LEE- Geomatik Mühendisliği-Doktora
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Yazar "Gülnerman Genç, Ayşe Giz" ile LEE- Geomatik Mühendisliği-Doktora'a göz atma
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ÖgeSocial media data valuation model for disaster incidence mapping(Graduate School, 2020-10-16) Gülnerman Genç, Ayşe Giz ; Karaman, Himmet ; 501142609 ; Geomathic Engineering ; Geomatik MühendisliğiSocial Media is a new age of data sources that emerged in the last decade. Users who have diverse different motivations (such as; entertainment, communicating or promoting) sign up the platforms worldwide. Currently, there is 3.5 billion active social media account worldwide. This growing number of account holders are accepted as human sensors that provide information about their environment. Unlike the traditional sensors, these human sensors have no certainty in their capacity to sense and share the information. In addition, the data provided by human sensors is unstructured. Still, social media is an invaluable data source for studies, especially that require continuous and real-time data widely. Currently, the data is widely used for politics, marketing, and most importantly in crisis management. In this thesis, social media data is assessed for incidence mapping during or shortly after a disaster with the motivation of increasing resilience to the expected major earthquake in Istanbul. The disaster management cycle has four phases as response, recovery, mitigation, and preparation. In the response phase, having real-time data from the affected area is important to properly allocate the resources. The conventional mapping technologies such as remote sensing and photogrammetry have the capacity of detecting the occurrence of a natural hazard however they are not eligible for information retrieval about the impacts of the natural hazards on human life such as emotions, opinions, and emergency situations. At this point, social media become forward as an immediate data source for incidence mapping during the response time of a disaster. Incidence mapping for resources management requires fine-grained data analyses. However, the uncertainty in data capacity, questions in the reliability of chosen techniques for pre-processing, and data bias are the key obstacles to the fine-grained analyses with the use of social media data. In this thesis, social media data is evaluated in terms of these key obstacles for Istanbul City since the data varies to the area that belongs to depending on its own human sensors. The main objective of this thesis is the determination of social media data potential for its use during the response phase of disaster management. There are three sub-objectives in order to reach the main objective; revealing the adequacy of the data for incidence mapping, adapting the pre-processing steps to Turkish language and questioning the reliability of the used filtering and classifying techniques with the quantification of its impacts on mapping, and investigating the intrinsic quality of the data (such as anomalies, trends, and biases) for the further interpretation of the incidence maps. The thesis is composed of three papers tackling these three objectives. Istanbul City is determined as the case area of each paper. In the first paper, the capacity of social media data to detect incidences in a fine-grained spatiotemporal perspective is investigated. For the case, the coup attempt data georeferenced within Istanbul city boundary is used and a series of incidences by the hour is mapped with the hotspots. According to that study, it is revealed that social media data has the capacity to identify an incidence with a fine-grain spatiotemporal resolution. In the second paper, the reliability of the chosen techniques for pre-processing and filtering social media data is researched with its effects on incidence mapping. Two terror attacks data that is georeferenced within Istanbul City is used for the case of this study. The study is not also testing the adaptation of the current pre-processing and filtering techniques to the Turkish language and also proposes a quantitative comparative index for quantifying the spatial reliability of each filtering process. This index named Giz Index which can be replicated for the similarity searches between two incidence maps. It is found in this study, with the proposed methodology for pre-processing and filtering, over 80% of spatial reliability can be achieved for incidence mapping based on social media data. In the third paper, the intrinsic quality of data is researched for the right interpretation of the incidence maps. The study overviews the weekly sampled social media data from each month during a year that is georeferenced within the Istanbul City. The data is assessed from the perspective of data anomaly, trend, and bias with the spatial statistical tests. The study infers that the data has spatial representation bias, anomaly tendency in some parts of the city, the spatiotemporal bias in terms of the time of day and day of the week. The results of the study contribute to the incidence mapping with the reference maps to avoid biased hot spot occurrences or missing information due to less amount of data.