OSCUR is transforming urban computing by making complex city data accessible and actionable through open-source tools. Funded by a $5M National Science Foundation grant, we empower researchers, city planners, and communities to tackle urban challenges like transportation, climate change, and environmental justice.
"While open data is increasingly available across a host of urban areas - from resource consumption to housing and infrastructure — its potential remains largely untapped because of unique challenges related to the diversity and scale of the data and the complex computations required to obtain trustworthy insights. OSCUR intends to unlock that potential"
"With OSCUR, we're trying to democratize urban computing and empower a broad range of stakeholders to analyze urban data at scale. We want to remove the silos in which urban data typically lives, lower the barrier to entry for urban computing and facilitate better collaboration. By providing a unified platform with powerful tools, we hope to accelerate research and development in this critical field."
"OSCUR empowers us to turn data into action for healthier cities. One of the key aspects is lowering the barriers to accessing this wealth of technologies we have been building over the past years. We’re creating analyses and visualizations that ensure that community members are aware of how they are impacted."
"Many cities and governments and industries are putting data out there, but it’s just very hard to use. Imagine if you hire the best chef in the world and give them the best ingredients, but you don’t give them any tools. No matter how good the chef is, if they don’t have the proper tools, they can’t make a good dish."
"The true beauty and promise of OSCUR is in how it attempts to unify long-standing and deeply interconnected problems in urban science that often have disparate approaches spread across disciplines."
"Because of their history, relationships between government and marginalized communities can be fraught with distrust — including distrust towards the data that government releases. You need to put time and effort into building something that is trustworthy and valuable for the community, in terms of allowing them to analyze the data in a way that matters to them."
Sources: Urban computing gets a boost — NYU Tandon | UIC researchers join national project — UIC Today
Note: Access our curated datasets with ease! For example, to load NYC Taxi Vis is the taxisvis1M
dataset, use the following code:
from datasets import load_dataset
dataset = load_dataset("OSCUR/taxisvis1M")
Simply replace taxisvis1M
with other dataset names to explore additional datasets. Here are some examples:
taxisvis1M
: Public New York City taxi pickup and drop-off datasets.NYC_311
: 311 Service on-street Requests from 2010 to present in New York City datasets.pluto
: The Primary Land Use Tax Lot Output (PLUTO™) containing extensive land use and geographic data at the tax lot level in NYC.Let’s build smarter cities together! 🌟
Cheers,
@OSCUR team