Computational Spatial Science - Models and Methods to build smarter cities

Type: Paper
Sponsor Groups: Spatial Analysis and Modeling Specialty Group, Transportation Geography Specialty Group, Geographic Information Science and Systems Specialty Group
Organizers: Avipsa Roy, Ziqi Li
Chairs: Avipsa Roy

Call for Submissions

We invite you to submit papers to the “Computational Spatial Science – Methods & Models” session. The purpose of this session is to bring together researchers, practitioners and academics to address the challenges of integrating computational methods, geospatial datasets and urban policymaking in building smarter cities. This session will provide a platform to discuss research areas, common issues and future directions in modeling urban design by considering sensor technology, crowdsourced data, visualization techniques, and advanced spatial analytics platforms.
Examples of topics for papers include but are not limited to:
• Analytics to integrate heterogeneous spatio-temporal data for prediction
• Anomaly detection and feature detection for infrastructure monitoring from remotely sensed data
• Real-time analytics of dynamic and distributed data for policymaking
• Spatial social network analytics in the built environment
• Theoretical and practical applications of Internet of Things in urban settings
• Urban mobility data management and visualization using crowdsourced data
• Spatial statistical models using multivariate data to generate an integrated decision-making tool for smart cities
• Policies and theoretical framework needed for developing smart city applications using spatial data by means of suitable use-cases
• Review of models/methods that discuss existing state-of-the-art and future directions to integrate GPS data and cyber-physical systems to achieve both smart and resilient cities

Please submit your abstracts to Avipsa Roy ( or Ziqi Li ( by October 30, 2019. These sessions are hosted by the Spatial Analysis and Research Center, School of Geographical Sciences and Urban Planning, Arizona State University.


The advances in sensor technologies and availability of connected devices have enabled the generation of large volumes of disparate, dynamic and geolocated data by both scientific communities and citizens. Such technological innovations have given birth to the concept of smart and connected cities. A smart city is forward-looking and progressive and has the potential to provide a higher-quality of life while meeting the daily needs of its citizens. The growing awareness among state authorities and local agencies regarding health, environmental, and economic benefits of smarter cities have motivated development of new methods in order to plan and design smarter cities.
Resilience is of utmost importance to design smarter cities. Therefore, the need for computational methods that can combine online and real-time knowledge discovery from dynamic geospatial data streams in conjunction with socio-economic data has risen enormously. These methods are designed to help practitioners and researchers with improved policymaking. The recent developments in data science and artificial intelligence have enabled integration of online (social-media) and static data sets (census/demographics/socio-economic/land-use) to develop robust spatial analytics approaches. The issue of modeling urban designs to build a smart city which is also resilient needs to be well understood to maximize the benefits of connected technologies - smartphones, GPS devices, health tracking devices etc. Visualization techniques are also important to allow public participation not only in data collection, but also in analytics and decision-making efforts to reduce error and uncertainties. Incorporating good quality and bias-corrected crowdsourced data with traditional data sources is another approach to improve data quality for connected applications and ease the decision-making process by policymakers.


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