An approach to represent and analyze socioeconomic
contexts as well as to reason with them, in order to
extract useful conclusions about the social perception
emerging from citizens’ beliefs and feelings, is introduced.
We concentrate here in the formal aspects of the solution,
completing this way our workMinisterio de Economía y Competitividad TIN2013-41086-PJunta de Andalucía TIC-606

Borrego Díaz, Joaquín

Galán Páez, Juan

Miguel Rodríguez, Jaime de

idUS. Depósito de Investigación Universidad de Sevilla

Extracting Emergent Knowledge about the Socioeconomic Urban ContextsJuan Gala´n-Pa´ezDept. Computer Science and AI - Universidad de SevillaE.T.S. Ingenier´ıa Informa´tica, Avda. Reina Mercedes s.n. 41012-Seville, Spain juangalan@us.esJoaqu´ın Borrego-D´ıazDept. Computer Science and AI - Universidad de SevillaE.T.S. Ingenier´ıa Informa´tica, Avda. Reina Mercedes s.n. 41012-Seville, Spain jborrego@us.esJaime de Miguel-Rodr´ıguez Sushant School of Art and Architecture - Ansal University Sector 55, Golf Course Road 122003 (Haryana) Gurgaon, Indiademiguel.jaime@gmail.comAbstractAn approach to represent and analyze socioeconomic contexts as well as to reason with them, in order to extract useful conclusions about the social perception emerging from citizens’ beliefs and feelings, is introduced. We concentrate here in the formal aspects of the solution, completing this way our work [4].Introduction and technical backgroundThe increasing availability of digital information about cities and their dynamics represents an enormous challenge for urban planners, data scientists and, in general, for the scientiﬁc community involved in the development of smart cities. The collection, processing and use of urban data applied to projects such as designing new applications, urban services or analyzing cities’ social dimension, is a key research & development ﬁeld.The human understanding of the knowledge information provides, as well as the extraction and processing of digital information, are particularly diﬃcult to tackle dueto the big amount of data. For example, the use ofportals where citizens interact and, in a more challengingsituation, where citizenship socializes their feelings [3].The problem has a semantic nature, as it is essentiallyqualitative and context-sensitive.The aim of the paper is to show how to transforminformation from the WWW about speciﬁc topics of thecity, generated by institutions, companies and citizens,into (emergent) semantic information useful to study howcitizens understand the particular socioeconomic context.Background: Formal Concept AnalysisFormal Concept Analysis (FCA) [5] provides powerfulsemantic tools for classiﬁcation, data mining and KE andDiscovery (KD). Among these tools, particularlyinteresting are concepts extraction and organization, andimplication basis. FCA also provides reasoning tools,which can be used to re-organize, enrich and even topredict the evolution of the system [2]. A formal contextM = (O,A, I) consists of two sets, O (objects) and A(attributes), and a relation I ⊆ O ×A. Finite contextscan be represented by a 1-0-table (identifying I with aboolean function on O ×A). Given X ⊆ O, Y ⊆ A,X ′ = {a ∈ A | oIa for all o ∈ X} andY ′ = {o ∈ O | oIa for all a ∈ Y } can be deﬁned.Knowledge Bases in FCA consist of implications betweenattributes. An implication is a pair of sets of attributes,written as Y1 → Y2. An implication is true with respect toM = (O,A, I) according to the following deﬁnition: Asubset T ⊆ A respects Y1 → Y2 if Y1 ⊆ T or Y2 ⊆ T .Y1 → Y2 is said to hold in M (M |= Y1 → Y2 or Y1 → Y2is an implication of M) if for all o ∈ O, the set {o}′respects Y1 → Y2.Let L be a set of implications and L be an implication.An implication L follows from L (L |= L) if each subsetof A respecting L also respects L. L is complete if forevery implication L, if M |= L then L |= L. L isnon-redundant if for each L ∈ L, L \ {L} |= L. FinallyL is a (implication) basis for M if L is complete andnon-redundant. A particular basis is theDuquenne-Guigues or so called Stem Basis (SB) (see[5]). The reasoning system we use is a production systemon the selected implication basis. In FCA, associationrules are also implications between sets of attributes.Conﬁdence and support are deﬁned as usual in datamining. The analogous to Stem Basis for association rulesis the Luxenburger basis [6]. The reasoning system for SBcan be adapted for reasoning with Luxenburger basis.Recall that Y is closed if Y ′′ = Y . Let be M = (O,A, I)a formal context and Y, Y1, Y2 ⊂ A.• Given Y1, Y2 closed, Y1 ≺ Y2 if there is not Y closedsuch that Y1 ⊂ Y ⊂ Y2.