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Week 5

Lab 7 Report: Table Operations and Joins in ArcGIS Pro

In Lab 7, the main objective was to develop stronger skills in tabular data management within ArcGIS Pro. The lab focused on viewing, selecting, calculating, reordering, and joining attribute data while producing three required maps. This lab emphasized that GIS analysis is heavily dependent on attribute tables and not just visual mapping. The datasets used included USCounties.shp in a Continental Albers projection and soils.shp in UTM Zone 17 NAD83 meters. The final deliverables consisted of Map 1 Oldster Counties, Map 2 Cow Density, and Map 3 Macon County Soil Fertility, along with a newly created soil properties table.

To begin, I opened ArcGIS Pro and added USCounties.shp to a new project. I examined the attribute table to understand the variables provided, which included census and agricultural data such as POP2000, Med_Age, BURG01, CropAcres, and Cows. One of the first required tasks was working with burglary statistics. I created a county burglary map using the BURG01 field and symbolized it with a quantile classification and 10 classes. This initial map showed that burglary counts were closely tied to county population, which indicated that normalization was necessary for meaningful comparisons.

To correct this, I added a new numeric field called BurgRate and used Calculate Field to compute a burglary rate based on population. Once calculated, I updated the symbology to display burglary rate rather than raw counts. I then used the Select by Attributes tool to build a query that identified counties with high burglary rates. Creating and running the query reinforced the importance of correctly defining clauses and operators. I confirmed the selection by comparing both the map and table views.

Next, I calculated vacancy rates by creating a new field called VacRate. The calculation used vacant housing units divided by total housing units multiplied by 100. I verified the results using table statistics to ensure the values were reasonable. I then created Map 1 Oldster Counties by calculating an age index representing the ratio of people over 65 to people 21 and under multiplied by 100. Using Select by Attributes, I applied conditions that selected counties with an age index greater than 85 and total population greater than 150000. After confirming the correct number of counties were selected, I exported them to a new shapefile and symbolized them with a contrasting color for the final layout.

For Map 2 Cow Density, I started a new project and joined USCounties.shp with USAgdat.dbf using the combined FIPS code as the key. After completing the join, I calculated cows per square mile. Since there are 640 acres in a square mile, I either calculated square miles first or used a combined calculation. I symbolized the CPSM field using quantile classification with 10 classes and created a layout with standard map elements.

For Map 3 Macon County Soil Fertility, I added soils.shp and symbolized it using unique values based on Soil_Type. I examined the soils table to verify the soil_type field properties. I then created a new table called soi lprops within the project geodatabase and added the required fields with matching data types. After manually entering soil property data, I joined soil props to soils using soil_type as the key. I symbolized soils by fertility class using quantile classification with 5 classes and created a layout that included both the map and table frame.

Overall, this lab reinforced how critical attribute tables are in GIS workflows. Calculations, queries, joins, and table updates are essential for producing accurate maps and meaningful analysis. While some steps required careful checking, especially field calculations and query clauses, the lab improved my confidence in managing and analyzing tabular data.

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