This chapter critically analyses whether new data sources, generally referred to as ‘big data’ and volunteered geographic information (VGI), can represent a way of overcoming the limits of traditional geographical representations based on official statistics and indicators. It also asks whether this new availability of spatial information simply increases the amount of georeferenced data or, through the support of a theoretical framework, it can also open up new and more effective ways of revealing places and their identities and thus improving community participation as practice for policymaking and planning.

If read in its historical context, the recent emergence of the big data era and the so-called ‘fourth paradigm’, based on data-intensive analysis (Anderson, 2008), appears to be in perfect continuity with the late 19th-century quantitative turn in social sciences where statistical quantification of human behaviour played a key role in explaining the world and in forming modern Western identity (Hacking, 1990). This ‘objective’ knowledge also produced a new approach to social engineering based on managing natural and social facts more easily through numbers (Barnes, 2013). The current ‘datafication’ of knowledge represents the power accorded to data in the construction and reproduction of social representations based on the huge availability of digital information and the power of the Internet (Newell and Marabelli, 2015).

This approach also has epistemological and ontological implications, in particular when the analysis is carried out at subnational level. As Shelton (2017) states, data used to represent the phenomena under investigation are strongly influenced by what is simpler to quantify and easily counted, causing a misrepresentation or underrepresentation of all that is not visible or is difficult to operationalise through the traditional top-down approach to knowledge production. The result is the production of partial geographies in which all that is not easy to represent, like conflict, cultural, ethnical and social diversity, essentially becomes invisible (Vanolo, 2018).

All these aspects are part of the subjective/emotional side of place representation carried out on different scales, from the body to the home and neighbourhood, up to larger scales characterising what Lefebvre (1991) defined as ‘lived spaces’. These ‘lived spaces’ are traditionally neglected in positivist geographical studies based on a conception of space as an object bounded by administrative limits and by economic and technical criteria. As Davidson et al. (2005, p. 1) note: ‘The difficulties in communicating the affective elements at play beneath the topographies of everyday life have meant that, to a greater or lesser extent, geography has tended to deny, avoid, suppress or downplay its emotional entanglements’. A limit probably due both to the limited availability of data and to the intrinsic difficulty in quantifying and operationalising subjective aspects, especially in relation to their spatiality.

This approach is particularly inadequate when values are at stake in the analysis as, for example, in the recent indicators framework built to assess the progress of policies for human well-being and sustainability: the ongoing sustainable development goals (SDGs) promoted by the United Nations (UN) in 2015. Despite the significant shift in considering different measures of human progress beyond the criteria of gross domestic product (GDP), the way to represent the complexity stemming from this new approach to development remains bound to the traditional systems of indicators based on official statistics produced by national institutions. According to this perspective, the geographical dimension of sustainability is restricted to the study of spatial differences and quantified using administrative spatial units with a comparative approach at global level. By describing regions or cities only in terms of their contents – that is, wage earners, pupils’ achievements, number of unemployed people and so on – they become mere objects of comparison in the ranking exercise of attributing above/below scores with respect to the national average.

The container approach to space is not able to grasp some complex issues of the sustainability/well-being concept and constitutes a rather poor conceptual and methodological basis for dealing with a complex place-based concept like sustainability (Manderscheid, 2012). Where people reside and act, their subjective and relational dimension of spatiality from the body and its multilayered interrelations with others scaled up to global level, is an issue that requires a more complex vision of spatiality, which Lefebvre (1991) and Harvey (2006) effectively introduced in their conceptualisation of space. This framework of reference can help in decoding and understanding the role of new sources of data in the production of a more participatory geographic knowledge.

Starting from this field of observation, the chapter will test the possibilities offered by new sources of geographic data usually defined as big data (in particular those produced through people’s voluntary contributions) in supporting a research path that can lead to more participatory and coherent spatial representations with the complexity that emerges when values and people and the specificities of places are at stake. The chapter will also examine the role of VGI and explore the ways in which a new approach to spatiality can help to decode and understand this avalanche of data.

