Understanding neighborhood isolation through spatial interaction network analysis using location big data

Research article published at the journal of Environment and Planning A: Economy and Space on December 1, 2019

Download link: https://journals.sagepub.com/doi/full/10.1177/0308518X19891911

Abstract

Hidden biases of racial and socioeconomic preferences shape residential neighborhoods throughout the USA. Thereby, these preferences shape neighborhoods composed predominantly of a particular race or income class. However, the assessment of spatial extent and the degree of isolation outside the residential neighborhoods at large scale is challenging, which requires further investigation to understand and identify the magnitude and underlying geospatial processes. With the ubiquitous availability of location-based services, large-scale individual-level location data have been widely collected using numerous mobile phone applications and enable the study of neighborhood isolation at large scale. In this research, we analyze large-scale anonymized smartphone users’ mobility data in Milwaukee, Wisconsin, to understand neighborhood-to-neighborhood spatial interaction patterns of different racial classes. Several isolated neighborhoods are successfully identified through the mobility-based spatial interaction network analysis.

Keywords: Neighborhood isolation, human mobility, big data, spatial interaction

a dark background version of the neighborhood isolation map without a cartogram in Milwaukee, Wisconsin
Spatial interactions between Milwaukee communities and their demographic composition. The brighter flows are places where more flows are overlapping/converging together.
The community flows are overlaid on top of a cartogram distorted by the percent of the non-white population of the census block groups to promote a more socially just map.

Acknowledgments

The authors would like to thank Safegraph, Inc., for providing the anonymous mobile phone location big data support. T.P. and S.G. thank the UW-Madison Hilldale Undergraduate/Faculty Research Fellowship for their support for this research.

Call for papers: GIScience 2020 International Conference

11th International Conference on Geographic Information Science (GIScience 2020)

http://www.giscience.org

Poznań, Poland, 15-18 September, 2020

The 11th International Conference on Geographic Information Science will be held in Poznań, Poland, 15-18 September 2020. Hosted by Adam Mickiewicz University, GIScience 2020 continues the long tradition of the series as a flagship conference for researchers in geographic information science and related disciplines that are interested in spatial and temporal information.

The biennial conference series typically attracts over 300 international participants from academia, industry, and government to advance the state-of-the-art in geographic information science. The first conference day (September 15, 2020) will be dedicated to workshops and tutorials, while the main conference will be taking place on September 16-18, 2020. The conference offers two separate paper tracks, one for full papers and the other for short papers, both of which will undergo full peer-review. Authors of accepted papers will be given the opportunity to present their work at the conference in an oral presentation or as a poster.

The GIScience conference series is deeply interdisciplinary with contributions frequently involving domains such as geography, earth science, cognitive science, information science, computer science, linguistics, mathematics, philosophy, life sciences, and social science. It attracts contributions from experts in geo-visualization, geographic information retrieval, geostatistics, geo-semantics, geosimulation, spatial optimization, transportation, computational geometry, and data structures. Topics of interest are not restricted to the geo-spatial realm but involve spatial and temporal information more broadly.

Since 2018, GIScience proceedings are published in LIPIcs, the Leibniz International Proceedings in Informatics series (https://www.dagstuhl.de/en/publications/lipics). LIPIcs volumes are peer-reviewed and published according to the principle of open access, i.e., they are available online and free of charge. Each article is published under a Creative Commons CC BY license (http://creativecommons.org/licenses/by/3.0/), where the authors retain their copyright. Also, each article is assigned a DOI and a URN. The digital archiving of each volume is done in cooperation with the Deutsche Nationalbibliothek/German National Library. A number of other high-standing international conferences have already made the move to LIPIcs.

