Lab Five: Mini-Final Project

Introduction:

In this lab, I have been asked to pose a research question and carry out the steps necessary to answer it and present my findings.

Research Question

 Where are the best potential locations for a new park in the city of Eau Claire, WI based on community need and potential social impact?

 

 

Objectives

 

To help locate potential locations for a new park I established the following criteria:

 

• The new park site should not be located within .5 miles of a currently existing park
• The new park should be located in a census block group with population density > 2000 people/square mile.
• Block groups eligible for a new park should contain more than 100 children ages 5-9
• Median Household Income in eligible block groups should be < $40,000

These criterion aim to identify a population in need based on access. Furthermore they seek to maximize impact through population density and a relatively large population of young children compared to other block groups.

 

Intended Audience

 

City planners and community advocates working to improve the welfare of citizens in the city of Eau Claire will be able to utilize these findings to stimulate further discussion about future park locations. This is, by no means, exhaustive research into suitable park locations. It is meant as a starting point.

 

Data Sources:

In order to research the criteria I set for potential park locations, I employed data obtained on the UWEC Geography departmental server in their ESRI data folder as well as various geodatabases containing data specific to the city of Eau Claire. Shapefiles retrieved from this location were already equipped with U.S. Census Data used for later analysis. This data is marked with dates of 7/13/2009 and 11/16/2009 and was obtained from ESRI. The attribute tables revealed that 2000 and 2010 are included. Data retrieved includes the following:

 

• Eau Claire County boundary shapefile
• U.S. Block Groups shapefile
• Light Gray Canvas Base Map
• Eau Claire City_Limits_Area shapefile
• Water shapefile
• Eau Claire Zoning shapefile

Concerns


My initial concern had to do with locating Median Household Income data for block groups. Looking at the attribute tables for my block group shapefile revealed this information was not included. Going to U.S. Census American FactFinder revealed this information was only available at the block level from the year 2000. Due to the large gap with, not only my available data (2009,2010), but also with the current year (2014), I chose to forego this very useful (if it were current) piece of information.

Next, I realized that park data for the city of Eau Claire was not available in a clean form. It was necessary to draw out this information from zoning data. While I am confident I was able to locate all current relevant parks in the city of Eau Claire, I cannot claim this with 100% certainty.

City Parks do not exist in a separate file available to me. It will be left to my own interpretation to define which parks ought to be included for analysis. Others will certainly take issue with my interpretation. Choosing or excluding parks for any of a number of relevant reasons would most likely arrive at a different result than I will obtain.

Lastly, the city of Eau Claire has expanded in way where portions of the city are located in block groups that extend beyond the city boundaries. A decision was made to simply analyze the block groups as whole rather than trying to locate exactly which portions of the data could be attributed to sections of a block group actually in the Eau Claire city limits. These outlying areas are, most likely, irrelevant to this discussion due to their newly developing nature. Once again, though, there may be relevant areas on the outskirts of the city that were marginalized by the assessment methods employed.

Methods:

The data flow model below (as seen in Figure 1) was followed to obtain the visual results of this study.

Figure 1: Data flow model employed to identify ideal areas for a new park in Eau Claire, WI.

 



Density and 5-9 yr. Population

By placing the city limits boundary over the U.S. block groups shapefiles, I extracted the necessary block groups and created a separate layer greatly reducing the data I would need to sift through. The resulting layer was queried twice to identify block groups that met two of my criteria (Population 2010/Square Mile-Density > 2000 and Population Age 5-9 > 100.

The former limit was chosen based on the average Density per square mile for the city of Eau Claire. Thus, block groups chosen would contain above average density. The latter limit was chosen based on looking at the various 5-9 yr. old populations for each block group. I wanted to eliminate a significant portion of block groups while still leaving several for further analyzation. I will admit the process of choosing eligible block groups based on 5-9 yr. old population was somewhat arbitrary, though still arrived at deductively.

Each of these queried selections were separated into their own layer and the Intersect tool formed a layer containing only the block groups that met both criteria

Public Parks

This portion proved to be a little more tricky than I had hoped. Because public parks were not available in their own layer, I had to query the zoning shapefile for all public land. This category did not possess any further sub-categories, so I had to digitize and select each park in order to turn them into a separate layer.

