Monday, May 29, 2017

GIS 6110 Module 2: Physical Database Design

Figure 1: Parks table, first ten records ordered by "gid" column.

Figure 2: Parcels table, first ten records ordered by "gid" column.

The second module of Advanced Topics in GIS built upon the introduction to entity relationship diagrams (ERDs) last week to include a physical model, the next step after the logical model. The ERD of the physical model is similar to the logical model, but it includes the datatypes of each attribute. This gets us closer to a database model that can be directly implemented as tables in a database.

The bulk of the time spent on this module's exercises, however, focused on using PostgreSQL and PostGIS to create, import and query spatial and non-spatial data. This was a good introduction for me, and a welcome addition to skills learned previously from ArcGIS and Oracle products. The figures above show sample data from two tables populated by data imported from shapefiles using the PostGIS Shapefile Import/Export Manager utility. After connecting to the desired PostgreSQL database, the user must input the SRID number defining the spatial coordinate system of the data. The method used for this module was to utilize the online conversion tool located at Once the SRID number is defined in the PostGIS utility, the shapefile is imported as a PostgreSQL table.

This module was just the beginning of the integration of GIS and database skills covered in previous courses in the UWF program. I look forward to seeing how far we can go this semester.

Tuesday, April 4, 2017

GIS 6005 Lab 10: Temporal Mapping

Our task this module involved experimenting with two sets of sequential data in ArcMap as seen in the screenshots above. The first map depicts the most populous cities in the U.S. from 1790 to 2000. By including a column in the city layer attribute table for year, we can enable the use of ArcMap's time functions in the layer's properties. From here, the field, format, interval, and other options are set to display time as required. Finally, using the Time Slider window, we can cycle through each year and the symbology for that year's city populations. This can be captured in a video for playback outside ArcMap. The second map, depicting the eruptions of volcanoes, went through a similar process. In this case, the option to 'Display data cumulatively' was engaged, keeping the symbols for each eruption on the map as new ones are added. This allows the patterns of eruptions along fault lines to form as each eruption is symbolized through time.

Like most tools available in ArcMap and in GIS in general, creating videos of data symbolized through time may be helpful or irrelevant. In the cases above, there is too much temporal data to display simultaneously, thus creating a video or interactive interface to depict the data over time makes sense. Doing so may reveal patterns more directly than a series of static maps. As in most cases, the usefulness of these tools depends on the data, the audience, and the intention of the cartographer.

Tuesday, March 28, 2017

GIS 6005 Lab 9: Bivariate Choropleth Mapping

Bivariate choropleth maps provide an efficient way to display two variables and how they are related in a manner that is easy to interpret by the viewer. Like univariate choropleth maps, a bivariate map uses color progression to symbolize classes of data, the difference being its use of two color ramps for two related variables and the combination of those ramps to display the correlation of those variables.

Creating a bivariate choropleth map requires preparation of the data. We first must ensure the data for the two variables are normalized. In the case of the map above, the data for obesity and physical inactivity were already normalized by population. Next, we will create a new field in the attribute table for each variable and populate them with codes for each variable class. To keep the map understandable for the viewer, each variable is generally limited to three classes; this is because the number of classes displayed in the final map is exponential based on the number of classes for the two base variables. Thus, three classes for the two variables equals nine classes in the final map. Four classes for the variables would create a confusing map of 16 classes.

A third field is added to the attribute table and is populated with the concatenation of the two fields created above. This field will be used for the symbology of the final map and represents the combination of the two chosen variables (i.e., obesity and physical inactivity rates). The creation of the color symbology in the above map required some experimentation. The foundation of color choice for bivariate choropleth maps lies in choosing complementary colors for the two variables. Hue, saturation, and value options can then be adjusted for the overlapping classes. Once the symbology is finalized, the legend must be manually adjusted to create a suitable bivariate choropleth legend. This is accomplished by converting the legend to graphics, ungrouping the elements twice, and manually placing the color squares in their final placement. Text elements may then be added to label the legend as is appropriate.

Tuesday, March 21, 2017

GIS 6005 Lab 8: Analytical Data

Our task this week involved three distinct steps: Downloading and processing raw data, choosing variables that are suitable for comparison and visualization, and creating a map layout to display multiple maps, charts, and text. The data came from and included information on a host of variables broken down by state and county. We first chose two variables we thoughts may be correlated (negatively or positively); I chose obesity and unemployement with the hypothesis that there would be a positive correlation. In order to incorporate this raw data into ArcMap, we created new Excel worksheets containing only the data needed. These worksheets were then used to create charts visualizing our chosen variables. They were also joined to two separate United States maps in ArcMap and symbolized as choropleth maps.

