GIS I Lab 4: Mini-Final Project

Introduction
This lab was intended to utilize a variety of tools to provide an answer to a geospatial question. The question to be answered was "Where can I live in Aitkin County, Minnesota? I want to live near a park and a hospital, but away from highways and railways". The specific criteria used were: within 10km of a park, at least 2 km away from railways and highways, and 25 km or less from the nearest hospital. The intended audience for this map is other GIS users and anyone looking for a home in Aitkin County, Minnesota.


Data Sources
To meet the criteria for this map, data concerning park land area, hospital locations, railway and highway locations, and the county border of Aitkin county. All data was obtained from the ESRI database server ( Data concerns include: age of the data (and therefore reliability), whether or not "hospitals" includes ER's and general health clinics, and whether or not railways are active or not.

Methods

To begin creating this map, a new file geodatabase was created to store and easily access all feature classes used in the process. For the Study Area locator map, three data layers were added: a Minnesota boundary layer, Minnesota counties layer, and study area layer. To create the study area layer, a query in the Minnesota counties layer was used to create a new layer containing only Aitkin County.

For the Suitable Living Area map, the three layers used above were added as well as these data feature classes: US Hospitals, US railways, US highways, and US parks. The parks layer was given a buffer of 10 kilometers and then dissolved. The park land layer was then erased from this buffer in order to produce a new layer representing living area within 10 km of park land. This layer was then clipped to the Aitkin County layer. The railway and highway layers were merged and clipped to the study area. A buffer of 2 kilometers was then created and dissolved. Since the criteria demanded that suitable living area exclude any land within 2 km of railways and highways, the buffer was erased from the layer representing living area near parks. This layer was then clipped to the Aitkin County layer. Finally, the hospitals layer was buffered to 25 km and then dissolved and clipped to the feature class near parks and away from rails and highways, creating a final result of a suitable living area including all of the requested criteria.

Below is a data flow model as a visual representation of the steps that were taken and the tools that were used.

Results

The map below represents suitable places to live in Aitkin County for a person looking to live near a park and hospital but away from railways and highways. There are four hospitals in Aitkin County to choose from. Much of the county contains park land, so it is not difficult to live near parks. Avoiding highways and railways is more difficult but can be accomplished. There is ample space to find an appropriate living area in Aitkin County even when considering the criterium.

Source: Esri - GIS Mapping Software, Solutions, Services, Map Apps, and Data.

Evaluation
This project was enjoyable because I got to pick the type of map I wanted to create. It was fun to put my skills to use in a way that brought GIS into a real life application. Something that I struggled with was creating the data flow model, however after developing such an elaborate one for this project I feel very comfortable with that skill. If I were to repeat this project, I would choose a different county. Preferably a larger one. I would also explore something different like where to put a golf course or a community pool. I kept my research question simple to ensure that I could properly display the skills that I have learned without making it too challenging and elaborate.

GIS I Lab 3: Vector Analysis with ArcGIS

Goal
To utilize the various geoprocessing tools available in ArcGIS for vector analysis and to run script commands using Python to perform geoprocessing operations

Background
The DNR of Michigan wanted help determining where the most suitable bear habitats are located in Marquette County. This was found using these specific criteria: proximity to streams, the most common type of land cover in which bears are found, all within DNR management areas, and at least 5 km away from urban or built up lands.

Methods
Objective 1: Mapping an Excel file
Black bear locations of Marquette County, Michigan, were downloaded in an excel file containing XY coordinates. Since these data are in a non-spatial database, the coordinates were added as an "event theme" and after they were mapped on the NAD 1983 HARN Michigan GeoRef (Meters) coordinate system. They were then exported as a feature class to be brought into the geodatabase properly.

Objective 2: Determining preferred forest type of black bears
All feature classes from the bear management geodatabase were added to the map. A spatial join was performed between the bear locations layer and the land cover type layer. The MINOR_TYPE field was then summarized in order to find the three land cover types which contained the largest count of bears. These 3 forest types were considered the suitable bear habitats and therefore a criteria in determining the most suitable bear habitats. Using "select by attributes", these 3 forest types were selected and made into a new layer.

