amorsppapers.cumincad.org/data/works/att/ecaadesigradi2019_225.pdf · cial inequality and urban...

Amor SPUnderstanding socio-spatial emotions of urban poor in São Paulo

Maycon Sedrez1, Jarryer de Martino2

1TU Braunschweig 2University of Espírito Santo1,2{mayconsedrez|jarryermartino}@gmail.com

In the last decades, the quantity of information produced and distributed due todigital resources has been growing; big data is contributing to a better perceptionof our cities. The aim of this paper is to understand spatial segregation in the cityof São Paulo (a city known by its social inequality and urban poverty) byscraping social media tags of emotions. We compared aspects that suggestsocio-spatial inequalities: urban poor versus feelings of love and hate, versusfeelings of joy and fear and the social vulnerability index as background. Threeissues are considered in this research: the emergence of urban space big data,digital inclusion, and architects and urbanists' access to big data. To unveilurban poor singularities through social media is an opportunity to reconnectcommunities to urban design.

Keywords: Parametrics, Social media, Urban analytics, Socio-spatialinteractions

INTRODUCTIONIn the last decades, the quantity of information pro-duced and distributed due to digital resources hasbeen growing, enabling the computational sciencesto represent complex occurrences and models. To-day, scientists observe the emergence of a new fieldrelated to data analysis (eScience), unifying theory,experiment and simulation (Hey et al. 2011). Instru-mentation and technology contribute to the inten-sive use of data, stimulating the demand for toolsthat can assist researchers, planners and analysts toacquire, select, visualize and analyse big data; the im-plementation ofmethods to handle big data is a newscience field called ‘predictive analytics’ (Leszczynski2016).

The effective and convenient applicationof tech-

nologies might help researchers to significantly ad-vance the understanding of the world, including itssocial challenges and how to face it. Thus, the tech-nological process of acquiring, modelling, visualizingand analysing information contribute to the decisionmaking process (Picon 2015; Batty 2018). This alsocommit the adoption of a scientific stance when thesubjects are analysed and interpreted. In this sense,the design of information is essential for communica-tion to reveal knowledge to other people.

In this context, utilization of maps to representurban space related data assume a high complex-ity level. The possibility of combination of differ-ent types and data sources that present as referencethe same territorial parameters (geo coordinates) es-tablish a new condition for analysis and interpre-

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tation of urban events. Thus, the traditional map-ping techniques have limitations that impede to reg-ister a large amount of information in a same spatialdata. According to Loch (2006) maps are a mediumto transfer knowledge, which can be broad and di-verse or more restrict and objective. Maps are an ab-straction of reality, spatializing the events that arerepresented, being spatial or not, material or ideal(Dutenkefer 2010).

The rising of big data is contributing to a betterperceptionof our cities (Picon2015; Taylor et al. 2015;Leszczynski 2016), and the production of digital con-tent reached unprecedented levels (Carpo 2017; Tay-lor et al. 2015; Arribas-Bel 2014), especially whenit comes to volunteered geographic information insocial media platforms, such as Twitter, Flickr, Insta-gramor Foursquare. Volumes of data about and fromcity spaces (Leszczynski 2016) are generated everyminute, however, this data is not created in a struc-tured way to be used as research input (Ratti & Of-fenhuber 2014). Alternatively this information can bemapped, selected and interpreted to support design-ers and planners on understanding urban spaces. Inthis paper three issues are orbiting around this prob-lem:

• Emergence of urban big data is a topic widelydiscussed in the literature (Batty 2018; Pi-con 2015), including how to treat or visualizedata (Singleton et al. 2018; M’Closkey & Van-DerSys 2017) and the changes technologiesposes to design (de Waal 2014). Social me-dia is responsible to produce a big quantity ofuser-generated information, specifically datawith geographic information (Campagna etal. 2015). Comments, reviews, photos, videos,and other digital media content can certainlyhelp us to visualize human interactions withbuilt environment (Gerbaudo 2012; Good-child 2007). These studies can support the de-sign and planning of urban spaces by addingnew layers of information. This brings us tothe question of what type and quality of datais generated by social media platforms in São

Paulo?• Digital exclusion has been a problem on cities

(Picon 2015). The municipality of São Paulostarted in 2014 to install 120 free Wi-Fi areasin public spaces to attend a demand for ac-cess to online information and communica-tion, aiming at 240 freeWi-Fi spots in the sub-sequent years. Feeling of belonging is one is-sue tackled by this solution, which also rein-forces democracy in the city. By being activeonvirtual spacepeople canengageonpartici-pation processes, and socialmedia becomes atool for participative and interactive strategies(Zhou&Wang2014; Bonsónet al. 2012). Map-ping social-spatial interactions through socialmedia can help to unveil poor communities’conflicts, demands and actions. We under-stand the limitations of urban poor in termsof accessibility to computer, smartphone andinternet, so we propose the following ques-tion, would be possible to map the emotionsof poor communities in the city of São Paulo?

