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Urban Data Integration (UDI) using Proximity Relationship Learning for Design, Management and Operations of Sustainable Urban Systems

TitleUrban Data Integration (UDI) using Proximity Relationship Learning for Design, Management and Operations of Sustainable Urban Systems
Publication TypeTechnical Report
Year of Publication2018
AuthorsGupta, K, Yang, Z, Jain, R
IssueTR227
Date Published02/2018
KeywordsData Integration, graph database, Planning, proximity learning, smart city, urban data
Abstract

The world is rapidly urbanizing with 66% of the world’s population expected to reside in cities by 2050. This massive influx of new urban citizens is putting enormous pressure on city systems and bringing forth challenges at the intersection of urban infrastructure, governance and the environment. As a result, researchers and practitioners have turned to new advanced sensing and data analytics developed under the burgeoning “smart city” movement to improve the design, management and operations of urban systems. However, data emerging from urban systems has been challenging to integrate, organize and analyze due to their natural spatial, temporal and typological heterogeneity. In this paper, an Urban Data Integration (UDI) framework is introduced that is capable of integrating heterogeneous urban data. The proposed UDI framework is extensible to multiple types of urban systems, scalable to the growing amount and quickly changing urban data streams and interpretable enough to help inform municipal decision-making. The UDI framework utilizes a series of novel proximity relationship learning algorithms to automatically reconstruct urban data in a graph database. The merits, applicability and efficacy of the proposed framework is demonstrated by validating and testing it on data from a mid-size city in the United States and by benchmarking its interpretability and computational performance for a typical urban analytics scenario against current practice (i.e., a relational database). Results indicate that the UDI framework provides easier and more computationally efficient exploration and querying of urban data and in turn can enable new computational approaches to urban system design, management and operations.

URLhttps://purl.stanford.edu/dq583pv0055
Citation Key1550