Finding Site Locations

This week in Intro to GIS, we utilized buffer and overlay tools to analyze data in order to find possible campsites at Fort De Soto National Park. We looked at certain distances away from waterways to prevent choosing a site in a flood zone, as well as maintaining that the sites were not too far from roads for easy access. We then removed any conservation areas that matched the before said criteria to prevent the disturbance of native/local plants and animals in active conservation project areas. Lastly, we determined the area size of the possible campsite locations in hectares (ha). The possible camping sites that meet all three criteria are depicted below in Figure 1 by their area size.

Figure 1: Viable campsites at Foto de Soto National Park, that meet the criteria of assignment Vector 2.

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Positional Accuracy: NSSDA

In this analysis, I compared the street and road intersect data collected for Alburquerque, NM by the City of Alburquerque and the application StreetMaps. I used an orthophoto base layer as the reference for this analysis, to compare and determine the accuracy of both the City and Streetmap layers using NSSDA procedures. The most difficult part of this analysis for me was how to determine what 20% per quadrant looks like. Because the reference map was divided into 208 quadrants, I had to determine how to subdivide all the quadrant's equality into 20%. After multiple trials and error, I decided to subdivide the entire area (208 sub-quadrants) into 4 equal-area subsections. In this way, I could do 5 random right intersection points per subsection or 20% per subsection. Map 1: City of Albuquerque city map data. Map 2: City of Alburquerque SteetMap data When selecting a random intersection to place the points within each quadrant, I choose a location that had data f...

Utilizing ERDAS Imagine to Analyze Map Features

This week we learned how to utilize histograms and different bands to highlight different features in a map. On the following map that we worked on, dark bodies of water caused high peaks on the left of histograms while snow-peaked mountains were small blips on the far right. These simple distinctions help to quickly identify map features on a graph, that you can then utilize as a stepping stone to finding them on the image. I found it incredibly interesting how the different band layers highlighted different features on the map. Figure 1 below depicts three different features we found on the image. Figure 1: Distinct features found on an image using ERDAS Imagine. Feature 1: Large body of water. Feature 2: Snow-capped mountains transitioning to thick vegetation. Feature 3: Shallow turbulent body of water near urbanized land, transitioning to deep calm body of water.

Choropleth and Dot Mapping

This week we explored choropleth and dot mapping. Choropleth is a thematic form of mapping that focuses on color units, whose color intensity is proportional to its corresponding data value. Dot mapping is also thematic. It uses either a proportional or graduated thematic symbol (like a circle), whose size increases due to its data value. Using ArcGIS pro, I analyzed the population densities of countries in Europe (person per square kilometer), as well as their wine consumption (liters per capita) to determine if there was a correlation between the two. In my choropleth map, I decided to use a natural breaks classification. I chose not to use Equal Interval because only 2 classes (with slight 3 rd class) were represented in the map, and it looked like almost just one color in the lower range. The standard deviation classification appeared to be more diverse at first glance but was actually skewed to the top ranges. I was then between Quantile and Natural Breaks. While both t...

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