GIS 335 Final Project - Suitable Land-Area for a Real Estate Client

Introduction
This investigation will be focused on Eau Claire County in Wisconsin.  A client recently consulted with me and would like to look at potential areas in Eau Claire County to build a house.  The client wants to buy land, but he is very selective with the land and required specific criteria that must be met before he decides which land to buy in Eau Claire County.  The client stated that he does not want to live within 5 miles of a railroad, and 2 miles of a county highway due to sensitive hearing.  Furthermore, the client has stated he is a devout atheist and does not want to be within 2 miles of any church within Eau Claire County.  Any land that does not fall in these uninhabitable areas is suitable land to the client.  This project will determine what areas in Eau Claire Count meet all the criteria of the client for suitable living.  This specific project is important or future realty because, while it’s highly unlikely that other clients seeking realtor services will look for the same exact criteria of this specific client, similar methods could be used to find suitable land for other clients in real life.  ArcGIS is an extremely valuable tool which has potential to be used in several academic disciplines and industries in the real world.  This project provides substantial evidence that use of ArcGIS could actually be used by realtors as a spatial analysis tool.

Data Sources

To assist in answering this spatial question, the following feature classes were used for this project:

“Esri2013.DBO.rail100k_usadata,” “Esri201.DBO.highways_usadata,” and “Esri2013.DBO.gchurch_usadata.”  Rail100k_usadata is a polyline shapefile made by Esri 2013.  Highways_usadata is a polyline shapefile made by Esri 2013.  Gchurch_usadata is a point shapefile created by Esri 2013. 

There are some concerns with this data and the primary data concern during this project is that some of the data is not as recent as preferred.  The Esri data is only three years old, but there could be potential problems and new features that now exist.  For example, new churches could have been built since 2013. If time constraints were not given for this project, seeking out more recent data would be a priority for project accuracy. 

Methods

A flowchart of the methods used for this lab is displayed above.  Layers are displayed in blue, spatial tools are displayed in yellow, and the resulting output classes from using those spatial tools are displayed in green. 

The first step was to add the states and counties layer from the USA geodatabase in our mgisdata folder.  The next step was to select Eau Claire county using SQL and create a separate layer from that selection.  Then a blank File geodatabase was created in the lab 4 folder to store all the data for this project.  Data was then browsed from the Esri data that the server provided.  After all the Esri feature classes were dragged onto the data view, the clip tool was used to clip all those features of interest. 

To solve the problems of the client, a 5-mile buffer was made around all railroads within Eau Claire County.  Then, another 2-mile buffer was made around all churches and county highways within Eau Claire County.  All three buffers that were created were also dissolved to remove the lines between the polygons that were created from the aforementioned buffers. The union tool was then used to join all 3 buffers to determine all unsuitable land and to make it look more aesthetically pleasing.  Finally, to find the suitable area of the client, the resulting layers were all erased from the county layer.  The leftover area was created into a layer called “suitable area.”  The query (1), clip (3), buffer (3), dissolve (3), union (1), and erase (1) tools were all used for this project.

Results

The final map below is displaying suitable land displayed in green and unsuitable land displayed in a grey colored “100-year flood overlay” which was selected for clarity purposes.  The map also shows county highways in red, county railroads in yellow, and all county churches with a black-cross symbol.  Much of the county land-area is deemed unsuitable because of the City of Eau Claire in the NW part of the county which contains several churches, surrounding county highways, and county railroads that run through the city.  There are plenty of “pockets” of suitable land south of the City of Eau Claire, but these pockets still are in between and surrounded by churches (in spite of being far enough away from the 5 and 2 mile buffers).   The most desirable land-area appears to be in the eastern part of the county, just northeast of the town of Fall Creek and Highway 12 as there are churches sparsely located and a large portion of land without any railroads or highways. 

Evaluation

This study is part of an introductory GIS class and therefore has a limited scope and area of interest. Being asked to repeat the project with less time restraints would allow the option to pursue additional recent data. Also, it would be interesting to use the spatial layers to develop a zoned map where the further an area is from the unsuitable land, the more ideal it is for the client. That type of zoned map would likely require additional tools such as the multiple ring buffer, which was not used in this project.

This project was very valuable and skills were definitely developed in spatial analysis, which is the core part of GIS 335.   This project allowed GIS users to construct a complex methodology to solve a hypothetical problem. The methodology which was employed for this project could easily be applied to similar real-world problems and therefore, this project was very important in growing critical thinking skills.

Sources

 Esri ArcGIS content team (10th edition, 2010-06-30), rail100k_usadata, Provided on geography database, 12/15/1

Esri ArcGIS content team (10th edition, 2010-06-30), Esri2013.DBO.gchurch_usadata, Provided on geography database, 5/13/2016

Esri ArcGIS content team (10th edition, 2010-06-30), ESRI2013.DBOhighways_usadata, Provided on geography database, 5/13/2016                                              

Lab 3: Bear Habitats in Marquette County, Michigan

Goal

The purpose of this project is to introduce and work with geoprocessing tools and scripts within ArcGIS such as union, intersect, overlay, buffer, and dissolve.  

