Mapping threats at sea to marine birds throughout their annual cycle to inform conservation and management in Atlantic Canada - 4&nbsp; Threat layers</title>
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+  <a href="./1_research_compendium.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Research compendium</span></span></a>
+  </div>
+</li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./2_data.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Sources Overview</span></span></a>
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- <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Petroleum pollution based on past incidents</span></span></a>
+  <a href="./3_threats.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Threat layers</span></span></a>
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     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#tasks" id="toc-tasks" class="nav-link active" data-scroll-target="#tasks"><span class="header-section-number">3.1</span> Tasks</a></li>
-  <li><a href="#data-processing" id="toc-data-processing" class="nav-link" data-scroll-target="#data-processing"><span class="header-section-number">3.2</span> Data &amp; processing</a>
-  <ul class="collapse">
-  <li><a href="#neec" id="toc-neec" class="nav-link" data-scroll-target="#neec"><span class="header-section-number">3.2.1</span> NEEC</a></li>
-  <li><a href="#nasp" id="toc-nasp" class="nav-link" data-scroll-target="#nasp"><span class="header-section-number">3.2.2</span> NASP</a></li>
-  <li><a href="#istop" id="toc-istop" class="nav-link" data-scroll-target="#istop"><span class="header-section-number">3.2.3</span> ISTOP</a></li>
-  </ul></li>
-  <li><a href="#method" id="toc-method" class="nav-link" data-scroll-target="#method"><span class="header-section-number">3.3</span> Method</a></li>
-  <li><a href="#outstanding-questions" id="toc-outstanding-questions" class="nav-link" data-scroll-target="#outstanding-questions"><span class="header-section-number">3.4</span> Outstanding questions</a>
-  <ul class="collapse">
-  <li><a href="#neec-1" id="toc-neec-1" class="nav-link" data-scroll-target="#neec-1"><span class="header-section-number">3.4.1</span> NEEC</a></li>
-  <li><a href="#istop-1" id="toc-istop-1" class="nav-link" data-scroll-target="#istop-1"><span class="header-section-number">3.4.2</span> ISTOP</a></li>
-  </ul></li>
-  <li><a href="#notes-references" id="toc-notes-references" class="nav-link" data-scroll-target="#notes-references"><span class="header-section-number">3.5</span> Notes &amp; references</a></li>
+  <li><a href="#petroleum-pollution-based-on-past-incidents" id="toc-petroleum-pollution-based-on-past-incidents" class="nav-link active" data-scroll-target="#petroleum-pollution-based-on-past-incidents"><span class="header-section-number">5</span> Petroleum pollution based on past incidents</a></li>
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@@ -204,7 +148,7 @@ <h2 id="toc-title">Table of contents</h2>
 
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 <div class="quarto-title">
-<h1 class="title"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Petroleum pollution based on past incidents</span></h1>
+<h1 class="title"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Threat layers</span></h1>
 </div>
 
 
@@ -221,91 +165,59 @@ <h1 class="title"><span class="chapter-number">3</span>&nbsp; <span class="chapt
 </header>
 
 
-<section id="tasks" class="level2" data-number="3.1">
-<h2 data-number="3.1" class="anchored" data-anchor-id="tasks"><span class="header-section-number">3.1</span> Tasks</h2>
-<ol type="1">
-<li>Receive and format data on petroleum pollution based on past accidental spills for nearshore Canadian waters
-<ol type="1">
-<li>National Environmental Emergencies Centre (NEEC)</li>
-<li>National Aerial Surveillance Program (NASP)</li>
-<li>Integrated Satellite Tracking of Pollution (ISTOP)</li>
-</ol></li>
-<li>Prepare petroleum pollution based on past incidents threat layers for specified annual periods</li>
-</ol>
-</section>
-<section id="data-processing" class="level2" data-number="3.2">
-<h2 data-number="3.2" class="anchored" data-anchor-id="data-processing"><span class="header-section-number">3.2</span> Data &amp; processing</h2>
-<p>The data used to generate this threat layers were requested by the CWS and transferred to inSileco. They are stored on a secure Google Cloud Storage bucket to ensure the reproducibility of this project, although this arrangement will need to be revisited at the end of the contract with the CWS. There are three different datasets used, which are detailed below.</p>
-<section id="neec" class="level3" data-number="3.2.1">
-<h3 data-number="3.2.1" class="anchored" data-anchor-id="neec"><span class="header-section-number">3.2.1</span> NEEC</h3>
-<p>The <a href="https://www.canada.ca/en/environment-climate-change/services/environmental-emergencies-program/national-centre.html">National Environmental Emergency Centre (NEEC)</a> is a program led by Environment and Climate Change Canada that provides an inventory of environmental emergencies incidents in Canada. The data requested by the CWS covers notifications impacting or potentially impacting a waterbody for the Atlantic region (including Québec) for the period of 2016 to 2023 <span class="citation" data-cites="neec2024">(<a href="#ref-neec2024" role="doc-biblioref">Centre, 2024</a>)</span>. The data provided inventories 9570 incidents.</p>
-<p>The following filtering procedures were applied to the data in order to use it to produce a threat layer:</p>
+<section id="petroleum-pollution-based-on-past-incidents" class="level1" data-number="5">
+<h1 data-number="5"><span class="header-section-number">5</span> Petroleum pollution based on past incidents</h1>
+<p>The objective was to assess the risk associated with petroleum incidents by evaluating the frequency of past incidents at specific locations over a defined period. Ideally, a more detailed approach would model the diffusion of each spill based on factors such as total volume, substance type, and local or regional currents to estimate the likely spread of each event. However, this level of detail was beyond the scope of the current study. Given the retrospective nature of the dataset and its focus on historical rather than predictive risk, we chose a simplified approach. Each recorded or probable incident was treated as an individual, discrete event in the dataset to characterize exposure to petroleum-related incidents.</p>
+<p>This threat layer was constructed using the <a href="#istop">ISTOP</a>, <a href="#neec">NEEC</a> and <a href="#nasp">NASP</a> datasets. Each dataset was processed to create spatially and temporally explicit threat layers.</p>
+<p>For the ISTOP dataset, we retained only categories 1A (possible oil with a clearly associated target), 1B (possible oil with a target within 50 km), and 2 (possible oil without an identifiable source), resulting in 123 usable observations. Since the ISTOP data already includes polygons representing areas of exposure, these polygons were directly utilized as pollution events in the analysis.</p>
+<p>The NASP data was refined by removing records with missing coordinates, yielding 3403 usable observations. Each NASP incident is represented as a point with latitude/longitude coordinates and includes spill volumes in liters. Since this dataset lacks defined surface areas, we inferred exposure areas by categorizing spill volumes and applying a variable buffer around each point based on the reported volume (<a href="#tbl-spillvolumes" class="quarto-xref">Table&nbsp;<span>5.1</span></a>).</p>
+<p>The NEEC dataset underwent extensive filtering, including the removal of records with empty coordinates, selection of the highest quantity for multi-substance spills, and exclusion of terms like “mvi” or “furnace oil.” The final dataset includes 1663 observations within 1 km of the coastline. Similar to the NASP data, the NEEC incidents are represented as points with latitude/longitude coordinates and spill volumes. The same approach of categorizing by volume and buffering each point was applied to the NEEC data (<a href="#tbl-spillvolumes" class="quarto-xref">Table&nbsp;<span>5.1</span></a>). Additionally, NEEC data was classified by substance type, considering both chemical state (solid, liquid, gas) and the presence of an oil sheen. After filtering, incidents were categorized into liquid with oil sheen (1331), liquid without oil sheen (4), or unknown 328.These categories were retained to create distinct threat layers based on substance type.</p>
+<p>The full details of the data processing and analytical approach used is available in the script <code>ana_petroleum_pollution_incidents.R</code>.</p>
 <ul>
-<li>Remove empty coordinates</li>
-<li>Use data from “Notification Date Time” when not available from “Incident Local Date Time” field</li>
