Choropleth Mapping

   In this lab, our goal was to create a choropleth map showing overall population densities in European countries and tie in wine consumption using either graduated or proportional symbology. Our objectives were to be able to choose an appropriate color scheme, classification scheme, symbology, and legend. Additionally, we compiled our maps in accordance with cartographic design principles and polished up the end result in Adobe Illustrator.

 

My Choropleth Map

 

 

 

   The purpose of this map is to display population density in European countries and the wine consumption percentage of those countries. I started off the project in ArcMap with choosing a color scheme for my data. I chose a sequential color scheme to display the unipolar data. I used a part-spectral scheme of yellow-to-orange-to-redish-brown. Next, I chose my data classification scheme. I decided on quantile because it showed the most variation among countries while refraining from being too mono-colored. Then, I used graduated symbology to display the wine consumption data. I felt this better portrayed the percentage of wine consumption. Lastly, I touched up my map in AI. I manually labeled the countries, gave it a title, and wrote a short synopsis. 

Data Classification

Hello!

In this week's lab we learned four different methods in which to classify our data in ArcMap, which are: Natural Breaks, Equal Interval, Quantile, and Standard Deviation. Our goal was to demonstrate that we could make four maps portraying these different methods and be able to organize all four data frames onto one map deliverable. Furthermore, we were expected to appropriately symbolize our maps with a logical color ramp and implement cartographic design principles into our final product.

This lab's purpose was to compare and contrast data classification methods and presentations in order to choose which one best suited an audience scenario.

My Map

This map shows data presented by four differing methods: Natural Breaks, Equal Interval, Quantile, and Standard Deviation. The overall data presentation showcases these methods depicting the area location per square mile of individuals age 65 and older in Miami-Dade County Florida.

Projections Part 2

Hello!

In this lab we learned how to download data from websites and how to define and reproject that data in ArcMap.

More specifically, we downloaded aerial photos, topographic quadrangles and shapefiles from two online data sources for the state of Florida. We also learned how to navigate these sites and get important source information from them.

We learned how to define data that has an unknown geographic coordinate system and how to project it. For the sources with a defined coordinate system, we learned how to reproject them to the desired coordinate system for our project.

We used supplemental data with lat and long values in Excel and learned how to convert it into X & Y values that can be used in ArcMap. We also learned how to assign a coordinate system for these values, and reproject it, as well.

The end goal was to be able to assimilate our own data, define, project, or reproject that data, and ultimately have created a map that implements only one coordinate system.

My Map (raw form)

 

The purpose of this map is to show petroleum tank contamination sites in Escambia County Florida. We were given free reign over which quad of Escambia county we chose. I chose Pensacola, it is quad 5258 and I found it on Labins.org. I chose the major roads data on the FGDL metadata explorer under a Florida Department of Transportation listing. The county boundary and quad index were worked through in the lab and were not free choice. This screenshot shows my end result. All the is data visible on the map and my data frame properties tab shows that all my data is projected on the correct (and same) coordinate system.

ESRI Spatial Statistics Training

Hello!

In this exercise we went through virtual training on ESRI's virtual campus. In this training course, we learned how to:
- Utilize the Spatial Statistics Toolbox and Geostatistical Analyst Extension.
- Recognize what questions need to be asked about your data before choosing an analysis tool.
- Calculate the Mean Center, Median Center, and Directional Distribution of a dataset.
- Examine the Spatial Distribution of your dataset and identify clusters and spatial relationships in the data.
- Bring up a Histogram and Normal QQ Plot, how to interpret them, as well as understand the properties of a normally distributed dataset.
- Find outliers in your data using a Histogram, Normal QQ Plot, Semivariogram Cloud, and Voronoi map.
- Use Trend Analysis graphs to identify patterns in your data.
- And to assess which analysis tools are appropriate to use with the given spatial distribution and values of your data.

My Map

Map Overview

This map was made through ArcMap with the data we downloaded from ESRI's virtual training course. It was our base map for the various exercises we performed throughout the training course. This particular map was made in the beginning of the training course and was made for the purpose of locating the mean center, median center, and directional distribution of weather stations in Europe. We used the Spatial Statistics tool in order to find these values. Our goal at the end was to determine which areas of Europe should be put under a freeze advisory, based off of the temperature data collected. 

Projections Part 1

Hello!

In this week's lab our objective was to be able to utilize the Project and Project Raster Tools in order to re-project data to a common Projected Coordinate System, recognize .prj file projection information as associated with GIS data files, properly work with and display multiple data frames in ArcMap, find the difference in area between data files displayed in different projected coordinate systems, and to be able to create a map with multiple data frames.

 

Here is my map outcome:

Map Summary:

This map shows the state of Florida projected with three different projection systems, which are the Albers Conical Equal Area projection, the NAD 1983 UTM Zone 16N projection, and the Florida State Plane North (HARN) projection. Each projection system held differing value quantities for the area of the counties, which is noted in the table below the maps. For this map, Albers is the choice selection for map projection since it accurately represents area across the whole planet, even though it distorts the shape. The other two projections are less accurate for this map projection because they only cover a small portion of the state of Florida. While this makes their covered areas more accurate, it leaves the uncovered areas outside of their boundaries less accurate. The projection with the worst distortion is the UTM Zone 16N projection, since it only accurately represents a small part of the state located in the panhandle.

Cartographic Design and Perceptual Organization

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In this lab our goal was to create a map according to end user needs by establishing a visual hierarchy to emphasize important features of our map and to effectively contrast map features in order to imply importance. We did this by implementing figure-ground, contrast, and balance, in order to create a harmonious organization and presentation of our map elements.

My Map

My Process:
 

I used the TOC in ArcMap to organize my symbols and map elements more effectively. I placed the school symbology at the top of my layer, to not overlap them with less important elements. I properly symbolized and sized them in order to stand out on the map. Next, I placed my roads in rank order: Interstate, US Highway, State Highway, Major Streets, DC Streets, and Ward 7 Streets. With decreasing rank, I decreased the width line by half a point to a point. Also, the non-major roads were given a lighter color line in order to avoid a conglomerate of bold colors. Additionally, I clipped out the non-major roads and schools that are not inclusive to the Ward 7 area. This helped to emphasize the importance of the Ward 7 area by decluttering its surroundings. Next in place was parks and surface water. Surface water was made a duller blue in order to not be too contrasting with my overall theme.  Last are the neighborhoods, neighborhood clusters, and DC boundary. I used greens in order to create a distinct figure-ground. I emphasized importance on my legend, scale bar, inset map, and map title by making them proportionately larger and putting them in empty map space. I deemphasized the source data, cartographer’s info, date, and inset map titles by making them smaller.

Last, I used Adobe Illustrator solely for typography. There, I made my map title, inset map titles, cartographer information, source information, date, and labeling for the Potomac River.

Sharing GIS Maps and Data

Hello!

Lab Objectives:
In this lab we learned how to gather and prepare our own data to make a map and how to share that map via the internet through three ways. We made a map package in ArcMap and shared it via the internet by logging into our personal ArcGIS online account in the software and sharing it directly to our accounts, we used our data to make a basic map on the ArcGIS online website under our accounts, and we learned how to convert our maps into a kml file to be used in Google Earth.

Here is a link to the map I made on ArcGIS online: http://arcg.is/20cMi9e

My Experience:
I had a lot of fun making my own data into something usable in ArcMap. I had never made my own data before and I was initially concerned it would be done in some complex way. It was also exciting to see all the different ways we can share our maps and to see what each option had to offer in view-ability and functionality in using our maps.