One benefit was achieved through this process; I chose to exclude parks such as Putnam Park which exists only as a walking and nature trail. Because I am putting focus on smaller children (ages 5-9), I felt parks such as these have far less relevance than other community parks. A .5 mile buffer was placed around existing parks and combined with my Density and 5-9 yr. old population layer through the Erase tool to exlude areas within eligible block groups that were too close to other parks.

City Limits

Eau Claire has many areas that have avoided annexation and an ever-expanding outer limit. As a result, the actual boundary of the city can be somewhat confusing. To alleviate this issue I utilized the Dissolve tool on the city boundary which presented a much more clear picture. This was combined with the resulting layer from my combination of Parks and Census Criteria data through the Clip tool.

This eliminated any area that was included in eligible block group and more than .5 miles away from existing parks but outside of the city boundaries. After implementing the Clip tool, I was left with the final result of my research.

Water
A shapefile containing the water for the Eau Claire county area was added to provide some color definition on the otherwise grey base-map. This was merely an aesthetic addition.

Results:

The following map (as seen in Figure 2) identifies the ideal new park areas within the block groups meeting my criteria.

Figure 2: This map identifies the locations to be considered for a new park in Eau Claire, WI based on meeting the prescribed criteria.

 



 



Only two block groups toward the southeast of Eau Claire were able to meet the density and 5-9 yr. old population requirements employed. Two parks are located in one of them including the Meadowview School park as well as Fairfax Pool. Without the inclusion of Fairfax Pool, even more of these block groups would fall outside of a .5 mile buffer from surrounding parks. This further solidifies the need for an additional park within these two block groups.

 

There are three slivers toward the east of these block groups that are identified as eligible. While they may seem too small for a park, these areas indicate areas within the block group with the least amount of access and are not meant to exclude any sites within the block groups from consideration for a park.

 

That being said, the area to the north of these block groups would provide better access to the largest portion of these block groups with current low access. This is especially true considering that the park furthest north within these block groups is Fairfax Pool.

 

This study does not take into consideration the suitability of land for a potential park or its current usage. It merely identifies the areas where park accessibility is lowest and where the impact of a new park could be the greatest. Further study of these areas and their surroundings are necessary to identify a suitable park location.

 

Evaluation:

Overall Impression

 

I feel rather empowered upon the completion of this project. While my understand of GIS software is miniscule in comparison to its capabilities, I know that I can perform operations to achieve a desired result.

 

Personal Changes

 

Were I to engage in this project again, I would spend additional time considering what data would be most helpful to achieve my desired goal and what implementing that data would produce. While my goal was to identify ideal park locations, my project actually produced areas with relatively low access, high population, with a substantial population of young children that could benefit from new park. I did not really identify ideal park sites by any other measure.

 

I also should have spent more time understanding what data was accessible to me. I wasted a lot of time trying to find more up-to-date median household income information. Had I explored this sooner, I could have sought out other equivalents that might help produce the same kind of information.

 

Project Changes

 

I am having a hard time identifying changes I would make to this assignment. Having completed it in a way that meets my personal standards leaves me feeling each part of the exercise was essential to the learning experience.

 

Challenges

 

The challenges associated with this exercise were many. Envisioning a data flow model is very important but difficult to develop without more familiarity. Finding ways to get to data that is not in an ideal form, such as park locations, can be tricky and requires creativity.

 

Decisions about which elements to include and which to leave out are important and can really be the difference in a legible map and confusing map.

 

Managing a good geodatabase and keeping track of all of your data is a time-consuming, painstaking process. However, learning these habits is essential to producing responsible, accountable maps.

 

 

 



LAB 4: Vector Analysis With ArcGIS

Goal:

The purpose of this lab will be to provide experience implementing geoprocessing tools to locate and display desired data. In addition, it will involve developing a data flow model providing a roadmap of the tools and information used to build a visual representation of the data. As a general rule, the production of maps using GIS software will include both of these elements.

Background:

The study area for this exercise is located in Marquette County Michigan. The Michigan DNR would like to receive a recommendation outlining the most suitable bear habitat(s) located within the boundaries of their managed lands in this study area.

