Data for: Towards Sustainable Smart City by Particulate Matter Prediction using Urban Big Data, Excluding Expensive Air Pollution Infrastructures

Published: 29 Aug 2018 | Version 1 | DOI: 10.17632/mf35mkghmj.1
Contributor(s):

Description of this data

It is vital to capture and analyze, from various sources in smart cities, the data that are beneficial in urban planning and decision making for governments and individuals. Urban policy makers can find a suitable solution for urban development by using the opportunities and capacities of big data, and by combining different heterogeneous data resources in smart cities. This paper presents data related to urban computing with an aim of assessing the knowledge that can be obtained through integration of multiple independent data sources in Smart Cities. The data contains multiple sources in the city of Aarhus, Denmark from August 1, 2014 to September 30, 2014. The sources include land use, waterways, water barriers, buildings, roads, amenities, POI, weather, traffic, pollution, and parking lot data. The published data in this paper is an extended version of the City Pulse project data to which additional data sources collected from online sources have been added.

Experiment data files

peer reviewed

This data is associated with the following peer reviewed publication:

Towards Sustainable Smart City by Particulate Matter Prediction Using Urban Big Data, Excluding Expensive Air Pollution Infrastructures

Published in: Big Data Research

Latest version

  • Version 1

    2018-08-29

    Published: 2018-08-29

    DOI: 10.17632/mf35mkghmj.1

    Cite this dataset

    Honarvar, Ali (2018), “Data for: Towards Sustainable Smart City by Particulate Matter Prediction using Urban Big Data, Excluding Expensive Air Pollution Infrastructures ”, Mendeley Data, v1 http://dx.doi.org/10.17632/mf35mkghmj.1

Categories

Smart City

Mendeley Library

Organise your research assets using Mendeley Library. Add to Mendeley Library

Licence

CC BY NC 3.0 Learn more

The files associated with this dataset are licensed under a Attribution-NonCommercial 3.0 Unported licence.

What does this mean?

You are free to adapt, copy or redistribute the material, providing you attribute appropriately and do not use the material for commercial purposes.

Report