Lab 5

Goals/Background: The goals of this lab were to efficiently utilize the geoprocessing tools ArcGIS for vector data. Specifically looking at suitable bear management study areas in Marquette county, Michigan, as well as looking at good tourist resort lakes, and interstate pollution hazard zones in Wisconsin. For these three main task many of the common geoprocessing tools were employed (merge, intersect, buffer etc...). We also utilized python scripting, as another option to use these geoprocessing tools. Finally after the maps were created we also had to create a data flow model to depict the steps that we took to reach the final maps in a simple fashion.

Methods: Part 1(Figure 1) entailed finding suitable areas for bear management in Marquette county in Michigan. First the bear locations had to be added from an excel sheet by mapping the X-Y values, then merged with the landcover feature class to create the bear_cover feature class, merging both feature classes together. After the streams were found to be suitable habitat for the bears, so to include this in the management plan a 500 meter buffer was created around the streams, which then was dissolved to delete any internal boundaries. The dissolved layer underwent a select by location with the bear_cover as the target layer, then create layer from selected features resulting in the streams_buffer feature class with the bear locations found within the 500 meter buffer. Landcover then underwent a select by attributes/create layer from selected features to create the landcover_selection feature class, which only included the top three minor_type landcovers for bears. Landcover_selection was intersected after with the Streams_Buffer feature class to create the Suitable_Habitat feature class, which was dissolved to make the final Suitable_Habitat_Dissolve feature class that had the bear locations and suitable habitats that fell within the stream buffer. The DNR_mgmt feature class then had to be intersected with the Suitable_Habitat_Dissolve layer to bring in the rest of the state/data creating the Suitable_Study_dnr class, which was dissolved, to make the Dissolve_Suitable_Final layer. To then eliminate the urban areas from the study/management areas the landcover feature class had to again undergo a select by attributes creating Urban_Areas class, then a buffer around to create Urban_Buffer. Finally the Urban_Buffer and Dissolve_Suitable_Final classes were clipped together to exclude the urban buffer areas from the management area creating the final map product (Figure 2).

Part 2 (Figure 3) was broken up into two parts, the first part was to create a map that showed the lakes around Wisconsin cities that would be good for tourist looking to resort on lakes in Wisconsin. The first part included the buffering of the cities feature class that would pick the lakes that were close to cities, but the lakes as well had to be selected by their attributes to be bigger than 5sq.miles and a layer created from those selected features called Lake_ResortAE. After the two resulting class (WI_cities_buffered, Lake_ResortAE) were created they then were clipped to excise the leftover lakes not found within the buffer of the cities resulting in the final feature class: lakes_resort (Figure 4). The final part to Part 2 (Figure 3) had us creating a multiple ringed buffer around the interstates of Wisconsin, then creating 6 zones of Hazard levels from the multiple rings in the buffer (Figure 5). Python Scripting was used for all of Part 2 instead of the normal ArcToolbox or SQL expression query windows (Figure 6).


Results:


Figure 1: A data flow model showing the steps taken and geoprocessing tools used to create the final map.





Figure 2: Map depicting suitable habitats for bears in the study area in Marquette County, Wisconsin.





Figure 3: Data flow models for both scenarios in Part 2 of Lab 5, showing the utilization of the geoprocessing tools.

Figure 4: Map showing the lakes that would be ideal for tourists looking to resort in Wisconsin.



Figure 5: Map displaying the air pollution hazard zones for Wisconsin Interstates.







Figure 6: Example of Python Scripting used for Part 2.

Sources:

1. ArcMap 10.2.2.(2014) [Software]. ESRI Inc., Redlands, CA. [accessed 4/20,23/2015].

 

2. Michigan Department of Natural Resources (DNR).

 

3. Price, Maribeth. 2014. Mastering ArcGIS. 6th Edition data CD. McGraw Hill.

 

4.  Wilson, Cyril 2012, A comprehensive Lake features for

Wisconsin, Unpublished data. [accessed 4/21/15,4/23/15].





Lab 4

Multiple Criteria Queries

Lab #4

Andrew Evenson

4/3/15

 

 

Introduction: The goal of this lab was to utilize queries and Boolean expressions, which we have been covering in class, and then adding on the concept of multiple criteria queries. Learning this skill is very useful for selecting features with minimal amount of work/steps and Boolean expressions, making us more efficient queries. Then to build on the multiple criteria aspect we worked with spatial and attribute queries during this process, as well as mapping the results of the queries we performed onto maps that already had other data.

 

Methods/Results: The first query involved finding counties with a specific population range and population density per square mile (Figure 1). To do this I first set up the population ranges with the 'AND' expression (to make the results only from that range), and then added the population density with the 'OR' expression.

 



Figure 1: A query box showing the SQL and Boolean expressions used for the multiple criteria query.
 

Then after the query results appeared a layer was created from the selected features from the counties shapefile, after which the answers were obtained from the resulting tables, resulting in the first map (Figure 2)


Figure 2: A map of the United States depicting the counties that met the multiple criteria query.

The second query dealt with selecting multiple states and characteristics based upon them (Figure 3). Which required the use of 'IN' as the operator, then adding the 'AND' operator to finish up the rest of the query.


Figure 3: Query window showing the necessary language to extract the desired information.



After which, I repeated the same steps as the first query of creating a layer from the selected features, resulting in a map of the desired attributes. (Figure 4).

Figure 4: Map of the United States with counties highlighted that matched the multiple criteria query.

The third query was and addendum to the query for question 2, which added in 5 states. This was achieved by taking the original expression from question 2 and adding the 'OR' expression to include the new states and criteria for the query (Figure 5).

Figure 5: Added criteria from the previous query.

After, when creating another new layer, the number of  counties that fulfill the new criteria can be acquired; and a new map of the new results (Figure 6).

Figure 6: Map of the United States showing the additional counties that met the new criteria, which was built on from the previous question.

The fourth query involved the combination of spatial and attribute queries. First to get the correct cities that are within 2 miles of a lake I had to perform a spatial query, setting cities as the target layer. After which the attribute query can be performed to get the final results (Figure 7).

Figure 7: Attribute query performed after the spatial query for question #4.

 After the query was performed a map was again created using the same techniques as described in the prior queries (Figure 8).

Figure 8: Map showing cities that are within 2 miles of a lake.

 

The final query was finding rivers that matched certain criteria (names) to do this the same 'IN' expression was used (Figure 9), just like previous questions dealing with multiple states, after which a map was created showing the rivers that matched the criteria (Figure 10).

Figure 9: SQL of the multiple criteria query.

Figure 10: Map of the multiple criteria results.

 



 

Conclusion: This lab helped employ queries, specifically multiple criteria queries, in an efficient manner for further use, as well the combination of using attribute and spatial queries together to obtain the desired selected features of the data.



Sources
1. Mastering ArcGIS (2014) [Book]. Price, Maribeth. McGraw Hill, New York, NY.
2.  ArcMap 10.2.2.(2014) [Software]. ESRI Inc., Redlands, CA. [accessed 4/2/2015].