Methods:

Objective 1: Add bear locations to Map

The x and y coordinates for bear locations in the study area were provided in an excel table. Through the creation of a temporary event theme, this data could be converted into a feature class displayed spatially in the study area. It was also important to set the coordinate system of the points given to the coordinate system of the geodatabase. Without this step, bear locations would not be accurately represented on the map.

Objective 2: Identify general criteria for determining conducive locations for bear habitats

In order to learn more about the habitat bears are likely to choose, the attribute table connected to bear locations needed to be connected to other data to provide context. Land-cover data was joined to the bear location data. By summarizing this data based on land-cover type, I was able to locate the three most likely categorizations for bears to reside. The three most likely land-cover types for bears to reside in were found to be Evergreen Forest Land, Forested Wetlands, and Mixed Forest Land. These selected land-cover types were extracted from the data into their own layer for future use.

Secondly, biologist's studies have shown bears are likely to be found near streams. In order to test this theory, a 500 meter buffer was placed around all of the streams in the study area. Next a "select by location" was performed to identify all of the bear locations that are within 500 meters of a stream. It turns out approximately 72% of bears in the study area are located within this prescribed boundary. Biologists would identify findings of  > 30% to be significant; as a result, it does appear that proximity to a stream is an important factor in identifying ideal bear habitats.

Objective 3: Combine criteria to find the most ideal bear habitat locations

The two criteria were combined through the Intersect tool with a Dissolve applied to remove any overlapping boundaries and identify the locations that met both of our determined criteria. This intersected locations were selected and turned into their own layer for future use.

Objective 4: Combine identified area from Objective 3 with DNR management areas to provide a recommendation

The DNR provided all locations of their managed area within Marquette County. For our purposes only locations within the study area were needed. By intersecting this area with the ideal location criteria data from objective 3 and performing a dissolve, I was then able to select the management areas within our criteria and create another layer with just the area that met this new criteria.

Objective 5: Remove all areas from our ideal locations that lie within 5 km of urban land

The DNR decided, for their purposes, the ideal bear management area would be located beyond 5 km of urban urban or built up lands. After adding this data from our geodatabase, the urban land classification was selected and turned into its' own layer. Then a 5 km buffer was placed around these selected urban lands . An Erase was performed on this buffered area combined with our previously identified suitable locations to remove all selected locations within the buffer. This left us with the most ideal bear habitat locations based on the criteria established (as seen in Figure 1).

Objective 6: Building a Map

In addition to the typical map elements added to a map within ArcGIS, the legend titles needed to be adjusted for reader clarity. To provide context, Marquette County, Michigan was selected and extracted to its' own layer to provide an inset map. The same study area data was added from the geodatabase and applied to the county.

Figures:

 

Figure 1: This map identifies the most suitable bear habitat locations within the Marquette County, Michigan study area

 

 

Figure 2: This figure is the data flow map for the production of the map seen in Figure 1

 

Results:

Very few bears currently reside within the ideal locations for bear habitat identified in Figure 1. However, due to the relative scarcity of suitable locations that are also DNR managed areas, this is not that unexpected.

 

The DNR land to the east along Lake Michigan do not appear to be the best location for bear habitats despite its' specification as such. Bears are currently nonexistent in this area and more research should be done before such a move was made.

 

The DNR areas in the middle and to the west of the map appear to be the most ideal of the locations specified due to the proximity of several bear locations. From this location a line of good locations extends to the northeast.

 

It may be wise for the DNR to identify land to the northwest of the study area for purchase and subsequent use as a bear management area. Many bear reside in the northwest portion of the study area with a relative paucity of suitable DNR land for bear management purposes.

 

Sources:

 

All data was obtained from the Michigan Center for Geographic Information.

 

DNR Managed Areas: http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm

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

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

 

 



 

GIS 1 LAB 3: INTRODUCTION TO GPS

Introduction:

Global Positioning Systems (GPS) is a very powerful tool with many capabilities. However, without being familiar with the devices and tools used to collect data, it will be nothing but an hindrance. In order to avoid this happening in the future this lab assignment's goal is to walk through all of the steps necessary to carry out data collection using GPS software, tools, and devices from pre-data collection all the way to a final product (a map).

