Geospatial Analytical Methods - Week 1 Study Guide PDF

**Week 1 Study Guide** **1. Mean and Median Centers** - **Mean Center**: Indicates the geographic center of your data. If your wells are clustered to the south and west, the mean center will be located there. - **Median Center**: The middle value when data is listed in numeric ord...

**Week 1 Study Guide** **1. Mean and Median Centers** - **Mean Center**: Indicates the geographic center of your data. If your wells are clustered to the south and west, the mean center will be located there. - **Median Center**: The middle value when data is listed in numeric order. It is slightly west of the mean center, indicating no extreme outliers. **2. Spatial Statistics** - **Definition**: Analysis of data characteristics across space to identify patterns and relationships. - **Importance**: Helps in selecting appropriate tools for data analysis and avoiding incorrect conclusions. **3. Data Exploration Steps** - Determine data clustering or dispersion, orientation, and center. - Compare mean and median centers to identify skewness or outliers. - Analyze directional distribution using ellipses to understand data spread and orientation. **4. Normal Distribution** - **Characteristics**: Bell-shaped curve, symmetrical data, mean and median are similar. - **Skewness**: Indicates asymmetry in data. Positive skew (tail on right), negative skew (tail on left), bimodal distribution (two peaks). **5. Frequency and Histograms** - **Frequency**: How often a value occurs. - **Histograms**: Graphical representation of data distribution using bars. Helps identify skewness and distribution shape. **6. QQ Plot** - **Purpose**: Compares data distribution to a normal distribution using a reference line. - **Interpretation**: Deviations from the line indicate skewness or non-normal distribution. **7. Semivariogram and Spatial Autocorrelation** - **Semivariogram**: Graphical tool to determine spatial autocorrelation. Shows variation between data values based on distance. - **Spatial Autocorrelation**: Closer points have more similar values. Types: Isotropic (distance only), Anisotropic (distance and direction). **8. Data Variation and Stationarity** - **Stationarity**: Consistent data variation across the study area. - **Example**: Weather monitoring stations should show consistent temperatures unless influenced by external factors. **9. Tessellations and Voronoi Maps** - **Tessellations**: Polygons creating a surface with no overlaps or gaps. - **Voronoi Maps**: Tessellations used to explore spatial variation. Each polygon represents the area closest to a data point. **10. Data Outliers** - **Identification**: Outliers affect data significantly. Techniques include Voronoi maps, histograms, and QQ plots. - **Causes**: Mistakes, unusual occurrences, or shifts in data patterns.