Google Earth

For Module 12, we created a kmz file from our Module 10 Dot Density map for Google Earth and created a tour of Miami, Ft Lauderdale, St Petersburg, and Tampa, FL. Google Earth is an interactive 3D representation of Earth based on satellite imagery. Data can be uploaded into Google Earth and saved as a kml file that is accessible by any ESRI, KML, and Google Earth client. It is a simple way for users who lack GIS knowledge to access geographic data. To create the kmz file, I converted the appropriate features, Surface Waters and Florida Counties, to KML. To prevent ArcPro from crashing, I had to add an attribute field POP10000 on the dot density layer and calculate the field by using the Python expression: !Sheet0_Population!/10000. Once the new field was calculated, I used the Create Random Points tool for the dot density feature class. Then I ran the Layer to KML. I adjusted the legends for the features and dot density in ArcPro and used the snipping tool to save each image and added the image overlay into Google Earth. For the tour, I created placemarks for each location. I created the tour by recording each placemark and zooming in and around the area for an in depth view of the 3D buildings.

3D Mapping

For Module 11, I completed numerous Esri exercises about 3D mapping and converted a 2D map to a 3D scene. 3D view consists of four main elements: surfaces, textures, features, and marginalia and effects. This will include ground surface, aerial imagery/cartographic maps, relative to ground features/know their own absolute z's, and reference aids/atmospheric effects. They can be represented as photorealistic (real world) or cartographic (representative). 3D maps are powerful tools for a user because they are immersive and eye-catching.  Below is the 2D map that was converted to a 3D scene of Downtown Boston. There are two applications of the 3D building layer. The buildings polygons are extruded by height value to create 3-dimensional building shapes. This enables the viewer to visually focus on a specific area and to view the buildings from different perspectives as well as compare building heights. A map user could also focus on a single building and examine the shadow effects and sun exposure and its surrounding landscape. 

Dot Mapping

This week's Module 10 lab we learned about the dot mapping method. Dot maps are a type of thematic map used to help visualize distribution and densities of a large number of discrete numerical points. Dot maps rely on visual scatter to show spatial patterns and are commonly used for population maps.They display a good visual representation of variations and can also be used for statistical analysis. Dot maps have good spatial representation. Dot maps are easy for map readers to interpret. Dot maps visually match phenomena that changes smoothly over a space. There are a few disadvantages to using dot maps. The clustering can make it impossible to plot and interpret dot maps. Large numbers of dots are difficult to count and calculate actual figures. The size of the dot has to be carefully selected to display data clearly. The areas with no data give a false sense of emptiness. 
For our assignment this week, I mapped the population density of South Florida. I chose a size 3pt for the the dot symbol and the color red. I opted to no color for the county borders and the bodies of water for the main portion of the map because it made the map look cleaner. For the urban areas, I chose a mauve color and used a 50% transparency. I created an inset map for the state of Florida that included the county border outline for reference. I created two legends for more clarity for the map reader. One of the legends depicted the dot symbols and the proportional count. I created this by using the rectangle tool to make one square and copied it to make two more. I then used the circle tool and added the dot symbols. 

Flowline Mapping

For the Module 9 lab, we employed a radial flow map to depict the migration of immigrants from around the world to the United States. The map has a spoke like pattern in nodal form with all of the flow lines ending at the same destination, the United States. For my map, I opted to go with the Flowline Basemap A with the choropleth map located in the inset map. I chose to leave the continents in place and made all of the flow lines the same red color. I used the weight point that I calculated in excel to show the proportion of the migration count from each country. I set the opacity to 75% and added a drop shadow to all of the flow lines. I chose a brown to light tan graduated color scheme for the choropleth inset map. These colors were noted in the legend in the manual breaks method with the lightest colors correlating to the lowest immigration percentage. The legend also contained the immigration totals per country in descending order to match the color legend. I changed the color scheme for the countries and chose slightly vibrant colors since it was a world map and my map was not heavily stylized.

