From labs to cities: Mapping the social impact of ubiquitous technologies

UbiOulu

28 March 2012, University of Zurich, Switzerland

Vassilis KostakosUniversity of Oulu

From labs to cities:Mapping the social impact of ubiquitous technologies

Thursday, March 29, 12

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• Founded in 1958• 6 faculties• 16 000 students• 2900 employees• Total funding EUR 226 million• Four research focus areas:

– Information Technology– Biosciences and Health– Cultural Identity and Interaction– Environment, Natural Resources and Materials

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University of OuluFinland

University of Oulu!OULU!

Rovaniemi!

Vaasa!

Helsinki!Turku!

Tampere!Lappeenranta!

Joensuu!Kuopio!

Jyväskylä!


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USA!

Canada!

Mexico!

Brazil!

Argentina!

Zambia!

Namibia!

Botswana!South Africa!

Europe!

Russia!South Korea!

Hong Kong!

Singapore!

Australia!

New Zealand!

Bilateral, n2n, ISEP!

Bilateral, n2n, ISEP!UN-CEP!

Bilateral, ISEP!

ISEP!

Bilateral, ISEP!

Erasmus, Nordplus,!Bilateral, n2n!

FIRST, n2n!

North-South-South!

ISEP!

Japan!Bilateral, ISEP!

Bilateral!

Bilateral!

Bilateral!

Chile!Bilateral, ISEP!

Senegal!

China!Bilateral!

Active exchange programs


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• Ubiquitous computing (MediaTeam)• Machine vision• Intelligent systems and security• Biomedical engineering


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Impact


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Ongoing work

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Social and complex networks

Sensing for public transport

Security and privacy

PEOPLE

SPACESTECHNOLOGY

Human Computer Interaction,Trust, Privacy, Phishing

Spatial & Transpatial Social NetworksUrban Mobility & EncounterEpidemiology & Diffusion

Space Syntax

Augmented SpacesSituated Services

Delay Tolerant Networks

Instrumenting mobile platforms

Public interactive displays


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City-scale lab

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panOULU WLAN AP

panOULU BT AP

panOULU WSN AP

Loudspeaker

NFC/RFIDreader

Control PC

500 GBRAID 1 disk

Cameras

57" Full HD LCD panel

6 mm safety glass withcapacitive touch screen foil


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Face detectionRead UBI-keyTouch screen

Interaction model


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Interface

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panOulu WiFi

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Bluetooth network

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3D Oulu

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Which half is real?

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Reconstructing movement

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Catchment areas

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Where is it busy now?

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Live demo?

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Click and pray!


http://ufn.virtues.fi/~swproject12/maps_test.html

http://ufn.virtues.fi/~swproject12/maps_test.html

http://livepage.apple.com/

http://livepage.apple.com/

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Open to the community: UbiChallenge

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Ojala, T. and Kostakos, V. (2011). UBI Challenge: Research Coopetition on Real-World Urban Computing. In proceedings of Mobile and Ubiquitous Multimedia 2011, Beijing, China, pp. 205-208.


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Lessons learned

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Analysis of user needs

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Ethnographic studiesStorytelling

Brainstorming


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Location, location, location

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Kukka, H., Kruger, F., Kostakos V., Ojala, T., and Jurmu, M. (2011). Information to Go: Exploring In-Situ Information Pick-up “In the Wild”. In Proc. INTERACT, pp. 487-504.


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Visitor areaResident areaScale: 500 m

Main reception

Museum

Main library

Bus stop

Main cafeteria

Visitor areaResident areaScale: 1km

Market

MuseumExhibitions

Train station

City servicesCity hall

Residentialarea

Main square


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Public scrutiny


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Successful services

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HangmanUbiPostcard

Hosio, S., Kostakos, V., Kukka, H., Jurmu, M., Riekki, J., Ojala, T. (2012). From School Food to Skate Parks in a few Clicks: Using Public Displays to Bootstrap Civic Engagement of the Young. In Proc. Pervasive 2012, Newcastle, UK.

Ubinion


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Kukka, H., Kostakos, V., Ojala, T., Ylipulli, J., Suopajarvi, T., Jurmu, M., Hosio, S. (2011). This Is Not Classified: Everyday Information Seeking and Encountering in Smart Urban Spaces. Personal and Ubiquitous Computing (online first)


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Users don’t know what they want

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Kukka, H., Kostakos, V., Ojala, T., Ylipulli, J., Suopajarvi, T., Jurmu, M., Hosio, S. (2011). This Is Not Classified: Everyday Information Seeking and Encountering in Smart Urban Spaces. Personal and Ubiquitous Computing (online first)


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Sustainability

140 880 €

Ojala T, Orajärvi J, Puhakka K, Heikkinen I & Heikka J (2011). panOULU: Triple helix driven municipal wireless network providing open and free Internet access. Proc. Communities & Technologies (C&T 2011), Brisbane, Australia, 118-127.


