The data generated by social media such as Twitter are classified as big data and the usability of those data can provide a wide range of resources to various study areas including disaster management, tourism, political science, and health. However, apart from the acquisition of the data, the reliability and accuracy when it comes to using it concern scientists in terms of whether or not the use of social media data (SMD) can lead to incorrect and unreliable inferences. There have been many studies on the analyses of SMD in order to investigate their reliability, accuracy, or credibility, but that have not dealt with the filtering techniques applied to with the data before creating the results or after their acquisition. This study provides a methodology for detecting the accuracy and reliability of the filtering techniques for SMD and then a spatial similarity index that analyzes spatial intersections, proximity, and size, and compares them. Finally, we offer a comparison that shows the best combination of filtering techniques and similarity indices to create event maps of SMD by using the Getis-Ord Gi* technique. The steps of this study can be summarized as follows: an investigation of domain-based text filtering techniques for dealing with sentiment lexicons, machine learning-based sentiment analyses on reliability, and developing intermediate codes specific to domain-based studies; then, by using various similarity indices, the determination of the spatial reliability and accuracy of maps of the filtered social media data. The study offers the best combination of filtering, mapping, and spatial accuracy investigation methods for social media data, especially in the case of emergencies, where urgent spatial information is required. As a result, a new similarity index based on the spatial intersection, spatial size, and proximity relationships is introduced to determine the spatial accuracy of the fine-filtered SMD. The motivation for this research is to develop the ability to create an incidence map shortly after a disaster event such as a bombing. However, the proposed methodology can also be used for various domains such as concerts, elections, natural disasters, marketing, etc.

Social media (SM) can be an invaluable resource in terms of understanding and managing the effects of catastrophic disasters. In order to use SM platforms for public participatory (PP) mapping of emergency management activities, a bias investigation should be undertaken with regard to the data related to the study area (urban, regional or national, etc.) to determine the spatial data dynamics. Thus, such determinations can be made on how SM can be used and interpreted in terms of PP. In this study, the city of Istanbul was chosen for social media data research area, as it is one of the most crowded cities in the world and expecting a major earthquake. The methodology for the data investigation is: 1. Obtain data and engage sampling, 2. Identify the representation and temporal biases in the data and normalize it in response to representation bias, 3. Identify general anomalies and spatial anomalies, 4. Manipulate the trend of the dataset with the discretization of anomalies and 5. Examine the spatiotemporal bias. Using this bias investigation methodology, citizen footprint dynamics in the city were determined and reference maps (most likely regional anomaly maps, representation maps, time-space bias maps, etc.) were produced. The outcomes of the study can be summarized in four steps. First, highly active users generate the majority of the data and removing this data as a general approach within a pseudo-cleaning process means concealing a large amount of data. Second, data normalization in terms of activity levels, changes the anomaly outcome resulting from diverse representation levels of users. Third, spatiotemporally normalized data present strong spatial anomaly tendency in some parts of the central area. Fourth, trend data is dense in the central area and the spatiotemporal bias assessments show the data density varies in terms of the time of day, day of week and season of the year. The methodology proposed in this study can be used to extract the unbiased daily routines of the social media data of the regions for the normal days and this can be referred for the emergency or unexpected event cases to detect the change or impacts.

Navigation systems for impaired people is one of the recent developed applications for hearing or visually impaired people within the context of the in-door navigation technology that compatible with the smart phones. Loud steps is one of the examples of them. The application serves for guiding hearing and visually impaired people with the help of reading voice features of routing to independently around various places. Offices, homes, schools, hospitals, bus stations, airports, public institutions and other social and sport facilities ...etc. are among these destinations. It has been downloaded and used by 130.000 users including more than 20.000 visually impaired people at various institutions in 11 countries. The first implementation of the loud steps in Istanbul Technical University (ITU) was the Faculty of Civil Engineering in Ayazaga Campus area. Subsequently, the application has been established for another 4 buildings including lecture halls and 3 techno park buildings in campus. During the later stages of this project, the establishment of system will be carried out for the remained 12 faculties and 6 institutes located in the campus area. Moreover, outdoor navigation system also established to the campus to make all indoor system integrated for the benefits of hearing or visually impaired people around the whole 2.500.000 square meters campus area. Therefore, ITU Ayazaga Campus has an integrated indoor and outdoor navigation systems which taking these kind of people from any campus entrances using the outdoor navigation systems and to the any halls, lecturer's offices, laboratories, information desk, toilets, elevators also other emergency facilities such as fire exits or fire-escape stairs...etc. providing voice guidance with also visual navigation.

Sports has an important role for the health of present societies and the next generations as an element of urban design. Sports facilities have strong effects on the social life in urban areas. The facilities supply the communication between the dynamics of the cities and generate synergy in the city life of people interacting with each other for improvement in their lives. Before the construction of the sports facilities, the planners must evaluate the type and size of them in terms of the population of the whole city and, the population of neighboring settlement areas, which use the facility. Entropy can be used as a criterion for the quantitative measure of spatial information on maps. In the study, the location of the sports facilities in Istanbul are compared by using the entropy as a quantitative criterion. As a result, the success of the location selection for the sports facilities in urban areas are estimated by using the entropy as a component. Metric entropy method and applications were carried out by a case study in the city of Istanbul for defining the spatial distribution of sports facility locations with the posts of people including "sports" and sports-related keywords on social media.