From c516e2947f17bff543c1363432c9601290e21c91 Mon Sep 17 00:00:00 2001
From: Robinlovelace <Robinlovelace@users.noreply.github.com>
Date: Wed, 21 Aug 2024 12:27:26 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20Robinlov?=
 =?UTF-8?q?elace/simodels@ba19a7a943afdc74b9b216410deb92ed1e67bc1b=20?=
 =?UTF-8?q?=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 404.html                            | 2 +-
 LICENSE.html                        | 2 +-
 articles/index.html                 | 2 +-
 articles/simodels.html              | 2 +-
 articles/sims-first-principles.html | 2 +-
 articles/validation.html            | 2 +-
 authors.html                        | 6 +++---
 index.html                          | 2 +-
 news/index.html                     | 9 ++++++++-
 pkgdown.yml                         | 2 +-
 reference/destinations_york.html    | 2 +-
 reference/index.html                | 2 +-
 reference/od_aus.html               | 2 +-
 reference/si_calculate.html         | 2 +-
 reference/si_oa_wpz.html            | 2 +-
 reference/si_oa_wpz_d.html          | 2 +-
 reference/si_oa_wpz_o.html          | 2 +-
 reference/si_od_census.html         | 2 +-
 reference/si_predict.html           | 2 +-
 reference/si_pubs.html              | 2 +-
 reference/si_to_od.html             | 2 +-
 reference/si_zones.html             | 2 +-
 reference/simodels-package.html     | 2 +-
 reference/zones_aus.html            | 2 +-
 reference/zones_york.html           | 2 +-
 search.json                         | 2 +-
 26 files changed, 35 insertions(+), 28 deletions(-)

diff --git a/404.html b/404.html
index a2e196e..e2668a2 100644
--- a/404.html
+++ b/404.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="https://robinlovelace.github.io/simodels/index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/LICENSE.html b/LICENSE.html
index 641df2d..cfeab74 100644
--- a/LICENSE.html
+++ b/LICENSE.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/index.html b/articles/index.html
index 8d4cc8a..5f2c6c0 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/simodels.html b/articles/simodels.html
index fb09535..87230f7 100644
--- a/articles/simodels.html
+++ b/articles/simodels.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/sims-first-principles.html b/articles/sims-first-principles.html
index 52f16b3..6e4611c 100644
--- a/articles/sims-first-principles.html
+++ b/articles/sims-first-principles.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/validation.html b/articles/validation.html
index 7a21ef3..49373b6 100644
--- a/articles/validation.html
+++ b/articles/validation.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/authors.html b/authors.html
index c3f3ace..c1ee290 100644
--- a/authors.html
+++ b/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -56,14 +56,14 @@ <h2 id="citation">Citation</h2>
 
       <p>Lovelace R, Nowosad J (2024).
 <em>simodels: Flexible Framework for Developing Spatial Interaction Models</em>.
-R package version 0.1.0,
+R package version 0.2.0,
 https://robinlovelace.github.io/simodels/, <a href="https://github.com/robinlovelace/simodels" class="external-link">https://github.com/robinlovelace/simodels</a>.
 </p>
       <pre>@Manual{,
   title = {simodels: Flexible Framework for Developing Spatial Interaction Models},
   author = {Robin Lovelace and Jakub Nowosad},
   year = {2024},
-  note = {R package version 0.1.0,
+  note = {R package version 0.2.0,
     https://robinlovelace.github.io/simodels/},
   url = {https://github.com/robinlovelace/simodels},
 }</pre>
diff --git a/index.html b/index.html
index 6ef53a1..1c4a5f3 100644
--- a/index.html
+++ b/index.html
@@ -22,7 +22,7 @@
 
     <a class="navbar-brand me-2" href="index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/news/index.html b/news/index.html
index 8de8f9a..dcd21f6 100644
--- a/news/index.html
+++ b/news/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -38,6 +38,13 @@ <h1>Changelog</h1>
       <small>Source: <a href="https://github.com/robinlovelace/simodels/blob/main/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.2.0" id="simodels-020-2024-08">simodels 0.2.0 (2024-08)<a class="anchor" aria-label="anchor" href="#simodels-020-2024-08"></a></h2>
+<ul><li>Updated use of <code>max_dist</code> to make use of updates the {od} package (<a href="https://github.com/robinlovelace/simodels/issues/38" class="external-link">#38</a>)
+<ul><li>
+<code><a href="../reference/si_to_od.html">si_to_od()</a></code> is now much/infinitely faster for large datasets</li>
+</ul></li>
+</ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.1.0" id="simodels-010-2024-01">simodels 0.1.0 (2024-01)<a class="anchor" aria-label="anchor" href="#simodels-010-2024-01"></a></h2><p class="text-muted">CRAN release: 2024-07-23</p>
 <ul><li>Fix silent bug introduced by updatream changes to <code>dplyr</code>
diff --git a/pkgdown.yml b/pkgdown.yml
index ad601b1..fa2924e 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -5,7 +5,7 @@ articles:
   simodels: simodels.html
   sims-first-principles: sims-first-principles.html
   articles/validation: validation.html
-last_built: 2024-08-21T12:20Z
+last_built: 2024-08-21T12:26Z
 urls:
   reference: https://robinlovelace.github.io/simodels/reference
   article: https://robinlovelace.github.io/simodels/articles
diff --git a/reference/destinations_york.html b/reference/destinations_york.html
index 05b0ee6..a7725ff 100644
--- a/reference/destinations_york.html
+++ b/reference/destinations_york.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/index.html b/reference/index.html
index b992294..2463315 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/od_aus.html b/reference/od_aus.html
index 70af660..0d651d4 100644
--- a/reference/od_aus.html
+++ b/reference/od_aus.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/si_calculate.html b/reference/si_calculate.html
index 37e2ba7..089e229 100644
--- a/reference/si_calculate.html
+++ b/reference/si_calculate.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/si_oa_wpz.html b/reference/si_oa_wpz.html
index 342e243..510661e 100644
--- a/reference/si_oa_wpz.html
+++ b/reference/si_oa_wpz.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/si_oa_wpz_d.html b/reference/si_oa_wpz_d.html
index 7c27c13..bd3f8b8 100644
--- a/reference/si_oa_wpz_d.html
+++ b/reference/si_oa_wpz_d.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/si_oa_wpz_o.html b/reference/si_oa_wpz_o.html
index a2b0a9c..5e83726 100644
--- a/reference/si_oa_wpz_o.html
+++ b/reference/si_oa_wpz_o.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/si_od_census.html b/reference/si_od_census.html
index e0bc57b..0f37f56 100644
--- a/reference/si_od_census.html
+++ b/reference/si_od_census.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/si_predict.html b/reference/si_predict.html
index a5d557d..80e76ec 100644
--- a/reference/si_predict.html
+++ b/reference/si_predict.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/si_pubs.html b/reference/si_pubs.html
index 2800a04..2078dff 100644
--- a/reference/si_pubs.html
+++ b/reference/si_pubs.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/si_to_od.html b/reference/si_to_od.html
index 37d1b8b..855afb3 100644
--- a/reference/si_to_od.html
+++ b/reference/si_to_od.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/si_zones.html b/reference/si_zones.html
index 15c525b..48bf199 100644
--- a/reference/si_zones.html
+++ b/reference/si_zones.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/simodels-package.html b/reference/simodels-package.html
index e5f43e4..b153739 100644
--- a/reference/simodels-package.html
+++ b/reference/simodels-package.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/zones_aus.html b/reference/zones_aus.html
index 22cc47e..ce1fd43 100644
--- a/reference/zones_aus.html
+++ b/reference/zones_aus.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/zones_york.html b/reference/zones_york.html
index 16426a2..dc4d0ea 100644
--- a/reference/zones_york.html
+++ b/reference/zones_york.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">simodels</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/search.json b/search.json
index 085f0ba..ca2c39e 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://robinlovelace.github.io/simodels/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU Affero General Public License","title":"GNU Affero General Public License","text":"Version 3, 19 November 2007 Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/> Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://robinlovelace.github.io/simodels/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU Affero General Public License","text":"GNU Affero General Public License free, copyleft license software kinds works, specifically designed ensure cooperation community case network server software. licenses software practical works designed take away freedom share change works. contrast, General Public Licenses intended guarantee freedom share change versions program–make sure remains free software users. speak free software, referring freedom, price. General Public Licenses designed make sure freedom distribute copies free software (charge wish), receive source code can get want , can change software use pieces new free programs, know can things. Developers use General Public Licenses protect rights two steps: (1) assert copyright software, (2) offer License gives legal permission copy, distribute /modify software. secondary benefit defending users’ freedom improvements made alternate versions program, receive widespread use, become available developers incorporate. Many developers free software heartened encouraged resulting cooperation. However, case software used network servers, result may fail come . GNU General Public License permits making modified version letting public access server without ever releasing source code public. GNU Affero General Public License designed specifically ensure , cases, modified source code becomes available community. requires operator network server provide source code modified version running users server. Therefore, public use modified version, publicly accessible server, gives public access source code modified version. older license, called Affero General Public License published Affero, designed accomplish similar goals. different license, version Affero GPL, Affero released new version Affero GPL permits relicensing license. precise terms conditions copying, distribution modification follow.","code":""},{"path":[]},{"path":"https://robinlovelace.github.io/simodels/LICENSE.html","id":"id_0-definitions","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"0. 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Also add information contact electronic paper mail. software can interact users remotely computer network, also make sure provides way users get source. example, program web application, interface display “Source” link leads users archive code. many ways offer source, different solutions better different programs; see section 13 specific requirements. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU AGPL, see https://www.gnu.org/licenses/.","code":"<one line to give the program's name and a brief idea of what it does.>     Copyright (C) <year>  <name of author>      This program is free software: you can redistribute it and/or modify     it under the terms of the GNU Affero General Public License as     published by the Free Software Foundation, either version 3 of the     License, or (at your option) any later version.      This program is distributed in the hope that it will be useful,     but WITHOUT ANY WARRANTY; without even the implied warranty of     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the     GNU Affero General Public License for more details.      You should have received a copy of the GNU Affero General Public License     along with this program.  If not, see <https://www.gnu.org/licenses/>."},{"path":"https://robinlovelace.github.io/simodels/articles/simodels.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"Spatial interaction models with R","text":"need installed R suitable editor RStudio VSCode R plugin. See package’s README instructions installing {simodels} package.","code":"library(simodels) library(dplyr) library(ggplot2) library(sf)"},{"path":"https://robinlovelace.github.io/simodels/articles/simodels.html","id":"input-data","dir":"Articles","previous_headings":"","what":"Input data","title":"Spatial interaction models with R","text":"tutorial builds reproducible guide SIMs R (Dennett 2018). start importing open access data representing movement zones Australia (thanks Adam Dennett making files accessible): Let’s take quick look ‘minimize’ input datasets modelling SIMs: results printed show : zones_aus represents 15 regions Australia, columns containing zone identifiers (IDs), primarily GCCSA_CODE, area zone (AREA_SQKM). minimal zones object contains region code, name, area. od_aus contains 225 rows data representing movement people zones aus. Note 255 15 squared, meaning OD dataset contains complete combination migration flows, starting zone 1GSYD 1GSYD. codes present GCCSA_CODE column aus object. first row OD dataset represents ‘intra-zonal’ migrations Greater Sydney, presumably counting number people move house somewhere region another home region. 13 columns dataset, important Orig_code, Dest_code, Flow, captured minimal od dataset. Note: useful convention ‘long form’ OD datasets first two columns contain zone IDs correspond values first column zone dataset. R package {od}, {simodels} builds, assumes inputs functions form. good idea verify origin destination codes od dataset match zone codes zones: clear ‘clean’ input datasets, let’s begin modelling!","code":"# To get the data (pre-loaded in the package) u1 = \"https://github.com/Robinlovelace/simodels/releases/download/0.0.1/zones_aus.geojson\" zones_aus = sf::read_sf(u1) u2 = \"https://www.dropbox.com/s/wi3zxlq5pff1yda/AusMig2011.csv?raw=1\" od_aus = read.csv(u2) dim(zones_aus) names(zones_aus) key_zone_names = c(\"GCCSA_CODE\", \"GCCSA_NAME\", \"AREA_SQKM\") zones = zones_aus[key_zone_names] head(zones, 2) dim(od_aus) names(od_aus) key_od_names = c(\"Orig_code\", \"Dest_code\", \"Flow\") od = od_aus[key_od_names] head(od, 2) summary(od[[1]] %in% zones[[1]]) summary(od[[2]] %in% zones[[1]])"},{"path":"https://robinlovelace.github.io/simodels/articles/simodels.html","id":"preparing-a-sim","dir":"Articles","previous_headings":"","what":"Preparing a SIM","title":"Spatial interaction models with R","text":"Key workings {simodels} package conversion geographic objects representing origins destinations OD dataset. case, already OD dataset, step less relevant. However, take step case many SIMs start without comprehensive OD dataset case. Prepare OD dataset follows: Note output duplicate columns: si_to_od() joins data origin destination objects resulting OD object.","code":"od_sim = si_to_od(origins = zones, destinations = zones) names(od_sim)"},{"path":"https://robinlovelace.github.io/simodels/articles/simodels.html","id":"an-unconstrained-sim","dir":"Articles","previous_headings":"","what":"An unconstrained SIM","title":"Spatial interaction models with R","text":"simplistic SIM - case inverse power distance decay function (negative exponential another commonly used decay function) - can created just based distance points: approach, ignoring variables level trip origins destinations, results flow estimates units. learning run constrained SIMs, let’s scale result total flow see far reality, just focussing interzonal OD pairs: results show simple unconstrained model, without parameter fitting, can explain less 20% variability flows. can better!","code":"si_power = function(d, beta) (d / 1000)^beta od_calculated = si_calculate(   od_sim,   fun = si_power,   d = distance_euclidean,   beta = -0.8   ) plot(od_calculated[\"interaction\"], logz = TRUE) od_interzonal = od %>%   filter(Orig_code != Dest_code) od_calculated_interzonal = od_calculated %>%   filter(O != D)  scale_factor = sum(od_interzonal$Flow) /   sum(od_calculated_interzonal$interaction) od_calculated_interzonal = od_calculated_interzonal %>%    mutate(interaction_scaled = interaction * scale_factor) od_joined = inner_join(   od_calculated_interzonal,   od %>% rename(O = Orig_code, D = Dest_code)   ) od_joined %>%    ggplot() +   geom_point(aes(Flow, interaction_scaled)) cor(od_joined$Flow, od_joined$interaction_scaled)^2 od_joined %>%    mutate(decay = distance_euclidean^-0.8) %>%    mutate(decay = decay * (sum(Flow) / sum(decay))) %>%    ggplot() +   geom_point(aes(distance_euclidean, Flow)) +   geom_line(aes(distance_euclidean, decay), colour = \"red\")"},{"path":"https://robinlovelace.github.io/simodels/articles/simodels.html","id":"a-production-constrained-sim","dir":"Articles","previous_headings":"","what":"A production constrained SIM","title":"Spatial interaction models with R","text":"first logical way improve model fit run production constrained model. , ’ll first calculate total number people leaving zone use constraint_production argument: Progress! doubled predictive ability model using ‘production constrained’ SIM, defined mathematically simodels-first-principles vignette.","code":"od_originating = od_joined %>%    group_by(O) %>%    mutate(originating_per_zone = sum(Flow)) %>%    ungroup() od_constrained_p = si_calculate(   od_originating,   fun = si_power,   d = distance_euclidean,   beta = -0.8,   constraint_production = originating_per_zone   ) od_constrained_p %>%    ggplot() +   geom_point(aes(Flow, interaction)) cor(od_constrained_p$Flow, od_constrained_p$interaction)^2"},{"path":"https://robinlovelace.github.io/simodels/articles/simodels.html","id":"training-a-sim","dir":"Articles","previous_headings":"","what":"Training a SIM","title":"Spatial interaction models with R","text":"advantage flow data used example already know interaction. (raises question SIM needed, answer: test models demonstrate techniques.) can using nls() function follows:","code":"library(minpack.lm) f = Flow ~ a * (distance_euclidean)^b m = nlsLM(   formula = f,   data = od_originating,   ) m # Nonlinear regression model #   model: Flow ~ a * (distance_euclidean)^b #    data: od_originating #          a          b  #  2.182e+07 -5.801e-01 od_joined %>%    mutate(decay = distance_euclidean^-5.801e-01) %>%    mutate(decay = decay * 2.182e+07) %>%    ggplot() +   geom_point(aes(distance_euclidean, Flow)) +   geom_line(aes(distance_euclidean, decay), colour = \"red\") od_pred = si_predict(od_originating, model = m) cor(od_pred$Flow, od_pred$interaction)^2 od_pred_const = si_predict(od_originating, model = m,   constraint_production = originating_per_zone) cor(od_pred_const$Flow, od_pred_const$interaction)^2 library(tmap) ttm() tm_shape(od_pred_const) +   tm_lines(\"interaction_scaled\", palette = \"viridis\")"},{"path":[]},{"path":"https://robinlovelace.github.io/simodels/articles/sims-first-principles.html","id":"what-are-sims","dir":"Articles","previous_headings":"","what":"What are SIMs?","title":"An introduction to spatial interaction models: from first principles","text":"Spatial Interaction Models (SIMs) mathematical models estimating movement spatial entities developed Alan Wilson late 1960s early 1970, considerable uptake refinement transport modelling since Boyce Williams (2015). four main types traditional SIMs (Wilson 1971): Unconstrained Production-constrained Attraction-constrained Doubly-constrained early highly influential type SIM ‘gravity model’, defined Wilson (1971) follows (paper explored many iterations formulation): Tij=KWi(1)Wj(2)cijn T_{j}=K \\frac{W_{}^{(1)} W_{j}^{(2)}}{c_{j}^{n}} “TijT_{j} measure interaction zones ii Wi(1)W_{}^{(1)} measure ‘mass term’ associated zone ziz_i, Wj(2)W_{j}^{(2)} measure ‘mass term’ associated zone zjz_j, cijc_{ij} measure distance, generalised cost travel, zone ii zone jj”. KK ‘constant proportionality’ nn parameter estimated. Redefining WW terms mm nn origins destinations respectively (Simini et al. 2012), classic definition ‘gravity model’ can written follows: Tij=Kminjcijn T_{j}=K \\frac{m_{} n_{j}}{c_{j}^{n}} purposes project, focus production-constrained SIMs. can defined follows (Wilson 1971): Tij=AiOinjf(cij) T_{ij} = A_iO_in_jf(c_{ij}) AA balancing factor defined : Ai=1∑jmjf(cij) A_{}=\\frac{1}{\\sum_{j} m_{j} \\mathrm{f}\\left(c_{j}\\right)} OiO_i analogous travel demand zone ii, can roughly approximated population. recent innovations SIMs including ‘radiation model’ Simini et al. (2012). See Lenormand, Bassolas, Ramasco (2016) comparison alternative approaches.","code":""},{"path":"https://robinlovelace.github.io/simodels/articles/sims-first-principles.html","id":"implementation-in-r","dir":"Articles","previous_headings":"","what":"Implementation in R","title":"An introduction to spatial interaction models: from first principles","text":"using functions packages, may worth implementing SIMs first principles, gain understanding work. code presented written functions simodels package developed, building Dennett (2018). aim demonstrate common way running SIMs, loop, rather using vectorised operations (used simodels package) can faster.  unconstrained spatial interaction model can written follows, --less arbitrary value beta can optimised later:  can plot ‘distance decay’ curve associated SIM follows:  can make production constrained follows:","code":"library(tmap) library(dplyr) library(ggplot2) zones = simodels::si_zones centroids = simodels::si_centroids od = simodels::si_od_census tm_shape(zones) + tm_polygons(\"all\", palette = \"viridis\") od_df = od::points_to_od(centroids) od_sfc = od::odc_to_sfc(od_df[3:6]) sf::st_crs(od_sfc) = 4326 od_df$length = sf::st_length(od_sfc) od_df = od_df %>% transmute(   O, D, length = as.numeric(length) / 1000,   flow = NA, fc = NA   ) od_df = sf::st_sf(od_df, geometry = od_sfc, crs = 4326) beta = 0.3 i = 1 j = 2 for(i in seq(nrow(zones))) {   for(j in seq(nrow(zones))) {     O = zones$all[i]     n = zones$all[j]     ij = which(od_df$O == zones$geo_code[i] & od_df$D == zones$geo_code[j])     od_df$fc[ij] = exp(-beta * od_df$length[ij])     od_df$flow[ij] = O * n * od_df$fc[ij]   } } od_top = od_df %>%    filter(O != D) %>%    top_n(n = 2000, wt = flow)  tm_shape(zones) +   tm_borders() +   tm_shape(od_top) +   tm_lines(\"flow\") summary(od_df$fc) #>      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.  #> 0.0002404 0.0256166 0.0801970 0.1495649 0.2035826 1.0000000 od_df %>%    ggplot() +   geom_point(aes(length, fc)) od_dfj = left_join(   od_df,   zones %>% select(O = geo_code, all) %>% sf::st_drop_geometry() ) #> Joining with `by = join_by(O)` od_dfj = od_dfj %>%    group_by(O) %>%    mutate(flow_constrained = flow / sum(flow) * first(all)) %>%   ungroup() sum(od_dfj$flow_constrained) == sum(zones$all) #> [1] TRUE od_top = od_dfj %>%    filter(O != D) %>%    top_n(n = 2000, wt = flow_constrained)  tm_shape(zones) +   tm_borders() +   tm_shape(od_top) +   tm_lines(\"flow_constrained\")"},{"path":"https://robinlovelace.github.io/simodels/articles/sims-first-principles.html","id":"validation","dir":"Articles","previous_headings":"","what":"Validation","title":"An introduction to spatial interaction models: from first principles","text":"","code":"od_dfjc = inner_join(od_dfj %>% select(-all), od) #> Joining with `by = join_by(O, D)` od_dfjc %>%    ggplot() +   geom_point(aes(all, flow_constrained)) cor(od_dfjc$all, od_dfjc$flow_constrained)^2 #> [1] 0.1735933"},{"path":[]},{"path":"https://robinlovelace.github.io/simodels/articles/validation.html","id":"validation-of-2011-od-data-for-leeds-uk","dir":"Articles","previous_headings":"","what":"Validation of 2011 OD data for Leeds, UK","title":"Validation of spatial interaction models","text":"input datasets example follows: Geographic boundary centroid data {pct} package, trip representing origins destinations Origin-destination data representing trips work 2011 Census Explanatory variables associated zones, tbc key input datasets shown  output shows 90% possible combinations OD pairs going every zone present validation dataset. maximum number unique OD pairs given OD dataset number origins multiplied number destinations. case 107 * 107 1.1449^{4} proceeding ’s worth sanity checks find : spatial distribution travel work Leeds look like? OD pairs  figure clearly shows Leeds monocentric city relatively high levels active travel near centre low levels active travel outskirts. can predict ~8% OD pairs dataset: long distance OD pairs avoiding centre. Let’s find : expected, mean length OD pairs observed Census substantially less, ’s calculating weighted mean, just 5 km: case, let’s proceed estimates travel. simplest model imagine simulate movement zones city, given knowledge many people travel work within zones Leeds total (236k), divide movement equally OD pair. case : Plotting observed number trips census data shows , expect, correlation simplistic non-model reality:  , can better? Although classic gravity model often presented type, can actually go step simpler , using linear exponential decay approximate travel, without attention paid population zone factor. let’s get stuck fit models!   simple linear model implemented code chunk predicts flow solely basis distance OD pairs. results slightly better ‘equal trips OD pairs’ approach still pretty bad, explaining 7% variability observed trips OD pairs census dataset. Furthermore, result also reveals issue fitting models OD datasets: ‘negative flow’ make sense contexts, models can predict negative values questioned. approach prevents negative values, also captures fact non-linear (near exponential) decay distance travel, SIMs exponential decay. can implement model follows: shown code , model fails converge. alternative approach follows:   can overcome using self starting function:   Another approach minpack.lm package can make ‘production constrained’ follows:   ‘attraction constrained’ follows (first calculate number people arriving zone):","code":"si_od_census #> # A tibble: 10,536 × 18 #>    O     D       all from_home light_rail train   bus  taxi motorbike car_driver #>    <chr> <chr> <dbl>     <dbl>      <dbl> <dbl> <dbl> <dbl>     <dbl>      <dbl> #>  1 E020… E020…    66         0          0     0     0     0         1         29 #>  2 E020… E020…   742         0          0     1     5     1         1        426 #>  3 E020… E020…     2         0          0     0     0     0         0          2 #>  4 E020… E020…     9         0          0     0     0     0         0          6 #>  5 E020… E020…    50         0          0     0     3     1         0         43 #>  6 E020… E020…    23         0          0     0     0     0         0         20 #>  7 E020… E020…     4         0          0     0     0     0         0          4 #>  8 E020… E020…     2         0          0     0     0     0         0          2 #>  9 E020… E020…     1         0          0     0     0     0         0          1 #> 10 E020… E020…     3         0          0     0     0     0         0          3 #> # ℹ 10,526 more rows #> # ℹ 8 more variables: car_passenger <dbl>, bicycle <dbl>, foot <dbl>, #> #   other <dbl>, geo_name1 <chr>, geo_name2 <chr>, la_1 <chr>, la_2 <chr> plot(si_zones$geometry) plot(si_centroids$geometry, add = TRUE) od_from_si = si_to_od(origins = si_centroids, destinations = si_centroids) nrow(si_od_census) / nrow(od_from_si) #> [1] 0.920255 si_od_census_sf = si_od_census %>%    od_to_sf(si_centroids) %>%   mutate(all, `Percent active` = (bicycle + foot) / all * 100) #> 0 origins with no match in zone ids #> 0 destinations with no match in zone ids #>  points not in od data removed.  si_od_census_sf %>%   arrange(`Percent active`) %>%    tm_shape() +   tm_lines(col = \"Percent active\", lwd = \"all\", scale = 3, palette = \"viridis\",            breaks = c(0, 0.1, 0.2, 0.4, 1) * 100, legend.lwd.show = FALSE) +   tm_scale_bar() od_pairs_with_no_travel = od_from_si %>%    filter(!paste(O, D) %in% paste(si_od_census$O, si_od_census$D)) summary(od_pairs_with_no_travel$distance_euclidean) #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>    3362    9407   12881   13555   17598   27761 summary(sf::st_length(si_od_census_sf)) #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>       0    5042    8091    8795   11751   26698 weighted.mean(sf::st_length(si_od_census_sf), w = si_od_census_sf$all) #> 5258.856 [m] od_from_si$flow_equal = sum(si_od_census$all) / nrow(od_from_si) od_joined = left_join(od_from_si, si_od_census) %>%    mutate(all = case_when(is.na(all) ~ 0, TRUE ~ all)) #> Joining with `by = join_by(O, D)` od_joined %>%    ggplot(aes(all, flow_equal)) +   geom_point() total = sum(si_od_census$all) m1 = lm(all ~ distance_euclidean, data = od_joined) od_joined = si_predict(od_joined, model = m1, constraint_total = total, output_col = \"flow_m1\") #> Negative values in output, setting them to zero od_joined %>%    ggplot(aes(distance_euclidean, flow_m1)) +   geom_point() od_joined %>%    ggplot(aes(all, flow_m1)) +   geom_point() sum(od_joined$flow_m1) == sum(od_joined$all, na.rm = TRUE) #> [1] TRUE cor(od_joined$all, od_joined$flow_m1, use = \"complete.obs\")^2 #> [1] 0.07686445 od_joined = od_joined %>%    mutate(distance_nonzero = case_when(     distance_euclidean == 0 ~ 50,     TRUE ~ distance_euclidean)) m2 = nls(   formula = all ~ a + distance_nonzero^b,   start = list(a = 5, b = -0.8),   data = od_joined   ) #> Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral): Missing value or an infinity produced when evaluating the model m2 = minpack.lm::nlsLM(   formula = all ~ a + distance_nonzero^b,   start = list(a = 5, b = -0.8),   data = od_joined   ) # m2 = gslnls::gsl_nls( #   all ~ a + distance_nonzero^b, #   start = list(a = 10, b = -0.08), #   # control = gslnls::gsl_nls_control(scale = \"more\"), #   control = gslnls::gsl_nls_control( solver = \"svd\"), #   data = od_joined #   ) od_joined = od_joined %>%    si_predict(m2, constraint_total = total, output_col = \"flow_m2\") od_joined %>%    ggplot(aes(distance_euclidean, flow_m2)) +   geom_point() od_joined %>%    ggplot(aes(all, flow_m2)) +   geom_point() cor(od_joined$all, od_joined$flow_m2, use = \"complete.obs\")^2 #> [1] 0.1444543 m3 = nls(   formula = all ~ SSasymp(distance_nonzero, a, b, lrc = log_alpha),   data = od_joined   ) od_joined = od_joined %>%    si_predict(m3, constraint_total = total, output_col = \"flow_m3\") od_joined %>%    ggplot(aes(distance_euclidean, flow_m3)) +   geom_point() od_joined %>%    ggplot(aes(all, flow_m3)) +   geom_point() cor(od_joined$all, od_joined$flow_m3, use = \"complete.obs\")^2 #> [1] 0.1441077 od_joined = od_joined %>%    si_predict(m3, constraint_production = origin_all, output_col = \"flow_m4\") od_joined %>%    ggplot(aes(distance_euclidean, flow_m4)) +   geom_point() od_joined %>%    ggplot(aes(all, flow_m4)) +   geom_point() cor(od_joined$all, od_joined$flow_m4, use = \"complete.obs\")^2 #> [1] 0.1299208 sum(od_joined$flow_m4) #> [1] 20237 zones_attraction = inner_join(   si_centroids,   si_od_census %>%    group_by(geo_code = D) %>%    summarise(attractiveness = sum(all)) ) #> Joining with `by = join_by(geo_code)` od_attraction = si_to_od(origins = zones_attraction, destinations = zones_attraction) od_joined_attraction = left_join(od_attraction, si_od_census) %>%    mutate(all = case_when(is.na(all) ~ 0, TRUE ~ all)) #> Joining with `by = join_by(O, D)`  od_joined = od_joined_attraction %>%    si_predict(m3, constraint_attraction = destination_attractiveness, output_col = \"flow_m5\") od_joined %>%    ggplot(aes(distance_euclidean, flow_m5)) +   geom_point() od_joined %>%    ggplot(aes(all, flow_m5)) +   geom_point() cor(od_joined$all, od_joined$flow_m5, use = \"complete.obs\")^2 #> [1] 0.7261424 sum(od_joined$flow_m5) #> [1] 236326 sum(od_joined$all) #> [1] 236326"},{"path":[]},{"path":"https://robinlovelace.github.io/simodels/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Robin Lovelace. Author, maintainer. Jakub Nowosad. Author.","code":""},{"path":"https://robinlovelace.github.io/simodels/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lovelace R, Nowosad J (2024). simodels: Flexible Framework Developing Spatial Interaction Models. R package version 0.1.0, https://robinlovelace.github.io/simodels/, https://github.com/robinlovelace/simodels.","code":"@Manual{,   title = {simodels: Flexible Framework for Developing Spatial Interaction