CONFERENCE TOPICS

Contributions are invited from a wide range of disciplines related to geographic information science, such as geography, earth science, cognitive science, information science, computer science, linguistics, mathematics, philosophy, life sciences, and social science. Topics of interest include (but are not limited to):

  • Agent-based modeling
  • Computational geometry
  • Events and processes
  • GeoAI
  • Geo-APIs
  • Geo-knowledge graphs
  • Geo-semantics
  • Geographic information observatories
  • Geographic information retrieval
  • Geosimulation and spatio-temporal modelling
  • Geovisualization and visual analytics
  • High-performance computing algorithms for spatial-temporal data
  • Human-Computer Interaction (with mobile devices)
  • Image classification methods
  • Internet of Things
  • Location privacy
  • Location-Based Services
  • Navigation
  • Replicability and reproducibility in GIScience
  • Scene recognition
  • Sensitivity analysis for spatial-temporal models
  • Spatial and spatio-temporal statistics
  • Spatial and temporal language
  • Spatial aspects of social computing
  • Spatial data infrastructures
  • Spatial data structures and algorithms
  • Spatially-explicit decision support
  • Spatially-explicit machine learning
  • Standardization and interoperability
  • Time series analysis
  • Trajectory and movement analysis
  • Uncertainty quantification and error propagation
  • Virtual reality


INFORMATION FOR AUTHORS

Full Paper Track

Full research papers will be thoroughly reviewed by at least three members of the international program committee. For this edition of the GIScience series, we will include an optional rebuttal phase during which authors can respond to the (initial) reviews. The rebuttal phase provides an opportunity to address misunderstandings, answer questions, or provide further details on issues that remained unclear to the reviewers. The reviewers will be able to react to these rebuttals by adjusting their review scores, if appropriate. Review criteria include novelty, significance of results as compared to previous work, the quality of the presented evaluations (if applicable), the clarity of the research statement, as well as the quality of writing and supporting illustrations. High-quality submissions will be accepted for presentation at the conference and published in LIPIcs, the Leibniz International Proceedings in Informatics series. Manuscripts must describe original work that has neither been published before, nor is currently under review elsewhere. Papers must be written in English and should not exceed fifteen pages (including title, figures, and references) in the required layout (see below). 

Short Paper Track

Short papers can report on the latest breaking results, present visions for the future of the field, or describe early work and experiments, as well as novel application areas. Short papers will also be reviewed by at least three reviewers. Review criteria include novelty, expected impact of early results, evaluation or evaluation plans for the future, plausibility of presented visions, as well as the quality of writing and supporting illustrations. Accepted papers in this track will be selected for either oral or poster presentations. Short papers must be written in English and should not exceed six pages (including title, figures, and references) in the requested LIPIcs layout. In addition, each submission must include Short Paper as a subtitle. 

The submission Web page for both tracks of GIScience 2020 is: https://easychair.org/conferences/?conf=giscience2020.


IMPORTANT DATES 

  • FULL PAPER TRACK
    • Full paper submissions: March 16, 2020
    • Full paper rebuttal phase: April 24-30, 2020
    • Full paper notification: May 15, 2020
    • Camera-ready papers: June 1, 2020
    • Full paper author registration deadline: June 1, 2020
       
  • SHORT PAPER TRACK
    • Short paper submission: May 25, 2020
    • Short paper notification: July 3, 2020
    • Camera-ready papers: July 15, 2020
    • Short paper author registration deadline: July 15, 2020
FORMATTING INSTRUCTIONS (all tracks)

The layout of any PDF submission to GIScience, whether full paper or short paper, should follow the 2019 template provided by LIPIcs (http://drops.dagstuhl.de/styles/lipics-v2019/lipics-v2019-authors.tgz). LIPIcs also provides a LaTeX class and template for papers. Authors unfamiliar with LaTeX, but keen to try, are highly encouraged to use Overleaf (http://www.overleaf.com), an online LaTeX editor that is easy to use and does not require any local installation. Overleaf comes with the LIPIcs class and template pre-loaded. Authors who want to use other word processors or text editors should stay close to the sample article’s layout for their paper submitted for review. Should their papers be accepted for publication, they will have to be converted to LaTeX using the LIPIcs LaTeX class and template. Authors are responsible for the conversion of their papers to LaTeX. There are also commercial conversion services such as http://wordtolatex.com/upload providing a one-step solution in case you do not want to do the conversion yourself. 

Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Mobile Phone Data

Mingxiao Li, Song Gao, Feng Lu, Huang Tong, Hengcai Zhang. (2019) Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data. International Journal of Environmental Research and Public Health. 16(22), 4522. DOI: 10.3390/ijerph16224522

Abstract: The spatiotemporal variability in air pollutant concentrations raises challenges in linking air pollution exposure to individual health outcomes. Thus, understanding the spatiotemporal patterns of human mobility plays an important role in air pollution epidemiology and health studies. With the advantages of massive users, wide spatial coverage and passive acquisition capability, mobile phone data have become an emerging data source for compiling exposure estimates. However, compared with air pollution monitoring data, the temporal granularity of mobile phone data is not high enough, which limits the performance of individual exposure estimation. To mitigate this problem, we present a novel method of estimating dynamic individual air pollution exposure levels using trajectories reconstructed from mobile phone data. Using the city of Shanghai as a case study, we compared three different types of exposure estimates using (1) reconstructed mobile phone trajectories, (2) recorded mobile phone trajectories, and (3) residential locations. The results demonstrate the necessity of trajectory reconstruction in exposure and health risk assessment. Additionally, we measure the potential health effects of air pollution from both individual and geographical perspectives. This helped reveal the temporal variations in individual exposures and the spatial distribution of residential areas with high exposure levels. The proposed method allows us to perform large-area and long-term exposure estimations for a large number of residents at a high spatiotemporal resolution, which helps support policy-driven environmental actions and reduce potential health risks.

GeoDS Lab at the Emerging Technology Leadership Summit 2019

Hyper Innovation is working with the Wisconsin Institutes for Discovery to establish the Emerging Tech Hub@UW-Madison to highlight applications for emerging technologies (e.g., VR/AR), create inspiration for innovation, and provide collaboration opportunities for startups, universities, and corporations.

GeoDS Lab among other campus teams were invited to show the Augmented Reality Beacons demo at the Innovation and Emerging Technology Leadership Summit – November 14, 2019.

VR Playground
AR SandBox

GeoDS Lab at ACM SIGSPATIAL’19

During November 5 – 8, 2019, GeoDS lab members presented at the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019) held in Chicago. We had two presentations and got one “Best Poster Award” in the Workshop on Ride-hailing Algorithms, Applications, and Systems (RAAS 2019)

  1. Analyzing the Gap Between Ride-hailing Location and Pick-up Location with Geographical Contexts (Best Poster Award). Yunlei Liang, Song Gao, Mingxiao Li, Yuhao Kang, and Jinmeng Rao (2019). In Proceedings of 1st ACM SIGSPATIAL International Workshop on Ride-hailing Algorithms, Applications, and Systems (RAAS’19) DOI: 10.1145/3357140.3365493 [PDF]
  2. A Data-Driven Approach to Understanding and Predicting the Spatiotemporal Availability of Street Parking (Short Paper). Mingxiao Li, Song Gao, Yunlei Liang, Joseph Marks, Yuhao Kang, and Moyin Li (2019). In Proceedings of 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems(SIGSPATIAL’19) DOI: 10.1145/3347146.3359366 [PDF]

As the General Chairs, Professor Song Gao co-organized the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI 2019). There are two keynotes from both industry and academia and 17 oral presentations in the GeoAI workshop. The proceedings of the GeoAI’19 workshop is available at the ACM Digital Library (Table of Contents): https://dl.acm.org/citation.cfm?id=3356471

GeoAI’19
SIGSPATIAL’19

A roundtable discussion: defining urban data science

Reference: Wei Kang, Taylor Oshan, Levi J Wolf, Geoff Boeing, Vanessa Frias-Martinez, Song Gao, Ate Poorthuis, Wenfei Xu. (2019) A roundtable discussion: defining urban data science. Environment and Planning B: Urban Analytics and City Science. 46(9), 1756-1768.  DOI: 10.1177/2399808319882826 [PDF]

Abstract:

The field of urban analytics and city science has seen significant growth and development in the past 20 years. The rise of data science, both in industry and academia, has put new pressures on urban research, but has also allowed for new analytical possibilities. Because of the rapid growth and change in the field, terminology in urban analytics can be vague and unclear. This paper, an abridged synthesis of a panel discussion among scholars in Urban Data Science held at the 2019 American Association of Geographers Conference in Washington, D.C., outlines one discussion seeking a better sense of the conceptual, terminological, social, and ethical challenges faced by researchers in this emergent field. The panel outlines the difficulties of defining what is or is not urban data science, finding that good urban data science must have an expansive role in a successful discipline of “city science.” It suggests that “data science” has value as a “signaling” term in industrial or popular science applications, but which may not necessarily be well-understood within purely academic circles. The panel also discusses the normative value of doing urban data science, linking successful practice back to urban life. Overall, this panel report contributes to the wider discussion around urban analytics and city science and about the role of data science in this domain.

