Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees

Isam Mashhour Al Jawarneh, Luca Foschini () and Paolo Bellavista
Additional contact information
Isam Mashhour Al Jawarneh: Department of Computer Science, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
Luca Foschini: Dipartimento di Informatica—Scienza e Ingegneria, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
Paolo Bellavista: Dipartimento di Informatica—Scienza e Ingegneria, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy

Future Internet, 2023, vol. 15, issue 8, 1-28

Abstract: Numerous real-life smart city application scenarios require joint analytics on unified views of georeferenced mobility data with environment contextual data including pollution and meteorological data. particularly, future urban planning requires restricting vehicle access to specific areas of a city to reduce the adverse effect of their engine combustion emissions on the health of dwellers and cyclers. Current editions of big spatial data management systems do not come with over-the-counter support for similar scenarios. To close this gap, in this paper, we show the design and prototyping of a novel system we term as EMDI for the enrichment of human and vehicle mobility data with pollution information, thus enabling integrated analytics on a unified view. Our system supports a variety of queries including single geo-statistics, such as ‘mean’, and Top-N queries, in addition to geo-visualization on the combined view. We have tested our system with real big georeferenced mobility and environmental data coming from the city of Bologna in Italy. Our testing results show that our system can be efficiently utilized for advanced combined pollution-mobility analytics at a scale with QoS guarantees. Specifically, a reduction in latency that equals roughly 65%, on average, is obtained by using EMDI as opposed to the plain baseline, we also obtain statistically significant accuracy results for Top-N queries ranging roughly from 0.84 to 1 for both Spearman and Pearson correlation coefficients depending on the geo-encoding configurations, in addition to significant single geo-statistics accuracy values expressed using Mean Absolute Percentage Error on the range from 0.00392 to 0.000195.

Keywords: climate strategies; air pollution; urban planning; air pollution control; mobility; spatial data; smart city; geospatial analysis; air quality; geographic information systems (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1999-5903/15/8/263/pdf (application/pdf)
https://www.mdpi.com/1999-5903/15/8/263/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:15:y:2023:i:8:p:263-:d:1212205

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().