Models},   author = {Robin Lovelace and Jakub Nowosad},   year = {2024},   note = {R package version 0.1.0,     https://robinlovelace.github.io/simodels/},   url = {https://github.com/robinlovelace/simodels}, }"},{"path":"https://robinlovelace.github.io/simodels/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Flexible Framework for Developing Spatial Interaction Models","text":"Install package follows: get develoment version :","code":"install.packages(\"remotes\") # if not already installed remotes::install_github(\"robinlovelace/simodels\") devtools::load_all()"},{"path":"https://robinlovelace.github.io/simodels/index.html","id":"simodel-basics","dir":"","previous_headings":"","what":"simodel basics","title":"Flexible Framework for Developing Spatial Interaction Models","text":"Run basic SIM follows:  just happened? created ‘OD data frame’ function si_to_od() geographic origins destinations, estimated simple ‘production constrained’ (constraint_production argument) gravity model based population origin destination zones custom distance decay function si_calculate(). example shows, package allows/encourages define use functions estimate amount interaction/movement places. approach also ‘tidy’, allowing use {simodels} functions {dplyr} pipelines: resulting estimates interaction, returned column interaction plotted distance graphic , resulted choice spatial interaction model inputs, allowing wide range alternative approaches implemented. flexibility key aspect package, enabling small easily modified functions implemented tested. output si_calculate() geographic object can plotted sf’s plot method (geographic data visualisation packages):  si_to_od() function transforms geographic entities (typically polygons points) data frame representing full combination origin-destination pairs less max_dist meters apart. common saying data science 80% effort goes pre-processing stage. equally true spatial interaction modelling types data intensive analysis/modelling work. function return? function allows use variable origin destination data joining attributes onto OD data frame, column names appended origin destination. approach works equally well ‘bipartite’ SIMs, origin destination points different (Hasova et al. 2022). following example implements bipartite SIM estimates number trips administrative zones pubs Leeds: can plot top 20 desire lines zone centroids 2 pubs example dataset follows:","code":"library(simodels) library(dplyr) # prepare OD data od = si_to_od(   origins = si_zones,        # origin locations   destinations = si_zones,   # destination locations   max_dist = 5000            # maximum distance between OD pairs   ) # specify a function gravity_model = function(beta, d, m, n) {   m * n * exp(-beta * d / 1000) }  # perform SIM od_res = si_calculate(   od,   fun = gravity_model,   d = distance_euclidean,   m = origin_all,   n = destination_all,   constraint_production = origin_all,   beta = 0.9   ) # visualize the results plot(od_res$distance_euclidean, od_res$interaction) od_res = od %>%    si_calculate(fun = gravity_model,                 m = origin_all,                n = destination_all,                d = distance_euclidean,                constraint_production = origin_all,                beta = 0.3) od_res %>%    select(interaction) Simple feature collection with 2505 features and 1 field Geometry type: LINESTRING Dimension:     XY Bounding box:  xmin: -1.743949 ymin: 53.71552 xmax: -1.337493 ymax: 53.92906 Geodetic CRS:  WGS 84 # A tibble: 2,505 × 2    interaction                                 geometry          <dbl>                         <LINESTRING [°]>  1       2177. (-1.400108 53.92906, -1.400108 53.92906)  2        632. (-1.400108 53.92906, -1.346497 53.92305)  3        556. (-1.346497 53.92305, -1.400108 53.92906)  4       1382. (-1.346497 53.92305, -1.346497 53.92305)  5        449. (-1.346497 53.92305, -1.357667 53.88306)  6        794. (-1.704658 53.91073, -1.704658 53.91073)  7        749.   (-1.704658 53.91073, -1.6876 53.90066)  8        287. (-1.704658 53.91073, -1.743949 53.88035)  9        267. (-1.704658 53.91073, -1.710657 53.87087) 10        186. (-1.704658 53.91073, -1.694076 53.86729) # ℹ 2,495 more rows plot(od_res[\"interaction\"], logz = TRUE) # Set n. trips to pubs, assuming that for every trip to the pub there are # 50 trips to work (this would be validated/tested/modelled in empirical work) zones_pubs = si_zones %>%    mutate(to_pubs = all / 50) pubs_example = si_pubs %>%    filter(grepl(pattern = \"Chemic|Nag\", x = name)) pubs_example$size = c(100, 80) od_to_pubs = si_to_od(zones_pubs, pubs_example) od_to_pubs_result = od_to_pubs %>%    si_calculate(fun = gravity_model,                 m = origin_to_pubs,                n = destination_size,                d = distance_euclidean,                beta = 0.5,                constraint_production = origin_to_pubs) od_to_pubs_result %>%    select(O, D, destination_name, interaction) Simple feature collection with 214 features and 4 fields Geometry type: LINESTRING Dimension:     XY Bounding box:  xmin: -1.743949 ymin: 53.71552 xmax: -1.337493 ymax: 53.92906 Geodetic CRS:  WGS 84 # A tibble: 214 × 5    O         D         destination_name  interaction                    geometry    <chr>     <chr>     <chr>                   <dbl>            <LINESTRING [°]>  1 E02002330 127960333 The Chemic Tavern        18.4 (-1.400108 53.92906, -1.55…  2 E02002331 127960333 The Chemic Tavern        15.9 (-1.346497 53.92305, -1.55…  3 E02002332 127960333 The Chemic Tavern        25.5 (-1.704658 53.91073, -1.55…  4 E02002333 127960333 The Chemic Tavern        38.7 (-1.6876 53.90066, -1.5528…  5 E02002334 127960333 The Chemic Tavern        20.9 (-1.357667 53.88306, -1.55…  6 E02002335 127960333 The Chemic Tavern        19.9 (-1.470966 53.89184, -1.55…  7 E02002336 127960333 The Chemic Tavern        25.5 (-1.624775 53.88589, -1.55…  8 E02002337 127960333 The Chemic Tavern        37.2 (-1.743949 53.88035, -1.55…  9 E02002338 127960333 The Chemic Tavern        36.8 (-1.710657 53.87087, -1.55… 10 E02002339 127960333 The Chemic Tavern        29.0 (-1.694076 53.86729, -1.55… # ℹ 204 more rows library(tmap) tmap_mode(\"view\") od_to_pubs_result %>%    top_n(n = 50, wt = interaction) %>%    tm_shape() +   tm_lines(col = \"interaction\", palette = \"viridis\", scale = 2)"},{"path":"https://robinlovelace.github.io/simodels/index.html","id":"feedback","dir":"","previous_headings":"","what":"Feedback","title":"Flexible Framework for Developing Spatial Interaction Models","text":"interested hear approach presented package compared implementations presented links . anyone like try approach implement another language feel free get touch via issue tracker.","code":""},{"path":"https://robinlovelace.github.io/simodels/index.html","id":"further-reading","dir":"","previous_headings":"","what":"Further reading","title":"Flexible Framework for Developing Spatial Interaction Models","text":"details SIMs defined mathematically code first principles, see sims vignette. dive straight using {simodels} develop SIMs, see Get started vignette. detailed introduction SIMs, support reproducible R code, see Adam Dennett’s 2018 paper.","code":""},{"path":"https://robinlovelace.github.io/simodels/index.html","id":"other-sim-packages","dir":"","previous_headings":"","what":"Other SIM packages","title":"Flexible Framework for Developing Spatial Interaction Models","text":"spflow R package spint Python package gravity R package mobility R package gravity functions scikit-mobility Python package","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/destinations_york.html","id":null,"dir":"Reference","previous_headings":"","what":"Example destinations dataset: schools in York — destinations_york","title":"Example destinations dataset: schools in York — destinations_york","text":"Example dataset York, UK","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/destinations_york.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Example destinations dataset: schools in York — destinations_york","text":"See data-raw/zones_york.qmd details data source.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/destinations_york.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example destinations dataset: schools in York — destinations_york","text":"","code":"head(destinations_york) #> Simple feature collection with 6 features and 4 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1.129601 ymin: 53.94108 xmax: -1.075563 ymax: 53.97108 #> Geodetic CRS:  WGS 84 #> # A tibble: 6 × 5 #>      URN n_pupils phase          type_of_establishment             geometry #>    <dbl>    <dbl> <chr>          <chr>                          <POINT [°]> #> 1 121266       71 Nursery        Local authority nurs… (-1.129601 53.96334) #> 2 121270      160 Not applicable Pupil referral unit   (-1.076782 53.94353) #> 3 121273      211 Primary        Community school       (-1.129302 53.9638) #> 4 121276      308 Primary        Community school      (-1.110397 53.94108) #> 5 121277      309 Primary        Community school      (-1.075563 53.95181) #> 6 121283      423 Primary        Community school      (-1.092958 53.97108)"},{"path":"https://robinlovelace.github.io/simodels/reference/od_aus.html","id":null,"dir":"Reference","previous_headings":"","what":"Example OD dataset: flows between regions in Australia — od_aus","title":"Example OD dataset: flows between regions in Australia — od_aus","text":"Example dataset Australia","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/od_aus.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Example OD dataset: flows between regions in Australia — od_aus","text":"Regenerate data scripts data-raw directory.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/od_aus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example OD dataset: flows between regions in Australia — od_aus","text":"","code":"head(od_aus) #>           Origin Orig_code       Destination Dest_code    Flow vi1_origpop #> 1 Greater Sydney     1GSYD    Greater Sydney     1GSYD 3395015     4391673 #> 2 Greater Sydney     1GSYD       Rest of NSW     1RNSW   91031     4391673 #> 3 Greater Sydney     1GSYD Greater Melbourne     2GMEL   22601     4391673 #> 4 Greater Sydney     1GSYD       Rest of Vic     2RVIC    4416     4391673 #> 5 Greater Sydney     1GSYD  Greater Brisbane     3GBRI   22888     4391673 #> 6 Greater Sydney     1GSYD       Rest of Qld     3RQLD   27445     4391673 #>   wj1_destpop vi2_origunemp wj2_destunemp vi3_origmedinc wj3_destmedinc #> 1     4391673          5.74          5.74         780.64         780.64 #> 2     2512952          5.74          6.12         780.64         509.97 #> 3     3999981          5.74          5.47         780.64         407.95 #> 4     1345717          5.74          5.17         780.64         506.58 #> 5     2065998          5.74          5.86         780.64         767.08 #> 6     2253723          5.74          6.22         780.64         446.48 #>   vi4_origpctrent wj4_destpctrent #> 1           31.77           31.77 #> 2           31.77           27.20 #> 3           31.77           27.34 #> 4           31.77           24.08 #> 5           31.77           33.19 #> 6           31.77           32.57"},{"path":"https://robinlovelace.github.io/simodels/reference/si_calculate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate flow using a pre-existing function — si_calculate","title":"Calculate flow using a pre-existing function — si_calculate","text":"Executes spatial interaction model based OD data frame user-specified function","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_calculate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate flow using a pre-existing function — si_calculate","text":"","code":"si_calculate(   od,   fun,   constraint_production,   constraint_attraction,   constraint_total,   output_col = \"interaction\",   ... )"},{"path":"https://robinlovelace.github.io/simodels/reference/si_calculate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate flow using a pre-existing function — si_calculate","text":"od data frame representing origin-destination data, e.g. created si_to_od() fun function calculates interaction (e.g. number trips) OD pair constraint_production Character representing column od. argument, set, ensures outputs 'production constrained': total 'interaction' (e.g. n. trips) OD pairs set total zone origin go value. constraint_attraction Character representing column od. argument, set, ensures outputs 'attraction constrained': total 'interaction' (e.g. n. trips) OD pairs set sum trips destination equal mean value per destination. constraint_total Single number representing total interaction. argument, set, ensures sum interaction calculated equal value given. output_col Character string containing name new output column. \"interaction\" default. ... Arguments passed fun","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_calculate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate flow using a pre-existing function — si_calculate","text":"sf data frame","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_calculate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate flow using a pre-existing function — si_calculate","text":"","code":"od = si_to_od(si_zones, si_zones, max_dist = 4000) #> Converting p to centroids #> lon-lat points fun_dd = function(d = \"distance_euclidean\", beta = 0.3) exp(-beta * d / 1000) fun_dd(d = (1:5) * 1000) #> [1] 0.7408182 0.5488116 0.4065697 0.3011942 0.2231302 od_dd = si_calculate(od, fun = fun_dd, d = distance_euclidean) plot(od$distance_euclidean, od_dd$interaction)  fun = function(O, n, d, beta) O * n * exp(-beta * d / 1000) od_output = si_calculate(od, fun = fun, beta = 0.3, O = origin_all,    n = destination_all, d = distance_euclidean) head(od_output) #> Simple feature collection with 6 features and 28 fields #> Geometry type: LINESTRING #> Dimension:     XY #> Bounding box:  xmin: -1.704658 ymin: 53.90066 xmax: -1.346497 ymax: 53.92906 #> Geodetic CRS:  WGS 84 #>           O         D distance_euclidean origin_geo_name origin_lad11cd #> 1 E02002330 E02002330              0.000       Leeds 001      E08000035 #> 2 E02002330 E02002331           3579.804       Leeds 001      E08000035 #> 3 E02002331 E02002331              0.000       Leeds 002      E08000035 #> 4 E02002331 E02002330           3579.804       Leeds 002      E08000035 #> 5 E02002332 E02002332              0.000       Leeds 003      E08000035 #> 6 E02002332 E02002333           1582.213       Leeds 003      E08000035 #>   origin_lad_name origin_all origin_bicycle origin_foot origin_car_driver #> 1           Leeds       2809             52         300              2170 #> 2           Leeds       2809             52         300              2170 #> 3           Leeds       2387             53         469              1573 #> 4           Leeds       2387             53         469              1573 #> 5           Leeds       2471             39         341              1666 #> 6           Leeds       2471             39         341              1666 #>   origin_car_passenger origin_motorbike origin_train_tube origin_bus #> 1                  138               10                36         89 #> 2                  138               10                36         89 #> 3                  132               16                10        126 #> 4                  132               16                10        126 #> 5                  147               19                75        166 #> 6                  147               19                75        166 #>   origin_taxi_other destination_geo_name destination_lad11cd #> 1                14            Leeds 001           E08000035 #> 2                14            Leeds 002           E08000035 #> 3                 8            Leeds 002           E08000035 #> 4                 8            Leeds 001           E08000035 #> 5                18            Leeds 003           E08000035 #> 6                18            Leeds 004           E08000035 #>   destination_lad_name destination_all destination_bicycle destination_foot #> 1                Leeds            2809                  52              300 #> 2                Leeds            2387                  53              469 #> 3                Leeds            2387                  53              469 #> 4                Leeds            2809                  52              300 #> 5                Leeds            2471                  39              341 #> 6                Leeds            3743                  80              529 #>   destination_car_driver destination_car_passenger destination_motorbike #> 1                   2170                       138                    10 #> 2                   1573                       132                    16 #> 3                   1573                       132                    16 #> 4                   2170                       138                    10 #> 5                   1666                       147                    19 #> 6                   2340                       193                    26 #>   destination_train_tube destination_bus destination_taxi_other #> 1                     36              89                     14 #> 2                     10             126                      8 #> 3                     10             126                      8 #> 4                     36              89                     14 #> 5                     75             166                     18 #> 6                    221             335                     19 #>                         geometry interaction #> 1 LINESTRING (-1.400108 53.92...     