Merge branch 'master' into addexample

jrising · jrising · commit 1d0f5c543a84 · 2020-02-14T10:34:12.000Z
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
@@ -0,0 +1,19 @@
+name: CI
+
+on:
+  push:
+    branches:
+    - master
+      
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.7]
+    steps:
+    - name: Run a build
+      run: |
+        pip install jupyter-book
+        jupyter-book build ./weather-panel.github.io
diff --git a/_build/contributors.html b/_build/contributors.html
@@ -22,8 +22,8 @@
 <h1 id="Contributors">Contributors<a class="anchor-link" href="#Contributors"> </a></h1><p>The core contributors of this tutorial are, in alphabetic order:</p>
 <ul>
 <li><a href="https://azharhsain.github.io/">Azhar Hussain</a></li>
-<li>James Rising</li>
-<li>Kevin Schwarzwald</li>
+<li><a href="http://existencia.org/pro">James Rising</a></li>
+<li><a href="https://iri.columbia.edu/contact/staff-directory/kevin-schwarzwald/">Kevin Schwarzwald</a></li>
 <li><a href="https://anatrisovic.com">Ana Trisovic</a></li>
 </ul>
 <h2 id="Citing-the-Weather-Panel-Tutorial">Citing the <em>Weather Panel Tutorial</em><a class="anchor-link" href="#Citing-the-Weather-Panel-Tutorial"> </a></h2><p>You can reference this tutorial with the DOI 10.5281/zenodo.3629801. 
diff --git a/_build/images/cartoon_sec2.JPG b/_build/images/cartoon_sec2.JPG
diff --git a/_build/reduced-form-specification.html b/_build/reduced-form-specification.html
@@ -149,6 +149,7 @@ <h3 id="Transformation-before-aggregation">Transformation-before-aggregation<a c
 N_{it} g_2(T_{ps}) + \cdots + \beta_k N_{it} g_k(T_{ps})$$</p>
 <p>where $N_{it}$ is the number of agent-timestep observations represented within region $i$ and reporting period $t$.</p>
 <h3 id="Aggregation-before-transformation">Aggregation-before-transformation<a class="anchor-link" href="#Aggregation-before-transformation"> </a></h3><p>When an economic process is occurring at the county level, we need to first do the weather variable aggregation at the county level. We do the weather variable transformation after we have aggregated it to the county level using weighted averaging method, and then run our estimation on the county level data. For example, to estimate the effect of storm events on public service employment at the administrative block level, we need to take into account the fact that hiring/firing of public service employees happens at the block level only.  Estimating grid-level effects will lead to wrong estimation, as it would result in zero estimate for those (almost all) grid cells which do not have the block office coordinates, and extremely large values for those (very few) cells, which comprise of the block office coordinates. The mathematical formulation for aggregation-before-transformation can be learned through transformation-before-aggregation formulation described above, with a change that the aggregation step precedes the transformation step. Weather data products can have temporal resolution finer than scale of daily observations. Like spatial aggregation, we can do temporal aggregation to month, year, or decade; however, unlike spatial aggregation, the averaging process is standard in all general cases.</p>
+<p><img src="images/cartoon_sec2.JPG" alt="Humor"></p>
 
 </div>
 </div>
diff --git a/_site/content/contributors.md b/_site/content/contributors.md
@@ -3,8 +3,8 @@
 The core contributors of this tutorial are, in alphabetic order:
 
 - [Azhar Hussain](https://azharhsain.github.io/)
-- James Rising
-- Kevin Schwarzwald
+- [James Rising](http://existencia.org/pro)
+- [Kevin Schwarzwald](https://iri.columbia.edu/contact/staff-directory/kevin-schwarzwald/)
 - [Ana Trisovic](https://anatrisovic.com)
 
 ## Citing the _Weather Panel Tutorial_
diff --git a/_site/content/images/cartoon_sec2.JPG b/_site/content/images/cartoon_sec2.JPG
diff --git a/_site/content/reduced-form-specification.md b/_site/content/reduced-form-specification.md
@@ -159,3 +159,6 @@ where $N_{it}$ is the number of agent-timestep observations represented within r
 ### Aggregation-before-transformation
 