Background

Students were to use bear sighting data points within a given study area in Marquette County, MI.  Students were to determine areas suitable for bear habitat that fall in the following criteria:

-The landcover type must be in one of the three most visited bear habitats: Mixed Forest Lands, Forested Wetlands, or Evergreen Forest Lands.

-The area must be within 500 meters of a stream.

-The area must be within areas that the Michigan DNR has already designated to manage (area of an area). 

-Lastly, the area must be 5km from land uses that are used as urban spaces.

*A second part of this lab was to provide a basic introduction to python using ArcGIS Python window.

Methods

There were 8 objectives to this lab and students first needed to convert excel data into a feature class that is interactive in ArcMap. This was done by first downloading the data and adding it as an ‘XY event theme,’ and then exporting the shapefile data into a new feature class. The result is a new feature class of bear sighting data points that is able to be seen in ArcMap.  

Next, students had to determine which criteria should be used in determining an appropriate site for bear habitats. The first criterion was to use the intersect tool for the bear_locations and landcover feature classes and summarizing the results.  This determined conclusions about the types of environmental spaces bears visit frequently.  The environmental spaces were: Mixed Forest Lands, Forested Wetlands, or Evergreen Forest Lands. The next criterion determined the number of bear habitats to be located near 500m of streams using the buffer tool. The spatial join resulted in a new feature class “Bear_Streams,” and students used the statistic tool to calculate the mean of sightings within the designated area. Then, frequented bear habitats was intersected with "dnr_mgmt."  The intersection of these two features resulted in the total area for suitable bear habitats. By clipping this area to DNR_management areas, a new feature that the DNR may manage was created. Finally, a buffer was created around the urban feature and erased from the DNR management layer to create a final feature that represents all land that the DNR should manage for bears. 

Now having established the criteria necessary for bear habitat, students were to generate a cartographically pleasing data flow model of the respective workflows, as well as a cartographically pleasing map of our results.

Results  

I generated my data flow model by dragging the results of my tool uses to the model flow that ArcGIS gives.  I then manually arranged them by objective.

Map:  Most bears frequented within 500m of streams in the South-Central, North-Central, and Southwest part of the study area.  They tended not to frequent in the Southeastern part of the study area due to urban land-use.  

Sources  

The Michigan Geographic Data Library

GIS 1 Lab 2

Goals and Background: The purpose of this lab was to give me a background in obtaining GIS and other data from an outside source and format it into a way that I can map and analyze in ArcMap. The three main objectives of this lab were to transform a standalone table containing data into an attribute table that is functional to map, gain experience obtaining data from an outside source by navigating the US Census Bureau's website and downloading the data, and creating a map to compare two different variables in Wisconsin counties.


Methods: The first part of this lab involved downloading data and creating two  maps in ArcMap. First, I had to navigate through the US Census Bureau's website.   I first clicked "Topics", expanded "Dataset", and selected "2010 SF1 100% Data".  This gave me Census data from 2010. I wanted to narrow it down even more to give me data by county in Wisconsin so I clicked on "Geographies" and then "County".  I selected Wisconsin and then "All Counties Within Wisconsin" and clicked "Add to your selections".  This completed the search function, so now I could look for the specific data I wanted to find.  The first data set I downloaded was "P1 for Total Population".  I downloaded the data and extracted the files from the zipped folder. I opened the table "DEC_10_SF1_P1" in Excel and saved it as an Excel Workbook.  This put it into a format where I was able to actually use the table in ArcMap.  I then went back to the Census website to download the actual Wisconsin shapefile so I could actually map the data I had. Under the "Geographies" selection, I clicked the "Map" tab and then downloaded that map as a shapefile.  I unzipped that file as well so I could work with it in ArcMap.  All of this downloaded data was then put into my “lab_2” folder.


Then I started working in ArcMap.  I opened to set the default location for all new files. Then I added my Wisconsin shapefile, as well as the P1 table to the data frame. The tables then needed to be joined together, and the mutual field between them was GEO_ID so I right clicked on the shapefile and chose "Join" from my options.  I joined it to the standalone table and then exported the data to create a new shapefile containing the data I needed from the standalone table. I added the new shapefile to the map and then removed the join between the other tables. I ran into trouble trying to map the population data because the field type was a string, so I create a new long integer field in my attribute table and used the field calculator to populate it with the data I needed.  I then opened the symbology tab on that shapefile and under "Quantities" chose "Graduated Color" and mapped my population using a natural breaks (Jenks) method because it appeared to work best.  I then repeated the above workflow with another dataset from the Census, this time being median age by county in Wisconsin.  I followed the same procedures as above in a new map file until I had mapped data of both Population and median age.  I then changed the projections of both of these data frames to be the NAD83 Wisconsin Central State Plane because it seemed like the best way to display my created maps.