From drought to a deluge of spatial data

The term ‘big data’, typically used to mean a wide range of data sources with specific characteristics and epistemological, methodological and legal implications, has since around 2010 become a ‘popular technological meme’ (Gorman, 2013). Despite its popularity across a wide range of disciplines, from information science to medicine, sociology, economics and management, the term does not yet have a structured and universally accepted definition. Through a survey of 1,581 conference papers and journal articles that contained the full term ‘big data’, de Mauro et al. (2016, p. 131) propose a definition independent of the various fields of application and based on big data as an information asset ‘characterised by such a high volume, velocity and variety to require specific technology and analytical methods for its transformation into value’.

To the well-known ‘three V’ criteria of data (volume, velocity and varieties) (Laney, 2001), some scholars (Boyd and Crawford, 2011; Kitchin, 2014) have added other criteria, in particular: completeness (the ability to grasp the characteristics of entire populations or systems); relationality (generally data have common key fields attached to other data sets); and high-resolution information involving a fine level of detail, including spatial scale resolution.

Among different typologies of sources of big data identified by the current literature (Kitchin, 2014; Miller, 2010) and major statistical agencies, the classification of the sources provided by the UN (UNGGIM, 2013) is particularly interesting for its relationship with data contents:

One of the most important characteristics of this avalanche of data is the possibility of georeferencing the information produced. Mobile phone users, access to the Internet, weather sensors, tracking of vehicles providing location in real time, social networks, shopping in the point of sale (POS) circuits and so on, allow the analysis and prediction of events with a geographical precision that was unthinkable in the mid-2010s. The number of social media users worldwide was 2.46 billion in 2017 (European Commission, 2019), although not all digital tracks are georeferenced. Roughly 2 per cent of the total number of tweets collected (almost 500 million tweets per day and 326 million people using Twitter every month in 2017) showed the user’s location with a street level accuracy, mainly due to privacy problems (Leetaru et al., 2013). Moreover, despite the low percentage of geotagged data from social networks, some researchers are developing an algorithm able to predict a Twitter user’s location without the need of a single geotag (Krishnamurthy et al., 2014). Batty (2013) suggests that the big data revolution will profoundly change geographic analysis thanks to the availability of small-scale information that will allow the emergence of some phenomena previously not easily measurable.

Another relevant big data issue is the ongoing shift in the production of data. Traditionally, data are collected by national statistical authorities that monitor the whole process from production to dissemination. One of the most relevant effects of technological advances since 2010 has been the involvement of non-professional producers in mapping activities and spatial data collection, the so-called ‘produsers’, that is people playing the twin role of producer and user of data (Budhathoki et al., 2008). These new ways of geographical data production can take place either voluntarily (through ad hoc spatial interfaces and the use of social networks, blogs, e-commerce, opinions about products and services and the like) or through the involuntary tracks generated by the use of mobile devices or other everyday practices (use of credit cards, health cards, public transport, satellite box car insurance, for example). Many terms are used to classify citizen-derived geographical information, such as crowdsourcing, user-generated content and VGI (See et al., 2016). However, in general, this range of technologies and human participation practices is defined as the ‘Geoweb’: the integration of Web 2.0 and geospatial information technology (such as Google Earth, OpenStreetMap (OSM), geographic information system (GIS), quantum geographic information system (QGIS)) (Elwood and Leszczynski, 2012). Some scholars have defined this new production as a revolution, an ‘unprecedented moment in human history: we can now know where nearly everything, from genetic to global levels, is at all times’ (Sui and DeLyser, 2012, p. 13). Boyd and Crawford (2011) also highlight the ability of big data to make connections between different kinds of personal, collective, social, financial and spatial data, producing a significant informational value in the analysis of relationships and behaviour patterns and a new paradigm for sociospatial research (Jiang and Thill, 2015).

