The recent explosion in the number and size of spatio-temporal data sets from urban environments and social sensors creates new opportunities for data-driven approaches to understand and improve cities. Visual analytics systems like Urbane aim to empower domain experts to explore multiple data sets, at different time and space resolutions. Since these systems rely on computationally-intensive spatial aggregation queries that slice and summarize the data over different regions, an important challenge is how to attain interactivity. While traditional pre-aggregation approaches support interactive exploration, they are unsuitable in this setting because they do not support ad-hoc query constraints or polygons of arbitrary shapes. To address this limitation, we have recently proposed Raster Join, an approach that converts a spatial aggregation query into a set of drawing operations on a canvas and leverages the rendering pipeline of the graphics hardware (GPU). By doing so, Raster Join evaluates queries on the fly at interactive speeds on commodity laptops and desktops. In this demonstration, we showcase the efficiency of Raster Join by integrating it with Urbane and enabling interactivity. Demo visitors will interact with Urbane to filter and visualize several urban data sets over multiple resolutions.
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GUIDES: Geospatial Urban Infrastructure Data Engineering Solutions
The digitization of legacy infrastructure constitutes an important
component of smart cities. While most cities worldwide possess
digital maps of their transportation infrastructure, few have accurate
digital information on their electric, natural gas, telecom,
water, wastewater, and district heating and cooling systems. Digitizing
data on legacy infrastructure systems comes with several
challenges such as missing data, data conversion issues, data inconsistency,
differences in the data format, spatio-temporal resolutions,
structure, semantics and syntax, difficulty in providing controlled
access to the datasets, and so on. Therefore, we introduce GUIDES, a
new data conversion and management framework for urban infrastructure
systems, which is comprised of big data analytics, efficient
data management techniques, semantic web technologies, methods
to ensure information security, and tools that aid visual analytics.
The proposed framework facilitates: (i) mapping of urban infrastructure
systems; (ii) integration of heterogeneous geospatial data;
(iii) a secured way of storing, analyzing and querying data while
preserving the semantics; (iv) qualitative and quantitative analysis
over several spatio-temporal resolutions; and (v) visualization of
static (e.g., land use) and dynamic (e.g., road traffic) information.
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- NSF-PAR ID:
- 10059475
- Date Published:
- Journal Name:
- 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3139958.3139968