Isarithmic Mapping

Module 8 introduced the class to Isarithmic mapping. Isarithmic maps depict smooth, continuous phenomena across an area using varying symbology methods. The phenomena are measured at control points and interpolated using the appropriate method. We mapped the state of Washington depicting the annual average precipitation from 1981-2010. The precipitation data was derived by measuring at control points and interpolated using PRISM (Parameter-elevation Relationships on Independent Slopes Model). This interpolation method accounts for major physiological factors (location, elevation, coastal proximity, topographic orientation, vertical atmospheric layer, topographic position, and orthographic effectiveness of the terrain) influencing climate patterns. As precipitation generally increases with elevation, PRISM integrates elevation into the surface by utilizing a digital elevation model (DEM). I created two maps with one using continuous tone symbology and the other using hypsometric tinting. The first map is a continuous tone map showing the annual average rainfall in Washington state from 1981-2010 using smooth stretch symbology. The second map implements hypsometric tinting and utilized classified symbology with the data manually divided into 10 different classes. Relief was incorporated into both maps by employing the hillshade effect but the hypsometric tint map also shows contour lines. The hypsometric tint map was ultimately used for the end product because it is ideal for geographically smaller areas such as a state where the continuous tone map would have been more appropriate if the map was of the United States.

Choropleth Mapping

This week's lab we produced a choropleth map denoting the population density of European countries and their wine consumption. A choropleth map is a thematic map in which enumeration units are shaded by intensity proportional to the data values associated with those units. The data needed to be normalized by using population density versus raw population count because the latter can be deceptive when land polygons are not the same size. The eye naturally follows areas of the same color giving larger polygons undue ranking and minimizes the significance of smaller polygons. For this map, I used the Europe Albers Equal Area Conic because equal area projections are ideal for choropleth maps especially when choosing to map population density. It shows the map is displayed in true proportions to its size on Earth. For the data classification, I chose the quartile method because it included all of the classes and contained a clear breakdown of the data displayed. Some of the other classifications did not include the darkest color class. I chose a graduated color ramp of brown light to dark that is color blind friendly. Since choropleth maps display alot of data by using color, I wanted my map to be easily read by every potential user. For the wine consumption, I chose the graduated symbols because the proportional symbols overlapped my entire map. I did not normalize this data because graduated/proportional symbology represents  numerical data associated with point locations, not area. 

Data Classification

This week's lab we learned about the 4 common data classification methods: Equal Interval, Quantile, Standard Deviation, and Natural Breaks. We compiled two maps using the Miami Dade County 2010 Census tract data to display each classification method. The first map showed the population percent of senior citizens aged 65 and above. The second map showed the senior citizen population normalized by area. Equal Interval is a classification method where data is represented by classes that contain an equal amount of data values. The range of the data is divided by the amount of classes you want to have. This method is the easiest for the reader to interpret and it is also the easiest to prepare. However, there can be an unequal amount of distribution within the classes that can cause entire classes to be unrepresented with fill color on the map or for one class to dominate the map. Quantile is a classification method where data is sorted into a certain amount of categories with each category containing the same number of values. The total number of observations is divided by the total number of classes. While you will never have empty classes, you have to manually adjust your break values to compensate for tied classes. Similar features can be placed in adjacent classes or features with grossly different values can be placed in the same class. This distortion can be decreased by adding classes. Standard Deviation is a classification method where the standard deviation is added/subtracted from the mean of the data. The data needs to be normally distributed to give your classes clear dividing points. The audience target should be considered as this statistical representation might not be easily understood. Natural Break is a classification method where the natural groups in the dataset are considered. This minimized the differences between data values in the same class. It does consider outliers and places them in their own categories but clusters are placed in one or two classes. It can be difficult to compare two or more maps with the natural break classification because each map range is data specific.

The first map, under the symbology tab, I selected the graduated color (green hue, light to dark) with the field PCT_65ABV for all of the data classification methods except for the Standard Deviation. For the Standard Deviation method, I used the dark brown-tan to light blue-navy blue. The map contained all map essentials with four data frames that contained a legend in each. I used the same layout for the second map except I normalized the area in square miles under the field AGE_65_UP. The population count normalized by area more accurately depicts the distribution of senior citizens of Miami Dade County. The percent above 65 data presentation can be misleading in that a large tract can have a high percentage of senior citizens residing there but contain a low population count. Since the percent above 65 presentation does not factor in area, a small tract can be densely populated while a large tract can be sparsely populated. When the data is normalized by area, the reader can focus on the areas that are densely populated.