Detecting Socio-Economic Impact of Cultural Investment ThroughGeo-Social Network Analysis

Xiao ZhouComputer Laboratory

Univerisity of Cambridgexz331@cam.ac.uk

Desislava HristovaComputer Laboratory

Univerisity of Cambridgedesislava.hristova@cl.cam.ac.uk

Anastasios NoulasData Science Institute

University of Lancastera.noulas@lancaster.ac.uk

Cecilia MascoloComputer Laboratory

Univerisity of Cambridgececilia.mascolo@cl.cam.ac.uk

Abstract

Taking advantage of nearly 4 million transition records forthree years in London from a popular location-based socialnetwork service, Foursquare, we study how to track the im-pact and measure the effectiveness of cultural investmentin small urban areas. We reveal the underlying relationshipsbetween socio-economic status, local cultural expenditure,and network features extracted from user mobility trajecto-ries. This research presents how geo-social and mobile ser-vices more generally can be used as a proxy to track localchanges as government financial effort is put in developingurban areas, and thus gives evidence and suggestions forfurther policy-making and investment optimization.

Introduction

In 1997, the striking “Bilbao miracle” created by Guggen-heim Museum not only provided Bilbao, a depressedSpanish port town, with a dramatic socio-economicgrowth, but also provided evidence that city can blossomwith cultural investment (González 2011). Even thoughthe ability of cultural investment to promote local regen-eration has been widely accepted, large-scale evaluationand prediction of its impact are still not practiced. Thepotential of network science in providing insight on de-privation dynamics (Eagle et al. 2010) along with the mil-lions of human mobility traces made available by location-based applications has so far been untapped in culture-ledregeneration studies. In this paper, we present how geo-social network data from location-based social networkFoursquare1 can be used to quantify the effect of culturalinvestment on the urban regeneration process in London’sneighbourhoods. We define new metrics on cultural in-vestment and cultural features in geo-social networks tomeasure the priority level of culture for urban areas andshow how differences in these reflect on the network prop-erties of local areas. In addition, we propose an innova-tive approach to uncovering the underlying relationshipsbetween socio-economic status, cultural investment andgeo-social network properties using a fusion of networkanalysis and statistical analysis.

1https://foursquare.com

Related WorkConventionally, the investigation of socio-economic de-privation for urban areas has largely relied on governmentstatistics, by which data are generally obtained from tradi-tional surveys. It is usually costly to implement and takes afew years to carry out each time. With an aim to overcomethis limitation, researchers have recently started to minelow-cost, real-time, and fine-grained new data sourcesfor socio-economic deprivation study. For instance, Ea-gle et al (2010) discovered a high correlation between callnetwork diversity and urban area deprivation using callrecords data; Quercia et al (2012) found the topic of tweetsand deprivation level of urban area are correlated; Smithet al (2013) using Oyster Card data to identify areas of highdeprivation level in London; Quercia and Saez (2014) ex-plored the relationship between the presence of certainFoursquare venues with social deprivation; Venerandi etal. (2015) using Foursquare and OpenStreetMap datasetsto explore the correlation between urban elements anddeprivation of neighbourhoods; Hristova et al. (2016) dis-cussed the relationship between the prosperity of peo-ple and urban places, and distinguished between differentcategories and urban geographies using Foursquare andTwitter data. Inspired by previous qualitative works in theurban planning domain that found correlations betweencultural investment and urban development, as well asquantitative studies from data scientists, we propose touse geo-social network data to detect the change of socio-economic deprivation related to cultural investment in ur-ban environment.

HypothesesAs a base condition, we expect that different socioeco-nomic conditions and amounts of cultural expenditurein neighbourhoods will lead to significantly different net-work properties in the geo-social graph. Therefore, we hy-pothesize that:

[H1] Areas with high cultural investment and depriva-tion level have significantly different network and localproperties from areas with low cultural investment and de-privation level.

This assertion lays the foundation of further inves-tigation into the nature of culture-led urban regenera-tion, where based on existing case studies from literature

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(González 2011), we expect that cultural investment inmore deprived neighbourhoods results in growth. Specifi-cally:

[H2] Areas with high cultural investment and depriva-tion level experience significant growth with respect to net-work and local properties from areas with lower culturalinvestment and deprivation level.