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Maintenance & Moderation

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Curiosity

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Novelty

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Interaction blindness

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Ojala, T., Kostakos, V., Kukka, H., Heikkinen, T., Linden, T., Jurmu, M., Hosio, S., Kruger, F., Zanni, D. (to appear). Experiences from the long-term deployment of multipurpose public interactive displays in a city center. IEEE Computer.


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Privacy - Who benefits?

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tie formation. Previous studies have found that in-dividuals benefit from having social ties that bridgebetween communities. These benefits include accessto jobs and promotions (5–13), greater job mobility(14, 15), higher salaries (9, 16, 17), opportunities forentrepreneurship (18, 19), and increased power innegotiations (20, 21). Although these studies sug-gest the possibility that the individual-level bene-fits of having a diverse social networkmay scale tothe population level, the relation between networkstructure and community economic developmenthas never been directly tested (22).

As policy-makers struggle to revive ailing econ-omies, understanding this relation between net-work structure and economic development mayprovide insights into social alternatives to traditionalstimulus policies. To that end,we analyzed themostcomplete record of a national communication net-work studied to date and coupled this social networkdata with detailed socioeconomic indicators to mea-sure this relation directly, at the population level. Thecommunication network data were collected duringthe month of August 2005 in the UK. The datacontain more than 90% of the mobile phones andgreater than 99% of the residential and businesslandlines in the country. The resulting network has65 ! 106 nodes, 368 ! 106 reciprocated social ties, amean geodesic distance (minimumnumber of director indirect edges connecting two nodes) of 9.4, anaverage degree of 10.1 network neighbors, and agiant component (the largest connected subgraph)containing 99.5% of all nodes (23).

Although the nature of this communicationdata limits causal inference, we were able to testthe hypothesized correspondence between socialnetwork structure and economic developmentusing the 2004 UK government’s Index of Mul-tiple Deprivation (IMD), a composite measure ofrelative prosperity of 32,482 communities encom-passing the entire country (24), based on income,employment, education, health, crime, housing,and the environmental quality of each region (25).Each residential landline number was associatedwith the IMD rank of the exchange inwhich it waslocated, as shown in Fig. 1. Obtaining the socio-economic profile for a given telephone exchangearea involves aggregating over the census regionswithin the exchange area. First we uniquelymapped each census region to the exchange areawith which it has the greatest spatial overlap. Wesubsequently aggregated, for each exchange area,the population-weighted average of the IMD forthe census regions assigned to each exchange:

mweighted ! !n

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where xi is the census rank for the ith censusregion that makes up the exchange area and wi isthe population weight of the ith census regiongiven by the fraction of the total population of theexchange area residing in the ith census region.

We then compared the IMD rank of each com-munity with diversity metrics associated with eachmember’s social network. Mobile numbers wereincluded (alongwith landlines) in the calculation ofthe nonspatial diversity measures; however, theywere not used to identifymembers of a community(because of insufficient data on spatial location).Wedeveloped two newmetrics to capture the social andspatial diversity of communication ties within anindividual’s social network. We quantify topolog-ical diversity as a function of the Shannon entropy,

H"i# ! "!k

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where Vij is the volume between node i and j.Wethen define social diversity,Dsocial(i), as the Shannon

entropy associated with individual i’s communi-cation behavior, normalized by k:

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The above measure of topological diversitydoes not take into account the geographic diver-sity in the calling patterns within a community.We define a similar measure for spatial diversity,Dspatial(i), by replacing call volume with the geo-graphic distance spanned by an individual’s ties tothe 1992 telephone exchange areas in the UK,

Dspatial"i# !" !

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in which pia is the proportion of time i spendscommunicatingwith ath ofA total exchange areas.High diversity scores imply that an individualsplits her time more evenly among social ties andbetween different regions.

