Lab 3

Introduction:
The goal of this lab was to find the ideal location to build an environmental-friendly, nature-oriented grade school in Waukesha County named Nature View Grade School. This location was to be within one mile of a lake, within 1.5 miles of a forest and 1000 feet or closer to a stream so that field trips to natural areas could be created easily and cost-effectively. Additionally, this school was to be placed in block group with a high percentage of minorities, in order to inspire groups people who are underrepresented in the STEM area. The map created for this project is intended for investors, stakeholders, county planners, leaders and interested parents.

Data Sources:
The data was retrieved from the Esri 2013 database from the University of Wisconsin - Eau Claire server. To find the ideal location for Nature View the layer files of Waukesha County, lakes, rivers, block groups and state parks and forests. From the USA database in the mgisdata folder that was downloaded for class the cities layer was added. One problem maybe be that the projections of each layer may not be the same, since they are coming from two different sources. Also, it is of concern that the block groups population is from 2010, so the data may be outdated, which may affect where to put the school.

Methods:
In order to place Nature View near a forest or lake I needed to map all the lakes and forests in Waukesha county. So the lakes and forests were clipped so that only the lakes and forests/parks in Waukesha county displayed on the map. Next forests were queried in the forests/parks layer and a new layer was created labeled forests and there happens to be only one state forest in Waukesha county; the Kettle Moraine. A 1-mile buffer was created around the lakes feature and a 1.5-mile buffer around the forests. These two buffers were intersected to create an ideal nature area for Nature View, however, the lakes had to be erased from the nature area, because part of the nature area was in a lake and obviously a school can’t be built in a lake. In order to target an area with a relatively large amount of minorities (Waukesha is mostly white) the block group layer was displayed and clipped for just Waukesha county. A minority field was created in the block group attribute table and the percentage of minorities was calculated for every block group. Next the block group was clipped so that only the block groups inside of the designated nature area shown. From there, the block group with the largest percentage of minorities was determined, which led to Preferred Location B. Preferred Location B fit all of the project criteria except for the river criteria. To see if there was a river in this location the rivers layer was clipped for Waukesha county and a buffer of 1,000 feet was added. There was a river in Preferred Location B! So by intersecting the river buffer and Preferred Location B, Preferred Location A was created.

Results:
For this project, two locations were created to allow for more options. It was determined that the criteria of being within 1,000 feet of a river wasn’t completely necessary, so in the case that a river was not in the nature area the school could still be built. The preferred locations are within 1 mile of the Kettle Moraine State Forest (Lapham Peak Unit). This is an especially unique forest area, because Lapham peak is the highest point in Waukesha County and a 45-foot observation tower sits atop its peak, which will provide great views and activities for students. A downside is that Lapham peak is already very popular and many schools already use the forest for field trips often. The preferred locations are located within 1.5 miles of Nagawicka Lake, which could be used for students to study aquatic habitats. Preferred Location B is the part of the designated nature area that lies within block group 55, which has the highest percentage of minorities with 18 %. Preferred Location A lies within Preferred Location B, but it is within 1,000 feet of a river. If building Nature View within 1,000 feet of the river proves to be dangerous for the structure of the building or to the health of the river, then the school will be built in Preferred Location B.

Evaluation:

I thought that this was a fun project, but I struggled to come up with an idea at the beginning. Looking through the DNR database I noticed that they had county and national forests, but not state forests and I was looking specifically for the Kettle Moraine Forest since I live right next to eat. Luckily, the Esri 2013 database contained state forests in its State Parks and Forests feature class. After coming up with the criteria for the location of the school I realized that there was only one forest (with three units) in Waukesha County, so I’m glad that there were some lakes near it. If I were to repeat the project, I would spend less time coming up with an idea and more time making a more professional-looking map.

Lab 2

Goals:

The goal of this lab was to determine a suitable habitat for bears in Marquette County, Michigan using ArcGIS and also use Python coding within ArcGIS.

Background:

The DNR is looking to create a management plan for bears in Marquette County, Michigan and I was asked to map the DNR lands that are within suitable habitats for bears and are at least 5 kilometers away from urban areas.