The following objectives outline this start-to-finish process:

1.       Create a geodatabase.

2.       Prepare the geodatabase for deployment to the Trimble Juno for field data collection using ArcPad Data Manager.

3.       Load the Geodatabase onto the Trimble Juno.

4.       Become familiar with the basics of the Trimble Juno GPS and ArcPad through an instructor-

       led demonstration.

5.       Collect point, line, and polygon features in the field using ArcPad on the Trimble Juno GPS

6.       Check the collected data back into ArcGIS from the field.

7.    Create a map displaying the collected data.

Methods:

Each of these objectives required different tools and operations for successful completion.

Objective 1: Creating a geodatabase

ArcCatalog was used to build a geodatabase. A geodatabase must be carefully prepared before entering the field to ensure a smooth data collection process. Details to be considered include establishing a coordinate system, creating appropriate feature classes, choosing the correct shapes (points, lines, or polygons), and determining the fields you will want in order to help you recall details about data being collected.

For the purposes of this exercise 3 practice and 3 regular feature classes were built, two each of points, lines, and polygons. An additional feature class, already built, was added containing polygons for the different buildings on campus. Next, a raster dataset with the outlay of the University of Wisconsin - Eau Claire campus was imported. This would allow the user to get a better sense of whether or not the features were being correctly added based on where the campus buildings were located on the map.

All of these feature classes were then added to a new map in ArcMap. Before moving on from this objective, it was important to alter the symbology of the feature classes rather than leave the default colors chosen in ArcMap. This is done to ensure the colors chosen will be easily identifiable as data is being collected.

Objective 2: Preparing geodatabase for deployment

Within ArcMap, the ArcPad Data Manager tool was used to "check-out" all the geodatabase layers and "copy-out" all other layers including the base map in the Action Menu. This essentially created a folder that would be transferred to a GPS unit

This folder was given a unique name associated with the project. It may be that a GPS unit has other folders already loaded onto it. Selecting a specific title ensures you will be able to locate the correct folder for your project when you are in the field.

 All of this creates "ArcPad data," which is effectively deployed when you finish this process.

Objective 3: Load geodatabase onto GPS unit

The GPS unit used for this project is the Trimble Juno 3B. A USB cable was connected between the computer and this GPS unit. The folder prepared (as explained in Objective 2) was cut out of its current location and pasted into the appropriate folder on the storage card of the Juno GPS unit.

It was also necessary to ensure the transfer was successful. The Trimble Juno 3B was turned on and the map that had been deployed was located indicating the transfer had taken place successfully. Within the Table of Contents under Layers, it was also verified that all of the layers we expected to be there, were indeed there.

Objective 4: Become Familiar with the Trimble Juno 3B

Utilizing the practice point, line, and polygon feature classes we developed, data was gathered using ArcPad on the Trimble Juno 3B to become familiar with the process before actually locating the data for the rest of the lab assignment.

Objective 5: Collect Point, Line and Polygon Features using ArcPad on the Trimble Juno 3B

After waiting for a "fix" assuring the unit was in communication with satellites, 4 specified areas (polygons), a footbridge (line), and three trees and lightpoles (points) were collected using ArcPad on the Trimble Juno 3B by selecting Add GPS Vertex. As features were collected information was entered into a Type field to help categorize the features.

Objective 6: Check collected data back into ArcGIS

After all the data was collected, the GPS unit was reconnected to the computer with a USB cable. Using Windows Explorer the folder containing the data was cut from the Juno 3B and pasted back into my personal folder. The ArcPad data manager toolbar was used to "check in" all of the feature classes from the Juno 3B. This made the collected data visible in ArcMap.

Objective 7: Build a Map with collected data

ArcGIS  was then used to build a map containing all of the necessary elements and displaying the collected data. An updated basemap was added providing a better reflection of the current campus layout.

Within the properties of the feature classes, the different polygons and points were differentiated from each other under the Symbology tab. This way, when the legend was built light poles were signified differently than trees, etc.