-<li>Remove records containing “covid”, “istop”, “sipps”</li>
-<li>Select greatest quantity for individual spills with multiple substances</li>
-<li>Remove Incident Name containing “mvi” or “furnace oil” to remove a few dozen incidents that generally won’t impact marine birds</li>
-<li>Keep all incidents within 1 <span class="math inline">\(km\)</span> of the coastline</li>
+<li><strong>Processing Script</strong>: <code>ana_petroleum_pollution_incidents.R</code></li>
+<li><strong>Output File</strong>: <code>petroleum_pollution_incidents.gpkg</code></li>
 </ul>
-<p>This filtering process yields a total of 1677 usable observations to characterize this pollution threat layer.</p>
-<!-- ```{R neecmap}
-mapview::mapview(neec2)
-``` -->
-</section>
-<section id="nasp" class="level3" data-number="3.2.2">
-<h3 data-number="3.2.2" class="anchored" data-anchor-id="nasp"><span class="header-section-number">3.2.2</span> NASP</h3>
-<p>The <a href="https://tc.canada.ca/en/programs/national-aerial-surveillance-program">National Aerial Surveillance Program (NASP)</a> is a surveillance program led by Transport Canada that operates across the country to detect pollution through aerial surveys. Its goals are to prevent pollution in our waters, protect our marine environment and endangered marine life, and ensure a safe and efficient transportation industry along our massive and varied coastlines. The data requested by the CWS covers oil pollution detections from visual or aided by instruments (<em>e.g.</em> radar) for the Atlantic region between April 2012 and March 2023 <span class="citation" data-cites="tc2024">(<a href="#ref-tc2024" role="doc-biblioref">Canada, 2024</a>)</span>. The data provided inventories 3578 incidents. Data were cleaned and formatted, with missing spill volumes removed from the dataset, resulting in 3572 usable observations to characterize this pollution threat layer.</p>
-</section>
-<section id="istop" class="level3" data-number="3.2.3">
-<h3 data-number="3.2.3" class="anchored" data-anchor-id="istop"><span class="header-section-number">3.2.3</span> ISTOP</h3>
-<p>The <a href="https://www.canada.ca/en/environment-climate-change/services/ice-forecasts-observations/latest-conditions/oil-pollution-monitoring-overview/satellite-spills-overview.html">Integrated Satellite Tracking of Pollution (ISTOP)</a> program assists environmental monitoring of coastal waters through satellite imagery analysis to detect oil spills. Since 2006, satellite imagery is analyzed daily to detect and report illegal and accidental oil pollution in marine environments. The analyses performed identify sea-surface anomalies identified as pollution from satellite imagery that are then transformed as vectorized polygons representing the surface affected by the pollution. The data requested by the CWS spanned the period of 2006 to 2023 and includes 637 incidents <span class="citation" data-cites="eccc2024">(<a href="#ref-eccc2024" role="doc-biblioref">Environment and Climate Change Canada, 2024</a>)</span>. Only data of categories 1A (<em>i.e.</em> possible oil with a target clearly associated), 1B (<em>i.e.</em> possible oil with a target within 50-km), and 2 (<em>i.e.</em> possible oil but without an identifiable source) were retained to characterize this pollution threat layer, resulting in 123 usable observations.</p>
-</section>
-</section>
-<section id="method" class="level2" data-number="3.3">
-<h2 data-number="3.3" class="anchored" data-anchor-id="method"><span class="header-section-number">3.3</span> Method</h2>
-<p>Since accidental spills have an effect that goes beyond the immediate location of the incidents due to the diffusion of the spill in the environment, the first step in characterizing the intensity of spills was to establish a potential zone of influence for each incident. To characterize this, we explored weighted kernel density estimates with a Gaussian distribution. However, kernels have the drawback of considering the density around a constant radius surrounding individual points, whereas each spill may have a different spatial footprint as a function of the total volume spilled. For this first iteration of the threat layers, we therefore chose a passive diffusive model to establish the zone of influence and relative intensity of accidental spills in the study area <span class="citation" data-cites="halpern2008a">(see <a href="#ref-halpern2008a" role="doc-biblioref">Halpern et al., 2008</a>)</span>.</p>
-<p>The diffusion model establishes the zone of influence of the spills by creating concentric rings centred on each incident. An initial value equal to the volume of the incident is assigned to the centre ring and represents the relative intensity of the incident on the environment. A value is then successively assigned to the adjacent rings according to a decay function of a percent value of the previous ring until a minimum threshold is reached. This approach assumes a linear decrease in the influence of the incidents on the environment.</p>
-<p>Since the data used are not directly comparable, we decided to create a single threat layer for each dataset. Furthermore, the range of volumes reported in the NEEC ([]) and NASP ([]) datasets were too great to use directly without completely masking the effects of smaller spill events. As such, we log-transformed the reported volumes to give less weight to very high volumes. The ISTOP dataset, meanwhile, has no reported volume. We therefore used the area in <span class="math inline">\(km^2\)</span> of individual incidents as a proxy of spill volumes. As with the other datasets, we log-transformed these values to decrease the influence of very large spills. Finally, in order to make intensity of threats related to past incidents more comparable, we normalized volumes/areas between 0 and 1 before running the diffusive plume models. The intensity and spatial distribution of accidental spills in the study area were then evaluated as the sum of the values of all concentric rings intersecting with cells of our study grid.</p>
-<p>The parameters that are currently used for the diffusive plume model are the following:</p>
-<ul>
-<li>Concentric rings radius: 100 <span class="math inline">\(m\)</span></li>
-<li>Decay function percent: 2%</li>
-<li>Minimum threshold: 0.05% of the global minimum, <em>i.e.</em> the lowest spill volume recorded</li>
-<li>Initial values: log-transformed and normalized volumes/areas</li>
-</ul>
-<p>All reported parameters and analytical decisions made could be further explored and modified in future iterations until we agree on the final threat layers desired. For us, these serve as a basis for further discussion towards producing the final threat layers generated through this project. Also, if requested, these parameters could also be included as parameters in the <em>research compendium</em>. The code used to perform this assessment is available <a href="https://github.com/inSilecoInc/ceacws/blob/main/R/threat_pollution.R">here</a>.</p>
-</section>
-<section id="outstanding-questions" class="level2" data-number="3.4">
-<h2 data-number="3.4" class="anchored" data-anchor-id="outstanding-questions"><span class="header-section-number">3.4</span> Outstanding questions</h2>
-<p>The following are some outstanding questions that were not presented in the previous sections.</p>
-<section id="neec-1" class="level3" data-number="3.4.1">
-<h3 data-number="3.4.1" class="anchored" data-anchor-id="neec-1"><span class="header-section-number">3.4.1</span> NEEC</h3>
-<p>So far, we set all NA quantities to 1 to perform the analysis. This should be discussed, as entries without volume makeup 58% of the NEEC dataset. Furthermore, certain volumes are reported in litres and others in kilograms. At the moment we are considering that the units are the same, but this is not necessarily the case. There are 11 incidents reports in kilograms in the NEEC dataset.</p>
-</section>
-<section id="istop-1" class="level3" data-number="3.4.2">
-<h3 data-number="3.4.2" class="anchored" data-anchor-id="istop-1"><span class="header-section-number">3.4.2</span> ISTOP</h3>
-<p>There are many duplicates in the data. For now, we only kept unique rows, but we are wondering whether there is a reason we are unaware of for these duplicates to exist and be used in combination.</p>
-</section>
-</section>
-<section id="notes-references" class="level2" data-number="3.5">
-<h2 data-number="3.5" class="anchored" data-anchor-id="notes-references"><span class="header-section-number">3.5</span> Notes &amp; references</h2>
-
-
-<div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="1" data-line-spacing="5" role="list">
-<div id="ref-tc2024" class="csl-entry" role="listitem">
-Canada, T. (2024). National <span>Aerial Surveillance Program</span> (<span>NASP</span>). <span>Data</span> extraction for period between <span>April</span> 2012 to <span>March</span> 2023. Transport Canada.
