1 / 16

What’s the Point? Working with 0-D Spatial Data in ArcGIS

What’s the Point? Working with 0-D Spatial Data in ArcGIS. Advanced GIS Workshop April 28-29, 2012 Antioch University New England Chris Brehme Keene State College. Getting Centered: Measures of Centrality. Central Feature Most centrally located of existing points

halia
Download Presentation

What’s the Point? Working with 0-D Spatial Data in ArcGIS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. What’s the Point?Working with 0-D Spatial Data in ArcGIS Advanced GIS Workshop April 28-29, 2012 Antioch University New England Chris Brehme Keene State College

  2. Getting Centered: Measures of Centrality • Central Feature • Most centrally located of existing points • Mean Center or Centroid • Sum the X values and Y values; average of each = X,Y of centroid • Weighted Centroid • Same as centroid, except 1 or more values are weighted • Median Center or Manhattan Median • Evenly divide the points N & S with a line, and evenly divide the points E & W with a line; intersection of lines = median center

  3. Dispersion Analysis • Standard Distance • Analogous to standard deviation • Represented graphically as circles on a 2-D scatter plot • Standard Deviational Ellipse • Captures Directional Bias • Measures Dispersion along 2 axes of a point distribution • Calculate Home Range of an animal

  4. Point Pattern Analysis Analysis of spatial properties of points rather than a single summary measure (e.g. centroid, etc.) Two primary approaches: • Quadrat Analysis • Point Density approach • based on observing the frequency distribution or density of points within a set of grid squares. • Nearest Neighbor Analysis • Point Interaction Approach • based on distances of points from one another

  5. Quadrat Analysis in ArcGIS Generate Random Points Create Fishnet Spatial Join to Count Points in each Quadrat Map Point Density

  6. Nearest Neighbor Index • Advantages • takes into account distance • No quadrat size problem to be concerned with • Disadvantages • highly dependent on the size and shape of study area • based on only the mean distance • Doesn’t incorporate local variations • Based on point location only and doesn’t incorporate magnitude of phenomena at that point

  7. Output of ArcGIS Nearest Neighbor Tool:

  8. Other Measures • Point Distance • Calculate distance from one set of points to another

  9. Other Measures Spatial Autocorrelation Measures: - Join Count Statistic - Moran’s I - Geary’s C ratio - General (Getis-Ord) G

  10. Interpolation: Thiessen Polygons Points connected with (invisible) lines Polygon edges are drawn perpendicular to these Image from GISCommons.org

  11. Interpolation: IDW Image from Paul Bolstad, GIS Fundamentals 3rd Edition, 2010

  12. IDW in ArcGIS Z-value field (value to estimate) Output cell size (the size of the grid cell) Power (higher values emphasize nearer points) Search Radius (could lead to No Data values if no points are within the radius) # of Points (interacts with search radius option)

  13. Try it Yourself! Point Pattern Analysis

More Related