The UIC School of Public Health (UIC-SPH) Emergency Management and Resiliency Planning (EMRP) program, in collaboration with the Electronic Visualization Laboratory (EVL) and content experts, is presenting a series of Midwest Comprehensive Visualization Dashboard(s) (MCVD) focusing on environmental health and justice issues in this region. A MCVD has two components: the visualization interface(s), i.e., link to interactive maps, and the end-user documentation (i.e., background information, data sources, methodology, findings, etc.). 
<a href="https://univofillinois.maps.arcgis.com/apps/instant/nearby/index.html?appid=486f6e438ecf4b048deebc8dafd9f2c1&sliderDistance=1">Link to the interactive map on the MCVD dashboard.</a> 
MCVD has been created by Joel Flax-Hatch, Michael D. Cailas (School of Public Health), Fabio Miranda (EVL), and Apostolis Sambanis (Division of Environmental and Occupational Health Sciences - EOHS)) with the participation of Michael Siciliano (College of Urban Planning and Public Affairs), Sybil Derrible (Civil, Materials, and Environmental Engineering), Yuan Shao (EOHS), and Greg Arling (Purdue University). 
<a href="https://ago-item-storage.s3.us-east-1.amazonaws.com/0e1922e480134854918ed0f3f97319fe/MCVD_EJ.3.V1.1.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEKb%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJHMEUCID3FXdMyOfYIb%2Fafu1eOW49fXssLx10%2Bs%2FOh29tg7m7yAiEA1YqdmEA8DmO%2FPfoparcE3Mr%2BOmRA89eoTEPh8ZoxOPcqgwQI3v%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARAAGgw2MDQ3NTgxMDI2NjUiDITVsh%2FbXDCTjerOWyrXA9skMbqdx8pG2RMMt4k5ms%2BFG30KSO3pLnufYGx%2B7nvR%2FVMNwS4lJZoAEhMED%2B6y22UVHgV1N3lUcEhAuc6X8i2ZiFHReKQDLODjfuL9ZoU3ubq%2FhOXmxaqZTw%2BSZ852w%2FeZK77wD0TsrUcabCDNLManoYWOMxkwqiPnyHChuwCON8529O6KlCMp%2BoXYl%2BMk7AXVdS3Fd1onYEy10MpPnhVN%2Bp0cdixCuaxfcmN3EPxfbY4q667z5fyg1fcDBfKgW8tk%2B5kzXMxpk0V%2Bq3Q3pII%2B9i3872DDEZFicjSLaLTt1w%2BHGg22mn9wBH7tFDTzmDF6UJWLpuRXnw3Ek8MWmDqdB8BWgxAbFmJbmQBGEcB9Bb%2FxAKIReMXFtrM2nThAMcOfx%2BwuiPlqVuHAdbjN6gtUrd3tX8Kd7WI3Q2mRzok5nf8XZ96EmYr3dA3ck7fBiC2ywkCzhn6U%2B4919EFAy3gJkOEbSNileqOKg1L%2Fv%2FOn4MRXwsQbAXJsb6Fq4XO%2BzYfUUvHHviqYMDdyIZU1Za9mPX9UER9XdrBJ9D6%2BDiJmOnbEz6MKAdjzAjDwmHqR0VHWj%2B8zVk98xa0x%2FLxol1CHliq5YjWT%2FfPBl3GoVRXFtorDyjkJzDCil4aQBjqlATd4Im%2B%2F5Hnm3WhZD3xNPje0qXVNEpEczPEUg77lN747bEEo47cI25tpDgVFxS%2BTkWv4Bqv2ylplFaPThOnwvUW1ms4f7Br3Z%2BI64bISJNaBXGv%2F7hb68bFvHsCc8yqBo1TcZcw6CaOyLyLfA5dAz1BKQNpdoI0K5m9DLHlBtZaAIn2uXUp75ttP0UqMXjhTDsHJWwkuM%2FQNDPGMGtbHJMynIEjZ7w%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220207T221949Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAYZTTEKKE2RWCPQVC%2F20220207%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=3d12d4d450d382bd441b4d23e229780f45b867eae9a23cec52b1794716f8d1cc">Read the press release outlining this research project.</a>

MCVD: Environmental Justice Landscape Map Interface

Midwest Comprehensive Visualization Dashboards: A New Environmental Justice Tool for Chicago Communities

Deep Umbra is a novel computational framework that enables the quantification of sunlight access and shadows at a global scale. Our framework is based on a conditional generative adversarial network that considers the physical form of cities to compute high-resolution spatial information of accumulated shadow cast for the different seasons of the year. 
Deep Umbra&rsquo;s primary motivation is the impact that shadow management can have on people&rsquo;s quality of life, since it can affect levels of comfort, heat distribution, public parks, etc. We also present the Global Shadow Dataset, a comprehensive dataset with the accumulated shadow information for over 100 cities on 6 continents. 
A <a href="https://www.evl.uic.edu/shadows/map">webviewer</a> has been developed to visualize accumulated shadow cast over a specific geographic area. The map shows the accumulated shadow for three different days of the year - Summer, Spring/Fall, and Winter.