• Only recently, architects and urban design-ers were introduced to the idea of using so-cial media as information to support designand planning decisions (Taylor et al. 2015).Computer scientists are rapidly developingknowledge and methods for scraping datafrom social media, while architects and ur-ban designers have still to learn on how tobe proficient on programming methods. Pro-gramming gained a new impulse in the lastdecade with the creation of algorithmic de-sign software for architecture design. The lackof knowledge on big data might lead archi-tects and urbanists to be left unaware of de-velopments anddiscoveries that deeply affectour cities. Being able to understand theseconcepts and work with these tools will makearchitects to play an active role on the devel-opment of smart cities and buildings. Whatare the challenges for architects to use thesetools?

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This paper argues in favour of rethinking design pro-cesses by unveiling citizens’ affects, ephemeral dis-turbances and spatial connections, by that exposingurban poor’s virtual socio-spatial interactions. Theaim of this paper is to understand spatial segrega-tion in the city of São Paulo (a city known by its so-cial inequality and urban poverty) by scraping so-cial media tags of emotions. As starting point, theauthors draw inspiration from a song of Criolo, theBrazilian hip-hop artist who wrote: ”não existe amorem SP” (Love does not exist in SP) in 2011. He por-trays the city as a ’labyrinth’ full of violence and graf-fiti in poor neighbourhoods. The melancholic lyricsinspired the authors to investigate emotions and city,condition made possible by increasing social mediavolunteered generated content.

According to Thrift (2004) affection (feelingssuch as anger, fear, happiness and joy) are essentialelements of urban life, however, study of affectionhas been neglected on cities sciences. On the otherhand, recent studies are finding sophisticated meth-ods to visualize affect in cities via social media analy-sis, aiming support for urbanplanning (Goet al. 2009;Guthier et al. 2014), or security of public spaces, forinstance, the EMOTIVE initiative (Leszczynski 2016).These studies have strong impact on the field of com-puter science, which can be challenging for archi-tects and urban designers. The unique contributionof this is paper is to bring to light a tool for archi-tects and urbanists to immediately incorporate socialmedia analysis in their design process as investiga-tive support, to examine the availability and qualityof data in São Paulo and to explore urban poor socio-spatial data. Weexpect that this researchhelps todis-seminate representationsof abstract aspects of socialmedia interactions and the physicality of the city.

METHODSWith the ubiquitous presence of Grasshopper in ar-chitecture schools and practices, tools (add-ons) arecontinuously being developed to overcome the ar-chitects’ lack of knowledge on computing. Interfacesoriented towards architects to explore big data are

still in development; one alternative is the add-onMosquito for Grasshopper. This add-on has a set oftools to connect to social media allowing data crawl-ing. Mosquito is an application merging three dis-ciplines: architecture and urbanism, computer sci-ence and geography. Connecting data with histori-cal and geographical context (Wyly 2014) provide in-sights for thedata scientist. Thrift (2014) andBlumen-stock et al. (2015) suggests that bigdatamight be thekey to make urban poverty visible. Testing the imag-inary of São Paulo full with emotions has the poten-tial to reveal aspects of the city which were not pre-viously considered in its planning. It helps to expressthe segregationand limits of the city in termsof socialinequity, it also presents the invisible aspects of ur-ban data. This allows emotions scraped from onlineposts to be spatially aggregated in dynamic ’moodmaps’ (Leszczynski 2016).

To exemplify new possibilities of perception,analysis and representation of urban environmentsusing data visualization and social media tags, thispaper focus on mapping of search tags related toemotions on Flickr. Twitter was also tested, gen-erating a large number of results, but with an in-significant percentage of geo located tags. Then,we compared aspects that suggest socio-spatial in-equalities: urban poor versus feelings of love andhate, versus feelings of joy and fear and the so-cial vulnerability index as background. The basemap of São Paulo are open source GIS files thatcan be downloaded from the portal GeoSampa(geosampa.prefeitura.sp.gov.br), we used the streetnetwork, the districts‘ outlines and the favelas’ out-lines. We compare the information with the SãoPaulo’s Index of Social Vulnerability which can bedownloaded from the portal São Paulo GovernoAberto (governoaberto.sp.gov.br), and the CensusSectors from the Brazilian Institute for Geography -IBGE (mapas.ibge.gov.br).

