Exploring Spatial Patterns in Your Data PDF

EXPLORING SPATIAL PATTERNS IN YOUR DATA OBJECTIVES  Learn how to examine your data using the Geostatistical Analysis tools in ArcMap.  Learn how to use descriptive statistics in ArcMap and Geoda to analyze data.  Be able to identify Geostatistical Analysis tools that can be used for furthe...

EXPLORING SPATIAL PATTERNS IN YOUR DATA OBJECTIVES  Learn how to examine your data using the Geostatistical Analysis tools in ArcMap.  Learn how to use descriptive statistics in ArcMap and Geoda to analyze data.  Be able to identify Geostatistical Analysis tools that can be used for further analysis. WHY EXPLORE YOUR DATA?  It allows you to better select an appropriate tool to analyze your data.  If you skip exploring your data, you may miss key information about it that may lead to incorrect conclusions and decisions. GEODA VS. ARCMAP  Geoda – free, open-source, simple, software specifically for statistical analysis  ArcMap – proprietary, GIS software that can perform statistical analysis along with hundreds of other analyses GEODA VS. ARCMAP  With ArcMap you can view several data layers at once. In Geoda, you view only one data layer.  Some tools are found in both programs, while some are found in only one. EXPLORE THE LOCATION OF YOUR DATA EXPLORE THE LOCATION OF YOUR DATA  Explore:  size of the study area  mean  median  direction data are oriented  You will see where data are clustered relative to the rest of the data. MEAN CENTER  The geographic center for a set of features.  Constructed from the average x and y values for the input feature centroids (middle points, if input features are polygons). MEDIAN CENTER  Median Center is robust to outliers.  Uses an algorithm to find the point that minimizes travel from it to all other features in the dataset.  At each step (t) in the algorithm, a candidate Median Center is found (Xt, Yt) and refined until it represents the location that minimizes Euclidian Distance d to all features (i) in the dataset. DIRECTION DISTRIBUTION (STANDARD DEVIATIONAL ELLIPSE)  Standard deviational ellipses summarize the spatial characteristics of geographic features: central tendency, dispersion, and directional trends.  The ellipse allows you to see if the distribution of features is elongated and hence has a particular orientation.  When the underlying spatial pattern of features is concentrated in the center with fewer features toward the periphery (a spatial normal distribution),  a one standard deviation ellipse polygon will cover approximately 68 percent of the features  two standard deviations will contain approximately 95 percent of the features  three standard deviations will cover approximately 99 percent of the features EXPLORE THE VALUES OF YOUR DATA NORMAL DISTRIBUTION  Some analysis tools assume a normal distribution:  Mean and median are similar  Data are symmetrical DATA FREQUENCY USING HISTOGRAMS DATA DISTRIBUTION USING A QQ PLOT ManyAcharacteristics normally Notdistributed normal of a normal dataset dataset A normal QQ plot shows the relationship of your data to a normal distribution line. BOX PLOT  Displays the median and interquartile range (IQ) (25%-75%)  Hinge = multiple of interquartile range MAPS  For examining data values and frequencies:  Quantile Map  Natural breaks  Equal intervals  For finding outliers:  Percentile Map  Box Map  Standard Deviation Map QUANTILE MAP  Displays the distribution of values in categories with an equal number of observations in each category. EQUAL INTERVAL MAP  Sets the value ranges in each category equal in size.  The entire range of data values is divided equally into however many categories have been chosen. NATURAL BREAKS MAP  Seeks to reduce the variance within classes and maximize the variance between classes OTHER EXPLORATORY METHODS  Scatter Plot (2 variables)  Parallel coordinate plot (A pattern of lines is drawn that connects the coordinates of each observation across the variables on parallel x-axes.) DETECT OUTLIERS OUTLIERS  Outliers can reveal mistakes, unusual occurrences, and shift points in data patterns (a valley in a mountain range).  You should use more than one method to find outliers because some techniques will only highlight data values near the two ends of your range. PERCENTILE MAP  Groups ranked data into 6 categories  Lowest and highest 1% are potential outliers BOX MAP  Groups data into 4 categories, plus 2 outlier categories at both ends  Data are outliers if they are 1.5 or 3 times the IQ.  Detects outliers with more certainty than a percentile map STANDARD DEVIATION MAP  Displays data 3 standard deviations above and below the mean.  As a parametric map, it is sensitive to outliers. SEMIVARIOGRAM CLOUD  When points closer together have greater differences in their values, this may indicate an outlier in the data.  The selected points may be outliers. VORONOI MAP The gray polygons may be outliers.  