+<div class="cell">
+<div id="tbl-spillvolumes" class="cell quarto-float anchored">
+<figure class="quarto-float quarto-float-tbl figure">
+<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-spillvolumes-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+Table&nbsp;5.1: Volume categories and buffer size selected to transform incident as point locations to area of exposure from
+</figcaption>
+<div aria-describedby="tbl-spillvolumes-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+<div class="cell-output-display">
+<table class="do-not-create-environment cell table table-sm table-striped small">
+<thead>
+<tr class="header">
+<th style="text-align: left;">Volume (L)</th>
+<th style="text-align: right;">Buffer size (m)</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td style="text-align: left;">[0, 100]</td>
+<td style="text-align: right;">500</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">[100, 1000]</td>
+<td style="text-align: right;">2000</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">[1000, 10000]</td>
+<td style="text-align: right;">3000</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">[10000+ [</td>
+<td style="text-align: right;">5000</td>
+</tr>
+</tbody>
+</table>
 </div>
-<div id="ref-neec2024" class="csl-entry" role="listitem">
-Centre, N. E. E. (2024). Atlantic (including <span>Quebec</span>) notifications impacting or potentially impacting a waterbody with select substances, 2016-2023. <span>Extracted</span> by <span>Philippe Muise</span> 2024-01-29. <span>Environment and Climate Change Canada</span>.
 </div>
-<div id="ref-eccc2024" class="csl-entry" role="listitem">
-Environment, and Climate Change Canada (2024). Integrated <span>Satellite Tracking</span> of <span>Pollution</span> (<span>ISTOP</span>) program. <span>Data</span> extraction for period between 2006 and 2023. <span>Environment and Climate Change Canada</span>.
-</div>
-<div id="ref-halpern2008a" class="csl-entry" role="listitem">
-Halpern, B. S., Walbridge, S., Selkoe, K. A., Kappel, C. V., Micheli, F., D’Agrosa, C., et al. (2008). A <span>Global Map</span> of <span>Human Impact</span> on <span>Marine Ecosystems</span>. <em>Science</em> 319, 948–952. doi:<a href="https://doi.org/10.1126/science.1149345">10.1126/science.1149345</a>.
+</figure>
 </div>
 </div>
+
+
 </section>
 
 </main> <!-- /main -->
@@ -720,8 +632,8 @@ <h2 data-number="3.5" class="anchored" data-anchor-id="notes-references"><span c
 </script>
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
-      <a href="./1_shipping.html" class="pagination-link" aria-label="Shipping-based pollution">
-        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Shipping-based pollution</span></span>
+      <a href="./2_data.html" class="pagination-link" aria-label="Data Sources Overview">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Sources Overview</span></span>
       </a>          
   </div>
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diff --git a/docs/index.html b/docs/index.html
index 375c302..bcb446b 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -33,7 +33,7 @@
 <script src="site_libs/quarto-search/fuse.min.js"></script>
 <script src="site_libs/quarto-search/quarto-search.js"></script>
 <meta name="quarto:offset" content="./">
-<link href="./1_shipping.html" rel="next">
+<link href="./1_research_compendium.html" rel="next">
 <script src="site_libs/quarto-html/quarto.js"></script>
 <script src="site_libs/quarto-html/popper.min.js"></script>
 <script src="site_libs/quarto-html/tippy.umd.min.js"></script>
@@ -145,14 +145,20 @@
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./1_shipping.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Shipping-based pollution</span></span></a>
+  <a href="./1_research_compendium.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Research compendium</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./2_petroleum.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Petroleum pollution based on past incidents</span></span></a>
+  <a href="./2_data.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Sources Overview</span></span></a>
+  </div>
+</li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./3_threats.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Threat layers</span></span></a>
   </div>
 </li>
     </ul>
@@ -250,21 +256,7 @@ <h3 data-number="1.2.2" class="anchored" data-anchor-id="progress-report"><span
 </section>
 <section id="area-of-interest" class="level2" data-number="1.3">
 <h2 data-number="1.3" class="anchored" data-anchor-id="area-of-interest"><span class="header-section-number">1.3</span> Area of interest</h2>
-<p>The project is conducted in EEZ of eastern Canada and covers the St.&nbsp;Lawrence, the western Atlantic and adjacent International Waters. As currently defined, the study area covers 7293008 <span class="math inline">\(km^2\)</span> (<a href="#fig-aoi" class="quarto-xref">Figure&nbsp;<span>1.1</span></a>). All threat layers are integrated in a study grid that divides the study area into a regular grid formed of cells of ~1 <span class="math inline">\(km^2\)</span>, although this decision can be revisited at any time throughout the project.</p>
-<div class="cell">
-<div class="cell-output-display">
-<div id="fig-aoi" class="quarto-figure quarto-figure-center quarto-float anchored">
-<figure class="quarto-float quarto-float-fig figure">
-<div aria-describedby="fig-aoi-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<img src="index_files/figure-html/fig-aoi-1.png" class="img-fluid figure-img" style="width:90.0%">
-</div>
-<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-aoi-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-Figure&nbsp;1.1: Limits of the study area for the project on mapping threats at sea to marine birds throughout their annual cycle to inform conservation and management in Atlantic Canada.
-</figcaption>
-</figure>
-</div>
-</div>
-</div>
+<p>The project is conducted in EEZ of eastern Canada and covers the St.&nbsp;Lawrence, the western Atlantic and adjacent International Waters. As currently defined, the study area covers r aoi_area <span class="math inline">\(km^2\)</span> (<span class="quarto-unresolved-ref">?fig-aoi</span>). All threat layers are integrated in a study grid that divides the study area into a regular grid formed of cells of ~1 <span class="math inline">\(km^2\)</span>, although this decision can be revisited at any time throughout the project.</p>
 </section>
 <section id="data-management-and-reproducibility" class="level2" data-number="1.4">
 <h2 data-number="1.4" class="anchored" data-anchor-id="data-management-and-reproducibility"><span class="header-section-number">1.4</span> Data management and reproducibility</h2>
@@ -702,8 +694,8 @@ <h2 data-number="1.5" class="anchored" data-anchor-id="notes-references"><span c
   <div class="nav-page nav-page-previous">
   </div>
   <div class="nav-page nav-page-next">
-      <a href="./1_shipping.html" class="pagination-link" aria-label="Shipping-based pollution">
-        <span class="nav-page-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Shipping-based pollution</span></span> <i class="bi bi-arrow-right-short"></i>
+      <a href="./1_research_compendium.html" class="pagination-link" aria-label="Research compendium">
+        <span class="nav-page-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Research compendium</span></span> <i class="bi bi-arrow-right-short"></i>
       </a>
   </div>
 </nav>
diff --git a/docs/index_files/figure-html/fig-aoi-1.png b/docs/index_files/figure-html/fig-aoi-1.png
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     "title": "Mapping threats at sea to marine birds throughout their annual cycle to inform conservation and management in Atlantic Canada",
     "section": "1.3 Area of interest",
-    "text": "1.3 Area of interest\nThe project is conducted in EEZ of eastern Canada and covers the St. Lawrence, the western Atlantic and adjacent International Waters. As currently defined, the study area covers 7293008 \\(km^2\\) (Figure 1.1). All threat layers are integrated in a study grid that divides the study area into a regular grid formed of cells of ~1 \\(km^2\\), although this decision can be revisited at any time throughout the project.\n\n\n\n\n\n\n\n\nFigure 1.1: Limits of the study area for the project on mapping threats at sea to marine birds throughout their annual cycle to inform conservation and management in Atlantic Canada.",
+    "text": "1.3 Area of interest\nThe project is conducted in EEZ of eastern Canada and covers the St. Lawrence, the western Atlantic and adjacent International Waters. As currently defined, the study area covers r aoi_area \\(km^2\\) (?fig-aoi). All threat layers are integrated in a study grid that divides the study area into a regular grid formed of cells of ~1 \\(km^2\\), although this decision can be revisited at any time throughout the project.",
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-    "text": "Access to shipping AIS data through Global Fishing Watch\nPrepare monthly petroleum pollution and disturbance due to vessel traffic threat layers in the Atlantic Ocean\n\nAll vessels\nCargo/bulk vessels\nFishing vessels",
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+    "section": "4.1 Integrated Satellite Tracking of Pollution (ISTOP) {istop}",
+    "text": "4.1 Integrated Satellite Tracking of Pollution (ISTOP) {istop}\nThe Integrated Satellite Tracking of Pollution (ISTOP) program (Environment and Climate Change Canada, 2024) utilizes satellite imagery to monitor and detect oil spills in coastal waters. Since 2006, daily satellite analyses have been conducted to identify and report both illegal and accidental marine oil pollution. These analyses detect sea-surface anomalies, which are vectorized into polygons representing affected areas. The data obtained span 2006 to 2023 and include 637 recorded incidents. The data was transferred manually to the Canadian Wildlife Service and are currently stored on a secure Google Cloud Storage bucket accessible through an authentication key and maintained by inSileco.\n\nSource: Environment Canada Integrated Satellite Tracking of Pollution (ISTOP) Program\nData Type: Vectorized Polygons\nCoverage: North American coastal waters, 2006–2023\nProcessing Script: prc_istop.R\nOutput File: istop.gpkg",
