Merge pull request #543 from NASA-IMPACT/main

Catching up with main branch

datasets/emit-landfill.data.mdx

@@ -12,7 +12,6 @@ taxonomy:
   - name: Topics
     values:
       - Air Quality
-      - Environmental Justice
   - name: Source
     values:
       - NASA EMIT
@@ -83,7 +82,7 @@ layers:
       This dataset focuses on large methane (CH4) emissions from Landfills in the Stockton, CA and Dallas, TX regions. For more information on EMIT please visit NASA's [Land Processing Distributed Active Archive Center](https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/emit-overview/#emit-metadata) (LP DAAC) and the EMIT Dataset Overview page on the U.S. [Greenhouse Gas Center](https://earth.gov/ghgcenter/data-catalog/emit-ch4plume-v1).
 
       - **Temporal Extent:** June 22, and August 25, 2023
-      - **Temporal Resolution:** Variable (based on ISS orbit, solar illumination, and target mask)
+      - **Temporal Resolution:** Inconsistent
       - **Spatial Extent:** Stockton, CA and Dallas, TX
       - **Spatial Resolution:** 60 m
       - **Data Units:** Parts per million-meter (ppm m)

datasets/fb_population.ej.data.mdx

@@ -11,7 +11,7 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - Environmental Justice
+      - Socioeconomic
   - name: Source
     values:
       - Meta 

datasets/grdi-v1.data.mdx

@@ -11,7 +11,7 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - Environmental Justice
+      - Socioeconomic
   - name: Source
     values:
       - NASA CIESIN

datasets/hls-events.ej.data.mdx

@@ -18,7 +18,6 @@ media:
 taxonomy:
   - name: Topics
     values:
-     - Environmental Justice
      - Disasters
   - name: Source
     values:
@@ -110,7 +109,7 @@ The production of atmospherically corrected HLS products is a collaborative effo
 </Figure>
 <Prose>
 ## Interpreting the data
-HLS imagery in support of Environmental Justice shows the impact of flooding for Hurricanes Maria and Ida that made landfall in Puerto Rico (2017) and New Orleans, LA (2021) respectively. The imagery displayed is a shortwave infrared (SWIR) false color composite that provides enhanced contrast to detect flood extent. In SWIR false color composite imagery, water is identified by dark blue colors, vegetation is bright green, clouds are white, and ice is blue.
+HLS imagery shows the impact of flooding for Hurricanes Maria and Ida that made landfall in Puerto Rico (2017) and New Orleans, LA (2021) respectively. The imagery displayed is a shortwave infrared (SWIR) false color composite that provides enhanced contrast to detect flood extent. In SWIR false color composite imagery, water is identified by dark blue colors, vegetation is bright green, clouds are white, and ice is blue.
 </Prose>
 </Block>
 

datasets/nighttime-lights.data.mdx

@@ -1,6 +1,6 @@
 ---
 id: nighttime-lights
-name: 'Nighttime Lights'
+name: 'Black Marble Night Lights - COVID-19'
 description: 'During the COVID-19 pandemic, researchers are using night light observations to track variations in energy use, migration, and transportation in response to social distancing and lockdown measures.'
 media:
   src: ::file ./nighttime-lights--dataset-cover.jpg

datasets/nighttime-lights.ej.data.mdx

@@ -1,6 +1,6 @@
 ---
 id: nighttime-lights-ej
-name: 'Nighttime Lights supporting Environmental Justice'
+name: 'Black Marble Night Lights - Hurricanes Maria and Ida'
 description: 'High definition nighttime lights can be used to identify regions impacted by natural disaster and/or power outages to better inform disaster response efforts.'
 media:
   src: ::file ./nighttime-lights-ej--dataset-cover-neworleans.jpeg
@@ -11,7 +11,7 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - Environmental Justice
+      - Disasters
   - name: Source
     values:
       - Black Marble

