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.applications and is referred to as Volunteered Geographic Information (VGI) [4,5]. The users can be thought of as unconscious volunteers for social media (SM) VGI, as deliberate volunteers for peer production VGI and as public participators for in citizen science based VGI [6][7][8]. The way of producing these forms of VGI is referred to as neo-geography in that it adopts neo-geographers (i.e., volunteers) who contributes to mapping activity without being expert [9]. This inexperience with regards to data production is questioned in the context of data quality [10][11][12], demographic bias (such as, gender, socioeconomic and educational aspects) [13,14] sampling bias (referring to volunteer sampling) and its impact on the generated data [15,16].In their very first form, citizen science projects in the very first forms were carried out with the use of paper maps [1]. However, with the...
Spatial data by using public knowledge is the most popular way to gather data in terms of social media within the last decade. Literature defines, public or volunteers are accepted bionic sensors detecting their surroundings and share what they detect in terms of their social media applications or microblogs. Besides being cheapest and fastest and easy way of spatial data acquisition, public or volunteers provides not only spatial data but also attribute data which makes the data more valuable. To understand and interpret those data have some difficulties according to locality. Although some difficulties like difference of languages, society structure and the time period would affect tweets depending on locality, gathering public knowledge or volunteered data contribute many scientific or private researches like Urban, Environmental, and Market side. To extract information, data should be reviewed locally according to main aim of research. In this study, our aim is to draw a perspective for a PhD research about volunteered data in the case of Turkey.
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.
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