Crowdsourced data or the emergence of a new point of view on space

Within the more general phenomena of crowdsourcing and user-generated content, the result of the voluntary data production processes has been defined as VGI (Goodchild, 2007), a particular kind of georeferenced crowdsourcing that represents the contribution citizens make to local knowledge (‘citizens as sensors’). Citizens create, collect, publish and share geographic information on the Web, playing an increasing influence on government operations, on urban and regional planning and on a wide range of business activities. Citizen involvement also concerns mapping activities, which have previously been the exclusive responsibility of the central authorities. The development of GIS-oriented applications for mobile devices has further facilitated the creation and sharing of information maps, allowing the construction of user-friendly web platforms not requiring any professional cartographic skill (‘collaborative mapping’). This is the case with OSM,¹ a collaborative map of the world created by a community of mappers who contribute to acquiring, reviewing and updating data on roads, trails, cafés, railway stations and other open data not covered by copyright. In 2019, 15 years after its creation, the OSM community of mappers was made up of more than five million registered members around the world.

Another example of VGI is the use of geotagging in the daily activities of people moving through the city. Cranshaw et al. (2012) developed a new representation of cities not based on the administrative boundaries of districts, but on what they define as ‘livehoods’: geo-social neighbourhoods defined by geographic proximity and by the cultural similarities of people in terms of social behaviour and daily use of the city. EmoMap is an application developed by the Vienna University of Technology (Capineri et al., 2018) that tries to understand the relationship between the urban context and the emotional responses of people crossing and living in different areas of the city.

The information produced from the users of the Geoweb is increasingly employed by the governments and organisations that play a central role in stimulating and organising citizen input into local planning (Certomà and Rizzi, 2017; Johnson and Sieber, 2013). The UN is experimenting with VGI’s potentiality through U-Report,² an anonymous messaging service available through the Facebook Messenger app that allows young people to speak out on issues that matter in their area. Over time, they also have the opportunity to contribute to discussions on certain issues, giving feedback about their experience as a U-Reporter to the central or local authorities. This service works in more than 53 countries around the world, from Argentina to Liberia and New Zealand. Citizen crowdsourcing is also the basis of the social experiment carried out by the Barcelona municipality through Decidim, a free open-source participatory online platform helping ‘citizens, organizations and public institutions self-organize democratically at every scale’.³ Bria (2018) states that over 70 per cent of urban policies in Barcelona have been proposed and decided through the online participation of citizens.

Albeit without adopting a precise technological mandate as in the case of Barcelona, in 2018 Rome’s III Municipality government requested the collaboration of citizens (Morosi, 2018) in producing a collaborative map of under-used or abandoned spaces, in order to allocate them for cultural purposes. This ongoing mapping exercise, based on the open-data platform Reter,⁴ a critical and collaborative cartography project, has led to spaces emerging that had never been considered in the past, by means of the Rome municipality’s official map: parks, gardens, schools, associations, community centres, bookshops, parishes, public buildings, squares, open spaces, streets, urban stairs, private terraces, interior courtyards in popular condominiums, all potential spaces where people can meet and develop cultural programmes based on public pedagogy. A new way of providing collective knowledge about places is provided by local people living there, a living and non-static map, with an affective and emotional dimension. In such a context, the cultural and entertainment dimension acts as a motivation for citizens to get actively involved (Figure 3.1).

The active production of information provided by the citizen is only a part of the Geoweb universe. A larger proportion of it is produced involuntarily by social media and mobile app users, which Campagna (2016, p. 48) defines as ‘social media geographic information’ (SMGI), a sub-category of VGI: ‘any piece or collection of multimedia data or information with explicit (i.e. coordinates) or implicit (i.e. place names or toponyms) geographic reference collected through the social networking web or mobile applications’. An unstructured data set of content that, if integrated with other data sources, can improve the knowledge of citizens’ perceptions and mood through ‘sentiment analysis’. Feick and Roche (2013) identify the ‘geowebbers’ as Debord’s ‘psychogeographers’ – flâneurs in urban space, exploring, using and producing new unstructured, but potentially valuable, data for local understanding.

The Geoweb has also transformed the traditional and professional GIS-user interfaces into simple, yet compelling, web browser-like interfaces. As Sui (2008, p. 4) argues, the ‘wikification of GIS is perhaps one of the most exciting, and indeed revolutionary developments since the invention of [GIS] technology in the early 1960s’. VGI is a sort of postmodern GIS ‘in which individuals are able to assert their own views of their surroundings and play a part in local decision-making’ (Goodchild, 2010, p. 20). The UN has recognised that ‘VGI and crowdsourced data … has the potential to enable user’s view of the geography, which if utilized by policy and decision-makers, will allow for potentially more effectively targeted interventions and more tailored public services’ (UNGGIM, 2013, p. 29).