After going through all of this, I built a proper layout.  I added titles, scale bars, north arrows, source information, the date, my name, and legends.  I did my best to display all of this in a way that would make it legible and appealing to viewers.  I then exported my constructed maps as a PDF to add to this blogpost.

For the second part of this lab I turned the map with the housing data into a dynamic map online. To do this, I first had to create a feature service through ArcMap.  I signed into my enterprise account and then shared it as a published service to the "My Hosted Services" connection. I then unchecked "Tile Mapping" and checked "Feature Access" to ensure pop ups would be allowed on my web map.
I clicked the "Item Description" tab and entered a summary, description, and tags for my map.  I then clicked "Sharing" and chose to share it with "UW-Eau Claire- Geography and Anthropology".  I clicked the "Analyze" tool and then edited or deleted any of the features that gave errors since they weren't needed.  Then I was able to publish my service on ArcMap.

Then I signed into my ArcGis Online enterprise account through a web browser. I clicked on "My Content" and then saw the feature layer that I was just working with in ArcMap. I chose "Add layer to map". This opened it in an online geobrowser.  I opened the "Configure pop up" window and chose the features I wanted to be displayed in the pop-up windows on the map. I then saved it and shared it with "UW- Eau Claire- Geography and Anthropology".  Below is a screen shot of the final result of that process.
Results:

Sources: http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml

Lab 1 Report

Goal: To prepare base maps for the Confluence Project in a way that  integrates land management, land use, and general administration so the project is clear and manageable from start to finish.

The purpose of this lab was to analyze and map the different kinds of data that will affect the decisions made regarding the Confluence Project here in Eau Claire. Specifically, this lab was focused on the two lots of land that are being considered for the location of the new fine arts building and student housing. The overall goal in the lab was to explore and become familiar with different data sets that are involved in the project and created maps illustrating those data sets.

Methods:  I started out by exploring different types of data sets and answering questions about them. I explored the zoning feature, political features, transportation features, and many others that are related to the Confluence Project. Next, digitized the site for the Confluence Project. I started with a base map of Eau Claire and added the parcel feature class or the different chunks of land that Eau Claire is split into. I then activated the editing tool, used the snapping feature, and this allowed me to trace the two parcels (128 Graham Ave and Haymarket Landing), and then added a fill color to help distinguish them from other parcels in downtown Eau Claire.  Next, I learned about the Public Land Survey system. In other words, it is a way of categorizing each individual parcel of land into categories like township, county or city. Next, I made a legal description of the of the two parcels of land being considered for the project. This included information like the parcel number, street number, street name, owner's name and what the land is used for, industrial, commercial, residential, etc. All these steps were necessary to learn how to display the following 6 maps below.

 Map 1, or Civil Divisions is displaying the areas of cities, towns, and villages in Eau Claire County. I did this by adding the county boundary and then adding the civil divisions data. Then I changed the civil division properties so that cities, towns and villages would be different colors to help distinguish the divisions in this particular map.  Lastly, I added a callout label to label the proposed site in the confluence project area.

Map 2, Census Boundaries was simply showing the boundaries of census areas and I have changed the properties so that the boundaries are different shade of green based on the population density in them.  I left the projected site parcels red, and changed the transparency on the block groups so that the projected site could be easily seen. 

Map 3, or City of EC Parcel Data is displaying all the individual lad parcel contained in the city of Eau Claire. I did this by adding the parcel, centerline or street data, and water data. Then I highlighted just the outside of the parcels so the map is still visible under them. The centerline data highlights the roads in Eau Claire and the water data shows the river.

Map 4, or Zoning shows the different zones of Eau Claire and which zones are used for what, like commercial, industrial, residential, or transportation use. I changed the colors in the symbology tab for each zone to easily distinguish the differences,

Map 5, or Voting districts shows the different voting districts by their ward number. I added the voting districts data and went into the properties and edited the symbols using the halo tool, and made the actual district numbers bigger so they can be displayed accordingly.   

Map 6 or PLSS data just shows the quarters and sections that Eau Claire is split into from the survey service. I put in the Quarter data and I also included the proposed site so you can see what quarter it lies in. I also really enjoyed the lecture about quarters and land surveys given by Professor Strand’s colleague.  It helped me understand the purpose of this map more. 

For lab 1, this was a challenged assignment but very rewarding and I enjoyed piecing everything together. 

Sources: City of Eau Claire and Eau Claire County 2013

• http://www.eauclairewi.gov/departments/public-works/engineering/mapping-services