Big Data GIS Analytics Towards Efficient Waste Management in Stockholm
Hossein Shahrokni, Bram Van der Heijde, David Lazarevic, Nils Brandt
Available Online August 2014.
- https://doi.org/10.2991/ict4s-14.2014.17How to use a DOI?
- Big Data Analytics, GIS, Smart Cities, Transportation, Waste Management
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Hossein Shahrokni AU - Bram Van der Heijde AU - David Lazarevic AU - Nils Brandt PY - 2014/08 DA - 2014/08 TI - Big Data GIS Analytics Towards Efficient Waste Management in Stockholm BT - ICT for Sustainability 2014 (ICT4S-14) PB - Atlantis Press SP - 140 EP - 147 SN - 2352-538X UR - https://doi.org/10.2991/ict4s-14.2014.17 DO - https://doi.org/10.2991/ict4s-14.2014.17 ID - Shahrokni2014/08 ER -