Capineri (2016) suggests that the experiential nature of VGI content can challenge the dominant narratives, traditionally provided by statistical indicators, because VGI represents a situated knowledge, inscribed in places and based on local practices and culture. Despite the technocratic implications of an excessive confidence in technology and the risks of the exclusion of people who do not have the required skills, Certomà and Rizzi (2017) posit that crowdsourcing is a tool able to generate new forms of urban governance through the active participation of citizens in the local political life.

This promising universe of new data provides huge quantities of geographical information that need to be not only exploited, but also decoded and understood within clear theoretical and epistemological frameworks (Kitchin, 2014; Sui and DeLyser, 2012). The emergence of information produced by people and communities claiming their point of view, without the filter of statisticians and other experts, undermines and calls into question the role of a neutral and objective knowledge, mostly based on the quantitative paradigm described earlier.

A paradigm based on the idea of space as a geometric and bounded entity inspired the analyses of positivist geography and other disciplines such as regional science. If the place where people and their perceptions and emotions reside matters, a new dimension of spatiality should be considered, a multilayered and dynamic vision that is the basis of the so-called postmodern turn in geography theorised by scholars such as Lefebvre (1991) and Harvey (2006).

Beyond geotagging: towards a conceptual framework for big geodata

One of the most interesting outcomes of the postmodern turn in geography is the transition to a new way of looking at space. In the recent past, many scholars have suggested that space has a much deeper meaning and importance than just an absolute and Euclidean dimension and they have proposed a theory in which space is seen as absolute, relative or relational, or any combination of these depending on the circumstances. As Harvey (2006, p. 145) puts it: ‘An event or a thing at a point in space cannot be understood by appeal[ing] to what exists only at that point’, depending on a wide amount of spatial relations on different scales and subject to historical influences. In the attempt to reach an even more analytical conceptualisation that allows us to disentangle, reaggregate and, at the same time, unify the concept of space, Harvey (2006, p. 152) suggests proceeding through a ‘speculative leap’. This consists of associating his former three-dimensional conceptualisation of space (‘absolute/relative/relational’) (Harvey, 1969) with the spatial trialectic (‘experienced/conceptualized/lived space’) proposed by Lefebvre (1991). This association generates a 3 x 3 matrix in which each cell represents a specific way of conceiving the meaning of space (Harvey, 2006) (see Table 3.1).

By introducing Lefebvrian categories, Harvey (2006) assigns great relevance to the ‘subject’, that is, the inhabitant of the city, with all her/his perceptions of the real context of the place (‘experienced space’) involving feelings and emotions (‘lived spaces’). This, in turn, allows Harvey to stress – within a clear postmodern framework – the importance of the positionality of the subject in the knowledge and representation of space. Furthermore, by introducing the category of ‘representation of space’, Harvey raises the issue of political power, in its capacity through mediated representations, to produce and reproduce space and to influence its perception and use.

In the matrix, the different position of the cells allows, once crystallised, the identification of the phenomena taking place in space by decomposing them. What is more, through the inverse dialectic movement process across the cells, the matrix also allows us to recombine a complex, transcalar and dialectical view of space.

In this way, the matrix permits the deconstruction and, at the same time, the reassemblage of different types of space. It is possible to describe ‘absolute experienced space’, that is the space of walls, streets, bridges and all the elements that a human being is able to perceive; the ‘relative spaces of representation’, as in the case of the frustration of commuters generated by being trapped for hours in traffic on their way to or from work; the ‘relational spaces of representation’, that is the artistic production in space mediated by the artist. The same space, as in the case of a city square, can be described by the cadastre map, by a postcard, by the time needed to reach it from the nearest train station, by the quantity of air monitored by air-pollution control systems; it can be described by the city’s official tourist guide, containing all the information on how to access the square and presented through an artistic medium. Finally, it can assume several features if, along with this information, we add emotional factors: the feelings of people living there, of people passing by in the square for business or pleasure, the place that the square has in the individual as well as in the collective memory.