Deep Umbra: A Global-Scale Generative Adversarial Approach for Sunlight Access and Shadow Accumulation in Urban Spaces

Sidewalks impart a safe walkway for pedestrians, links public transportation facilities, and equip people with limited abilities with routing and navigation services.  There is a scarcity of open sidewalk data worldwide.  As one of the most famous crowdsourced cartographic data repositories and the only globally available open geospatial data, OpenStreetMap (OSM) could aid the lack of open sidewalk data to a large extent.  However, completeness and quality of OSM data have long been a major issue.  In this research, we analyze city-scale availability and trustworthiness of sidewalk networks.  As part of this work, we compare the coverage of sidewalks in OpenStreetMap across 50 cities in the US by computing the ratio of highways to sidewalk geometry and the percentage of highways containing sidewalk information.  Additionally, we plot the availability of OpenStreetMap sidewalk attributes in a number of cities, and compare the distribution of trustworthiness index of sidewalks, and highways with sidewalk information in these cities.

Sidewalk coverage of (a) Seattle, (b) a sportion of downtown Seattle, & (c) View Ridge neighborhood. Overall Seattle OSM has comparably higher sidewalk geometries mapped than other cities. However, smaller neighborhoods like View Ridge have poor coverage.

Crowdsourcing and Sidewalk Data: A Preliminary Study on the Trustworthiness of OpenStreetMap Data in the US

Computer Science assistant professor and EVL faculty member Fabio Miranda has been named a <a href="http://www.nasonline.org/programs/kavli-frontiers-of-science/news/2022-kavli-fellows.html">2022 Kavli Fellow</a> by the National Academy of Sciences (NAS). A total of 185 scientists across disciplines were selected as Kavli Fellows by a committee of NAS members. Kavli Fellows represent &ldquo;the brightest young scientists from industry, academia, and government&rdquo; and are invited to participate in the Kavli Frontiers of Science symposia of the NAS. 
The Frontiers of Science symposium series provides a forum for early career researchers in US science to share ideas within their disciplines while building relationships and prospective collaborations as they advance in their careers. Dr. Miranda presented his research in Urban Data Visualization at the 19th Chinese-American Symposium on July 8-10, 2022 in Irvine, California. <a href="http://www.nasonline.org/programs/kavli-frontiers-of-science/past-symposia/2022-cafos.html">See the full program.</a>

Miranda Named Kavli Fellow by the National Academy of Sciences

While cities around the world are looking for smart ways to channel new advances in data collection, management, and analysis to address their day-to-day problems, the complex nature of urban issues and the overwhelming amount of available structured and unstructured data have posed significant challenges in translating these efforts into actionable insights. In the past few years, urban visual analytics tools have significantly helped tackle these challenges. When analyzing a feature of interest, an urban scientist must transform, integrate and visualize different thematic (e.g., sunlight, flows) and physical (e.g., buildings, sidewalks) layers, oftentimes across multiple spatial and temporal scales. However, integrating and analyzing these layers requires expertise in different fields, increasing development time and effort. This makes the entire visual data exploration and system implementation hard for experienced programmers and also sets a high entry barrier for urban experts outside of computer science. With this in mind, in this paper, we present the Urban Toolkit (UTK), a flexible and extensible visualization framework that enables the easy authoring of web-based visualizations through a new high-level grammar specifically built with common urban use cases in mind. In order to facilitate the integration and visualization of different urban data, we also propose the concept of knots to merge thematic and physical urban layers. We evaluate our approach through use cases and a series of interviews with experts and practitioners from different domains, including urban accessibility, urban planning, architecture, and climate science. UTK is available at urbantk.org. 
Index Terms: Urban visual analytics, Urban analytics, Urban data, Visualization toolkit.

UTK abstracts spatial joins through the use of knots.

