The 30 U.S. cities where journalists can drop the state name when referencing them. This map highlights the 30 U.S. cities that can stand alone in AP Style, meaning journalists do not need to include the state name in datelines or many written references. AP Style (Associated Press Style) is a standardized writing guide used across much of journalism, covering rules for dates, titles, numbers, punctuation, capitalization, abbreviations, and geographic references. Under these guidelines, cities like “CHICAGO” or “ATLANTA” can appear without a state abbreviation because they are considered widely recognizable. So instead of writing “CHICAGO, Ill.,” journalists can simply use “CHICAGO.” The selection isn’t based purely on population size. It also reflects national familiarity, frequency in news coverage, and how easily a city can be identified without additional context. That’s why some major cities are excluded, while others like Honolulu, Salt Lake City, and Oklahoma City are included—they are strongly associated with their states and widely understood in national reporting. In AP Style datelines, these approved cities are written in all caps, so a story might begin with “BOSTON –” rather than “BOSTON, Mass. –”.

# Learning Palantir Foundry 🚀 Answer "where did this dashboard number come from?" in an instant. Data Lineage is an exploration tool that visualizes the entire flow of your data. 📌 Title and Feature URL Title: Data Lineage URL: 📝 Overview Data Lineage is an interactive visualization tool that comprehensively shows how data flows through the Foundry platform. It helps you understand data movement, dependencies, and transformations across your entire data ecosystem. Because you can trace the lineage from sources through pipelines, the Ontology, and apps as a graph, it sharply reduces the cost of incident response and audit explanations. 🔧 How It Works It represents data dependencies through a graph-based visualization. - Find datasets using project names, table identifiers, or row labels, and browse data directly from Foundry Projects - Expand or collapse ancestor (upstream) and descendant (downstream) relationships for any dataset - View multiple table attributes at once, down to schema details, build timestamps, and source code - Apply custom color schemes to highlight pipeline characteristics such as stale datasets - Create shareable pipeline snapshots to communicate within the team 🛠 Practical Usage - Trace upstream from a dashboard or output dataset to pinpoint the source of a number - When an upstream schema changes, trace downstream to map the blast radius - Color-code stale datasets to discover neglected pipelines - Drill down from a high-level overview into granular technical details like transformation code and execution history - Share pipeline snapshots to document data workflows across functions 🎯 Use Cases - Instantly answering "what is the origin of this dashboard's number" - Identifying the impact scope of upstream schema changes in advance to prevent incidents - Presenting data lineage during audits to cut explanation costs - Finding stale or unused datasets to tidy up pipelines ⚠️ Caveats - This overview page does not explicitly discuss performance or graph-complexity constraints with extremely large pipelines - Lineage covers data flow within the Foundry platform; processing outside the platform is out of visualization scope - The accuracy of lineage depends on transforms and pipelines being properly configured within Foundry #PalantirFoundry# #DataLineage#