However, this stimulating view of spatiality is limited by an operational impasse, because as we move away from a Cartesian and geometric conception of space, the representation and analysis of phenomena in terms of dialectic and emotional spatiality becomes increasingly difficult and complex. The space of sensations, emotions, imagination and meanings embedded in everyday life, experienced through the complex network of symbols and images of its inhabitants and users, is essentially qualitative, fluid and dynamic. As Zhang (2006) suggests, an important element in Lefebvre’s (1991) space theory is the introduction of the ‘viewer’s point of view’, because its trialectic is not intended as a cake cut into three slices, but rather as three different images that overlap. Each image represents a different moment of the human spatial experience.

This conceptual perspective can help to clarify the role that new sources of information can play in geographical studies. They can allow the analysis of spatial phenomena in quantitative terms, via the availability of a large amount of georeferencing data passively collected, not only in a static way, but also in terms of flows and movements, and also in qualitative terms, via the information about what people say and voluntarily produce through online content. This is a central point of the potential that big data can offer to geographical studies: a deeper knowledge of places and local dynamics beyond simple geotagging based on the possibility of locating available information.

SDGs: Indicators as usual

In order to test the heuristic power of the inspiring conceptualisation of spatiality described in the previous section, it must be understood how the categories identified in the matrix can provide a relational and complex representation of the phenomena analysed in their spatial dimension and if new sources of data like VGI can support it.

The exercise proposed consists of testing the operational potentialities of the matrix through the representation of the concept of sustainability in an urban context.

In 2015 UN member states adopted the 2030 Agenda for Sustainable Development, defined as ‘a shared blueprint for peace and prosperity for people and the planet’, comprised of 17 SDGs: a global call for action against poverty and other deprivations, the implementation of policies for health and education, the reduction of inequality, the support of economic growth, and the challenge of climate change in order to preserve the planet.

The goals were defined according to the concept of sustainability involving three dimensions (environmental, social and economic), with the aim of capturing both the time perspective of intergenerational sustainability and the spatial perspective of intragenerational sustainability among nations and regions. The 17 goals comprise of 230 indicators that cover the 169 targets expected to be achieved by 2030.

Despite this significant shift in the global approach to social and economic development, the way of representing the complexity stemming from this new route, in particular at the local level, remains bound to the traditional systems of indicators based on official statistical data produced by institutions to communicate a vision of the local as objective and neutral (Kaika, 2017).

The ‘Cape Town Global Action Plan for Sustainable Development Data’ (UN, 2017) only generically sketches the need for ‘new sources of data’ (Objective 2.3), without considering data highlighting the individual component of sustainability. The production of geographic information (Objective 3.4) is also requested in order to be integrated with statistical data, but without a specific reflection on the objectives and actors involved, as well as the choice of spatial units relevant to the analysis of the targets at sub-national level, such as in Goal 11: ‘Make cities and human settlement inclusive, safe, resilient and sustainable’.

Leaving aside the criticisms around the ideological bases, the effectiveness of the policies and the fundamental contradictions regarding the unsustainability of economic growth and environmental protection as consistent objectives of the SDGs expressed by some scholars (Easterly, 2015; Nightingale, 2018; Swain, 2018), many objections have been raised regarding the issue of a data-driven approach. Two of the main elements at stake that have been raised are the lack of a theoretical basis (Szirmai, 2015) and the inconsistency of the framework indicators due to the contested concept of sustainability not being directly observable or measurable (Spaiser et al., 2017). When measuring discrete or simple phenomena (like industrial production or goods transported by train), the role of indicators is quite simple and not questionable. However, when the phenomenon to be represented is a contested concept, like sustainability or human well-being, then it is necessary to adopt some care in its conceptualisation, construction and use. Mair et al. (2018) state that if sustainability is a non-univocal concept, the representation provided by indicators only reflects the vision of one of the possible interpretations of the concept and not a universal meaning. This problem is also intertwined with the ethical implications of indicators that in modern Western society are separable from technical issues seen as neutral and objective. If data do not consider the moral dimension of sustainability of different communities involved, there is a risk that data do not reflect people’s real values. Nevertheless, indicators produce visions of the world, shaping and determining policies and action (Liverman, 2018). It is precisely here that the problem of the scale of representation intervenes, being the values strictly connected to the local dimension where people live and act. If urban is not only a territory or a simple portion of space but the place where sociospatial relations act simultaneously (Massey, 1992), then it is difficult to synthesise it in just one way, and a rethinking of the traditional categories (such as centre and periphery) of urban representation is needed.