The Urban Toolkit: A Grammar-based Framework for Urban Visual Analytics

Roberto Cesar Jr., professor/department head of Computer Science at the University of Sao Paolo and director of eScience Research Center presents his research in computer vision and image processing for urban visualization. Dr. Cesar&rsquo; invited talk will be held: 
EVL&rsquo;s Continuum 
Engineering Research Facility, Room 2068 
Friday, September 29th, noon 
&ldquo;Computer vision and physical models for urban informatics&rdquo; 
Computer vision techniques have played an important role in urban data analysis. Such techniques are useful for capturing and analyzing visual information about cities&rsquo; infrastructure, social manifestations and mobility, to name just a few. However, the adoption of computer vision techniques in urban environments faces important challenges, such as the restricted heterogeneous hardware typically involved, outdoor image variations, and other uncontrolled aspects. In this talk, we present some recent results to overcome these limitations. Specifically, we discuss computer vision techniques for rain scenes, pedestrian mobility analysis, and physical models for urban green regions and graffiti distributions. 
Bio: 
Roberto is a professor at the University of Sao Paulo (USP) since 1998 (BSc in Computer Science - UNESP - 1991; MSc in Electrical Engineering - UNICAMP - 1993; Ph.D. in Physics - USP/Brazil, IPT-UCL/Belgium - 1997). He is currently a full professor in the Department of Computer Science - IME - USP working in the Data Science Research Group. He is currently a special advisor for Physical Sciences and Engineering at the Sao Paulo Research Foundation - FAPESP. He served as the Director of the eScience Research Center at USP and as the head of the Computer Science Department. He was a member of the Image and Vision Computing and the Signal, Image and Video Processing editorial boards, chair and invited speaker of conferences and workshops (Sibgrapi 2003, CIARP 2010, Sibgrapi 2011; SHAPES 2.0 - 2012, eSon - IEEE eScience 2013, IEEE eScience 2014). He has experience in computer science, with an emphasis on computer vision, machine learning, and artificial intelligence. 
<a href="https://www.ime.usp.br/~cesar/">https://www.ime.usp.br/~cesar/</a>

Roberto Cesar Jr. Invited to Present &ldquo;Computer vision and physical models for urban informatics&rdquo;

Sunlight and shadow play critical roles in how urban spaces are utilized, thrive, and grow. While access to sunlight is essential to the success of urban environments, shadows can provide shaded places to stay during the hot seasons, mitigate heat island effect, and increase pedestrian comfort levels. Properly quantifying sunlight access and shadows in large urban environments is key in tackling some of the important challenges facing cities today. In this paper, we propose Dee Umbra, a novel computational framework that enables the quantification of sunlight access and shadows at a global scale. Our framework is based on a conditional generative adversarial network that considers the physical form of cities to compute high-resolution spatial information of accumulated sunlight access for the different seasons of the year. We use data from seven different cities to train our model, and show, through an extensive set of experiments, its low overall RMSE (below 0.1) as well as its extensibility to cities that were not part of the training set. Additionally, we contribute a set of case studies and a comprehensive dataset with sunlight access information for more than 100 cities across six continents of the world. Deep Umbra is available at <a href="https://urbantk.org/shadows">https://urbantk.org/shadows</a>. 
Index Terms - Urban computing, Urban analytics, Sunlight access, Shadow, Generative adversarial networks.

Example where shadows are cast across tiles.

Deep Umbra: A Generative Approach for Sunlight Access Computation in Urban Spaces

e-JUST - Environmental Justice using Urban Scalable Toolkit, a state-of-the-art GIS-based web platform, is being developed to inform local and regional decision-making regarding air quality and heat in the Chicago metropolitan area, utilizing Earth science and high-resolution climate model outputs. e-JUST contributes to mitigating risks associated with extreme heat and air pollution by improving decision-making related to heat and air quality stressors in the Chicago metropolitan region. e-JUST technology architecture involves ReactJS, Vega-Lite, and Urban Toolkit's functionalities. e-JUST integrates a variety of meteorological and air quality datasets. Among these are NASA&rsquo;s remote sensing observations, such as GOES 16 and ECOSTRESS, that measure surface temperatures at 100 m and hourly intervals. The environmental data layers include high-resolution (1 km and higher) outputs from urban climate models related to temperature, NO2 and PM2.5, ozone, and other pollutants. To account for demographic data, we use the 2020 US Census. Our data fusion approaches include further downscaling environmental data layers to street scale using machine learning algorithms for spatiotemporal interpolation, enabling more accurate predictions at scales relevant to communities. e-JUST&rsquo;s statistical view includes a heat matrix that illustrates correlations between several environmental and socioeconomic variables at a grid point in time, as well as a scatterplot that allows users to compare observed data and forecasts of two different variables. Overall, this toolkit provides information for the near past and near-real-time forecasting to aid in decision-making and emergency management preparedness for Chicago communities. Additionally, our platform is designed to provide users with easy access to data, enabling them to view and analyze data immediately. The e-JUST toolkit is anticipated to benefit a wide audience, ranging from the general public to academic researchers.

