Affiliation Spatially Expressed: How Social Networks Structure Residential Mobilities in London

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1 Affiliation Spatially Expressed: How Social Networks Structure Residential Mobilities in London Anna Tuononen, Stephen Law UCL, Bartlett School of Architecture, London, United Kingdom Keywords Urban migrations; mobilities; social networks; residential location; social economics; spatial analysis Abstract This study explores people s migrations in the city, investigated as a socially networked effect. The definition of affiliation lends itself to explore migrations as the state or process of affiliating or being affiliated (Oxford Dictionaries 2017). This work proposes, that social influences motivate urban co-location and migrations; it also captures these motivations enacted using mixed quantitive approaches. Exploring a range of spatial and social decisions met by people relocating in London, this study aims to theoretically connect ideas of migration and residential location with the body of work in space syntax theory. Space syntax theory views city s formation and urban movement as entangled, interactive processes between social and material, spatial networks (Hillier 2016a). The analyses of this study point to that a spatial structuring is still vital to produce and reproduce essential sides of certain social relationships. The effect exhibited through the social choices among the vast majority of migrations in this study; moreover, its participants deployed spatial distance to strengthen, maintain or to dissolve their social networks. In the migrations of this study, metric distance as a measure best described how participants sought nearness [in comparison to travel time], that from the point of spatial affordances increased the probabilities for the participants to encounter their affiliations. Importantly, this study s social networks appeared to exhibit a more nuanced role than locational pulls in residential choice. Instead, social networks appeared to not only direct participants preference but also to constitute their knowledge of living in the city - influencing residential mobilities and the participants relative knowledge of the housing market. Migrations as cities events have significance since they cumulatively reconfigure cities social processes long-term, such as how neighbourhoods and the city itself restructure over time. This paper draws from collected data and the methodological core of a research project completed by Anna Tuononen in fulfilment of their MSc in autumn Proceedings of the 11th International Space Syntax Symposium. 1

2 Background Studying migrations as a dynamic that socially restructures the city appears little commented in wider literature; readings into the different understandings of social theory, urban studies and economics seem mostly not to connect at the nexus of social interactions, urban form and land bidding. Within social theory, many ideas would support that people exercise social choices as they migrate. George Akerlof (1997) and Mark Granovetter (2005) contend that our social and economic lives embed as processes and their consequences. Mobilities theorist John Urry (2012) describes social networking resource-intensive but also asserts their workings to reproduce inequalities. This suggests, that movements, migrations and accesses depend on the unequal distributions of resources between bodies, such as time, money, emotional labour and ability. Again, within space syntax theory Bill Hillier and Vinicius Netto (2002) note, that copresence links to spatial proximity to create and recreate of one s desired social relations. Hillier (2016b) identifies the ties between social and spatial networks as a key research priority in urban studies. Until this point, the slower citizen-focused change has received little note that undoubtedly drives a city s formation. This study is directed to address a basal divide between social theory and the economic field. The latter theorises the dynamics of residential markets mostly through financial incentives (Schirmer et al. 2014) in which a person s social subjectivity remains undiscussed. More recently, the influence of social networks on economic choice is recognised in parts (a.o. Dutta and Jackson 2003). However, to think of housing markets as social, city forming processes, is yet to surface in the mainstream consciousness. At the time of writing, current ideas fail to describe some significant phenomena in cities, such as the pull of skilled people into the major cities (FT Data 2016), or why so many people in precarity remain in places with a region's highest living costs. It seems that a price- or utility-focused reasoning does not explain why the city exists and functions as it does. Many of the ideas together frame that spatial distance and even proximity to one s social circles likely matter, while living near or far are unlikely unified ideas. In the case, a wider structural analysis is needed to grasp the nuances to social circumstances in which people decide to relocate. Methodology Data collection To observe a larger-scale social choice among many, the research takes an interest in the spatial relations of people s social networks before and after they relocated. With no available data to draw from, the MSc study collected a new dataset. To do so, a total of 125 residents in case locations of London were approached via structured interviews. The interview sessions inquired participants about their reasons to relocate, as well as the locations of old and new residences. Important members of the participant's affiliations were also asked, such as central people, groups, or communities in participants' lives. The Proceedings of the 11th International Space Syntax Symposium. 2