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-    "section": "2.2 Global Fishing Watch",
-    "text": "2.2 Global Fishing Watch\nThe purpose of Global Fishing Watch is to create and publicly share knowledge about human activity at sea to enable fair and sustainable use of our ocean. They use cutting-edge technology to turn big data into actionable information. They believe human activity at sea should be common knowledge in order to safeguard the global ocean for the good of all. Global Fishing Watch’s open-access technology products, datasets, and code accelerate research and innovation and support sustainable ocean management. They offer a variety of products related to human activity at sea, including shipping and fisheries, but also data on ocean conditions and biology. Access to their data can be achieved through their API, which is also accessible through an R Package called gfwr.\nThe gfwr R package offers wrapper functions to access data through the GFW APIs. It provides convenient functions to freely pull GFW data directly into R in tidy formats. The package currently works with the following APIs:\n\nVessels API: vessel search and identity based on AIS self-reported data\nEvents API: encounters, loitering, port visits and fishing events based on AIS data\nMap Visualization (4Wings API: apparent fishing effort based on AIS data\n\nThe incentive of fetching data from such an API is that the current project could have been built so that continuous integration of new data could be performed, ensuring an updated assessment of this particular threat layer through time. However, after exploring the APIs and contacting representatives of GFW, we had to come to the conclusion that the data needed for this project were not and would likely not be available through their API. They stated that other products that might be useful are planned to be released in the future, such as vessel presence raster resources. However, the release dates for these resources extends beyond the timelines of the current project. We therefore had to pivot to an alternative data source to complete this part of the contract.",
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+    "section": "4.2 National Aerial Surveillance Program (NASP) {nasp}",
+    "text": "4.2 National Aerial Surveillance Program (NASP) {nasp}\nThe National Aerial Surveillance Program (NASP) (Transport Canada, 2024) is a country-wide aerial surveillance initiative designed to detect pollution and protect Canadian waters. The program, covering pollution detections in the Atlantic region from April 2012 to March 2023, aims to safeguard marine environments, endangered marine life, and promote safe maritime transport along Canada’s coastlines. The dataset includes 3578 incidents. The data was transferred manually to the Canadian Wildlife Service and are currently stored on a secure Google Cloud Storage bucket accessible through an authentication key and maintained by inSileco.\n\nSource: Transport Canada National Aerial Surveillance Program (NASP)\nData Type: CSV\nCoverage: Atlantic Region, Canada, 2012–2023\nProcessing Script: prc_nasp.R\nOutput File: nasp.gpkg",
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-    "section": "2.3 Vessel Density Mapping",
-    "text": "2.3 Vessel Density Mapping\nDuring the same timeframe of this project, Veinot et al. (2023) published a Technical Report detailing a project seeking to map vessel density in the Northwest Atlantic in 2019 using Automatic Identification System (AIS) data. Their work covers our study area, but was not available publicly yet. Since, their work has been extended to include vessel density mapping for the years 2013 through 2022 and has been made public on the Open Government data platform as a series entitled Vessel Density Mapping Series in the Northwest Atlantic. These data provide vessel density raster layers monthly for all vessel types combined, and yearly for different vessel types. This does not match perfectly with the requirements for this project. Namely, we would need to differentiate between daytime and nighttime vessel traffic, and use monthly data for each vessel type. In light of this, we agreed on a strategy to address this issue.\nFirst, we built the code necessary to access the openly available data and integrate it in our study grid.; this data serves as an acceptable backup plan in case we cannot access more detailed data. The code that fetches the data from the Open Government platform and integrates it in our study grid has therefore been developed and is available in the research compendium.\nSecond, the team from the CWS contacted the authors of Veinot et al. (2023) to discuss the possibility of either accessing the raw AIS data that they used to generate their own vessel density mapping, collaborating on a refined version of their work that addresses the needs of the CWS, or a combination of both. After discussing with their team, it was agreed that we would meet with a representative from their team to discuss the approach that they used to prepare their vessel density layers and potentially collaborate on updating their code to generate the layers that are required for this project. It was also agreed that the Northwest Atlantic region track lines and AIS data points, with MMSI data and times could be shared with us, under condition of a signed agreement and the transfer of anonymized MMSI numbers to ensure confidentiality. The data were transferred to the CWS on March 20th and we are currently waiting to have access to continue with our work. The threat layers using shipping data will therefore be completed during the second part of this project.",
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+    "section": "4.3 National Environmental Emergency Centre (NEEC) {neec}",
+    "text": "4.3 National Environmental Emergency Centre (NEEC) {neec}\nThe National Environmental Emergency Centre (NEEC) (National Environmental Emergency Centre, 2024), a program under Environment and Climate Change Canada, provides an inventory of environmental emergency incidents across Canada. The NEEC dataset provided to the Canadian Wildlife Service covers incidents impacting waterbodies in the Atlantic region, including Québec, from 2016 to 2023, comprising 9570 recorded incidents. The data was transferred manually to the Canadian Wildlife Service and are currently stored on a secure Google Cloud Storage bucket accessible through an authentication key and maintained by inSileco.\n\nSource: National Environmental Emergency Centre (NEEC)\nData Type: CSV\nCoverage: Atlantic Region, Canada (including Québec), 2016–2023\nProcessing Script: prc_neec.R\nOutput Files: neec.csv and substances.csv",
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-    "text": "2.4 Notes & references\n\n\n\n\nVeinot, T., Nicoll, A., Rozalska, K., and Coffen-Smout, S. (2023). Vessel Density Mapping of 2019 Automatic Identification System (AIS) Data in the Northwest Atlantic. Can. Tech. Rep. Fish. Aquat. Sci. 3520: Vi + 29 p. Department of Fisheries and Oceans.",
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-    "text": "Receive and format data on petroleum pollution based on past accidental spills for nearshore Canadian waters\n\nNational Environmental Emergencies Centre (NEEC)\nNational Aerial Surveillance Program (NASP)\nIntegrated Satellite Tracking of Pollution (ISTOP)\n\nPrepare petroleum pollution based on past incidents threat layers for specified annual periods",
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-    "section": "3.2 Data & processing",
-    "text": "3.2 Data & processing\nThe data used to generate this threat layers were requested by the CWS and transferred to inSileco. They are stored on a secure Google Cloud Storage bucket to ensure the reproducibility of this project, although this arrangement will need to be revisited at the end of the contract with the CWS. There are three different datasets used, which are detailed below.\n\n3.2.1 NEEC\nThe National Environmental Emergency Centre (NEEC) is a program led by Environment and Climate Change Canada that provides an inventory of environmental emergencies incidents in Canada. The data requested by the CWS covers notifications impacting or potentially impacting a waterbody for the Atlantic region (including Québec) for the period of 2016 to 2023 (Centre, 2024). The data provided inventories 9570 incidents.\nThe following filtering procedures were applied to the data in order to use it to produce a threat layer:\n\nRemove empty coordinates\nUse data from “Notification Date Time” when not available from “Incident Local Date Time” field\nRemove records containing “covid”, “istop”, “sipps”\nSelect greatest quantity for individual spills with multiple substances\nRemove Incident Name containing “mvi” or “furnace oil” to remove a few dozen incidents that generally won’t impact marine birds\nKeep all incidents within 1 \\(km\\) of the coastline\n\nThis filtering process yields a total of 1677 usable observations to characterize this pollution threat layer.\n\n\n\n3.2.2 NASP\nThe National Aerial Surveillance Program (NASP) is a surveillance program led by Transport Canada that operates across the country to detect pollution through aerial surveys. Its goals are to prevent pollution in our waters, protect our marine environment and endangered marine life, and ensure a safe and efficient transportation industry along our massive and varied coastlines. The data requested by the CWS covers oil pollution detections from visual or aided by instruments (e.g. radar) for the Atlantic region between April 2012 and March 2023 (Canada, 2024). The data provided inventories 3578 incidents. Data were cleaned and formatted, with missing spill volumes removed from the dataset, resulting in 3572 usable observations to characterize this pollution threat layer.\n\n\n3.2.3 ISTOP\nThe Integrated Satellite Tracking of Pollution (ISTOP) program assists environmental monitoring of coastal waters through satellite imagery analysis to detect oil spills. Since 2006, satellite imagery is analyzed daily to detect and report illegal and accidental oil pollution in marine environments. The analyses performed identify sea-surface anomalies identified as pollution from satellite imagery that are then transformed as vectorized polygons representing the surface affected by the pollution. The data requested by the CWS spanned the period of 2006 to 2023 and includes 637 incidents (Environment and Climate Change Canada, 2024). Only data of categories 1A (i.e. possible oil with a target clearly associated), 1B (i.e. possible oil with a target within 50-km), and 2 (i.e. possible oil but without an identifiable source) were retained to characterize this pollution threat layer, resulting in 123 usable observations.",
