text: import React, { Component } from "react";
  
  class Info extends Component {
    render() {
      return (
        <div>
          <div style={iStyle}>
            {/* <section style={isectionStyle}>
              <h1 style={ih1Style}></h1>
              <p style={ipStyle}></p>
            </section> */}
            <section style={isectionStyle}>
              <h1 style={ih1Style}>Map Layers</h1>
              <p style={ipStyle}>
                Our layers show the risks of various natural disasters for each
                county in the country. They come from a few different sources and
                mean different things.
              </p>
            </section>
            <section style={isectionStyle}>
              <h1 style={ih1Style}>Danger</h1>
              <p style={ipStyle}>
                This layer assigns each county an overall subjective danger score
                between 1 and 6. This score combines all our records of past
                events for that county, and weigh events by their magnitude when
                that number was available. We calculated the score by the
                following algorithm.
              </p>
              <br />
              <ol>
                <li>
                  1. Sum the number of events for the following categories: Winter
                  Weather, Storm, Flood, Fire, Heat, and Drought (from the NOAA
                  dataset, see below).
                </li>
                <li>
                  2. Add that to 100 times the number of Hurricanes that were
                  declared federal disasters (from the FEMA dataset, see below).
                </li>
                <li>
                  3. Normalize and scale the base-10 logarithm of that number
                  (which we call ‘storms’) so that the distribution over all
                  counties has a standard deviation of 1 and a mean of 0.
                </li>
                <li>
                  4. Calculate the energy released by all earthquakes above
                  magnitude 4.0, and add up that energy per county. Normalize and
                  scale as before.
                </li>
                <li>
                  5. Calculate the damage caused by tornadoes, using an
                  exponential scale that gives ~300x more weight to category 5
                  tornadoes as to category 0. Normalize and scale as before.
                </li>
                <li>
                  6. Add up the normalized numbers, creating a normal distribution
                  with a mean of 0 and a standard deviation of 1.
                </li>
                <li>
                  7. Bin the results into six symmetrical bins around the mean,
                  with bin boundaries at 0, 1, and 2 standard deviations from the
                  mean.
                </li>
              </ol>
              <p style={ipStyle}>
                We chose this method because it allows us to aggregate events by
                magnitude (when available) and create a normal distribution of
                danger levels from what would otherwise be extremely lopsided data
                dominated by zeros and by extreme events. This is necessary
                because the natural distribution of all these events is
                log-normal. Thus, our custom danger metric highlights the counties
                around the USA that suffer a disproportionate number of severe
                events. This layer displays the bins.
              </p>
            </section>
            <section style={isectionStyle}>
              <h1 style={ih1Style}>Cost</h1>
              <p style={ipStyle}>
                In order to create a single metric of the costs associated with
                natural disasters, we used the NOAA dataset (see below) and
                recorded the property damages, injuries, and deaths associated
                with all the extreme weather events between 1996 and 2018.
              </p>
              <p style={ipStyle}>
                We then turned everything into dollars. For dollars, we used the{" "}
                <a
                  style={iaStyle}
                  href="https://en.wikipedia.org/wiki/Value_of_life#United_States"
                  target="_blank"
                >
                  statistical value of a human life in the USA
                </a>
                , which we approximate as 9 million dollars. We turned injuries
                into dollars by assuming that an injury costs on average 1 year of
                quality-adjusted life, which is commonly set at 50,000 dollars. I
                scaled and normalized the base-10 logarithm of this total cost,
                and binned it as described before. The result is also normally
                distributed throughout the county. This layer displays the bins.
              </p>
            </section>
  
            <section style={isectionStyle}>
              <h2 style={ih2Style}>Earthquakes</h2>
              <p style={ipStyle}>
                We got our list of earthquakes from the Advanced National Seismic
                System (ANSS), a collaboration of the U.S. Geological Survey
                (USGS) and regional, state, and academic partners that collects
                and analyzes data on significant earthquakes to provide near
                real-time information to emergency responders and officials, the
                news media, and the public. We used a{" "}
                <a
                  style={iaStyle}
                  href="https://github.com/usgs/libcomcat"
                  target="_blank"
                >
                  Python API
                </a>{" "}
                to query their
                <a
                  style={iaStyle}
                  href="https://earthquake.usgs.gov/data/comcat/"
                  target="_blank"
                >
                  Comprehensive Earthquake Catalog
                </a>
                , collecting all the earthquakes in the coterminous 48 states,
                Alaska, and Hawaii. We collected all the earthquake events of
                magnitude greater than 4.0 on the Richter scale that happened
                between 1996 and 2018. This layer displays a heatmap of those
                individual earthquake events.
              </p>
            </section>
  