Results:

Figure 1: This map shows the features collected per the direction of the assignment

 

 

The map produced (as seen in Figure 1) leaves something to be desired in my opinion. Many of the features at the bottom-right of the map are bunched together, and some even overlap. Clearly, this particular GPS unit does not contain the necessary point accuracy to produce features sufficient for mapping. In addition, the blue "circle" is not in any way shaped properly. While many points were taken to ensure accuracy, the GPS unit is just not capable of the precision required to build a feature that better represents what is actually there. One of the lawn portions, especially, falls short of the actuall lawn. Here again, the GPS unit, was just not able to precisely locate the data being collected.

 

Due to the spread out nature of the data (with the bridge being so far away from the other points), this map appears very empty. and does not contain good balance.

When looking at my points, I had taken the point of one tree twice. I had to delete this data point for accuracy. Also, the line for the bridge was hard to replicate. I probably should have made the line thicker and just covered the entire bridge for the sake of being aesthetically pleasing.

Sources:

Data Source: GPS data collected by Nathan Schaffer
Aerial Map: ArcGIS Online, UWECCampusBaseMap
Topographic Base Map: UWEC Server, W:\geog\LidarData\EauClaireCity_3in_2013\MrSids folder

GIS 1 LAB 2: DOWNLOADING GIS DATA

 

 

Introduction: 

 

A tool like ArcMAP is invaluable for producing high-quality, effective maps. However, without adequate understanding of the process of transferring data and images from other sources into ArcMAP, quite the opposite may occur.

 

 

Lab 2 is an exercise meant to build familiarity with downloading data from the U.S. Census Bureau, transferring it to ArcMAP, and building maps with that data.

 

The U.S. Census Bureau

The U.S. Census Bureau is one of the main sources of concentrated data for the purpose of geographic comparisons (as well as for many other fields). Their mission "is to serve as the leading source of quality data about the nation's people and economy." More directly, U.S. Census data is used to determine Congressional seats in each state, "to make decisions about what community services to provide," and "to distribute more than $400 billion dollars in federal funds." Mastering the process of transferring data from this vast source of information is a key skill for map-makers to develop.

 

Before doing so, it is important to familiarize with the terminology used by the U.S. Census Bureau as well as any nuances to be aware of. To learn more about U.S. Census terms and processes please access the following links:

 

 

 http://www.census.gov/

 

 

Exercise Objective

 



 In this exercise, I will access and download from the U.S. Census Bureau. This data will be

transferred to ArcMAP in order to produce two maps reflecting this information.

 

 Methods:

Downloading 2010 Census Data from U.S. Census Bureau

 

 

After navigating to the U.S. Census Bureau website at the following link

 

http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t

 

 I completed the following steps to download data:

 

1.  Choose Data Set & Geography





First, I selected Advanced Search (as seen in Figure 1).

 

Figure 1: This figure shows the U.S. Census Bureau American Fact Finder Home Page. By selecting Advanced Search (circled in red), you can choose both the data and geography specifications you desire.



 

 

 This opens up a screen from which you can select your data set (topics) and geographies needed (as seen in figure 2).

 

Figure 2: This figure shows the selections from which data sets and geography specifications can be selected.

 

2. Choose Specific Survey Source

Once you have chosen you data set and geography specifications, you are provided with many different options to choose from as the specific source of information. The U.S. Census Bureau produces many different reports, so it is necessary to specify which report you would like to access.

 

3. Download Data Set

After selecting the appropriate source for this exercise, I simply had to select "Download" and specify where I wanted the file to be downloaded to.

 

4. Unzip Downloaded File

The file that is downloaded to your location of choice is delivered in the form of a zip file. After right clicking on the zip file in your folder, I chose to extract (or "unzip") all the data I had requested from the U.S. Census Bureau. After doing this there were several files containing the data I had selected.

 

5. Save CSV file as an MS Excel File

The files received from the U.S. Census Bureau come mostly in CSV files. After opening the CSV file containing my data, I chose to "save as" an xlsx file. This is an important step especially for combining data. In order to merge data, a program such as Microsoft Excel must be used. All of your data needs to be in the same format in order to be merged.

 

6. Download Shape Files from U.S. Census Bureau

Next, I navigated back to the U.S. Census Bureau geographies selection. The U.S. Census Bureau has shape files coincide with the data you select. For my purposes, I clicked on the map tab and made sure that all the regions of  my data set were represented (all the counties of Wisconsin), clicked download, spatial data formats, and shapefile.zip. Just as was seen with the data set, I downloaded this zip file to my folder, extracted the files from the zip file, and saved them into my folder.

