Final Project

Goals and Background: The goal of this project was to apply my geospatial skills I have learned in the semester to a real life problem. I asked the question, where should we look to find a place that we can study high permeability soil while being near an interstate for ease of travel and near a hospital so that a member of our team could be rushed to the hospital if he or she needed medical attention for a condition that could possibly necessitate it. I used various tools that I had learned in the semester to answer this question and produce a map showing the areas meeting my criteria.

Methods: To solve this question I used data from ESRI and from the Wisconsin DNR that was stored on my university's servers. All data I found I checked the appropriate scales that they could be used at in the meta data, making sure that they would be appropriate to display at the scale I would be mapping at. I found datasets of counties, then on top of these overlaid data of hospitals, interstates, and soils. With the counties layer, I selected the counties by attributes that were in Wisconsin, then created a new layer from the selection. I then used the these counties to clip the hospitals and interstates layers down to only Wisconsin. I then selected by attributes the high permeability soils and made a new layer from the selection. This layer I then intersected with a 5 mile buffer I made from the hospital layer. This new layer I then intersected with a 10 mile buffer I made from the interstate layer. This final layer took into consideration all of my criteria and is what I displayed on my final map. Below (figure 1) is the flow chart I made for this geoprocessing.

Figure 1

Results: The following map shows the areas of interest that one could study at given my criteria. 

Sources: 

ESRI:

Esri2013.DBO.USAData

WI DNR:

Widnr2014.dbo.gcsm_soil_characteristics

GIS 1 Lab 5

Goals and Background: The goal of this lab was to learn how to apply various vector geoprocessing methods and tools. In order to do this we applied our skills regarding the various methods and tools to determine the best habitats for bears in Marquette County, Michigan. The second part of this lab was also geared toward learning how to apply geoprocessing methods, but through the use of Python scripting in ArcGIS. We also practiced making data flow models for all of our processing work. Finally, after geoprocessing and data flow modeling, we produced cartographically pleasing maps of our final processed data.

Methods: 

Part 1: Bear habitat suitability modeling

For part 1, I were tasked with finding suitable bear habitat that the DNR could manage meeting certain criteria that was supplied. The bear habitat had to be within 500 meters of a stream, in the three top land cover types I found that the tracked bears' locations were in, and in DNR managed lands that were at least 5 km away from urban or built up areas. All files created through geoprocessing or other techniques were stored in an geoprocessing output folder, and all files were supplied by my professor.

I was first supplied with an excel table with x and y coordinates of tracked bears in the study area. This table I right clicked on in ArcCatalog, selecting create feature class from XY table. Given the projection that these XY coordinates were collected in, I was able to select the correct coordinate system in the "Create Feature Class From XY Table" window, and create the feature class. I named this feature class bear_locations.

Next, we added all feature classes from the "bear_management_area" feature dataset, ordered their layers logically, and symbolized the landcover layer so that each landcover classification (the minor type field) was displayed with a unique color. I then intersected the "bear_locations" and landcover feature classes to create a new feature class named "bear_covertype". In order to find out the top three landcover types bears were located in, I summarized the minor type field and observed the counts.

Next to find out if being near a stream was a necessary criteria to include in finding areas that were bear habitats, I found the percentage of bears near streams. To do this I ran the buffer tool creating a 500 m buffer around streams that was completely dissolved named "500m_buffer", then intersected it with my bear locations feature class to create the feature class "bears_buffer". Finding that a significant majority of bears were included in this buffer zone in my study area, I concluded that this was a important criteria.

Next I found the area that fit all of the criteria that I explained in the first paragraph of this methods section. To do this I first created a layer of the 3 top cover types where bears were located that I found earlier. To do this I first selcted by attributes these three land cover types in the landcover feature class, then used the "create new layer from selected features" option after right clicking on the layer in the "Table Of Contents." I named this new layer "landcover_suitable" This layer I intersected with the "500m_buffer" feature class I explained earlier to make the land_suitable layer. This layer I then dissolved, in order to erase the arbitrary internal boundries, creating the feature class, "land_suitable_dissolve. I then intersected this layer with the "dnr_mgmt" (areas of DNR management within the study area) feature class to create the "suit_dnr" feature class which I then dissolved in order to create the "suit_dnr_dissolve" feature class. This feature class was all of the areas of DNR bear management that I wanted but still included areas near urban and built-up areas.

In order to get rid of the areas, and the areas within a 5 km buffer of them I first selected these areas by attributes, using the condition "MAJOR_CODE" = 1 in the landcover layer. I then created a new layer using the "create new layer from selected features" option after right clicking on the layer in the "Table Of Contents." This layer I created I then sent to the buffer tool, creating a 5 km buffer around it. I named this buffer urbanbuiltup_buffer. This buffer layer I then used with the suit_dnr_dissolve layer and the erase (analysis) tool in order to create my final area without the urban and built-up areas in it.

My final cartographic work using this data (map 1), as well as the data flow model (flow chat 1) that I created with this data flow model I have been describing can be seen under results below.

Part 2: Introduction to python scripting

All code written in this section was saved via a right click on the python scripting window to a python output folder, for later analysis. Work and scratch spaces in the options window under geoprocessing and environments were also set to this folder.

Section 1

This section tasked me with finding areas of Wisconsin that would be suitable for the establishment of resorts. The criteria was that the resort should be created not more than 10 miles from a city, and in the precinct of a lake that is above 5 square miles in area.

In a new map in ArcMap I opened the python window after adding the "WI_Cities," "Interstates," "Lakes," and "Counties' features. I then typed the following in order to create a 10 mile buffer around cities:

After creating this buffer, I found the lakes that were greater than 5 square miles, then created a new layer from these selected lakes using the following code:

Next, I  clipped the areas of the lakes not within 5 miles of a city using the two feature classes that I made earlier in this section using the following code:

I then created a cartographically pleasing map of these lakes shown in the results section below (map 2.1).

Section 2

This section asked us to create a multiple ring buffer zone near interstates to show where air polution would most likely be highest and lowest near the interstate. I used the code below to create this multiple ring buffer zone, then right clicked on the buffer layer in the "Table of Contents," clicked on properties, then clicked on symbology, finally selecting a unique colors symbolization on the distance field and assigning a monochromatic color scheme, then switching the direction of it so it would get lighter as it went away from the interstate. The map I created is in the results section below (map 2.2). I also created a flow chart for both sections 1 and 2 of part 2 of this lab which are also below.

Results:

Map 1

Flow Chart 1

Map 2.1

Map 2.2

Flow Chart 2

Sources

Price, M. (2016). Mastering ArcGIS (Seventh ed., pp. 9-378). New York, NY: McGraw-Hill.

Data used was given to me in a zip file and downloaded to my computer.  Available upon request with the permission of my instructor. This data came from ESRI, the Michigan DNR, and Lake features are from Wilson, Cyril 2012, A comprehensive Lake features for Wisconsin, Unpublished data.