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>

Urban Data Visualization

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