 When an economic process is occurring at the county level, we need to first do the weather variable aggregation at the county level. We do the weather variable transformation after we have aggregated it to the county level using weighted averaging method, and then run our estimation on the county level data. For example, to estimate the effect of storm events on public service employment at the administrative block level, we need to take into account the fact that hiring/firing of public service employees happens at the block level only.  Estimating grid-level effects will lead to wrong estimation, as it would result in zero estimate for those (almost all) grid cells which do not have the block office coordinates, and extremely large values for those (very few) cells, which comprise of the block office coordinates. The mathematical formulation for aggregation-before-transformation can be learned through transformation-before-aggregation formulation described above, with a change that the aggregation step precedes the transformation step. Weather data products can have temporal resolution finer than scale of daily observations. Like spatial aggregation, we can do temporal aggregation to month, year, or decade; however, unlike spatial aggregation, the averaging process is standard in all general cases.
+
+![Humor](images/cartoon_sec2.JPG)
+
diff --git a/_site/contributors.html b/_site/contributors.html
@@ -26,8 +26,8 @@
   "@type": "NewsArticle",
   "mainEntityOfPage": "YOUR URL//contributors.html",
   "headline": "Contributors",
-  "datePublished": "2020-01-31T10:30:17-05:00",
-  "dateModified": "2020-01-31T10:30:17-05:00",
+  "datePublished": "2020-02-13T11:38:03-05:00",
+  "dateModified": "2020-02-13T11:38:03-05:00",
   "description": "        Contributors The core contributors of this tutorial are, in alphabetic order:Azhar HussainJames RisingKevin SchwarzwaldAna TrisovicCiting the Weather...",
   "author": {
     "@type": "Person",
@@ -634,8 +634,8 @@ <h2 class="c-sidebar__title">Weather Panel Tutorial</h2>
 <h1 id="Contributors">Contributors<a class="anchor-link" href="#Contributors"> </a></h1><p>The core contributors of this tutorial are, in alphabetic order:</p>
 <ul>
 <li><a href="https://azharhsain.github.io/">Azhar Hussain</a></li>
-<li>James Rising</li>
-<li>Kevin Schwarzwald</li>
+<li><a href="http://existencia.org/pro">James Rising</a></li>
+<li><a href="https://iri.columbia.edu/contact/staff-directory/kevin-schwarzwald/">Kevin Schwarzwald</a></li>
 <li><a href="https://anatrisovic.com">Ana Trisovic</a></li>
 </ul>
 <h2 id="Citing-the-Weather-Panel-Tutorial">Citing the <em>Weather Panel Tutorial</em><a class="anchor-link" href="#Citing-the-Weather-Panel-Tutorial"> </a></h2><p>You can reference this tutorial with the DOI 10.5281/zenodo.3629801. 
diff --git a/_site/geographical-unit-data.html b/_site/geographical-unit-data.html
@@ -26,8 +26,8 @@
   "@type": "NewsArticle",
   "mainEntityOfPage": "YOUR URL//geographical-unit-data.html",
   "headline": "Generating geographical unit data",
-  "datePublished": "2020-01-31T10:30:17-05:00",
-  "dateModified": "2020-01-31T10:30:17-05:00",
+  "datePublished": "2020-02-13T11:38:03-05:00",
+  "dateModified": "2020-02-13T11:38:03-05:00",
   "description": "        4. Generating geographical unit data 4.1 Introduction A geographical unit, area or region, is a portion of a country or other region delineated for t...",
   "author": {
     "@type": "Person",
diff --git a/_site/getting-started.html b/_site/getting-started.html
@@ -26,8 +26,8 @@
   "@type": "NewsArticle",
   "mainEntityOfPage": "YOUR URL//getting-started.html",
   "headline": "Getting started",
-  "datePublished": "2020-01-31T10:30:17-05:00",
-  "dateModified": "2020-01-31T10:30:17-05:00",
+  "datePublished": "2020-02-13T11:38:03-05:00",
+  "dateModified": "2020-02-13T11:38:03-05:00",
   "description": "        Weather Panel Tutorial Introduction to the Tutorial Welcome to the weather panel data regression tutorial! Usingeconometrics to study how social, eco...",
   "author": {
     "@type": "Person",
diff --git a/_site/images/cartoon_sec2.JPG b/_site/images/cartoon_sec2.JPG
diff --git a/_site/reduced-form-specification.html b/_site/reduced-form-specification.html
@@ -26,8 +26,8 @@
   "@type": "NewsArticle",
   "mainEntityOfPage": "YOUR URL//reduced-form-specification.html",
   "headline": "Developing a reduced-form specification",
-  "datePublished": "2020-01-31T10:30:17-05:00",
-  "dateModified": "2020-01-31T10:30:17-05:00",
+  "datePublished": "2020-02-13T11:38:03-05:00",
+  "dateModified": "2020-02-13T11:38:03-05:00",
   "description": "        2. Developing a reduced-form specification This section describes some of the considerations that go into developing a reduced-form specification usi...",
   "author": {
     "@type": "Person",
@@ -761,6 +761,7 @@ <h3 id="Transformation-before-aggregation">Transformation-before-aggregation<a c
 N_{it} g_2(T_{ps}) + \cdots + \beta_k N_{it} g_k(T_{ps})$$</p>
 <p>where $N_{it}$ is the number of agent-timestep observations represented within region $i$ and reporting period $t$.</p>
 <h3 id="Aggregation-before-transformation">Aggregation-before-transformation<a class="anchor-link" href="#Aggregation-before-transformation"> </a></h3><p>When an economic process is occurring at the county level, we need to first do the weather variable aggregation at the county level. We do the weather variable transformation after we have aggregated it to the county level using weighted averaging method, and then run our estimation on the county level data. For example, to estimate the effect of storm events on public service employment at the administrative block level, we need to take into account the fact that hiring/firing of public service employees happens at the block level only.  Estimating grid-level effects will lead to wrong estimation, as it would result in zero estimate for those (almost all) grid cells which do not have the block office coordinates, and extremely large values for those (very few) cells, which comprise of the block office coordinates. The mathematical formulation for aggregation-before-transformation can be learned through transformation-before-aggregation formulation described above, with a change that the aggregation step precedes the transformation step. Weather data products can have temporal resolution finer than scale of daily observations. Like spatial aggregation, we can do temporal aggregation to month, year, or decade; however, unlike spatial aggregation, the averaging process is standard in all general cases.</p>
+<p><img src="images/cartoon_sec2.JPG" alt="Humor"></p>
 
