Abstract
Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI) for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations.
Highlights
Urban areas, as characterized by very high population density and vast human activities, are where over 50% of the world’s population lives and where over 75% of the world’s energy is used [1]
We found that moving spatial analytical tools to cyberspace and providing adequate provenance information greatly facilitates the collaborative decision-making process
This geospatial cyberinfrastructure (GCI) is built upon a service-oriented architecture that allows (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positioning errors introduced by fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task
Summary
As characterized by very high population density and vast human activities, are where over 50% of the world’s population lives and where over 75% of the world’s energy is used [1]. It promotes widespread sharing of geospatial data and analytical functionalities based upon a service-oriented architecture [12] and empowers data-driven scientific analysis in an open and collaborative fashion [13] In this vein, the GCI provides a promising solution framework to address several technical challenges encountered in the domain of urban economic modeling. We found that moving spatial analytical tools to cyberspace and providing adequate provenance information greatly facilitates the collaborative decision-making process This GCI is built upon a service-oriented architecture that allows (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positioning errors introduced by fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task.
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