e-JUST - Environmental Justice using Urban Scalable Toolkit

EVL faculty and Computer Science (CS) Assistant Professor Fabio Miranda (PI) and Civil, Materials, and Environmental Engineering (CME) Professor Sybil Derrible (co-PI) have been awarded $1.75 million NSF grant entitled: "Collaborative Research: Frameworks: Cyberinfrastructure to Catalyze and Sustain the Urban Computing Community.” Also known as <a href="https://oscur.org/">&ldquo;The Open-Source Cyberinfrastructure for Urban Computing Research&rdquo; (OSCUR)</a>, the grant is part of an overall $5 million project that is collaborative with the University of Washington and NYU. Here at UIC, the overall effort includes the departments of CS, CME, and the College of Urban Planning and Public Affairs (CUPPA). The grant will run from September 2024 to August 2029. 
Project Overview: 
Cities are the loci of resource consumption, economic activity, and innovation; they are where our looming sustainability problems were born and where those problems must be solved. Given our increasing ability to collect, transmit, store, and analyze data, we have the opportunity to go beyond today&rsquo;s imperfect and often anecdotal understanding of cities to enable better operations, better planning, and better policy. To understand cities, we must analyze the data exhaust from their components - infrastructure, environment, and people - and how they interact over space and time. While there are already troves of open data about cities, their potential remains largely untapped due to unique challenges related to diversity, scale, and complexity of both urban data and computations required to obtain insights from these data. In this project, we aim to democratize data-driven and data-intensive approaches in urban computing to empower a broad range of stakeholders to analyze urban data at scale and tackle problems from different domains, including transportation, accessibility, and disaster management. We will address two critical obstacles to greater progress in urban computing: the lack of documented, robust, well-engineered tools and open computing platforms, and a dispersed community of researchers and developers. At the core of our proposal is the development of a cyberinfrastructure that integrates methods and tools for the exploration of urban data that are scalable, reusable, interoperable, and offer solutions to common challenges, including data cleaning, management, and visualization. We will deploy an integrated suite of tools in a collaborative cloud environment, enabling users from different domains to easily reuse existing methods across domains to ensure sustainability, facilitate collaboration between researchers and developers, and develop a cohesive community. 
Keywords: urban data, urban science, big data

UIC Researchers Join National Project to Turn Urban Data into Healthier Cities

Project Overview 
Cities are the loci of resource consumption, economic activity, and innovation; they are where our looming sustainability problems were born and where those problems must be solved. Given our increasing ability to collect, transmit, store, and analyze data, we have the opportunity to go beyond today’s imperfect and often anecdotal understanding of cities to enable better operations, better planning, and better policy. To understand cities, we must analyze the data exhaust from their components - infrastructure, environment, and people - and how they interact over space and time. While there are already troves of open data about cities, their potential remains largely untapped due to unique challenges related to diversity, scale, and complexity of both urban data and computations required to obtain insights from these data. In this project, we aim to democratize data-driven and data-intensive approaches in urban computing to empower a broad range of stakeholders to analyze urban data at scale and tackle problems from different domains, including transportation, accessibility, and disaster management. We will address two critical obstacles to greater progress in urban computing: the lack of documented, robust, well-engineered tools and open computing platforms, and a dispersed community of researchers and developers. At the core of our proposal is the development of a cyberinfrastructure that integrates methods and tools for the exploration of urban data that are scalable, reusable, interoperable, and offer solutions to common challenges, including data cleaning, management, and visualization. We will deploy an integrated suite of tools in a collaborative cloud environment, enabling users from different domains to easily reuse existing methods across domains to ensure sustainability, facilitate collaboration between researchers and developers, and develop a cohesive community. 
Keywords: urban data, urban science, big data 
Intellectual Merit 
We will develop a software infrastructure to address a wide range of urban computing challenges by integrating and extending existing open-source tools as well as translating academic research products. We will also deploy a cloud-based, open collaborative environment that will support the use of this infrastructure over large and diverse urban data sets (e.g., spatiotemporal, geometry, image). The intellectual merit of the proposed effort lies within the novelty of the tools and techniques it will produce, as well as in the software engineering challenges involved in developing, maintaining and supporting code that will be deployed and widely adopted. 
Broader Impacts 
This project will contribute to the creation of a sustainable environment of urban software and tools, readily available for researchers to use and extend. The proposed infrastructure aims to propel urban computing into the open-source and open-science movement, making urban computing research more tractable, replicable/reproducible, and impactful, and delivering immediate benefit to a broad range of disciplines, including urban planning, urban design, transportation, GIS, data science, and human behavior studies. It will reduce barriers to entry for newcomers to urban computing, through intuitive interfaces, easy access to data, and participation in an active community. This project will contribute to education in computer science and urban computing-related areas through new graduate-level curricula to educate the next generation of urban scientists about computing, and expose computer and data scientists to urban-related problems. We will lead collaborative projects, seminars, and workshops to incentivize the creation of deeper connections across urban computing-related areas. We will release all software produced using a permissive open-source license to afford dissemination, commercialization, and incorporation into other software packages. We will actively contribute to our open-source projects to build community and ensure sustainability. The PIs of this project have a proven record of releasing data, widely used tools, and frameworks that are distributed as open source.