            <section style={isectionStyle}>
              <h1 style={ih1Style}>Tornadoes</h1>
              <p style={ipStyle}>
                This layer displays individual tornado events from the same NOAA
                data that created the tornado risk layer. That layer aggregated
                tornadoes by county. This layer displays them individually, with
                colors proportional to the tornado magnitude. Greener points are
                lower intensity, redder points are higher.
              </p>
            </section>
  
            <section style={isectionStyle}>
              <h2 style={ih2Style}>Earthquake Risk</h2>
              <p style={ipStyle}>
                This seismic hazard map is part of the 1997-2014 edition of the
                National Atlas of the United States, a publication by the US
                Department of the Interior. This map summarizes the quantitative
                information, available from geologic and geophysical sources,
                about seismic ground motion hazard in the United States.
              </p>
  
              <p style={ipStyle}>
                The data represent a model showing the probability that ground
                motion will reach a certain level. Specifically, this map layer
                shows peak horizontal ground acceleration (the fastest measured
                change in speed, for a particle at ground level that is moving
                horizontally due to an earthquake) with a 10% probability of
                exceedance in 50 years. Values are given in %g, where g is
                acceleration due to gravity, or 9.8 meters/second^2.
              </p>
  
              <p style={ipStyle}>
                Dataset available at the{" "}
                <a
                  style={iaStyle}
                  href="https://geo.nyu.edu/catalog/stanford-rm034qp5477"
                  target="_blank"
                >
                  NYU Spatial Data Repository
                </a>
                , and more information about the dataset available at the{" "}
                <a
                  style={iaStyle}
                  href="https://purl.stanford.edu/rm034qp5477"
                  target="_blank"
                >
                  Stanford Digital Repository.`
                </a>
              </p>
            </section>
  
            <section style={isectionStyle}>
              <h1 style={ih1Style}>Hurricane Risk</h1>
              <p style={ipStyle}>
                Our data on hurricanes comes from the Federal Emergency Management
                Agency (FEMA){" "}
                <a
                  style={iaStyle}
                  href="https://www.fema.gov/media-library/assets/documents/28318"
                  target="_blank"
                >
                  Disaster Declarations Summary
                </a>
                , a summarized dataset describing all federally declared
                disasters. We collected data for 1996-2018 and summed up all the
                times that a hurricane was declared a federal disaster for that
                county. This layer displays the number of events.
              </p>
            </section>
  
            <section style={isectionStyle}>
              <h2 style={ih2Style}>
                Tornado, Major Storm, Flood, Winter Weather, Fire, and Heat Wave
                Risks
              </h2>
              <p style={ipStyle}>
                We used a{" "}
                <a
                  style={iaStyle}
                  href="https://www.climate.gov/maps-data/dataset/severe-storms-and-extreme-events-data-table"
                  target="_blank"
                >
                  database of severe storms and extreme events
                </a>{" "}
                from the National Oceanic and Atmospheric Administration (NOAA).
                These data are collected by the National Weather Service. Weather
                offices detect events using instruments and visual observations,
                and they also receive information from storm spotters—people who
                call in to report severe events. Tornadoes, high wind speeds, and
                storm cell data are collected with radar.
              </p>
  
              <ul>
                <strong style={istrongStyle}>
                  Events in the official NOAA database are selected based upon the
                  following criteria:
                </strong>
                <li>
                  Storm has sufficient intensity to cause loss of life, injuries,
                  significant property damage, and/or disruption to commerce.
                </li>
                <li>
                  Storm event contains rare, unusual, weather phenomena or
                  significant meteorological events, such as extreme temperature.
                </li>
              </ul>
              <p style={ipStyle}>
                The data itself is available{" "}
                <a
                  style={iaStyle}
                  href="https://www1.ncdc.noaa.gov/pub/data/swdi/stormevents/csvfiles/"
                  target="_blank"
                >
                  here
                </a>
                , and the corresponding metadata is{" "}
                <a
                  style={iaStyle}
                  href="https://www1.ncdc.noaa.gov/pub/data/swdi/stormevents/csvfiles/Storm-Data-Export-Format.pdf"
                  target="_blank"
                >
                  here
                </a>
                . Our county-by-county maps are colored according to the logarithm
                of event counts. We counted all the events that occurred in each
                county between 1996 and 2018, aggregated them in a few broad
                categories, and created a color display that varies according to
                the base-10 natural logarithm of the event count. NOAA classified
                extreme weather events into 48 categories, which we combined into
                a few broad categories as follows:
              </p>
              <br />
              <div>
                <table style={itableStyle}>
                  <tbody>
                    <tr style={itrStyle}>
                      <th>Category</th>
                      <th>NOAA extreme weather event categories</th>
                    </tr>
  
                    <tr style={itrStyle}>
                      <td>Flood</td>
                      <td>
                        Flood, Flash Flood, Coastal Flood, Storm Surge/Tide,
                        Lakeshore Flood, Debris Flow
                      </td>
                    </tr>
  