Objective 3: Determining correlation between streams and black bear habitation
In order to determine if distance to streams is an important factor in bear habitat selection, a buffer of 500 feet was created around all streams in Marquette County, and then the dissolve tool was used to connect the buffers. Next, the clip tool was used on the streams buffer layer with the bear locations layer to discover if most bears (at least 30%) were located near streams.

Objective 4: Finding suitable habitat (near streams in top 3 forest types)
An intersect was performed to find habitats near streams within the top 3 forest types. This provides the best suitable habitat for bears.

Objective 5: Finding suitable habitat within DNR management land
Once the best suitable habitat was determined, the DNR management land needed to be added to the criterion. A clip was performed to limit the DNR land to the study area being researched. The clip was then dissolved to eliminate the individual divisions inside of the DNR management lands. After this, the intersect tool was used to determine where the best suitable habitats were found within DNR management lands in the study area.

Objective 6: Eliminating areas near urban or built up areas
Urban and built up areas are hazardous to bears, so those needed to be eliminated from the possible study locations. To accomplish this, a spatial query was used in the land cover layer to create a new layer containing only urban and built up areas. In this new layer, a buffer of 5 km was created around all urban and built up areas, and then an erase was performed to eliminate these areas from the most suitable habitat on DNR management land layer. This provided the most suitable study area for bears in Marquette County, Michigan (Figure B).

Objective 7: Generating a data flow model
A data flow model was created including each step that was taken and the tools which were used to accomplish each task (Figure A)

Objective 8: Using Python
A few simple command scripts for geoprocessing tools were run using Python to demonstrate a more efficient way of running tools in ArcGIS (Figure C).

Results
The top 3 suitable forest types for bears were Mixed Forest Land, Forested Wetlands, and Evergreen Forest Land. It was also found that approx.  72% of bears lived within 500 meters of a stream, so streams were designated an important criterion in selecting suitable bear habitats. Figure B shows the most suitable bear habitat locations in Marquette County based on these two criterion, as well as the limitations of being within DNR management land and at least 5 km away from urban or built up lands.

Figures

Figure A. Data Flow Model

Figure B. Map of Suitable Bear Habitat in Marquette County, Michigan

Figure C. Python script

Sources
State of Michigan Open GIS Data:
http://gis.michigan.opendata.arcgis.com/

Landcover is from USGS NLCD:
http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html

DNR management units:
http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm

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

GIS I Lab 2: Downloading data and creating a webmap

Introduction
The purpose of this lab was to demonstrate the downloading and mapping of data from an online service (in this case, the U.S. Census Bureau). This involved understanding how to download the data, convert it into a usable from for ArcGIS software, and then creating and uploading a map as a web map.

Methods
Objective 1: Downloading census data
An Advanced Search was opened in the US Census Bureau Fact Finder website. "People", "Basic Count/Estimate" and "population total" were chosen for the topics. "County 050" and "Wisconsin" were chosen for geography. The data TOTAL POPULATION from the 2010 SF1 Dataset was selected, downloaded, and zipped to a specified folder. The files were then extracted as CSV files and an extra row was removed in order to make the data usable. The file was then saved as an Excel Workbook file to be uploaded into ArcGIS as tabular data.

Objective 2: Downloading shapefile for WI census data 
On the US Census Bureau website, the Wisconsin Counties were highlighted on the map and downloaded as a zipped shapefile. The data was then unzipped.

Objective 3: Joining the data
A blank map was opened in ArcMap and the Layers data frame was renamed Population. The shape file and the TOTAL POPULATION excel worksheet obtained from the US Census Bureau were added to the map. The shapefile attribute table was joined to the population table using the common attribute field "GEO_ID".

Objective 4: Mapping the data
A new field was added in the data table in which the D001 field data (population) was inserted using the field calculator and then named "D001_new" so that it could be mapped quantitatively. The population was then mapped with graduated colors.

Objective 5: Mapping a variable
Another data file was downloaded from the US Census Bureau, this time the data containing information about households in Wisconsin counties. It was unzipped and converted to an excel worksheet file. A new data frame was inserted and the WI county shapefile and household data table were added. The tables were joined by the common attribute "NAME" this time. The percentage of family households data was mapped quantitatively using a graduated colors symbol and normalized by total households per each WI county.