If the concept of sustainability is widely defined as a pillar of the strategy of the SDGs, the concept of ‘city’ that inspires Goal 11 of the SDGs remains opaque and difficult to operate. Unlike the general consensus on the importance of cities for sustainable development that appears in the background as an unquestionable truth, a clear definition of the city does not emerge from UN documents, and the SDG devoted to cities seems to be somewhat of a compromise between the different schools of theoretical thought (Barnett and Parnell, 2016). A consequence of this indefinite theoretical assumption is the lack of an adequate scale of analysis for the implementation system, centred on indicators at the national level, without conceptualising and undertaking ‘new forms of relational comparative analysis, those that escape the normalizing assumptions on traditional styles of comparative analysis’ (Barnett and Parnell, 2016, p. 11).

With these considerations in mind, we propose testing the matrix of spatialities outlined above (Table 3.1) in order to understand whether urban sustainability can be better represented by traditional indicators and, at the same time, if the subjective dimension of the communities involved in SDG policies can be integrated into the framework of indicators. The matrix, through a holistic approach, also allows the vision of the connections that link the different aspects of the problem analysed and of the different spatial scales involved in the implementation of the strategy.

In SDGs, Goal 11 is devoted to policies that will make cities and human settlements inclusive, safe, resilient and sustainable. Urban sprawl is one of the main problems that world cities are facing: ‘Urban sprawl is a complex phenomenon that is difficult to quantify and measure accurately … moving from sprawl to compact form is more likely to be a direction in a continuum rather than across fixed and measurable categories’ (Frenkel and Ashkenazi, 2008, p. 57). The official indicator proposed to monitor the implementation of policies is ‘average ratio of land consumption rate to population growth rate, 1990−2000 and 2000−2015’, based on a stratified sample of 194 cities. As stated in The Sustainable Development Goals Report 2016 (UN, 2016, p. 33), this indicator has a significant disclaimer:

It is clear that the indicator simplifies a highly complex problem that cannot be measured in the same way for all world cities or through a single measure and a spatial administrative unit.

In this exercise urban sprawl is proposed as a ‘stylised fact’ of spatial injustice, a multidimensional ‘space-embedded’ phenomenon that can be considered as a good example of the circular and cumulative relationship between spatial forms and social behaviours.

Starting from the material aspects of urban sprawl in terms of ‘absolute/relative/relational/experienced space’ (first row of the matrix), we will then broaden the analysis to the other social and spatial phenomena connected to it.

The first step consists of deconstructing urban sprawl into its main spatial and social forms and effects:

These dimensions are closely interdependent, and have a clear spatial dimension that is, however, difficult to grasp through static analysis.

In Table 3.2 we describe urban sprawl, in a relational way, and its unsustainable human impact, that is the daily home–workplace commuting of people living in urban-dispersed peripheries. By integrating the different spatialities and scale of urban sprawl, the matrix allows us to bring out the complexity of the phenomenon:

Each cell identifies a specific type of original space that helps to deconstruct a concept (the mobility of commuters within a sprawled urban context) and to incorporate its intrinsic spatiality.

The next step consists of filling up the matrix with the quantitative and qualitative information according to their spatiality. As shown in Table 3.2, the matrix contains different types of information at different spatial scales. In our case, the spatial reference of commuting flows comprises people resident in a given urban area with diffused urban characteristics that need to be related to the other spatial levels of the analysis.

The result of the reassemblage of the information contained in the matrix is a relational representation of the investigated phenomenon. This allows us to grasp and disentangle different levels of the analysis and different points of view of the urban sprawl and to focus, in turn, on both quantitative and qualitative aspects, or choose an overall narrative in which space is integrated into the analysis.

