updates to weather data section

Author: ks905383

_build/contributors.html

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   Contributors
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+search: tutorial github weather panel io kevin schwarzwald com doi zenodo contributors azhar hussain james rising org ana trisovic contributions atrisovic core alphabetic order azharhsain existencia pro iri columbia edu contact staff directory anatrisovic citing reference represents versions always resolve latest cite january version v welcome any mistake please dont hesitate open issue issues send us pull request
 
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_build/geographical-unit-data.html

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   Generating geographical unit data
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+search: data shapefile regions administrative region grid units r matrix unit weather shape vector need example spatial cell common geographic names information files qgis shp cells states across also points shapefiles not python level using polygons used gridded dataset myshapefile pbsmapping w text values generating geographical economic countries those com gis analysis software www library set name same projection import within next generate entries sparse convert column listed events matching korea area defined ways city web where center adm political usually relevant granularity gadm org study features should much system representation geometry create polygon image arcgis latitude longitude working step fiona
 
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_build/getting-started.html

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   Getting started
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-    Using Weather and Climate Data
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+search: data weather tutorial climate point region science econometrics resource examples hsiang org socioeconomic resolution variables work particular not gridded being geographic unit grid t panel regression using economic better understand change responses carleton through relate outcomes high read assume experience language try provide case specific question important useful www theoretical foundation following describes space location across information always structure notation introduction welcome study social biophysical systems respond opened huge doors allowing us impacts disaster risk management human behavior sustainable development here relationships uncovered recent years images jpg source sciencemag content aad walk steps necessary cover should aware develop specification shapefiles
 
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@@ -27,7 +26,8 @@ <h1 id="Introduction-to-the-Tutorial">Introduction to the Tutorial<a class="anch
 responses, resource management, human behavior, and sustainable
 development. Here are some of the relationships that have been
 uncovered in recent years:</p>
-<p><img src="images/examples.jpg" alt="Examples from Carleton and Hsiang (2016)."></p>
+<p><img src="images/examples.jpg" alt="Examples from Carleton and Hsiang (2016).">
+Source: <a href="https://science.sciencemag.org/content/353/6304/aad9837">Carleton and Hsiang (2016)</a>.</p>
 <p>This tutorial will walk you through the steps necessary to relate
 socioeconomic outcomes to weather data at high resolution. We will
 cover:</p>

_build/reduced-form-specification.html

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   Developing a reduced-form specification
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-    Using Weather and Climate Data
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+search: t theta k temperature transformation sum level ps g grid data weather c f precipitation aggregation n example county polynomial nabla variable degree humidity variables below response before where circ knots into using not radiation aggregate bins psi used average region model our linear weighted spline y j beta form important across e cubic need cross validation agent s specification also local etc year process carbon values doing cells available methods good forms min days above temperatures metric while often air highly since however et should different effect shortwave relative sst functional method grids terms betak betakf observations let take
 
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@@ -121,7 +120,8 @@ <h2 id="2.2.-Common-functional-forms-(pros,-cons,-and-methods)">2.2. Common func
 </li>
 </ul>
 <p><strong>Example of reduced-form regression plots for different functional forms</strong>
-<img src="images/fform_cil.JPG" alt="Data from  Greenstone et al. (2019)!"></p>
+<img src="images/fform_cil.JPG" alt="Data from  Greenstone et al. (2019)!">
+Source: <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3224365">Carleton et al. (2019)</a>.</p>
 <h2 id="2.3.-Cross-validation">2.3. Cross-validation<a class="anchor-link" href="#2.3.-Cross-validation"> </a></h2><p>Cross-validation can be done to check the <em>internal validity</em> and the <em>external validity</em> of the model estimates. For checking
 internal validity, the model can be fit to a subset of the dataset, and evaluated on the remainder. For example, you can leave particular regions out of your regression or remove a random <em>1/k</em> of your data (k-fold cross validation) instead of running a full-sample regression. For gauging external validity, model is run on some new dataset that has not been not used in the model-evaluation process. For example, by predicting the response for a new country using global regression model estimates, and comparing it to the actual observations.</p>
 <p>Although cross-validation is not universally performed by researchers, and many people continue to rely on the measure of R-squared statistic. However, we know from our basic statistics learning, how badly R-squared statistic can perform even in very simple cases. Therefore, cross-validation can be an effective approach for doing model-selection.</p>