The most difficult part of this assignment, however, was the final task of creating a layout to contain the various maps and charts. I chose to keep the layout as simple as possible with a white background and no neatlines or borders. This allows the viewer's gaze to flow easily from one element to the next. I settled on a tabloid-sized page in landscape orientation in order to stack the two maps on the left and to make room for the charts on the right.

Based on my end product, there does not appear to be a correlation between obesity and unemployment. If a correlation is present, it is relatively minor.

Wednesday, March 8, 2017

GIS 6005 Lab 7: Terrain Visualization

The various tasks for module 7 have taught us ways to aid map viewers in visualizing the terrain that is being displayed. One of the more precise methods is to display and label contour lines; such maps are familiar to hikers and anyone familiar with USGS map products. Another method is to apply hillshade to a DEM (digital elevation model). Hillshade tools, such as provided by ArcMap, take the elevation data of a DEM and simulate the visual effect of the sun at a specified angle and direction. This visual effect greatly aids the viewer in reaching an intuitive understanding of the topography of the displayed area. Hillshade can be combined with other map elements, such as the above symbology representing tree and landcover types for Yellowstone National Park, to provide the viewer with more information. In this case, the landcover layer is set to a transparency of 30% to allow the hillshade layer to be seen. This gives the viewer an understanding of the relationship between landcover, topography, and elevation that would not be possible if one used hillshade or landcover alone.

Wednesday, February 22, 2017

GIS 6005 Lab 6: Choropleth Mapping

One of our tasks this week was to use a diverging color scheme to represent positive and negative population change in the counties of our chosen state. We first needed to apply a suitable projection for our state. In my case, I chose UTM Zone 13N for Colorado. This UTM zone covers the majority of Colorado, leaving only a strip along the westernmost border outside the zone and thus mildly distorted. If we were required to work with detailed spatial statistics this week or were focused on that section of Colorado, I would have chosen a different projection or created a custom one.

The next step was to normalize the data. Rather than use the normalization options in the layer properties, we created a new attribute in which we used the field calculator to calculate the percentage of population change by county from 2010 to 2014. This was the field we used as the basis for our choropleth maps.

We then faced three main tasks: Decide on the classification details (method and number of classes), how to symbolize the classes, and how to design the legend. I began by using the Natural Breaks classification method with seven classes. Seven classes would allow for one class representing minimal population change and three classes each for population increase and decrease. Natural Breaks was a good start, but, for this general reference map without any other specified purpose, I wanted to create a more symmetrical and directly comparable classification scheme to more easily see how counties compared. I manually adjusted the population increase and decrease classes to mirror each other.

For symbolizing these classes, I based the diverging color scheme on one included in ArcMap. I did adjust the HSV values, primarily saturation, to even out the contrast somewhat. The orange to red color ramp seems natural to represent a decline and is easily distinguished from the green color ramp. The yellow representing the middle class is distinguishable from both ramps while fitting in to both color schemes.

The final piece of the layout was the legend. My original intention was to use the legend property options to create the legend, but these were too limited. The data naturally fall into three categories (as I have classified them), so I converted the legend to graphics in order to split the legend into three categories. This created a legend that was more legible and intuitive to interpret.

Wednesday, February 15, 2017

GIS 6005 Lab 5: Symbol Mapping

Our lab exercises this week introduced us to some of the difficulties cartographers face when displaying data using proportional symbols. It can be an intuitive way to present data, yet it can also be a struggle to create a map that does not create unnecessary confusion. In the above example, we were required to use proportional symbols to communicate job gains (a positive number) and job losses (a negative number) by state. The first hurdle was dealing with the negative job numbers; directly symbolizing both positive and negative numbers from a single layer in ArcMap does not produce an acceptable result. We needed to export selections of states with positive and negative job numbers into two new layers. A new field was then added to the 'states with job losses' layer in which the field calculator was used to convert the negative numbers to positive. The resulting two layers were then used as the basis for the proportional map above.

The primary variable in creating this style of map is choosing how large the symbols should be and if Flannery's compensation should be applied. In this case, compensation was not applied. However, there was a discrepancy in size between the two layers. Experimentation with the minimum symbol sizes created proportional symbol progressions that were equivalent. Once I finalized the legend layout (converting it to graphics and editing it within ArcMap), I wanted to apply a type of transparency that is not available within ArcMap. I exported the layout to Adobe Illustrator and applied a 'multiply' transparency to create the transparency effect seen above. This minimizes the interpretive problems created by symbol overlap.