DataseSocio-economic DataThe dataset used to evaluate the socio-economic statusis the English Indices of Deprivation, a measure of depri-vation for small areas in England calculated by the De-partment for Communities and Local Government. It pro-duces the Index of Multiple Deprivation (IMD), which re-flects the overall deprivation level of areas. The versionsused in this research are the 2010 Index2 and 2015 Index3,through a comparative analysis of which, an insight of so-cioeconomic deprivation changes of urban areas can bederived.

Cultural Expenditure DataThe cultural expenditure data used in this work is the lo-cal authority revenue expenditure and financing4. Thisdataset is based on returns from all 444 local authoritiesin England, showing how they allocate their yearly spend-ing on various services, including “cultural and related ser-vices”. In this study, revenue spending data for the finan-cial years 2010/11, 2011/12, and 2012/13 are involved.

Foursquare DataAlongside the two official datasets from government intro-duced above, we also use user mobility records and infor-mation of venues in London from Foursquare. It containsall “transitions” (pairs of check-ins by users between twodifferent venues) occurring within London for three yearsfrom January 2011 to December 2013. In total, there are3,992,664 transitions generated between 17804 venues inLondon during the study period.

Geo-social NetworkThe Foursquare dataset can be depicted as a spatial net-work of venues connected by transition flows of users.It is a directed graph where nodes represent start andend venues, while edges correspond to transitions. Theweight of the edge is proportional to the number of transi-tions made by all users between the two venues. We as-sume the impact of expenditure can be observed after9 months through Foursquare. Formally, we define ouryearly dataset as a directed graph Gt = (Vt ,Et ) for t =1,2,3, where t indicates the different snapshots in time ofthe dataset. The network properties are shown in Table 1.

2https://www.gov.uk/government/statistics/english-indices-of-deprivation-2010

3https://www.gov.uk/government/statistics/english-indices-of-deprivation-2015

4https://www.gov.uk/government/collections/local-authority-revenueexpenditure-and-financing

t Duration |V | |E| 〈C〉 〈k〉1 2011.1-2011.12 15832 469229 0.221 592 2012.1-2012.12 16189 715113 0.228 703 2013.1-2013.12 17684 742017 0.240 84

Table 1: Network Properties at Each Snapshot: Number ofNodes |V |, Number of Edges |E |, Average Clustering Coef-ficient ⟨C⟩, and Average Degree ⟨k⟩.

Figure 1: Initial IMD Score, CEA, and CVA of London Bor-oughs

MetricsNetwork Metrics

The network measures used are in-degree centrality, out-degree centrality, and average clustering coefficient. In-degree centrality represents how many in-flow transitionsthe nodes of an area receive from other areas. Out-degreecentrality of an area represents how many transitions startfrom this area, but end in other areas. The local clusteringcoefficient captures the degree to which the neighbours ofa given node are connected with each other. For a node iwith degree ki , the local clustering coefficient (Watts andStrogatz 1998) is defined as:

Ci = Li

ki (ki −1)(1)

where Li represents the number of edges between the kineighbours of node i . Then, average clustering coefficient,which reflects the overall level of clustering in an area ismeasured by averaging the local clustering coefficients ofall the nodes within it.

Cultural Advantage Metrics

Cultural Expenditure Advantage To evaluate the prior-ity of cultural expenditure for a local area than the cityaverage, we define cultural expenditure advantage (CEA).The CEA for area i is represented as:

C E Ai = C Ei

T Ei·(∑

i∈I C Ei∑i∈I T Ei

)−1

(2)

where C Ei is the cultural expenditure of neighbourhoodi ; T Ei is the total expenditure of i; and I is the set of neigh-bourhoods in the city.

Cultural Venue Advantage Similarly, a metric of culturalvenue advantage (CVA) is given to measure the extent towhich an area provides more cultural venues than thecity average. Here, cultural venue includes 58 categories ofFoursquare culture related venues, such as museums, artgalleries, theatres, libraries, and parks. The CVA for neigh-bourhood i can be defined as:

CV Ai = CVi

T Vi·(∑

i∈I CVi∑i∈I T Vi

)−1

(3)

where CVi is the number of cultural venues in i and T Viis the number of all venues in I .

Evaluation & Results

Socio-cultural Neighbourhood Groups

In this subsection, we provide a general view at boroughlevel to exam the observability of cultural investments’ ef-fects in the geo-social data and investigate whether dif-ferent socio-economic status and cultural expenditurein neighbourhoods would result in significantly differentproperties in the geo-social network graph. This study laysthe foundation for deeper discussions on smaller areas.