If the first column of the matrix represents discrete phenomena easily operationalised through quantitative data and indicators, the column relating to the ‘lived space’ is instead difficult to be represented through traditional quantitative tools, but it nevertheless plays an important role in our understanding of the urban sprawl. The role of big data can be supportive in all the different phases/cells of the matrix, in particular for the relational aspects of the observed phenomena (e.g. the daily commuting of passengers through transport card chips, the use of the city made by the commuter through their geotags). However, it is in the third column relating to the lived space that the use of crowdsourcing reveals its most interesting potentialities for all the reasons explained in the previous paragraphs. It is here that people involved in daily commuting can express their point of view, which cannot emerge through the indicators selected to implement Goal 11 of the SDGs. These voices can produce new spatialities and contribute to the inclusion of citizen and communities in the decision-making process, allowing those minority views to be taken into consideration (Kharrazi et al., 2016).

The coexistence of multiple scales and sources involved in the analysis invites us to explore the possibility of using different – although interrelated – analytical tools (Sui and DeLyser, 2012), including methods, techniques and different types of sources, trying to make a hybrid integration that is able to reassemble as much as possible the complexity of the theme, where ‘mathematics, poetry, and music converge if not merge’ (Harvey, 2006, p. 124).

Conclusions

The large and increasing availability of geospatial data about individual and social preferences, opinions, values, movements and relationships, although still largely unstructured and in search of new measures for the assessment of its quality, can contribute to a new information base for improving our collective knowledge of places. In this chapter, we have tried to clarify, on conceptual grounds, the role that this avalanche of data, especially those voluntarily produced by web users, can play in improving the research agenda of geographical studies. These data sources represent not only a chance to enlarge the availability of geographic data, but also, and more importantly, they can support the development of new representations of spatial processes at various scales, allowing them to move from a static to a dynamic level in terms of flows, processes and relationships. VGI, in particular, can also challenge the representation provided by traditional indicators, producing different narratives and discourses about places. People can act as living indicators, incorporating dimensions like subjectivities and emotions still largely neglected in the case of policies devoted to well-being like the UN SDGs.

The idea of spatiality that emerges from the conceptual perspective proposed in this chapter represents a critical topic to be considered in order to avoid the risk associated with the technoscientific epistemology produced by the growing and pervasive availability of data. The possibility of representing living spaces, through the voice of local communities, without the intermediation of power and experts, is nevertheless a politically relevant issue. It also requires the development of a new methodological approach where the integration of data, formats, methods, tools and subjects producing information becomes crucial and a major challenge for the future research agenda.

Despite this promising scenario, many issues need to be explored. Inclusiveness and participation in the Geoweb still show patterns of inequality both geographically and socially. The participation in crowdsourced information takes place mainly in urban areas where infrastructural information and communications technology (ICT) facilities (internet, Wi-Fi and so on) are more available; a distribution that unfortunately confirms that the digital divide follows the traditional lines of social injustice on a global scale. Moreover, as is evident from this chapter, the voluntary contribution to the production of data directly usable for collective information concerns only a part of the big data universe. To the traditional debate on the exclusion from the Web of those who do not have the means and the computer skills necessary for participation, we must add the problem of those who own and process these large amounts of data. These new subjects only partially coincide with the public institutions and represent a potential threat to democratic information and to the privacy of people. Citizens’ digital rights include the rights of privacy, security and information self-determination and must be placed at the centre of digital policies (Bria and Bain, 2018).

The scientific community, in particular the producers of official statistics, is still wary of the role and quality of crouwdsourced data. If traditional statistics are produced through documented and reproducible phases and methodologies, in the world of big data information comes from multiple sources, the synthesis is done by the user and the sampling process is not provided before data gathering. The assessment of quality and reliability of these new data sources is a complex issue that cannot be limited to the field of statistical studies but implies the rethinking of the epistemological framework for social analysis in an era where the production of knowledge is no longer left exclusively to the scientific community (Saltelli et al., 2016).

The practices of online democratic participation reported in this chapter are still wide-ranging in nature and intensity and need a more comprehensive assessment of their ability to become integrated in local government processes. To avoid the risk of a new technocratic way of producing knowledge, the information asset of big data and online participation is only a part of the debate between citizens and institutions. Technology is not an endpoint but only a tool to improve broad participation in place construction.

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