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Digital Data

Introduction

In this lab, we were introduced to available digital data sets that are commonly used, while also integrating different, less common digital data. There is a lot of data available online, and it is important to know how to use and manipulate it. For this lab, we learned how to use basic selection by location, basic table operations, learning how to use data’s attribute tables, and how to change, add, or remove table columns and values. Digital data is important to GIS as it enables dynamic, real-time, and precise spatial analysis that is not achievable on paper maps.

Resources Used

ArcGIS Pro

Process

The first exercise was to learn and create a map by manually selecting its features. Luckily for this lab, we were provided a file containing all the public digital data we would need. We started with a map that showed all the cities in the United States. We were focused on a small island on the coast of south-central Alaska called Kodiak Island. After finding this island, we changed our map projection to Alaska State Plane NAD 1983 (2011) State Plane zone Alaska 5 (FIPS 5005), US feet. We then used the select tool in ArcGIS Pro to select the region we wanted to study. The following map was our final product.

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The next exercise we were focused on was using Census Data. This digital data was the census data for a part of the Minnesota/Wisconsin border. We first examined the attribute table and statistics and concluded that the dataset was considered to have “long-tailed” distributions. Next, we opened the data’s symbology and used graduated colors, showing the population density, with no normalization, a geometric interval method, and 20 classes. The following map was our final product.

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The next exercise was still using census data, but was focused on the population density of the lower 48 states in America. For this exercise, we used a shapefile. This shapefile showed all 50 states. We used the select tool to project just the lower 48. In the attribute table, there were many columns that were not useful to our exercise. We deleted every column other than the ObjectID, NAMELSAD10, and DP0010001, using the Delete Field tool in our tool utility. This allows us to rapidly delete many fields quickly and efficiently. Then, we were introduced to a new way to symbolize data by using the graduated symbols quantities in the symbology properties. We set the constraints to a specific value field, 5 classes, natural break classification, and the symbol size ranging from 1 to 18. This places circles that were proportional to the relative size of the counties. We then wanted to show the population differences for counties, so we modified a single population county by modifying the largest class to have a size of 48 to better reflect the relative county populations.

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The next exercise was focused on digital elevation and NHD data. For this map, we used the UTM Zone 15N NAD 83 projection, and our file was from the United States Geological Survey. Using the label properties, we inserted all the counties in the Sub-basin. We then exported the data set for only the Lower St. Croix watershed. Next, we created a shaded-relief elevation map using the Hillshade tool in our spatial analysis, which is a familiar tool that we have used in the past. Then, we added the waterbody that contained lakes, ponds, and reservoirs. Using symbology, we classified certain colors to specific layers. The following map was our final project.

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The last exercise was the NWI data and basic table manipulations. In this new map, we wanted to assign three classes of wetlands: small, medium, and large, as well as Upland and Wisconsin polygons. Using the attribute table, we sorted the area column in descending order. The table did not label the size classes, so we learned how to manipulate and add a column. After adding a size column, we calculated the field, which ran and added the new column to the attribute table. To sort the data into small, medium, and large, we used the select tool to highlight all the rows. Small was assigned to the values between 222.62295 and 2993.85555. Medium was assigned to the values between 3001.8695 and 9969.29615. Large was assigned to the values between 10076.1794 and 5103526.79121. All values labeled “U” were assigned to the size ”Upland”, and confirmed that the one value labeled “OUT” was assigned to the size “Wisconsin”. The following map was our final project.

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Summary

This lab focused on working with digital data and using it to create and analyze visual maps. We created five different maps to explore how data can be represented spatially and how changes in data classification, symbology, and scale affect interpretation. Through this process, we practiced importing digital datasets, organizing and managing data, and applying mapping techniques to clearly communicate information. The lab emphasized the importance of accurate data representation and how maps can be used as effective tools for analyzing patterns and relationships within digital data.

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