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Big Data GIS Analytics Towards Efficient Waste Management in Stockholm
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering.ORCID iD: 0000-0003-2621-4253
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering.
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering.ORCID iD: 0000-0002-9869-9707
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering.
2014 (English)In: Proceedings of the 2014 conference ICT for Sustainability / [ed] Höjer, Lago, Wangel, Stockholm, 2014, 140-147 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents preliminary findings from a big data analysis and GIS to identify the efficiency of waste management and transportation in the City of Stockholm. The aim of this paper is to identify inefficiencies in waste collection routes in the city of Stockholm, and to suggest potential improvements. Based on a large data set consisting of roughly half a million entries of waste fractions, weights, and locations, a series of new waste generation maps was developed. This was the outcome of an extensive data curation process, followed by batch geocoding of the curated entries. Thereafter, the maps were generated that describe what waste fraction comes from where and how it is collected. Finally, a preliminary analysis of the route efficiency was conducted. Maps of selected vehicle routes were constructed in detail and the efficiencies of the routes for the first half of July 2013 were assessed using the efficiency index (kg waste/km). It is concluded that substantial inefficiencies were revealed, and a number of intervention measures are discussed to increase the efficiency of waste management, including a shared waste collection vehicle fleet.

Place, publisher, year, edition, pages
Stockholm, 2014. 140-147 p.
Keyword [en]
Big Data Analytics, GIS, Smart Cities, Transportation, Waste Management
National Category
Other Environmental Engineering
Identifiers
URN: urn:nbn:se:kth:diva-149939ISI: 000346245500017Scopus ID: 2-s2.0-84928029537ISBN: 978-94-62520-22-6 (print)OAI: oai:DiVA.org:kth-149939DiVA: diva2:741573
Conference
2nd International Conference on ICT for Sustainability (ICTS), Stockholm, SWEDEN, AUG 24-27, 2014
Note

QC 20150122

Available from: 2014-08-28 Created: 2014-08-28 Last updated: 2015-11-20Bibliographically approved
In thesis
1. Smart Urban Metabolism: Toward a New Understanding of Causalities in Cities
Open this publication in new window or tab >>Smart Urban Metabolism: Toward a New Understanding of Causalities in Cities
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

For half a century, urban metabolism has been used to provide insights to support transitions to sustainable urban development (SUD). Internet and Communication Technology (ICT) has recently been recognized as a potential technology enabler to advance this transition. This thesis explored the potential for an ICT-enabled urban metabolism framework aimed at improving resource efficiency in urban areas by supporting decision-making processes. Three research objectives were identified: i) investigation of how the urban metabolism framework, aided by ICT, could be utilized to support decision-making processes; ii) development of an ICT platform that manages real-time, high spatial and temporal resolution urban metabolism data and evaluation of its implementation; and iii) identification of the potential for efficiency improvements through the use of resulting high spatial and temporal resolution urban metabolism data. The work to achieve these objectives was based on literature reviews, single-case study research in Stockholm, software engineering research, and big data analytics of resulting data. The evolved framework, Smart Urban Metabolism (SUM), enabled by the emerging context of smart cities, operates at higher temporal (up to real-time), and spatial (up to household/individual) data resolution. A key finding was that the new framework overcomes some of the barriers identified for the conventional urban metabolism framework. The results confirm that there are hidden urban patterns that may be uncovered by analyzing structured big urban data. Some of those patterns may lead to the identification of appropriate intervention measures for SUD.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. xiii, 73 p.
Series
TRITA-IM, ISSN 1402-7615 ; 2015:01
Keyword
industrial ecology, urban metabolism, smart cities, big data, data science
National Category
Other Environmental Engineering Environmental Analysis and Construction Information Technology
Research subject
Industrial Ecology
Identifiers
urn:nbn:se:kth:diva-176892 (URN)978-91-7595-737-1 (ISBN)
Public defence
2015-12-16, F3, Lindstedtsvägen 26, KTH, Stockholm, 14:00 (English)
Opponent
Supervisors
Projects
Smart City SRS
Funder
VINNOVA, 2012-01148
Note

QC 20151120

Available from: 2015-11-20 Created: 2015-11-11 Last updated: 2015-11-20Bibliographically approved

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Shahrokni, HosseinLazarevic, David

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