Method:

Objective 1:

The first task of this lab was to plot the location of bears in the study area using a table provided. By simply adding the XY Data and selecting the coordinate system NAD 1983 HARN Michigan GeoRef (Meters) a feature class was created and added to the map.

Objective 2:

Next, the most common landcover that the bears were found in were determined by changing the landcover layer symbology to a unique category map. Once the different land covers were revealed the intersect tool was used to spatially join the landcover layer and the bear_location layer to form the bear_cover layer. In the bear_cover layer the top 3 land covers (Evergreen Forest Land, Forested Wetlands and Mixed Forest Land) that bears were found in were determined through summarization.

Objective 3:

Knowing that bears tend to live near streams, it was important to determine how many bears were found near a stream. By creating a 500 meter buffer using the stream layer a new layer, stream_buffer, was created. Using the clip tool any bears located outside of 500 meters of a stream was excluded from the data in the bears_streams_clip layer. Through this layer is was determined that 72% of the located bears were found near a stream.

Objective 4:

By using the information found from objective 2 and 3 a suitable habitat for bears was determined to be in one of the top 3 land covers and within 500 meters of a stream. First the top three land covers were queried in the land_covers layer. Those land covers were intersected with stream_buffer to create the suitable_habitat layer. The dissolve tool was used to eliminate internal boundaries within the suitable_habitat layer.

Objective 5:

In order to find all of the DNR land that lies within the suitable_habitat layer the clip tool was used, which created the dnr_mgmt_suitable_habitat layer. To rid this layer of the internal boundaries that divides the DNR land into units the dissolve tool was used.

Objective 6:

The next task involved removing any suitable habitat data that was within 5 kilometers of Urban or Built Up land. The Urban land had to be queried in the landcover layer. A 5 kilometer buffer was created around the Urban or Built Up land and then that area was erased from the dnr_mgmt_suitable_habitat layer to create the dnr_mgmt_suitable_outside_urban layer. This layer displays the specific areas that the DNR manages, that is 5 kilometers away from any urban areas and is a suitable habitat for bears.

Objective 7:

The task of this objective was to create a cartographically pleasing map of the information collected throughout the other objectives and a data flow model to show the workflow of the lab.

Objective 8:

For the task python coding was used to perform simple functions. First a code was written to create a buffer of one kilometer around every stream. Next a code was written to intersect the new stream_buf layer with the suitable_habitat layer. Lastly, code was written to erase the urban buffer away from the suitable_habitat layer. 

Results:

Objective 7:

Figure 1 is the map of the study area in Marquette County, Michigan that includes all of the layers that were created in the first six objectives. Figure 2 are the data flow models that show how each layer was found.

Figure 1.

Figure 2.

Objective 8:

Below is the python script that shows how the last few layers were created.

First a one kilometer buffer was created around the streams layer.

>>> import arcpy

>>> arcpy.Buffer_analysis("Streams", "streams_buf", "1 kilometer", "FULL", "ROUND", "ALL")

<Result 'H:\\Documents\\ArcGIS\\Default.gdb\\streams_buf'>

Next the buffered streams layer and the suitable habitat layer were intersected. 

>>> arcpy.Intersect_analysis(["streams_buf","suitable_habitat"], "land_stream")

<Result 'H:\\Documents\\ArcGIS\\Default.gdb\\land_stream'>

Lastly the urban buffer was erased form the suitable habitat away form urban areas layer.

>>> arcpy.Erase_analysis("urban_buffer","Suitable Habitat Away From Urban Areas")

<Result 'H:\\Documents\\ArcGIS\\Default.gdb\\urban_buffer_Erase'>

Sources:

All data were downloaded from the Stat of Michigan Open GIS data.

http://gis.michigan.opendata.arcgis.com/ 

Landcover from USGS NLCD

http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html 

DNR management units from the Michigan DNR
http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_uni ts.htm

Streams
http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html 

Lab 1

Introduction:

The goal of this lab was to download and use data from the US Census Bureau in order to create a map and also create a web map. These tasks were achieved through seven objectives.