                    <tr style={itrStyle}>
                      <td>Storm</td>
                      <td>
                        Thunderstorm Wind, Marine Thunderstorm Wind, Marine High
                        Wind, High Wind, Funnel Cloud, Dust Storm, Strong Wind,
                        Dust Devil, Tropical Depression, Lightning, Tropical
                        Storm, High Surf, Heavy Rain, Hail, Marine Hail, Marine
                        Strong Wind, Waterspout
                      </td>
                    </tr>
  
                    <tr style={itrStyle}>
                      <td>Winter weather</td>
                      <td>
                        Winter Storm, Heavy Snow, Frost/Freeze, Freezing Fog, Ice
                        Storm, Sleet, Lake-Effect Snow, Cold/Wind Chill, Extreme
                        Cold/Wind Chill, Blizzard
                      </td>
                    </tr>
  
                    <tr style={itrStyle}>
                      <td>Fire</td>
                      <td>Wildfire, Dense Smoke</td>
                    </tr>
  
                    <tr style={itrStyle}>
                      <td>Heat</td>
                      <td>Heat, Excessive Heat</td>
                    </tr>
  
                    <tr style={itrStyle}>
                      <td>Hurricane</td>
                      <td>Hurricane, Hurricane (typhoon)</td>
                    </tr>
                  </tbody>
                </table>
              </div>
  
              {/* <p style={ipStyle}>
                We also collected the total number of deaths, injuries, and
                property caused by extreme weather events in each county over the
                same timeframe. We counted both direct and indirect
                deaths/injuries.
              </p> */}
            </section>
  
            <section style={isectionStyle}>
              <h1 style={ih1Style}>Past events for a given county</h1>
              <p style={ipStyle}>
                Our past event graphs for individual counties contain data from
                the NOAA dataset described above, separated by year between 1996
                and 2018. Total events for the county were summed.
              </p>
            </section>
  
            <section style={isectionStyle}>
              <h1 style={ih1Style}>Predicted events for a given location</h1>
              <p style={ipStyle}>
                Predicted events are not associated with counties, but with
                specific locations where the user double-clicks. Those coordinates
                are used as a query for a third-party API, the{" "}
                <a
                  style={iaStyle}
                  href="https://climate.azavea.com/"
                  target="_blank"
                >
                  Azavea Climate API
                </a>
                , which provides projections for that location. Azavea gives us
                access predictions from the{" "}
                <a style={iaStyle} href="http://loca.ucsd.edu/" target="_blank">
                  UCSD LOCA dataset
                </a>
                . These predictions are based on a specific climate change
                scenario, RCP4.5. Representative Concentration Pathways (RCPs) are
                projections of greenhouse gas concentration (not emission)
                trajectories adopted by the{" "}
                <a
                  style={iaStyle}
                  href="https://en.wikipedia.org/wiki/Intergovernmental_Panel_on_Climate_Change"
                  target="_blank"
                >
                  IPCC
                </a>{" "}
                for its fifth Assessment Report in 2014. For more details refer{" "}
                <a
                  style={iaStyle}
                  href="https://skepticalscience.com/rcp.php"
                  target="_blank"
                >
                  Representative Concentration Pathways
                </a>
                .
              </p>
              <p style={ipStyle}>
                Many climate indicators are available through Azavea. We used
                yearly aggregations of maximum and average values of dry_spells,
                extreme_cold_events, extreme_heat_events,
                extreme_precipitation_events and heat_wave_incidents for the next
                20 years.
              </p>
            </section>
  
            <section style={isectionStyle}>
              <h2 style={ih2Style}>County shapes, names, and codes</h2>
              <p style={ipStyle}>
                We got our map of the shapes of US counties from{" "}
                <a
                  style={iaStyle}
                  href="https://community.esri.com/thread/24614"
                  target="_blank"
                >
                  this source
                </a>
                , which itself is a lower-resolution version of a map published by
                the{" "}
                <a
                  style={iaStyle}
                  href="https://www.arcgis.com/home/item.html?id=a00d6b6149b34ed3b833e10fb72ef47b"
                  target="_blank"
                >
                  Environmental Systems Research Institute
                </a>
                . The following explanation for what counts as a county is from
                the
                <a
                  style={iaStyle}
                  href="https://catalog.data.gov/dataset/tiger-line-shapefile-2016-nation-u-s-current-county-and-equivalent-national-shapefile"
                  target="_blank"
                >
                  US Census Bureau
                </a>
              </p>
  