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-    "text": "3.3 Method\nSince accidental spills have an effect that goes beyond the immediate location of the incidents due to the diffusion of the spill in the environment, the first step in characterizing the intensity of spills was to establish a potential zone of influence for each incident. To characterize this, we explored weighted kernel density estimates with a Gaussian distribution. However, kernels have the drawback of considering the density around a constant radius surrounding individual points, whereas each spill may have a different spatial footprint as a function of the total volume spilled. For this first iteration of the threat layers, we therefore chose a passive diffusive model to establish the zone of influence and relative intensity of accidental spills in the study area (see Halpern et al., 2008).\nThe diffusion model establishes the zone of influence of the spills by creating concentric rings centred on each incident. An initial value equal to the volume of the incident is assigned to the centre ring and represents the relative intensity of the incident on the environment. A value is then successively assigned to the adjacent rings according to a decay function of a percent value of the previous ring until a minimum threshold is reached. This approach assumes a linear decrease in the influence of the incidents on the environment.\nSince the data used are not directly comparable, we decided to create a single threat layer for each dataset. Furthermore, the range of volumes reported in the NEEC ([]) and NASP ([]) datasets were too great to use directly without completely masking the effects of smaller spill events. As such, we log-transformed the reported volumes to give less weight to very high volumes. The ISTOP dataset, meanwhile, has no reported volume. We therefore used the area in \\(km^2\\) of individual incidents as a proxy of spill volumes. As with the other datasets, we log-transformed these values to decrease the influence of very large spills. Finally, in order to make intensity of threats related to past incidents more comparable, we normalized volumes/areas between 0 and 1 before running the diffusive plume models. The intensity and spatial distribution of accidental spills in the study area were then evaluated as the sum of the values of all concentric rings intersecting with cells of our study grid.\nThe parameters that are currently used for the diffusive plume model are the following:\n\nConcentric rings radius: 100 \\(m\\)\nDecay function percent: 2%\nMinimum threshold: 0.05% of the global minimum, i.e. the lowest spill volume recorded\nInitial values: log-transformed and normalized volumes/areas\n\nAll reported parameters and analytical decisions made could be further explored and modified in future iterations until we agree on the final threat layers desired. For us, these serve as a basis for further discussion towards producing the final threat layers generated through this project. Also, if requested, these parameters could also be included as parameters in the research compendium. The code used to perform this assessment is available here.",
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-    "text": "3.4 Outstanding questions\nThe following are some outstanding questions that were not presented in the previous sections.\n\n3.4.1 NEEC\nSo far, we set all NA quantities to 1 to perform the analysis. This should be discussed, as entries without volume makeup 58% of the NEEC dataset. Furthermore, certain volumes are reported in litres and others in kilograms. At the moment we are considering that the units are the same, but this is not necessarily the case. There are 11 incidents reports in kilograms in the NEEC dataset.\n\n\n3.4.2 ISTOP\nThere are many duplicates in the data. For now, we only kept unique rows, but we are wondering whether there is a reason we are unaware of for these duplicates to exist and be used in combination.",
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-    "section": "3.5 Notes & references",
-    "text": "3.5 Notes & references\n\n\n\n\nCanada, T. (2024). National Aerial Surveillance Program (NASP). Data extraction for period between April 2012 to March 2023. Transport Canada.\n\n\nCentre, N. E. E. (2024). Atlantic (including Quebec) notifications impacting or potentially impacting a waterbody with select substances, 2016-2023. Extracted by Philippe Muise 2024-01-29. Environment and Climate Change Canada.\n\n\nEnvironment, and Climate Change Canada (2024). Integrated Satellite Tracking of Pollution (ISTOP) program. Data extraction for period between 2006 and 2023. Environment and Climate Change Canada.\n\n\nHalpern, B. S., Walbridge, S., Selkoe, K. A., Kappel, C. V., Micheli, F., D’Agrosa, C., et al. (2008). A Global Map of Human Impact on Marine Ecosystems. Science 319, 948–952. doi:10.1126/science.1149345.",
+    "text": "5 Petroleum pollution based on past incidents\nThe objective was to assess the risk associated with petroleum incidents by evaluating the frequency of past incidents at specific locations over a defined period. Ideally, a more detailed approach would model the diffusion of each spill based on factors such as total volume, substance type, and local or regional currents to estimate the likely spread of each event. However, this level of detail was beyond the scope of the current study. Given the retrospective nature of the dataset and its focus on historical rather than predictive risk, we chose a simplified approach. Each recorded or probable incident was treated as an individual, discrete event in the dataset to characterize exposure to petroleum-related incidents.\nThis threat layer was constructed using the ISTOP, NEEC and NASP datasets. Each dataset was processed to create spatially and temporally explicit threat layers.\nFor the ISTOP dataset, we retained only categories 1A (possible oil with a clearly associated target), 1B (possible oil with a target within 50 km), and 2 (possible oil without an identifiable source), resulting in 123 usable observations. Since the ISTOP data already includes polygons representing areas of exposure, these polygons were directly utilized as pollution events in the analysis.\nThe NASP data was refined by removing records with missing coordinates, yielding 3403 usable observations. Each NASP incident is represented as a point with latitude/longitude coordinates and includes spill volumes in liters. Since this dataset lacks defined surface areas, we inferred exposure areas by categorizing spill volumes and applying a variable buffer around each point based on the reported volume (Table 5.1).\nThe NEEC dataset underwent extensive filtering, including the removal of records with empty coordinates, selection of the highest quantity for multi-substance spills, and exclusion of terms like “mvi” or “furnace oil.” The final dataset includes 1663 observations within 1 km of the coastline. Similar to the NASP data, the NEEC incidents are represented as points with latitude/longitude coordinates and spill volumes. The same approach of categorizing by volume and buffering each point was applied to the NEEC data (Table 5.1). Additionally, NEEC data was classified by substance type, considering both chemical state (solid, liquid, gas) and the presence of an oil sheen. After filtering, incidents were categorized into liquid with oil sheen (1331), liquid without oil sheen (4), or unknown 328.These categories were retained to create distinct threat layers based on substance type.\nThe full details of the data processing and analytical approach used is available in the script ana_petroleum_pollution_incidents.R.\n\nProcessing Script: ana_petroleum_pollution_incidents.R\nOutput File: petroleum_pollution_incidents.gpkg\n\n\n\n\n\nTable 5.1: Volume categories and buffer size selected to transform incident as point locations to area of exposure from\n\n\n\n\n\n\nVolume (L)\nBuffer size (m)\n\n\n\n\n[0, 100]\n500\n\n\n[100, 1000]\n2000\n\n\n[1000, 10000]\n3000\n\n\n[10000+ [\n5000",
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+---
+
+# Data Sources Overview
+
+## Integrated Satellite Tracking of Pollution (ISTOP) {istop}
+
+```{r}
+#| message: false
+istop <- sf::st_read("../../workspace/data/harvested/istop-1.0.0/processed/istop.gpkg", quiet = TRUE)
+```
+
+The [Integrated Satellite Tracking of Pollution (ISTOP)](https://www.canada.ca/en/environment-climate-change/services/ice-forecasts-observations/latest-conditions/oil-pollution-monitoring-overview/satellite-spills-overview.html) program [@eccc2024] utilizes satellite imagery to monitor and detect oil spills in coastal waters. Since 2006, daily satellite analyses have been conducted to identify and report both illegal and accidental marine oil pollution. These analyses detect sea-surface anomalies, which are vectorized into polygons representing affected areas. The data obtained span 2006 to 2023 and include `r nrow(istop)` recorded incidents. The data was transferred manually to the Canadian Wildlife Service and are currently stored on a secure Google Cloud Storage bucket accessible through an authentication key and maintained by inSileco.
