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Learning Objectives for Module 4
This module explored ways to manipulate the data for the purposes of understanding their spatial relationships. Buffers are drawn to isolate areas of known parameters. Distances around the objects of interest, for example, nests of endangered birds, can help apply appropriate management strategies to that parcel of land. This would be a point-in-polygon type of arrangement. Other useful tools include the union and clip tools, IDW, and Kriging. Spatial interpolation is the estimation of unknown values from known values, and is driven by the concepts expressed in Tobler's Law, which suggests that all places are related to nearby places, and are more related to those that are close then to those that are far removed. Methods by which to express the data were presented in this module including summary stats and charting strategies.
Problem:
Understanding the distributions of features, events, or characteristics across a study area can be facilitated by using a technique that assesses density. This technique calculates a density value for each cell across the surface of the study area allowing us to visualize the distributions of the values.
Analysis Procedure:
Once the ArcMap document is opened, it is appropriate to set the geoprocessing environment and load the data. The Kernel Density tool can be used with varying search radii and on the various layers to create density surfaces across the study area. It is also possible to use selected features to develop the density surfaces by using query expressions.
Results:
Figure 1. Map of night burglaries using the Kernel Density tool and a query expression specific for night time events
Application and Reflection:
Evaluating spatial relationships among features or events in a study area can help better understand the dynamics that influence why and how the characteristics of that area came to be the way they are. These evaluations are made possible via the various tools that cut or expand or join or interpolate that data. In our assessments of the mushrooms in Eno River State Park (see Our Mushroom Hunting), we may wish to know how mushroom sightings of individual varieties spread outward from a known hot spot, perhaps identified from the Hot Spot Analysis tool in the Spatial Statistics (Spatial Statistics) tool set. To do so, we might use IDW, Kriging, or Kernel Density analysis on the sightings of the varieties of interest. These data could be useful to compare, for example, with soil water content values to identify the relative of importance of soil water content on mushroom distribution, or even to show us where to go to collect soil water content information.
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