In Figure 1, we reveal the relationship between IMD andcultural advantage in 2010. Here, the colour bar presentsIMD scores in 2010, where higher numbers indicate moredeprived areas. As we can see from this plot, yellow bub-bles cluster in the middle/lower part, suggesting moredeprived boroughs spent relatively less on culture andshowed average cultural venue advantage in the begin-ning. Then in Figure 2, we discuss how the local author-ities spent their money in the next two years, and hownetwork features changed accordingly. It can be observedthat more deprived boroughs spent more money on cul-

Group 1 Group 2 Group 3 Group4InitialIMD

lessdeprived

moredeprived

moredeprived

lessdeprived

CEAmore

advantagedless

avantagedmore

advantagedless

advantagedN 160 192 88 114

Table 2: Groups of London Wards in ANOVA Analyses.

ture and had larger number of venues and transitions cre-ated.

Overall, boroughs with different initial IMD scores andculture expenditure priorities show different network pat-terns. Based on this observation, we identify four groupsof London wards outlined in Table 2 for further investiga-tion. Here, wards are firstly grouped into more deprivedand less deprived classes according to whether their de-privation level in 2010 is higher or lower than the city av-erage. Then, they are further classified according to theircultural spending priority. Specifically, if the CEA of a wardis more than 1, it is clustered into the more advantagedgroup; otherwise, it is put into the less advantaged group.

Hypotheses EvaluationIn the following hypotheses evaluation, two types ofANOVA analyses will be applied to get a deeper under-standing of the patterns we found at borough level, andtest how they vary between ward groups and time periods.

[H1] Network and Local Properties. To test [H1], we em-ploy independent one-way ANOVA to examine whetherstatistically significant differences exist in terms of a set ofnetwork and local features between different ward groups.

From the output results, we can see that there are sig-nificant effects of groups on six features at the p < .05level. The factor that distinguishes less and more deprivedgroups is average clustering coefficient with a statisticallysignificant main effect of F (3,550) = 4.15, p = .006. Fur-thermore, less deprived wards have higher means of av-

Figure 2: Culture Expenditure, and Foursquare Features Changes of London Boroughs in Later Years

Figure 3: Means Plots for Five Variables with Statistically Significant Effects in Independent One-way ANOVA Analysis

Figure 4: Means Plots for Variables with Statistically Significant Effects in Factorial Repeated Measures ANOVA Analysis

erage clustering coefficient. The other five features revealsignificant difference between cultural advantaged anddisadvantaged groups. Their means are plotted in Figure3, from which we can find areas that gave higher priorityto culture had larger venue created number, node num-ber, in-degree centrality, out-degree centrality, and venuecreated density on average. Additionally, Group 3, thatwas more deprived in 2010, but invested relatively largeramount of money in culture later, performed best in mostcases.

Through One-way ANOVA analysis, we find that ur-ban areas with high cultural investment and deprivationlevel show significantly higher values in network proper-ties than those areas with low cultural investment and de-privation level, which suggests that [H1] is true.

[H2] Growth of Network and Local Properties. Afterdiscussing the overall differences between groups, wetest whether groups experienced significant differences ingrowth with respect to network and local properties in thispart. Technically, we examine whether there are statisti-cally significant differences between different years andwhether interaction effects exist between group and timepoint by using factorial repeated measures ANOVA.

Figure 4 gives the means plots of the features that showsignificant effects. It can be found that Group 3 and Group1 had dramatic advantages for all the three years. To con-sider groups separately, Group 3 had the highest values,while Group 4 had the lowest. In addition, significant in-teraction effects between group and year are found in

three features: venue created density (p = .008), in-degreecentrality (p = .038), and out-degree centrality (p = .037).

In this subsection, we detect how the growth of networkand local properties varied between groups and confirm[H2] by finding that areas with high cultural investmentand deprivation level experience significant growth withrespect to network and local properties from areas withlower cultural investment and deprivation level.

Discussion & Conclusions

In this work, we have investigated the socio-economic im-pact of cultural expenditure on London neighbourhoods,visible through the lens of location-based mobile data.We propose an innovative approach to giving insights onunderlying relationships between socio-economic status,cultural investment and geo-social network properties.By applying new cultural metrics and traditional networkmetrics to the geo-social graph of transitions betweenvenues on Foursquare, we show that areas with high cul-tural investment and deprivation experience significantgrowth. It will have significant implications for location-based mobile systems, local governments and policymak-ers alike.

Acknowledgement

We would like to thank Foursquare for supporting this re-search by providing the dataset employed in the analysis.

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