 

 Joining Data Together in ArcMAP

 



Import shape file to ArcMAP

Next, I opened the ArcMAP program to a blank map, and imported the shape file of the counties of Wisconsin that I had downloaded from the U.S. Census Bureau as well as the Excel file I had saved containing my data set.

 

This shape file contained data pertaining to each county which could be seen by right clicking on the shape file and opening the attribute table. However, it was not connected to the data set information from the U.S. Census Bureau.

 

Table Join the Two Attribute Tables

By right clicking on my shapefile, I was able to open up Arrange Tables and link the attributes in my shape file to the attributes of my data set. The key was to identify the common attribute, GEO#id, the two data sets shared. Now, I had a combined data set to facilitate making maps.

 

 

Mapping Data

 

Map 1

Two maps needed to be produced for this exercise. The first one was based on population data imported from the U.S. Census Bureau. This entailed defining the symbology for a graduated color map.

 

By selecting the shape file in the Table of Contents, I was able to open up the attributes and choose the number of classes and color scheme to represent my data. I also adjusted the numbers and number breaks to make the map easier to read and interpret.

 

Map 2

For the second map, I had to go back to the U.S. Census Bureau Website and select data for a variable of my choosing and import it to a new data frame in the arcMAP table of contents. Following the same steps employed previously, I imported data that would allow me to portray the percentage of males age 25-29 in each county compared to the total population.

 

In order to do this, when I set the symbology, I had to normalize the number of 25-29 year old men in each county by the total population for each county.

 

I also had to adjust the format of the numbers associated with this data. Due to normalization, the numbers were in long decimal form. This was not easy to read or interpret. Therefore, I switched the number format to percentage. This made it a little better, but I still needed to "even out" the numbers for easy interpretation by adjusting the ranges and labels of my data.

 

Both Maps

Because both of these maps were of the entire state of Wisconsin I projected each data frame using NAD_1983_Wisconsin_TM. This Transverse Mercator Projection is specifically suited to the entire state of Wisconsin.

 

Also, I added a base map to each data from to provide a little area context and enhance the appearance of the map. These basemaps came with a separate file that included references connected to the areas being shown. As a result, these references were visible through my shape file map. I had to turn off the reference layer to avoid this problem.

 

Lastly, titles, north arrows, reference information, a scale bar, and a legend were added to each map. The legend shows numbers that were altered to make the map easier to interpret.

Results:

The following maps were produced as a result of this exercise.

 

Map 1

The first (as seen in Figure 3) shows the total population for each county in Wisconsin.

 

Figure 3: This is a map showing total population for each county in Wisconsin categorized into 6 categories. The major population concentrations are around Madison and Milwaukee especially in Dane, Waukesha, and Milwaukee counties.



 

I know that 6 classes is a little bit of stretch because colors can be hard to differentiate. Still, using Jenks Natural Breaks, counties with between 4,000 and 60,000 people were being categorized together. This seemed a bit of a stretch. By adding the extra class, I was able to cut that number in half which seemed far more reasonable. I carefully chose my color gradient, and judged whether or not I could distinguish the six different colors. Because of the way the data is dispersed, I found it to be quite easy, thankfully.

 

Highly populated counties are right where they would be expected - in the Madison and Milwaukee Areas, around Green Bay, Wausau, Lacrosse, and along the corridor between Eau Claire and the Twin Cities. Equally expected is the tendency for less populated counties to be found in the northern-most counties This map is more important as a reference point for the second map

 

Map 2

The second map (as seen in Figure 4) represents the percentage of males age 25-29 in all of the Wisconsin counties.

 

Figure 4: This map shows the percentage of 25-29 year old men in each Wisconsin county as a proportion of the total population. Similar patterns to what can be seen in Figure 3 exist with the most notable exception being Waukesha county. 

 

Because this data needed to be normalized by the total population in each county, percentages are seen in the legend instead of raw numbers. I considered normalizing using the total male population, rather than total population, but I really was trying to identify where there were large concentrations of, potentially, young professional men. This would be an indicator of the areas where employment is being found, etc. The point was also to look at the "brain drain" concept where young people move to large cities with better paying jobs and a more vibrant atmosphere.