datasets/nlcd.data.mdx

@@ -14,7 +14,6 @@ taxonomy:
     values:
       - Agriculture
       - Biomass
-      - Environmental Justice
       - Land Cover
   - name: Source
     values:

datasets/nldas2.data.mdx

@@ -17,7 +17,10 @@ taxonomy:
       - Surface Meteorology
       - Drought    
       - Agriculture
-      - Disasters       
+      - Disasters
+infoDescription: |
+  ::markdown
+    NLDAS-2 is a surface meteorological analysis and land-surface model dataset running in operations to produce outputs of soil moisture, snow, surface fluxes, streamflow, etc. for drought monitoring and other applications.             
 layers:
   - id: nldas2
     stacCol: nldas2
@@ -35,8 +38,8 @@ layers:
         - 200
       nodata: 0
     compare:
-      datasetId: nldas3
-      layerId: nldas3
+      datasetId: nldas2
+      layerId: nldas2
       mapLabel: |
         ::js ({dateFns, datetime, compareDatetime}) => {
           return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
@@ -54,6 +57,10 @@ layers:
         - "#F37C21"
         - "#FCFFA4"
         - "#fc8d59"
+    info:
+      spatialExtent: CONUS 
+      temporalResolution: Monthly 
+      unit: mm/month        
 ---
 <Block>
   <Prose>

datasets/nldas3.data.mdx

@@ -17,7 +17,10 @@ taxonomy:
       - Surface Meteorology
       - Drought    
       - Agriculture
-      - Disasters       
+      - Disasters
+infoDescription: |
+  ::markdown
+    NASA is co-developing high-resolution retrospective and real-time data for water resources and agricultural applications.             
 layers:
   - id: nldas3
     stacCol: nldas3
@@ -35,8 +38,8 @@ layers:
         - 200
       nodata: 0
     compare:
-      datasetId: nldas3
-      layerId: nldas3
+      datasetId: nldas2
+      layerId: nldas2
       mapLabel: |
         ::js ({dateFns, datetime, compareDatetime}) => {
           return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
@@ -54,6 +57,10 @@ layers:
         - "#F37C21"
         - "#FCFFA4"
         - "#fc8d59"
+    info:
+      spatialExtent: North and Central America 
+      temporalResolution: Monthly 
+      unit: mm/month          
 ---
 <Block>
   <Prose>

datasets/ps_blue_tarp_detections.ej.data.mdx

@@ -1,7 +1,7 @@
 ---
 id: ps_blue_tarp_detections
-name: "Blue tarp detections"
-description: "Machine learning generated blue tarp detections using Planetscope 3-band RGB imagery"
+name: "PlanetScope Blue tarp Detections"
+description: "Machine learning generated blue tarp detections using PlanetScope 3-band RGB imagery"
 media:
   src: ::file ./ps-bluetarp--dataset-cover.jpg
   alt: Blue tarp detections for Jefferson Parish, LA on February 12, 2022
@@ -11,19 +11,18 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - Environmental Justice
       - Disasters
   - name: Source
     values:
       - Planet
 infoDescription: |
   ::markdown
-    Planetscope provides 3-band RGB imagery at 3-meter ground resolution which
+    PlanetScope provides 3-band RGB imagery at 3-meter ground resolution which
     can support building-scale analysis of the land surface. In the aftermath of
     natural disasters associated with high wind speeds, homes with damaged roofs
     typically are covered with blue tarps to protect the interior of the home
     from further damage. Using machine learning, blue tarps can be detected from
-    the Planetscope imagery using pre-event cloud free images to detect blue
+    the PlanetScope imagery using pre-event cloud free images to detect blue
     pixels and potential impacts after a natural disaster.
 layers:
   - id: blue-tarp-detection
@@ -49,9 +48,9 @@ layers:
 