Urbane&rsquo;s map view. The number of NYC taxi pickups (b) is visualized for a chosen time range (a,c).

Collaborative Research: Frameworks: Cyberinfrastructure to Catalyze and Sustain the Urban Computing Community

EVL faculty and Computer Science assistant professor Fabio Miranda along with colleagues Froehlich (UW, CS), Li (UW, CS), Hosseini (Berkeley, Planning), Sevtsuk (MIT, Planning), Eisenberg (UIC, Health) are organizing a virtual workshop focused on the role of AI in enhancing urban accessibility. All researchers and practitioners interested in Urban+AI and disability should consider submitting a proposal to the <a href="https://accessiblecities.github.io/UrbanAccess2024/">ASSETS&rsquo;24 Workshop &ldquo;The Future of Urban Accessibility: The Role of AI&rdquo;</a>. The deadline is September 20, 2024 
How will AI transform urban infrastructure for people with disabilities--from public transportation and sidewalks to the design and use of buildings, housing, parks, and transit stations? What new mobility tools and techniques will AI enable from autonomous vehicles and micro-mobility solutions to personalized routing and accessible interactive maps? How can AI be used to effectively and ethically to solve urban accessibility problems and improve the quality of life for all? How can and should people with disabilities be involved with decisions about how AI is used in cities? 
In this workshop, we examine the emerging role of AI in the design of equitable and accessible cities, transportation systems, and interactive tools for mapping and navigation. We invite short papers (broadly construed), including experience reports, position papers, vision pieces, demonstrations, pictorials, or research summaries up to approximately 2,000 words on this topic (approximately 3 pages of formatted, two-column output). See example topic areas. For the word count, you can exclude figures, tables, captions, and the reference list. As we aim for a broad representation of viewpoints, disciplines, and work practices, please choose a format that you feel best conveys your work (e.g., single column, double column, ACM template). 
Submitted artifacts should not be anonymized and, in addition to their primary content, should include a bio of each author and rationale for attending the workshop. PDF submissions should be accessible following the ASSETS&rsquo;24 Accessibility Guidelines. Artifacts will be reviewed and selected by the co-organizers to balance topics, geographies, and communities of focus. Accepted authors will be required to register and virtually attend the workshop via Zoom on a date in October. 
Our overarching goals are to identify open challenges in Urban AI + disability, share current work across disciplines, and spur new collaborations in Urban AI + disability. As a secondary goal, we aim to synthesize and publish our discussions together in a jointly authored report perhaps to the SIGACCESS Newsletter or beyond. 
The Workshop will be held the week of October 21st (tdb).

Call for Participation ASSETS&rsquo;24 Workshop &ldquo;The Future of Urban Accessibility: The Role of AI&rdquo;

Over the past decade, several urban visual analytics systems and tools have been proposed to tackle a host of challenges faced by cities, in areas as diverse as transportation, weather, and real estate. Many of these tools have been designed through collaborations with urban experts, aiming to distill intricate urban analysis workflows into interactive visualizations and interfaces. However, the design, implementation, and practical use of these tools still rely on siloed approaches, resulting in bespoke systems that are difficult to reproduce and extend. At the design level, these tools undervalue rich data workflows from urban experts, typically treating them only as data providers and evaluators. At the implementation level, they lack interoperability with other technical frameworks. At the practical use level, they tend to be narrowly focused on specific fields, inadvertently creating barriers to cross-domain collaboration. To address these gaps, we present Curio, a framework for collaborative urban visual analytics. Curio uses a dataflow model with multiple abstraction levels (code, grammar, GUI elements) to facilitate collaboration across the design and implementation of visual analytics components. The framework allows experts to intertwine data preprocessing, management, and visualization stages while tracking the provenance of code and visualizations. In collaboration with urban experts, we evaluate Curio through a diverse set of usage scenarios targeting urban accessibility, urban microclimate, and sunlight access. These scenarios use different types of data and domain methodologies to illustrate Curio&rsquo;s flexibility in tackling pressing societal challenges. Curio is available at <a=href"urbantk.org/curio.">urbantk.org/curio</a>. 
Index Terms - Urban analytics, urban data, spatial data, dataflow, provenance, visualization framework, visualization system.