3 interviews also took note of participants regular meeting places to maintain these relationships. London case areas The chosen participants to this study had moved to a few different comparable areas in London. London suited for this study as an accessible research field, while also described by high levels of residential mobilities (Landlord Today 2016), and diverse neighbourhoods. Diversity helps to frame a multitude of stories of choice that realised in the same location. The control factors by this study selects its care areas are: - residents whose social networks are diverse (taking Hristova et al as a reference point), - care areas belonging roughly to the same submarket when accounting for features of the street grid (Law et al. 2016, Figure 1) - similar accessibility values (NAIN, NACH) and - a matching demographics by age, ethnicity and occupation. Applying these criteria, Seven Sisters in Haringey Borough and Deptford in Lewisham Borough were chosen. Figure 1 Areas Seven Sisters and Deptford overlaid on a submarket division proposed by Law et al Proceedings of the 11th International Space Syntax Symposium. 3

4 Spatial Analyses Space syntax theory and analysis, GIS and statistical analysis were used to derive tendencies from the interviews. GIS All map representations were made with the open-source GIS (geographical information systems) software QGIS; some of the GIS techniques deployed were geocoding and heatmaps. Transport analysis To calculate the distances and travel times between each respondent and their closest contacts, a transport network analysis using the Google Maps API (Application Programming Interface) was made. The Google Maps API essentially is an app that allows its users to calculate spatial distance indices using the Google Map engine and its database. This database entails: - pedestrian network data to calculate walking distances between the origin and destination - a detailed public transport network and timetable data to calculate travel times between origin and destination. A Python-based function was developed to calculate spatial measures in a respondent s egocentric graph (Figure 2). Affiliation Closest affiliation Person Figure 2 An egocentric network graph describing the respondent (black) and their closest affiliations (2 greys). The length of the edge is conceptually depicting the social distance between a respondent and their contacts. These measures are the metric distance, travel time and angular distance between the respondent and their closest contacts. Proceedings of the 11th International Space Syntax Symposium. 4

5 Dist (ij) = network walking distance between i and j where i is the interviewed correspondent j is each of its closest social contacts Time (ij) = public transport travel time between i and j where i is the interviewed correspondent j is each of its closest social contacts Ang (ij) = network angular distance between i and j where i is the interviewed correspondent j is each of its closest social contacts Space syntax Two commonly used space syntax measures calculated for the central street segments of each case area (Table 1). This table charts Seven Sisters' and Deptford s normalised integration and normalised choice measures for radii of 800, 2000 and N. Min Radii Seven Sisters Deptford N Table 1 The normalised integration and choice values of central segments of Deptford and Seven Sisters. One of the measures is the segment angular integration (1) (Hillier and Hanson 1984; Hillier et al. 2012), which measures the reciprocal average shortest path between every origin (i) to every destination (j) - or more simply its to-movement potential to reach all nodes in the system (Hillier and Iida 2005). The segment angular integration is also known as closeness centrality in network science literature. CC i (r) = N(r) 1 d ij (r) N is the number of nodes in the network CC_i is the closeness centrality at i d_ij is a measure for impedance between i and j r is the radius (1) Proceedings of the 11th International Space Syntax Symposium. 5

6 Another space syntax measure is the segment angular choice (2) (Hillier and Iida 2005). It measures the sum of shortest paths overlap (N) for a particular segment (i) between all pairs of origins (s) and destinations (t) or more simply the potential of moving through a street segment (Hillier and Iida 2005). This measure is also referred to as betweenness centrality. BC i = (s t) θ st i BC_i is the Betweenness centrality at i N_st is the overlap between s and t on segment i (2) The research follows Hillier et al. (2012) method to normalise the choice measure (NACH) and the integration measure (NAIN). Proceedings of the 11th International Space Syntax Symposium. 6

7 Grasping the spatialised market: Affordances of the street grid vs. scales of residential mobilities This part visually explores (Figure 3) the migration trajectories between respondents' origins to Seven Sisters and Deptford. Figure 3 Point density clusters of respondents previous residential locations by case area. Proceedings of the 11th International Space Syntax Symposium. 7

8 The mappings yield that the River Thames is distinctly a divisive element to the residential mobilities shown. Previous work to residential choice (Zondag and Pieters 2005) describes a strong relation between a participants new residential location and the site of their last residence. The visual study confirms this in part while showing a variation in this relationship. A significant share of participants had moved locally (Figure 3), shown as bright, dense clusters around both case areas. Moreover, the number of those moving declines when exceeding locality for greater distances. This effect initially suggests that intraurban migrations are geographically confined, in that distance and spatial configuration likely influence people s mental maps of the city through the areas that people become exposed. Laying fundaments to residential mobility: Motivations and improvements of housing circumstances In urban economics, a more traditional definition describes relocations stemming from a housing arrangement not meeting the current tenant s expectations (Clark and Onaka 1985). The MSc re-visited this definition and asked how the study participants found their new expectations met where they moved. Table 2 summarises the responses to this question. These reveals that the access to one s work or study location (31), local social opportunities (22) and price (20) of housing were most prominent factors in the location choice for the interviewees of this study. The term social opportunities is used to combine a few motives that people consistently named. These are, for example, the presence of outdoor places to meet people [new and known], experiencing a sense of community and that people of similar demographics are residing in the same area. Proceedings of the 11th International Space Syntax Symposium. 8

