Tuesday, June 6, 2017

Physical Spatial Database Design

Above is my entity relationship diagram (ERD) is used to create and import data into PostGreSQL (PGSQL).  The diagram above shows how individual entities within the database are linked to one another.  For example, the parcels entity is related to the sales entity via a one to zero or many relationship.  Similarly, the parks are related to the parcels by a one to at least one or many entity.  The development of these diagrams greatly help when importing the data into PGSQL.  Each entity within the diagram is able to be exported in PGSQL format that allows for the creation of a PGSQL database.  Furthermore, .csv files are able to be imported into the PGSQL database to give the entities values or information associated with them specifically.  The shapefiles themselves are also able to be imported into the PGSQL database further adding to the database.

Tuesday, April 4, 2017

Temporal & 3D Mapping

In this weeks lab we looked at the possibilities to display temporal data over time using ArcGIS's time functionality.  This function allows for the display of information in an interactive map format over the course of a given time period.  In the example above, the map shows the extent Global volcanic activity over the time span of 589 years in relation to the Earth's tectonic plates.  By enabling timing within a certain data layer based on an attribute, in this case years, the above map was able to exported into a movie form, more specifically, an .avi file.  When played, the map sequences through volcanic activity for each individual year, subsequently adding each event to the map.  We also looked at the population growth of the contiguous United States over the course of time by using proportional symbols.  The population growth map adjusted the proportional symbols in relation to population with each individual year in a similar fashion.  The use of time enabled sequence mapping coupled with the ability to produce interactive movies of the data allows analyst to show temporal information in a format that is very easily comprehendible.  Rather than displaying said temporal information in separate maps side by side or in a slide deck format for comparison, the map itself is able to show the changes in data over extensive periods of time.  As you can imagine, this function can give an audience a much better understanding of the temporal information.  I like to think of it in a sense of weather mapping.  Weather is generally mapped in a very similar sense, only on a minute by minute basis as opposed to years.

Tuesday, March 28, 2017

Bivariate Choropleth Mapping

The use of bivariate choropleth mapping can be exceptionally useful when comparing the data associated with two variables.  Rather than using the single variable side by side maps for comparison, the use of bivariate mapping allows readers to see the comparison all in one map.  In order to make the two variables usable in such a format the individual values must be given a new value that designates the classification that the normalized value fits under.  In other words, all the values that fell into a particular classification will receive a new value defining the classification.  By assigning each variable a 3 class classification, the resulting map will be comprised of a total of 9 different classes displaying the relationship between the variables.  The color choice of the legend can literally make or break you final map product, a contrasting color choice that has enough variation to also include suitable colors for the intermediate classes is imperative.  The bivariate style of choropleth mapping can be confusing if not property displayed.  However, as you can see above, the successful combination of variables along with colors can ultimately lead to an easily understandable map and information.

Wednesday, March 22, 2017

Analytical Data Lab

Overall this lab was rather challenging.  The ability to accurately, effectively, and understandably display analytical data coupled with geographical data is not as easy as one may think.  First, given a set of data we were tasked with choosing a set of variables to analyze and effectively show the relationship the two variables share between one another, more specifically, how that relationship relates to the geographic relationship the two variables share between one another.  Unfortunately, I was unsuccessful in showing the relationship geographically.  However, I was able to show a positive relationship between my variables with the assistance of a scatterplot and consequent trend line.  I was not able to directly relate that relationship to geographical aspects, such as counties, that were included in the data.  Overall, I was able to display my variables in the form of an infographic.  By displaying national county wide data for each variable, I was able to come up with what I though was a fairly decent display of the data.  Having used only two variables, I elected to use contrasting colors, blue and red, for each respective variable.  I was also able to pull out some interesting features of the data in the form of bar graphs and display them in a fashion similar to the mapped data, the bar graphs retained the color of their representative variable in order to alleviate any confusion that may arise due to the seemingly overwhelming amount of information included in the document.  I also elected to use written statements to convey the analysis of the data, I found this to be the most effective means, but do agree that it took away from the 'wow' factor of my document.  In short, I needed a significant amount of more room to work with in the document.  The limited space limited far more than just the display, I felt as though the variable warranted a more in depth analysis all of which needed to be included within the info graphic, a successive chain to results style, but we made do with what we were allotted.


Wednesday, March 8, 2017

Terrain Visualization

This lab exercised tasked us with giving a two dimensional set of land cover data depth and a 3-D effect by incorporating a hillshade effect into the display of the data.  By utilizing a digital elevation model of the same area that comprises the land cover data and deriving a hillshade raster data set from the DEM, I was able to successfully portray the land cover data into a depth implied map.  As you can see from the image above, the data in the land cover map begins to show changes at specific elevations and areas.  I was able to achieve this by simply applying a transparency to the land cover data while displaying the hillshade model in a gray scale behind it.  This took a lot of trial and error to complete.  When applying a transparency setting, some color is lost or even has the potential to blend with other colors.  I had to continually go back and forth to ensure each color representation retained the same amount of hierarchy as the next.  I also needed to make sure the different colors were distinguishable between one another.  Overall, I am pleased and impressed with the turnout.  The ability to show a relation such as elevation and subsequent land cover can prove to be imperative in terms of environmental analysis.

Wednesday, February 15, 2017

Choropleth Mapping

Choropleth mapping is a fairly common style of cartography used for many different applications to show how a measurement varies across a geographic area through the use of color and  classifications.  In this weeks lab, we were tasked with showing the change in population from 2010 to 2014 across the counties of a particular state, I decided to map the change of population by county for Colorado.  By normalizing the population data for the time period, I was able to construct a choropleth map showing the negative and positive difference in population for the entire state.  The loss and/or gain for each county is represented in the form of a percent of the population for the time period.  As you can see, the range of change in population varied from approximately (-15%(a loss) to 15% (a gain)).  Rather than using a color ramp of decreasing or increasing shades of a single color, I chose to use individual colors for the scale.  The use of individual colors within the scale was possible due to my decision to only classify the data into 6 different classes.  By limiting the number of classes and still portraying the data as accurately as possible, I was able to avoid a large confusing color scale and/or color ramp.  Furthermore, I inserted a legend into the map to represent my choice in classification and color.  Notice the indicator lines remarking which colors represent loses and which colors represent gains.  Overall, when it comes to choropleth mapping, simplicity sells.  The easier it is for the reader to interpret the information displayed within the map in the most holistic way possible the better.

Monday, February 13, 2017

Symbol Mapping

For this weeks lab we were tasked with mapping proportional symbols for job variations within the United States over a period of time beginning in Dec. 2007 and ending in July 2015.  Part of the challenge was to show individual states' job increases or decreases and denote legibly whether or not the individual state had experienced loses or gains over the period in time represented.  In order to do so, I used proportional symbols (in this case circles) to show the loses or gains in jobs for each state.  The loses are represented in red proportional circles, while the blue proportional circles represent the states that saw gains in jobs throughout the time period.  Furthermore, the states depicted in green also represent gains in jobs while the yellow states represent loses in jobs.  Another task for this weeks lab was to make the information displayed in the map as legible and reader friendly as possible in the map's legend.  I chose to stack the proportional circles on top of one another to 1) show size comparison, 2) save room rather than itemizing each symbol size, and 3) make the legend more legible.