New IJGIS Editorial on GeoAI

Abstract: What is the current state-of-the-art in integrating results from artificial intelligence research into geographic information science and the earth sciences more broadly? Does GeoAI research contribute to the broader field of AI, or does it merely apply existing results? What are the historical roots of GeoAI? Are there core topics and maybe even moonshots that jointly drive this emerging community forward? In this editorial, we answer these questions by providing an overview of past and present work, explain how a change in data culture is fueling the rapid growth of GeoAI work, and point to future research directions that may serve as common measures of success.

Moonshot (Editorial): Can we develop an artificial GIS analyst that passes a domain-specific Turing Test by 2030?

Keywords: Spatial Data Science, GeoAI, Machine Learning, Knowledge Graphs, Geo-Semantics, Data Infrastructure

Acknowledgement: we sincerely thank all the reviewers who contribute their time to the peer-review process and ensure the quality of the accepted papers.

Special Issue Papers (up to date):

Janowicz, K., Gao, S., McKenzie, G., Hu, Y., and Bhaduri, B. (2020, Editorial). GeoAI: Spatially Explicit Artificial Intelligence Techniques for Geographic Knowledge Discovery and Beyond. International Journal of Geographical Information Science, 1-13.

Acheson, E., Volpi, M., & Purves, R. S. (2020). Machine learning for cross-gazetteer matching of natural features. International Journal of Geographical Information Science, 1-27.

Duan, W., Chiang, Y., Leyk, S., Uhl, J. and Knoblock, C. (2020). Automatic alignment of contemporary vector data and georeferenced historical maps using reinforcement learning. International Journal of Geographical Information Science, forthcoming.

Guo, Z., & Feng, C. C. (2020). Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds. International Journal of Geographical Information Science, 1-20.

Law, S., Seresinhe, C. I., Shen, Y., & Gutierrez-Roig, M. (2020). Street-Frontage-Net: urban image classification using deep convolutional neural networks. International Journal of Geographical Information Science, 1-27.

Li, W., & Hsu, C. Y. (2020). Automated terrain feature identification from remote sensing imagery: a deep learning approach. International Journal of Geographical Information Science, 1-24.

Ren, Y., Chen, H., Han, Y., Cheng, T., Zhang, Y., & Chen, G. (2020). A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes. International Journal of Geographical Information Science, 1-22.

Sparks, K., Thakur, G., Pasarkar, A., & Urban, M. (2020). A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation. International Journal of Geographical Information Science, 1-18.

Xie, Y., Cai, J., Bhojwani, R., Shekhar, S., & Knight, J. (2020). A locally-constrained YOLO framework for detecting small and densely-distributed building footprints. International Journal of Geographical Information Science, 1-25.

Zhu, D., Cheng, X., Zhang, F., Yao, X., Gao, Y., & Liu, Y. (2020). Spatial interpolation using conditional generative adversarial neural networks. International Journal of Geographical Information Science, 1-24.

Prof. Song Gao received a new NSF Research Grant

Recently, Dr. Song Gao (Co-PI) received a NSF grant together with Dr. Qunying Huang (PI), Dr. Daniel Wright (Co-PI), Dr. Nick Fang (Co-PI), and Dr. Yi Qiang (Co-PI).

Title: A GeoAI Data-Fusion Framework for Real-Time Assessment of Flood Damage and Transportation Resilience by Integrating Complex Sensor Datasets