 

I realize I could have, and probably should have, just looked at all people age 25-29 rather than just men. I simply got pigeon-holed into looking at certain data and how to think about it. This map is not really a fair representation of what I wanted to look at for that reason.

 

That being said, I feel the idea of brain drain is fairly well-represented by this map, though it is definitely incomplete. Most of the counties with larger population centers have larger populations of 25-29 year old men as a proportion of the total population in the county. I would assume that adding 25-29 year old women would only serve to enhance this pattern. Still, it would be interesting to compare the two sexes to identify if there are areas young men  or young women are going in greater proportion than the other and to identify the reasons.

 

Also, there is a noticeable pattern of fewer 25-29 year old men as a percentage of total population in many of the northern-most counties.

 

One thing that really started to develop more strongly to me by looking at this map was the tendency for larger percentages of 25-29 year old men along the major highway corridors from Milwaukee over to Madison and up through Appleton and Green Bay as well as from Madison up to Eau Claire and then over to the Twin Cities. Even in counties where there are smaller population totals in Figure 3, there are still some of the higher concentrations of 25-29 year old men. Mobility could be a key factor for this demographic even if they choose not to live in counties with large population centers.

 

Sources:

Factfinder2.census.gov. (2014). American factfinder - search. [online] Retrieved from: http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t [Accessed: 6 Mar 2014].

 

 

 

 

 

 

 



GIS 1 Lab 1: Technical Report on Potential Confluence Project in Eau Claire, WI

Introduction

The Confluence Project
The Eau Claire Confluence Project is a joint effort between public and private entities to invest approximately $50 million dollars into mixed development located in downtown Eau Claire. As it currently stands this project would center around a 150,000 sq. foot community arts center and would also include retail/commercial space, public parking, and university student housing.

Location
The site being considered for this venture is known as "the Haymarket site." It is situated at the confluence of the Eau Claire and Chippewa rivers along Eau Claire Street and Graham Avenue. Two buildings (the Formers Store and Market Square) along with a current parking lot would need to be removed in order to make way for the new development.

Principal Stakeholders
As a public/private partnership, the stakeholders bring together the University of Wisconsin-Eau Claire and the Eau Claire Regional Arts Council from the public sector as well as Commonweal Development Corp. and Market & Johnson Inc. from the private sector. The ownership of the Haymarket Property is currently owned by the principal stakeholders (outside of the Arts Council) under the umbrella of Haymarket Concepts LLC.

Report Objective
Any time such a large venture is considered, there are many aspects to analyze in order to ascertain impact, potential, and feasibility. Through mapping techniques and the use of geographic information systems important information can be placed in the hands of interested parties. Toward that end, I will produce six maps portraying various regional and political boundaries for consideration.

Links to Confluence Center Information
http://volumeone.org/news/1/posts/2014/01/21/6112_county_board_will_pledge_3_5m_to_confluence_if

http://www.uwec.edu/News/more/confluenceprojectFAQs.htm

http://www.eauclairearts.com/confluence/

Methods

Data Collection
The first step in producing informative maps is the collection and organization of data. Information about political and regional boundaries was provided to me from city and county of Eau Claire. Importing the data from the files I had been given into ArcMAP allowed me to build a geodatabase in which to house the relevant data. From there I would be able to organize data into pertinent layers for the purpose of map production.

Confluence Center Site Specification
In addition to the data sets given to me, it was also necessary to produce a template specifying the exact area of the Confluence Center. Producing this template is an extremely important step because each map should make it very clear to the reader how the other information presented on the map relates to the specific item of importance (The Confluence Center).

To do this, I first added World Imagery as my base map. Secondly, I added the "proposed site" feature class to my base map. This feature class would act as my template for all subsequent maps. In order for it to serve that purpose, I had to correlate the legal parcels specified for the Confluence Project location to imported parcel area data from the city of Eau Claire.

