This week's module focused on the creation and interpretation of contours and hillshades in ArcGIS Pro. In order to create a hillshade for the land use map I used the default traditional options in the Raster Functions pane. I then adjusted the transparency of the land use layer to show the hillshade underneath. The land use layer consisted of natural features, so I tried to use natural colors in the symbology as much as I could without negatively impacting the visual clarity of the map.
This week's lab focused on examining various coordinate systems and map projections. We were tasked to create a map layout of a chosen US State, using an appropriately selected projected coordinate system. I chose Nevada as my area of interest, and used the NAD 1983 (2011) UTM Zone 11N coordinate system. The State Plane coordinate system was less appropriate as Nevada had three State Plane zones, versus it's singular UTM zone.
This introductory module emphasized the importance of proper symbology, typography and general map design. We were tasked to design a tourism map for the City of Austin showing the location of rec centers, golf courses and the county's water bodies.
This week's module involved performing unsupervised and supervised classification using ERDAS Imagine. We were provided satellite imagery and created a land use classification using spectral signatures. Additionally, I generated signatures for roads and water, but due to spectral overlap between road and deciduous forest signatures, I had to create additional signatures to address this issue.
This week's module delved further into the features of ERDAS Imagine. We learned how high-pass and low-pass filters change aerial imagery, how image histograms are used to assist in feature identification, and how different band combinations enhance specific features such as water bodies.
This week's module introduced us to ERDAS Imagine and it's features. Specifically, we were tasked with creating a subset of an aerial image in ERDAS to use as a base for a map layout in ArcGIS.
This map represents the land classification of a subset of Washington State by hectares. This map frame was clipped from within ERDAS.
This week's module involved digitizing an area of Pascagoula, Mississippi. We had to classify aerial imagery of the area by land use or land cover based on features identified. The classification used was the USGS Standard Land Use / Land Cover classification system and the imagery was classified up to Level II. The creation of this map involved heavy use of Clip within the Modify Features pane of the Edit tab.
The objective of this first module was to teach us how to interpret aerial imagery based on criteria such as Tone (brightness), Texture, and recognition elements such as Shape Size, Shadow, Pattern and Association. These criteria help to distinguish features on aerial imagery. The map layout below shows different tones and textures found within aerial imagery. The layout was made without a north arrow or scale bar due to the raster image having an unknown coordinate system.
This final module explores the effects of scale and resolution differences on vector and raster data. We were given a data set of hydrographic vectors rendered at three different scales: 1:1200, 1:24000 and 1:100000. After exploring the dataset I found that larger scales such as 1:1200 can capture smaller details and finer geometric shapes better than smaller scales such as 1:24000 and 1:100000 since detail is lost as intricate features are simplified. These smaller scales can also cause small features to be completely excluded as the scale makes them comparably insignificant.
We also examined how the Modified Area Unit Problem (MAUP) can effect spatial data by using a nonwhite % vs. below poverty line % dataset as a case study. The data shows that the Scale Effect of the MAUP can cause results to vary if the units of analyses are differently sized spatial units (Zip Codes v.s. Counties, for example). The smaller the spatial scale, the more divergent the results can be.
Lastly, we examined a map of the U.S. Congressional districts for gerrymandering. Gerrymandering is a political process in which a group in charge of redistricting draws districts in a way that would devalue an opposing group's vote. This can be done by clumsily clumping a target population's entire area into a single, messy district, resulting in them only winning one district. Another method of gerrymandering is done by drawing the districts in a way where the target population's area is heavily split up so their votes get drowned out by the other voters, resulting in them winning no districts.
Pictured above is an example of one of the worst cases of gerrymandering in terms of compactness. Compactness is defined as the area a feature occupies in relation to its area center, an can be scored using the Polsby-Popper Score. This score is calculated using the formula 4π(Area)/(Perimeter)^2, resulting in scores between 0 and 1, where lower scores are considered bad. The district in the screenshot above achieved a very low score of 0.08.
