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GISG 112 Final Presentation
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GISG 112 Final Presentation

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Slideshow for my final class project involving Spatial Analysis

Slideshow for my final class project involving Spatial Analysis

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  • The question or problem I chose to answer was how to determine the temperature and precipitation for two seasons of the year at a location that is not reasonably close to a National Weather Service Weather Station.
  • Sample and definition of interpolation
  • Wet Season is October – March and the dry season is April - September
  • This slide shows all of the area weather stations in California that I considered for this study. The stations are identified by their three letter National Weather Service location code. I narrowed it down to these stations at first because they are “reasonably” close for the area. As you can see, around Old Dale there are not many weather reporting stations.
  • This slide shows all of the National Weather Service stations located within 150 kilometers of Old Dale. I had to use such a broad range because of the lack of close (other than NXP, Twentynine Palms) official weather stations. I widened my range until I felt I had enough data points to try to make some reasonable analyses.
  • I chose IDW for my interpolation method because it considers both the sample point’s distance and value from the estimated cells. Points closer to the cell have a greater influence on the cell’s estimated value than those that are farther away.
  • Average dry season precipitation at Old Dale using IDW. Precipitation for the dry season (April – September) is in the range of 0.2-0.4 centimeters (.07 - .15 inches). As you can see from the analysis the precipitation closely follows that of the NXP weather station (29 Palms). This data was in the appropriate range for a location in the Mojave Desert region.
  • Analysis of IDW Average Dry Season Temperatures. Again the value closely follows that of the nearest reporting station of 29 Palms. Both locations received an average temperature of 25 degrees Celsius (77 degrees Fahrenheit). For the dry season this would be an appropriate value for the desert.
  • Analysis of the average IDW Wet Season Temperature. Values closely follow station NXP (29 Palms). For the months of October – March the average wet season precipitation was in the range of 0.2-1.2 centimeters (.07 - .47 inches). Being a desert areas even in the “wet” seasons the average rain received is not that high an amount.
  • Analysis of IDW Average Wet Season Temperatures. The average temperature again closely followed Twentynine Palms at 12.8-13.8 degrees Celsius (around 55.4 degrees Fahrenheit).

GISG 112 Final Presentation GISG 112 Final Presentation Presentation Transcript

  • Interpolation: Determining Weather in Old Dale, California Steve Ruge GISG 112 Final Project Wednesday, January 27, 2010
  • Question/Problem
    • How to determine the temperature and precipitation for two seasons of the year at a location that is not reasonably close to a National Weather Service Weather Station
  • Why?
    • Have a friend who recently purchased property in the ghost town of Old Dale, CA and he is curious as to what to expect weather-wise at his location. Through interpolation I will attempt to provide him with the average temperature and precipitation to be expected at his location.
  • Interpolation
    • The process of estimating unknown values that fall between known values
    • -Source: ESRI Virtual Campus
  • Project Background Information
  • Seasonal Definitions
    • For this project I chose and defined two custom seasons
      • Wet Season
        • October – March
      • Dry Season
        • April – September
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  • NWS Weather Station Selection
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  • Analysis
  • Interpolation Method Chosen
    • The interpolation method I have chose for this project is IDW.
    • IDW “…considers the values of the sample points and the distance separating them from the estimated cell. Sample points closer to the cell have a greater influence on the cell's estimated value than sample points that are further away.”
    • – ESRI Virtual Campus
    • Therefore, this method works the best for precipitation and temperature where closer weather stations have more of an influence than those farther apart.
  • Inverse Distance Weighted (IDW)
    • The Inverse Distance Weighted method is the practical, easy-to-understand interpolator. When you use IDW, you are applying a "one size fits all" assumption to your sample points.
    • IDW works best for dense, evenly-spaced sample point sets. It does not consider any trends in the data, so, for example, if actual surface values change more in the north-south direction than they do in the east-west direction (because of slope, wind, or some other factor), the interpolated surface will average out this bias rather than preserve it.
    • -Source: ESRI Virtual Campus
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  • IDW Dry vs. Wet Season Comparisons
  • IDW Conclusions
    • Using IDW to perform the interpolation showed that reliable data could have been assumed by using the data of the nearest NWS station at Twentynine Palms, CA.
    • This make sense since IDW gives priority to those points closest to it (the Twentynine Palms station is the closest to Old Dale).
  • Other Interpolation Methods Tried
  • Kriging
  • Kriging
    • Kriging is a weighted average technique, except that the weighting formula in Kriging uses much more sophisticated math. Kriging measures distances between all possible pairs of sample points and uses this information to model the spatial autocorrelation for the particular surface you're interpolating.
    • Tailors its calculations to your data by analyzing all the data points to find out how much autocorrelation they exhibit and then factors that information into the weighted average estimation.
    • -Source: ESRI Virtual Campus
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  • Kriging Dry vs. Wet Season Comparison
  • Spline
  • Spline
    • Spline interpolation method fits a flexible surface, as if it were stretching a rubber sheet across all the known point values.
    • Estimates unknown values by bending a surface through known values
    • This stretching effect is useful if you want estimated values that are below the minimum or above the maximum values found in the sample data. This makes the Spline interpolation method good for estimating lows and highs where they are not included in the sample data.
    • -Source: ESRI Virtual Campus
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  • Spline Dry vs. Wet Season Analysis
  • Natural Neighbors
  • Natural Neighbors
    • In Natural Neighbors interpolation, the value of an estimated location is a weighted average of the values of the natural neighbors. The weighting is proportional to the area in the estimation location’s Voronoi polygon that was contributed by each natural neighbor’s polygon.
    • Since the output is a raster, the estimation locations are a regularly-spaced array equal to the number of raster cells.
    • -Source: ESRI Virtual Campus
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  • Natural Neighbors Dry vs. Wet Season
  • Data Sources
  • Coverage/Shapefiles
    • For the states and selected counties of California, Arizona, and Nevada:
      • US Census Bureau Cartographic Boundary Files
        • http://www.census.gov/geo/www/cob/co2000.html#shp
          • Accessed May 9, 2006
  • XY Data For Plotting
    • Old Dale, California location information:
      • Ghosttown.com write-up of Old Dale, CA
        • http://www.ghosttowns.com/states/ca/dale.html
          • Accessed April 25, 2006
    • Lake Havasu City, Arizona NWS Weather Station location information:
      • National Weather Service Telecommunications Operations Center Meteorological Station Information Lookup Page
        • http://weather.noaa.gov/cgi-bin/nsd_lookup.pl?station=KHLU
          • Accessed May 5, 2006
  • California Weather Station Point Locations
    • California Spatial Information Library (CaSIL):
      • http://gis.ca.gov/meta.epl?oid=270
        • Accessed May 9, 2006
  • Average Weather Observation Data
    • Weatherbase.com website
      • http://www.weatherbase.com
        • Accessed April 25, 2006
  • Interpolation Definitions
    • ESRI Virtual Campus “Learning ArcGIS 9 Spatial Analysis -- Module 4: Interpolating Raster Surfaces”
      • http://campus.esri.com/Courses/LearnSA9/index.cfm?c=137&CFID=22817678&CFTOKEN=59523191
        • Accessed May 20, 2006