Module 4 - Vector Analysis

 Vector Analysis lab was divided into two parts. In part one, I learned about geodatabases and the differences between a shapefile and a Feature Class in a geodatabase. I created a new geodatabase and imported Feature Class(es). We also imported mxd file, which is another ESRI ArcGIS data file, to the project and used query expression to select features based on spatial relationship, such as distance, containments, intersection, and adjacent.

In part two of the lab, I used ArcGIS modeling tools, such as buffer, union and erase, to create a map that will highlight potential campground sites in DeSoto National Forest, located in southeastern of Mississippi State. For a site to be considered suitable, it must be within 300 meters of a road for easy vehicle access, 500 meters from a river, and 150 meters from any lakes. Lastly, all proposed sites must avoid conservation areas, as these are designated to protect plants and wildlife.

First, I used the buffer tool to perform a 300 meters buffer on the Roads feature layer, and selected Dissolve All as the Dissolve Type, to dissolve the buffer into a single feature. This made it easy to visualize; otherwise, I would have had multiple buffer borders overlap.

Second, I created a variable distance buffer, 150 meters for lakes and 500 meters for rivers, from the water layer feature class. In order to do that, I created a new field “buffdist” in the water layer attribute table and populated the field with the appropriate attribute; 150 for lakes and 500 for rivers. To achieve that, I used Select by Attribute tool to select the lakes and assigned the value 150 to the selected records. I performed the same process to add 500 to the lakes’ records. Clearing all selection and using the buffer tool from the geoprocessing pane, I performed a new buffer analysis based on the new criteria, using “buffdist” as the field for Distance [value or Field] and Dissolve features using the listed fields for the Dissolve type with the “buffdist” as the Dissolve Field(s).

Subsequently, I performed a Union overlay on both Roads_Buffer and Water_Buffer to create a new layer that will include both buffers, which represents the best places suitable for campground sites. Only places that falls within the two buffers are considered as the best places.

To conduct this analysis, it is necessary to prepare both buffer layers. I created a new field in the attribute table for each layer Roads_Buffer and Water_Buffer. The new field is designated as insd_rbuf for the Roads_Buffer layer and insd_wbuf for the Water_Buffer layer, with both fields populated with the value 1, indicating that the records are located within the buffer. Subsequently, I used the Union analysis tool, with Roads_Buffer and Water_Buffer as inputs, resulting in an output layer named Buffers_Union. Since we are focusing on regions that fall within the Roads and Water buffers, records that show a value of 1 in both insd_rbuf and insd_wbuf fields are identified as potential campground locations. I applied Select by Attribute query to select records with a value of 1 in both fields, then exported the selected records to a new layer. Lastly, I applied the erase tool to the newly created layer to eliminate conservation areas from the layer indicating potential campground sites.

The resulting feature class was classified as a multipart layer, indicating that although the different polygons seemed distinct, they were collectively associated with a single attribute record or a single feature. The Multipart to Singlepart tool was utilized to divide the data into individual features. The final step required the addition of the Area field to the attribute table, followed by the calculation of the area for each site feature in hectares.

For symbology, I utilized Area for the field, selected manual intervals for the method, and organized the data into 5 classes to represent the areas in hectares. I then manually adjusted the values to present whole numbers rather than fractions, making them easier to comprehend. I chose red for the largest features and yellow for the smallest, which prioritizes attention on the most significant potential sites.