Using Curio to build a dataflow to analyze the shadow impact of a proposed building in Boston.

Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics

Curio is a framework for collaborative urban visual analytics that uses a dataflow model with multiple abstraction levels (code, grammar, GUI elements) to facilitate collaboration across the design and implementation of visual analytics components. Curio allows users to build and iterate on dataflows with reusable modules, supporting collaborative design and tracking changes. We evaluated Curio with domain experts on urban accessibility, climate, and sunlight access. 
Video overview, source code, examples, and tutorials are available here: <a=href"https://urbantk.org/curio/">https://urbantk.org/curio/</a>

The &ldquo;Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics&rdquo; paper was accepted to IEEE VIS 2024. Curio is a framework for collaborative urban visual analytics that uses a dataflow model with multiple abstraction levels (code, grammar, GUI elements) to facilitate collaboration across the design and implementation of visual analytics components. Curio allows users to build and iterate on dataflows with reusable modules, supporting collaborative design and tracking changes. We evaluated Curio with domain experts on urban accessibility, climate, and sunlight access. 
Video overview, source code, examples, and tutorials are available here: <a=href"https://urbantk.org/curio/">https://urbantk.org/curio/</a> 
The paper was led by PhD student Gustavo Moreira under EVL faculty and Computer Science assistant professor Fabio Miranda.

&ldquo;Curio&rdquo; Paper Accepted to IEEE VIS 2024

The Open-Source Cyberinfrastructure for Urban Computing Research (OSCUR) will create a cohesive ecosystem of tools and resources designed to transform city data into actionable knowledge. This multi-institutional collaboration is supported by a $5 million grant from the National Science Foundation, with $1.75 million allocated to UIC. 
The project includes researchers from UIC's College of Engineering and College of Urban Planning and Public Affairs who will collaborate with investigators from New York University and the University of Washington. UIC will leverage its expertise in visual data analysis, civil engineering, and government and community partnerships to design accessible computational tools focused on environmental justice, transportation, and other public priorities.

OSCUR

Over the past decade, there has been a significant increase in the development of visual analytics systems dedicated to addressing urban issues. These systems distill intricate urban analysis workflows into intuitive, interactive visual representations and interfaces, enabling users to explore, understand, and derive insights from large and complex data, including street-level imagery, street networks, and building geometries. Developing urban visual analytics systems, however, is a challenging endeavor that requires considerable programming expertise and interaction between various multidisciplinary stakeholders. This situation often leads to monolithic and isolated prototypes that are hard to reproduce, combine, or extend. Concurrently, there has been an increase in the availability of general and urban-specific toolkits, frameworks, and authoring tools that are open source and abstract away the need to implement low-level visual analytics functionalities. This paper provides a hierarchical taxonomy of urban visual analytics systems to contextualize how they are usually designed, implemented, and evaluated. We develop this taxonomy across three distinct levels (i.e., dimensions, categories, and tags), juxtaposing visualization with analytics, data, and system dimensions. We then assess the extent to which current open-source toolkits, frameworks, and authoring tools can effectively support the development of components tailored to urban visual analytics, identifying their strengths and limitations in addressing the unique challenges posed by urban data. In doing so, we offer a roadmap that can guide the effective employment of existing resources and chart a pathway for developing and refining future systems. 
Keywords: Visual Analytics, Visualization Toolkits, Visualization Grammars, Visualization Authoring, Urban Visual Analytics

Assessing the Landscape of Toolkits, Frameworks, and Authoring Tools for Urban Visual Analytics Systems

