Working with Attribute Tables in ArcGIS Pro

Working with Attribute Tables

Kendrick Wittmer

Date: 2/24/2025

    The objective of this lab focused on working with attribute tables, specifically creating new fields, calculating values, performing selections using queries, and joining external data tables. These tasks are key to making meaningful maps from raw data. Each map represents a real-world application of GIS table operations to reveal patterns and relationships in county-level data across the U.S.

Ratio of Old to Young Populations in U.S. Counties

    This map shows the ratio of people aged 65 and older to those aged 21 and under for each county. A new field was added to the data table to calculate this ratio, and counties with both a high ratio (over 85) and a total population above 150,000 were selected and highlighted in yellow. This allowed for easy identification of older population centers, most notably in Florida and parts of Arizona and Massachusetts. The rest of the counties are color-coded by ratio using graduated shading.

Burglary Rates in U.S. Counties

    Burglary data was normalized by population to create a more accurate representation of crime risk. A new field was added to the county dataset to calculate burglaries per 100 people. Counties were then shaded using a quantile classification with 10 classes to show differences in burglary rates. Darker shades represent higher rates. This map highlights regional crime trends while addressing issues like population bias in raw burglary totals.

Median Age in U.S. Counties

    This map displays the median age of county residents. It uses data directly from the attribute table and applies a graduated color ramp to show age distribution across the country. Older populations are more common in parts of the Northeast, Midwest, and Florida, while younger median ages are found in areas like Texas and the West.

Soil Fertility in Macon County, NC

    This map required creating a new table of soil properties and joining it to a soils shapefile using a common field (soil_type). Fields were added for fertility class, drainage class, and name. Once joined, soil polygons were symbolized by fertility class using unique values. A table of soil properties was also inserted into the layout. This exercise demonstrates how to enrich spatial data with detailed tabular information for land management applications.

    Each of these maps illustrates how powerful GIS can be when attribute data is properly managed and analyzed. From crime and aging populations to agriculture and soil science, these visualizations show the importance of combining spatial features with meaningful table data.