Downloading GIS Data

Goal

The goal of this lab was to introduce the different ways you collect data sets and how to download that data from the U.S. Census Bureau. The U.S. Census Bureau is one of the largest sources of quality data in the nation, all of the data is relevant to the nations people and economy. 

Methodology/Results

In order to start this lab we first had to collect two data sets, one of which was required while the other was our choice. The first data set that we had to collect was the total population for each county in the state of Wisconsin. While the other data set that was needed to be collected was our choice, for this lab I wanted to choose a data set that would be relevant to the total population and that would create a good and reasonable comparison. That is why I chose the total number of housing units in each county for the state of Wisconsin. After we collected the data the next step was to download the shape file for the state of Wisconsin, after that step was complete we needed to map the data by adding both data sets to different data frames on Arc Map. In order to get the data on the map for each county we needed to join the data so than the map could correctly show the data within each county. Once all of the data was joined with the map we could then start to create the layout for the final map, to do this we needed to add a legend, north arrow, our name as the designer of the map and a title for the entire map. Once this step was complete we were on our final step of the lab was to publish our final map onto ArcGIS Online, we had to go through many steps in order to properly upload our map as a web map. But after we got everything finished our final map looked like the figure below. 

Figure 1: Population vs Housing Units

As you can notice on the map the amount of housing in each county is almost directly related to the total population in each county for the state of Wisconsin. The only area where it might differ is the more northern part of Wisconsin because that is a much less populated area than that of the southern part of Wisconsin. As expected though, the more populated counties do indeed have the most housing since the population is so large in Milwaukee so are the amount of housing units. 

Sources

 http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=DEC_10_SF1_P1&prodType=table

http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=DEC_10_SF1_H1&prodType=table

http://uwec.maps.arcgis.com/home/webmap/viewer.html?webmap=3453e8379eb142ddb0c42dc47cfa1913

Eau Claire Confluence Project

Lab 1: Confluence Project

Introduction

            The future of Eau Claire is very important to most people that live there, as an intern at Clear Vision Eau Claire I would like to strive to make this city one of the greatest. In order to do that my company announced a plan in 2012 for a public private partnership between local developers. This development is referred to as the “Confluence Project” this project is supposed to be completed by the beginning of 2014 and will contain three performance spaces, galleries, offices, classrooms, studios and much more. What I have to do for this project so than it will succeed is to create an initial report or relevant information of the layout as well as including base maps for each data set. I will create this relevant information and base maps using ArcMap and ArcCatalog.

Methodology

            In order to create the relevant information and base maps we first had to create the initial data for the confluence project. First we opened ArcCatalog so than we could create the data, after this program was opened we had to create a geodatabase and a feature dataset for that database. After the feature dataset was created we could move on to mapping the confluence project while being displayed at its real life location. ArcMap is the next program that is needed to be used so than we can create the maps. Once this program is opened we need to upload a world imagery base map so than we can see where the actual confluence project is going to take place. The next objective was to find the parcel information for each of the parcels that were being used for the confluence project. In order to find out this information we have to use the “identify tool” in ArcMap, this tool helps us get vast amounts of information about each location. But the only information that we wanted was the parcel number so than we can get the full parcel description of each location. Another tool that was very useful in this project was the “editor tool” and the “snapping tool” both were very helpful in defining where the proposed site for the confluence project actually was in relation to real world features.

            The next step after collecting all of the relevant information for the confluence project is to start creating all of the maps that are needed to make this project successful. The task at hand is to create six different maps all for the same location but each map representing a different variable. The first map represents “Civil Divisions” in order to create this map we had to change the symbology of the map and add the civil divisions feature class so than the information on the map is relevant. We also had to create maps that represented other variables, for instance we had to create maps for “Census Boundaries”, “Zoning”, “PLSS”, “Voting Districts” and “EC City Parcel Data.” Each of these maps required adding different types of data and different feature classes. For example, the “Zoning Map” needed the zoning_areas feature class to be added to it since that has information on it relevant to that map, while the “Census Boundaries Map” needed the BlockGroups and Tract Group feature class to be added since that has information relevant to this map. Each map needed different feature classes to be added to each one some required the same since they are showing somewhat similar information. After we made all of the maps and included all of the information that was needed all we have to do now is change the view to layout and start to construct the map correctly making each data frame the same size, while including a legend, scale and title for each map.

Results

            After looking carefully at all of the information and analyzing each one of the maps carefully you can see that the proposed site is placed at a perfect position. Especially if they are trying to draw in a younger crowd, if you were to look at the figure below at the Census Boundaries map you would see that the area that the Confluence project is in is among the youngest in Eau Claire. Also one of the goals of this Confluence project is to try to have local businesses work together to help promote Eau Claire. If you were to look at the Zoning map below you would see that the proposed site is in the Central Business District for zoning classes making it a great spot for local businesses to meet up and share ideas with each other. Each map tells vast amounts of information about the confluence project but the one thing that all maps are telling us is that it is in a very good position for accomplishing its goals. 

Figure 1: Confluence Project Maps