&ldquo;Assessing the Landscape of Toolkits, Frameworks, and Authoring Tools for Urban Visual Analytics Systems&rdquo; has been accepted for publication by Science Direct (Elsevier) in the Computer Graphics 2024 Journal. In this paper, researchers reviewed 135 works to assess the extent to which current open-source toolkits, frameworks, and authoring tools can effectively support the development of components tailored to urban visual analytics, identifying strengths and limitations in addressing the unique challenges posed by urban data. 
Project Overview 
The creation of visual analytics systems focused on urban issues has seen a notable surge. These systems simplify intricate urban analysis workflows into intuitive, interactive visual representations, enabling users to explore, understand, and derive insights from large, complex data. Examples of these datasets include street-level imagery, street networks, and building geometries. Developing urban visual analytics systems, however, is a challenging task that necessitates
significant programming expertise to handle several critical aspects, such as: (1) visualization to allow for data exploration in urban environments, (2) data management to integrate diverse urban data types and sources, (3) data analytics to highlight patterns and trends, and (4) system performance to ensure interactive response times. Furthermore, such development requires collaboration between stakeholders from different disciplines, including urban planning, public policy, public health, and climate sciences. The complexity involved and the tendency for these efforts to occur within the framework of one-off collaborations often lead to the creation of monolithic and isolated prototypes that are rarely made publicly available. Such a situation, with a notable lack of emphasis on interoperability as a design requirement, makes it difficult to extend existing systems and integrate discrete components into other tools.
The paper was led by PhD student Leonardo Ferreira under the advisement of EVL faculty and Computer Science assistant professor Fabio Miranda.

Typical steps in a workflow for urban data analysis.

Urban Visual Analytics Systems Research Published in Computer Graphics 2024 Journal

This year, students at the Electronic Visualization Laboratory (EVL) at the University of Illinois Chicago College of Engineering participated in the <a href="https://ieeevis.org/year/2024/welcome">IEEE VIS 2024 Conference</a>. 
Prof. Liz Marai&rsquo;s students had several presentations. PhD Student Siyuan Zhao presented his work &ldquo;A Part-to-Whole Circular Cell Explorer&rdquo; at the Bio+Med+Vis Workshop on Oct 13. PhD Student Andrew Wentzel presented his paper <a href="https://www.evl.uic.edu/pubs/2827">&ldquo;DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer&rdquo;</a> on Oct 17. 
Prof. Fabio Miranda&rsquo;s PhD Student Gustavo Moreira presented his work <a href="https://www.evl.uic.edu/pubs/2843">&ldquo;Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics&rdquo;</a> on Oct 16.
Dr. Saeed BoorBoor presented his two papers, &ldquo;VoxAR: Adaptive Visualization of Volume Rendered Objects in Optical See-Through Augmented Reality&rdquo; and &ldquo;Submerse: Visualizing Storm Surge Flooding Simulations in Immersive Display Ecologies&rdquo;. He will join as an EVL Faculty member at the University of Illinois Chicago next term. 
UIC&rsquo;s EVL faculty Liz Marai, PhD Students Md Nafiul Alam Nipu, Siyuan Zhao, and Hossein Fathollahian also participated at the VIS Chicago Satellite Event hosted by the University of Chicago Data Science Institute. After the watch party for the plenary session, we heard from faculty and PhD students at UChicago, UIC&rsquo;s EVL, Northwestern, and DePaul.

IEEE VIS 2024: Visualization & Virtual Analytics Virtual Conference

September 2023 marked the 50th anniversary of the Electronic Visualization Laboratory (EVL). This paper summarizes EVL&rsquo;s efforts in Visual Data Science, with a focus on the many networked, immersive, collaborative visualization and virtual reality (VR) systems and applications the Lab has developed and deployed, as
well as lessons learned and future plans. 
Index terms: Visualization, Virtual Reality, Visual Data Science, Collaboration

An EVL CAD Rendering of the DOCC Space

Immersive Analytics at the Electronic Visualization Laboratory

The Data Observation and Computation Collaboratory (DOCC, &rsquo;dock&rsquo;) is a visual data science instrument for University of Illinois Chicago (UIC) science and engineering research and research training. Many UIC researchers are part of multi-disciplinary teams investigating and contributing to solutions for societal issues, in such fields as biology, cybersecurity, healthcare, manufacturing, urban sustainability, and cyber-physical systems (e.g., autonomous cars). DOCC is a digital &rsquo;Project Room&rsquo; that enables domain scientists and computer scientists to collaborate on real-world problems by providing them with high-performance computing, high-resolution displays, and sophisticated Artificial Intelligence (AI), data analytic, data simulation, image processing, visualization, and virtual-reality software and services. 
Societal challenges can only be addressed by research teams, with each contributing expertise in complementary areas. DOCC&rsquo;s Intellectual Merit is two-fold: it provides onsite computing with adequate support, and it provides collaboration software services. With respect to the former, DOCC is a configurable computing environment that prioritizes real-time, interactive, collaborative, data-intensive applications, but can be scheduled off-hours for batch-mode processing. Users can execute codes faster, apply more sophisticated analytics to large-scale problems, gain greater insights, and open new avenues of research. With respect to the latter, DOCC&rsquo;s collaboration software lets researchers either sit around one of DOCC&rsquo;s large tables and display walls or video-teleconference in while simultaneously sharing and interacting with their data on the large displays – which remote viewers can access online. 
To encourage adoption and ensure Broader Impact, DOCC has a visual front end, enabling users to easily access familiar collaboration tools, scientific notebooks, AI workflows, and popular visualization software. Alternatively, users can add new AI models or create them from scratch. And because DOCC is connected to the UIC research network, data and/or computer codes can be ported to remote computer and cloud resources located on campus, regionally, nationally, or internationally. DOCC personnel can assist new users make their applications more robust, dynamic and configurable. DOCC can transform scientific research, data exploration and discovery, recruit and retain new faculty, make cyberinfrastructure more accessible to students, and help educate the next-generation workforce. UIC is a Minority Serving Institution, a Hispanic Serving Institution and an Asian American and Native American Pacific Islander Serving Institution. 
DOCC and its associated data will remain active for at least three years after the completion of this project, as demand dictates. Software will be stored and archived via EVL&rsquo;s storage servers, as well as code repositories.