9 Factor in choice MOTIVATIONS from 162 total mentions Frequency Share LOW-TO-NO SOCIAL FACTOR Shift from renting to buying first property 8 27 % Contract ended 6 Improvement in home s condition 2 Price 20 Free from commute costs 1 Social housing 4 Finding a workhome 2 IMPLICIT SOCIAL FACTOR Access to work or study location % Bustle 2 Generally new opportunities 2 Leisure / things to do 2 Finding somewhere more suitable for family living 3 Safer 1 Amenities, services or consumption opportunities 5 Work opportunities 2 EXPLICIT SOCIAL FACTOR Access to family members(s) 3 42 % Access to friend(s) 10 Social opportunities from this area 22 Tension with cohabitants/ neighbours 10 Moving in with partner 4 Moving in with family member(s) 2 Moving in with friend(s) 7 Extra room (for child) 10 Table 2 The opportunities and improvements claimed by the new dwelling and location, by frequency of mention. Proceedings of the 11th International Space Syntax Symposium. 9

10 The relocation motives were categorised by the degree to which a social factor was involved in choosing the new home and location. Social factors were defined through the following: - Low-to-no factor; mainly economic or physical improvements - Implicit factor; a factor having an implicit influence on one s social networks - Explicit social factor; an attempt to rearrange one s existing social networks Arranging the relocation motives helps to identify that only 27% of them are solely physical or economic improvements of housing. Again, 72% of named improvements had consequences on social networks either explicitly or implicitly. An implicit social factor, is for example a safer living environment, in which a person is likely inclined to spend time outdoors and be exposed to more social contact. The motives (Table 2) interestingly trace the improved circumstances towards a multi-faceted sociality: current or potential, close or distant. The motives also point to potent directions for quantitative analyses later. Social networks: a factor worth noting in housing economics How noteworthy is the influence of social motivations over the scale and dynamics that these migration take? This section attempts to numerically describe this effect, and later "weight" the attraction that social networks perform compared to the participant s workplace - often named a principal factor among residential choice studies (Schirmer et al. 2014). Figure 4 depicts the changes in distances to social networks of the participants upon their move. This study finds a trend among respondents staying within same distance (29 % saying within +/ km distance) or shifting closer (65% moving more than 1.25 km closer) to their affiliations. One in three (35%) increased their average distance more than 1.25 km. A look at the symmetry of the results show a trend towards the spatial attraction of social networks (only accounting for existing contacts): there is a significantly higher deviation among respondents moving closer to their contacts (94.24) than among those moving away (3.27). This difference implies that people decided to move closer to their contacts over a wide range of distances. Proceedings of the 11th International Space Syntax Symposium. 10

11 Figure 4 This study s migrations depicted as the change of distance to participants social network members. The numbers are average distances to the respondents contacts (individuals, groups and communities), which means all affiliations mentioned are accorded the same weight. Thus, for example, information, where the respondent had moved closer to one particular contact and further away from other social contacts is averaged out. Present affiliations of the participants were asked, of which those living within meeting distance this study included in the sample. To ascertain, a spatial preference towards affiliations took place this phase introduces a comparison between work location and participants' affiliations. Among a subset of working participants, via linear regression, the changed distances to social contacts as well as work, were analysed. Then, choice behaviours were observed by socioeconomics, which this study defined through the use professional groups and the level of education they require. Proceedings of the 11th International Space Syntax Symposium. 11

12 Professional group Regression equation Y = Spat.pref. *X + Constant Unstandardise d coeff. Beta values Interpretation RMSE University educated Constant 3.79 balanced between Spatial preference 1.03 affiliations and work 6.14 With vocational education Constant 0.55 balanced between Spatial preference 0.99 affiliations and work 3.08 Requiring no formal education Constant notably towards work at Spatial preference 0.55 the expense of affiliations 4.16 Table 3 The linear regression model coefficients on the spatial preference among working participants. The occupational categories in use are applied from Census definitions (2011). The coefficients (Table 3) describe that the majority of people allocate distance somewhat equally between and work. An exception are people in professions requiring no formal education. Significantly, a gap among working participants in groups 1-6 and 7-9 describes, that there is a trend to exercise choice towards one s social networks, as there are circumstances to allocate notably more proximity towards work than to social affiliations. This observed gap aligns with Urry s position (2012), in which the reproduction of one s social networks is closely linked with a participant's socioeconomic opportunities, to which residential location appears to contribute as a social practice. Asking respondents what their relationship to their current living area was before they moved revealed that a majority of 59% had contacts that resided in the area already (Table 4). Indirectly, the figures are telling of a preference towards areas that people already knew through someone in their social circles. no family member(s) partner/ spouse friend(s) several affiliations employer has always lived there 15 out of 44 3 out of 44 2 out of 44 5 out of out of 44 1 of 44 2 of % 7 % 5 % 11 % 36 % 2 % 5 % 59 % Table 4 Responses to question: Did you know someone in this area before moving here? Proceedings of the 11th International Space Syntax Symposium. 12