Methods:

Objective One: Download 2010 Census Data

In order to find specific data an “Advanced Search” was used on the US Census Fact Finder website.  In the “Topics” category People, Basic Count/Estimate and Population total were selected. In the “Geography” category County 050 and Wisconsin/all counties in Wisconsin were chosen. The data TOTAL POPULATION from the 2010 SF1 dataset was selected and downloaded into a personal folder labeled LAB2. The files were extracted as CSV files, however an extra row had to be deleted in the excel file as to not disrupt the rest of the data. That file was saved as an Excel Workbook file and was uploaded into ArcGIS as TABULAR data.

Objective Two: Download the shapefile for the WI census data

On the Census Bureau website, the counties in Wisconsin were still selected, so they were downloaded and saved as a shapefile(.zip) in order to obtain spatial data on Wisconsin.

Objective Three: Join the data together

A blank map was opened in ArcMap and the Layers data frame was labeled Population. The shape file and the TOTAL POPULATION excel worksheet that were downloaded from the Census Bureau were added to the map. Then the attribute tables of the shape file and the excel worksheet were joined using “GEO_ID” as the common attribute field.

Objective Four: Mapping the data

A new field was added to the joined attribute table. Using the field calculator, the D001 field data (population) was added to the new field as D001new and as a Double field type so that it could be mapped quantitatively. The data was then added to the map using graduated colors.

Objective Five: Mapping another variable

Back on the US Census Bureau website another variable was selected. Going through the same process described in Objective One “Age & Sex” and “All Counties within Wisconsin” were selected and the tabular data for “Median Age by Sex” 2010 SF1 100% Data were downloaded. After deleting an unwanted row and saving the file as an Excel Workbook and the shape file of the counties in Wisconsin data were added to ArcMap under a new data frame labeled “Median Age by Sex (Male)”. The attribute tables were joined and a new field was created using the data from D003 and was labeled D003new so that the data could be mapped quantitatively. D003 represents the median age of the male population in each county. The symbology was changed to graduated blue colors.

Objective 6: Building the layout

The coordinate system for both data frames were changed to NAD 1983 Wisconsin TM (US Feet) in order to lessen map distortion of Wisconsin. Basic map elements such as the title, legend, north arrow, scale, source and author were added to the map. The maps were saved and the map created in Objective Five was deleted, because it was no longer need for Objective Seven.

Objective 7: Creating a web map

In order to create a web map ArcGIS Online for Organizations was accessed by signing into the University of Wisconsin Geography & Anthropology Organization account using my own UWEC username and password. Once logged in, the map was published as a service under the UW-Eau Claire – Geography and Anthropology connection. The service was named Wisconsin_Demographic_Information_Kleinschmidt. The capabilities were enabled as Feature Access, the item description was added and the service was shared to UW-Eau Claire – Geography and Anthropology. After analyzing the map and eliminating any errors the map was published!

Next, in the ArcGIS website after signing in again and navigating to the home page of the UWEC Geography & Anthropology account the feature layer that was just created was found under the My Content tab. After viewing this layer, the previously-created map appears. Next the Population and County names were configured and saved as pop-ups. Now when a county in Wisconsin is clicked the population in each county and the county name appears. The map was saved and then shared.

Results

This lab produced two maps. The first map contains two maps of Wisconsin for comparison. The first image of Wisconsin (left) shows the population of Wisconsin by county.

 Through analyses of this map, one can conclude that the counties with the largest population are located in the southern part of the state, with Dane, Waukesha and Milwaukee counties appearing to contain the largest populations. There appears to be less population in the northern part of the state.

This second map (right) displays the median age of Wisconsin’s male population by county. By analyzing this map one can tell that the median age of males is higher in Northern Wisconsin. By comparing these to maps one can conclude that the median age of males is lower where there is a higher population. For example, Milwaukee county is in the highest population range in the state, but also is in the lowest median age of males range in the state.

The final map is a web map of the previously-made Population by County map of Wisconsin.

With the handy pop-up tool, the name and population of each county can be seen by simply clicking on it!

Sources:

http://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t
http://www.arcgis.com/home