The findings provided by the number of reviewed approaches and studies found in literature, summarize the current development stage for advanced social media analysis methods and tools, which are easing the use of SMGI in several application domains. Focusing the attention on the spatial planning domain, several approaches and methodologies are proposed in order to take advantage of SMGI, which represents a potentially affordable and boundless source of information regarding people interests and concerns.
Summarizing the findings of the reviewed approaches, it is evident that novel methodologies require advanced methods and tools for properly managing and analyzing the various facets of SMGI. As a matter of fact, SMGI exhibits a particular data structure, requiring the use of suitable tools and analytical methods to deal with the spatial, temporal and user dimension, as well as, to investigate the embedded multimedia contents. All the reviewed methodologies may manage and analyze the sheer volume of this data, although in several approaches a manual intervention is strongly required for guiding the analysis, the proper information extraction, or the results classification.
From an analytical perspective, the approaches introduced in the different domains are mainly based on the analysis of one or more dimensions of SMGI, namely spatial, temporal, textual or users, through the use of different methods and tools to elicit useful knowledge. Nonetheless, the approaches reviewed in the field of spatial planning, and in particular the one proposed by Campagna et al. (2013), stress the major opportunities arising from a parallel or integrated analysis of multiple dimensions, which may enable to inquiry more effectively the spatial and temporal patterns of contributions, as well as the social and urban dynamics, thus easing the investigation of users preferences and concerns in urban systems. Overall, the reviewed studies show how quantitative and qualitative analyses may be conducted on SMGI using spatio-temporal and statistical techniques to verify different hypothesis, unleashing the knowledge enclosed in the sheer volume of qualitative descriptive SMGI (Campagna et al., 2015). Indeed, the wealth of information available from social media about facts, opinions and feelings of users could affect the current practices in design, analysis and decision-making, and could inform smart strategies with a real-time monitoring of needs and requirements of local communities. In addition, the availability of geographic social network platforms may ease the processes of Public Participation, or Participatory GIS (PPGIS), both in technology and social terms. Recently, PPGIS initiatives have required major efforts in order to establish a suitable technological and management framework. By taking advantage of already available social networks and SMGI, both no technology setup and less commitment by the potential participants may be required, inasmuch involved participants voluntary use one or more social networks during their daily routines (Campagna et al., 2015). Nevertheless, it is important to underline how different combinations of analytical approaches may be required in order to interpret the local contexts proficiently thanks to the use of SMGI.
Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 57 In the lights of these considerations, the summary of the results obtained by the reviewed studies and approaches, mainly in the domain of spatial planning, is provided in Table 1. In the table are exposed the different dimensions of SMGI and the main potential findings that an advanced analysis of each dimension, or multiple dimensions, may provide for eliciting knowledge in spatial planning.
SMGI DIMENSION MAIN FINDINGS FOR SPATIAL PLANNING
Space
Time + Content i) change in general interests and concerns
User + Content i) user profiling
Space + User + Content i) user interests and concerns in space
Space + Time + Content i) change in general interests and concerns in space Time + User + Content i) change in user interests and concerns
Space + Time + User + Content i) change in user interests and concerns in space ii) (near) real-time information for spatial governance Table 1. SMGI dimensions and opportunities for spatial planning analysis.
Moreover, the table summarizes the main results that advanced analyses on SMGI dimensions may provide for spatial planning in order to support analysis, design and decision-making. Despite SMGI may represent an innovative source of information regarding facts, opinions and preferences of users in space and time, it is important to be aware that this information may benefit from the official geographic information related to the referring context. Therefore, listed findings could be obtained by integrating SMGI with the available A-GI in order to extract profitable knowledge. In addition, the integration of datasets, originating from different social networks, might further improve the presented analytical opportunities.
Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 58
4.5 Discussion
This chapter discusses the increased availability of SMGI over the global Internet and the opportunities that this type of information may disclose for analysis in different application domains, such as disaster and emergency management, political science, social science, media studies, as well as, urban and regional planning. In spite of the novel mechanism that VGI initiatives are fostering for the production and dissemination of geographic information, major concerns persist regarding the quality, reliability, accuracy and credibility of this information for practices and research. Indeed, VGI and its SMGI subset are heterogeneous data and usually unstructured data, which may enclose different knowledge basis, leading toward difficulties in the seamless use of this information for practices or in analytical framework.