              <p style={ipStyle}>
                The primary legal divisions of most states are termed counties. In
                Louisiana, these divisions are known as parishes. In Alaska, which
                has no counties, the equivalent entities are the organized
                boroughs, city and boroughs, municipalities, and for the
                unorganized area, census areas. The latter are delineated
                cooperatively for statistical purposes by the State of Alaska and
                the Census Bureau. In four states (Maryland, Missouri, Nevada, and
                Virginia), there are one or more incorporated places that are
                independent of any county organization and thus constitute primary
                divisions of their states. These incorporated places are known as
                independent cities and are treated as equivalent entities for
                purposes of data presentation. The District of Columbia and Guam
                have no primary divisions, and each area is considered an
                equivalent entity for purposes of data presentation.
              </p>
            </section>
  
            {/* Save for additional documentation------------------------------------------------------------------------------------------- */}
            {/* <section style={isectionStyle}>
              <h1 style={ih1Style}>FEMA Disasters</h1>
              <p style={ipStyle}>
                The Federal Emergency Management Agency (FEMA) Disaster
                Declarations Summary is a summarized dataset describing all
                federally declared disasters. This information begins with the
                first disaster declaration in 1953 and features all three disaster
                declaration types: major disaster, emergency and fire management
                assistance. The dataset includes declared recovery programs and
                geographic areas (county not available before 1964; Fire
                Management records are considered partial due to historical nature
                of the dataset).
              </p>
              <p style={ipStyle}>
                We performed data wrangling on FEMA archive of disasters
                throughout the USA to consolidate data by{" "}
                <strong style={istrongStyle}>County FIPS Code</strong> and by Year
                and County FIPS Code. This data was in turn used for data
                visualization via choropleth maps.
              </p>
              <h2 style={ih2Style}>Datasets:</h2>
              <a
                style={iaStyle}
                href="https://www.fema.gov/media-library/assets/documents/28318"
                target="_blank"
              >
                FEMA Disaster Declarations Summary
              </a>
              <h2 style={ih2Style}>DFIPS Code:</h2>
              FIPS codes are unique identifiers used by the US government for
              identifying states and counties (or county-equivalent areas). Each
              county has a full FIPS code that contains two digits for the state
              and three for the county within that state.
            </section>
  
            <section style={isectionStyle}>
              <h1 style={ih1Style}>Azavea Climate API</h1>
              <p style={ipStyle}>
                Azavea Climate API provides different models and scenarios for
                universally-recognized temperature and precipitation indicators.
                Azavea provides access to research and application development
                through free and open API.
              </p>
              <h2 style={ih2Style}>Datasets:</h2>
              <a
                style={iaStyle}
                href="https://cds.nccs.nasa.gov/nex-gddp/"
                target="_blank"
              >
                NASA NEX-GDDP
              </a>
              <br />
              <a style={iaStyle} href="http://loca.ucsd.edu/" target="_blank">
                UCSD LOCA
              </a>
              <br />
              For better prediction accuracy with respect to location, we are
              using <strong style={istrongStyle}>LOCA dataset.</strong>
              <h2 style={ih2Style}>Scenarios:</h2>
              <p style={ipStyle}>
                A Representative Concentration Pathway (RCP) is a greenhouse gas
                concentration (not emissions) trajectory adopted by the IPCC for
                its fifth Assessment Report (AR5) in 2014. For more details refer{" "}
                <a
                  style={iaStyle}
                  href="https://skepticalscience.com/rcp.php"
                  target="_blank"
                >
                  Representative Concentration Pathways.
                </a>
              </p>
              <p style={ipStyle}>RCP4.5</p>
              <p style={ipStyle}>RCP8.5</p>
              <p style={ipStyle}>
                To avoid inflating the projections and for displaying more
                realistic estimate, we are using{" "}
                <strong style={istrongStyle}>RCP45</strong> scenario.
              </p>
              <h2 style={ih2Style}>Indicators:</h2>
              <p style={ipStyle}>
                There are many climate indicators available but we shall fetch and
                use{" "}
                <strong style={istrongStyle}>
                  yearly aggregation of maximum and average values of dry_spells,
                  extreme_cold_events, extreme_heat_events,
                  extreme_precipitation_events and heat_wave_incidents from
                  current year till 2100.
                </strong>
              </p>
            </section> */}
          </div>
        </div>
      );
    }
  }
  const iStyle = {
    background: "floralwhite",
    color: "black",
    padding: "1.5rem",
    justifyContent: "center",
    fontSize: "1.2em"
  };
  const isectionStyle = {
    width: "100%",
    maxWidth: "600px",
    margin: "0 auto"
  };
  const ipStyle = {
    marginTop: "10px"
  };
  const ih1Style = {
    marginTop: "10px",
    fontWeight: "bold"
  };
  const ih2Style = {
    marginTop: "10px",
    fontWeight: "bold"
  };
  const iaStyle = {
    marginTop: "10px"
  };
  const itableStyle = {
    width: "100%"
  };
  const istrongStyle = {
    fontWeight: "bold"
  };
  const itrStyle = {
    marginTop: "10px"
  };
  export default Info;
  