Fig. 1. An image of regional communication diversity and socioeconomic ranking for the UK. We findthat communities with diverse communication patterns tend to rank higher (represented from light blueto dark blue) than the regions with more insular communication. This result implies that communicationdiversity is a key indicator of an economically healthy community. [(29) Crown copyright material isreproduced with the permission of the Controller of Her Majesty’s Stationery Office]

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Although both social and spatial network diver-sity scores were strongly correlated with IMD rank(r = 0.73 and r = 0.58, respectively), we found aweaker positive correlation present using number ofcontacts (r = 0.44) and a negative correlation forcommunication volume (r = –0.33). For example,whereas inhabitants of Stoke-on-Trent, one of theleast prosperous regions in the UK, averaged ahigher monthly call volume than the national aver-age, they have one of the lowest diversity scores inthe country. Similarly prosperous Stratford-upon-Avon has inhabitants with extremely diverse net-works, despite no more communication than thenational average.

The strong association between diversity andIMD rank persists using other network diversitymetrics, including Burt’s measure of “structuralholes” (9). A structural hole is a missing relationbetween any two of a node’s neighbors, creating anopen triad. Burt's seminal work showed that remu-neration within an organization increases with thenumber of structural holes that surround a node.Ourresults show that this relation scales to the level ofcommunities, whose socioeconomic opportunitiesincrease with the number of structural holes in theego networks of themembers (r= 0.72).Moreover,a composite measure constructed via principalcomponent analysis was an even better predictorof economic development than either componentalone, as illustrated in Fig. 2 (r = 0.78).

By coupling the most complete population-level social network studied to datewith community-level economic outcomes,wewere able to validatea central assumption that is widely accepted in net-work science but was untested at the populationlevel: Domore diverse ties provide greater access tosocial and economic opportunities? Although thecausal direction of this relation—whether networkdiversity promotes opportunity or economic devel-opment leads to more diversified contacts—cannotbe established, social network diversity seems to beat the very least a strong structural signature for theeconomic development of a community. On a pop-ulation level, the surprisingly strong correspondencewe discovered between the structure of social con-tacts and the economic well-being of populationshighlights the potential benefit of socially targetedpolicies for economic development. However,additional researchwill be required to derive reliable

policy implications. In particular, establishing thecausal mechanisms underlying the observed corre-spondencebetweennetworkdiversity andeconomicdevelopment may require additional longitudinalsocial network and economic data (26–28).

References and Notes1. M. Newman, SIAM Rev. 45, 167 (2003).2. S. Page, The Difference: How the Power of Diversity

Creates Better Groups, Firms, Schools, and Societies(Princeton Univ. Press, Princeton, NJ, 2007).

3. M. Granovetter, Sociol. Theory 1, 201 (1983).4. Y. Bian, Am. Sociol. Rev. 62, 366 (1997).5. M. Granovetter, Am. J. Sociol. 78, 1360 (1973).6. R. Fernandez, N. Weinberg, Stanford GSB Research Paper

Series no. 1382, 1 (1994).7. D. Brass, Adm. Sci. Q. 29, 518 (1984).8. D. Brass, Acad. Manage. J. 28, 327 (1985).9. R. Burt, Structural Holes: The Social Structure of

Competition (Harvard Univ. Press, Cambridge, MA, 1992).10. P. Marsden, J. Hurlbert, Soc. Forces 66, 1038 (1988).11. N. Lin, W. Ensel, J. Vaughn, Am. Sociol. Rev. 46, 393 (1981).12. H. Flap, N. Degraaf, Netherlands J. Sociol. 22, 145 (1986).13. N. Degraaf, H. Flap, Soc. Forces 67, 453 (1988).14. B. Wegener, Am. Sociol. Rev. 56, 60 (1991).15. J. Podolny, J. Baron, Am. Sociol. Rev. 62, 673 (1997).16. M.-D. Seidel, J. Polzer, K. Stewart, Adm. Sci. Q. 45, 1 (2000).17. S. Seibert, M. Kraimer, R. Liden, Acad. Manage. J. 44,

219 (2001).

18. H. Aldrich, C. Zimmer, in The Art and Science ofEntrepreneurship, D. L. Sexton, R. W. Smilor, Eds.(Ballinger Publishing, Cambridge, MA, 1986), pp. 3–24.

19. P. Dubini, H. Aldrich, J. Bus. Venturing 6, 305 (1991).20. M. Burkhardt, D. Brass, Adm. Sci. Q. 35, 104 (1990).21. D. Brass, M. Burkhardt, Acad. Manage. J. 36, 441 (1993).22. Previous studies have used longitudinal designs to test the

hypothesized one-way causal direction between networkposition and individual economic benefits. These resultssuggest a similar causal direction at the population level, butour national data do not allow us to test for causal direction,and we cannot rule out the possibility that economicadvantages may also lead to changes in network structure.