Nonetheless, VGI and SMGI may present important benefits in terms of affordability and timely data, as well as in the capability to provide information usually neglected in official information. Thus, several authors are concerned in the development of methodologies to evaluate and assess the quality and the fit-to-purpose of SMGI for different practices and research.
Furthermore, the reviewed studies found in literature were able to depict several methods and approach which may be conducted to investigate proficiently this type of information. SMGI requires advanced technologies, methodologies and ad-hoc tools to be analyzed and elicit knowledge; however, a lack of common methodologies or analytical frameworks to take advantage of this information for practices and research is detectable. In the spatial planning domain, the knowledge enclosed in SMGI may play a major role for supporting governance processes oriented to the local communities needs, fostering the development of smart cities initiatives tailored on real requirements of people and social dynamics. For this reason, the development of advanced tools, able to deal with the challenges of extraction, management and analysis of this information may be considered as the first milestone for easing and increasing the use of SMGI in practices. Despite of traditional data, SMGI refers to dynamic processes and requires new kind of tools to treat monitoring and decision-making in real-time about information that is continually changing, as well as, into finding suitable practices and procedures to integrate this experiential information with A-GI.
The reviewed studies provide several suggestions toward this direction and offer a glimpse concerning the current opportunities for a proper integration of SMGI with official information, as summarized above in Table 1. Altogether, the proposed methodologies and the different analytical opportunities contribute to show how SMGI might be used to elicit information, not only about the physical geography of places, but, overall, to investigate the perceptions of places and issues in time and spaces by the involved community, which may add a multifaceted perspective for spatial planning and decision-making.
Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 59
CHAPTER 5
OBJECTIVES OF RESEARCH AND PROPOSED METHODOLOGY
5.1 Introduction
The increased availability and production of SMGI over the global Internet are paving the way to innovative analysis scenarios in urban and regional planning, as depicted by the findings of the several approaches found in literature. Operationally, the integration of SMGI with A-GI may allow the development of analyses based upon quantitative and qualitative information, enriching the current capabilities of traditional spatial planning analytical methodologies. Commonly, urban and regional planning processes need large amounts of information for developing sustainable decision-making and implementing public policies. This information may be official or derived from direct observations or questionnaires conducted on a representative sample of population. Nevertheless, the traditional methods for gathering information may be highly expensive and time consuming, limiting the capability to have frequently updated information (Frias-Martinez et al., 2012; Jankowski et al., 2010). Hence, the data commonly used to study social phenomena is static and reports the particular instant at which the information is collected, dismissing opportunities to provide change in interests or preferences over a period of time (Antony, 2010).
SMGI may represent an innovative way to deal with the requirements for updated datasets on urban environments (Goodchild, 2007) or to favor the collection of opinions and requirements from local communities (Williams, 2010), easing social participative practices (Miller, 2006). However, the ‘Big Data’
nature of SMGI may require ad-hoc tools and analytical methodologies to take advantage of the enclosed knowledge. As a matter of fact, one major issue to extract useful knowledge from these innovative sources is to find an efficient way to manage the avalanche of information. The management issues for information from Big Data sources (Caverlee, 2010) is giving rise to an emerging new research field, namely Computational Social Science (CSS) (Lazer et al., 2009). This discipline may be described as the “integrated and interdisciplinary pursuit of social inquiry with emphasis on information processing and through the medium of advanced computation” (Cioffi-Revilla, 2010). Therefore, the discipline exploits a computational approach to the social sciences, that is the use of advanced instruments, tools and models to enable the collection and the analysis of massive amounts of data (Lazer et al., 2009). CSS is starting to revolutionize the way research is carried out, affecting both the empirical work by means of ‘big data’ and the theoretical model through computer simulation models for investigating social phenomena (Hilbert, 2015). The main CSS domains are automated information extraction systems, social network analysis, social GIS, complexity modeling, and social simulation models. Nonetheless, significant barriers persist to the advancement of
Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 60 CSS, but an increased availability of user-friendly tools and methodologies might magnify the extent of CSS to other domains of interest (Lazer et al., 2009).