 </div>
 </div>
diff --git a/_site/search.html b/_site/search.html
@@ -26,8 +26,8 @@
   "@type": "NewsArticle",
   "mainEntityOfPage": "YOUR URL//search",
   "headline": "Search the site",
-  "datePublished": "2020-01-31T10:30:17-05:00",
-  "dateModified": "2020-01-31T10:30:17-05:00",
+  "datePublished": "2020-02-13T11:38:03-05:00",
+  "dateModified": "2020-02-13T11:38:03-05:00",
   "description": "        ",
   "author": {
     "@type": "Person",
diff --git a/_site/suggestions.html b/_site/suggestions.html
@@ -26,8 +26,8 @@
   "@type": "NewsArticle",
   "mainEntityOfPage": "YOUR URL//suggestions.html",
   "headline": "Suggestions for work organization",
-  "datePublished": "2020-01-31T10:30:17-05:00",
-  "dateModified": "2020-01-31T10:30:17-05:00",
+  "datePublished": "2020-02-13T11:38:03-05:00",
+  "dateModified": "2020-02-13T11:38:03-05:00",
   "description": "        5. Suggestions when producing a panel dataset 5.1 Organization of data and code Having a single directory for a project, containing all data and code...",
   "author": {
     "@type": "Person",
diff --git a/_site/weather-and-climate-data.html b/_site/weather-and-climate-data.html
@@ -26,8 +26,8 @@
   "@type": "NewsArticle",
   "mainEntityOfPage": "YOUR URL//weather-and-climate-data.html",
   "headline": "Using Weather and Climate Data",
-  "datePublished": "2020-01-31T10:30:17-05:00",
-  "dateModified": "2020-01-31T10:30:17-05:00",
+  "datePublished": "2020-02-13T11:38:03-05:00",
+  "dateModified": "2020-02-13T11:38:03-05:00",
   "description": "        1. Using Weather and Climate Data When using weather data as independent variables in an economic model, or climate data to project your research res...",
   "author": {
     "@type": "Person",
diff --git a/_site/weighting-schemes.html b/_site/weighting-schemes.html
@@ -26,8 +26,8 @@
   "@type": "NewsArticle",
   "mainEntityOfPage": "YOUR URL//weighting-schemes.html",
   "headline": "Weighting schemes",
-  "datePublished": "2020-01-31T10:30:17-05:00",
-  "dateModified": "2020-01-31T10:30:17-05:00",
+  "datePublished": "2020-02-13T11:38:03-05:00",
+  "dateModified": "2020-02-13T11:38:03-05:00",
   "description": "        3. Weighting schemes This section describes how to use different weighting schemes when aggregating gridded data to data regions.3.1 Why spatial weig...",
   "author": {
     "@type": "Person",
diff --git a/content/contributors.md b/content/contributors.md
@@ -3,8 +3,8 @@
 The core contributors of this tutorial are, in alphabetic order:
 
 - [Azhar Hussain](https://azharhsain.github.io/)
-- James Rising
-- Kevin Schwarzwald
+- [James Rising](http://existencia.org/pro)
+- [Kevin Schwarzwald](https://iri.columbia.edu/contact/staff-directory/kevin-schwarzwald/)
 - [Ana Trisovic](https://anatrisovic.com)
 
 ## Citing the _Weather Panel Tutorial_
diff --git a/content/images/cartoon_sec2.JPG b/content/images/cartoon_sec2.JPG
diff --git a/content/reduced-form-specification.md b/content/reduced-form-specification.md
@@ -159,3 +159,6 @@ where $N_{it}$ is the number of agent-timestep observations represented within r
 ### Aggregation-before-transformation
 
 When an economic process is occurring at the county level, we need to first do the weather variable aggregation at the county level. We do the weather variable transformation after we have aggregated it to the county level using weighted averaging method, and then run our estimation on the county level data. For example, to estimate the effect of storm events on public service employment at the administrative block level, we need to take into account the fact that hiring/firing of public service employees happens at the block level only.  Estimating grid-level effects will lead to wrong estimation, as it would result in zero estimate for those (almost all) grid cells which do not have the block office coordinates, and extremely large values for those (very few) cells, which comprise of the block office coordinates. The mathematical formulation for aggregation-before-transformation can be learned through transformation-before-aggregation formulation described above, with a change that the aggregation step precedes the transformation step. Weather data products can have temporal resolution finer than scale of daily observations. Like spatial aggregation, we can do temporal aggregation to month, year, or decade; however, unlike spatial aggregation, the averaging process is standard in all general cases.
+
+![Humor](images/cartoon_sec2.JPG)
+