+
+- **Source**: Environment Canada Integrated Satellite Tracking of Pollution (ISTOP) Program
+- **Data Type**: Vectorized Polygons
+- **Coverage**: North American coastal waters, 2006–2023
+- **Processing Script**: `prc_istop.R`
+- **Output File**: `istop.gpkg`
+
+## National Aerial Surveillance Program (NASP) {nasp}
+
+```{r}
+#| message: false
+nasp <- sf::st_read("../../workspace/data/harvested/nasp-1.0.0/processed/nasp.gpkg", quiet = TRUE)
+```
+
+The [National Aerial Surveillance Program (NASP)](https://tc.canada.ca/en/programs/national-aerial-surveillance-program) [@tc2024] is a country-wide aerial surveillance initiative designed to detect pollution and protect Canadian waters. The program, covering pollution detections in the Atlantic region from April 2012 to March 2023, aims to safeguard marine environments, endangered marine life, and promote safe maritime transport along Canada’s coastlines. The dataset includes `r nrow(nasp)` incidents. The data was transferred manually to the Canadian Wildlife Service and are currently stored on a secure Google Cloud Storage bucket accessible through an authentication key and maintained by inSileco.
+
+- **Source**: Transport Canada National Aerial Surveillance Program (NASP)
+- **Data Type**: CSV
+- **Coverage**: Atlantic Region, Canada, 2012–2023
+- **Processing Script**: `prc_nasp.R`
+- **Output File**: `nasp.gpkg`
+
+## National Environmental Emergency Centre (NEEC) {neec}
+
+```{r}
+#| message: false
+neec <- vroom::vroom("../../workspace/data/harvested/neec-1.0.0/processed/neec.csv")
+```
+
+The [National Environmental Emergency Centre (NEEC)](https://www.canada.ca/en/environment-climate-change/services/environmental-emergencies-program/national-centre.html) [@neec2024], a program under Environment and Climate Change Canada, provides an inventory of environmental emergency incidents across Canada. The NEEC dataset provided to the Canadian Wildlife Service covers incidents impacting waterbodies in the Atlantic region, including Québec, from 2016 to 2023, comprising `r nrow(neec)` recorded incidents. The data was transferred manually to the Canadian Wildlife Service and are currently stored on a secure Google Cloud Storage bucket accessible through an authentication key and maintained by inSileco.
+
+- **Source**: National Environmental Emergency Centre (NEEC)
+- **Data Type**: CSV
+- **Coverage**: Atlantic Region, Canada (including Québec), 2016–2023
+- **Processing Script**: `prc_neec.R`
+- **Output Files**: `neec.csv` and `substances.csv`
+
+
+# References
\ No newline at end of file
diff --git a/pubs/report/2_petroleum.qmd b/pubs/report/2_petroleum.qmd
deleted file mode 100644
index a05c974..0000000
--- a/pubs/report/2_petroleum.qmd
+++ /dev/null
@@ -1,98 +0,0 @@
----
-title: "Petroleum pollution based on past incidents"
----
-
-## Tasks 
-
-1. Receive and format data on petroleum pollution based on past accidental spills for nearshore Canadian waters
-   1. National Environmental Emergencies Centre (NEEC)
-   2. National Aerial Surveillance Program (NASP)
-   3. Integrated Satellite Tracking of Pollution (ISTOP)
-2. Prepare petroleum pollution based on past incidents threat layers for specified annual periods
-
-## Data & processing
-
-The data used to generate this threat layers were requested by the CWS and transferred to inSileco. They are stored on a secure Google Cloud Storage bucket to ensure the reproducibility of this project, although this arrangement will need to be revisited at the end of the contract with the CWS. There are three different datasets used, which are detailed below. 
-
-### NEEC
-
-```{r neec}
-neec <- pipedat::importdat("77f8d683", "format")[["neec_cws-77f8d683.csv"]]
-neec2 <- sf::st_read("../../project-data/partials/neec.gpkg", quiet = TRUE)
-na_vol <- neec2 |>
-  dplyr::filter(is.na(quantity) | is.na(unit))
-na_vol <- round(((nrow(na_vol) / nrow(neec2)) * 100), 0)
-vol_kg <- neec2 |>
-  dplyr::filter(!is.na(unit) & unit == "Kilogram(s)") |>
-  nrow()
-```
-
-The [National Environmental Emergency Centre (NEEC)](https://www.canada.ca/en/environment-climate-change/services/environmental-emergencies-program/national-centre.html) is a program led by Environment and Climate Change Canada that provides an inventory of environmental emergencies incidents in Canada. The data requested by the CWS covers notifications impacting or potentially impacting a waterbody for the Atlantic region (including Québec) for the period of 2016 to 2023 [@neec2024]. The data provided inventories `r nrow(neec)` incidents. 
-
-The following filtering procedures were applied to the data in order to use it to produce a threat layer: 
-
-  - Remove empty coordinates
-  - Use data from "Notification Date Time" when not available from "Incident Local Date Time" field
-  - Remove records containing "covid", "istop", "sipps"
-  - Select greatest quantity for individual spills with multiple substances
-  - Remove Incident Name containing "mvi" or "furnace oil" to remove a few dozen incidents that generally won't impact marine birds
-  - Keep all incidents within 1 $km$ of the coastline
-
-This filtering process yields a total of `r nrow(neec2)` usable observations to characterize this pollution threat layer. 
-
-<!-- ```{R neecmap}
-mapview::mapview(neec2)
-``` -->
-
-
-### NASP
-
-```{R nasp} 
-nasp <- pipedat::importdat("376f0891", "format")[["nasp_cws-376f0891.gpkg"]]
-nasp2 <- sf::st_read("../../project-data/partials/nasp.gpkg", quiet = TRUE)
-```
-
-The [National Aerial Surveillance Program (NASP)](https://tc.canada.ca/en/programs/national-aerial-surveillance-program) is a surveillance program led by Transport Canada that operates across the country to detect pollution through aerial surveys. Its goals are to prevent pollution in our waters, protect our marine environment and endangered marine life, and ensure a safe and efficient transportation industry along our massive and varied coastlines. The data requested by the CWS covers oil pollution detections from visual or aided by instruments (*e.g.* radar) for the Atlantic region between April 2012 and March 2023 [@tc2024]. The data provided inventories `r nrow(nasp)` incidents. Data were cleaned and formatted, with missing spill volumes removed from the dataset, resulting in `r nrow(nasp2)` usable observations to characterize this pollution threat layer.
-
-
-### ISTOP
-
-```{R istop} 
-istop <- pipedat::importdat("48ea8a05", "format")[["istop_cws-48ea8a05.gpkg"]]
-istop2 <- sf::st_read("../../project-data/partials/istop.gpkg", quiet = TRUE)
-```
-
-The [Integrated Satellite Tracking of Pollution (ISTOP)](https://www.canada.ca/en/environment-climate-change/services/ice-forecasts-observations/latest-conditions/oil-pollution-monitoring-overview/satellite-spills-overview.html) program assists environmental monitoring of coastal waters through satellite imagery analysis to detect oil spills. Since 2006, satellite imagery is analyzed daily to detect and report illegal and accidental oil pollution in marine environments. The analyses performed identify sea-surface anomalies identified as pollution from satellite imagery that are then transformed as vectorized polygons representing the surface affected by the pollution. The data requested by the CWS spanned the period of 2006 to 2023 and includes `r nrow(istop)` incidents [@eccc2024]. Only data of categories 1A (*i.e.* possible oil with a target clearly associated), 1B (*i.e.* possible oil with a target within 50-km), and 2 (*i.e.* possible oil but without an identifiable source) were retained to characterize this pollution threat layer, resulting in `r nrow(istop2)` usable observations.