The shape files (.shp) are converted to .dwg byQGIS and imported to Rhino. This procedure involvesto adjust theprojectionof themaponaWorld Projec-tion for São Paulo:


Figure 1Grasshopper dataflow definitionusing Mosquito(Authors, 2019).

SIRGAS 2000 UTM Zone 23S EPSG 31983 (Brazil)exported from QGIS as SAD 69(96) EPSG 5530 (BrazilPolyconic).

By doing so it is necessary to inform the follow-ing projection to Mosquito:

PROJCS[“SIRGAS_2000_UTM_zone_23S”,GEOGCS[“GCS_SIRGAS 2000”,DATUM[“D_SIRGAS_2000”,SPHEROID[“GRS_1980”,6378137,298.257222101]],PRIMEM[“Greenwich”,0],UNIT[“Degree”,0.017453292519943295]],PROJECTION[“Transverse_Mercator”],PARAMETER[“latitude_of_origin”,0],PARAMETER[“central_meridian”,-45],PARAMETER[“scale_factor”,0.9996],PARAMETER[“false_easting”,500000],PARAMETER[“false_northing”,10000000],UNIT[“Meter”,1]]Mosquito is relatively easy to use tool (fig 1), be-

ing themost difficult part to set up the correct projec-tion between themap and collected tags. We limitedthe data scraping from 2016.01.01 to 2018.12.31, ona 20km radius from the coordinates: -23.5475 and -46.63611, points outside the political borders of SãoPaulo are not counted and are deleted from the vi-sualizations. Since Mosquito returns a maximum of

250 results for Flickr, the date range must be shiftedto accumulate the dataset for each year. We rec-ognize that the poorest communities in São Paulomight probably have less computers, smartphonesand tablets, but Wi-Fi points in São Paulo are con-nected to public equipment such as libraries, culturecentres, educational facilities, parks, fablabs, touris-tic places or squares, which offer computers for thesecommunities.

The tags are searched individually, and the geopoints are then transferred to Rhino by baking them.A second definition parametrize the visualization byusing a hexagonal grid to sum the number of resultsinside each cell and then inform the radius of the cir-cles (fig. 2).

We produced two maps using tags scrappedfrom Flickr: 1. love and hate São Paulo (fig.3 ), 2.joy and fear São Paulo (fig. 4); using as backgroundthe social vulnerability index, streets and favelas. Allsearches were conducted exclusively in Portugueseusing the following words, for the map 1 (love andhate) we got 614 results for the search tags: amor(love), paixão (passion), amar (to love), carinho (affec-tion), ‘eu amo’ (I love); and 38 results for the searchtags: ódio (hate), abandono (abandonment), odiar(to hate), indiferença (indifference), raiva (rage). For


the map 2 (joy and fear) we got 1131 results for thesearch tags: amizade (friendship), feliz (happy), fe-licidade (happiness), alegria (joy), amigos (friends);and 354 results for the search tags: triste (sad), tris-teza (sadness), medo (fear), confronto (clash), vio-lento (violent), inimigo (enemy), violência (violence).We got 498 results for the search tags related to ur-ban poor: COHAB (social housing sets of buildings),cortiço (tenement), favela (slum), comunidade (com-munity), gueto (gueto), periferia (periphery) used inboth maps.

The search tags for urban poor can have bothnegative and positive aspects attached to it; somedwellers proudly apply the word COHAB to their ori-gins and community. In this sense, it is not possibleto disconnect some ambiguous results, and that isnot the intention. However, we emphasize the ambi-guities introduced by somewords and the possibilityof having search returns not referring to the specificsearch. Since it is not possible to examine every sin-gle result, this aspect of data scraping must be bal-anced. This is one of the challenges of working withbig data, try to avoid ambiguities to accumulate adataset that can represent the research as accurateas possible.

The maps show the concentration of results foremotions on the old city centre and Paulista Avenue.In the case of Paulista Av., we recognize it as a hubof activity in São Paulo, especially due to public man-ifestations taking place in this street, therefore, thelarge set of results in this area. The lower Paulista Av.has more love tags, while the upper part a mix of joyand fear. The socio-spatial interactions of this spe-

cific socialmediaplatform (Flickr) arenot commononfavelas, but, are consistent with some of our data forurban poor communities. A special attention to onepoint in the north (around the district Jardim JoanaD’Arc) highly active in termsof community andalso infear. The Favela Paraisópolis in the southwest part ofthe city did not generate any result, which is surpris-ing, since it is one of the largest favelas in São Paulo.A high concentration of hate in four points, one in thecity centre and three in the periphery, must be sub-ject of a detailed study, being one of these points afavela in the district Jaguaré - west part of the city.Future research will verify the tags in detail to under-stand in which context these citizens use social me-dia to communicate their feelings. The social vulner-ability index is helpful to visualize that there are vir-tual activity on vulnerable communities and favelas.