Research Infrastructure: MRI: Track 2 Acquisition of Data Observation and Computation Collaboratory (DOCC)

Candidate: Gustavo Moreira 
Committee: Fabio Miranda (Chair and Advisor), UIC 
G. Elisabeta Marai, UIC 
Michael Papka, UIC 
Remco Chang, Tufts University 
Ross Maciejewski, Arizona State University 
Date/Time: 1pm CT, May 12, 2025 
Location: UIC ERF 2068 (Continuum) 
Zoom details: 
<a href="https://uic.zoom.us/j/5980237609?pwd=b2p3SlZMekR5Vjlidllnb1F6Qm5XUT09">https://uic.zoom.us/j/5980237609?pwd=b2p3SlZMekR5Vjlidllnb1F6Qm5XUT09</a> 
Abstract: 
Urban environments generate a large amount of complex and heterogeneous data, which creates opportunities for all kinds of data analysis across domains such as accessibility, climate, real estate, and infrastructure. Interested in these analyses are a diverse range of stakeholders each bringing distinct expertise, analytical workflows, and priorities. However, among many others, urban planners, architects, environmental scientists, and public health experts employ different workflows while participating in a highly collaborative analytical setting. This reality results in gaps in contribution and communication, impacting the ability of such stakeholders to work together and the inclusion of important opinions and insights. Trying to solve some of those challenges, many urban analytics tools have been developed over the years. They offer powerful means to explore the complexity of data while accounting for the iterative nature of the process. However, these tools are often tailored to particular domains and are the product of disjointed collaborations between visualization and domain experts. That being said, the complexity of urban phenomena, combined with the fragmented nature of current urban visual analytics systems, limits the ability of researchers and practitioners to construct reproducible and adaptable workflows. Existing tools often require specialized technical expertise, treat domain experts primarily as end users, and lack mechanisms for integrating workflows across design, implementation, and evaluation. 
Aiming to tackle the complexity of urban data and the collaborative nature of urban analytics, while remedying for current gaps in urban visual analytics tools, this dissertation will propose an ecosystem of tools and theoretical frameworks that try to answer the following question: &ldquo;how to support accessible, flexible, extensible, reproducible, and scalable tools across domains and users?&rdquo;. Across this ecosystem of tools, there is a common thread of a meta approach. Meaning that the tools proposed enable a large spectrum of users to build urban visual analytics systems at different levels of expertise. This dissertation will present three solutions developed iteratively and a fourth ongoing work. They are a product of close cooperation with experts and practitioners who supported the evaluation through a series of real-life case studies. First, the Urban Toolkit (UTK) introduces a flexible grammar-powered visualization framework for urban data integration, showcasing an example library with public datasets from: Chicago Data Portal, OpenStreetMap, NYC Open Data, and Analyze Boston. Curio builds on UTK to add a collaborative, dataflow-driven approach, presenting three usage scenarios with public datasets from OpenStreetMap and Google Street View. Then, Urbanite extends the previous tools to propose a framework for human-AI collaboration, fostering alignment for a declarative construction of urban VA systems. Three usage scenarios are presented using public datasets from: OpenStreetMap, OpenTopography, Project Sidewalk, and Urban Pulse's paper. Finally, I present ongoing research that investigates challenges and opportunities in translating a domain problem into a VA system in an AI-assisted environment. The three tools bridge the gap between stakeholders by increasing accessibility to urban data analysis while making sure that solutions are extensible and scalable enough to be reused in different contexts, thus supporting an already existing diverse set of urban visual analytics workflows.

Urban Data Visualization

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