-
-
-
-## Method
-
-Since accidental spills have an effect that goes beyond the immediate location of the incidents due to the diffusion of the spill in the environment, the first step in characterizing the intensity of spills was to establish a potential zone of influence for each incident. To characterize this, we explored weighted kernel density estimates with a Gaussian distribution. However, kernels have the drawback of considering the density around a constant radius surrounding individual points, whereas each spill may have a different spatial footprint as a function of the total volume spilled. For this first iteration of the threat layers, we therefore chose a passive diffusive model to establish the zone of influence and relative intensity of accidental spills in the study area [see @halpern2008a]. 
-
-The diffusion model establishes the zone of influence of the spills by creating concentric rings centred on each incident. An initial value equal to the volume of the incident is assigned to the centre ring and represents the relative intensity of the incident on the environment. A value is then successively assigned to the adjacent rings according to a decay function of a percent value of the previous ring until a minimum threshold is reached. This approach assumes a linear decrease in the influence of the incidents on the environment.
-
-Since the data used are not directly comparable, we decided to create a single threat layer for each dataset. Furthermore, the range of volumes reported in the NEEC ([]) and NASP ([]) datasets were too great to use directly without completely masking the effects of smaller spill events. As such, we log-transformed the reported volumes to give less weight to very high volumes. The ISTOP dataset, meanwhile, has no reported volume. We therefore used the area in $km^2$ of individual incidents as a proxy of spill volumes. As with the other datasets, we log-transformed these values to decrease the influence of very large spills. Finally, in order to make intensity of threats related to past incidents more comparable, we normalized volumes/areas between 0 and 1 before running the diffusive plume models. The intensity and spatial distribution of accidental spills in the study area were then evaluated as the sum of the values of all concentric rings intersecting with cells of our study grid. 
-
-The parameters that are currently used for the diffusive plume model are the following: 
-
-- Concentric rings radius: 100 $m$
-- Decay function percent: 2%
-- Minimum threshold: 0.05% of the global minimum, *i.e.* the lowest spill volume recorded
-- Initial values: log-transformed and normalized volumes/areas
-
-All reported parameters and analytical decisions made could be further explored and modified in future iterations until we agree on the final threat layers desired. For us, these serve as a basis for further discussion towards producing the final threat layers generated through this project. Also, if requested, these parameters could also be included as parameters in the *research compendium*. The code used to perform this assessment is available [here](https://github.com/inSilecoInc/ceacws/blob/main/R/threat_pollution.R).
-
-## Outstanding questions
-
-The following are some outstanding questions that were not presented in the previous sections. 
-
-### NEEC 
-
-So far, we set all NA quantities to 1 to perform the analysis. This should be discussed, as entries without volume makeup `r na_vol`% of the NEEC dataset. Furthermore, certain volumes are reported in litres and others in kilograms. At the moment we are considering that the units are the same, but this is not necessarily the case. There are `r vol_kg` incidents reports in kilograms in the NEEC dataset. 
-
-### ISTOP
-
-There are many duplicates in the data. For now, we only kept unique rows, but we are wondering whether there is a reason we are unaware of for these duplicates to exist and be used in combination.
-
-## Notes & references
diff --git a/pubs/report/3_threats.qmd b/pubs/report/3_threats.qmd
new file mode 100644
index 0000000..6245680
--- /dev/null
+++ b/pubs/report/3_threats.qmd
@@ -0,0 +1,53 @@
+---
+title: "Threat layers"
+---
+
+# Petroleum pollution based on past incidents
+
+```{r}
+#| message: false
+istop <- sf::st_read(
+  "../../workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/petroleum_pollution_incidents_istop.gpkg",
+  quiet = TRUE
+)
+nasp <- sf::st_read(
+  "../../workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/petroleum_pollution_incidents_nasp.gpkg",
+  quiet = TRUE
+)
+neec <- sf::st_read(
+  "../../workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/petroleum_pollution_incidents_neec.gpkg",
+  quiet = TRUE
+)
+ppi <- sf::st_read(
+  "../../workspace/data/analyzed/petroleum_pollution_incidents-1.0.0/petroleum_pollution_incidents.gpkg",
+  quiet = TRUE
+)
+```
+
+The objective was to assess the risk associated with petroleum incidents by evaluating the frequency of past incidents at specific locations over a defined period. Ideally, a more detailed approach would model the diffusion of each spill based on factors such as total volume, substance type, and local or regional currents to estimate the likely spread of each event. However, this level of detail was beyond the scope of the current study. Given the retrospective nature of the dataset and its focus on historical rather than predictive risk, we chose a simplified approach. Each recorded or probable incident was treated as an individual, discrete event in the dataset to characterize exposure to petroleum-related incidents.
+
+This threat layer was constructed using the [ISTOP](#istop), [NEEC](#neec) and [NASP](#nasp) datasets. Each dataset was processed to create spatially and temporally explicit threat layers. 
+
+For the ISTOP dataset, we retained only categories 1A (possible oil with a clearly associated target), 1B (possible oil with a target within 50 km), and 2 (possible oil without an identifiable source), resulting in `r nrow(istop)` usable observations. Since the ISTOP data already includes polygons representing areas of exposure, these polygons were directly utilized as pollution events in the analysis. 
+
+The NASP data was refined by removing records with missing coordinates, yielding `r nrow(nasp)` usable observations.  Each NASP incident is represented as a point with latitude/longitude coordinates and includes spill volumes in liters. Since this dataset lacks defined surface areas, we inferred exposure areas by categorizing spill volumes and applying a variable buffer around each point based on the reported volume (@tbl-spillvolumes).
+
+The NEEC dataset underwent extensive filtering, including the removal of records with empty coordinates, selection of the highest quantity for multi-substance spills, and exclusion of terms like "mvi" or "furnace oil." The final dataset includes `r nrow(neec)` observations within 1 km of the coastline. Similar to the NASP data, the NEEC incidents are represented as points with latitude/longitude coordinates and spill volumes. The same approach of categorizing by volume and buffering each point was applied to the NEEC data (@tbl-spillvolumes). Additionally, NEEC data was classified by substance type, considering both chemical state (solid, liquid, gas) and the presence of an oil sheen. After filtering, incidents were categorized into liquid with oil sheen (`r neec |> dplyr::filter(substance == "liquid_with_oil_sheen") |> nrow()`), liquid without oil sheen (`r neec |> dplyr::filter(substance == "liquid_without_oil_sheen") |> nrow()`), or unknown `r neec |> dplyr::filter(substance == "unknown") |> nrow()`.These categories were retained to create distinct threat layers based on substance type.
+
+The full details of the data processing and analytical approach used is available in the script `ana_petroleum_pollution_incidents.R`. 
