Urban morphology has long been recognized as a factor shaping human mobility, yet comparative and formal classifications of urban form across metropolitan areas remain limited. Building on theoretical principles of urban structure and advances in unsupervised learning, we systematically classified the built environment of nine U.S. metropolitan areas using structural indicators such as density, connectivity, and spatial configuration. The resulting morphological types were linked to mobility patterns through descriptive statistics, marginal effects estimation, and post hoc statistical testing. Here we show that distinct urban forms are systematically associated with different mobility behaviors, such as reticular morphologies being linked to significantly higher public transport use (marginal effect = 0.49) and reduced car dependence (-0.41), while organic forms are associated with increased car usage (0.44), and substantial declines in public transport (-0.47) and active mobility (-0.30). These effects are statistically robust (p < 1e-19), highlighting that the spatial configuration of urban areas plays a fundamental role in shaping transportation choices. Our findings extend previous work by offering a reproducible framework for classifying urban form and demonstrate the added value of morphological analysis in comparative urban research. These results suggest that urban form should be treated as a key variable in mobility planning and provide empirical support for incorporating spatial typologies into sustainable urban policy design.

Most days, Athena Frederick goes grocery shopping and picks up her grandson from school without ever getting into a car. The same is true of her teenage daughter, who takes herself to and from high school. That's possible because she lives in Peachtree City, Ga., a small town just south of Atlanta that started building a network of paths in 1974 that are accessible to golf carts, but not cars. It now extends more than 100 miles, serving 38,000 residents and their more than 11,000 registered carts. Nearly every destination and domicile in the town is accessible via a class of vehicle most Americans regard as a toy.

Teen scientists use machine learning and neural networks to detect and diagnose diseases, track space debris, design drones and justify conclusions at Intel ISEF 2017. While sentient computer beings like HAL from the classic 2001: A Space Odyssey or Samantha from the 2013 film Her may still be on the distant horizon, some forms of artificial intelligence (AI) are already improving lives. At the 2017 Intel International Science and Engineering Fair (ISEF) – where nearly 1,800 high school students gathered to present original research and compete for more than $4 million in prizes – the next generation of scientists used machine learning and artificial neural networks to find solutions to some of today's most vexing problems. "AI is critical to our future," said Christopher Kang, a budding computer scientist from Richland, Washington, who won an ISEF award in the robotics and intelligent machines category. "Humans have a limit as to how much data we can analyze," he said.

The basic unit of meaning on the Semantic Web is the RDF statement, or triple, which combines a distinct subject, predicate and object to make a definite assertion about the world. A set of triples constitutes a graph, to which they give a collective meaning. It is upon this simple foundation that the rich, complex knowledge structures of the Semantic Web are built. Yet the very expressiveness of RDF, by inviting comparison with real-world knowledge, highlights a fundamental shortcoming, in that RDF is limited to statements of absolute fact, independent of the context in which a statement is asserted. This is in stark contrast with the thoroughly context-sensitive nature of human thought. The model presented here provides a particularly simple means of contextualizing an RDF triple by associating it with related statements in the same graph. This approach, in combination with a notion of graph similarity, is sufficient to select only those statements from an RDF graph which are subjectively most relevant to the context of the requesting process.