The goal of this exercise is to learn about some of the spatial analysis capabilities in ArcGIS and other software packages.   

Part 1: Nearest Neighbor Analysis

Do the exercise called "Nearest Neighbor Analysis" on the course Blackboard site under Assignments. 

Part 2: Raster Analysis, Sampling, and Hawth's Tools in ArcGIS

You will need to know how to use ArcGIS Spatial Analyst to complete this exercise.  If you are not proficient I suggest doing the free ESRI Virtual Campus exercise called "Learning ArcGIS 9 Spatial Analyst."  You'll need to sign up for it by going to the following website and following the directions- http://www.lib.unc.edu/reference/gis/virtual_campus.html. The goal of this exercise is to create new spatial layers that might be useful in predicting some health outcome.

Section 1: Interpolating a Surface

Download the following dataset to practice with these tools- http://www.unc.edu/~emch/gisph/CAdata.zip and unzip it. First use the IDW interpolation tool to interpolate a surface of the NastyWater variable.  Use the mask tool and mask the result to the California boundary area.  Print the resulting map.  Where is the water nastiest in California based on this interpolation?

Section 2: Calculating Densities

Using the CAdata calculate the density using the population field and a 50,000 meter search radius.  Print the map.  Now do the same using the clinic field.  Then using the raster calculator, calculate a map that is the ratio of clinic density to population density.  Are there some areas that don't have a lower ratio than others? 

Section 3: Reclassification and Vector Conversion

Reclassify the population density raster into 3 classes: high, medium, and low based on natural breaks in the data. Then convert the dataset into vector, make a map that labels the different density categories, and print the resulting map.

Section 4: Hawth's Tools

Hawth's Tools http://www.spatialecology.com/htools/index.php are a wonderful set of common spatial analysis functions that ArcGIS can't do out of the box.  First, read through the different tool descriptions at http://www.spatialecology.com/htools/tooldesc.php.  Then turn the Hawth's tools menu on and create a random sample of 100 points within California using the CAdata.  Print the map.  Then choose at least one other Hawth's tool, read about it, and run it using the CAdata or some other data set.  Write a paragraph that explains what you did.  

Part 3: Spatial Statistics in ArcGIS

Go through the free ESRI Virtual Campus exercise called "Understanding Spatial Statistics in ArcGIS 9."  It is free to anyone even though it isn't on the UNC list.  You'll need to sign up for an ESRI Virtual Campus account at http://campus.esri.com.  Download the following dataset to practice with these tools- http://www.unc.edu/~emch/gisph/CAdata.zip.

Section 1: Measuring Geographic Distributions

In ArcGIS under the "Measuring Geographic Distributions" tools of the "Spatial Statistics" toolbox run the following programs using the towns point layer: 1. Central feature, 2. Mean Center (without weighting), 3. Mean Center (weighted by the population field), 4. Directional Distribution (Standard Deviational Ellipse) using 1 SD, 5. Standard Distance using 1 SD.  Make a map or maps that labels all of the features that you created.  Then describe what each one means.

Section 2: Analyzing Spatial Patterns

In ArcGIS under the "Analyzing Spatial Patterns" tools of the "Spatial Statistics" toolbox run the global Moran's I program using the NastyWater variable and print the result.  Is NastyWater spatially autocorrelated in California?  If so, how much and what does that mean?  Calculate the average nearest neighbor distance on towns.  Are the towns clustered or dispersed?  What is the average nearest neighborhood value and Z-score? Calculate the Getis-Ord General G Index on NastyWater.  What is the value and Z-score? Is there high or low clustering?.

Section 3: Mapping Clusters

In ArcGIS under the "Mapping Clusters" tools of the "Spatial Statistics" toolbox run the program called "Hot Spot Analysis: Getis-Ord Gi*" for the NastyWater variable  Use a distance band of 10,000.  The result is a table that has a new field in it called Gi10000.  Map that out with the small numbers (cool spots) in blue points and the large numbers (hot spots) in red.  Does there seem to be spatial clustering of those hot spots? Then calculate the Anselin's Local Moran's I values for the NastyWater variable using the Town layer.  Map the local Moran's I value using red and blue?  Where is there high local spatial autocorrelation of NastyWater and what does that mean?

Lab Deliverable Summary: Print out all of the outputs, put your name on them, and give them to the instructor. They include: Part 1: printout of results of nearest neighbor analysis and paragraph describing distribution. Part 2: a map of the interpolated nasty water surface and a short description of the spatial pattern, a raster map of clinic density/ population density and short description of the spatial distribution, a reclassified vector map that labels the different density categories, two Hawth's tools map and paragraph explaining the second tool analysis. In Part 2, organize the deliverables by the four sections that are listed.  Part 3: map or maps showing the 3 geographic distribution summary statistics and a description of what each means, Moran's I result printout and description of what the result means, average nearest neighbor calculation result and description of what it means, Getis-Ord General G Index result and description of what it means. In Part 3, also organize the deliverables by the three sections that are listed.