7890481 #> 2 LINESTRING (-1.400108 53.92...     2290854 #> 3 LINESTRING (-1.346497 53.92...     5697769 #> 4 LINESTRING (-1.346497 53.92...     2290854 #> 5 LINESTRING (-1.704658 53.91...     6105841 #> 6 LINESTRING (-1.704658 53.91...     5753720 plot(od$distance_euclidean, od_output$interaction)  od_pconst = si_calculate(od, fun = fun, beta = 0.3, O = origin_all,   n = destination_all, d = distance_euclidean, constraint_production = origin_all) # Origin totals in OD data should equal origin totals in zone data library(dplyr) #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union origin_totals_zones = od_pconst |>   group_by(geo_code = O) |>   summarise(all_od = sum(interaction)) |>   sf::st_drop_geometry() zones_joined = left_join(si_zones, origin_totals_zones) #> Joining with `by = join_by(geo_code)` plot(zones_joined$all, zones_joined$all_od)  plot(od_pconst$distance_euclidean, od_pconst$interaction)  plot(od_pconst[\"interaction\"], logz = TRUE)  od_dd = si_calculate(od, fun = fun_dd, d = distance_euclidean, output_col = \"res\") head(od_dd$res) #> [1] 1.0000000 0.3416593 1.0000000 0.3416593 1.0000000 0.6220942 od_dd = si_calculate(od, fun = fun_dd, d = distance_euclidean, constraint_total = 10) sum(od_dd$interaction) #> [1] 10"},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz.html","id":null,"dir":"Reference","previous_headings":"","what":"Origin-Destination Data for Leeds — si_oa_wpz","title":"Origin-Destination Data for Leeds — si_oa_wpz","text":"dataset contains origin-destination data Leeds, including number trips output areas (OAs) workplace zones (WPZs).","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Origin-Destination Data for Leeds — si_oa_wpz","text":"","code":"head(si_oa_wpz) #> # A tibble: 6 × 3 #>   OA11CD    wz11cd        n #>   <chr>     <chr>     <dbl> #> 1 E00056848 E33009546     1 #> 2 E00056848 E33009547     1 #> 3 E00056848 E33009548     1 #> 4 E00056848 E33009553     1 #> 5 E00056848 E33010351     1 #> 6 E00056848 E33010352     1"},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz_d.html","id":null,"dir":"Reference","previous_headings":"","what":"Destination Data for Leeds — si_oa_wpz_d","title":"Destination Data for Leeds — si_oa_wpz_d","text":"dataset contains number trips destined workplace zone (WPZ) Leeds.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz_d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Destination Data for Leeds — si_oa_wpz_d","text":"See wicid.ukdataservice.ac.uk details data source data-raw/leeds_wpz.R details example dataset generated.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz_d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Destination Data for Leeds — si_oa_wpz_d","text":"","code":"head(si_oa_wpz_d) #> Simple feature collection with 6 features and 2 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -1.557363 ymin: 53.77863 xmax: -1.523513 ymax: 53.80146 #> Geodetic CRS:  WGS 84 #>      wz11cd n_d                       geometry #> 1 E33010402  58 MULTIPOLYGON (((-1.532786 5... #> 2 E33010372 160 MULTIPOLYGON (((-1.546048 5... #> 3 E33010502 135 MULTIPOLYGON (((-1.55139 53... #> 4 E33010472  34 MULTIPOLYGON (((-1.54637 53... #> 5 E33010442  60 MULTIPOLYGON (((-1.551074 5... #> 6 E33009594  15 MULTIPOLYGON (((-1.523878 5... sf:::plot.sf(si_oa_wpz_d[\"n_d\"])"},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz_o.html","id":null,"dir":"Reference","previous_headings":"","what":"Origin Data for Leeds — si_oa_wpz_o","title":"Origin Data for Leeds — si_oa_wpz_o","text":"dataset contains number trips originating output area (OA) Leeds.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz_o.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Origin Data for Leeds — si_oa_wpz_o","text":"","code":"head(si_oa_wpz_o) #> Simple feature collection with 6 features and 2 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -1.589455 ymin: 53.79168 xmax: -1.576951 ymax: 53.80234 #> Geodetic CRS:  WGS 84 #>      OA11CD n_o                       geometry #> 1 E00056848  24 MULTIPOLYGON (((-1.581762 5... #> 2 E00056849  27 MULTIPOLYGON (((-1.577746 5... #> 3 E00056874  92 MULTIPOLYGON (((-1.585058 5... #> 4 E00056876 103 MULTIPOLYGON (((-1.58512 53... #> 5 E00056885  48 MULTIPOLYGON (((-1.583388 5... #> 6 E00056886  29 MULTIPOLYGON (((-1.584578 5... sf:::plot.sf(si_oa_wpz_o[\"n_o\"])"},{"path":"https://robinlovelace.github.io/simodels/reference/si_od_census.html","id":null,"dir":"Reference","previous_headings":"","what":"Example OD dataset — si_od_census","title":"Example OD dataset — si_od_census","text":"Example OD dataset 2011 UK Census","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_od_census.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Example OD dataset — si_od_census","text":"Regenerate data scripts data-raw directory.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_od_census.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example OD dataset — si_od_census","text":"","code":"head(si_od_census) #> # A tibble: 6 × 18 #>   O      D       all from_home light_rail train   bus  taxi motorbike car_driver #>   <chr>  <chr> <dbl>     <dbl>      <dbl> <dbl> <dbl> <dbl>     <dbl>      <dbl> #> 1 E0200… E020…    66         0          0     0     0     0         1         29 #> 2 E0200… E020…   742         0          0     1     5     1         1        426 #> 3 E0200… E020…     2         0          0     0     0     0         0          2 #> 4 E0200… E020…     9         0          0     0     0     0         0          6 #> 5 E0200… E020…    50         0          0     0     3     1         0         43 #> 6 E0200… E020…    23         0          0     0     0     0         0         20 #> # ℹ 8 more variables: car_passenger <dbl>, bicycle <dbl>, foot <dbl>, #> #   other <dbl>, geo_name1 <chr>, geo_name2 <chr>, la_1 <chr>, la_2 <chr>"},{"path":"https://robinlovelace.github.io/simodels/reference/si_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict spatial interaction based on pre-trained model — si_predict","title":"Predict spatial interaction based on pre-trained model — si_predict","text":"Predict spatial interaction based pre-trained model","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict spatial interaction based on pre-trained model — si_predict","text":"","code":"si_predict(   od,   model,   constraint_production,   constraint_attraction,   constraint_total,   output_col = \"interaction\",   ... )"},{"path":"https://robinlovelace.github.io/simodels/reference/si_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict spatial interaction based on pre-trained model — si_predict","text":"od data frame representing origin-destination data, e.g. created si_to_od() model model object, e.g. lm() glm() constraint_production Character representing column od. argument, set, ensures outputs 'production constrained': total 'interaction' (e.g. n. trips) OD pairs set total zone origin go value. constraint_attraction Character representing column od. argument, set, ensures outputs 'attraction constrained': total 'interaction' (e.g. n. trips) OD pairs set sum trips destination equal mean value per destination. constraint_total Single number representing total interaction. argument, set, ensures sum interaction calculated equal value given. output_col Character string containing name new output column. \"interaction\" default. ... Arguments passed fun","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict spatial interaction based on pre-trained model — si_predict","text":"sf data frame","code":""},{"path":[]},{"path":"https://robinlovelace.github.io/simodels/reference/si_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict spatial interaction based on pre-trained model — si_predict","text":"","code":"od = si_to_od(si_zones, si_zones, max_dist = 4000) #> Converting p to centroids #> lon-lat points m = lm(od$origin_all ~ od$origin_bicycle) od_updated = si_predict(od, m)"},{"path":"https://robinlovelace.github.io/simodels/reference/si_pubs.html","id":null,"dir":"Reference","previous_headings":"","what":"Example destinations dataset: pubs in Leeds — si_pubs","title":"Example destinations dataset: pubs in Leeds — si_pubs","text":"Example dataset Leeds, UK","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_pubs.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Example destinations dataset: pubs in Leeds — si_pubs","text":"Regenerate data scripts data-raw directory.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_pubs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example destinations dataset: pubs in Leeds — si_pubs","text":"","code":"head(si_pubs) #> Simple feature collection with 6 features and 7 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1.696442 ymin: 53.72551 xmax: -1.42461 ymax: 53.90583 #> Geodetic CRS:  WGS 84 #>      osm_id                  name barrier place man_made shop amenity #> 2    611105   The Mexborough Arms    <NA>  <NA>     <NA> <NA>     pub #> 4  21029699 The Black Horse Hotel    <NA>  <NA>     <NA> <NA>     pub #> 5  26124686 The Waggon and Horses    <NA>  <NA>     <NA> <NA>     pub #> 6  26124687            The Castle    <NA>  <NA>     <NA> <NA>     pub #> 19 27297278           Cross Pipes    <NA>  <NA>     <NA> <NA>     pub #> 20 27305438             Bay Horse    <NA>  <NA>     <NA> <NA>     pub #>                      geometry #> 2    POINT (-1.42461 53.7281) #> 4  POINT (-1.694605 53.90571) #> 5  POINT (-1.495596 53.72551) #> 6  POINT (-1.495613 53.72573) #> 19 POINT (-1.696442 53.90481) #> 20  POINT (-1.69363 53.90583)"},{"path":"https://robinlovelace.github.io/simodels/reference/si_to_od.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare OD data frame — si_to_od","title":"Prepare OD data frame — si_to_od","text":"Prepares OD data frame next used estimate movement origins destinations spatial interaction model.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_to_od.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare OD data frame — si_to_od","text":"","code":"si_to_od(origins, destinations, max_dist = Inf, intrazonal = TRUE)"},{"path":"https://robinlovelace.github.io/simodels/reference/si_to_od.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare OD data frame — si_to_od","text":"origins sf object representing origin locations/zones destinations sf object representing destination locations/zones max_dist Euclidean distance meters (numeric). OD pairs distance apart less returned therefore included SIM. intrazonal Include intrazonal OD pairs? Intrazonal OD pairs represent movement one place zone another place zone. TRUE default.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_to_od.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare OD data frame — si_to_od","text":"sf data frame","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_to_od.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prepare OD data frame — si_to_od","text":"origin-destination datasets spatial entities constitute origins (typically administrative zones) also represent destinations. 'unipartite' case origins destinations passed object, sf data frame representing administrative zones. 'Bipartite' datasets, contrast, represent \"spatial interaction systems origins act destinations vice versa\" (Hasova et al. 2022). different sf object can passed destinations argument.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_to_od.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare OD data frame — si_to_od","text":"","code":"library(sf) #> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE origins = si_centroids[c(1, 2, 99), ] destinations = origins plot(origins$geometry)  odsf = si_to_od(origins, destinations, max_dist = 1200) #> lon-lat points plot(odsf) #> Warning: plotting the first 9 out of 27 attributes; use max.plot = 27 to plot all  # note: result contains intrazonal flows represented by linestrings # with a length of 0, e.g.: sf::st_coordinates(odsf$geometry[1]) #>              X        Y L1 #> [1,] -1.501878 53.81544  1 #> [2,] -1.501878 53.81544  1 # With different destinations compared with origins library(sf) origins = si_centroids[c(2, 99), c(1, 6, 7)] destinations = si_centroids[1, c(1, 6, 8)] odsf = si_to_od(origins, destinations) nrow(odsf) # no intrazonal flows #> [1] 2 plot(odsf)"},{"path":"https://robinlovelace.github.io/simodels/reference/si_zones.html","id":null,"dir":"Reference","previous_headings":"","what":"Example zones and centroids — si_zones","title":"Example zones and centroids — si_zones","text":"si_zones si_centroids represent administrative zones flows estimated.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_zones.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Example zones and centroids — si_zones","text":"schema data can (re-)generated using code data-raw","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_zones.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example zones and centroids — si_zones","text":"","code":"si_zones #> Simple feature collection with 107 features and 13 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -1.800362 ymin: 53.699 xmax: -1.292229 ymax: 53.94589 #> Geodetic CRS:  WGS 84 #> # A tibble: 107 × 14 #>    geo_code  geo_name  lad11cd   lad_name   all bicycle  foot car_driver #>    <chr>     <chr>     <chr>     <chr>    <dbl>   <dbl> <dbl>      <dbl> #>  1 E02002330 Leeds 001 E08000035 Leeds     2809      52   300       2170 #>  2 E02002331 Leeds 002 E08000035 Leeds     2387      53   469       1573 #>  3 E02002332 Leeds 003 E08000035 Leeds     2471      39   341       1666 #>  4 E02002333 Leeds 004 E08000035 Leeds     3743      80   529       2340 #>  5 E02002334 Leeds 005 E08000035 Leeds     2995      53   170       2421 #>  6 E02002335 Leeds 006 E08000035 Leeds     3056      33   106       2549 #>  7 E02002336 Leeds 007 E08000035 Leeds     2690      35    94       2178 #>  8 E02002337 Leeds 008 E08000035 Leeds     3187      31   238       2222 #>  9 E02002338 Leeds 009 E08000035 Leeds     3155      52   343       2024 #> 10 E02002339 Leeds 010 E08000035 Leeds     2501      41   360       1552 #> # ℹ 97 more rows #> # ℹ 6 more variables: car_passenger <dbl>, motorbike <dbl>, train_tube <dbl>, #> #   bus <dbl>, taxi_other <dbl>, geometry <POLYGON [°]> sf:::plot.sfg(si_zones$geometry) sf:::plot.sfg(si_centroids$geometry, add = TRUE)"},{"path":"https://robinlovelace.github.io/simodels/reference/simodels-package.html","id":null,"dir":"Reference","previous_headings":"","what":"simodels: Flexible Framework for Developing Spatial Interaction Models — simodels-package","title":"simodels: Flexible Framework for Developing Spatial Interaction Models — simodels-package","text":"Develop spatial interaction models (SIMs). SIMs predict amount interaction, example number trips per day, geographic entities representing trip origins destinations. Contains functions creating origin-destination datasets geographic input datasets calculating movement origin-destination pairs constrained, production-constrained, attraction-constrained models (Wilson 1979) doi:10.1068/a030001 .","code":""},{"path":[]},{"path":"https://robinlovelace.github.io/simodels/reference/simodels-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"simodels: Flexible Framework for Developing Spatial Interaction Models — simodels-package","text":"Maintainer: Robin Lovelace rob00x@gmail.com (ORCID) Authors: Jakub Nowosad nowosad.jakub@gmail.com (ORCID)","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/zones_aus.html","id":null,"dir":"Reference","previous_headings":"","what":"Example zones dataset: regions of Australia — zones_aus","title":"Example zones dataset: regions of Australia — zones_aus","text":"Example dataset Australia","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/zones_aus.