23. The anonymized call logs were recorded by the networkoperator as required by law and for billing purposes andnot for the purpose of this project. Although thesecommunication data and the exact location of thetelephone exchanges are not publicly available, theregional aggregates are available from the first author.

24. Department of Communities and Local Government,Indices of Deprivation 2004—Summary (revised)(Department of Communities and Local Government,Stationery Office, London, 2004); www.communities.gov.uk/archived/general-content/communities/indicesofdeprivation/indicesofdeprivation/?view=Standard.

25. The 2004 IMD was constructed before the 2005telephone call logs were recorded. We assume that the2004 IMD rankings are relatively constant and, therefore,make no causal inference from the temporal order.Additionally, exchange areas can also be scored with specificIMD components, such as education. Higher education maypromote more diverse network ties and lead to jobs withhigher income, which might account for part of thecorrespondence between IMD and network diversity.

26. S. Aral, L. Muchnik, A. Sundararajan, Proc. Natl. Acad.Sci. U.S.A. 106, 21544 (2009).

27. S. Currarini, M. O. Jackson, P. Pin, Proc. Natl. Acad. Sci.U.S.A. 107, 4857 (2010).

28. C. Manski, Rev. Econ. Stud. 60, 531 (1993).29. Office for National Statistics, Super Output Area

Boundaries (Her Majesty’s Stationery Office, London, 2004).30. This work was supported by the Santa Fe Institute,

the U.S. NSF (BCS-0537606), and British Telecom.

Supporting Online Materialwww.sciencemag.org/cgi/content/full/328/5981/1029/DC1Materials and MethodsFigs. S1 to S6References4 January 2010; accepted 14 April 201010.1126/science.1186605

Coadministration of a Tumor-PenetratingPeptide Enhances the Efficacyof Cancer DrugsKazuki N. Sugahara,1* Tambet Teesalu,1* Priya Prakash Karmali,2 Venkata Ramana Kotamraju,1Lilach Agemy,1 Daniel R. Greenwald,3 Erkki Ruoslahti1,2†Poor penetration of anticancer drugs into tumors can be an important factor limiting their efficacy. Westudied mouse tumor models to show that a previously characterized tumor-penetrating peptide, iRGD,increased vascular and tissue permeability in a tumor-specific and neuropilin-1–dependent manner,allowing coadministered drugs to penetrate into extravascular tumor tissue. Importantly, this effect didnot require the drugs to be chemically conjugated to the peptide. Systemic injection with iRGD improvedthe therapeutic index of drugs of various compositions, including a small molecule (doxorubicin),nanoparticles (nab-paclitaxel and doxorubicin liposomes), and a monoclonal antibody (trastuzumab).Thus, coadministration of iRGD may be a valuable way to enhance the efficacy of anticancer drugs whilereducing their side effects, a primary goal of cancer therapy research.

The therapeutic efficacy of many anticancerdrugs is limited by their poor penetrationinto tumor tissue and by their adverse ef-

fects on healthy cells, which limits the dose ofdrug that can be safely administered to cancerpatients. In solid tumors, many anticancer drugs

Fig. 2. The relation between socialnetwork diversity and socioeconomicrank. Diversity was constructed as acomposite of Shannon entropy andBurt’s measure of structural holes,by using principal component anal-ysis. A fractional polynomial was fitto the data.

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The end

Vassilis Kostakos - [email protected]

• 3rd Ubi Summer School 2012– http://www.ubioulu.fi/en/UBI-summer-school-2012

• Researcher in Residence program– http://www.ubioulu.fi/en/UBI-RIR-program– UbiChallenge: http://www.ubioulu.fi/en/UBI-challenge

• Special issue of IJHCS on Social Networks and Ubiquitous Interactions (May 2012).

• Or just come visit!


http://www.ubioulu.fi/en/UBI-summer-school-2012

http://www.ubioulu.fi/en/UBI-summer-school-2012

http://www.ubioulu.fi/en/UBI-RIR-program

http://www.ubioulu.fi/en/UBI-RIR-program

http://www.ubioulu.fi/en/UBI-challenge

http://www.ubioulu.fi/en/UBI-challenge

From labs to cities: Mapping the social impact of ubiquitous technologies

Technology

Transcript of From labs to cities: Mapping the social impact of ubiquitous technologies