Greetings!

Charles Q.C. LI.
Urban Researcher,
Designer,
& Romantic Pragmatist @QLabs

About

Who Am I ?

[Researcher; Planner; Designer]
Sustainable & Equitable Cities; Urban Sensing & Computing.

Hello!

I am an urban researcher, designer, and romantic pragmatist based in the U.S. I believe in harnessing [1] 🧑‍💻📱🛰️ data & technology, 👩🏻‍🎨🗺️📈 [2] design & planning, and 🌟🌎📚 [3] critical history & theory to foster public ⚖️debates, shaping 🌆cities toward sustainable, healthy, and equitable futures. My recent research examines how metropolitan form influences active mobility, such as walking, through the lenses of climate mitigation, environmental health and social segregation. I combine cutting-edge AI advancements with statistically rigorous methods like causal inference. I also value qualitative approaches, including site observation, ground-truthing, and theoretical integration.

Passionate about life, knowledge, and human connections, I cherish old-school communication—emails, calls, and face-to-face chats with 👁️ eye contact.

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See My Skills

Recent Experience

Leading multiple research projects focused on mobility, perceived street safety, livability and transportation sustainability by leveraging large-scale urban data and advanced analytical techniques, including AI, to inform and enrich policy development. Execute end-to-end urban research processes, from proposal design to final outcomes. Collaborate with multiple PIs and facilitate weekly briefings for high-profile industry and municipal partners. Featured as a speaker at the 2024 MIT Senseable Forum on Future Cities: Being Physical, attended by over 100 participants from five continents.

Serve as the methodology lead for environmental research investigating PM2.5 exposure during school hours in racially and socioeconomically vulnerable regions of the San Francisco Bay Area. Expertise includes statistical analyses, air quality data engineering—such as ground-truthing and analyzing hyperlocal air quality data from stationary and mobile sensors—and web development. Developed platforms integrating multi-scale air quality datasets (e.g., PurpleAir, AirNow, BEACO2N, NASA) with CDR data using Mapbox.

Entrepreneurship

QLabs

[Human Behavior; Sustainability; Health; Equity]

PRINCIPLE

Questioning + Quest

Great research begins and ends with great questions!

Our core value is to ask and answer meaningful research questions. Leveraging technologies, we aim to contribute to existing and emerging ideas of urban research. We prioritize insights from daily observations, lived experiences, and comprehensive literature reviews to uncover conflicts, pursuing them through dedicated research to benefit humanity.

METHOD

Quantitative + Qualitative

Great research utilizes mixed methods!

Great urban research bridges divides—between disciplines, technical and non-technical audiences, and experts and the public. While we focus on technological advancements like AI, we equally value the humanities and traditional methods. Our work spans diverse fields, including law, anthropology, and psychology, fostering interdisciplinary collaboration.

Academic Degrees

Key Courses:
A+ - Scalable Spatial Analytics
A+ - Analytic and Research Methods for Planners: GIS
A - Back-End Web Architecture
A - Locative Media
A - Land and Housing Market Economics
A - Urbanization in Developing Countries
A- - Urban Informatics and Visualization
A- - Advanced Study of Energy & Environment
A- - Citizen Involvement in the City Planning Process

Key Courses:
A - GIS & Its Application in Urban Planning I & II
A - City Policy Analysis
A - Urban Ecology & Environmental Planning
A - Physical Environment Analysis
A - Data Analysis of Urban Planning System
A - Community Planner Intership
A - History of Foreign Landscape
A - Advanced Mathematics
A - Linear Algebra

Work

Featured Projects.

My portfolio spans urban planning, behavioral science, economics, architecture, design, and spatial data analytics.

Urban Science & Studies

Ongoing - lead

Visual AI, Land Use and 15-minute Cities in the U.S.

We use AI to explore the link between land use and 15-minute city usage. Findings reveal that a 50% increase in land use mix boosts national 15-minute usage by ~1% on national average and promotes equitable spatial distribution. Southern cities benefit most from small increases, while northeastern cities show the highest potential under larger targets.

Ongoing - lead

Urban Form and Travel Mode Inequity in the U.S.

We analyze how the built environment influence walking versus driving across income groups in NYC, LA, and Tampa using large-scale GPS data. Low-income individuals walk more, likely due to economic constraints. Sidewalk proximity promotes walking universally but affects groups differently: low-income individuals in NYC are more responsive, while high-income groups in LA and Tampa are more sensitive.

Ongoing - lead

Text AI and Critical Place Identity in the U.S.

Inspired by Doreen Massey's view that places defy single-dimensional categorization, we use social media, POI, zoning data, neural networks, and NLP (e.g., ChatGPT) to uncover dominant urban narratives in U.S. cities. This helps planners, investors, and the broad public develop insurgent planning strategies.

Ongoing - lead (tech)

Air Pollution and K-12 Students in Public Schools.

We are conducting an analysis that integrates multi-scale air quality data with mobility CDR data to support equitable environmental planning. Our findings aim to inform stakeholders about PM 2.5 exposure among K-12 student groups of different racial demographics, both while stationary and in motion.

Ongoing - lead

Graph AI and the Paradox of Connections in the U.S.

We use graph-based AI models, social media data, and mobility data to analyze alignments and mismatches between social media connections and travel volumes in U.S. cities, predicting emerging regional centers.

Ongoing - lead

Jan Gehl, Street Design and Perceived Safety

Using large-scale street view imagery in Stockholm, we evaluate Jan Gehl's urban safety theory. Greenery and sidewalks boost safety, while walls and speed limits reduce it, with unexpected results for bike lanes. We propose two strategies: Territory Control for city centers and Pedestrian-Friendly Design for suburbs.

Published - Lead

Visual AI, Land Cover and Surface Temperature. (view)

We construct a generative adversarial network (GAN) to predict detailed land surface temperature maps based on land cover data in New York City. It provides designers with fast, fine-grained predictions and visualizations of the environmental impacts of their design proposals.

Published - Lead

Urban Form and Mobility Pattern. (view)

Using the "5Ds" urban design framework, we applied principal component analysis (PCA) and K-means clustering to GPS-based travel data from the Twin Cities region. This analysis identified four trip clusters, offering valuable insights for agencies aiming to understand mobility patterns.

Design & Media

Dancing Wrap

Facade Design

Parametric designed component creates dynamic urban interface.