13 There are a few ways in which affiliations likely have an indirect role to the location choices here. One of them is, to act as influencers in the residential choices within the same social circles [..]social networks affect the flow and the quality of information. Much information is subtle, nuanced and difficult to verify, so actors do not believe impersonal sources and instead rely on people they know. (Granovetter 2003, p. 33). Networks should also be noted in their implicit role as steerers of other people s movements. Sam Griffiths (2016) suggests that everyday trajectories equate to areas and accumulate the experience of the lived city without obligations: Mundane mobility practices also have effects on identity, belonging and understanding of place (as often understood to be the case of exotic journeys to far-away destinations). (ibid., p. 242). One interviewee mentions knowing their new neighbourhood Deptford as a switch point on their commute. Perceiving local nearness to one s affiliations This section explores how residents in Seven Sisters and Deptford interacted with the distance incorporated into their social networks as they moved. Two parameters, metric distance and travel time, are used to describe perceived nearness to one s contacts; this section will examine which of them may better explain the logic by which people move closer to each other. To elaborate on the parameters, spatial distance is considered influencing the likelihood of one s spontaneous and planned meetings (a.o. Hillier and Netto, 2002), whereas, travel time, is regarded as the mobility costs to meet one s social contacts. Figure 5 Route distance and travel time to respondents networks. The regression analysis in Figure 5 depicts different behaviours between participants relocating away from their affiliations, and those moving closer. With increases in both distance and travel time, the results comply with a linear trend; again those who relocated with decreased distance and travel time acted with lesser uniformity. These participants appeared to an extent Proceedings of the 11th International Space Syntax Symposium. 13

14 " drawn towards minimising metric distance over travel time to their contacts. Meaning that minimizing mobility costs to travel did not attract behaviour. Furthermore, those people moving closer to their contacts, while not experiencing a proportional decrease in travel time, likely shifted from motorised transport to walking. The acted preference towards lesser metric distance aligns with motivations to increase the chance of encounter as described by Hillier and Netto (2002). Earlier in the paper, the visual study in Figure 3 displayed the rapid decline in the number of people moving to either Seven Sisters and Deptford, the further away the people relocated. A breakdown to this effect to histograms (Figure 6) interestingly shows varying patterns of that decline. Compared to Seven Sisters, Deptford attracted an unexpected number of people further away, as far as approximately km and from its immediate vicinity. In Seven Sisters the same tendency is closer to something that is inverse, where a majority of participants relocated from within km. Figure 6 Change of residential location measured as route distance. Here notable is the difference between the distributions of distances from which residents have located to either case area. Seven Sisters and Deptford's spatial network measures NAIN and NACH (Table 1) suggest the likelihood by which each other centre areas is known in their surroundings. A comparison between the two highlights a note-worthy trait, in which Seven Sisters evidences higher centrality values than Deptford, yet Deptford surprisingly shows an influx of new residents from a greater area than Seven Sisters. The pull to Deptford within larger surrounding potentially relates to the social networks of the newcomers and the effect of networked co-location (Table 4), since Deptford is in the mental map of a widely spread population despite its relative position in the spatial network of London. Proceedings of the 11th International Space Syntax Symposium. 14

15 It seems some of Deptford's residents show broader knowledge of London than those moving to Seven Sisters; what allows these longer shifts to take place when drawing upon Urry (2012) is likely is better socioeconomic or other social standing - also potentially meaning materially different futures to the areas. The analysis in Figure 7 compares moving distances of the study participants with the distances they regularly travelled to maintain their social relations. The scales of moved distances show a link to the geographic spread of social contacts, which again is links with Urry's (2012) proposition on networking inequalities. The resulted moving distances may be a multiplied result of networks influence: such as (1) co-locating with social connections in the area or (2) a person communicating with their contacts that in turn directs their imagery of the area and (3) accumulating expanded mental maps of the city during trips to visit others. Figure 7 Respondents networking mobilities and residential mobilities. Figure 7 illustrates that those with geographically diffused networks simultaneously performed more extended distance relocations. It suggests a person s greater freedom to relocate in the city or beyond and likely a knowledge advantage over many in the property market. In contrast, residential immobility exhibited through limited, and local residential relocation by people whose social contacts lived close-by. Proceedings of the 11th International Space Syntax Symposium. 15