   - id: blue-tarp-planetscope
     stacCol: blue-tarp-planetscope
-    name: Planetscope input RGB imagery used for blue tarp detection
+    name: PlanetScope input RGB imagery used for blue tarp detection
     type: raster
-    description: "Planetscope input RGB imagery used for blue tarp detection. Includes copyrighted material of Planet. All rights reserved."
+    description: "PlanetScope input RGB imagery used for blue tarp detection. Includes copyrighted material of Planet. All rights reserved."
     zoomExtent:
       - 14
     compare:
@@ -71,12 +70,12 @@ layers:
 
 <Block>
   <Prose>
-    Planetscope provides 3-band RGB imagery at 3-meter ground resolution which
+    PlanetScope provides 3-band RGB imagery at 3-meter ground resolution which
     can support building-scale analysis of the land surface. In the aftermath of
     natural disasters associated with high wind speeds, homes with damaged roofs
     typically are covered with blue tarps to protect the interior of the home
     from further damage. Using machine learning, blue tarps can be detected from
-    the Planetscope imagery using pre-event cloud free images to detect blue
+    the PlanetScope imagery using pre-event cloud free images to detect blue
     pixels and potential impacts after a natural disaster.
   </Prose>
 </Block>
@@ -85,7 +84,7 @@ layers:
   <Prose>
     ## Scientific research Detection of blue tarps from high resolution imagery
     can inform disaster response of the most impacted locations. Additionally,
-    given the frequency with which Planetscope scenes are retrieved from the
+    given the frequency with which PlanetScope scenes are retrieved from the
     satellite, the rate of recovery for a given location can also be tracked
     over time. This can also support disaster response to provide aid to
     specific locations where recovery efforts are lacking.

datasets/svi_household.ej.data.mdx

@@ -11,7 +11,7 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - Environmental Justice
+      - Socioeconomic
   - name: Source
     values:
       - ATSDR

datasets/svi_housing.ej.data.mdx

@@ -11,7 +11,7 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - Environmental Justice
+      - Socioeconomic
   - name: Source
     values:
       - ATSDR

datasets/svi_minority.ej.data.mdx

@@ -11,7 +11,7 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - Environmental Justice
+      - Socioeconomic
   - name: Source
     values:
       - ATSDR

datasets/svi_overall.ej.data.mdx

@@ -18,7 +18,7 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - Environmental Justice
+      - Socioeconomic
   - name: Source
     values:
       - ATSDR

datasets/svi_socioeconomic.ej.data.mdx

@@ -11,7 +11,7 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - Environmental Justice
+      - Socioeconomic
   - name: Source
     values:
       - ATSDR

datasets/urban-heating.data.mdx

@@ -11,7 +11,6 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - Environmental Justice
       - Land Cover
       - Temperature
   - name: Source

overrides/about.mdx

@@ -10,7 +10,6 @@ description: "Visualization, Exploration, and Data Analysis (VEDA): Scalable and
 
     - an evolution of COVID-19 dashboard to provide the interactive storytelling for various environmental changes using Earth observation data;
     - a transformation of high-valued NASA datasets to dynamic visualization, enabling users to perform on-the-fly visual analysis;
-    - support for environmental justice initiatives through the integration of NASA Earth observation data and socio-economic data;
     - a tool by which users can visually explore and localize data, and perform independent analysis; data-driven stories that are exportable in multiple formats;
     - and a situational awareness system that brings together Earth observation datasets such as greenhouse gasses, air quality, and sea level rise, among others.
 