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Example zones dataset: regions of Australia — zones_aus","text":"Regenerate data scripts data-raw directory.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/zones_aus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example zones dataset: regions of Australia — zones_aus","text":"","code":"head(zones_aus) #> Simple feature collection with 6 features and 6 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: 137.9943 ymin: -39.12579 xmax: 153.6298 ymax: -10.73851 #> Geodetic CRS:  GDA94 #> # A tibble: 6 × 7 #>   GCCSA_CODE GCC_CODE16 GCCSA_NAME        STATE_CODE STATE_NAME      AREA_SQKM #>   <chr>      <chr>      <chr>             <chr>      <chr>               <dbl> #> 1 1RNSW      1RNSW      Rest of NSW       1          New South Wales   788443. #> 2 1GSYD      1GSYD      Greater Sydney    1          New South Wales    12368. #> 3 2GMEL      2GMEL      Greater Melbourne 2          Victoria            9993. #> 4 2RVIC      2RVIC      Rest of Vic.      2          Victoria          217503. #> 5 3RQLD      3RQLD      Rest of Qld       3          Queensland       1714330. #> 6 3GBRI      3GBRI      Greater Brisbane  3          Queensland         15842. #> # ℹ 1 more variable: geometry <MULTIPOLYGON [°]>"},{"path":"https://robinlovelace.github.io/simodels/reference/zones_york.html","id":null,"dir":"Reference","previous_headings":"","what":"Example zones dataset: administrative zones of York — zones_york","title":"Example zones dataset: administrative zones of York — zones_york","text":"See data-raw/zones_york.qmd details data source.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/zones_york.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example zones dataset: administrative zones of York — zones_york","text":"","code":"head(zones_york) #> Simple feature collection with 6 features and 13 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -1.143303 ymin: 53.90614 xmax: -1.075275 ymax: 53.97628 #> Geodetic CRS:  WGS 84 #>    LSOA21CD  LSOA21NM total f0_to_15 f16_to_29 f30_to_44 f45_to_64 f65_and_over #> 1 E01013335 York 012A  1626      164       115       215       202          158 #> 2 E01013336 York 012B  1545      109        75       162       221          210 #> 3 E01013337 York 012C  1651      127        98       167       236          198 #> 4 E01013338 York 012D  2358      203       194       268       300          258 #> 5 E01013339 York 012E  1602      128        91       149       246          194 #> 6 E01013340 York 024A  1275      117        53        95       217          160 #>   m0_to_15 m16_to_29 m30_to_44 m45_to_64 m65_and_over #> 1      169       151       170       180          102 #> 2      138       108       143       215          164 #> 3      151       104       175       229          166 #> 4      250       186       236       291          172 #> 5      133       109       134       251          167 #> 6      129       100        71       188          145 #>                         geometry #> 1 MULTIPOLYGON (((-1.128299 5... #> 2 MULTIPOLYGON (((-1.123884 5... #> 3 MULTIPOLYGON (((-1.133394 5... #> 4 MULTIPOLYGON (((-1.122007 5... #> 5 MULTIPOLYGON (((-1.11843 53... #> 6 MULTIPOLYGON (((-1.087652 5... sf:::plot.sfg(zones_york$geometry)"},{"path":"https://robinlovelace.github.io/simodels/news/index.html","id":"simodels-010-2024-01","dir":"Changelog","previous_headings":"","what":"simodels 0.1.0 (2024-01)","title":"simodels 0.1.0 (2024-01)","text":"CRAN release: 2024-07-23 See https://github.com/Robinlovelace/simodels/pull/32 Minor tweaks documentation","code":""},{"path":"https://robinlovelace.github.io/simodels/news/index.html","id":"simodels-005","dir":"Changelog","previous_headings":"","what":"simodels 0.0.5","title":"simodels 0.0.5","text":"CRAN release: 2022-08-31 Remove dependency files hosted Dropbox CRAN (#28)","code":""},{"path":"https://robinlovelace.github.io/simodels/news/index.html","id":"simodels-004-2022-06","dir":"Changelog","previous_headings":"","what":"simodels 0.0.4 (2022-06)","title":"simodels 0.0.4 (2022-06)","text":"CRAN release: 2022-06-15 Package renamed avoid clash existing SI package (#20)","code":""},{"path":"https://robinlovelace.github.io/simodels/news/index.html","id":"simodels-003","dir":"Changelog","previous_headings":"","what":"simodels 0.0.3","title":"simodels 0.0.3","text":"New NEWS.md file track changes package. New intrazonal argument od_to_si() (@R-icntay #16)","code":""}]
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Also add information contact electronic paper mail. software can interact users remotely computer network, also make sure provides way users get source. example, program web application, interface display “Source” link leads users archive code. many ways offer source, different solutions better different programs; see section 13 specific requirements. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU AGPL, see https://www.gnu.org/licenses/.","code":"<one line to give the program's name and a brief idea of what it does.>     Copyright (C) <year>  <name of author>      This program is free software: you can redistribute it and/or modify     it under the terms of the GNU Affero General Public License as     published by the Free Software Foundation, either version 3 of the     License, or (at your option) any later version.      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See package’s README instructions installing {simodels} package.","code":"library(simodels) library(dplyr) library(ggplot2) library(sf)"},{"path":"https://robinlovelace.github.io/simodels/articles/simodels.html","id":"input-data","dir":"Articles","previous_headings":"","what":"Input data","title":"Spatial interaction models with R","text":"tutorial builds reproducible guide SIMs R (Dennett 2018). start importing open access data representing movement zones Australia (thanks Adam Dennett making files accessible): Let’s take quick look ‘minimize’ input datasets modelling SIMs: results printed show : zones_aus represents 15 regions Australia, columns containing zone identifiers (IDs), primarily GCCSA_CODE, area zone (AREA_SQKM). minimal zones object contains region code, name, area. od_aus contains 225 rows data representing movement people zones aus. Note 255 15 squared, meaning OD dataset contains complete combination migration flows, starting zone 1GSYD 1GSYD. codes present GCCSA_CODE column aus object. first row OD dataset represents ‘intra-zonal’ migrations Greater Sydney, presumably counting number people move house somewhere region another home region. 13 columns dataset, important Orig_code, Dest_code, Flow, captured minimal od dataset. Note: useful convention ‘long form’ OD datasets first two columns contain zone IDs correspond values first column zone dataset. R package {od}, {simodels} builds, assumes inputs functions form. good idea verify origin destination codes od dataset match zone codes zones: clear ‘clean’ input datasets, let’s begin modelling!","code":"# To get the data (pre-loaded in the package) u1 = \"https://github.com/Robinlovelace/simodels/releases/download/0.0.1/zones_aus.geojson\" zones_aus = sf::read_sf(u1) u2 = \"https://www.dropbox.com/s/wi3zxlq5pff1yda/AusMig2011.csv?raw=1\" od_aus = read.csv(u2) dim(zones_aus) names(zones_aus) key_zone_names = c(\"GCCSA_CODE\", \"GCCSA_NAME\", \"AREA_SQKM\") zones = zones_aus[key_zone_names] head(zones, 2) dim(od_aus) names(od_aus) key_od_names = c(\"Orig_code\", \"Dest_code\", \"Flow\") od = od_aus[key_od_names] head(od, 2) summary(od[[1]] %in% zones[[1]]) summary(od[[2]] %in% zones[[1]])"},{"path":"https://robinlovelace.github.io/simodels/articles/simodels.html","id":"preparing-a-sim","dir":"Articles","previous_headings":"","what":"Preparing a SIM","title":"Spatial interaction models with R","text":"Key workings {simodels} package conversion geographic objects representing origins destinations OD dataset. case, already OD dataset, step less relevant. However, take step case many SIMs start without comprehensive OD dataset case. Prepare OD dataset follows: Note output duplicate columns: si_to_od() joins data origin destination objects resulting OD object.","code":"od_sim = si_to_od(origins = zones, destinations = zones) names(od_sim)"},{"path":"https://robinlovelace.github.io/simodels/articles/simodels.html","id":"an-unconstrained-sim","dir":"Articles","previous_headings":"","what":"An unconstrained SIM","title":"Spatial interaction models with R","text":"simplistic SIM - case inverse power distance decay function (negative exponential another commonly used decay function) - can created just based distance points: approach, ignoring variables level trip origins destinations, results flow estimates units. learning run constrained SIMs, let’s scale result total flow see far reality, just focussing interzonal OD pairs: results show simple unconstrained model, without parameter fitting, can explain less 20% variability flows. can better!","code":"si_power = function(d, beta) (d / 1000)^beta od_calculated = si_calculate(   od_sim,   fun = si_power,   d = distance_euclidean,   beta = -0.8   ) plot(od_calculated[\"interaction\"], logz = TRUE) od_interzonal = od %>%   filter(Orig_code != Dest_code) od_calculated_interzonal = od_calculated %>%   filter(O != D)  scale_factor = sum(od_interzonal$Flow) /   sum(od_calculated_interzonal$interaction) od_calculated_interzonal = od_calculated_interzonal %>%    mutate(interaction_scaled = interaction * scale_factor) od_joined = inner_join(   od_calculated_interzonal,   od %>% rename(O = Orig_code, D = Dest_code)   ) od_joined %>%    ggplot() +   geom_point(aes(Flow, interaction_scaled)) cor(od_joined$Flow, od_joined$interaction_scaled)^2 od_joined %>%    mutate(decay = distance_euclidean^-0.8) %>%    mutate(decay = decay * (sum(Flow) / sum(decay))) %>%    ggplot() +   geom_point(aes(distance_euclidean, Flow)) +   geom_line(aes(distance_euclidean, decay), colour = \"red\")"},{"path":"https://robinlovelace.github.io/simodels/articles/simodels.html","id":"a-production-constrained-sim","dir":"Articles","previous_headings":"","what":"A production constrained SIM","title":"Spatial interaction models with R","text":"first logical way improve model fit run production constrained model. , ’ll first calculate total number people leaving zone use constraint_production argument: Progress! doubled predictive ability model using ‘production constrained’ SIM, defined mathematically simodels-first-principles vignette.","code":"od_originating = od_joined %>%    group_by(O) %>%    mutate(originating_per_zone = sum(Flow)) %>%    ungroup() od_constrained_p = si_calculate(   od_originating,   fun = si_power,   d = distance_euclidean,   beta = -0.8,   constraint_production = originating_per_zone   ) od_constrained_p %>%    ggplot() +   geom_point(aes(Flow, interaction)) cor(od_constrained_p$Flow, od_constrained_p$interaction)^2"},{"path":"https://robinlovelace.github.io/simodels/articles/simodels.html","id":"training-a-sim","dir":"Articles","previous_headings":"","what":"Training a SIM","title":"Spatial interaction models with R","text":"advantage flow data used example already know interaction. (raises question SIM needed, answer: test models demonstrate techniques.) can using nls() function follows:","code":"library(minpack.lm) f = Flow ~ a * (distance_euclidean)^b m = nlsLM(   formula = f,   data = od_originating,   ) m # Nonlinear regression model #   model: Flow ~ a * (distance_euclidean)^b #    data: od_originating #          a          b  #  2.182e+07 -5.801e-01 od_joined %>%    mutate(decay = distance_euclidean^-5.801e-01) %>%    mutate(decay = decay * 2.182e+07) %>%    ggplot() +   geom_point(aes(distance_euclidean, Flow)) +   geom_line(aes(distance_euclidean, decay), colour = \"red\") od_pred = si_predict(od_originating, model = m) cor(od_pred$Flow, od_pred$interaction)^2 od_pred_const = si_predict(od_originating, model = m,   constraint_production = originating_per_zone) cor(od_pred_const$Flow, od_pred_const$interaction)^2 library(tmap) ttm() tm_shape(od_pred_const) +   tm_lines(\"interaction_scaled\", palette = \"viridis\")"},{"path":[]},{"path":"https://robinlovelace.github.io/simodels/articles/sims-first-principles.html","id":"what-are-sims","dir":"Articles","previous_headings":"","what":"What are SIMs?","title":"An introduction to spatial interaction models: from first principles","text":"Spatial Interaction Models (SIMs) mathematical models estimating movement spatial entities developed Alan Wilson late 1960s early 1970, considerable uptake refinement transport modelling since Boyce Williams (2015). four main types traditional SIMs (Wilson 1971): Unconstrained Production-constrained Attraction-constrained Doubly-constrained early highly influential type SIM ‘gravity model’, defined Wilson (1971) follows (paper explored many iterations formulation): Tij=KWi(1)Wj(2)cijn T_{j}=K \\frac{W_{}^{(1)} W_{j}^{(2)}}{c_{j}^{n}} “TijT_{j} measure interaction zones ii Wi(1)W_{}^{(1)} measure ‘mass term’ associated zone ziz_i, Wj(2)W_{j}^{(2)} measure ‘mass term’ associated zone zjz_j, cijc_{ij} measure distance, generalised cost travel, zone ii zone jj”. KK ‘constant proportionality’ nn parameter estimated. Redefining WW terms mm nn origins destinations respectively (Simini et al. 2012), classic definition ‘gravity model’ can written follows: Tij=Kminjcijn T_{j}=K \\frac{m_{} n_{j}}{c_{j}^{n}} purposes project, focus production-constrained SIMs. can defined follows (Wilson 1971): Tij=AiOinjf(cij) T_{ij} = A_iO_in_jf(c_{ij}) AA balancing factor defined : Ai=1∑jmjf(cij) A_{}=\\frac{1}{\\sum_{j} m_{j} \\mathrm{f}\\left(c_{j}\\right)} OiO_i analogous travel demand zone ii, can roughly approximated population. recent innovations SIMs including ‘radiation model’ Simini et al. (2012). See Lenormand, Bassolas, Ramasco (2016) comparison alternative approaches.","code":""},{"path":"https://robinlovelace.github.io/simodels/articles/sims-first-principles.html","id":"implementation-in-r","dir":"Articles","previous_headings":"","what":"Implementation in R","title":"An introduction to spatial interaction models: from first principles","text":"using functions packages, may worth implementing SIMs first principles, gain understanding work. code presented written functions simodels package developed, building Dennett (2018). aim demonstrate common way running SIMs, loop, rather using vectorised operations (used simodels package) can faster.  unconstrained spatial interaction model can written follows, --less arbitrary value beta can optimised later:  can plot ‘distance decay’ curve associated SIM follows:  can make production constrained follows:","code":"library(tmap) library(dplyr) library(ggplot2) zones = simodels::si_zones centroids = simodels::si_centroids od = simodels::si_od_census tm_shape(zones) + tm_polygons(\"all\", palette = \"viridis\") od_df = od::points_to_od(centroids) od_sfc = od::odc_to_sfc(od_df[3:6]) sf::st_crs(od_sfc) = 4326 od_df$length = sf::st_length(od_sfc) od_df = od_df %>% transmute(   O, D, length = as.numeric(length) / 1000,   flow = NA, fc = NA   ) od_df = sf::st_sf(od_df, geometry = od_sfc, crs = 4326) beta = 0.3 i = 1 j = 2 for(i in seq(nrow(zones))) {   for(j in seq(nrow(zones))) {     O = zones$all[i]     n = zones$all[j]     ij = which(od_df$O == zones$geo_code[i] & od_df$D == zones$geo_code[j])     od_df$fc[ij] = exp(-beta * od_df$length[ij])     od_df$flow[ij] = O * n * od_df$fc[ij]   } } od_top = od_df %>%    filter(O != D) %>%    top_n(n = 2000, wt = flow)  tm_shape(zones) +   tm_borders() +   tm_shape(od_top) +   tm_lines(\"flow\") summary(od_df$fc) #>      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.  #> 0.0002404 0.0256166 0.0801970 0.1495649 0.2035826 1.0000000 od_df %>%    ggplot() +   geom_point(aes(length, fc)) od_dfj = left_join(   od_df,   zones %>% select(O = geo_code, all) %>% sf::st_drop_geometry() ) #> Joining with `by = join_by(O)` od_dfj = od_dfj %>%    group_by(O) %>%    mutate(flow_constrained = flow / sum(flow) * first(all)) %>%   ungroup() sum(od_dfj$flow_constrained) == sum(zones$all) #> [1] TRUE od_top = od_dfj %>%    filter(O != D) %>%    top_n(n = 2000, wt = flow_constrained)  tm_shape(zones) +   tm_borders() +   tm_shape(od_top) +   tm_lines(\"flow_constrained\")"},{"path":"https://robinlovelace.github.io/simodels/articles/sims-first-principles.html","id":"validation","dir":"Articles","previous_headings":"","what":"Validation","title":"An introduction to spatial interaction models: from first principles","text":"","code":"od_dfjc = inner_join(od_dfj %>% select(-all), od) #> Joining with `by = join_by(O, D)` od_dfjc %>%    ggplot() +   geom_point(aes(all, flow_constrained)) cor(od_dfjc$all, od_dfjc$flow_constrained)^2 #> [1] 0.1735933"},{"path":[]},{"path":"https://robinlovelace.github.io/simodels/articles/validation.html","id":"validation-of-2011-od-data-for-leeds-uk","dir":"Articles","previous_headings":"","what":"Validation of 2011 OD data for Leeds, UK","title":"Validation of spatial interaction models","text":"input datasets example follows: Geographic boundary centroid data {pct} package, trip representing origins destinations Origin-destination data representing trips work 2011 Census Explanatory variables associated zones, tbc key input datasets shown  output shows 90% possible combinations OD pairs going every zone present validation dataset. maximum number unique OD pairs given OD dataset number origins multiplied number destinations. case 107 * 107 1.1449^{4} proceeding ’s worth sanity checks find : spatial distribution travel work Leeds look like? OD pairs  figure clearly shows Leeds monocentric city relatively high levels active travel near centre low levels active travel outskirts. can predict ~8% OD pairs dataset: long distance OD pairs avoiding centre. Let’s find : expected, mean length OD pairs observed Census substantially less, ’s calculating weighted mean, just 5 km: case, let’s proceed estimates travel. simplest model imagine simulate movement zones city, given knowledge many people travel work within zones Leeds total (236k), divide movement equally OD pair. case : Plotting observed number trips census data shows , expect, correlation simplistic non-model reality:  , can better? Although classic gravity model often presented type, can actually go step simpler , using linear exponential decay approximate travel, without attention paid population zone factor. let’s get stuck fit models!   simple linear model implemented code chunk predicts flow solely basis distance OD pairs. results slightly better ‘equal trips OD pairs’ approach still pretty bad, explaining 7% variability observed trips OD pairs census dataset. Furthermore, result also reveals issue fitting models OD datasets: ‘negative flow’ make sense contexts, models can predict negative values questioned. approach prevents negative values, also captures fact non-linear (near exponential) decay distance travel, SIMs exponential decay. can implement model follows: shown code , model fails converge. alternative approach follows:   can overcome using self starting function:   Another approach minpack.lm package can make ‘production constrained’ follows:   ‘attraction constrained’ follows (first calculate number people arriving zone):","code":"si_od_census #> # A tibble: 10,536 × 18 #>    O     D       all from_home light_rail train   bus  taxi motorbike car_driver #>    <chr> <chr> <dbl>     <dbl>      <dbl> <dbl> <dbl> <dbl>     <dbl>      <dbl> #>  1 E020… E020…    66         0          0     0     0     0         1         29 #>  2 E020… E020…   742         0          0     1     5     1         1        426 #>  3 E020… E020…     2         0          0     0     0     0         0          2 #>  4 E020… E020…     9         0          0     0     0     0         0          6 #>  5 E020… E020…    50         0          0     0     3     1         0         43 #>  6 E020… E020…    23         0          0     0     0     0         0         20 #>  7 E020… E020…     4         0          0     0     0     0         0          4 #>  8 E020… E020…     2         0          0     0     0     0         0          2 #>  9 E020… E020…     1         0          0     0     0     0         0          1 #> 10 E020… E020…     3         0          0     0     0     0         0          3 #> # ℹ 10,526 more rows #> # ℹ 8 more variables: car_passenger <dbl>, bicycle <dbl>, foot <dbl>, #> #   other <dbl>, geo_name1 <chr>, geo_name2 <chr>, la_1 <chr>, la_2 <chr> plot(si_zones$geometry) plot(si_centroids$geometry, add = TRUE) od_from_si = si_to_od(origins = si_centroids, destinations = si_centroids) nrow(si_od_census) / nrow(od_from_si) #> [1] 0.920255 si_od_census_sf = si_od_census %>%    od_to_sf(si_centroids) %>%   mutate(all, `Percent active` = (bicycle + foot) / all * 100) #> 0 origins with no match in zone ids #> 0 destinations with no match in zone ids #>  points not in od data removed.  si_od_census_sf %>%   arrange(`Percent active`) %>%    tm_shape() +   tm_lines(col = \"Percent active\", lwd = \"all\", scale = 3, palette = \"viridis\",            breaks = c(0, 0.1, 0.2, 0.4, 1) * 100, legend.lwd.show = FALSE) +   tm_scale_bar() od_pairs_with_no_travel = od_from_si %>%    filter(!paste(O, D) %in% paste(si_od_census$O, si_od_census$D)) summary(od_pairs_with_no_travel$distance_euclidean) #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>    3362    9407   12881   13555   17598   27761 summary(sf::st_length(si_od_census_sf)) #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>       0    5042    8091    8795   11751   26698 weighted.mean(sf::st_length(si_od_census_sf), w = si_od_census_sf$all) #> 5258.856 [m] od_from_si$flow_equal = sum(si_od_census$all) / nrow(od_from_si) od_joined = left_join(od_from_si, si_od_census) %>%    mutate(all = case_when(is.na(all) ~ 0, TRUE ~ all)) #> Joining with `by = join_by(O, D)` od_joined %>%    ggplot(aes(all, flow_equal)) +   geom_point() total = sum(si_od_census$all) m1 = lm(all ~ distance_euclidean, data = od_joined) od_joined = si_predict(od_joined, model = m1, constraint_total = total, output_col = \"flow_m1\") #> Negative values in output, setting them to zero od_joined %>%    ggplot(aes(distance_euclidean, flow_m1)) +   geom_point() od_joined %>%    ggplot(aes(all, flow_m1)) +   geom_point() sum(od_joined$flow_m1) == sum(od_joined$all, na.rm = TRUE) #> [1] TRUE cor(od_joined$all, od_joined$flow_m1, use = \"complete.obs\")^2 #> [1] 0.07686445 od_joined = od_joined %>%    mutate(distance_nonzero = case_when(     distance_euclidean == 0 ~ 50,     TRUE ~ distance_euclidean)) m2 = nls(   formula = all ~ a + distance_nonzero^b,   start = list(a = 5, b = -0.8),   data = od_joined   ) #> Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral): Missing value or an infinity produced when evaluating the model m2 = minpack.lm::nlsLM(   formula = all ~ a + distance_nonzero^b,   start = list(a = 5, b = -0.8),   data = od_joined   ) # m2 = gslnls::gsl_nls( #   all ~ a + distance_nonzero^b, #   start = list(a = 10, b = -0.08), #   # control = gslnls::gsl_nls_control(scale = \"more\"), #   control = gslnls::gsl_nls_control( solver = \"svd\"), #   data = od_joined #   ) od_joined = od_joined %>%    si_predict(m2, constraint_total = total, output_col = \"flow_m2\") od_joined %>%    ggplot(aes(distance_euclidean, flow_m2)) +   geom_point() od_joined %>%    ggplot(aes(all, flow_m2)) +   geom_point() cor(od_joined$all, od_joined$flow_m2, use = \"complete.obs\")^2 #> [1] 0.1444543 m3 = nls(   formula = all ~ SSasymp(distance_nonzero, a, b, lrc = log_alpha),   data = od_joined   ) od_joined = od_joined %>%    si_predict(m3, constraint_total = total, output_col = \"flow_m3\") od_joined %>%    ggplot(aes(distance_euclidean, flow_m3)) +   geom_point() od_joined %>%    ggplot(aes(all, flow_m3)) +   geom_point() cor(od_joined$all, od_joined$flow_m3, use = \"complete.obs\")^2 #> [1] 0.1441077 od_joined = od_joined %>%    si_predict(m3, constraint_production = origin_all, output_col = \"flow_m4\") od_joined %>%    ggplot(aes(distance_euclidean, flow_m4)) +   geom_point() od_joined %>%    ggplot(aes(all, flow_m4)) +   geom_point() cor(od_joined$all, od_joined$flow_m4, use = \"complete.obs\")^2 #> [1] 0.1299208 sum(od_joined$flow_m4) #> [1] 20237 zones_attraction = inner_join(   si_centroids,   si_od_census %>%    group_by(geo_code = D) %>%    summarise(attractiveness = sum(all)) ) #> Joining with `by = join_by(geo_code)` od_attraction = si_to_od(origins = zones_attraction, destinations = zones_attraction) od_joined_attraction = left_join(od_attraction, si_od_census) %>%    mutate(all = case_when(is.na(all) ~ 0, TRUE ~ all)) #> Joining with `by = join_by(O, D)`  od_joined = od_joined_attraction %>%    si_predict(m3, constraint_attraction = destination_attractiveness, output_col = \"flow_m5\") od_joined %>%    ggplot(aes(distance_euclidean, flow_m5)) +   geom_point() od_joined %>%    ggplot(aes(all, flow_m5)) +   geom_point() cor(od_joined$all, od_joined$flow_m5, use = \"complete.obs\")^2 #> [1] 0.7261424 sum(od_joined$flow_m5) #> [1] 236326 sum(od_joined$all) #> [1] 236326"},{"path":[]},{"path":"https://robinlovelace.github.io/simodels/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Robin Lovelace. Author, maintainer. Jakub Nowosad. Author.","code":""},{"path":"https://robinlovelace.github.io/simodels/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lovelace R, Nowosad J (2024). simodels: Flexible Framework Developing Spatial Interaction Models. R package version 0.2.0, https://robinlovelace.github.io/simodels/, https://github.com/robinlovelace/simodels.","code":"@Manual{,   title = {simodels: Flexible Framework for Developing Spatial Interaction Models},   author = {Robin Lovelace and Jakub Nowosad},   year = {2024},   note = {R package version 0.2.0,     https://robinlovelace.github.io/simodels/},   url = {https://github.com/robinlovelace/simodels}, }"},{"path":"https://robinlovelace.github.io/simodels/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Flexible Framework for Developing Spatial Interaction Models","text":"Install package follows: get develoment version :","code":"install.packages(\"remotes\") # if not already installed remotes::install_github(\"robinlovelace/simodels\") devtools::load_all()"},{"path":"https://robinlovelace.github.io/simodels/index.html","id":"simodel-basics","dir":"","previous_headings":"","what":"simodel basics","title":"Flexible Framework for Developing Spatial Interaction Models","text":"Run basic SIM follows:  just happened? created ‘OD data frame’ function si_to_od() geographic origins destinations, estimated simple ‘production constrained’ (constraint_production argument) gravity model based population origin destination zones custom distance decay function si_calculate(). example shows, package allows/encourages define use functions estimate amount interaction/movement places. approach also ‘tidy’, allowing use {simodels} functions {dplyr} pipelines: resulting estimates interaction, returned column interaction plotted distance graphic , resulted choice spatial interaction model inputs, allowing wide range alternative approaches implemented. flexibility key aspect package, enabling small easily modified functions implemented tested. output si_calculate() geographic object can plotted sf’s plot method (geographic data visualisation packages):  si_to_od() function transforms geographic entities (typically polygons points) data frame representing full combination origin-destination pairs less max_dist meters apart. common saying data science 80% effort goes pre-processing stage. equally true spatial interaction modelling types data intensive analysis/modelling work. function return? function allows use variable origin destination data joining attributes onto OD data frame, column names appended origin destination. approach works equally well ‘bipartite’ SIMs, origin destination points different (Hasova et al. 2022). following example implements bipartite SIM estimates number trips administrative zones pubs Leeds: can plot top 20 desire lines zone centroids 2 pubs example dataset follows:","code":"library(simodels) library(dplyr) # prepare OD data od = si_to_od(   origins = si_zones,        # origin locations   destinations = si_zones,   # destination locations   max_dist = 5000            # maximum distance between OD pairs   ) # specify a function gravity_model = function(beta, d, m, n) {   m * n * exp(-beta * d / 1000) }  # perform SIM od_res = si_calculate(   od,   fun = gravity_model,   d = distance_euclidean,   m = origin_all,   n = destination_all,   constraint_production = origin_all,   beta = 0.9   ) # visualize the results plot(od_res$distance_euclidean, od_res$interaction) od_res = od %>%    si_calculate(fun = gravity_model,                 m = origin_all,                n = destination_all,                d = distance_euclidean,                constraint_production = origin_all,                beta = 0.3) od_res %>%    select(interaction) Simple feature collection with 2505 features and 1 field Geometry type: LINESTRING Dimension:     XY Bounding box:  xmin: -1.743949 ymin: 53.71552 xmax: -1.337493 ymax: 53.92906 Geodetic CRS:  WGS 84 # A tibble: 2,505 × 2    interaction                                 geometry          <dbl>                         <LINESTRING [°]>  1       2177. (-1.400108 53.92906, -1.400108 53.92906)  2        632. (-1.400108 53.92906, -1.346497 53.92305)  3        556. (-1.346497 53.92305, -1.400108 53.92906)  4       1382. (-1.346497 53.92305, -1.346497 53.92305)  5        449. (-1.346497 53.92305, -1.357667 53.88306)  6        794. (-1.704658 53.91073, -1.704658 53.91073)  7        749.   (-1.704658 53.91073, -1.6876 53.90066)  8        287. (-1.704658 53.91073, -1.743949 53.88035)  9        267. (-1.704658 53.91073, -1.710657 53.87087) 10        186. (-1.704658 53.91073, -1.694076 53.86729) # ℹ 2,495 more rows plot(od_res[\"interaction\"], logz = TRUE) # Set n. trips to pubs, assuming that for every trip to the pub there are # 50 trips to work (this would be validated/tested/modelled in empirical work) zones_pubs = si_zones %>%    mutate(to_pubs = all / 50) pubs_example = si_pubs %>%    filter(grepl(pattern = \"Chemic|Nag\", x = name)) pubs_example$size = c(100, 80) od_to_pubs = si_to_od(zones_pubs, pubs_example) od_to_pubs_result = od_to_pubs %>%    si_calculate(fun = gravity_model,                 m = origin_to_pubs,                n = destination_size,                d = distance_euclidean,                beta = 0.5,                constraint_production = origin_to_pubs) od_to_pubs_result %>%    select(O, D, destination_name, interaction) Simple feature collection with 214 features and 4 fields Geometry type: LINESTRING Dimension:     XY Bounding box:  xmin: -1.743949 ymin: 53.71552 xmax: -1.337493 ymax: 53.92906 Geodetic CRS:  WGS 84 # A tibble: 214 × 5    O         D         destination_name  interaction                    geometry    <chr>     <chr>     <chr>                   <dbl>            <LINESTRING [°]>  1 E02002330 127960333 The Chemic Tavern        18.4 (-1.400108 53.92906, -1.55…  2 E02002331 127960333 The Chemic Tavern        15.9 (-1.346497 53.92305, -1.55…  3 E02002332 127960333 The Chemic Tavern        25.5 (-1.704658 53.91073, -1.55…  4 E02002333 127960333 The Chemic Tavern        38.7 (-1.6876 53.90066, -1.5528…  5 E02002334 127960333 The Chemic Tavern        20.9 (-1.357667 53.88306, -1.55…  6 E02002335 127960333 The Chemic Tavern        19.9 (-1.470966 53.89184, -1.55…  7 E02002336 127960333 The Chemic Tavern        25.5 (-1.624775 53.88589, -1.55…  8 E02002337 127960333 The Chemic Tavern        37.2 (-1.743949 53.88035, -1.55…  9 E02002338 127960333 The Chemic Tavern        36.8 (-1.710657 53.87087, -1.55… 10 E02002339 127960333 The Chemic Tavern        29.0 (-1.694076 53.86729, -1.55… # ℹ 204 more rows library(tmap) tmap_mode(\"view\") od_to_pubs_result %>%    top_n(n = 50, wt = interaction) %>%    tm_shape() +   tm_lines(col = \"interaction\", palette = \"viridis\", scale = 2)"},{"path":"https://robinlovelace.github.io/simodels/index.html","id":"feedback","dir":"","previous_headings":"","what":"Feedback","title":"Flexible Framework for Developing Spatial Interaction Models","text":"interested hear approach presented package compared implementations presented links . anyone like try approach implement another language feel free get touch via issue tracker.","code":""},{"path":"https://robinlovelace.github.io/simodels/index.html","id":"further-reading","dir":"","previous_headings":"","what":"Further reading","title":"Flexible Framework for Developing Spatial Interaction Models","text":"details SIMs defined mathematically code first principles, see sims vignette. dive straight using {simodels} develop SIMs, see Get started vignette. detailed introduction SIMs, support reproducible R code, see Adam Dennett’s 2018 paper.","code":""},{"path":"https://robinlovelace.github.io/simodels/index.html","id":"other-sim-packages","dir":"","previous_headings":"","what":"Other SIM packages","title":"Flexible Framework for Developing Spatial Interaction Models","text":"spflow R package spint Python package gravity R package mobility R package gravity functions scikit-mobility Python package","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/destinations_york.html","id":null,"dir":"Reference","previous_headings":"","what":"Example destinations dataset: schools in York — destinations_york","title":"Example destinations dataset: schools in York — destinations_york","text":"Example dataset York, UK","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/destinations_york.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Example destinations dataset: schools in York — destinations_york","text":"See data-raw/zones_york.qmd details data source.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/destinations_york.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example destinations dataset: schools in York — destinations_york","text":"","code":"head(destinations_york) #> Simple feature collection with 6 features and 4 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1.129601 ymin: 53.94108 xmax: -1.075563 ymax: 53.97108 #> Geodetic CRS:  WGS 84 #> # A tibble: 6 × 5 #>      URN n_pupils phase          type_of_establishment             geometry #>    <dbl>    <dbl> <chr>          <chr>                          <POINT [°]> #> 1 121266       71 Nursery        Local authority nurs… (-1.129601 53.96334) #> 2 121270      160 Not applicable Pupil referral unit   (-1.076782 53.94353) #> 3 121273      211 Primary        Community school       (-1.129302 53.9638) #> 4 121276      308 Primary        Community school      (-1.110397 53.94108) #> 5 121277      309 Primary        Community school      (-1.075563 53.95181) #> 6 121283      423 Primary        Community school      (-1.092958 53.97108)"},{"path":"https://robinlovelace.github.io/simodels/reference/od_aus.html","id":null,"dir":"Reference","previous_headings":"","what":"Example OD dataset: flows between regions in Australia — od_aus","title":"Example OD dataset: flows between regions in Australia — od_aus","text":"Example dataset Australia","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/od_aus.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Example OD dataset: flows between regions in Australia — od_aus","text":"Regenerate data scripts data-raw directory.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/od_aus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example OD dataset: flows between regions in Australia — od_aus","text":"","code":"head(od_aus) #>           Origin Orig_code       Destination Dest_code    Flow vi1_origpop #> 1 Greater Sydney     1GSYD    Greater Sydney     1GSYD 3395015     4391673 #> 2 Greater Sydney     1GSYD       Rest of NSW     1RNSW   91031     4391673 #> 3 Greater Sydney     1GSYD Greater Melbourne     2GMEL   22601     4391673 #> 4 Greater Sydney     1GSYD       Rest of Vic     2RVIC    4416     4391673 #> 5 Greater Sydney     1GSYD  Greater Brisbane     3GBRI   22888     4391673 #> 6 Greater Sydney     1GSYD       Rest of Qld     3RQLD   27445     4391673 #>   wj1_destpop vi2_origunemp wj2_destunemp vi3_origmedinc wj3_destmedinc #> 1     4391673          5.74          5.74         780.64         780.64 #> 2     2512952          5.74          6.12         780.64         509.97 #> 3     3999981          5.74          5.47         780.64         407.95 #> 4     1345717          5.74          5.17         780.64         506.58 #> 5     2065998          5.74          5.86         780.64         767.08 #> 6     2253723          5.74          6.22         780.64         446.48 #>   vi4_origpctrent wj4_destpctrent #> 1           31.77           31.77 #> 2           31.77           27.20 #> 3           31.77           27.34 #> 4           31.77           24.08 #> 5           31.77           33.19 #> 6           31.77           32.57"},{"path":"https://robinlovelace.github.io/simodels/reference/si_calculate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate flow using a pre-existing function — si_calculate","title":"Calculate flow using a pre-existing function — si_calculate","text":"Executes spatial interaction model based OD data frame user-specified function","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_calculate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate flow using a pre-existing function — si_calculate","text":"","code":"si_calculate(   od,   fun,   constraint_production,   constraint_attraction,   constraint_total,   output_col = \"interaction\",   ... )"},{"path":"https://robinlovelace.github.io/simodels/reference/si_calculate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate flow using a pre-existing function — si_calculate","text":"od data frame representing origin-destination data, e.g. created si_to_od() fun function calculates interaction (e.g. number trips) OD pair constraint_production Character representing column od. argument, set, ensures outputs 'production constrained': total 'interaction' (e.g. n. trips) OD pairs set total zone origin go value. constraint_attraction Character representing column od. argument, set, ensures outputs 'attraction constrained': total 'interaction' (e.g. n. trips) OD pairs set sum trips destination equal mean value per destination. constraint_total Single number representing total interaction. argument, set, ensures sum interaction calculated equal value given. output_col Character string containing name new output column. \"interaction\" default. ... Arguments passed fun","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_calculate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate flow using a pre-existing function — si_calculate","text":"sf data frame","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_calculate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate flow using a pre-existing function — si_calculate","text":"","code":"od = si_to_od(si_zones, si_zones, max_dist = 4000) #> Converting p to centroids #> lon-lat points fun_dd = function(d = \"distance_euclidean\", beta = 0.3) exp(-beta * d / 1000) fun_dd(d = (1:5) * 1000) #> [1] 0.7408182 0.5488116 0.4065697 0.3011942 0.2231302 od_dd = si_calculate(od, fun = fun_dd, d = distance_euclidean) plot(od$distance_euclidean, od_dd$interaction)  fun = function(O, n, d, beta) O * n * exp(-beta * d / 1000) od_output = si_calculate(od, fun = fun, beta = 0.3, O = origin_all,    n = destination_all, d = distance_euclidean) head(od_output) #> Simple feature collection with 6 features and 28 fields #> Geometry type: LINESTRING #> Dimension:     XY #> Bounding box:  xmin: -1.704658 ymin: 53.90066 xmax: -1.346497 ymax: 53.92906 #> Geodetic CRS:  WGS 84 #>           O         D distance_euclidean origin_geo_name origin_lad11cd #> 1 E02002330 E02002330              0.000       Leeds 001      E08000035 #> 2 E02002330 E02002331           3579.804       Leeds 001      E08000035 #> 3 E02002331 E02002331              0.000       Leeds 002      E08000035 #> 4 E02002331 E02002330           3579.804       Leeds 002      E08000035 #> 5 E02002332 E02002332              0.000       Leeds 003      E08000035 #> 6 E02002332 E02002333           1582.213       Leeds 003      E08000035 #>   origin_lad_name origin_all origin_bicycle origin_foot origin_car_driver #> 1           Leeds       2809             52         300              2170 #> 2           Leeds       2809             52         300              2170 #> 3           Leeds       2387             53         469              1573 #> 4           Leeds       2387             53         469              1573 #> 5           Leeds       2471             39         341              1666 #> 6           Leeds       2471             39         341              1666 #>   origin_car_passenger origin_motorbike origin_train_tube origin_bus #> 1                  138               10                36         89 #> 2                  138               10                36         89 #> 3                  132               16                10        126 #> 4                  132               16                10        126 #> 5                  147               19                75        166 #> 6                  147               19                75        166 #>   origin_taxi_other destination_geo_name destination_lad11cd #> 1                14            Leeds 001           E08000035 #> 2                14            Leeds 002           E08000035 #> 3                 8            Leeds 002           E08000035 #> 4                 8            Leeds 001           E08000035 #> 5                18            Leeds 003           E08000035 #> 6                18            Leeds 004           E08000035 #>   destination_lad_name destination_all destination_bicycle destination_foot #> 1                Leeds            2809                  52              300 #> 2                Leeds            2387                  53              469 #> 3                Leeds            2387                  53              469 #> 4                Leeds            2809                  52              300 #> 5                Leeds            2471                  39              341 #> 6                Leeds            3743                  80              529 #>   destination_car_driver destination_car_passenger destination_motorbike #> 1                   2170                       138                    10 #> 2                   1573                       132                    16 #> 3                   1573                       132                    16 #> 4                   2170                       138                    10 #> 5                   1666                       147                    19 #> 6                   2340                       193                    26 #>   destination_train_tube destination_bus destination_taxi_other #> 1                     36              89                     14 #> 2                     10             126                      8 #> 3                     10             126                      8 #> 4                     36              89                     14 #> 5                     75             166                     18 #> 6                    221             335                     19 #>                         geometry interaction #> 1 LINESTRING (-1.400108 53.92...     7890481 #> 2 LINESTRING (-1.400108 53.92...     2290854 #> 3 LINESTRING (-1.346497 53.92...     5697769 #> 4 LINESTRING (-1.346497 53.92...     2290854 #> 5 LINESTRING (-1.704658 53.91...     6105841 #> 6 LINESTRING (-1.704658 53.91...     5753720 plot(od$distance_euclidean, od_output$interaction)  od_pconst = si_calculate(od, fun = fun, beta = 0.3, O = origin_all,   n = destination_all, d = distance_euclidean, constraint_production = origin_all) # Origin totals in OD data should equal origin totals in zone data library(dplyr) #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union origin_totals_zones = od_pconst |>   group_by(geo_code = O) |>   summarise(all_od = sum(interaction)) |>   sf::st_drop_geometry() zones_joined = left_join(si_zones, origin_totals_zones) #> Joining with `by = join_by(geo_code)` plot(zones_joined$all, zones_joined$all_od)  plot(od_pconst$distance_euclidean, od_pconst$interaction)  plot(od_pconst[\"interaction\"], logz = TRUE)  od_dd = si_calculate(od, fun = fun_dd, d = distance_euclidean, output_col = \"res\") head(od_dd$res) #> [1] 1.0000000 0.3416593 1.0000000 0.3416593 1.0000000 0.6220942 od_dd = si_calculate(od, fun = fun_dd, d = distance_euclidean, constraint_total = 10) sum(od_dd$interaction) #> [1] 10"},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz.html","id":null,"dir":"Reference","previous_headings":"","what":"Origin-Destination Data for Leeds — si_oa_wpz","title":"Origin-Destination Data for Leeds — si_oa_wpz","text":"dataset contains origin-destination data Leeds, including number trips output areas (OAs) workplace zones (WPZs).","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Origin-Destination Data for Leeds — si_oa_wpz","text":"","code":"head(si_oa_wpz) #> # A tibble: 6 × 3 #>   OA11CD    wz11cd        n #>   <chr>     <chr>     <dbl> #> 1 E00056848 E33009546     1 #> 2 E00056848 E33009547     1 #> 3 E00056848 E33009548     1 #> 4 E00056848 E33009553     1 #> 5 E00056848 E33010351     1 #> 6 E00056848 E33010352     1"},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz_d.html","id":null,"dir":"Reference","previous_headings":"","what":"Destination Data for Leeds — si_oa_wpz_d","title":"Destination Data for Leeds — si_oa_wpz_d","text":"dataset contains number trips destined workplace zone (WPZ) Leeds.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz_d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Destination Data for Leeds — si_oa_wpz_d","text":"See wicid.ukdataservice.ac.uk details data source data-raw/leeds_wpz.R details example dataset generated.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz_d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Destination Data for Leeds — si_oa_wpz_d","text":"","code":"head(si_oa_wpz_d) #> Simple feature collection with 6 features and 2 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -1.557363 ymin: 53.77863 xmax: -1.523513 ymax: 53.80146 #> Geodetic CRS:  WGS 84 #>      wz11cd n_d                       geometry #> 1 E33010402  58 MULTIPOLYGON (((-1.532786 5... #> 2 E33010372 160 MULTIPOLYGON (((-1.546048 5... #> 3 E33010502 135 MULTIPOLYGON (((-1.55139 53... #> 4 E33010472  34 MULTIPOLYGON (((-1.54637 53... #> 5 E33010442  60 MULTIPOLYGON (((-1.551074 5... #> 6 E33009594  15 MULTIPOLYGON (((-1.523878 5... sf:::plot.sf(si_oa_wpz_d[\"n_d\"])"},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz_o.html","id":null,"dir":"Reference","previous_headings":"","what":"Origin Data for Leeds — si_oa_wpz_o","title":"Origin Data for Leeds — si_oa_wpz_o","text":"dataset contains number trips originating output area (OA) Leeds.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_oa_wpz_o.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Origin Data for Leeds — si_oa_wpz_o","text":"","code":"head(si_oa_wpz_o) #> Simple feature collection with 6 features and 2 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -1.589455 ymin: 53.79168 xmax: -1.576951 ymax: 53.80234 #> Geodetic CRS:  WGS 84 #>      OA11CD n_o                       geometry #> 1 E00056848  24 MULTIPOLYGON (((-1.581762 5... #> 2 E00056849  27 MULTIPOLYGON (((-1.577746 5... #> 3 E00056874  92 MULTIPOLYGON (((-1.585058 5... #> 4 E00056876 103 MULTIPOLYGON (((-1.58512 53... #> 5 E00056885  48 MULTIPOLYGON (((-1.583388 5... #> 6 E00056886  29 MULTIPOLYGON (((-1.584578 5... sf:::plot.sf(si_oa_wpz_o[\"n_o\"])"},{"path":"https://robinlovelace.github.io/simodels/reference/si_od_census.html","id":null,"dir":"Reference","previous_headings":"","what":"Example OD dataset — si_od_census","title":"Example OD dataset — si_od_census","text":"Example OD dataset 2011 UK Census","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_od_census.