16 Findings on the interrelations between space and social networks This paper discussed the mixed approaches examining the link between residential relocation and social networks. The explorations of the embedded social factor suggest that social networks are incorporated in the notion of space and entail the choice of distance to them. For the majority of participants, spatial distance is likely a signifier of the social priorities that people place among their social relationships while forming new, maintaining, or dissolving existing relationships. Another central suggestion is that the link between social choice and residential location works in much more intricate ways from economic interpretations previously. This study also encourages further research and exploration beyond what was possible with a limited data set used for this paper. This paper sums a few findings on the role of both social and spatial networks in migrations process, which may be potent as future research directions: Findings on the role of space a. The spatial configuration of London likely structures the mental perception of the city, shown in the fluxes of migrations, and the dynamics between neighbourhoods. b. Social proximity appears more strongly perceived in spatial terms than time, potentially linking to the ideas of increased co-presence within neighbourhoods (Hillier and Netto 2002) Findings on the role of social networks Social networks were shown interwoven into residential choice both directly and indirectly, while not statically a. Directly, social affiliations appear to provide material everyday support, and social surroundings to enforce distinct identities. b. Indirectly, by providing information advantage, providing trusted information and directing preference over universal media (Granovetter 2005). The expectation of physical presence still shows prevalent to inducing movement in cities, through which people become acquainted with new areas. c. Social priorities of participants were shown to change over time, instigating new living arrangements and living locations that socially re-configured their social networks. References Akerlof, G., Social distance and social decisions. Econometrica, 65(5), pp Dutta, B. and Jackson, M., On the formation of networks and groups. Studies in Economic Design, (Networks and Groups), pp FT Data, Are Londoners leaving London? Available at: /06/09/are-londoners-leaving-london-internal-migration-uk/ [Accessed August 20, 2016]. Granovetter, M., The impact of social structure on economic outcomes. The Journal of Economic Perspectives, 19(1), pp Granovetter, M., Economic action and the problem of embeddedness. American Journal of Sociology, 91(3), pp Griffiths, S., and Lünen, A. von., Spatial cultures: towards a new social morphology of cities past and present. New York, US: Routledge. Hillier, B., 2016a. The fourth sustainability, creativity: statistical associations and credible mechanisms. In J. Portugali and E. Stolk, eds. Complexity, Cognition, Urban Planning Proceedings of the 11th International Space Syntax Symposium. 16

17 and Design: Post-Proceedings of the 2nd Delft International Conference. Cham: Springer International Publishing, pp Hillier, B., 2016b. What are cities for? And how does it relate to their spatial form? The Journal of Space Syntax. Available at: article/view/282 [Accessed February 5, 2017]. Hillier, B. and Hanson, J., The social logic of space. Cambridge: Cambridge University Press. Hillier, B. and Iida, S., Network effects and psychological effects: A theory of urban movement. In A. Van Ness, Proceedings of the Fifth International Space Syntax Symposium. Delft: University of Technology, pp Hillier, B. and Netto, V., Society seen through the prism of space: outline of a theory of society and space. Urban Design International. Hillier, B. Yang, T. and Turner, A., Normalising least angle choice in Depthmap and how it opens up new perspectives on the global and local analysis of city space. Journal of Space Syntax (3)2, pp Hristova, D., Williams, M. and Panzarasa, P., Measuring urban social diversity using interconnected geo-social networks. Proceedings of the 25th International Conference on World Wide Web, pp Landlord Today, Average tenancy is now 18 months. Available at: [Accessed June 14, 2016]. Law, S., Karimi, K. and Penn, A., An empirical study on applying community detection methods in defining spatial housing submarkets in London. Proceedings from 10th Space Syntax Symposium. UCL: London Schirmer, P. Van Eggermond, M. and Axhausen, K., The role of location in residential location choice models: a review of literature, pp Oxford Dictionaries. English. (2018). affiliation Definition of affiliation in English by Oxford Dictionaries. [online] Available at: [Accessed 17 May 2017]. Urry, J., Social networks, mobile lives and social inequalities. Journal of Transport Geography, 21, pp Zondag, B. and Pieters, M., Influence of accessibility on residential location choice. Transportation Research Record: Journal of the Transportation Research Board, pp Proceedings of the 11th International Space Syntax Symposium. 17