Three questions proposed in the beginning ofthis paper are now examined.

• What type and quality of data is generated bysocial media platforms in São Paulo? Regard-ing data for emotions in São Paulo, the resultsare satisfactory, allowing similar future stud-ies with other tags. Brazilians are well knownfor being active on social media in all socialclasses, for instance, the meme “Please cometo Brasil” in which internet users ask for in-ternational artists to perform in Brazil, reflectsthe poor and middle class access to interna-tional culture. However, a significant amountof noise is generated, what Leszczynski (2016)calls unstructured real-time content; a smart

Figure 2Grasshopperdefinition forvisualization ofresults in ahexagonal grid(Authors, 2019)


Figure 3Love and hate fromFlickr in São Paulo(Authors, 2019).Social VulnerabilityIndex (Source:Governo Aberto)levels: 1. super low,2. very low, 3. low,4. vulnerable, 5.high, 6. very high;for the CensusSector (Source:IBGE); on top ofstreets, slums anddistricts’ outlines(Source:GeoSampa).

selection of search expressions is fundamen-tal for ameaningful analysis. It canbe focusona second part of this study to look on spacesand which are their physical characteristics.

• Would be possible tomap the feelings of poorcommunities in the city of São Paulo? Webelieve that in combination with other socialmedia platforms, the results can be more ac-curate and complex. We aggregate a signif-icant number of geo located tags to createtwo mood maps that have actual informationand can be updated constantly, registeringthe evolution of emotion in the city. Flickrposts can be a very good data source, but thedesigner must be aware of its limitations: ingeneral, the photos present positive aspects.

• What are the challenges for architects to usethese tools? To understand the limitations

and potentialities of such analysis to designand plan of cities. Fundamentally the toolcan assist architects to collect information oncertain places and scales, allowing them tobe introduced to concepts of big data. Wenoticed that employing Mosquito alone wasnot enough; some knowledge on GIS is nec-essary. Moreover, it is important to estab-lish clear parameters to collect relevant infor-mation. Words might have double meaningor being applied for different contexts, for in-stance, the word “park” alone can be relatedto green spaces or car spaces. This ambiguityhave to be addressed beforehand.


Figure 4Joy and fear fromFlickr in São Paulo(Authors, 2019).Social VulnerabilityIndex (Source:Governo Aberto)levels: 1. super low ,2. very low, 3. low,4. vulnerable, 5.high, 6. very high;for the CensusSector (Source:IBGE); on top ofstreets, slums anddistricts’ outlines(Source:GeoSampa).

CONCLUSIONIn this paper we experiment with a social media datascraper for Grasshopper. We speculate that urbanpoor emotions canbemapped throughsocial-media,and create two mood maps of love and hate, joy andfear. The urban inequalities cannot be solved by thisanalysis, but the results offer hints to support strate-gies for planning anddesigning, for instance, PaulistaAv. is perceived as the contemporary Agora, the oldcity as place of complex urban dynamics, and the ur-ban poor manifesting emotions in this specific so-cial network is a sign of accessibility to technology inthese communities.

The data generated via social media tags whenconfronted with the statistical data of the city, mightreveal the virtualizationof a collective’s intention. Ac-cording to Levy (2011), the virtualization is the mu-tation of identity, a displacement from the ontolog-

ical gravity centre of the object in discussion. Bythat mean it is possible the detachment of here andnow, and, the words used to generate the map canbe considered vectors of virtualization that aban-doned the presence, being released from physicalspace. Thus, manifestations in social networks couldbe interpreted as an extension, complementation oreven realizationof desires of individuals that extrapo-late the space and the physical body. Schopenhauer(2005) believes that our body is an object amongother objects that brings meaning to the world and,in this way, the individual manifests his knowledgethrough representations that will serve as a depar-ture point for the world’s intuition. Thus, these mapscan be analysed from two perspectives: the one thatreveals the intentions and desires of individuals whofind in virtual space the opportunity to accomplishtheir wills, and who have limited chances tomanifest


them in physical space, configuring the universe ofdesire; or that which corresponds to the record of thepresent andbecomes the representationof theworldas it is.

Nevertheless, mapping the manifestations of in-dividuals on the territory corresponds to the repre-sentation of the simultaneity of the physical and vir-tual space, contributing in a singular way, even be-fore the possibility of ambiguities, for the productionof information that will reveal and complement theanalysis and interpretation of the urban space. Hav-ing this understanding of two separated worlds thatinterlace in severalmomentswill allow architects andurbanists to interpret these visualizations in favourof their designs. To unveil urban poor singularitiesthrough social media is an opportunity to reconnectcommunities to urban design.

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