Objective 6: Building the layout
The data frame projection was changed to NAD 1983 Central WI State Plane. Titles, legends, north arrows, and scales were added to each map and positioned to be cartographically pleasing.

Objective 7: Creating the webmap
The 2010 Family Households map and Esri basemap were both eliminated. The remaining map layer was exported as a shapefile and added as a new layer. The old was deleted. This made the join permanent so the a feature service could be created from the ArcMap document and published to ArcGIS online. Once the web map was created, the attributes were configured so that only the County name and Population are visible. Aliases were changed to be more clear and population was transformed to 0 decimal places because people cannot be represented fractionally. The map was then shared to the UWEC Geography and Anthropology organization and is visible to the public (see link below).

Results
Below are the resulting maps created from the data obtained from the U.S. Census Bureau. Left: a map displaying the population density of each county in Wisconsin in 2010 (also found as a webmap in the link below). Trends show a higher concentration of people in the southeast corner of the state, and a low density in the northern half. Right: a map displaying the percentage of households in each county which contain families. There is a lower density of family households in the northern part of the state (a trend seen on both maps), and each county appears to have at least 57% of the households occupied by families.

Web map link: uwec.maps.arcgis.com/home/webmap/viewer.html?webmap=9561a511fec549d7bf601573deeda41e

Sources
U.S. Census Bureau, Esri, ArcGIS online.

GIS I Lab 1: The Confluence Project

Introduction: The purpose of this map collection is to provide a cartographic representation of all relevant information needed for the Confluence Project proposed by UW-Eau Claire and the Eau Claire Regional Arts Center.


Methods:
Civil Divisions Map
To make this map, the feature class containing the digitized proposed site of the Confluence Project was added, as well as the World Imagery base map, the county_boundary feature class, and the civil divisions feature class. Each layer was given a transparency of 50% and different symbol colors were designated for city, town, and village categories. A callout label was placed to identify the proposed site on the map. A legend displaying the proposed site and each type of municipality was inserted along with a scale bar with miles for the units, and a title above the data frame.

Census Boundaries Map
To display the census boundaries, a new data frame was inserted and the proposed site feature class, the World Imagery base map, and both the tracts and block groups feature classes from the City of Eau Claire geodatabase were added. The transparency was set to 50% once again, and the block group data was normalized to population per square mile. Then the legend, title, and scale bar were entered.

PLSS Features Map
In a third data frame the proposed site was displayed on a World Imagery base map with the PLSS Quarter Quarter feature class set as a hollow symbol in order to view the imagery below and the location of the Confluence Project within the Public Land Survey System. The map was completed by including a legend, title, and scale bar.

EC City Parcel Data Map
A fourth data frame was inserted, in which the World Imagery base map was added once again, along with the centerlines, water, parcel area, and proposed site features classes. The water was made blue with 40% transparency, and a bright, hollow symbol was designed for the parcel area. The centerlines were thickened and changed to a bright color. This map was created to demonstrate the locate band reference of the Confluence Project. The legend, title, and scale bar were added to complete the map.

Zoning Map
To create the zoning map, a new data frame was inserted which included the World Imagery base map, as well as the centerlines, proposed site, and zoning class feature classes. The values were grouped together to condense the number of zone classes, and each were given different colors to make them easy to distinguish. To finish the map a title, legend, and scale bar were inserted. The goal of this map is to show the zoning classes surrounding the Confluence Project.

Voting Districts Map
To represent the voting districts, a final data frame was inserted including the World Imagery base map, proposed site, and the voting wards of 2011 feature class. The labels for each ward were made visible using a halo mask, and a title, scale bar, and callout label were added to clarify the location of the proposed site.


Results:

Sources:
http://www.eauclairearts.com/confluence/
http://www.uwec.edu/News/more/confluenceprojectFAQs.htm
http://volumeone.org/news/1/posts/2012/05/15/3134_arts_center
City of Eau Claire and Eau Claire County 2013