Merge pull request #96 from SchmidtDSE/update-viz-site

Update viz site

CITATION.cff

@@ -1,6 +1,6 @@
 cff-version: '1.1.0'
 message: 'Please cite the following works to reference this software.'
-abstract: 'Climate change not only threatens agricultural producers but also strains financial institutions. These important food system actors include government entities tasked with both insuring grower livelihoods and supporting response to continued global warming. We use an artificial neural network to predict future maize yields in the U.S. Corn Belt, finding alarming changes to institutional risk exposure within the Federal Crop Insurance Program. Specifically, our machine learning method anticipates more frequent and more severe yield losses that would result in the annual probability of Yield Protection (YP) claims to more than double at mid-century relative to simulations without continued climate change. Furthermore, our dual finding of relatively unchanged average yields paired with decreasing yield stability reveals targeted opportunities to adjust coverage formulas to include variability. This important structural shift may help regulators support grower adaptation to continued climate change by recognizing the value of risk-reducing strategies such as regenerative agriculture. Altogether, paired with open source interactive tools for deeper investigation, our risk profile simulations fill an actionable gap in current understanding, bridging granular historic yield estimation and climate-informed prediction of future insurer-relevant loss. '
+abstract: 'Climate change not only threatens agricultural producers but also strains related public agencies and financial institutions. These important food system actors include government entities tasked with insuring grower livelihoods and supporting response to continued global warming. We examine future risk within the U.S. Corn Belt geographic region for one such crucial institution: the U.S. Federal Crop Insurance Program. Specifically, we predict the impacts of climate-driven crop loss at a policy-salient "risk unit" scale. Built through our presented neural network Monte Carlo method, simulations anticipate both more frequent and more severe losses that would result in a costly doubling of the annual probability of maize Yield Protection insurance claims at mid-century. We also provide an open source pipeline and interactive visualization tools to explore these results with configurable statistical treatments. Altogether, we fill an important gap in current understanding for climate adaptation by bridging existing historic yield estimation and climate projection to predict crop loss metrics at policy-relevant granularity.'
 authors:
 
 doi: '10.48550/ARXIV:2408.02217'

README.md

@@ -38,7 +38,7 @@ A summary of the pipeline is created in `stats.json`. See local package below fo
 ### Interactive tools
 Written in [Sketchingpy](https://sketchingpy.org/), the tools can be executed locally on your computer, in a static context for building the paper, or through a web browser. First, one needs to get data from the pipeline or download prior results:
 
- - **Download prior results**: Retrieve the [latest results](https://ag-adaptation-study.pub/archive/data.zip) and move them into the viz directory (`paper/viz/data`). Simply use wget when in the `paper/viz directory` as so: `wget https://ag-adaptation-study.pub/archive/data.zip; unzip data.zip`.
+ - **Download prior results**: Retrieve the [latest results](https://ag-adaptation-study.pub/archive/data.zip) and move them into the viz directory (`paper/viz/data`). Simply use wget to gather model outputs when in the `paper/viz directory` as so: `wget https://ag-adaptation-study.pub/archive/data.zip; unzip data.zip`. If using prior sweep results, download full sweep information like so: `cd data; wget http://ag-adaptation-study.pub/data/sweep_ag_all.csv; cd ..`.
  - **Use your own results**: Update the output data per instructions regarding local package below.
 
 There are two options for executing the tools:

export_tasks.py

@@ -328,10 +328,10 @@ def is_record_in_scope(target, threshold, geohash_sim_size=4, historic=False):
         return False
 
     if historic:
-        if target['series'] != 'historic':
+        if str(target['series']) != '2010':
             return False
     else:
-        if target['series'] == 'historic':
+        if str(target['series']) == '2010':
             return False
 
     if abs(float(target['unitSize'])) <= 1.0001:
@@ -704,9 +704,7 @@ def requires(self):
         """
         return [
             HistExportSubTask(geohash_size=4, historic=False),
-            HistExportSubTask(geohash_size=4, historic=True),
-            HistExportSubTask(geohash_size=5, historic=False),
-            HistExportSubTask(geohash_size=5, historic=True)
+            HistExportSubTask(geohash_size=5, historic=False)
         ]
 
     def output(self):

paper/viz/hist_viz.py

@@ -654,7 +654,7 @@ def _draw_axis_bottom(self):
         self._sketch.set_text_align('center', 'center')
 
         if self._comparison == 'vs historic':
-            self._sketch.draw_text(0, 0, 'Historic Values (Approx 2007)')
+            self._sketch.draw_text(0, 0, 'Predicted 2010 Series')
         else:
             self._sketch.draw_text(0, 0, 'Climate Change Stops')
 
@@ -716,7 +716,7 @@ def _draw_top_claims(self, claims):
         self._sketch.set_text_font(const.FONT_SRC, 11)
 
         is_catastrophic = self._target_threshold == '75% cov'
-        max_val = 30 if is_catastrophic else 35
+        max_val = 35 if is_catastrophic else 45
 
         y = SUB_CHART_HEIGHT - self._get_y(15)
         start_x = self._get_x(-100) - 5
@@ -757,7 +757,7 @@ def _draw_bottom_claims(self, claims):
         self._sketch.set_text_font(const.FONT_SRC, 11)
 
         is_catastrophic = self._target_threshold == '75% cov'
-        max_val = 30 if is_catastrophic else 35
+        max_val = 35 if is_catastrophic else 45
 
         y = self._get_y(15)
         start_x = self._get_x(-100)

paper/viz/history_viz.py

@@ -520,7 +520,7 @@ def _draw_content(self):
         std = statistics.stdev(self._values)
 
         if self._use_std:
-            threshold = -2.1
+            threshold = -1.5
             format_str = '%s%.1f std'
             get_delta = lambda x: (x - average) / std
         else:
@@ -597,7 +597,7 @@ def _draw_average(self):
 
         if self._use_std:
             std = statistics.stdev(self._values)
-            with_loss = filter(lambda x: (x - average) / std < -2.1, self._values)
+            with_loss = filter(lambda x: (x - average) / std < -1.5, self._values)
         else:
             with_loss = filter(lambda x: (x - average) / average < -0.25, self._values)
 
@@ -734,7 +734,7 @@ def step(self, mouse_x_abs, mouse_y_abs, clicked):
         self._sketch.draw_text(4, self._height - 16, '0%')
 
         self._sketch.set_text_align('right', 'top')
-        self._sketch.draw_text(self._width - 4, self._height - 16, '10%')
+        self._sketch.draw_text(self._width - 4, self._height - 16, '30%')
 
         # Draw top bar
         historic_claims = self._data_facade.get_claims(2010, 'historic', self._using_std) * 100
@@ -755,7 +755,7 @@ def step(self, mouse_x_abs, mouse_y_abs, clicked):
         self._sketch.draw_text(2, 64, 'Future: %.1f%% (<-%.2f%s)' % future_str_vals)
 
         self._sketch.set_rect_mode('corner')
-        get_width = lambda x: x / 10 * (self._width - 8)
+        get_width = lambda x: x / 30 * (self._width - 8)
         self._sketch.draw_rect(4, 41, get_width(historic_claims), 3)
         self._sketch.draw_rect(4, 65, get_width(future_claims), 3)