Cluster Voronoi maps show spatial outliers in your data; simple Voronoi maps can pinpoint data values that are many class breaks removed from surrounding polygons. HISTOGRAM  Values in the last bars to the left or right, if far removed from the adjacent values, may indicate outliers. NORMAL QQ PLOT  Values at the tails of a normal QQ plot can also be outliers. This can happen when the tail values do not fall along the reference line. BOXPLOT  Points outside the hinges (represented by the black, horizontal lines), maybe outliers. EXPLORE SPATIAL RELATIONSHIPS IN YOUR DATA SPATIAL AUTOCORRELATION  Everything is related, but objects closer together are more related than objects farther apart.  Explore using a semivariogram graph or cloud  Can also be explored using Moran’s I and Getis-Ord G statistics Height (sill) = variation between data values. Range = distance between points at which the semivariogram flattens out. As the range increase, height should increase, since points further away from each other are not as related, so there should be more variation. If a semivariogram is a horizontal line, there is no spatial autocorrelation. VARIATION IN YOUR DATA  Many spatial statistics analysis techniques assume your data are stationary, meaning the relationship between two points and their values depends on the distance between them, not their exact location.  Explore variation using a Voronoi map.  A Voronoi map is created by defining Thiessen polygons around each point in your dataset.  Any location inside a polygon represents the area closer to that data point than to any other data point.  This allows you to explore the variation of each sample point based on its relationship to surrounding sample points. A SIMPLE VORONOI MAP Green = little local variation Orange and Red = greater local variation  A simple Voronoi map shows the data value at each location. The map is symbolized using a geometrical interval classification. This will show the variation in data values across your entire dataset. TYPES OF VORONOI MAPS  Simple: The value assigned to a polygon is the value recorded at the sample point within that polygon.  Mean: The value assigned to a polygon is the mean value that is calculated from the polygon and its neighbors.  Mode: All polygons are categorized using five class intervals. The value assigned to a polygon is the mode (most frequently occurring class) of the polygon and its neighbors.  Cluster: All polygons are categorized using five class intervals. If the class interval of a polygon is different from each of its neighbors, the polygon is colored gray and put into a sixth class to distinguish it from its neighbors.  Entropy: All polygons are categorized using five classes based on a natural grouping of data values (smart quantiles). The value assigned to a polygon is the entropy that is calculated from the polygon and its neighbors. Entropy = - Σ (pi * Log pi ), EXPLORE TRENDS IN YOUR DATA TREND ANALYSIS  You can use the trend analysis tool in Arcmap to visually compare the trend lines with any patterns in your data.  When exploring trends, your data locations are mapped along the x- and y-axes. The values of each data location are mapped as height (z-axis).  Trends are analyzed based on direction and on the order of the line that fits the trend. The trend line is a mathematical function, or polynomial, that describes the variation in the data.  You can determine whether the order of the polynomial fits your data based on the shape created by the line. A second-order polynomial will appear as an upward or a downward curve (known as a parabola). These polynomials show a clear curve, indicating a second-order trend in the data. SELECTING AN ANALYSIS TECHNIQUE  Each of the following techniques are types of interpolation. Interpolation creates surfaces based on spatially continuous data.  Each surface uses the values and locations of your points to create (or interpolate) the values for the remaining points in the surface. GEOSTATISTICAL INTERPOLATION  Creates surfaces using the relationships between your data locations and their values.  Predicts values based on your existing data.  Assumptions:  Data is not clustered. (Simple kriging technique has a declustering option.)  Data is normally distributed. (Transformation options are available.)  Data is stationary (no local variation).  Data is autocorrelated.  Data has no local trends. (You can remove trends from data as part of the interpolation process. ) GLOBAL DETERMINISTIC INTERPOLATION  Creates surfaces using the existing values at each location.  Uses your entire dataset to create your surface.  Assumptions:  Outliers have been removed from the data.  Global trends exist in the data. LOCAL DETERMINISTIC INTERPOLATION  Uses several subsets, or neighborhoods, within an entire dataset to create the different components of the surface.  Assumption:  Data is normally distributed. INVERSE DISTANCE WEIGHTED INTERPOLATION (IDW)  A type of local deterministic interpolation.  Assumptions:  Data is not clustered.  Data is autocorrelated. OTHER SPATIAL STATISTICAL TESTS  Tests for spatial autocorrelation  Getis-Ord General G and Global Moran’s I (to determine overall clustering and dispersion of values)  Hot Spot Analysis (Getis-Ord Gi*) and Anselin’s Local Moran’s I (to determine specific clusters of high and low values)  Regression  Used to evaluate relationships between two or more feature attributes. Are location, crime rates, racial make- up, and income related to housing values in a census tract?