+
+
+- **Processing Script**: `ana_petroleum_pollution_incidents.R`
+- **Output File**: `petroleum_pollution_incidents.gpkg`
+
+
+
+```{r}
+#| label: tbl-spillvolumes
+#| tbl-cap: "Volume categories and buffer size selected to transform incident as point locations to area of exposure from "
+data.frame(
+  volume = c("[0, 100]", "[100, 1000]", "[1000, 10000]", "[10000+ ["),
+  buffer = c(500, 2000, 3000, 5000)
+) |>
+  knitr::kable(col.names = c("Volume (L)", "Buffer size (m)"))
+```
\ No newline at end of file
diff --git a/pubs/report/_quarto.yml b/pubs/report/_quarto.yml
index 74d5fe4..ea77ec9 100644
--- a/pubs/report/_quarto.yml
+++ b/pubs/report/_quarto.yml
@@ -14,10 +14,11 @@ book:
       degrees: MSc
   chapters:
     - index.qmd
-    - 1_shipping.qmd
-    - 2_petroleum.qmd
+    - 1_research_compendium.qmd
+    - 2_data.qmd
+    - 3_threats.qmd
 
-bibliography: [references.bib, ../../project-data/pipedat/pipedat.bib]
+bibliography: [references.bib, ../../workspace/bibliographies/data.bib]
 csl: frontiers.csl
 
 format:
diff --git a/pubs/report/3_VIIRS.qmd b/pubs/report/del_VIIRS.qmd
similarity index 100%
rename from pubs/report/3_VIIRS.qmd
rename to pubs/report/del_VIIRS.qmd
diff --git a/pubs/report/1_shipping.qmd b/pubs/report/del_shipping.qmd
similarity index 100%
rename from pubs/report/1_shipping.qmd
rename to pubs/report/del_shipping.qmd
diff --git a/pubs/report/index.qmd b/pubs/report/index.qmd
index 19cb1fe..1a6530b 100644
--- a/pubs/report/index.qmd
+++ b/pubs/report/index.qmd
@@ -46,7 +46,7 @@ The current report covers the work that was to be accomplished before March 31st
 
 ## Area of interest 
 
-```{r aoiarea, echo = FALSE}
+```{r aoiarea, echo = FALSE, eval = FALSE}
 aoi_area <- sf::st_read("../../project-data/aoi/aoi.gpkg", quiet = TRUE) |>
   sf::st_make_valid() |>
   sf::st_area() |>
@@ -56,9 +56,9 @@ aoi_area <- sf::st_read("../../project-data/aoi/aoi.gpkg", quiet = TRUE) |>
   round(0)
 ```
 
-The project is conducted in EEZ of eastern Canada and covers the St. Lawrence, the western Atlantic and adjacent International Waters. As currently defined, the study area covers `r aoi_area` $km^2$ (@fig-aoi). All threat layers are integrated in a study grid that divides the study area into a regular grid formed of cells of ~1 $km^2$, although this decision can be revisited at any time throughout the project. 
+The project is conducted in EEZ of eastern Canada and covers the St. Lawrence, the western Atlantic and adjacent International Waters. As currently defined, the study area covers r aoi_area $km^2$ (@fig-aoi). All threat layers are integrated in a study grid that divides the study area into a regular grid formed of cells of ~1 $km^2$, although this decision can be revisited at any time throughout the project. 
 
-```{r aoi, echo = FALSE}
+```{r aoi, echo = FALSE, eval = FALSE}
 #| fig-cap: "Limits of the study area for the project on mapping threats at sea to marine birds throughout their annual cycle to inform conservation and management in Atlantic Canada."
 #| out-width: '90%'
 #| label: fig-aoi
diff --git a/workspace/pipelines/analytical/petroleum_pollution_incidents.yaml b/workspace/pipelines/analytical/petroleum_pollution_incidents.yaml
index 2a6941f..01fd61f 100644
--- a/workspace/pipelines/analytical/petroleum_pollution_incidents.yaml
+++ b/workspace/pipelines/analytical/petroleum_pollution_incidents.yaml
@@ -8,26 +8,35 @@ pipeline:
   description: 'Pipeline for analytical data: petroleum_pollution_incidents'
   analysis:
     sources:
-    - name: petroleum pollution incidents istop
-      description: Preparing ISTOP data for diffusive model 
-      script: ana_petroleum_pollution_incidents_istop
-      input_files:
-        - id: 835bc0d1-e57a-4e63-a327-908ee871bf2d
-          name: istop
-      output_files: petroleum_pollution_incidents_istop.gpkg
+      - name: petroleum pollution incidents istop
+        description: Preparing ISTOP data for event count
+        script: ana_petroleum_pollution_incidents_istop
+        input_files:
+          - id: 835bc0d1-e57a-4e63-a327-908ee871bf2d
+            name: istop
+        output_files: petroleum_pollution_incidents_istop.gpkg
 
-    - name: petroleum pollution incidents nasp
-      description: Preparing NASP data for diffusive model 
-      script: ana_petroleum_pollution_incidents_nasp
-      input_files:
-      - id: e566fb8c-42d8-4013-8002-17af44a15a46
-        name: nasp
-      output_files: petroleum_pollution_incidents_nasp.gpkg
+      - name: petroleum pollution incidents nasp
+        description: Preparing NASP data for event count
+        script: ana_petroleum_pollution_incidents_nasp
+        input_files:
+        - id: e566fb8c-42d8-4013-8002-17af44a15a46
+          name: nasp
+        output_files: petroleum_pollution_incidents_nasp.gpkg
 
-    - name: petroleum pollution incidents neec
-      description: Preparing NEEC data for diffusive model 
-      script: ana_petroleum_pollution_incidents_neec
-      input_files:
-      - id: 56c710c3-1859-4eab-821f-e1e41dbbfd29
-        name: neec
-      output_files: petroleum_pollution_incidents_neec.gpkg
+      - name: petroleum pollution incidents neec
+        description: Preparing NEEC data for event count
+        script: ana_petroleum_pollution_incidents_neec
+        input_files:
+        - id: 56c710c3-1859-4eab-821f-e1e41dbbfd29
+          name: neec
+        output_files: petroleum_pollution_incidents_neec.gpkg
+
+      - name: petroleum pollution incidents
+        description: Petroleum pollution based on past incidents
+        script: ana_petroleum_pollution_incidents
+        input_files:
+          - petroleum pollution incidents istop
+          - petroleum pollution incidents nasp
+          - petroleum pollution incidents neec
+        output_files: petroleum_pollution_incidents.gpkg
diff --git a/workspace/scripts/ana_petroleum_pollution_incidents.R b/workspace/scripts/ana_petroleum_pollution_incidents.R
index ffd8b87..3479bf3 100644
--- a/workspace/scripts/ana_petroleum_pollution_incidents.R
+++ b/workspace/scripts/ana_petroleum_pollution_incidents.R
@@ -63,6 +63,15 @@ ana_petroleum_pollution_incidents_neec <- function(input_files, output_path) {
     dplyr::mutate(
       chemical_state = ifelse(is.na(chemical_state), "unknown", chemical_state),
       oil_sheen = ifelse(is.na(oil_sheen), "unknown", oil_sheen)
+    ) |>
+    dplyr::mutate(
+      oil_sheen = dplyr::case_when(
+        oil_sheen == "yes" ~ "with_oil_sheen",
+        oil_sheen == "no" ~ "without_oil_sheen",
+        .default = oil_sheen
+      ),
+      substance = paste0(chemical_state, "_", oil_sheen),
+      substance = stringr::str_replace(substance, "unknown_unknown", "unknown")
     )
 
   # ----------------------------------------------------------------
@@ -130,7 +139,7 @@ ana_petroleum_pollution_incidents_neec <- function(input_files, output_path) {
       year = format(date, "%Y"),
       uid = sprintf("neec_%04d", dplyr::row_number())
     ) |>
-    dplyr::select(uid, date, year, chemical_state, oil_sheen)
+    dplyr::select(uid, date, year, substance)
 
   # Export partial data for use in report (this should be revisited)
   sf::st_write(
@@ -238,3 +247,22 @@ categorize_by_quantity <- function(dat) {
       .default = NA_real_
     ))
 }
+
+ana_petroleum_pollution_incidents <- function(input_files, output_path) {
+  dplyr::bind_rows(
+    input_files[stringr::str_detect(unlist(input_files), "istop.gpkg")] |>
+      sf::st_read(quiet = TRUE) |>
+      sf::st_transform(4326),
+    input_files[stringr::str_detect(unlist(input_files), "nasp.gpkg")] |>
+      sf::st_read(quiet = TRUE) |>
+      sf::st_transform(4326),
+    input_files[stringr::str_detect(unlist(input_files), "neec.gpkg")] |>
+      sf::st_read(quiet = TRUE) |>
+      sf::st_transform(4326)
+  ) |>
+    sf::st_write(
+      dsn = file.path(output_path, "petroleum_pollution_incidents.gpkg"),
+      quiet = TRUE,
+      delete_dsn = TRUE
+    )
+}