Digitizing Confluence Center Site
After using the Identify tool to locate the parcels for the confluence center site, I needed to differentiate the two parcels from all of the other parcels represented on the map. To do this, I turned on the Edit toolbar . In this toolbar, there is a Snapping toolbar. One important part of digitizing is making sure you maintain consistency in your maps. By selecting End snapping and Vertex Snapping, I ensured that the vertices and nodes I would build to differentiate the Confluence Center Site parcels would match the vertices and nodes in the parcel layer. The outlined parcels can be seen in Figure 1.

Figure 1: This figure shows the outlined parcels for the proposed Confluence Center. Using the snapping tool, this outline would serve as my template for the confluence center site on my subsequent maps.

Map Building

Now that I had established access to my data sets in a geodatabase and built a template for th Confluence Center site, I could simply import the necessary data into newly inserted data frames. Separate data frames allows you to build several maps utilizing the same geodatabase. This is especially useful in a case such as this where 6 similar maps are being produced of one site. In order to stay organized I renamed each new data frame according to the details of the map. The 6 maps, and thus the 6 titles of my data frames were as follows:

• Civil Divisions (map showing the boundaries of cities, towns and villages in the area)
• In this case, no villages were visible on the map so I removed the data from my dataset. Had I left it, it would have been possible to confuse the readers. This is especially true due to the fact that the world imagery is visible behind the slightly transparent civil divisions data. It could be assumed that colors showing through from the world imagery represented non-existent villages.
• Census Boundaries (map showing population density per square mile in census tracts)
• The important steps for this map involved normalizing the data. Had I just separated tracts by population, a very different picture would be painted. Normalizing by square mile paints a better picture of where population is centered.
• Secondly, I had to adjust the number breaks in my legend to allow for greater clarity. This involved eliminating decimal places and placing my breaks at easy to follow numbers.
• PLSS Features (map showing quarter - quarter township boundaries around the site)
• Townships are approximately 360 sq. foot parcels of land. The grids in this map are technically 1/16th of a township. Due to the legal definitions attached to land and properties. It is important to understand this aspect.
• Eau Claire City Parcels (map showing exact parcels of Confluence Center site and region)
• Also having to do with legal definitions, this map outlines the exact parcels of the proposed Confluence Center site.
• Zoning (map specifying the different zones of property around the Confluence Center site)
• Properties are broken down into classifications based on their usage. Because their are many different specific classifications of sites, for the purpose of this map, I combined data into 6 generalized classes (Industrial, Central Business District, Residential, Commercial, Public, and Transportation). This will make it much easier for readers to analyze. Too many colors are difficult to differentiate.
• In Symbology, I also manually changed the labels to my new grouped classifications.
• Also, I eliminated data for "Transportation" parcels because none were visible on the map. This helps avoids confusion for readers.
• This map would like nicer if roads were accounted for in some kind of classification or otherwise differentiated. There are places on the map that are not consistent with the top layer as a result. The World Imagery below is visible along roadways.
• Voting Districts (map showing the breakdown of voting districts around the site)
• Because this map did not require a legend, it was important that I specify the Confluence Center site with a label. Because of the many numbers on this map, it meant adjusting the X, Y coordinates to place the label in a place where it would not obscure numbers.

Figure 2: The following maps correspond with the specifications above by title.  

                                                            Source: City of Eau Claire and County of Eau Claire, 2013

Generalized Methods

Some aspects of cartography were employed on multiple maps. I will list them here for your consideration.

• Background color (white) on legends, scale bars, and north arrows for clarity.
• Adjusting the scale bar to familiar and whole numbers for clarity.
• Titles above maps were produced in ArcMAP.
• Transparency levels were adjusted to varying degrees to provide the layering affect of the World Imagery below the map data.

Legal Descriptions

Using the Property Assessment Search website for Eau Claire, I gathered the necessary information to build a basic legal description of the two parcels for the proposed Confluence Center as seen in Figure 3. This also included providing a snapshot of the parcels with labels. Once again, by building an initial template, this was very easy process. All I had to do was add the labels.



 Figure 3: This figure shows the compilation of legal descriptions for the two proposed parcels for the Confluence Center project.

Conclusion 

Using geographic information skills and principals of map making allows you to make detailed, accurate representations of many different types of data. The six maps produced for this technical report are just one example of what can be done. Due to the many different types of political and geographical boundaries and classifications, this information is not just "nice to be able to show" but a real necessity for decision makers.