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Example OD dataset — si_od_census","text":"Regenerate data scripts data-raw directory.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_od_census.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example OD dataset — si_od_census","text":"","code":"head(si_od_census) #> # A tibble: 6 × 18 #>   O      D       all from_home light_rail train   bus  taxi motorbike car_driver #>   <chr>  <chr> <dbl>     <dbl>      <dbl> <dbl> <dbl> <dbl>     <dbl>      <dbl> #> 1 E0200… E020…    66         0          0     0     0     0         1         29 #> 2 E0200… E020…   742         0          0     1     5     1         1        426 #> 3 E0200… E020…     2         0          0     0     0     0         0          2 #> 4 E0200… E020…     9         0          0     0     0     0         0          6 #> 5 E0200… E020…    50         0          0     0     3     1         0         43 #> 6 E0200… E020…    23         0          0     0     0     0         0         20 #> # ℹ 8 more variables: car_passenger <dbl>, bicycle <dbl>, foot <dbl>, #> #   other <dbl>, geo_name1 <chr>, geo_name2 <chr>, la_1 <chr>, la_2 <chr>"},{"path":"https://robinlovelace.github.io/simodels/reference/si_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict spatial interaction based on pre-trained model — si_predict","title":"Predict spatial interaction based on pre-trained model — si_predict","text":"Predict spatial interaction based pre-trained model","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict spatial interaction based on pre-trained model — si_predict","text":"","code":"si_predict(   od,   model,   constraint_production,   constraint_attraction,   constraint_total,   output_col = \"interaction\",   ... )"},{"path":"https://robinlovelace.github.io/simodels/reference/si_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict spatial interaction based on pre-trained model — si_predict","text":"od data frame representing origin-destination data, e.g. created si_to_od() model model object, e.g. lm() glm() constraint_production Character representing column od. argument, set, ensures outputs 'production constrained': total 'interaction' (e.g. n. trips) OD pairs set total zone origin go value. constraint_attraction Character representing column od. argument, set, ensures outputs 'attraction constrained': total 'interaction' (e.g. n. trips) OD pairs set sum trips destination equal mean value per destination. constraint_total Single number representing total interaction. argument, set, ensures sum interaction calculated equal value given. output_col Character string containing name new output column. \"interaction\" default. ... Arguments passed fun","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict spatial interaction based on pre-trained model — si_predict","text":"sf data frame","code":""},{"path":[]},{"path":"https://robinlovelace.github.io/simodels/reference/si_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict spatial interaction based on pre-trained model — si_predict","text":"","code":"od = si_to_od(si_zones, si_zones, max_dist = 4000) #> Converting p to centroids #> lon-lat points m = lm(od$origin_all ~ od$origin_bicycle) od_updated = si_predict(od, m)"},{"path":"https://robinlovelace.github.io/simodels/reference/si_pubs.html","id":null,"dir":"Reference","previous_headings":"","what":"Example destinations dataset: pubs in Leeds — si_pubs","title":"Example destinations dataset: pubs in Leeds — si_pubs","text":"Example dataset Leeds, UK","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_pubs.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Example destinations dataset: pubs in Leeds — si_pubs","text":"Regenerate data scripts data-raw directory.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_pubs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example destinations dataset: pubs in Leeds — si_pubs","text":"","code":"head(si_pubs) #> Simple feature collection with 6 features and 7 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1.696442 ymin: 53.72551 xmax: -1.42461 ymax: 53.90583 #> Geodetic CRS:  WGS 84 #>      osm_id                  name barrier place man_made shop amenity #> 2    611105   The Mexborough Arms    <NA>  <NA>     <NA> <NA>     pub #> 4  21029699 The Black Horse Hotel    <NA>  <NA>     <NA> <NA>     pub #> 5  26124686 The Waggon and Horses    <NA>  <NA>     <NA> <NA>     pub #> 6  26124687            The Castle    <NA>  <NA>     <NA> <NA>     pub #> 19 27297278           Cross Pipes    <NA>  <NA>     <NA> <NA>     pub #> 20 27305438             Bay Horse    <NA>  <NA>     <NA> <NA>     pub #>                      geometry #> 2    POINT (-1.42461 53.7281) #> 4  POINT (-1.694605 53.90571) #> 5  POINT (-1.495596 53.72551) #> 6  POINT (-1.495613 53.72573) #> 19 POINT (-1.696442 53.90481) #> 20  POINT (-1.69363 53.90583)"},{"path":"https://robinlovelace.github.io/simodels/reference/si_to_od.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare OD data frame — si_to_od","title":"Prepare OD data frame — si_to_od","text":"Prepares OD data frame next used estimate movement origins destinations spatial interaction model.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_to_od.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare OD data frame — si_to_od","text":"","code":"si_to_od(origins, destinations, max_dist = Inf, intrazonal = TRUE)"},{"path":"https://robinlovelace.github.io/simodels/reference/si_to_od.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare OD data frame — si_to_od","text":"origins sf object representing origin locations/zones destinations sf object representing destination locations/zones max_dist Euclidean distance meters (numeric). OD pairs distance apart less returned therefore included SIM. intrazonal Include intrazonal OD pairs? Intrazonal OD pairs represent movement one place zone another place zone. TRUE default.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_to_od.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare OD data frame — si_to_od","text":"sf data frame","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_to_od.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prepare OD data frame — si_to_od","text":"origin-destination datasets spatial entities constitute origins (typically administrative zones) also represent destinations. 'unipartite' case origins destinations passed object, sf data frame representing administrative zones. 'Bipartite' datasets, contrast, represent \"spatial interaction systems origins act destinations vice versa\" (Hasova et al. 2022). different sf object can passed destinations argument.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_to_od.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare OD data frame — si_to_od","text":"","code":"library(sf) #> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE origins = si_centroids[c(1, 2, 99), ] destinations = origins plot(origins$geometry)  odsf = si_to_od(origins, destinations, max_dist = 1200) #> lon-lat points plot(odsf) #> Warning: plotting the first 9 out of 27 attributes; use max.plot = 27 to plot all  # note: result contains intrazonal flows represented by linestrings # with a length of 0, e.g.: sf::st_coordinates(odsf$geometry[1]) #>              X        Y L1 #> [1,] -1.501878 53.81544  1 #> [2,] -1.501878 53.81544  1 # With different destinations compared with origins library(sf) origins = si_centroids[c(2, 99), c(1, 6, 7)] destinations = si_centroids[1, c(1, 6, 8)] odsf = si_to_od(origins, destinations) nrow(odsf) # no intrazonal flows #> [1] 2 plot(odsf)"},{"path":"https://robinlovelace.github.io/simodels/reference/si_zones.html","id":null,"dir":"Reference","previous_headings":"","what":"Example zones and centroids — si_zones","title":"Example zones and centroids — si_zones","text":"si_zones si_centroids represent administrative zones flows estimated.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_zones.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Example zones and centroids — si_zones","text":"schema data can (re-)generated using code data-raw","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/si_zones.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example zones and centroids — si_zones","text":"","code":"si_zones #> Simple feature collection with 107 features and 13 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -1.800362 ymin: 53.699 xmax: -1.292229 ymax: 53.94589 #> Geodetic CRS:  WGS 84 #> # A tibble: 107 × 14 #>    geo_code  geo_name  lad11cd   lad_name   all bicycle  foot car_driver #>    <chr>     <chr>     <chr>     <chr>    <dbl>   <dbl> <dbl>      <dbl> #>  1 E02002330 Leeds 001 E08000035 Leeds     2809      52   300       2170 #>  2 E02002331 Leeds 002 E08000035 Leeds     2387      53   469       1573 #>  3 E02002332 Leeds 003 E08000035 Leeds     2471      39   341       1666 #>  4 E02002333 Leeds 004 E08000035 Leeds     3743      80   529       2340 #>  5 E02002334 Leeds 005 E08000035 Leeds     2995      53   170       2421 #>  6 E02002335 Leeds 006 E08000035 Leeds     3056      33   106       2549 #>  7 E02002336 Leeds 007 E08000035 Leeds     2690      35    94       2178 #>  8 E02002337 Leeds 008 E08000035 Leeds     3187      31   238       2222 #>  9 E02002338 Leeds 009 E08000035 Leeds     3155      52   343       2024 #> 10 E02002339 Leeds 010 E08000035 Leeds     2501      41   360       1552 #> # ℹ 97 more rows #> # ℹ 6 more variables: car_passenger <dbl>, motorbike <dbl>, train_tube <dbl>, #> #   bus <dbl>, taxi_other <dbl>, geometry <POLYGON [°]> sf:::plot.sfg(si_zones$geometry) sf:::plot.sfg(si_centroids$geometry, add = TRUE)"},{"path":"https://robinlovelace.github.io/simodels/reference/simodels-package.html","id":null,"dir":"Reference","previous_headings":"","what":"simodels: Flexible Framework for Developing Spatial Interaction Models — simodels-package","title":"simodels: Flexible Framework for Developing Spatial Interaction Models — simodels-package","text":"Develop spatial interaction models (SIMs). SIMs predict amount interaction, example number trips per day, geographic entities representing trip origins destinations. Contains functions creating origin-destination datasets geographic input datasets calculating movement origin-destination pairs constrained, production-constrained, attraction-constrained models (Wilson 1979) doi:10.1068/a030001 .","code":""},{"path":[]},{"path":"https://robinlovelace.github.io/simodels/reference/simodels-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"simodels: Flexible Framework for Developing Spatial Interaction Models — simodels-package","text":"Maintainer: Robin Lovelace rob00x@gmail.com (ORCID) Authors: Jakub Nowosad nowosad.jakub@gmail.com (ORCID)","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/zones_aus.html","id":null,"dir":"Reference","previous_headings":"","what":"Example zones dataset: regions of Australia — zones_aus","title":"Example zones dataset: regions of Australia — zones_aus","text":"Example dataset Australia","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/zones_aus.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Example zones dataset: regions of Australia — zones_aus","text":"Regenerate data scripts data-raw directory.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/zones_aus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example zones dataset: regions of Australia — zones_aus","text":"","code":"head(zones_aus) #> Simple feature collection with 6 features and 6 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: 137.9943 ymin: -39.12579 xmax: 153.6298 ymax: -10.73851 #> Geodetic CRS:  GDA94 #> # A tibble: 6 × 7 #>   GCCSA_CODE GCC_CODE16 GCCSA_NAME        STATE_CODE STATE_NAME      AREA_SQKM #>   <chr>      <chr>      <chr>             <chr>      <chr>               <dbl> #> 1 1RNSW      1RNSW      Rest of NSW       1          New South Wales   788443. #> 2 1GSYD      1GSYD      Greater Sydney    1          New South Wales    12368. #> 3 2GMEL      2GMEL      Greater Melbourne 2          Victoria            9993. #> 4 2RVIC      2RVIC      Rest of Vic.      2          Victoria          217503. #> 5 3RQLD      3RQLD      Rest of Qld       3          Queensland       1714330. #> 6 3GBRI      3GBRI      Greater Brisbane  3          Queensland         15842. #> # ℹ 1 more variable: geometry <MULTIPOLYGON [°]>"},{"path":"https://robinlovelace.github.io/simodels/reference/zones_york.html","id":null,"dir":"Reference","previous_headings":"","what":"Example zones dataset: administrative zones of York — zones_york","title":"Example zones dataset: administrative zones of York — zones_york","text":"See data-raw/zones_york.qmd details data source.","code":""},{"path":"https://robinlovelace.github.io/simodels/reference/zones_york.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example zones dataset: administrative zones of York — zones_york","text":"","code":"head(zones_york) #> Simple feature collection with 6 features and 13 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -1.143303 ymin: 53.90614 xmax: -1.075275 ymax: 53.97628 #> Geodetic CRS:  WGS 84 #>    LSOA21CD  LSOA21NM total f0_to_15 f16_to_29 f30_to_44 f45_to_64 f65_and_over #> 1 E01013335 York 012A  1626      164       115       215       202          158 #> 2 E01013336 York 012B  1545      109        75       162       221          210 #> 3 E01013337 York 012C  1651      127        98       167       236          198 #> 4 E01013338 York 012D  2358      203       194       268       300          258 #> 5 E01013339 York 012E  1602      128        91       149       246          194 #> 6 E01013340 York 024A  1275      117        53        95       217          160 #>   m0_to_15 m16_to_29 m30_to_44 m45_to_64 m65_and_over #> 1      169       151       170       180          102 #> 2      138       108       143       215          164 #> 3      151       104       175       229          166 #> 4      250       186       236       291          172 #> 5      133       109       134       251          167 #> 6      129       100        71       188          145 #>                         geometry #> 1 MULTIPOLYGON (((-1.128299 5... #> 2 MULTIPOLYGON (((-1.123884 5... #> 3 MULTIPOLYGON (((-1.133394 5... #> 4 MULTIPOLYGON (((-1.122007 5... #> 5 MULTIPOLYGON (((-1.11843 53... #> 6 MULTIPOLYGON (((-1.087652 5... sf:::plot.sfg(zones_york$geometry)"},{"path":"https://robinlovelace.github.io/simodels/news/index.html","id":"simodels-020-2024-08","dir":"Changelog","previous_headings":"","what":"simodels 0.2.0 (2024-08)","title":"simodels 0.2.0 (2024-08)","text":"si_to_od() now much/infinitely faster large datasets","code":""},{"path":"https://robinlovelace.github.io/simodels/news/index.html","id":"simodels-010-2024-01","dir":"Changelog","previous_headings":"","what":"simodels 0.1.0 (2024-01)","title":"simodels 0.1.0 (2024-01)","text":"CRAN release: 2024-07-23 See https://github.com/Robinlovelace/simodels/pull/32 Minor tweaks documentation","code":""},{"path":"https://robinlovelace.github.io/simodels/news/index.html","id":"simodels-005","dir":"Changelog","previous_headings":"","what":"simodels 0.0.5","title":"simodels 0.0.5","text":"CRAN release: 2022-08-31 Remove dependency files hosted Dropbox CRAN (#28)","code":""},{"path":"https://robinlovelace.github.io/simodels/news/index.html","id":"simodels-004-2022-06","dir":"Changelog","previous_headings":"","what":"simodels 0.0.4 (2022-06)","title":"simodels 0.0.4 (2022-06)","text":"CRAN release: 2022-06-15 Package renamed avoid clash existing SI package (#20)","code":""},{"path":"https://robinlovelace.github.io/simodels/news/index.html","id":"simodels-003","dir":"Changelog","previous_headings":"","what":"simodels 0.0.3","title":"simodels 0.0.3","text":"New NEWS.md file track changes package. New intrazonal argument od_to_si() (@R-icntay #16)","code":""}]