stories/black-belt-climate-ej.stories.mdx

@@ -12,7 +12,6 @@ pubDate: 2024-11-11
 taxonomy:
   - name: Topics
     values:
-      - Environmental Justice
       - Heat
       - Land Use
       - Natural Disasters
@@ -35,11 +34,8 @@ taxonomy:
     <p style={{ marginBottom: '0.5em' }}>
       <sup>[3]</sup> University of Maryland and Global Modeling and Assimilation Office, NASA
     </p>
-    <p style={{ marginBottom: '0.5em', fontWeight: 'bold' }}>
-      Mission: NASA Earth Action: A thriving world, driven by trusted, actionable Earth science
-    </p>
     <p style={{ fontSize: '0.9em', fontStyle: 'italic' }}>
-      This study demonstrates innovative applications of NASA and other datasets to highlight environmental inequities. Please note that these results are preliminary and have not yet undergone peer review.
+      <mark>🚧 This Data Story presents work in progress and not peer-reviewed results, but is being expanded into an article that will undergo peer review and publication. 🚧</mark>
     </p>
   </Prose>
 </Block>
@@ -89,14 +85,17 @@ taxonomy:
 <Block style={{justifyContent: 'center', alignItems: 'center', margin: '0 auto', width: '80%' }}>
   <Prose style={{ textAlign: 'justify', display: 'flex', alignItems: 'center' }}>
     <p>
-     The Black Belt’s rich soil played a pivotal role in shaping the region's history and demographics. This fertile land transformed the area into an agricultural powerhouse, making it a cornerstone of the cotton economy, which was heavily reliant on enslaved African American labor. After the Civil War, many former slaves remained in the area, working as sharecroppers and tenant farmers. The region became a significant cultural and political area, particularly noted for its role in the civil rights movement. Despite its agricultural legacy, the Black Belt has faced economic and social challenges, including poverty and limited access to education and healthcare, which continue to impact its predominantly Black population.
+     The Black Belt’s rich soil played a pivotal role in shaping the region's history and demographics. This fertile land transformed the area into an agricultural powerhouse, making it a cornerstone of the cotton economy. After the Civil War, many African Americans remained in the area, working as sharecroppers and tenant farmers. The region became a significant cultural and political area, particularly noted for its role in the civil rights movement. Despite its agricultural legacy, the Black Belt has faced economic and social challenges, including poverty and limited access to education and healthcare, which continue to impact its predominantly Black population.
     </p>
   </Prose>
   <Figure style={{ flex: '0 0 auto', marginLeft: '5px' }}>
     <Image
       src={new URL('./Black_cotton_farming_family.jpg', import.meta.url).href}
       style={{ maxWidth: '100%', height: 'auto' }}
     />
+    <Caption attrUrl='http://kaufmann-mercantile.com/images/organic-cotton-farming.jpg'>
+      African-American cotton tenant farmers (c.1890s)
+    </Caption>
   </Figure> 
 </Block>
 
@@ -229,7 +228,7 @@ taxonomy:
 <Block style={{ display: 'flex', justifyContent: 'center', alignItems: 'center', margin: '0 auto', width: '80%' }}>
   <Prose style={{ textAlign: 'justify', display: 'flex', alignItems: 'center' }}>
     <p>
-     The combination of rising temperatures and an older workforce underscores the urgent need for adaptive strategies to protect both the health of the population and the viability of these economic sectors.Addressing these intertwined challenges requires targeted policies and investments in climate resilience measures. By enhancing education, healthcare, and job opportunities, along with adopting sustainable agricultural practices, the Black Belt can work towards a more resilient and prosperous future in the face of climate change.
+     The combination of rising temperatures and an older workforce underscores the urgent need for adaptive strategies to protect both the health of the population and the viability of these economic sectors. Addressing these intertwined challenges requires targeted policies and investments in climate resilience measures. By enhancing education, healthcare, and job opportunities, along with adopting sustainable agricultural practices, the Black Belt can work towards a more resilient and prosperous future in the face of climate change.
      ### To mitigate heat-related risks, several safeguards can be implemented:
      - Improved Access to Cooling Centers: Establishing more cooling centers in rural areas can provide relief during extreme heat events
      - Health Monitoring Programs: Implementing regular health check-ups for older adults working in agriculture can help prevent heat-related illnesses

stories/houston-aod.stories.mdx

@@ -12,7 +12,6 @@ pubDate: 2023-10-31
 taxonomy:
   - name: Topics
     values:
-      - Environmental Justice
       - Air Quality
       - Urban
   - name: Source