Urban Modeling and Social Media

Itzhak Omer
2017 Geoinformatics & Geostatistics An Overview  
SM data can also be integrated with other sources of "big spatial data, " obtained by Information and Communication Technologies (ICTs), including real time and Volunteered Geographic Information (VGI) [7] . For example, studies have used the smart card databases of individual person movements through public transportation to investigate mobility patterns in the metropolitan areas of London, Singapore and Beijing [8] . The integration of SM data with such large detailed movement flow data sets
more » ... nables the revelation of polycentric structures in metropolitan areas, such as the spatial relations between employment centres and residential housing. SM data have also great potential for enhancing agent-based (AB) models, especially movement flow models [1, 2] . For example, current AB pedestrian movement models are based on observed aggregate volume data that were collected in selected places, with no information on individual movement paths (i.e., origin, destination, and length of the movement path), or people's intentions and feelings during movement. As a result, application of these models in existing and planned urban environments is limited to predicting movement volume distributions (e.g., identifying activity hotspots) throughout the street network but not movement flows between diverse locations in the street network [9] . However, the rapid development of GPSbased devices (including smartphones) for tracking pedestrian routes and of geographic information technology has enabled the capture of pedestrian movement flows in time and space at the level of individual movement paths. These GPS-based data can be integrated with social media data in order to capture how people behave and feel in different places. In AB modeling, such subjective data are essential for the definition of behavioral rules regarding agents' interactions with other agents and with urban environments, and thus advance our understanding of the pedestrian flow patterns created and the conditions affecting these patterns. It follows that geo-referenced social media data may potentially be used for the calibration and validation in urban modelling, especially spatial interaction and agent based models at the large and local scale, respectively. However, some data usability issues have arisen when employing SM data [2], particularly with regard to sampling (i.e., the data may not reliably represent the true underlying population), privacy and ethics (i.e., it is not clear if people agree or are aware that their social media contributions are being used for research) and context-related uncertainty (e.g., it is difficult to derive a person's intended meaning from the texts). These usability issues may explain the limited application of SM data so far in urban modeling. Therefore, much more effort should be dedicated to the verification and improvement of SM data usability, considering their potential. However, SM contains the potential for enhancing urban modelling not only as a source of data but also as an integral part of those data. This is so because SM not only tells us how, and when, individuals are using urban spaces; they themselves also have an effect on how, and when, individuals use urban space. Herrera-Yagüe et al. [10] have reported that on the urban scale, unlike the country scale, geography plays only a minor role in the formation of social networking communities within cities. It is also well known, as suggested by the concepts 'Network Individualism' or 'individual-based network' [11] , that contemporary society is in a Urban models are essential for understanding the structure and dynamics of cities and for testing the impact of changes in the locations of land use and transportation on the urban environment. As a result of the increased involvement of social media (SM) or digital social networks (e.g., Facebook, Twitter, Gowalla and Foursquare data) in contemporary urban society they can be relevant for urban modeling in two was. First, they can serve as an appropriate unique provider of data that can tell us how, and when, individuals are using urban spaces. Second, as they have potential effect on individuals' spatial behavior and urban dynamics they should be taken into consideration in urban modeling. In the following I discuss the relevance and potential of SM for urban modeling. Social media (SM) data are contributed by individual users and generally include check-ins (e.g., in business and services), points of interest, images, textual messages, and the time and location of when and where the message was posted. Hence, these updated and detailed data can tell us how, and when, individuals are using urban spaces, how they feel in different places, and what are their intentions and preferences [1] . In contrast to "traditional" social and functional data, collected by surveys and censuses, SM provide objective and subjective data on various social and functional activities, travel behaviour and daily space-time movements at different times and spaces, thus having great potential for urban research and modelling [2] .
doi:10.4172/2327-4581.1000e103 fatcat:ksgd3bqxqnaxhkmrjmnvrvfatu