Tuesday, May 30, 2017

Physical ERD Diagrams

Physical ERDs are a bit more intuitive than logical diagrams. They account for the attribute type for each attribute within an entity. For this reason, they are often more complete and complex than a logical diagram but offer more information at the same time and can offer better insight about how a database is meant to be used and how it will store data.

In this hypothetical database which is meant to support analyzing historical home sales and proximity to parks. The parcel data and the sales data must be linked to one another to start the analysis and this has been shown in the physical ERD above. The various attributes have been distinguished by their data type so that the reader of this diagram can see how data will be represented within the database. Most attributes are based on a series of characters that are used to give each data item a unique identifier, such as the pin attribute on the parcels entity. For this reason, a VARCHAR data type is used since these attributes will neither be entirely numeric nor entirely text. The same idea is used to distinguish the other attributes based on how they need to be represented in accordance with their data characteristics.

Tuesday, April 4, 2017

Temporal Mapping

Temporal mapping is a useful communication tool in GIS. Temporal Mapping allows GIS users to show various change over time effects in maps. This can be used to show changing trends, data levels, and event events over time.

For this week, we made a temporal map detailing the change in population over time for the largest cities in the United States. This map also shows how the overall population center of the U.S. has shifted from the East Coast towards the middle of the Contiguous states.

To make a temporal map, you need to have data that has some sort of date element to it, such as days, years, months, etc. The next step is to enable time capability with your data. After that, you can incorporate the built in Time Slider from ArcMap to have your data cycle through as time increases. 
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For my map, there was also the incorporation of visual elements such as a moving indicator that went along a time scale to indicate at which point in time the map was being shown. There are also dynamic text elements that help indicate the time. 

This type of map making can be very helpful to show trends. Scientiests can use them to display their research into the way things such as diseases, invasive species, pollution, etc. spread over a geographic area over time.

Sunday, March 26, 2017

Bi-variate Choropleth Mapping

For this week, our task was to create a Bi-Variate Choropleth map. This map was to utilize two different variables that were shown to have at least some correlation between one another. Here is my product for this task:


Preparing a Bi-Variate Choropleth can be quite tricky, as there are not built-in functions in ArcGIS for such a task. This requires manipulating the data so that it can be manually used to create the values needed to display the two variables together.

The first step was to rank the two variables. This was done by classify the data using quantiles and then assigning the data a rank based on which class it was in. Essentially, if the data was in class one, it was assigned the rank of "1" in a new field. For the second variable, the ranks were assigned the same way but instead of a number, it was given a letter (A, B, C, etc.). The two newly created rank fields were then combined to create an overall ranking system of A1, B1, C2, B3, etc.

The way this ranking worked is simple. Ranks of A1 were low on both variable scales. A3 values were high on the first variable scale and C1 values were high on the second. C3 values were high on both scales and indicated a strong correlation between both variables.

The values were then assigned a Bi-Variate color scale. This was created by taking two 3-class color scales and blending them to create a bi-variate scale with 9 overall color values. When these colors were shown on the map with their individual values, it created the map as shown above.

Overall, creating a Bi-Variate Color map is a manual process. Hopefully one day ArcGIS will have a program or toolset integrated into it that will allow for quicker and more efficient creation of Bi-Variate Choropleth maps.

Wednesday, March 22, 2017

GIS and Infographics

The task for this week was to use GIS data to create an Infogrpahic. Infographics are graphical elements that contain one or more data comparisons and are used to present data while simultaneously explain the relationships or correlations within that data.

Here is my product for this task:

My map focuses on 2015 data for overall population at the county level and deaths due to vehicular accidents. These two variables were shown to be correlated in the sense that if the population increases then there will be more auto accident deaths as well.

I visually represented this data by using two separate chloropleth maps. The fact that these maps are very similar looking to one another shows the correlation between the two variables. I also used a scatter plot with a trend-line added to reinforce the correlation. I then added two more charts that should help the reader visualize the data clearly.

Overall, the theme of the map was to utilize darker colors as the background with lighter colors as the main visual elements. I did this with an overall black back ground with the various elements having a dark grey background. This allowed the different elements to have enough separation from one another so that they could be easily distinguished.

Tuesday, March 7, 2017

Terrain Visualization

Terrain visualization is an important skill to have when working with GIS data. It helps to know how to convert a two-dimensional image into something that appears three-dimensional.
For this week, we were tasked with taking a land cover assessment of various tree species in Yellowstone national park and along with a Digital Elevation Model (DEM), make a map that showed the land cover as well as showing the terrain features. My product for this task is above. I first made sure that the land cover data was easy to read and made sense. I updated the color scale to group the different assessments for the tree species together with varying shades of color. I also made sure that the non-forested areas were an easy to identify color (white) and that water features were blue. I then used the DEM to create a hill-shade layer. The land cover data was place over the hill-shade as an overlay and made semi-transparent. This made it so that the hillshade showed through and the colors of the land cover were correctly represented on the legend. 

This map in the end, clearly shows both sets of data in a clear and concise way. Both datasets work well together and neither one out-competes the other for attention.

Tuesday, February 21, 2017

Choropleth Mapping

Choropleth mapping is a common mapping style which allows a GIS user to display data over a wide area. Choropleth maps are used to display a particular field (or fields in some cases) against a color gradient. These maps are often used to show changes in data such as population changes.

For this week we were tasked in creating a Choropleth map for a state of our choosing using the change in population on the county level from the year 2010 to 2014. Here is my final product:


My main goal with this map was to make everything as easy as possible to read. There are a lot of variables to be considered with this type of map. Things such as the class breaks for the data and the color scale used to display it are all factors that can have a huge impact on the final map.

For my data breaks, I used a defined break classification system were I made each of the classes have a data range of 5%. ArcGIS will then auto create the right number of classes needed to display all of the data with the given range, in this case that was 6.

My focus after that was to make sure the map flowed well. I used colorbrewer2.org/ to create an appropriate color scale that would allow my data classes to have enough separation from one another and also be easily distinguishable.

Arguably the most important part was the legend. I made sure to make it easily understandable and also on the same plane as the rest of the map, hence why it is in the main data frame. The legend title directly corresponds with what the data represents so that there is little to no confusion for the reader.

After that, all I needed to do was to make sure everything else looked good for a publishable map. I made the rest of the United States a background feature and made the frame background a light blue color to represent the Atlantic Ocean.

Overall, I am very proud of this map and it will definitely be going into my map portfolio!

Monday, February 13, 2017

Proportional Symbol Mapping Using US Job Fluctuations

Proportional symbolism assigns graphical increments to map data so that the user can easily visualize changes using a method other than examining statistics or other values. It makes map usage much quicker to provide a point of insight rather than just a list of numbers. For this week, we were tasked with creating a proportional symbolized map using US job number fluctuations. This was represented by the number of jobs a state gained or how many it lost. For negative values, we had to get creative and make an absolute value column in the attribute table of a separate dataset containing only the states with declining jobs so that they could be represented using the same symbolism for the positive values.


To distinguish the values, the negative values were symbolized using red proportional circles and the positive with blue. The resulting map makes it easy to visualize areas of the US that are having success in growing jobs, as well as the magnitude of that success, and vice versa with negative job growth.