Lab 4: Final Project

Introduction: The research question I proposed for this final project was "Where should I buy a house in Outagamie County, Wisconsin?". This is the county that I grew up in and the data sets that I chose were based off of where I would want my house to be located if I was looking to start a family. The objectives for this project were to take the geoprocessing skills that I have learned over the course of this semester and apply them in several different ways as a means of answering my spatial question. There were certain parameters that had to be met such as 1) at least four tools had to be used (excluding clip, query, add field, project, and project define), 2) ideally, the area of interest (AOI) was to be at the county level, and 3) existing data and a minimum of three different spatial data layers (excluding the AOI) had to be used. We were also strongly encouraged to use the Mastering ArcGIS data, data provided by Esri, and/or data from the Wisconsin Department of Natural Resources (WDNR) which were readily available on the department server. I believe anyone who has just started a family or is looking to start a family and wants to live near or within the Appleton area in Outagamie County would be interested in this information, and it is these people who this information is intended for. 

Data Sources: The data and corresponding metadata I used to complete this project are as follows:

1. WiDNR2014.DBO.County_Boundaries: Wisconsin County Boundaries
2. Esri2013.DBO.blkgrp_usacen: U.S. Census Block Groups
3. Esri2013.DBO.mjr_hwys_usadata: U.S. Major Highways
4. Esri2013.DBO.rail100k_usadata: U.S. National Transportation Atlas Railroads
5. Esri2013.DBO.gschools_usadata: U.S. Geographic Names Information System Schools
6. Esri2013.DBO.park_dtl_usadata: U.S. Parks

The Esri data was provided with ArcGIS and the WDNR data came from the WDNR database at http://dnr.wi.gov/maps/gis/metadata.html.

There were a few data concerns that materialized as I worked through this project. One was with the completeness of the data set, in particular with the schools feature class. As I was looking through the public schools in the attribute table I noticed that Einstein Middle School, a large Middle School in Appleton, was not included in the data. This made me concerned that other public schools were not featured as well. Another concern I had was with the block group data set. I wanted to find the density of families based on the 'Families' and 'SQMI' fields as a way of finding neighborhoods with large percentages of families with children. However, it is hard to tell what the dynamics are of 'Families' with the U.S. Census data so I had to make an educated assumption that the likelihood of people who are reporting that their household is considered a "Family" is based on their being children in the household. 

Methods: There were numerous steps that I took to get the final result that I was looking for. I first dropped in the Wisconsin county boundaries data set and then selected and exported Outagamie County to get outagamie_co. This is the feature class I used to clip the rest of the data sets that I used in this project. Using the schools data I first created two features classes, cnty_grade_schools and cnty_middleandhigh_schools, set a four mile buffer on both, and then intersected the resulting classes so that I would have a proximity to both grade schools and middle and high schools. With the block group data set, I added a family density field which I then calculated using the 'Families' and 'SQMI' fields. I then selected and exported the data that produced high family density block groups (outagamie_highfamdens). Using the parks data set I set a one and a half mile buffer so that my final result would be within this distance of any park (outagamie_parks_final). I wanted my final result to be a certain distance away from both highways and railroads to cut down on the potential high level of noise, so I set a half mile buffer on major highways, a one mile buffer on railroads, and then combined the two results using a union (outagamie_hwyrail_buffer). Now that I had my base feature classes I desired it was time for the finalizing steps. I intersected the schools, high family density, and park feature classes to get areas that featured all three data sets (outagamie_parks_fam_schools), and then with that result I performed an erase with the highways and railroads feature class (potentail_house_area). To get a clean looking result, I then used the dissolve and clip tools which answered my question with potential house areas. The data flow model below summarizes my workflow through this project (Figure 1).

 

Figure 1: Data Flow Model

Results: The final result I achieved can be seen below (Figure 2), and I included an enlarged version of the county map for better clarity as well (Figure 3). Outagamie County is the red square-shaped figure in the locator map. The small, black symbols represent schools, the orange lines represent major highways, and the brown, hashed lines are the railroads. The tan polygons with gray outlines are the block groups, the green polygons are parks, and the purple polygons represent my final result of potential areas to live based on my criteria. The final resulting areas are all concentrated in the Appleton city area.

Figure 2: Final Result Map

Figure 3: Final Result County Map

Evaluation: I was overall pleased with how smoothly this project went, for the most part, and that I was able to find the data sets that I was looking for and could get the final result that I desired. The data provided by Esri and the WDNR was sufficient enough to allow me complete this project, although the data was not 100% accurate (the absence of Einstein Middle School for instance). If I had to do this project again I would possibly try to find a better method to figuring out which areas have a high density of families with young children such as using different parameters other than block groups and a different field aside from 'Families'. A faced a couple of minor challenges along the way. First, there were several different highway data sets to choose from so I had to be sure I chose the right one. I believe the major highways feature class was the right choice for the answer I was trying to attain. Another challenge was I had to use a trial and error method of figuring out my buffer distances. Too much buffer and I wouldn't have large enough areas in my answer; too little buffer and my areas would be too large. In the large scope of the project, these were minor challenges that I overcame without too much difficulty.