• The support of Y ⊆ A is supp(Y ) = |Y ′|. and thesupport of L = Y1 → Y2 is supp(L) = |(Y1 ∪ Y2)′|• The conﬁdence of L is conf(L) = supp(Y1 ∪ Y2)supp(Y1)Given γ, δ, the Luxenburger basis of a context M withconﬁdence γ and support δ, denoted by L(M,γ, δ), isL(M,γ, δ) := {L : Y1 → Y2 | Y1, Y2 closed,Y1 ≺ Y2, conf(L) ≥ γ, sup(L) ≥ δ}By convention, L(M, 1, 0) is the SB basis. Luxenburgerbasis can be interpreted as an association rules set, thusthey allow reasoning under uncertainty. The particularfeature of the Luxenburger basis is that it shows someexplicit information on how concepts from the conceptlattice are related (according to the conﬁdence andsupport).A Methodology based on FCA. The methodologyapplied in this work is based on the processing of data toobtain a formal context representing the global knowledge(the called Monster Model). This context is usually builtby collecting a huge amount of information by means ofWWW services. From this global context, a conceptlattice can be extracted. This lattice is a complexsemantic network with complex topology, providing someinsights on the robustness of the data set [1]. The secondstage involves the selection of a number of features andthresholds (to discretize data) to produce KBs forreasoning tasks. In this way both, concept lattices andimplication basis, are obtained, providing knowledge fromthe information.In this paper data resources come from housing market,which represent a highly dynamic data set that plays a keyrole in the understanding of urban processes. This dataset provides a meaningful view of the socioeconomiclandscape of our cities. For instance, when focusing on acertain neighborhood, the data can be used to measuresales-price variation indexes upon which substantialknowledge can be gained regarding issues like socialintegration and diversity of urban areas.In this case Data comes from Self-City platform1. Thisplatform allows obtaining real time urban information in ameaningful way, striving to make dynamic data botheﬃcient and essential for city management anddecision-making. In order to achieve these goals, Self Cityinvolves a complex articulation of visualization techniques,crossing of planning data, clustering algorithms and most1http://self-city.com/importantly, monitoring strategies in tune with agencies’needs. The right setup of these diﬀerent apparatuses willdetermine till what extent will, the information served, beassimilated and with this, the chances of reaction upondata.Reasoning with emergent conceptsCurrently, Self-City manages digital information only.Such information can be visualized, but a soundcomprehension of its dynamics and conceptual structurerequires semantic processing. The understanding of thefull dataset needs a semantic interpretation of theconcepts involved in this complex system. Contexts arebuilt from the Self-City information ﬂow as for instancetemporal and spatial information on for sale housing:objects are for sale homes and attributes provide aqualitative description of these at a given moment;description of the item (i.e. price, dimensions,environment, etc.) and description of the item evolution(i.e. price changes, environment evolution, etc.).Thresholds for attributes have to be selected.The formal context considered in this work consist of 6000(approx.) for sale homes in the city of Seville. Alsosubareas of Seville can be considered (i.e. streets, zones,etc.) for a more detailed analysis. A more precise analysisof conceptual diﬀerences can be provided by means oflogical reasoning with association rules related to thelattice. Also, it is possible to consider how qualitativepatterns are reproduced within the city.The study of this kind of semantic structures allows, forexample, discovering new concepts, as a result ofexploratory analysis of the Concept Lattice for a particularcity area. This allows both understanding and comparingdiﬀerent city areas and neighborhoods. The estimation ofsimilarity among concept lattices from diﬀerent citiesallows comparing socioeconomic contexts. Fig. 1 showsthe similarity between the main streets of a district and aspeciﬁc Avenue.Figure 1: Semantic (social) Patterns in a district from SevilleFigure 2: Comparing concept lattices from diﬀerent streetsIt is also possible to compare citizens’ perceptions onthe socioeconomic context for diﬀerent neighborhoods(see Fig. 2) by using Luxenburger rules. In this case, ifMK is the context for the ﬁrst street and MA for thesecond, it is possible to see if the concept ”large ﬂoor” isdiﬀerently perceived by citizens in diﬀerent neighbors: If”large size ﬂoor” attribute is permuted with ”medium sizeﬂoor” in the context MA, obtaining M′A, it holds that:• L(MK , .85, 0) |= L(M ′A, 0.97, 0)• L(M ′A, 1, 0) |= L(MA, 0.94, 0)AcknowledgementsTIC-6064 Excellence project (Junta Andaluc´ıa) andTIN2013- 41086-P (Spanish Ministry of Economy andCompetitiveness), co-ﬁnanced with FEDER fundsReferences[1] G.A. Aranda-Corral, J. Borrego-D´ıaz, J. Gala´n-Pa´ez,On the Phenomenological Reconstruction of ComplexSystems—The Scale-Free ConceptualizationHypothesis, Systems Research and BehavioralScience 30(6): 716-734 (2013).[2] G. A. Aranda-Corral, J. Borrego-D´ıaz, J. Gala´n-Pa´ez,Complex Concept Lattices for Simulating HumanPrediction in Sport. J. Systems Science andComplexity 26(1):117-136 (2012)[3] D Beraldo, J Gala´n–Pa´ez. The #OCCUPY networkon Twitter and the challenges to social movementstheory and research. International Journal ofElectronic Governance 6 (4), 319-341 (2014)[4] J. Borrego-D´ıaz, J. Gala´n-Pa´ez, J. DeMiguel-Rodr´ıguez, An approach to social perceptionof socioeconomic contexts by means of conceptreasoning. VII International Meeting in Dynamics ofSocio-Economic Systems (2014).[5] B. Ganter and R. Wille, Formal Concept Analysis -Mathematical Foundations, Springer-Verlag, 1999.[6] Luxenburger, M.: Implications partielles dans uncontexte. Math. Inf. Sci. hum. 113 (1991) 35–55

Extracting emergent knowledge about the socioeconomic urban contexts

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Extracting emergent knowledge about the socioeconomic urban contexts

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