University of Twente Student Theses


A guideline to support the monitoring of SDG 11 : using open geospatial data extracted by Earth observation and Machine Learning

Torabi Dashti, Zahra (2022) A guideline to support the monitoring of SDG 11 : using open geospatial data extracted by Earth observation and Machine Learning.

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Abstract:Agenda 2030 have defined Sustainable Development Goal 11 to make cities and human settlements inclusive, safe, resilient, and sustainable. There are also specified targets of SDG11 to help cities overcome their challenges, grow, and thrive. 15 indicators have been defined for monitoring the achievement towards the targets. But, due to poor access to open and standardized data and the lack of institutions for comprehensive data collection, monitoring the progress toward these goals becomes difficult which makes it hard to accomplish them. This study proposes a guideline for monitoring the three SDG indicators; access to public transport, land use efficiency, and access to public spaces, with the aim of solving the problem of obtaining required data to evaluate them for the city of Tehran (whose organizations and institutions face data restrictions). For assessing the access to public transport, the walking distance to the public transport stations has been set as a criteria. This criterion is used for determining the service area of public transport system. For calculating this indicator, data for stations and roads are captured from Open Street Map and analyzed through the Network Analysis tool in ArcMap. Since the monitoring of the indicator progress needs the calculation of indicator in a past date, historical data of public transport stations and roads were collected from the Tehran municipality dataset. Land use efficiency is assessed by computing the land consumption rate per population growth rate. For calculating the land consumption rate data of built-up areas of two different times are captured from the World Settlement Footprint layer. The population data of the Tehran municipality statistical year book is used for computing population growth rate. Access to public spaces is assessed by calculation of the share of the people within its service area, which is defined by the 400-meter walking distance toward them. Data on the open/green spaces are captured through Sentinel-2 imageries. The CART algorithm in Google Earth Engine (GEE) platform is used for land cover classification. The Street network for this analysis is captured from OSM. In the next step, the results of the computation are analyzed, considering the context of the city of Tehran and in the two defined times to monitor the progress of SDG indicators. Finally, the guideline for the evaluation and monitoring of SDG11 indicators is formed by organizing the whole process. The guideline consists of all available data sources, the methods for extracting required data, and the ways of computation of indicators which is useful for relevant stakeholders in evaluating and monitoring SDG indicators. The guideline can fill the gap of data restriction and helps to analyze data with minimal manual interaction and at the lowest cost and in the shortest time. Although there are some limitations to applying the guideline; It is not addressed social and economic conditions, and some aspects of indicators cannot be observed by EO tools. There could be an improvement in assessing and interpreting the result of monitoring indicators by accompanying the other auxiliary data such as demographic, social, and economic data in future studies.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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