Abstract: Traditional modeling approaches for flood damage assessment are often labor-intensive and time-consuming due to requirements for domain expertise, training data, and field surveys. Additionally, the lack of data and standard methodologies makes it more challenging to assess transportation network resilience in real-time during flood disasters. To address these challenges, this project aims to integrate novel data streams from both physical sensor networks (e.g., remotely-sensed data using unmanned aerial vehicles [UAVs]), and citizen sensor networks (e.g., crowdsourced traffic data, social media and community responsive teams connected through a developed mobile app). The goal is to develop a framework for real-time assessment of damage and the resilience of urban transportation infrastructures after coastal floods via the state-of-the-art computer vision, deep learning and data fusion technologies. The project will also advance Data Science through multi-disciplinary and multi-institutional collaborations. The project is expected to improve the sustainability, resilience, livability, and general well-being of coastal communities by having a direct impact on the effectiveness, capability, and potential of using both physical and social sensor data. This will in turn enable and transform damage assessments, and identify critical and vulnerable components in transportation networks in a more effective and efficient manner. The interdisciplinary research team, along with students and collaborators from different coastal regions, will facilitate the sharing of knowledge and technologies from different socio-environmental contexts and testing the transferability of the research outcomes.

The project will harmonize physical and citizen sensors within a geospatial artificial intelligence (GeoAI) data-fusion framework with a focus on three research thrusts: (1) unsupervised flood extent detection by integrating UAV images collected throughout this project with existing geospatial data (e.g., road networks and building footprints); (2) flood depth estimation using deep learning and computer vision techniques combined with crowdsourced photos and UAV imagery; and (3) assessment of the impact on and resilience of transportation networks based on near real-time flood and damage information. The innovative methodology will be demonstrated and deployed through collaborative efforts in response to future flood events as well as several historical storms. The project will produce open-source algorithms for future educational use, raw and processed datasets and associated processing software, a mobile app to engage community responsive science teams, and three research publications.

Source: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940091

The fusion of knowledge-driven and data-driven approaches to discovering urban functional regions

Papadakis, E., Gao, S., & Baryannis, G. (2019). Combining Design Patterns and Topic Modeling to Discover Regions Supporting Particular Functionality. ISPRS International Journal of Geo-Information. 8(9), 385; https://doi.org/10.3390/ijgi8090385.

Abstract

The problem of discovering regions that support particular functionalities in an urban setting has been approached in literature using two general methodologies: top-down, encoding expert knowledge on urban planning and design and discovering regions that conform to that knowledge; and bottom-up, using data to train machine learning models, which can discover similar regions. Both methodologies face limitations, with knowledge-based approaches being criticized for scalability and transferability issues and data-driven approaches for lacking interpretability and depending heavily on data quality.

To mitigate these disadvantages, we propose a novel framework that fuses a knowledge-based approach using design patterns and a data-driven approach using latent Dirichlet allocation (LDA) topic modeling in three different ways: Functional regions discovered using either approach are evaluated against each other to identify cases of significant agreement or disagreement; knowledge from patterns is used to adjust topic probabilities in the learning model; and topic probabilities are used to adjust pattern-based results. The proposed methodologies are demonstrated through the use case of identifying shopping-related regions in the Los Angeles metropolitan area. Results show that the combination of pattern-based discovery and topic modeling extraction helps uncover discrepancies between the two approaches and smooth inaccuracies caused by the limitations of each approach.

Figure. The proposed framework of fusing knowledge-based and data-driven approaches
Figure. Extracted shopping regions by combining data-to-knowledge and knowledge-to-data approaches.

Prof. Song Gao received an AI for Earth Grant from Microsoft

[Madison, WI/USA] – [August 8, 2019] – Professor Song Gao as the Principal Investigator (PI) has been awarded an AI for Earth research grant from Microsoft to help further the efforts in the area of Geospatial Artificial Intelligence (GeoAI).

This new grant will provide Dr. Song Gao and his research assistants Yuhao Kang and Jake Kruse at the GeoDS@UW-Madison lab, and Dr. Fan Zhang (Postdoc Researcher at the MIT Senseable city Lab and Peking University) with the Azure cloud computing resources and AI data labelling services to accelerate their work on understanding the playability of cities and metropolitan areas from the human-environment interaction perspective using multi-source geospatial big data (e.g., images, texts, and videos).

The Microsoft AI for Earth is a $50 million, 5-year program that brings the full advantage of Microsoft technology to those working to solve global environmental challenges in the key focus areas of climate, agriculture, water and biodiversity. Through grants that provide access to cloud and AI tools, opportunities for education and training on AI, and investments in innovative, scalable solutions, AI for Earth works to advance sustainability across the globe. 

Learn more about the Microsoft AI for Earth program: https://www.microsoft.com/en-us/aiforearth